arkouda¶
Subpackages¶
Submodules¶
arkouda.accessorarkouda.alignmentarkouda.array_viewarkouda.categoricalarkouda.clientarkouda.client_dtypesarkouda.dataframearkouda.dtypesarkouda.groupbyclassarkouda.historyarkouda.indexarkouda.infoclassarkouda.ioarkouda.io_utilarkouda.joinarkouda.loggerarkouda.matcharkouda.matcherarkouda.numericarkouda.pdarrayclassarkouda.pdarraycreationarkouda.pdarraysetopsarkouda.plottingarkouda.rowarkouda.securityarkouda.segarrayarkouda.seriesarkouda.sortingarkouda.stringsarkouda.timeclassarkouda.util
Package Contents¶
Classes¶
A multi-dimensional view of a pdarray. Arkouda |
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Represent integers as bit vectors, e.g. a set of flags. |
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Custom property-like object. |
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Represents an array of values belonging to named categories. Converting a |
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Represents an array of values belonging to named categories. Converting a |
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Represents an array of values belonging to named categories. Converting a |
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A DataFrame structure based on arkouda arrays. |
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A DataFrame structure based on arkouda arrays. |
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Represents a date and/or time. |
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Represents a date and/or time. |
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Represents a date and/or time. |
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A column in a GroupBy that has been differenced. |
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Generic enumeration. |
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An integer-backed representation of a set of named binary fields, e.g. flags. |
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Group an array or list of arrays by value, usually in preparation |
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Group an array or list of arrays by value, usually in preparation |
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Group an array or list of arrays by value, usually in preparation |
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Group an array or list of arrays by value, usually in preparation |
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Group an array or list of arrays by value, usually in preparation |
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Group an array or list of arrays by value, usually in preparation |
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Represent integers as IPv4 addresses. |
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Generic enumeration. |
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The results of a power divergence statistical test. |
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This class is useful for printing and working with individual rows of a |
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One-dimensional arkouda array with axis labels. |
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Represents a duration, the difference between two dates or times. |
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Represents a duration, the difference between two dates or times. |
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The basic arkouda array class. This class contains only the |
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The basic arkouda array class. This class contains only the |
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The basic arkouda array class. This class contains only the |
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The basic arkouda array class. This class contains only the |
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The basic arkouda array class. This class contains only the |
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The basic arkouda array class. This class contains only the |
Functions¶
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Make a callback (i.e. function) that can be called on an |
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Return the element-wise absolute value of the array. |
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Return the element-wise absolute value of the array. |
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Cast an array to another dtype. |
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Cast an array to another dtype. |
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Map multiple arrays of sparse identifiers to a common 0-up index. |
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Return True iff all elements of the array evaluate to True. |
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Return True iff any element of the array evaluates to True. |
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arange([start,] stop[, stride,] dtype=int64) |
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arange([start,] stop[, stride,] dtype=int64) |
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arange([start,] stop[, stride,] dtype=int64) |
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arange([start,] stop[, stride,] dtype=int64) |
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arange([start,] stop[, stride,] dtype=int64) |
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arange([start,] stop[, stride,] dtype=int64) |
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Return the element-wise inverse cosine of the array. The result is between 0 and pi. |
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Return the element-wise inverse hyperbolic cosine of the array. |
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Return the element-wise inverse sine of the array. The result is between -pi/2 and pi/2. |
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Return the element-wise inverse hyperbolic sine of the array. |
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Return the element-wise inverse tangent of the array. The result is between -pi/2 and pi/2. |
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Return the element-wise inverse tangent of the array pair. The result chosen is the |
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Return the element-wise inverse hyperbolic tangent of the array. |
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Return the index of the first occurrence of the array max value. |
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Find the indices corresponding to the k maximum values of an array. |
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Return the index of the first occurrence of the array min value. |
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Finds the indices corresponding to the k minimum values of an array. |
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Return the permutation that sorts the array. |
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Return the permutation that sorts the array. |
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Return the permutation that sorts the array. |
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Convert a Python or Numpy Iterable to a pdarray or Strings object, sending |
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Convert a Python or Numpy Iterable to a pdarray or Strings object, sending |
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Convert a Python or Numpy Iterable to a pdarray or Strings object, sending |
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Convert a Python or Numpy Iterable to a pdarray or Strings object, sending |
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Attach to all objects registered with the names provide |
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class method to return a pdarray attached to the registered name in the arkouda |
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Create a bigint pdarray from an iterable of uint pdarrays. |
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Broadcast a dense column vector to the rows of a sparse matrix or grouped array. |
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Broadcast a dense column vector to the rows of a sparse matrix or grouped array. |
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Broadcast a dense column vector to the rows of a sparse matrix or grouped array. |
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Broadcast a dense column vector to the rows of a sparse matrix or grouped array. |
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Algorithm to determine shape of broadcasted PD array given two array shapes |
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expand an array's rank to the specified shape using broadcasting |
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Cast an array to another dtype. |
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Cast an array to another dtype. |
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Return the element-wise ceiling of the array. |
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Assert that numpy dtype dt is one of the dtypes supported |
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Computes the chi square statistic and p-value. |
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Send a clear message to clear all unregistered data from the server symbol table |
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Clip (limit) the values in an array to a given range [lo,hi] |
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Count leading zeros for each integer in an array. |
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Return the permutation that groups the rows (left-to-right), if the |
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Return the permutation that groups the rows (left-to-right), if the |
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Return the permutation that groups the rows (left-to-right), if the |
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Compute the internal size of a hypothetical join between a and b. Returns |
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Concatenate a list or tuple of |
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Concatenate a list or tuple of |
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Concatenate a list or tuple of |
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Convert a Categorical array to Strings for display |
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Return the correlation between x and y |
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Return the element-wise cosine of the array. |
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Return the element-wise hyperbolic cosine of the array. |
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Return the covariance of x and y |
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Return a pdarray instance pointing to an array created by the arkouda server. |
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Return a pdarray instance pointing to an array created by the arkouda server. |
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Return a pdarray instance pointing to an array created by the arkouda server. |
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Return a pdarray instance pointing to an array created by the arkouda server. |
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Count trailing zeros for each integer in an array. |
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Return the cumulative product over the array. |
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Return the cumulative sum over the array. |
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Return the cumulative sum over the array. |
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Creates a fixed frequency Datetime range. Alias for |
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Creates a fixed frequency Datetime range. Alias for |
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Converts angles element-wise from degrees to radians. |
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Disables verbose logging (DEBUG log level) for all ArkoudaLoggers, setting |
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Returns the sum of the elementwise product of two arrays of the same size (the dot product) or |
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Enables verbose logging (DEBUG log level) for all ArkoudaLoggers |
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Return the element-wise exponential of the array. |
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Return the element-wise exponential of the array minus one. |
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Export data from Arkouda file (Parquet/HDF5) to Pandas object or file formatted to be |
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Return indices of query items in a search list of items (-1 if not found). |
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Return the element-wise floor of the array. |
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Returns the element-wise remainder of division. |
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Converts a Pandas Series to an Arkouda pdarray or Strings object. If |
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Converts a Pandas Series to an Arkouda pdarray or Strings object. If |
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Create a pdarray filled with fill_value. |
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Create a pdarray filled with fill_value. |
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Create a pdarray filled with fill_value of the same size and dtype as an existing |
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Generate a segmented array of variable-length, contiguous ranges between pairs of |
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Generate a segmented array of variable-length, contiguous ranges between pairs of |
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A convenience method for instantiating an ArkoudaLogger that retrieves the |
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Get a concrete byteorder (turns '=' into '<' or '>') |
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Get a list of column names from CSV file(s). |
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Get the names of the datasets in the provide files |
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Get the type of a file accessible to the server. Supported |
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Get null indices of a string column in a Parquet file. |
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Get the server's byteorder |
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Return an element-wise hash of the array or list of arrays. |
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Create a grid plot histogramming all numeric columns in ak dataframe |
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Compute a histogram of evenly spaced bins over the range of an array. |
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Compute a histogram of evenly spaced bins over the range of an array. |
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Compute the bi-dimensional histogram of two data samples with evenly spaced bins |
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Compute the multidimensional histogram of data in sample with evenly spaced bins. |
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Import data from a file saved by Pandas (HDF5/Parquet) to Arkouda object and/or |
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Test whether each element of a 1-D array is also present in a second array. |
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Test whether each element of a 1-D array is also present in a second array. |
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Test whether each element of a 1-D array is also present in a second array. |
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Test each value for membership in any of a set of half-open (pythonic) |
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Returns an integer array of the index values where the values of the first |
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Returns JSON formatted string containing information about the objects in names |
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Find the intersection of two arkouda arrays. |
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Find the intersection of two arrays. |
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Apply a function defined over intervals to an array of arguments. |
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Find all the rows that are in both dataframes. |
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Find the inverse of a permutation array. |
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Convert values to an Arkouda array of IP addresses. |
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Return True iff the arrays are cosorted, i.e., if the arrays were columns in a table |
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Indicate which values are ipv4 when passed data containing IPv4 and IPv6 values. |
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Indicate which values are ipv6 when passed data containing IPv4 and IPv6 values. |
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Determine if the name provided is associated with a registered Object |
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Return True iff the array is monotonically non-decreasing. |
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Return True iff the array is monotonically non-decreasing. |
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Return the element-wise isfinite check applied to the array. |
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Return the element-wise isinf check applied to the array. |
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Return the element-wise isnan check applied to the array. |
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Return the element-wise isnan check applied to the array. |
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Performs an inner-join on equality between two integer arrays where |
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Map two arrays of sparse identifiers to the 0-up index set implied by the left array, |
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Create a pdarray of linearly-spaced floats in a closed interval. |
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Return a list containing the names of all registered objects |
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Return a list containing the names of all objects in the symbol table |
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Load a pdarray previously saved with |
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Load multiple pdarrays, Strings, SegArrays, or Categoricals previously |
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Return the element-wise natural log of the array. |
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Return the element-wise base 10 log of the array. |
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Return the element-wise natural log of one plus the array. |
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Return the element-wise base 2 log of the array. |
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Apply the function defined by the mapping keys --> values to arguments. |
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This function calls the h5ls utility on a HDF5 file visible to the |
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Used for identifying the datasets within a file when a CSV does not |
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Return the maximum value of the array. |
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Find the k maximum values of an array. |
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Return the mean of the array. |
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Merge Arkouda DataFrames with a database-style join. |
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Return the minimum value of the array. |
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Find the k minimum values of an array. |
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Returns the element-wise remainder of division. |
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Create a pdarray filled with ones. |
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Create a pdarray filled with ones. |
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Create a pdarray filled with ones. |
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Create a pdarray filled with ones. |
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Create a one-filled pdarray of the same size and dtype as an existing |
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Find the bit parity (XOR of all bits) for each integer in an array. |
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Plot the distribution and cumulative distribution of histogram Data |
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Find the population (number of bits set) for each integer in an array. |
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Raises an array to a power. If where is given, the operation will only take place in the positions |
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Computes the power divergence statistic and p-value. |
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Prints verbose information for each object in names in a human readable format |
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Return the product of all elements in the array. Return value is |
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Converts angles element-wise from radians to degrees. |
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Generate a pdarray of randomized int, float, or bool values in a |
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Generate a pdarray of randomized int, float, or bool values in a |
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Generate random strings with log-normally distributed lengths and |
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Generate random strings with lengths uniformly distributed between |
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Read datasets from files. |
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Read CSV file(s) into Arkouda objects. If more than one dataset is found, the objects |
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Read Arkouda objects from HDF5 file/s |
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Read Arkouda objects from Parquet file/s |
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Read datasets from files and tag each record to the file it was read from. |
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Receive a pdarray sent by pdarray.transfer(). |
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Receive a pdarray sent by dataframe.transfer(). |
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Register all objects in the provided dictionary |
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Try to infer what dtype arkouda_server should treat val as. |
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Return data saved using ak.snapshot |
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Map two arrays of sparse values to the 0-up index set implied by the right array, |
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Rotate bits of <x> to the left by <rot>. |
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Rotate bits of <x> to the left by <rot>. |
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Return the element-wise rounding of the array. |
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DEPRECATED |
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Given an array of query vals and non-overlapping, closed intervals, return |
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Alias for the from_parts function. Prevents user from needing to call ak.SegArray constructor |
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Find the set difference of two arrays. |
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Find the set exclusive-or (symmetric difference) of two arrays. |
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Return the element-wise sign of the array. |
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Return the element-wise sine of the array. |
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Return the element-wise hyperbolic sine of the array. |
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Computes the sample skewness of an array. |
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Create a snapshot of the current Arkouda namespace. All currently accessible variables containing |
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Return a sorted copy of the array. Only sorts numeric arrays; |
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Takes the square root of array. If where is given, the operation will only take place in |
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Return the element-wise square of the array. |
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Draw real numbers from the standard normal distribution. |
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Draw real numbers from the standard normal distribution. |
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Return the standard deviation of values in the array. The standard |
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Return the sum of all elements in the array. |
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Return the element-wise tangent of the array. |
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Return the element-wise hyperbolic tangent of the array. |
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Return a fixed frequency TimedeltaIndex, with day as the default |
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Return a fixed frequency TimedeltaIndex, with day as the default |
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Write Arkouda object(s) to CSV file(s). All CSV Files written by Arkouda |
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Save multiple named pdarrays to HDF5 files. |
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Save multiple named pdarrays to Parquet files. |
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Split numpy dtype dt into its kind and byte size, raising |
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Return the element-wise truncation of the array. |
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Generate a pdarray with uniformly distributed random float values |
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Generate a pdarray with uniformly distributed random float values |
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Find the union of two arrays/List of Arrays. |
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Find the unique elements of an array. |
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Find the unique elements of an array. |
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Find the unique elements of an array. |
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Unregister all names provided |
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Unregister a named pdarray in the arkouda server which was previously |
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Overwrite the datasets with name appearing in names or keys in columns if columns |
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Count the occurrences of the unique values of an array. |
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Return the variance of values in the array. |
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Returns an array with elements chosen from A and B based upon a |
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Returns an array with elements chosen from A and B based upon a |
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Returns an array with elements chosen from A and B based upon a |
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Allows the user to write custom logs. |
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Computes x * log(y). |
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Map an array of sparse values to 0-up indices. |
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Create a pdarray filled with zeros. |
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Create a pdarray filled with zeros. |
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Create a pdarray filled with zeros. |
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Create a pdarray filled with zeros. |
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Create a zero-filled pdarray of the same size and dtype as an existing |
Attributes¶
The DType enum defines the supported Arkouda data types in string form. |
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- arkouda.ARKOUDA_SUPPORTED_DTYPES¶
- arkouda.AllSymbols = '__AllSymbols__'¶
- class arkouda.ArrayView(base: arkouda.pdarrayclass.pdarray, shape, order='row_major')[source]¶
A multi-dimensional view of a pdarray. Arkouda
ArraryViewbehaves similarly to numpy’s ndarray. The base pdarray is stored in 1-dimension but can be indexed and treated logically as if it were multi-dimensional- dtype¶
The element type of the base pdarray (equivalent to base.dtype)
- Type:
dtype
- size¶
The number of elements in the base pdarray (equivalent to base.size)
- Type:
int_scalars
- ndim¶
Number of dimensions (equivalent to shape.size)
- Type:
int_scalars
- itemsize¶
The size in bytes of each element (equivalent to base.itemsize)
- Type:
int_scalars
- order¶
Index order to read and write the elements. By default or if ‘C’/’row_major’, read and write data in row_major order If ‘F’/’column_major’, read and write data in column_major order
- Type:
str {‘C’/’row_major’ | ‘F’/’column_major’}
- objType = 'ArrayView'¶
- to_hdf(prefix_path: str, dataset: str = 'ArrayView', mode: str = 'truncate', file_type: str = 'distribute')[source]¶
Save the current ArrayView object to hdf5 file
- Parameters:
prefix_path (str) – Path to the file to write the dataset to
dataset (str) – Name of the dataset to write
mode (str (truncate | append)) – Default: truncate Mode to write the dataset in. Truncate will overwrite any existing files. Append will add the dataset to an existing file.
file_type (str (single|distribute)) – Default: distribute Indicates the format to save the file. Single will store in a single file. Distribute will store the date in a file per locale.
- to_list() list[source]¶
Convert the ArrayView to a list, transferring array data from the Arkouda server to client-side Python. Note: if the ArrayView size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A list with the same data as the ArrayView
- Return type:
list
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the ArrayView size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(6).reshape(2,3) >>> a.to_list() [[0, 1, 2], [3, 4, 5]] >>> type(a.to_list()) list
- to_ndarray() numpy.ndarray[source]¶
Convert the ArrayView to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the ArrayView size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A numpy ndarray with the same attributes and data as the ArrayView
- Return type:
np.ndarray
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the ArrayView size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.Examples
>>> a = ak.arange(6).reshape(2,3) >>> a.to_ndarray() array([[0, 1, 2], [3, 4, 5]]) >>> type(a.to_ndarray()) numpy.ndarray
- update_hdf(prefix_path: str, dataset: str = 'ArrayView', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this array view object. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the array view
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data
- class arkouda.BitVector(values, width=64, reverse=False)[source]¶
Bases:
arkouda.pdarrayclass.pdarrayRepresent integers as bit vectors, e.g. a set of flags.
- Parameters:
values (pdarray, int64) – The integers to represent as bit vectors
width (int) – The number of bit fields in the vector
reverse (bool) – If True, display bits from least significant (left) to most significant (right). By default, the most significant bit is the left-most bit.
- Returns:
bitvectors – The array of binary vectors
- Return type:
Notes
This class is a thin wrapper around pdarray that mostly affects how values are displayed to the user. Operators and methods will typically treat this class like a uint64 pdarray.
- conserves¶
- special_objType = 'BitVector'¶
- register(user_defined_name)[source]¶
Register this BitVector object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the BitVector is to be registered under, this will be the root name for underlying components
- Returns:
The same BitVector which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different BitVectors with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the BitVector with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- arkouda.BitVectorizer(width=64, reverse=False)[source]¶
Make a callback (i.e. function) that can be called on an array to create a BitVector.
- Parameters:
width (int) – The number of bit fields in the vector
reverse (bool) – If True, display bits from least significant (left) to most significant (right). By default, the most significant bit is the left-most bit.
- Returns:
bitvectorizer – A function that takes an array and returns a BitVector instance
- Return type:
callable
- class arkouda.CachedAccessor(name: str, accessor)[source]¶
Custom property-like object. A descriptor for caching accessors. :param name: Namespace that will be accessed under, e.g.
df.foo. :type name: str :param accessor: Class with the extension methods. :type accessor: clsNotes
For accessor, The class’s __init__ method assumes that one of
Series,DataFrameorIndexas the single argumentdata.
- class arkouda.Categorical(values, **kwargs)[source]¶
Represents an array of values belonging to named categories. Converting a Strings object to Categorical often saves memory and speeds up operations, especially if there are many repeated values, at the cost of some one-time work in initialization.
- Parameters:
values (Strings) – String values to convert to categories
NAvalue (str scalar) – The value to use to represent missing/null data
- permutation¶
The permutation that groups the values in the same order as categories
- Type:
pdarray, int64
- size¶
The number of items in the array
- Type:
Union[int,np.int64]
- nlevels¶
The number of distinct categories
- Type:
Union[int,np.int64]
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
Union[int,np.int64]
- shape¶
The sizes of each dimension of the array
- Type:
tuple
- property nbytes¶
The size of the Categorical in bytes.
- Returns:
The size of the Categorical in bytes.
- Return type:
int
- BinOps¶
- RegisterablePieces¶
- RequiredPieces¶
- dtype¶
- objType = 'Categorical'¶
- permutation¶
- segments¶
- static attach(user_defined_name: str) Categorical[source]¶
DEPRECATED Function to return a Categorical object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which Categorical object was registered under
- Returns:
The Categorical object created by re-attaching to the corresponding server components
- Return type:
- Raises:
TypeError – if user_defined_name is not a string
- concatenate(others: Sequence[Categorical], ordered: bool = True) Categorical[source]¶
Merge this Categorical with other Categorical objects in the array, concatenating the arrays and synchronizing the categories.
- Parameters:
others (Sequence[Categorical]) – The Categorical arrays to concatenate and merge with this one
ordered (bool) – If True (default), the arrays will be appended in the order given. If False, array data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.
- Returns:
The merged Categorical object
- Return type:
- Raises:
TypeError – Raised if any others array objects are not Categorical objects
Notes
This operation can be expensive – slower than concatenating Strings.
- contains(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element contains the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that contain substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- endswith(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element ends with the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that end with substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- classmethod from_codes(codes: arkouda.pdarrayclass.pdarray, categories: arkouda.strings.Strings, permutation=None, segments=None, **kwargs) Categorical[source]¶
Make a Categorical from codes and categories arrays. If codes and categories have already been pre-computed, this constructor saves time. If not, please use the normal constructor.
- Parameters:
- Returns:
The Categorical object created from the input parameters
- Return type:
- Raises:
TypeError – Raised if codes is not a pdarray of int64 objects or if categories is not a Strings object
- classmethod from_return_msg(rep_msg) Categorical[source]¶
Create categorical from return message from server
Notes
This is currently only used when reading a Categorical from HDF5 files.
- group() arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that groups the array, placing equivalent categories together. All instances of the same category are guaranteed to lie in one contiguous block of the permuted array, but the blocks are not necessarily ordered.
- Returns:
The permutation that groups the array by value
- Return type:
Notes
This method is faster than the corresponding Strings method. If the Categorical was created from a Strings object, then this function simply returns the cached permutation. Even if the Categorical was created using from_codes(), this function will be faster than Strings.group() because it sorts dense integer values, rather than 128-bit hash values.
- hash() Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Compute a 128-bit hash of each element of the Categorical.
- Returns:
A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
- Return type:
Notes
The implementation uses SipHash128, a fast and balanced hash function (used by Python for dictionaries and sets). For realistic numbers of strings (up to about 10**15), the probability of a collision between two 128-bit hash values is negligible.
- in1d(test: arkouda.strings.Strings | Categorical) arkouda.pdarrayclass.pdarray[source]¶
Test whether each element of the Categorical object is also present in the test Strings or Categorical object.
Returns a boolean array the same length as self that is True where an element of self is in test and False otherwise.
- Parameters:
test (Union[Strings,Categorical]) – The values against which to test each value of ‘self`.
- Returns:
The values self[in1d] are in the test Strings or Categorical object.
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if test is not a Strings or Categorical object
See also
Notes
in1d can be considered as an element-wise function version of the python keyword in, for 1-D sequences.
in1d(a, b)is logically equivalent toak.array([item in b for item in a]), but is much faster and scales to arbitrarily largea.Examples
>>> strings = ak.array([f'String {i}' for i in range(0,5)]) >>> cat = ak.Categorical(strings) >>> ak.in1d(cat,strings) array([True, True, True, True, True]) >>> strings = ak.array([f'String {i}' for i in range(5,9)]) >>> catTwo = ak.Categorical(strings) >>> ak.in1d(cat,catTwo) array([False, False, False, False, False])
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
register,attach,unregister,unregister_categorical_by_nameNotes
Objects registered with the server are immune to deletion until they are unregistered.
- static parse_hdf_categoricals(d: Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings]) Tuple[List[str], Dict[str, Categorical]][source]¶
This function should be used in conjunction with the load_all function which reads hdf5 files and reconstitutes Categorical objects. Categorical objects use a naming convention and HDF5 structure so they can be identified and constructed for the user.
In general you should not call this method directly
- Parameters:
d (Dictionary of String to either Pdarray or Strings object)
- Returns:
2-Tuple of List of strings containing key names which should be removed and Dictionary of
base name to Categorical object
See also
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- register(user_defined_name: str) Categorical[source]¶
Register this Categorical object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Categorical is to be registered under, this will be the root name for underlying components
- Returns:
The same Categorical which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Categoricals with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Categorical with the user_defined_name
See also
unregister,attach,unregister_categorical_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- reset_categories() Categorical[source]¶
Recompute the category labels, discarding any unused labels. This method is often useful after slicing or indexing a Categorical array, when the resulting array only contains a subset of the original categories. In this case, eliminating unused categories can speed up other operations.
- Returns:
A Categorical object generated from the current instance
- Return type:
- save(prefix_path: str, dataset: str = 'categorical_array', file_format: str = 'HDF5', mode: str = 'truncate', file_type: str = 'distribute', compression: str | None = None) str[source]¶
DEPRECATED Save the Categorical object to HDF5 or Parquet. The result is a collection of HDF5/Parquet files, one file per locale of the arkouda server, where each filename starts with prefix_path and dataset. Each locale saves its chunk of the Strings array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in HDF5 files (must not already exist) :type dataset: str :param file_format: The format to save the file to. :type file_format: str {‘HDF5 | ‘Parquet’} :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, create a new Categorical dataset within existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
compression (str (Optional)) – {None | ‘snappy’ | ‘gzip’ | ‘brotli’ | ‘zstd’ | ‘lz4’} The compression type to use when writing. This is only supported for Parquet files and will not be used with HDF5.
- Return type:
String message indicating result of save operation
- Raises:
ValueError – Raised if the lengths of columns and values differ, or the mode is neither ‘truncate’ nor ‘append’
TypeError – Raised if prefix_path, dataset, or mode is not a str
Notes
Important implementation notes: (1) Strings state is saved as two datasets within an hdf5 group: one for the string characters and one for the segments corresponding to the start of each string, (2) the hdf5 group is named via the dataset parameter.
See also
-,-
- set_categories(new_categories, NAvalue=None)[source]¶
Set categories to user-defined values.
- Parameters:
new_categories (Strings) – The array of new categories to use. Must be unique.
NAvalue (str scalar) – The value to use to represent missing/null data
- Returns:
A new Categorical with the user-defined categories. Old values present in new categories will appear unchanged. Old values not present will be assigned the NA value.
- Return type:
- classmethod standardize_categories(arrays, NAvalue='N/A')[source]¶
Standardize an array of Categoricals so that they share the same categories.
- Parameters:
arrays (sequence of Categoricals) – The Categoricals to standardize
NAvalue (str scalar) – The value to use to represent missing/null data
- Returns:
A list of the original Categoricals remapped to the shared categories.
- Return type:
List of Categoricals
- startswith(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element starts with the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that start with substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- to_hdf(prefix_path, dataset='categorical_array', mode='truncate', file_type='distribute')[source]¶
Save the Categorical to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale.
- Return type:
None
See also
- to_list() List[source]¶
Convert the Categorical to a list, transferring data from the arkouda server to Python. This conversion discards category information and produces a list of strings. If the arrays exceeds a built-in size limit, a RuntimeError is raised.
- Returns:
A list of strings corresponding to the values in this Categorical
- Return type:
list
Notes
The number of bytes in the Categorical cannot exceed
ak.client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but proceed with caution.
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the arkouda server to Python. This conversion discards category information and produces an ndarray of strings. If the arrays exceeds a built-in size limit, a RuntimeError is raised.
- Returns:
A numpy ndarray of strings corresponding to the values in this array
- Return type:
np.ndarray
Notes
The number of bytes in the array cannot exceed
ak.client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but proceed with caution.
- to_parquet(prefix_path: str, dataset: str = 'categorical_array', mode: str = 'truncate', compression: str | None = None) str[source]¶
This functionality is currently not supported and will also raise a RuntimeError. Support is in development. Save the Categorical to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in HDF5 files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Categorical dataset within existing files.
compression (str (Optional)) – Default None Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4
- Return type:
String message indicating result of save operation
- Raises:
RuntimeError – On run due to compatability issues of Categorical with Parquet.
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.See also
- to_strings() List[source]¶
Convert the Categorical to Strings.
- Returns:
A Strings object corresponding to the values in this Categorical.
- Return type:
Examples
>>> from arkouda import ak >>> ak.connect() >>> a = ak.array(["a","b","c"]) >>> a >>> c = ak.Categorical(a) >>> c.to_strings() c.to_strings()
>>> isinstance(c.to_strings(), ak.Strings) True
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a Categorical object to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the Categorical is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unique() Categorical[source]¶
- unregister() None[source]¶
Unregister this Categorical object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
register,attach,unregister_categorical_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_categorical_by_name(user_defined_name: str) None[source]¶
Function to unregister Categorical object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the Categorical object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- update_hdf(prefix_path, dataset='categorical_array', repack=True)[source]¶
Overwrite the dataset with the name provided with this Categorical object. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the Categorical
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, the repack option allows for automatic creation of a file without the inaccessible data.
- class arkouda.Categorical(values, **kwargs)[source]¶
Represents an array of values belonging to named categories. Converting a Strings object to Categorical often saves memory and speeds up operations, especially if there are many repeated values, at the cost of some one-time work in initialization.
- Parameters:
values (Strings) – String values to convert to categories
NAvalue (str scalar) – The value to use to represent missing/null data
- permutation¶
The permutation that groups the values in the same order as categories
- Type:
pdarray, int64
- size¶
The number of items in the array
- Type:
Union[int,np.int64]
- nlevels¶
The number of distinct categories
- Type:
Union[int,np.int64]
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
Union[int,np.int64]
- shape¶
The sizes of each dimension of the array
- Type:
tuple
- property nbytes¶
The size of the Categorical in bytes.
- Returns:
The size of the Categorical in bytes.
- Return type:
int
- BinOps¶
- RegisterablePieces¶
- RequiredPieces¶
- dtype¶
- objType = 'Categorical'¶
- permutation¶
- segments¶
- static attach(user_defined_name: str) Categorical[source]¶
DEPRECATED Function to return a Categorical object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which Categorical object was registered under
- Returns:
The Categorical object created by re-attaching to the corresponding server components
- Return type:
- Raises:
TypeError – if user_defined_name is not a string
- concatenate(others: Sequence[Categorical], ordered: bool = True) Categorical[source]¶
Merge this Categorical with other Categorical objects in the array, concatenating the arrays and synchronizing the categories.
- Parameters:
others (Sequence[Categorical]) – The Categorical arrays to concatenate and merge with this one
ordered (bool) – If True (default), the arrays will be appended in the order given. If False, array data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.
- Returns:
The merged Categorical object
- Return type:
- Raises:
TypeError – Raised if any others array objects are not Categorical objects
Notes
This operation can be expensive – slower than concatenating Strings.
- contains(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element contains the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that contain substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- endswith(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element ends with the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that end with substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- classmethod from_codes(codes: arkouda.pdarrayclass.pdarray, categories: arkouda.strings.Strings, permutation=None, segments=None, **kwargs) Categorical[source]¶
Make a Categorical from codes and categories arrays. If codes and categories have already been pre-computed, this constructor saves time. If not, please use the normal constructor.
- Parameters:
- Returns:
The Categorical object created from the input parameters
- Return type:
- Raises:
TypeError – Raised if codes is not a pdarray of int64 objects or if categories is not a Strings object
- classmethod from_return_msg(rep_msg) Categorical[source]¶
Create categorical from return message from server
Notes
This is currently only used when reading a Categorical from HDF5 files.
- group() arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that groups the array, placing equivalent categories together. All instances of the same category are guaranteed to lie in one contiguous block of the permuted array, but the blocks are not necessarily ordered.
- Returns:
The permutation that groups the array by value
- Return type:
Notes
This method is faster than the corresponding Strings method. If the Categorical was created from a Strings object, then this function simply returns the cached permutation. Even if the Categorical was created using from_codes(), this function will be faster than Strings.group() because it sorts dense integer values, rather than 128-bit hash values.
- hash() Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Compute a 128-bit hash of each element of the Categorical.
- Returns:
A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
- Return type:
Notes
The implementation uses SipHash128, a fast and balanced hash function (used by Python for dictionaries and sets). For realistic numbers of strings (up to about 10**15), the probability of a collision between two 128-bit hash values is negligible.
- in1d(test: arkouda.strings.Strings | Categorical) arkouda.pdarrayclass.pdarray[source]¶
Test whether each element of the Categorical object is also present in the test Strings or Categorical object.
Returns a boolean array the same length as self that is True where an element of self is in test and False otherwise.
- Parameters:
test (Union[Strings,Categorical]) – The values against which to test each value of ‘self`.
- Returns:
The values self[in1d] are in the test Strings or Categorical object.
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if test is not a Strings or Categorical object
See also
Notes
in1d can be considered as an element-wise function version of the python keyword in, for 1-D sequences.
in1d(a, b)is logically equivalent toak.array([item in b for item in a]), but is much faster and scales to arbitrarily largea.Examples
>>> strings = ak.array([f'String {i}' for i in range(0,5)]) >>> cat = ak.Categorical(strings) >>> ak.in1d(cat,strings) array([True, True, True, True, True]) >>> strings = ak.array([f'String {i}' for i in range(5,9)]) >>> catTwo = ak.Categorical(strings) >>> ak.in1d(cat,catTwo) array([False, False, False, False, False])
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
register,attach,unregister,unregister_categorical_by_nameNotes
Objects registered with the server are immune to deletion until they are unregistered.
- static parse_hdf_categoricals(d: Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings]) Tuple[List[str], Dict[str, Categorical]][source]¶
This function should be used in conjunction with the load_all function which reads hdf5 files and reconstitutes Categorical objects. Categorical objects use a naming convention and HDF5 structure so they can be identified and constructed for the user.
In general you should not call this method directly
- Parameters:
d (Dictionary of String to either Pdarray or Strings object)
- Returns:
2-Tuple of List of strings containing key names which should be removed and Dictionary of
base name to Categorical object
See also
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- register(user_defined_name: str) Categorical[source]¶
Register this Categorical object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Categorical is to be registered under, this will be the root name for underlying components
- Returns:
The same Categorical which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Categoricals with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Categorical with the user_defined_name
See also
unregister,attach,unregister_categorical_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- reset_categories() Categorical[source]¶
Recompute the category labels, discarding any unused labels. This method is often useful after slicing or indexing a Categorical array, when the resulting array only contains a subset of the original categories. In this case, eliminating unused categories can speed up other operations.
- Returns:
A Categorical object generated from the current instance
- Return type:
- save(prefix_path: str, dataset: str = 'categorical_array', file_format: str = 'HDF5', mode: str = 'truncate', file_type: str = 'distribute', compression: str | None = None) str[source]¶
DEPRECATED Save the Categorical object to HDF5 or Parquet. The result is a collection of HDF5/Parquet files, one file per locale of the arkouda server, where each filename starts with prefix_path and dataset. Each locale saves its chunk of the Strings array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in HDF5 files (must not already exist) :type dataset: str :param file_format: The format to save the file to. :type file_format: str {‘HDF5 | ‘Parquet’} :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, create a new Categorical dataset within existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
compression (str (Optional)) – {None | ‘snappy’ | ‘gzip’ | ‘brotli’ | ‘zstd’ | ‘lz4’} The compression type to use when writing. This is only supported for Parquet files and will not be used with HDF5.
- Return type:
String message indicating result of save operation
- Raises:
ValueError – Raised if the lengths of columns and values differ, or the mode is neither ‘truncate’ nor ‘append’
TypeError – Raised if prefix_path, dataset, or mode is not a str
Notes
Important implementation notes: (1) Strings state is saved as two datasets within an hdf5 group: one for the string characters and one for the segments corresponding to the start of each string, (2) the hdf5 group is named via the dataset parameter.
See also
-,-
- set_categories(new_categories, NAvalue=None)[source]¶
Set categories to user-defined values.
- Parameters:
new_categories (Strings) – The array of new categories to use. Must be unique.
NAvalue (str scalar) – The value to use to represent missing/null data
- Returns:
A new Categorical with the user-defined categories. Old values present in new categories will appear unchanged. Old values not present will be assigned the NA value.
- Return type:
- classmethod standardize_categories(arrays, NAvalue='N/A')[source]¶
Standardize an array of Categoricals so that they share the same categories.
- Parameters:
arrays (sequence of Categoricals) – The Categoricals to standardize
NAvalue (str scalar) – The value to use to represent missing/null data
- Returns:
A list of the original Categoricals remapped to the shared categories.
- Return type:
List of Categoricals
- startswith(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element starts with the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that start with substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- to_hdf(prefix_path, dataset='categorical_array', mode='truncate', file_type='distribute')[source]¶
Save the Categorical to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale.
- Return type:
None
See also
- to_list() List[source]¶
Convert the Categorical to a list, transferring data from the arkouda server to Python. This conversion discards category information and produces a list of strings. If the arrays exceeds a built-in size limit, a RuntimeError is raised.
- Returns:
A list of strings corresponding to the values in this Categorical
- Return type:
list
Notes
The number of bytes in the Categorical cannot exceed
ak.client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but proceed with caution.
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the arkouda server to Python. This conversion discards category information and produces an ndarray of strings. If the arrays exceeds a built-in size limit, a RuntimeError is raised.
- Returns:
A numpy ndarray of strings corresponding to the values in this array
- Return type:
np.ndarray
Notes
The number of bytes in the array cannot exceed
ak.client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but proceed with caution.
- to_parquet(prefix_path: str, dataset: str = 'categorical_array', mode: str = 'truncate', compression: str | None = None) str[source]¶
This functionality is currently not supported and will also raise a RuntimeError. Support is in development. Save the Categorical to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in HDF5 files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Categorical dataset within existing files.
compression (str (Optional)) – Default None Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4
- Return type:
String message indicating result of save operation
- Raises:
RuntimeError – On run due to compatability issues of Categorical with Parquet.
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.See also
- to_strings() List[source]¶
Convert the Categorical to Strings.
- Returns:
A Strings object corresponding to the values in this Categorical.
- Return type:
Examples
>>> from arkouda import ak >>> ak.connect() >>> a = ak.array(["a","b","c"]) >>> a >>> c = ak.Categorical(a) >>> c.to_strings() c.to_strings()
>>> isinstance(c.to_strings(), ak.Strings) True
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a Categorical object to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the Categorical is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unique() Categorical[source]¶
- unregister() None[source]¶
Unregister this Categorical object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
register,attach,unregister_categorical_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_categorical_by_name(user_defined_name: str) None[source]¶
Function to unregister Categorical object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the Categorical object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- update_hdf(prefix_path, dataset='categorical_array', repack=True)[source]¶
Overwrite the dataset with the name provided with this Categorical object. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the Categorical
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, the repack option allows for automatic creation of a file without the inaccessible data.
- class arkouda.Categorical(values, **kwargs)[source]¶
Represents an array of values belonging to named categories. Converting a Strings object to Categorical often saves memory and speeds up operations, especially if there are many repeated values, at the cost of some one-time work in initialization.
- Parameters:
values (Strings) – String values to convert to categories
NAvalue (str scalar) – The value to use to represent missing/null data
- permutation¶
The permutation that groups the values in the same order as categories
- Type:
pdarray, int64
- size¶
The number of items in the array
- Type:
Union[int,np.int64]
- nlevels¶
The number of distinct categories
- Type:
Union[int,np.int64]
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
Union[int,np.int64]
- shape¶
The sizes of each dimension of the array
- Type:
tuple
- property nbytes¶
The size of the Categorical in bytes.
- Returns:
The size of the Categorical in bytes.
- Return type:
int
- BinOps¶
- RegisterablePieces¶
- RequiredPieces¶
- dtype¶
- objType = 'Categorical'¶
- permutation¶
- segments¶
- static attach(user_defined_name: str) Categorical[source]¶
DEPRECATED Function to return a Categorical object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which Categorical object was registered under
- Returns:
The Categorical object created by re-attaching to the corresponding server components
- Return type:
- Raises:
TypeError – if user_defined_name is not a string
- concatenate(others: Sequence[Categorical], ordered: bool = True) Categorical[source]¶
Merge this Categorical with other Categorical objects in the array, concatenating the arrays and synchronizing the categories.
- Parameters:
others (Sequence[Categorical]) – The Categorical arrays to concatenate and merge with this one
ordered (bool) – If True (default), the arrays will be appended in the order given. If False, array data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.
- Returns:
The merged Categorical object
- Return type:
- Raises:
TypeError – Raised if any others array objects are not Categorical objects
Notes
This operation can be expensive – slower than concatenating Strings.
- contains(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element contains the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that contain substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- endswith(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element ends with the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that end with substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- classmethod from_codes(codes: arkouda.pdarrayclass.pdarray, categories: arkouda.strings.Strings, permutation=None, segments=None, **kwargs) Categorical[source]¶
Make a Categorical from codes and categories arrays. If codes and categories have already been pre-computed, this constructor saves time. If not, please use the normal constructor.
- Parameters:
- Returns:
The Categorical object created from the input parameters
- Return type:
- Raises:
TypeError – Raised if codes is not a pdarray of int64 objects or if categories is not a Strings object
- classmethod from_return_msg(rep_msg) Categorical[source]¶
Create categorical from return message from server
Notes
This is currently only used when reading a Categorical from HDF5 files.
- group() arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that groups the array, placing equivalent categories together. All instances of the same category are guaranteed to lie in one contiguous block of the permuted array, but the blocks are not necessarily ordered.
- Returns:
The permutation that groups the array by value
- Return type:
Notes
This method is faster than the corresponding Strings method. If the Categorical was created from a Strings object, then this function simply returns the cached permutation. Even if the Categorical was created using from_codes(), this function will be faster than Strings.group() because it sorts dense integer values, rather than 128-bit hash values.
- hash() Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Compute a 128-bit hash of each element of the Categorical.
- Returns:
A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
- Return type:
Notes
The implementation uses SipHash128, a fast and balanced hash function (used by Python for dictionaries and sets). For realistic numbers of strings (up to about 10**15), the probability of a collision between two 128-bit hash values is negligible.
- in1d(test: arkouda.strings.Strings | Categorical) arkouda.pdarrayclass.pdarray[source]¶
Test whether each element of the Categorical object is also present in the test Strings or Categorical object.
Returns a boolean array the same length as self that is True where an element of self is in test and False otherwise.
- Parameters:
test (Union[Strings,Categorical]) – The values against which to test each value of ‘self`.
- Returns:
The values self[in1d] are in the test Strings or Categorical object.
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if test is not a Strings or Categorical object
See also
Notes
in1d can be considered as an element-wise function version of the python keyword in, for 1-D sequences.
in1d(a, b)is logically equivalent toak.array([item in b for item in a]), but is much faster and scales to arbitrarily largea.Examples
>>> strings = ak.array([f'String {i}' for i in range(0,5)]) >>> cat = ak.Categorical(strings) >>> ak.in1d(cat,strings) array([True, True, True, True, True]) >>> strings = ak.array([f'String {i}' for i in range(5,9)]) >>> catTwo = ak.Categorical(strings) >>> ak.in1d(cat,catTwo) array([False, False, False, False, False])
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
register,attach,unregister,unregister_categorical_by_nameNotes
Objects registered with the server are immune to deletion until they are unregistered.
- static parse_hdf_categoricals(d: Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings]) Tuple[List[str], Dict[str, Categorical]][source]¶
This function should be used in conjunction with the load_all function which reads hdf5 files and reconstitutes Categorical objects. Categorical objects use a naming convention and HDF5 structure so they can be identified and constructed for the user.
In general you should not call this method directly
- Parameters:
d (Dictionary of String to either Pdarray or Strings object)
- Returns:
2-Tuple of List of strings containing key names which should be removed and Dictionary of
base name to Categorical object
See also
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- register(user_defined_name: str) Categorical[source]¶
Register this Categorical object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Categorical is to be registered under, this will be the root name for underlying components
- Returns:
The same Categorical which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Categoricals with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Categorical with the user_defined_name
See also
unregister,attach,unregister_categorical_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- reset_categories() Categorical[source]¶
Recompute the category labels, discarding any unused labels. This method is often useful after slicing or indexing a Categorical array, when the resulting array only contains a subset of the original categories. In this case, eliminating unused categories can speed up other operations.
- Returns:
A Categorical object generated from the current instance
- Return type:
- save(prefix_path: str, dataset: str = 'categorical_array', file_format: str = 'HDF5', mode: str = 'truncate', file_type: str = 'distribute', compression: str | None = None) str[source]¶
DEPRECATED Save the Categorical object to HDF5 or Parquet. The result is a collection of HDF5/Parquet files, one file per locale of the arkouda server, where each filename starts with prefix_path and dataset. Each locale saves its chunk of the Strings array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in HDF5 files (must not already exist) :type dataset: str :param file_format: The format to save the file to. :type file_format: str {‘HDF5 | ‘Parquet’} :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, create a new Categorical dataset within existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
compression (str (Optional)) – {None | ‘snappy’ | ‘gzip’ | ‘brotli’ | ‘zstd’ | ‘lz4’} The compression type to use when writing. This is only supported for Parquet files and will not be used with HDF5.
- Return type:
String message indicating result of save operation
- Raises:
ValueError – Raised if the lengths of columns and values differ, or the mode is neither ‘truncate’ nor ‘append’
TypeError – Raised if prefix_path, dataset, or mode is not a str
Notes
Important implementation notes: (1) Strings state is saved as two datasets within an hdf5 group: one for the string characters and one for the segments corresponding to the start of each string, (2) the hdf5 group is named via the dataset parameter.
See also
-,-
- set_categories(new_categories, NAvalue=None)[source]¶
Set categories to user-defined values.
- Parameters:
new_categories (Strings) – The array of new categories to use. Must be unique.
NAvalue (str scalar) – The value to use to represent missing/null data
- Returns:
A new Categorical with the user-defined categories. Old values present in new categories will appear unchanged. Old values not present will be assigned the NA value.
- Return type:
- classmethod standardize_categories(arrays, NAvalue='N/A')[source]¶
Standardize an array of Categoricals so that they share the same categories.
- Parameters:
arrays (sequence of Categoricals) – The Categoricals to standardize
NAvalue (str scalar) – The value to use to represent missing/null data
- Returns:
A list of the original Categoricals remapped to the shared categories.
- Return type:
List of Categoricals
- startswith(substr: bytes | arkouda.dtypes.str_scalars, regex: bool = False) arkouda.pdarrayclass.pdarray[source]¶
Check whether each element starts with the given substring.
- Parameters:
substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)
- Returns:
True for elements that start with substr, False otherwise
- Return type:
pdarray, bool
- Raises:
TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown
See also
Notes
This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.
- to_hdf(prefix_path, dataset='categorical_array', mode='truncate', file_type='distribute')[source]¶
Save the Categorical to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale.
- Return type:
None
See also
- to_list() List[source]¶
Convert the Categorical to a list, transferring data from the arkouda server to Python. This conversion discards category information and produces a list of strings. If the arrays exceeds a built-in size limit, a RuntimeError is raised.
- Returns:
A list of strings corresponding to the values in this Categorical
- Return type:
list
Notes
The number of bytes in the Categorical cannot exceed
ak.client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but proceed with caution.
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the arkouda server to Python. This conversion discards category information and produces an ndarray of strings. If the arrays exceeds a built-in size limit, a RuntimeError is raised.
- Returns:
A numpy ndarray of strings corresponding to the values in this array
- Return type:
np.ndarray
Notes
The number of bytes in the array cannot exceed
ak.client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but proceed with caution.
- to_parquet(prefix_path: str, dataset: str = 'categorical_array', mode: str = 'truncate', compression: str | None = None) str[source]¶
This functionality is currently not supported and will also raise a RuntimeError. Support is in development. Save the Categorical to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in HDF5 files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Categorical dataset within existing files.
compression (str (Optional)) – Default None Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4
- Return type:
String message indicating result of save operation
- Raises:
RuntimeError – On run due to compatability issues of Categorical with Parquet.
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.See also
- to_strings() List[source]¶
Convert the Categorical to Strings.
- Returns:
A Strings object corresponding to the values in this Categorical.
- Return type:
Examples
>>> from arkouda import ak >>> ak.connect() >>> a = ak.array(["a","b","c"]) >>> a >>> c = ak.Categorical(a) >>> c.to_strings() c.to_strings()
>>> isinstance(c.to_strings(), ak.Strings) True
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a Categorical object to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the Categorical is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unique() Categorical[source]¶
- unregister() None[source]¶
Unregister this Categorical object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
register,attach,unregister_categorical_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_categorical_by_name(user_defined_name: str) None[source]¶
Function to unregister Categorical object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the Categorical object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- update_hdf(prefix_path, dataset='categorical_array', repack=True)[source]¶
Overwrite the dataset with the name provided with this Categorical object. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the Categorical
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, the repack option allows for automatic creation of a file without the inaccessible data.
- arkouda.DTypeObjects¶
- arkouda.DTypes¶
- class arkouda.DataFrame(initialdata=None, index=None, columns=None)[source]¶
Bases:
collections.UserDictA DataFrame structure based on arkouda arrays.
- Parameters:
initialdata (List or dictionary of lists, tuples, or pdarrays) – Each list/dictionary entry corresponds to one column of the data and should be a homogenous type. Different columns may have different types. If using a dictionary, keys should be strings.
index (Index, pdarray, or Strings) – Index for the resulting frame. Defaults to an integer range.
columns (List, tuple, pdarray, or Strings) – Column labels to use if the data does not include them. Elements must be strings. Defaults to an stringified integer range.
Examples
Create an empty DataFrame and add a column of data:
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame() >>> df['a'] = ak.array([1,2,3]) >>> display(df)
a
0
1
1
2
2
3
Create a new DataFrame using a dictionary of data:
>>> userName = ak.array(['Alice', 'Bob', 'Alice', 'Carol', 'Bob', 'Alice']) >>> userID = ak.array([111, 222, 111, 333, 222, 111]) >>> item = ak.array([0, 0, 1, 1, 2, 0]) >>> day = ak.array([5, 5, 6, 5, 6, 6]) >>> amount = ak.array([0.5, 0.6, 1.1, 1.2, 4.3, 0.6]) >>> df = ak.DataFrame({'userName': userName, 'userID': userID, >>> 'item': item, 'day': day, 'amount': amount}) >>> display(df)
userName
userID
item
day
amount
0
Alice
111
0
5
0.5
1
Bob
222
0
5
0.6
2
Alice
111
1
6
1.1
3
Carol
333
1
5
1.2
4
Bob
222
2
6
4.3
5
Alice
111
0
6
0.6
Indexing works slightly differently than with pandas:
>>> df[0]
keys
values
userName
Alice
userID
111
item
0
day
5
amount
0.5
>>> df['userID'] array([111, 222, 111, 333, 222, 111])
>>> df['userName'] array(['Alice', 'Bob', 'Alice', 'Carol', 'Bob', 'Alice'])
>>> df[ak.array([1,3,5])]
userName
userID
item
day
amount
0
Bob
222
0
5
0.6
1
Carol
333
1
5
1.2
2
Alice
111
0
6
0.6
Compute the stride:
>>> df[1:5:1]
userName
userID
item
day
amount
0
Bob
222
0
5
0.6
1
Alice
111
1
6
1.1
2
Carol
333
1
5
1.2
3
Bob
222
2
6
4.3
>>> df[ak.array([1,2,3])]
userName
userID
item
day
amount
0
Bob
222
0
5
0.6
1
Alice
111
1
6
1.1
2
Carol
333
1
5
1.2
>>> df[['userID', 'day']]
userID
day
0
111
5
1
222
5
2
111
6
3
333
5
4
222
6
5
111
6
- property columns¶
An Index where the values are the column names of the dataframe.
- Returns:
The values of the index are the column names of the dataframe.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df
col1
col2
0
1
3
1
2
4
>>> df.columns Index(array(['col1', 'col2']), dtype='<U0')
- property dtypes¶
The dtypes of the dataframe.
- Returns:
dtypes – The dtypes of the dataframe.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': ["a", "b"]}) >>> df
col1
col2
0
1
a
1
2
b
>>> df.dtypes
keys
values
col1
int64
col2
str
- property empty¶
Whether the dataframe is empty.
- Returns:
True if the dataframe is empty, otherwise False.
- Return type:
bool
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({}) >>> df 0 rows x 0 columns >>> df.empty True
- property index¶
The index of the dataframe.
- Returns:
The index of the dataframe.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df
col1
col2
0
1
3
1
2
4
>>> df.index Index(array([0 1]), dtype='int64')
- property info¶
Returns a summary string of this dataframe.
- Returns:
A summary string of this dataframe.
- Return type:
str
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': ["a", "b"]}) >>> df
col1
col2
0
1
a
1
2
b
>>> df.info "DataFrame(['col1', 'col2'], 2 rows, 20 B)"
- property shape¶
The shape of the dataframe.
- Returns:
Tuple of array dimensions.
- Return type:
tuple of int
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df
col1
col2
0
1
4
1
2
5
2
3
6
>>> df.shape (3, 2)
- property size¶
Returns the number of bytes on the arkouda server.
- Returns:
The number of bytes on the arkouda server.
- Return type:
int
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df
col1
col2
0
1
4
1
2
5
2
3
6
>>> df.size 6
- objType = 'DataFrame'¶
- GroupBy(keys, use_series=False, as_index=True, dropna=True)[source]¶
Group the dataframe by a column or a list of columns.
- Parameters:
keys (str or list of str) – An (ordered) list of column names or a single string to group by.
use_series (bool, default=False) – If True, returns an arkouda.dataframe.GroupBy object. Otherwise an arkouda.groupbyclass.GroupBy object.
as_index (bool, default=True) – If True, groupby columns will be set as index otherwise, the groupby columns will be treated as DataFrame columns.
dropna (bool, default=True) – If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Returns:
If use_series = True, returns an arkouda.dataframe.GroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.
- Return type:
arkouda.dataframe.GroupBy or arkouda.groupbyclass.GroupBy
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1.0, 1.0, 2.0, np.nan], 'col2': [4, 5, 6, 7]}) >>> df
col1
col2
0
1
4
1
1
5
2
2
6
3
nan
7
>>> df.GroupBy("col1") <arkouda.groupbyclass.GroupBy at 0x7f2cf23e10c0> >>> df.GroupBy("col1").size() (array([1.00000000000000000 2.00000000000000000]), array([2 1]))
>>> df.GroupBy("col1",use_series=True) col1 1.0 2 2.0 1 dtype: int64 >>> df.GroupBy("col1",use_series=True, as_index = False).size()
col1
size
0
1
2
1
2
1
- all(axis=0) arkouda.series.Series | bool[source]¶
Return whether all elements are True, potentially over an axis.
Returns True unless there at least one element along a Dataframe axis that is False.
Currently, will ignore any columns that are not type bool. This is equivalent to the pandas option bool_only=True.
- Parameters:
axis ({0 or ‘index’, 1 or ‘columns’, None}, default = 0) –
Indicate which axis or axes should be reduced.
0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.
- Return type:
arkouda.series.Series or bool
- Raises:
ValueError – Raised if axis does not have a value in {0 or ‘index’, 1 or ‘columns’, None}.
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({"A":[True,True,True,False],"B":[True,True,True,False], ... "C":[True,False,True,False],"D":[True,True,True,True]})
A
B
C
D
0
True
True
True
True
1
True
True
False
True
2
True
True
True
True
3
False
False
False
True
>>> df.all(axis=0) A False B False C False D True dtype: bool >>> df.all(axis=1) 0 True 1 False 2 True 3 False dtype: bool >>> df.all(axis=None) False
- any(axis=0) arkouda.series.Series | bool[source]¶
Return whether any element is True, potentially over an axis.
Returns False unless there is at least one element along a Dataframe axis that is True.
Currently, will ignore any columns that are not type bool. This is equivalent to the pandas option bool_only=True.
- Parameters:
axis ({0 or ‘index’, 1 or ‘columns’, None}, default = 0) –
Indicate which axis or axes should be reduced.
0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.
- Return type:
arkouda.series.Series or bool
- Raises:
ValueError – Raised if axis does not have a value in {0 or ‘index’, 1 or ‘columns’, None}.
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({"A":[True,True,True,False],"B":[True,True,True,False], ... "C":[True,False,True,False],"D":[False,False,False,False]})
A
B
C
D
0
True
True
True
False
1
True
True
False
False
2
True
True
True
False
3
False
False
False
False
>>> df.any(axis=0) A True B True C True D False dtype: bool >>> df.any(axis=1) 0 True 1 True 2 True 3 False dtype: bool >>> df.any(axis=None) True
- append(other, ordered=True)[source]¶
Concatenate data from ‘other’ onto the end of this DataFrame, in place.
Explicitly, use the arkouda concatenate function to append the data from each column in other to the end of self. This operation is done in place, in the sense that the underlying pdarrays are updated from the result of the arkouda concatenate function, rather than returning a new DataFrame object containing the result.
- Parameters:
other (DataFrame) – The DataFrame object whose data will be appended to this DataFrame.
ordered (bool, default=True) – If False, allow rows to be interleaved for better performance (but data within a row remains together). By default, append all rows to the end, in input order.
- Returns:
Appending occurs in-place, but result is returned for compatibility.
- Return type:
self
Examples
>>> import arkouda as ak >>> ak.connect() >>> df1 = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
col1
col2
0
1
3
1
2
4
>>> df2 = ak.DataFrame({'col1': [3], 'col2': [5]})
col1
col2
0
3
5
>>> df1.append(df2) >>> df1
col1
col2
0
1
3
1
2
4
2
3
5
- apply_permutation(perm)[source]¶
Apply a permutation to an entire DataFrame. The operation is done in place and the original DataFrame will be modified.
This may be useful if you want to unsort an DataFrame, or even to apply an arbitrary permutation such as the inverse of a sorting permutation.
- Parameters:
perm (pdarray) – A permutation array. Should be the same size as the data arrays, and should consist of the integers [0,size-1] in some order. Very minimal testing is done to ensure this is a permutation.
- Return type:
None
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
col1
col2
0
1
4
1
2
5
2
3
6
>>> perm_arry = ak.array([0, 2, 1]) >>> df.apply_permutation(perm_arry) >>> display(df)
col1
col2
0
1
4
1
3
6
2
2
5
- argsort(key, ascending=True)[source]¶
Return the permutation that sorts the dataframe by key.
- Parameters:
key (str) – The key to sort on.
ascending (bool, default = True) – If true, sort the key in ascending order. Otherwise, sort the key in descending order.
- Returns:
The permutation array that sorts the data on key.
- Return type:
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1.1, 3.1, 2.1], 'col2': [6, 5, 4]}) >>> display(df)
col1
col2
0
1.1
6
1
3.1
5
2
2.1
4
>>> df.argsort('col1') array([0 2 1]) >>> sorted_df1 = df[df.argsort('col1')] >>> display(sorted_df1)
col1
col2
0
1.1
6
1
2.1
4
2
3.1
5
>>> df.argsort('col2') array([2 1 0]) >>> sorted_df2 = df[df.argsort('col2')] >>> display(sorted_df2)
col1
col2
0
2.1
4
1
3.1
5
2
1.1
6
- static attach(user_defined_name: str) DataFrame[source]¶
Function to return a DataFrame object attached to the registered name in the arkouda server which was registered using register().
- Parameters:
user_defined_name (str) – user defined name which DataFrame object was registered under.
- Returns:
The DataFrame object created by re-attaching to the corresponding server components.
- Return type:
- Raises:
RegistrationError – if user_defined_name is not registered
See also
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister() >>> df.is_registered() False
- coargsort(keys, ascending=True)[source]¶
Return the permutation that sorts the dataframe by keys.
Note: Sorting using Strings may not yield correct sort order.
- Parameters:
keys (list of str) – The keys to sort on.
- Returns:
The permutation array that sorts the data on keys.
- Return type:
Example
>>> df = ak.DataFrame({'col1': [2, 2, 1], 'col2': [3, 4, 3], 'col3':[5, 6, 7]}) >>> display(df)
col1
col2
col3
0
2
3
5
1
2
4
6
2
1
3
7
>>> df.coargsort(['col1', 'col2']) array([2 0 1]) >>>
- copy(deep=True)[source]¶
Make a copy of this object’s data.
When deep = True (default), a new object will be created with a copy of the calling object’s data. Modifications to the data of the copy will not be reflected in the original object.
When deep = False a new object will be created without copying the calling object’s data. Any changes to the data of the original object will be reflected in the shallow copy, and vice versa.
- Parameters:
deep (bool, default=True) – When True, return a deep copy. Otherwise, return a shallow copy.
- Returns:
A deep or shallow copy according to caller specification.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> display(df)
col1
col2
0
1
3
1
2
4
>>> df_deep = df.copy(deep=True) >>> df_deep['col1'] +=1 >>> display(df)
col1
col2
0
1
3
1
2
4
>>> df_shallow = df.copy(deep=False) >>> df_shallow['col1'] +=1 >>> display(df)
col1
col2
0
2
3
1
3
4
- corr() DataFrame[source]¶
Return new DataFrame with pairwise correlation of columns.
- Returns:
Arkouda DataFrame containing correlation matrix of all columns.
- Return type:
- Raises:
RuntimeError – Raised if there’s a server-side error thrown.
See also
Notes
Generates the correlation matrix using Pearson R for all columns.
Attempts to convert to numeric values where possible for inclusion in the matrix.
Example
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [-1, -2]}) >>> display(df)
col1
col2
0
1
-1
1
2
-2
>>> corr = df.corr()
col1
col2
col1
1
-1
col2
-1
1
- count(axis: int | str = 0, numeric_only=False) arkouda.series.Series[source]¶
Count non-NA cells for each column or row.
The values np.NaN are considered NA.
- Parameters:
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
numeric_only (bool = False) – Include only float, int or boolean data.
- Returns:
For each column/row the number of non-NA/null entries.
- Return type:
- Raises:
ValueError – Raised if axis is not 0, 1, ‘index’, or ‘columns’.
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> import numpy as np >>> df = ak.DataFrame({'col_A': ak.array([7, np.nan]), 'col_B':ak.array([1, 9])}) >>> display(df)
col_A
col_B
0
7
1
1
nan
9
>>> df.count() col_A 1 col_B 2 dtype: int64
>>> df = ak.DataFrame({'col_A': ak.array(["a","b","c"]), 'col_B':ak.array([1, np.nan, np.nan])}) >>> display(df)
col_A
col_B
0
a
1
1
b
nan
2
c
nan
>>> df.count() col_A 3 col_B 1 dtype: int64
>>> df.count(numeric_only=True) col_B 1 dtype: int64
>>> df.count(axis=1) 0 2 1 1 2 1 dtype: int64
- drop(keys: str | int | List[str | int], axis: str | int = 0, inplace: bool = False) None | DataFrame[source]¶
Drop column/s or row/s from the dataframe.
- Parameters:
keys (str, int or list) – The labels to be dropped on the given axis.
axis (int or str) – The axis on which to drop from. 0/’index’ - drop rows, 1/’columns’ - drop columns.
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.
- Returns:
DateFrame when inplace=False; None when inplace=True
- Return type:
arkouda.dataframe.DataFrame or None
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> display(df)
col1
col2
0
1
3
1
2
4
Drop column
>>> df.drop('col1', axis = 1)
col2
0
3
1
4
Drop row
>>> df.drop(0, axis = 0)
col1
col2
0
2
4
- drop_duplicates(subset=None, keep='first')[source]¶
Drops duplcated rows and returns resulting DataFrame.
If a subset of the columns are provided then only one instance of each duplicated row will be returned (keep determines which row).
- Parameters:
subset (Iterable) – Iterable of column names to use to dedupe.
keep ({'first', 'last'}, default='first') – Determines which duplicates (if any) to keep.
- Returns:
DataFrame with duplicates removed.
- Return type:
Example
>>> df = ak.DataFrame({'col1': [1, 2, 2, 3], 'col2': [4, 5, 5, 6]}) >>> display(df)
col1
col2
0
1
4
1
2
5
2
2
5
3
3
6
>>> df.drop_duplicates()
col1
col2
0
1
4
1
2
5
2
3
6
- dropna(axis: int | str = 0, how: str | None = None, thresh: int | None = None, ignore_index: bool = False) DataFrame[source]¶
Remove missing values.
- Parameters:
axis ({0 or 'index', 1 or 'columns'}, default = 0) –
Determine if rows or columns which contain missing values are removed.
0, or ‘index’: Drop rows which contain missing values.
1, or ‘columns’: Drop columns which contain missing value.
Only a single axis is allowed.
how ({'any', 'all'}, default='any') –
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.
’any’: If any NA values are present, drop that row or column.
’all’: If all values are NA, drop that row or column.
thresh (int, optional) – Require that many non - NA values.Cannot be combined with how.
ignore_index (bool, default
False) – IfTrue, the resulting axis will be labeled 0, 1, …, n - 1.
- Returns:
DataFrame with NA entries dropped from it.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> import numpy as np >>> df = ak.DataFrame( { "A": [True, True, True, True], "B": [1, np.nan, 2, np.nan], "C": [1, 2, 3, np.nan], "D": [False, False, False, False], "E": [1, 2, 3, 4], "F": ["a", "b", "c", "d"], "G": [1, 2, 3, 4], } )
>>> display(df)
A
B
C
D
E
F
G
0
True
1
1
False
1
a
1
1
True
nan
2
False
2
b
2
2
True
2
3
False
3
c
3
3
True
nan
nan
False
4
d
4
>>> df.dropna()
A
B
C
D
E
F
G
0
True
1
1
False
1
a
1
1
True
2
3
False
3
c
3
>>> df.dropna(axis=1)
A
D
E
F
G
0
True
False
1
a
1
1
True
False
2
b
2
2
True
False
3
c
3
3
True
False
4
d
4
>>> df.dropna(axis=1, thresh=3)
A
C
D
E
F
G
0
True
1
False
1
a
1
1
True
2
False
2
b
2
2
True
3
False
3
c
3
3
True
nan
False
4
d
4
>>> df.dropna(axis=1, how="all")
A
B
C
D
E
F
G
0
True
1
1
False
1
a
1
1
True
nan
2
False
2
b
2
2
True
2
3
False
3
c
3
3
True
nan
nan
False
4
d
4
- filter_by_range(keys, low=1, high=None)[source]¶
Find all rows where the value count of the items in a given set of columns (keys) is within the range [low, high].
To filter by a specific value, set low == high.
- Parameters:
keys (str or list of str) – The names of the columns to group by.
low (int, default=1) – The lowest value count.
high (int, default=None) – The highest value count, default to unlimited.
- Returns:
An array of boolean values for qualified rows in this DataFrame.
- Return type:
Example
>>> df = ak.DataFrame({'col1': [1, 2, 2, 2, 3, 3], 'col2': [4, 5, 6, 7, 8, 9]}) >>> display(df)
col1
col2
0
1
4
1
2
5
2
2
6
3
2
7
4
3
8
5
3
9
>>> df.filter_by_range("col1", low=1, high=2) array([True False False False True True])
>>> filtered_df = df[df.filter_by_range("col1", low=1, high=2)] >>> display(filtered_df)
col1
col2
0
1
4
1
3
8
2
3
9
- classmethod from_pandas(pd_df)[source]¶
Copy the data from a pandas DataFrame into a new arkouda.dataframe.DataFrame.
- Parameters:
pd_df (pandas.DataFrame) – A pandas DataFrame to convert.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> import pandas as pd >>> pd_df = pd.DataFrame({"A":[1,2],"B":[3,4]}) >>> type(pd_df) pandas.core.frame.DataFrame >>> display(pd_df)
A
B
0
1
3
1
2
4
>>> ak_df = DataFrame.from_pandas(pd_df) >>> type(ak_df) arkouda.dataframe.DataFrame >>> display(ak_df)
A
B
0
1
3
1
2
4
- classmethod from_return_msg(rep_msg)[source]¶
Creates a DataFrame object from an arkouda server response message.
- Parameters:
rep_msg (string) – Server response message used to create a DataFrame.
- Return type:
- groupby(keys, use_series=True, as_index=True, dropna=True)[source]¶
Group the dataframe by a column or a list of columns. Alias for GroupBy.
- Parameters:
keys (str or list of str) – An (ordered) list of column names or a single string to group by.
use_series (bool, default=True) – If True, returns an arkouda.dataframe.GroupBy object. Otherwise an arkouda.groupbyclass.GroupBy object.
as_index (bool, default=True) – If True, groupby columns will be set as index otherwise, the groupby columns will be treated as DataFrame columns.
dropna (bool, default=True) – If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Returns:
If use_series = True, returns an arkouda.dataframe.GroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.
- Return type:
arkouda.dataframe.GroupBy or arkouda.groupbyclass.GroupBy
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1.0, 1.0, 2.0, np.nan], 'col2': [4, 5, 6, 7]}) >>> df
col1
col2
0
1
4
1
1
5
2
2
6
3
nan
7
>>> df.GroupBy("col1") <arkouda.groupbyclass.GroupBy at 0x7f2cf23e10c0> >>> df.GroupBy("col1").size() (array([1.00000000000000000 2.00000000000000000]), array([2 1]))
>>> df.GroupBy("col1",use_series=True) col1 1.0 2 2.0 1 dtype: int64 >>> df.GroupBy("col1",use_series=True, as_index = False).size()
col1
size
0
1
2
1
2
1
- head(n=5)[source]¶
Return the first n rows.
This function returns the first n rows of the the dataframe. It is useful for quickly verifying data, for example, after sorting or appending rows.
- Parameters:
n (int, default = 5) – Number of rows to select.
- Returns:
The first n rows of the DataFrame.
- Return type:
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': ak.arange(10), 'col2': -1 * ak.arange(10)}) >>> display(df)
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
5
5
-5
6
6
-6
7
7
-7
8
8
-8
9
9
-9
>>> df.head()
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
>>> df.head(n=2)
col1
col2
0
0
0
1
1
-1
- is_registered() bool[source]¶
Return True if the object is contained in the registry.
- Returns:
Indicates if the object is contained in the registry.
- Return type:
bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mismatch of registered components.
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister() >>> df.is_registered() False
- isin(values: arkouda.pdarrayclass.pdarray | Dict | arkouda.series.Series | DataFrame) DataFrame[source]¶
Determine whether each element in the DataFrame is contained in values.
- Parameters:
values (pdarray, dict, Series, or DataFrame) – The values to check for in DataFrame. Series can only have a single index.
- Returns:
Arkouda DataFrame of booleans showing whether each element in the DataFrame is contained in values.
- Return type:
See also
ak.Series.isinNotes
Pandas supports values being an iterable type. In arkouda, we replace this with pdarray.
Pandas supports ~ operations. Currently, ak.DataFrame does not support this.
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col_A': ak.array([7, 3]), 'col_B':ak.array([1, 9])}) >>> display(df)
col_A
col_B
0
7
1
1
3
9
When values is a pdarray, check every value in the DataFrame to determine if it exists in values.
>>> df.isin(ak.array([0, 1]))
col_A
col_B
0
0
1
1
0
0
When values is a dict, the values in the dict are passed to check the column indicated by the key.
>>> df.isin({'col_A': ak.array([0, 3])})
col_A
col_B
0
0
0
1
1
0
When values is a Series, each column is checked if values is present positionally. This means that for True to be returned, the indexes must be the same.
>>> i = ak.Index(ak.arange(2)) >>> s = ak.Series(data=[3, 9], index=i) >>> df.isin(s)
col_A
col_B
0
0
0
1
0
1
When values is a DataFrame, the index and column must match. Note that 9 is not found because the column name does not match.
>>> other_df = ak.DataFrame({'col_A':ak.array([7, 3]), 'col_C':ak.array([0, 9])}) >>> df.isin(other_df)
col_A
col_B
0
1
0
1
1
0
- isna() DataFrame[source]¶
Detect missing values.
Return a boolean same-sized object indicating if the values are NA. numpy.NaN values get mapped to True values. Everything else gets mapped to False values.
- Returns:
Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> import numpy as np >>> df = ak.DataFrame({"A": [np.nan, 2, 2, 3], "B": [3, np.nan, 5, 6], ... "C": [1, np.nan, 2, np.nan], "D":["a","b","c","d"]}) >>> display(df)
A
B
C
D
0
nan
3
1
a
1
2
nan
nan
b
2
2
5
2
c
3
3
6
nan
d
>>> df.isna() A B C D 0 True False False False 1 False True True False 2 False False False False 3 False False True False (4 rows x 4 columns)
- classmethod load(prefix_path, file_format='INFER')[source]¶
Load dataframe from file. file_format needed for consistency with other load functions.
- Parameters:
prefix_path (str) – The prefix path for the data.
file_format (string, default = "INFER")
- Returns:
A dataframe loaded from the prefix_path.
- Return type:
Examples
To store data in <my_dir>/my_data_LOCALE0000, use “<my_dir>/my_data” as the prefix.
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'hdf5_output','my_data') >>> Path(my_path).mkdir(parents=True, exist_ok=True) >>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)}) >>> df.save(my_path, file_type="distribute") >>> df.load(my_path)
A
B
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
- memory_usage(index=True, unit='B') arkouda.series.Series[source]¶
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of the index.
- Parameters:
index (bool, default True) – Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If
index=True, the memory usage of the index is the first item in the output.unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.
- Returns:
A Series whose index is the original column names and whose values is the memory usage of each column in bytes.
- Return type:
See also
arkouda.pdarrayclass.nbytes,arkouda.index.Index.memory_usage,arkouda.index.MultiIndex.memory_usage,arkouda.series.Series.memory_usageExamples
>>> import arkouda as ak >>> ak.connect() >>> dtypes = [ak.int64, ak.float64, ak.bool] >>> data = dict([(str(t), ak.ones(5000, dtype=ak.int64).astype(t)) for t in dtypes]) >>> df = ak.DataFrame(data) >>> display(df.head())
int64
float64
bool
0
1
1
True
1
1
1
True
2
1
1
True
3
1
1
True
4
1
1
True
>>> df.memory_usage()
0
Index
40000
int64
40000
float64
40000
bool
5000
>>> df.memory_usage(index=False)
0
int64
40000
float64
40000
bool
5000
>>> df.memory_usage(unit="KB")
0
Index
39.0625
int64
39.0625
float64
39.0625
bool
4.88281
To get the approximate total memory usage:
>>> df.memory_usage(index=True).sum()
- memory_usage_info(unit='GB')[source]¶
A formatted string representation of the size of this DataFrame.
- Parameters:
unit (str, default = "GB") – Unit to return. One of {‘KB’, ‘MB’, ‘GB’}.
- Returns:
A string representation of the number of bytes used by this DataFrame in [unit]s.
- Return type:
str
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': ak.arange(1000), 'col2': ak.arange(1000)}) >>> df.memory_usage_info() '0.00 GB'
>>> df.memory_usage_info(unit="KB") '15 KB'
- merge(right: DataFrame, on: str | List[str] | None = None, how: str = 'inner', left_suffix: str = '_x', right_suffix: str = '_y', convert_ints: bool = True, sort: bool = True) DataFrame[source]¶
Merge Arkouda DataFrames with a database-style join. The resulting dataframe contains rows from both DataFrames as specified by the merge condition (based on the “how” and “on” parameters).
Based on pandas merge functionality. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
- Parameters:
right (DataFrame) – The Right DataFrame to be joined.
on (Optional[Union[str, List[str]]] = None) – The name or list of names of the DataFrame column(s) to join on. If on is None, this defaults to the intersection of the columns in both DataFrames.
how ({"inner", "left", "right}, default = "inner") – The merge condition. Must be “inner”, “left”, or “right”.
left_suffix (str, default = "_x") – A string indicating the suffix to add to columns from the left dataframe for overlapping column names in both left and right. Defaults to “_x”. Only used when how is “inner”.
right_suffix (str, default = "_y") – A string indicating the suffix to add to columns from the right dataframe for overlapping column names in both left and right. Defaults to “_y”. Only used when how is “inner”.
convert_ints (bool = True) – If True, convert columns with missing int values (due to the join) to float64. This is to match pandas. If False, do not convert the column dtypes. This has no effect when how = “inner”.
sort (bool = True) – If True, DataFrame is returned sorted by “on”. Otherwise, the DataFrame is not sorted.
- Returns:
Joined Arkouda DataFrame.
- Return type:
Note
Multiple column joins are only supported for integer columns.
Examples
>>> import arkouda as ak >>> ak.connect() >>> left_df = ak.DataFrame({'col1': ak.arange(5), 'col2': -1 * ak.arange(5)}) >>> display(left_df)
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
>>> right_df = ak.DataFrame({'col1': 2 * ak.arange(5), 'col2': 2 * ak.arange(5)}) >>> display(right_df)
col1
col2
0
0
0
1
2
2
2
4
4
3
6
6
4
8
8
>>> left_df.merge(right_df, on = "col1")
col1
col2_x
col2_y
0
0
0
0
1
2
-2
2
2
4
-4
4
>>> left_df.merge(right_df, on = "col1", how = "left")
col1
col2_y
col2_x
0
0
0
0
1
1
nan
-1
2
2
2
-2
3
3
nan
-3
4
4
4
-4
>>> left_df.merge(right_df, on = "col1", how = "right")
col1
col2_x
col2_y
0
0
0
0
1
2
-2
2
2
4
-4
4
3
6
nan
6
4
8
nan
8
>>> left_df.merge(right_df, on = "col1", how = "outer")
col1
col2_y
col2_x
0
0
0
0
1
1
nan
-1
2
2
2
-2
3
3
nan
-3
4
4
4
-4
5
6
6
nan
6
8
8
nan
- notna() DataFrame[source]¶
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA. numpy.NaN values get mapped to False values.
- Returns:
Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> import numpy as np >>> df = ak.DataFrame({"A": [np.nan, 2, 2, 3], "B": [3, np.nan, 5, 6], ... "C": [1, np.nan, 2, np.nan], "D":["a","b","c","d"]}) >>> display(df)
A
B
C
D
0
nan
3
1
a
1
2
nan
nan
b
2
2
5
2
c
3
3
6
nan
d
>>> df.notna() A B C D 0 False True True True 1 True False False True 2 True True True True 3 True True False True (4 rows x 4 columns)
- classmethod read_csv(filename: str, col_delim: str = ',')[source]¶
Read the columns of a CSV file into an Arkouda DataFrame. If the file contains the appropriately formatted header, typed data will be returned. Otherwise, all data will be returned as a Strings objects.
- Parameters:
filename (str) – Filename to read data from.
col_delim (str, default=",") – The delimiter for columns within the data.
- Returns:
Arkouda DataFrame containing the columns from the CSV file.
- Return type:
- Raises:
ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server.
See also
Notes
CSV format is not currently supported by load/load_all operations.
The column delimiter is expected to be the same for column names and data.
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”\n”) at this time.
Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing
bytes as uint(8).
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'csv_output','my_data') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_csv(my_path) >>> df2 = DataFrame.read_csv(my_path + "_LOCALE0000") >>> display(df2)
A
B
0
1
3
1
2
4
- register(user_defined_name: str) DataFrame[source]¶
Register this DataFrame object and underlying components with the Arkouda server.
- Parameters:
user_defined_name (str) – User defined name the DataFrame is to be registered under. This will be the root name for underlying components.
- Returns:
The same DataFrame which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different DataFrames with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str.
RegistrationError – If the server was unable to register the DataFrame with the user_defined_name.
See also
unregister,attach,unregister_dataframe_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
Any changes made to a DataFrame object after registering with the server may not be reflected in attached copies.
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister() >>> df.is_registered() False
- rename(mapper: Callable | Dict | None = None, index: Callable | Dict | None = None, column: Callable | Dict | None = None, axis: str | int = 0, inplace: bool = False) DataFrame | None[source]¶
Rename indexes or columns according to a mapping.
- Parameters:
mapper (callable or dict-like, Optional) – Function or dictionary mapping existing values to new values. Nonexistent names will not raise an error. Uses the value of axis to determine if renaming column or index
column (callable or dict-like, Optional) – Function or dictionary mapping existing column names to new column names. Nonexistent names will not raise an error. When this is set, axis is ignored.
index (callable or dict-like, Optional) – Function or dictionary mapping existing index names to new index names. Nonexistent names will not raise an error. When this is set, axis is ignored.
axis (int or str, default=0) – Indicates which axis to perform the rename. 0/”index” - Indexes 1/”column” - Columns
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.
- Returns:
DateFrame when inplace=False; None when inplace=True.
- Return type:
arkouda.dataframe.DataFrame or None
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({"A": ak.array([1, 2, 3]), "B": ak.array([4, 5, 6])}) >>> display(df)
A
B
0
1
4
1
2
5
2
3
6
Rename columns using a mapping:
>>> df.rename(column={'A':'a', 'B':'c'})
a
c
0
1
4
1
2
5
2
3
6
Rename indexes using a mapping:
>>> df.rename(index={0:99, 2:11})
A
B
0
1
4
1
2
5
2
3
6
Rename using an axis style parameter:
>>> df.rename(str.lower, axis='column')
a
b
0
1
4
1
2
5
2
3
6
- reset_index(size: int | None = None, inplace: bool = False) None | DataFrame[source]¶
Set the index to an integer range.
Useful if this dataframe is the result of a slice operation from another dataframe, or if you have permuted the rows and no longer need to keep that ordering on the rows.
- Parameters:
size (int, optional) – If size is passed, do not attempt to determine size based on existing column sizes. Assume caller handles consistency correctly.
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.
- Returns:
DateFrame when inplace=False; None when inplace=True.
- Return type:
arkouda.dataframe.DataFrame or None
Note
Pandas adds a column ‘index’ to indicate the original index. Arkouda does not currently support this behavior.
Example
>>> df = ak.DataFrame({"A": ak.array([1, 2, 3]), "B": ak.array([4, 5, 6])}) >>> display(df)
A
B
0
1
4
1
2
5
2
3
6
>>> perm_df = df[ak.array([0,2,1])] >>> display(perm_df)
A
B
0
1
4
1
3
6
2
2
5
>>> perm_df.reset_index()
A
B
0
1
4
1
3
6
2
2
5
- sample(n=5)[source]¶
Return a random sample of n rows.
- Parameters:
n (int, default=5) – Number of rows to return.
- Returns:
The sampled n rows of the DataFrame.
- Return type:
Example
>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)}) >>> display(df)
A
B
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
Random output of size 3:
>>> df.sample(n=3)
A
B
0
0
0
1
1
-1
2
4
-4
- save(path, index=False, columns=None, file_format='HDF5', file_type='distribute', compression: str | None = None)[source]¶
DEPRECATED Save DataFrame to disk, preserving column names.
- Parameters:
path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (list, default=None) – List of columns to include in the file. If None, writes out all columns.
file_format (str, default='HDF5') – ‘HDF5’ or ‘Parquet’. Defaults to ‘HDF5’
file_type (str, default=distribute) – “single” or “distribute” If single, will right a single file to locale 0.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Compression type. Only used for Parquet
Notes
This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'hdf5_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)}) >>> df.save(my_path + '/my_data', file_type="single") >>> df.load(my_path + '/my_data')
A
B
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
- sort_index(ascending=True)[source]¶
Sort the DataFrame by indexed columns.
Note: Fails on sort order of arkouda.strings.Strings columns when multiple columns being sorted.
- Parameters:
ascending (bool, default = True) – Sort values in ascending (default) or descending order.
Example
>>> df = ak.DataFrame({'col1': [1.1, 3.1, 2.1], 'col2': [6, 5, 4]}, ... index = Index(ak.array([2,0,1]), name="idx"))
>>> display(df)
idx
col1
col2
0
1.1
6
1
3.1
5
2
2.1
4
>>> df.sort_index()
idx
col1
col2
0
3.1
5
1
2.1
4
2
1.1
6
- sort_values(by=None, ascending=True)[source]¶
Sort the DataFrame by one or more columns.
If no column is specified, all columns are used.
Note: Fails on order of arkouda.strings.Strings columns when multiple columns being sorted.
- Parameters:
by (str or list/tuple of str, default = None) – The name(s) of the column(s) to sort by.
ascending (bool, default = True) – Sort values in ascending (default) or descending order.
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [2, 2, 1], 'col2': [3, 4, 3], 'col3':[5, 6, 7]}) >>> display(df)
col1
col2
col3
0
2
3
5
1
2
4
6
2
1
3
7
>>> df.sort_values()
col1
col2
col3
0
1
3
7
1
2
3
5
2
2
4
6
>>> df.sort_values("col3")
col1
col2
col3
0
1
3
7
1
2
3
5
2
2
4
6
- tail(n=5)[source]¶
Return the last n rows.
This function returns the last n rows for the dataframe. It is useful for quickly testing if your object has the right type of data in it.
- Parameters:
n (int, default=5) – Number of rows to select.
- Returns:
The last n rows of the DataFrame.
- Return type:
See also
arkouda.dataframe.headExamples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': ak.arange(10), 'col2': -1 * ak.arange(10)}) >>> display(df)
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
5
5
-5
6
6
-6
7
7
-7
8
8
-8
9
9
-9
>>> df.tail()
col1
col2
0
5
-5
1
6
-6
2
7
-7
3
8
-8
4
9
-9
>>> df.tail(n=2)
col1
col2
0
8
-8
1
9
-9
- to_csv(path: str, index: bool = False, columns: List[str] | None = None, col_delim: str = ',', overwrite: bool = False)[source]¶
Writes DataFrame to CSV file(s). File will contain a column for each column in the DataFrame. All CSV Files written by Arkouda include a header denoting data types of the columns. Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing bytes as uint(8).
- Parameters:
path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
index (bool, default=False) – If True, the index of the DataFrame will be written to the file as a column.
columns (list of str (Optional)) – Column names to assign when writing data.
col_delim (str, default=",") – Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, default=False) – If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
- Return type:
None
- Raises:
ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server.
Notes
CSV format is not currently supported by load/load_all operations.
The column delimiter is expected to be the same for column names and data.
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”\n”) at this time.
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'csv_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_csv(my_path + "/my_data") >>> df2 = DataFrame.read_csv(my_path + "/my_data" + "_LOCALE0000") >>> display(df2)
A
B
0
1
3
1
2
4
- to_hdf(path, index=False, columns=None, file_type='distribute')[source]¶
Save DataFrame to disk as hdf5, preserving column names.
- Parameters:
path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (List, default = None) – List of columns to include in the file. If None, writes out all columns.
file_type (str (single | distribute), default=distribute) – Whether to save to a single file or distribute across Locales.
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray.
Notes
This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'hdf_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_hdf(my_path + "/my_data") >>> df.load(my_path + "/my_data")
A
B
0
1
3
1
2
4
- to_markdown(mode='wt', index=True, tablefmt='grid', storage_options=None, **kwargs)[source]¶
Print DataFrame in Markdown-friendly format.
- Parameters:
mode (str, optional) – Mode in which file is opened, “wt” by default.
index (bool, optional, default True) – Add index (row) labels.
tablefmt (str = "grid") – Table format to call from tablulate: https://pypi.org/project/tabulate/
storage_options (dict, optional) – Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a non-fsspec URL. See the fsspec and backend storage implementation docs for the set of allowed keys and values.
**kwargs – These parameters will be passed to tabulate.
Note
This function should only be called on small DataFrames as it calls pandas.DataFrame.to_markdown: https://pandas.pydata.org/pandas-docs/version/1.2.4/reference/api/pandas.DataFrame.to_markdown.html
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}) >>> print(df.to_markdown()) +----+------------+------------+ | | animal_1 | animal_2 | +====+============+============+ | 0 | elk | dog | +----+------------+------------+ | 1 | pig | quetzal | +----+------------+------------+
Suppress the index:
>>> print(df.to_markdown(index = False)) +------------+------------+ | animal_1 | animal_2 | +============+============+ | elk | dog | +------------+------------+ | pig | quetzal | +------------+------------+
- to_pandas(datalimit=maxTransferBytes, retain_index=False)[source]¶
Send this DataFrame to a pandas DataFrame.
- Parameters:
datalimit (int, default=arkouda.client.maxTransferBytes) – The maximum number size, in megabytes to transfer. The requested DataFrame will be converted to a pandas DataFrame only if the estimated size of the DataFrame does not exceed this value.
retain_index (bool, default=False) – Normally, to_pandas() creates a new range index object. If you want to keep the index column, set this to True.
- Returns:
The result of converting this DataFrame to a pandas DataFrame.
- Return type:
pandas.DataFrame
Examples
>>> import arkouda as ak >>> ak.connect() >>> ak_df = ak.DataFrame({"A": ak.arange(2), "B": -1 * ak.arange(2)}) >>> type(ak_df) arkouda.dataframe.DataFrame >>> display(ak_df)
A
B
0
0
0
1
1
-1
>>> import pandas as pd >>> pd_df = ak_df.to_pandas() >>> type(pd_df) pandas.core.frame.DataFrame >>> display(pd_df)
A
B
0
0
0
1
1
-1
- to_parquet(path, index=False, columns=None, compression: str | None = None, convert_categoricals: bool = False)[source]¶
Save DataFrame to disk as parquet, preserving column names.
- Parameters:
path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (list) – List of columns to include in the file. If None, writes out all columns.
compression (str (Optional), default=None) – Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4
convert_categoricals (bool, default=False) – Parquet requires all columns to be the same size and Categoricals don’t satisfy that requirement. If set, write the equivalent Strings in place of any Categorical columns.
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'parquet_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_parquet(my_path + "/my_data") >>> df.load(my_path + "/my_data")
B
A
0
3
1
1
4
2
- transfer(hostname, port)[source]¶
Sends a DataFrame to a different Arkouda server.
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the DataFrame is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Returns:
A message indicating a complete transfer.
- Return type:
str
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unregister()[source]¶
Unregister this DataFrame object in the arkouda server which was previously registered using register() and/or attached to using attach().
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister.
See also
register,attach,unregister_dataframe_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister() >>> df.is_registered() False
- static unregister_dataframe_by_name(user_defined_name: str) str[source]¶
Function to unregister DataFrame object by name which was registered with the arkouda server via register().
- Parameters:
user_defined_name (str) – Name under which the DataFrame object was registered.
- Raises:
TypeError – If user_defined_name is not a string.
RegistrationError – If there is an issue attempting to unregister any underlying components.
See also
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister_dataframe_by_name("my_table_name") >>> df.is_registered() False
- update_hdf(prefix_path: str, index=False, columns=None, repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this dataframe. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (List, default=None) – List of columns to include in the file. If None, writes out all columns.
repack (bool, default=True) – HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Returns:
Success message if successful.
- Return type:
str
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray.
Notes
- If file does not contain File_Format attribute to indicate how it was saved,
the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added.
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'hdf_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_hdf(my_path + "/my_data") >>> df.load(my_path + "/my_data")
A
B
0
1
3
1
2
4
>>> df2 = ak.DataFrame({"A":[5,6],"B":[7,8]}) >>> df2.update_hdf(my_path + "/my_data") >>> df.load(my_path + "/my_data")
A
B
0
5
7
1
6
8
- class arkouda.DataFrame(initialdata=None, index=None, columns=None)[source]¶
Bases:
collections.UserDictA DataFrame structure based on arkouda arrays.
- Parameters:
initialdata (List or dictionary of lists, tuples, or pdarrays) – Each list/dictionary entry corresponds to one column of the data and should be a homogenous type. Different columns may have different types. If using a dictionary, keys should be strings.
index (Index, pdarray, or Strings) – Index for the resulting frame. Defaults to an integer range.
columns (List, tuple, pdarray, or Strings) – Column labels to use if the data does not include them. Elements must be strings. Defaults to an stringified integer range.
Examples
Create an empty DataFrame and add a column of data:
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame() >>> df['a'] = ak.array([1,2,3]) >>> display(df)
a
0
1
1
2
2
3
Create a new DataFrame using a dictionary of data:
>>> userName = ak.array(['Alice', 'Bob', 'Alice', 'Carol', 'Bob', 'Alice']) >>> userID = ak.array([111, 222, 111, 333, 222, 111]) >>> item = ak.array([0, 0, 1, 1, 2, 0]) >>> day = ak.array([5, 5, 6, 5, 6, 6]) >>> amount = ak.array([0.5, 0.6, 1.1, 1.2, 4.3, 0.6]) >>> df = ak.DataFrame({'userName': userName, 'userID': userID, >>> 'item': item, 'day': day, 'amount': amount}) >>> display(df)
userName
userID
item
day
amount
0
Alice
111
0
5
0.5
1
Bob
222
0
5
0.6
2
Alice
111
1
6
1.1
3
Carol
333
1
5
1.2
4
Bob
222
2
6
4.3
5
Alice
111
0
6
0.6
Indexing works slightly differently than with pandas:
>>> df[0]
keys
values
userName
Alice
userID
111
item
0
day
5
amount
0.5
>>> df['userID'] array([111, 222, 111, 333, 222, 111])
>>> df['userName'] array(['Alice', 'Bob', 'Alice', 'Carol', 'Bob', 'Alice'])
>>> df[ak.array([1,3,5])]
userName
userID
item
day
amount
0
Bob
222
0
5
0.6
1
Carol
333
1
5
1.2
2
Alice
111
0
6
0.6
Compute the stride:
>>> df[1:5:1]
userName
userID
item
day
amount
0
Bob
222
0
5
0.6
1
Alice
111
1
6
1.1
2
Carol
333
1
5
1.2
3
Bob
222
2
6
4.3
>>> df[ak.array([1,2,3])]
userName
userID
item
day
amount
0
Bob
222
0
5
0.6
1
Alice
111
1
6
1.1
2
Carol
333
1
5
1.2
>>> df[['userID', 'day']]
userID
day
0
111
5
1
222
5
2
111
6
3
333
5
4
222
6
5
111
6
- property columns¶
An Index where the values are the column names of the dataframe.
- Returns:
The values of the index are the column names of the dataframe.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df
col1
col2
0
1
3
1
2
4
>>> df.columns Index(array(['col1', 'col2']), dtype='<U0')
- property dtypes¶
The dtypes of the dataframe.
- Returns:
dtypes – The dtypes of the dataframe.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': ["a", "b"]}) >>> df
col1
col2
0
1
a
1
2
b
>>> df.dtypes
keys
values
col1
int64
col2
str
- property empty¶
Whether the dataframe is empty.
- Returns:
True if the dataframe is empty, otherwise False.
- Return type:
bool
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({}) >>> df 0 rows x 0 columns >>> df.empty True
- property index¶
The index of the dataframe.
- Returns:
The index of the dataframe.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df
col1
col2
0
1
3
1
2
4
>>> df.index Index(array([0 1]), dtype='int64')
- property info¶
Returns a summary string of this dataframe.
- Returns:
A summary string of this dataframe.
- Return type:
str
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': ["a", "b"]}) >>> df
col1
col2
0
1
a
1
2
b
>>> df.info "DataFrame(['col1', 'col2'], 2 rows, 20 B)"
- property shape¶
The shape of the dataframe.
- Returns:
Tuple of array dimensions.
- Return type:
tuple of int
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df
col1
col2
0
1
4
1
2
5
2
3
6
>>> df.shape (3, 2)
- property size¶
Returns the number of bytes on the arkouda server.
- Returns:
The number of bytes on the arkouda server.
- Return type:
int
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df
col1
col2
0
1
4
1
2
5
2
3
6
>>> df.size 6
- objType = 'DataFrame'¶
- GroupBy(keys, use_series=False, as_index=True, dropna=True)[source]¶
Group the dataframe by a column or a list of columns.
- Parameters:
keys (str or list of str) – An (ordered) list of column names or a single string to group by.
use_series (bool, default=False) – If True, returns an arkouda.dataframe.GroupBy object. Otherwise an arkouda.groupbyclass.GroupBy object.
as_index (bool, default=True) – If True, groupby columns will be set as index otherwise, the groupby columns will be treated as DataFrame columns.
dropna (bool, default=True) – If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Returns:
If use_series = True, returns an arkouda.dataframe.GroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.
- Return type:
arkouda.dataframe.GroupBy or arkouda.groupbyclass.GroupBy
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1.0, 1.0, 2.0, np.nan], 'col2': [4, 5, 6, 7]}) >>> df
col1
col2
0
1
4
1
1
5
2
2
6
3
nan
7
>>> df.GroupBy("col1") <arkouda.groupbyclass.GroupBy at 0x7f2cf23e10c0> >>> df.GroupBy("col1").size() (array([1.00000000000000000 2.00000000000000000]), array([2 1]))
>>> df.GroupBy("col1",use_series=True) col1 1.0 2 2.0 1 dtype: int64 >>> df.GroupBy("col1",use_series=True, as_index = False).size()
col1
size
0
1
2
1
2
1
- all(axis=0) arkouda.series.Series | bool[source]¶
Return whether all elements are True, potentially over an axis.
Returns True unless there at least one element along a Dataframe axis that is False.
Currently, will ignore any columns that are not type bool. This is equivalent to the pandas option bool_only=True.
- Parameters:
axis ({0 or ‘index’, 1 or ‘columns’, None}, default = 0) –
Indicate which axis or axes should be reduced.
0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.
- Return type:
arkouda.series.Series or bool
- Raises:
ValueError – Raised if axis does not have a value in {0 or ‘index’, 1 or ‘columns’, None}.
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({"A":[True,True,True,False],"B":[True,True,True,False], ... "C":[True,False,True,False],"D":[True,True,True,True]})
A
B
C
D
0
True
True
True
True
1
True
True
False
True
2
True
True
True
True
3
False
False
False
True
>>> df.all(axis=0) A False B False C False D True dtype: bool >>> df.all(axis=1) 0 True 1 False 2 True 3 False dtype: bool >>> df.all(axis=None) False
- any(axis=0) arkouda.series.Series | bool[source]¶
Return whether any element is True, potentially over an axis.
Returns False unless there is at least one element along a Dataframe axis that is True.
Currently, will ignore any columns that are not type bool. This is equivalent to the pandas option bool_only=True.
- Parameters:
axis ({0 or ‘index’, 1 or ‘columns’, None}, default = 0) –
Indicate which axis or axes should be reduced.
0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.
1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.
None : reduce all axes, return a scalar.
- Return type:
arkouda.series.Series or bool
- Raises:
ValueError – Raised if axis does not have a value in {0 or ‘index’, 1 or ‘columns’, None}.
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({"A":[True,True,True,False],"B":[True,True,True,False], ... "C":[True,False,True,False],"D":[False,False,False,False]})
A
B
C
D
0
True
True
True
False
1
True
True
False
False
2
True
True
True
False
3
False
False
False
False
>>> df.any(axis=0) A True B True C True D False dtype: bool >>> df.any(axis=1) 0 True 1 True 2 True 3 False dtype: bool >>> df.any(axis=None) True
- append(other, ordered=True)[source]¶
Concatenate data from ‘other’ onto the end of this DataFrame, in place.
Explicitly, use the arkouda concatenate function to append the data from each column in other to the end of self. This operation is done in place, in the sense that the underlying pdarrays are updated from the result of the arkouda concatenate function, rather than returning a new DataFrame object containing the result.
- Parameters:
other (DataFrame) – The DataFrame object whose data will be appended to this DataFrame.
ordered (bool, default=True) – If False, allow rows to be interleaved for better performance (but data within a row remains together). By default, append all rows to the end, in input order.
- Returns:
Appending occurs in-place, but result is returned for compatibility.
- Return type:
self
Examples
>>> import arkouda as ak >>> ak.connect() >>> df1 = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
col1
col2
0
1
3
1
2
4
>>> df2 = ak.DataFrame({'col1': [3], 'col2': [5]})
col1
col2
0
3
5
>>> df1.append(df2) >>> df1
col1
col2
0
1
3
1
2
4
2
3
5
- apply_permutation(perm)[source]¶
Apply a permutation to an entire DataFrame. The operation is done in place and the original DataFrame will be modified.
This may be useful if you want to unsort an DataFrame, or even to apply an arbitrary permutation such as the inverse of a sorting permutation.
- Parameters:
perm (pdarray) – A permutation array. Should be the same size as the data arrays, and should consist of the integers [0,size-1] in some order. Very minimal testing is done to ensure this is a permutation.
- Return type:
None
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
col1
col2
0
1
4
1
2
5
2
3
6
>>> perm_arry = ak.array([0, 2, 1]) >>> df.apply_permutation(perm_arry) >>> display(df)
col1
col2
0
1
4
1
3
6
2
2
5
- argsort(key, ascending=True)[source]¶
Return the permutation that sorts the dataframe by key.
- Parameters:
key (str) – The key to sort on.
ascending (bool, default = True) – If true, sort the key in ascending order. Otherwise, sort the key in descending order.
- Returns:
The permutation array that sorts the data on key.
- Return type:
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1.1, 3.1, 2.1], 'col2': [6, 5, 4]}) >>> display(df)
col1
col2
0
1.1
6
1
3.1
5
2
2.1
4
>>> df.argsort('col1') array([0 2 1]) >>> sorted_df1 = df[df.argsort('col1')] >>> display(sorted_df1)
col1
col2
0
1.1
6
1
2.1
4
2
3.1
5
>>> df.argsort('col2') array([2 1 0]) >>> sorted_df2 = df[df.argsort('col2')] >>> display(sorted_df2)
col1
col2
0
2.1
4
1
3.1
5
2
1.1
6
- static attach(user_defined_name: str) DataFrame[source]¶
Function to return a DataFrame object attached to the registered name in the arkouda server which was registered using register().
- Parameters:
user_defined_name (str) – user defined name which DataFrame object was registered under.
- Returns:
The DataFrame object created by re-attaching to the corresponding server components.
- Return type:
- Raises:
RegistrationError – if user_defined_name is not registered
See also
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister() >>> df.is_registered() False
- coargsort(keys, ascending=True)[source]¶
Return the permutation that sorts the dataframe by keys.
Note: Sorting using Strings may not yield correct sort order.
- Parameters:
keys (list of str) – The keys to sort on.
- Returns:
The permutation array that sorts the data on keys.
- Return type:
Example
>>> df = ak.DataFrame({'col1': [2, 2, 1], 'col2': [3, 4, 3], 'col3':[5, 6, 7]}) >>> display(df)
col1
col2
col3
0
2
3
5
1
2
4
6
2
1
3
7
>>> df.coargsort(['col1', 'col2']) array([2 0 1]) >>>
- copy(deep=True)[source]¶
Make a copy of this object’s data.
When deep = True (default), a new object will be created with a copy of the calling object’s data. Modifications to the data of the copy will not be reflected in the original object.
When deep = False a new object will be created without copying the calling object’s data. Any changes to the data of the original object will be reflected in the shallow copy, and vice versa.
- Parameters:
deep (bool, default=True) – When True, return a deep copy. Otherwise, return a shallow copy.
- Returns:
A deep or shallow copy according to caller specification.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> display(df)
col1
col2
0
1
3
1
2
4
>>> df_deep = df.copy(deep=True) >>> df_deep['col1'] +=1 >>> display(df)
col1
col2
0
1
3
1
2
4
>>> df_shallow = df.copy(deep=False) >>> df_shallow['col1'] +=1 >>> display(df)
col1
col2
0
2
3
1
3
4
- corr() DataFrame[source]¶
Return new DataFrame with pairwise correlation of columns.
- Returns:
Arkouda DataFrame containing correlation matrix of all columns.
- Return type:
- Raises:
RuntimeError – Raised if there’s a server-side error thrown.
See also
Notes
Generates the correlation matrix using Pearson R for all columns.
Attempts to convert to numeric values where possible for inclusion in the matrix.
Example
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [-1, -2]}) >>> display(df)
col1
col2
0
1
-1
1
2
-2
>>> corr = df.corr()
col1
col2
col1
1
-1
col2
-1
1
- count(axis: int | str = 0, numeric_only=False) arkouda.series.Series[source]¶
Count non-NA cells for each column or row.
The values np.NaN are considered NA.
- Parameters:
axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
numeric_only (bool = False) – Include only float, int or boolean data.
- Returns:
For each column/row the number of non-NA/null entries.
- Return type:
- Raises:
ValueError – Raised if axis is not 0, 1, ‘index’, or ‘columns’.
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> import numpy as np >>> df = ak.DataFrame({'col_A': ak.array([7, np.nan]), 'col_B':ak.array([1, 9])}) >>> display(df)
col_A
col_B
0
7
1
1
nan
9
>>> df.count() col_A 1 col_B 2 dtype: int64
>>> df = ak.DataFrame({'col_A': ak.array(["a","b","c"]), 'col_B':ak.array([1, np.nan, np.nan])}) >>> display(df)
col_A
col_B
0
a
1
1
b
nan
2
c
nan
>>> df.count() col_A 3 col_B 1 dtype: int64
>>> df.count(numeric_only=True) col_B 1 dtype: int64
>>> df.count(axis=1) 0 2 1 1 2 1 dtype: int64
- drop(keys: str | int | List[str | int], axis: str | int = 0, inplace: bool = False) None | DataFrame[source]¶
Drop column/s or row/s from the dataframe.
- Parameters:
keys (str, int or list) – The labels to be dropped on the given axis.
axis (int or str) – The axis on which to drop from. 0/’index’ - drop rows, 1/’columns’ - drop columns.
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.
- Returns:
DateFrame when inplace=False; None when inplace=True
- Return type:
arkouda.dataframe.DataFrame or None
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> display(df)
col1
col2
0
1
3
1
2
4
Drop column
>>> df.drop('col1', axis = 1)
col2
0
3
1
4
Drop row
>>> df.drop(0, axis = 0)
col1
col2
0
2
4
- drop_duplicates(subset=None, keep='first')[source]¶
Drops duplcated rows and returns resulting DataFrame.
If a subset of the columns are provided then only one instance of each duplicated row will be returned (keep determines which row).
- Parameters:
subset (Iterable) – Iterable of column names to use to dedupe.
keep ({'first', 'last'}, default='first') – Determines which duplicates (if any) to keep.
- Returns:
DataFrame with duplicates removed.
- Return type:
Example
>>> df = ak.DataFrame({'col1': [1, 2, 2, 3], 'col2': [4, 5, 5, 6]}) >>> display(df)
col1
col2
0
1
4
1
2
5
2
2
5
3
3
6
>>> df.drop_duplicates()
col1
col2
0
1
4
1
2
5
2
3
6
- dropna(axis: int | str = 0, how: str | None = None, thresh: int | None = None, ignore_index: bool = False) DataFrame[source]¶
Remove missing values.
- Parameters:
axis ({0 or 'index', 1 or 'columns'}, default = 0) –
Determine if rows or columns which contain missing values are removed.
0, or ‘index’: Drop rows which contain missing values.
1, or ‘columns’: Drop columns which contain missing value.
Only a single axis is allowed.
how ({'any', 'all'}, default='any') –
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.
’any’: If any NA values are present, drop that row or column.
’all’: If all values are NA, drop that row or column.
thresh (int, optional) – Require that many non - NA values.Cannot be combined with how.
ignore_index (bool, default
False) – IfTrue, the resulting axis will be labeled 0, 1, …, n - 1.
- Returns:
DataFrame with NA entries dropped from it.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> import numpy as np >>> df = ak.DataFrame( { "A": [True, True, True, True], "B": [1, np.nan, 2, np.nan], "C": [1, 2, 3, np.nan], "D": [False, False, False, False], "E": [1, 2, 3, 4], "F": ["a", "b", "c", "d"], "G": [1, 2, 3, 4], } )
>>> display(df)
A
B
C
D
E
F
G
0
True
1
1
False
1
a
1
1
True
nan
2
False
2
b
2
2
True
2
3
False
3
c
3
3
True
nan
nan
False
4
d
4
>>> df.dropna()
A
B
C
D
E
F
G
0
True
1
1
False
1
a
1
1
True
2
3
False
3
c
3
>>> df.dropna(axis=1)
A
D
E
F
G
0
True
False
1
a
1
1
True
False
2
b
2
2
True
False
3
c
3
3
True
False
4
d
4
>>> df.dropna(axis=1, thresh=3)
A
C
D
E
F
G
0
True
1
False
1
a
1
1
True
2
False
2
b
2
2
True
3
False
3
c
3
3
True
nan
False
4
d
4
>>> df.dropna(axis=1, how="all")
A
B
C
D
E
F
G
0
True
1
1
False
1
a
1
1
True
nan
2
False
2
b
2
2
True
2
3
False
3
c
3
3
True
nan
nan
False
4
d
4
- filter_by_range(keys, low=1, high=None)[source]¶
Find all rows where the value count of the items in a given set of columns (keys) is within the range [low, high].
To filter by a specific value, set low == high.
- Parameters:
keys (str or list of str) – The names of the columns to group by.
low (int, default=1) – The lowest value count.
high (int, default=None) – The highest value count, default to unlimited.
- Returns:
An array of boolean values for qualified rows in this DataFrame.
- Return type:
Example
>>> df = ak.DataFrame({'col1': [1, 2, 2, 2, 3, 3], 'col2': [4, 5, 6, 7, 8, 9]}) >>> display(df)
col1
col2
0
1
4
1
2
5
2
2
6
3
2
7
4
3
8
5
3
9
>>> df.filter_by_range("col1", low=1, high=2) array([True False False False True True])
>>> filtered_df = df[df.filter_by_range("col1", low=1, high=2)] >>> display(filtered_df)
col1
col2
0
1
4
1
3
8
2
3
9
- classmethod from_pandas(pd_df)[source]¶
Copy the data from a pandas DataFrame into a new arkouda.dataframe.DataFrame.
- Parameters:
pd_df (pandas.DataFrame) – A pandas DataFrame to convert.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> import pandas as pd >>> pd_df = pd.DataFrame({"A":[1,2],"B":[3,4]}) >>> type(pd_df) pandas.core.frame.DataFrame >>> display(pd_df)
A
B
0
1
3
1
2
4
>>> ak_df = DataFrame.from_pandas(pd_df) >>> type(ak_df) arkouda.dataframe.DataFrame >>> display(ak_df)
A
B
0
1
3
1
2
4
- classmethod from_return_msg(rep_msg)[source]¶
Creates a DataFrame object from an arkouda server response message.
- Parameters:
rep_msg (string) – Server response message used to create a DataFrame.
- Return type:
- groupby(keys, use_series=True, as_index=True, dropna=True)[source]¶
Group the dataframe by a column or a list of columns. Alias for GroupBy.
- Parameters:
keys (str or list of str) – An (ordered) list of column names or a single string to group by.
use_series (bool, default=True) – If True, returns an arkouda.dataframe.GroupBy object. Otherwise an arkouda.groupbyclass.GroupBy object.
as_index (bool, default=True) – If True, groupby columns will be set as index otherwise, the groupby columns will be treated as DataFrame columns.
dropna (bool, default=True) – If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Returns:
If use_series = True, returns an arkouda.dataframe.GroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.
- Return type:
arkouda.dataframe.GroupBy or arkouda.groupbyclass.GroupBy
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [1.0, 1.0, 2.0, np.nan], 'col2': [4, 5, 6, 7]}) >>> df
col1
col2
0
1
4
1
1
5
2
2
6
3
nan
7
>>> df.GroupBy("col1") <arkouda.groupbyclass.GroupBy at 0x7f2cf23e10c0> >>> df.GroupBy("col1").size() (array([1.00000000000000000 2.00000000000000000]), array([2 1]))
>>> df.GroupBy("col1",use_series=True) col1 1.0 2 2.0 1 dtype: int64 >>> df.GroupBy("col1",use_series=True, as_index = False).size()
col1
size
0
1
2
1
2
1
- head(n=5)[source]¶
Return the first n rows.
This function returns the first n rows of the the dataframe. It is useful for quickly verifying data, for example, after sorting or appending rows.
- Parameters:
n (int, default = 5) – Number of rows to select.
- Returns:
The first n rows of the DataFrame.
- Return type:
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': ak.arange(10), 'col2': -1 * ak.arange(10)}) >>> display(df)
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
5
5
-5
6
6
-6
7
7
-7
8
8
-8
9
9
-9
>>> df.head()
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
>>> df.head(n=2)
col1
col2
0
0
0
1
1
-1
- is_registered() bool[source]¶
Return True if the object is contained in the registry.
- Returns:
Indicates if the object is contained in the registry.
- Return type:
bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mismatch of registered components.
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister() >>> df.is_registered() False
- isin(values: arkouda.pdarrayclass.pdarray | Dict | arkouda.series.Series | DataFrame) DataFrame[source]¶
Determine whether each element in the DataFrame is contained in values.
- Parameters:
values (pdarray, dict, Series, or DataFrame) – The values to check for in DataFrame. Series can only have a single index.
- Returns:
Arkouda DataFrame of booleans showing whether each element in the DataFrame is contained in values.
- Return type:
See also
ak.Series.isinNotes
Pandas supports values being an iterable type. In arkouda, we replace this with pdarray.
Pandas supports ~ operations. Currently, ak.DataFrame does not support this.
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col_A': ak.array([7, 3]), 'col_B':ak.array([1, 9])}) >>> display(df)
col_A
col_B
0
7
1
1
3
9
When values is a pdarray, check every value in the DataFrame to determine if it exists in values.
>>> df.isin(ak.array([0, 1]))
col_A
col_B
0
0
1
1
0
0
When values is a dict, the values in the dict are passed to check the column indicated by the key.
>>> df.isin({'col_A': ak.array([0, 3])})
col_A
col_B
0
0
0
1
1
0
When values is a Series, each column is checked if values is present positionally. This means that for True to be returned, the indexes must be the same.
>>> i = ak.Index(ak.arange(2)) >>> s = ak.Series(data=[3, 9], index=i) >>> df.isin(s)
col_A
col_B
0
0
0
1
0
1
When values is a DataFrame, the index and column must match. Note that 9 is not found because the column name does not match.
>>> other_df = ak.DataFrame({'col_A':ak.array([7, 3]), 'col_C':ak.array([0, 9])}) >>> df.isin(other_df)
col_A
col_B
0
1
0
1
1
0
- isna() DataFrame[source]¶
Detect missing values.
Return a boolean same-sized object indicating if the values are NA. numpy.NaN values get mapped to True values. Everything else gets mapped to False values.
- Returns:
Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> import numpy as np >>> df = ak.DataFrame({"A": [np.nan, 2, 2, 3], "B": [3, np.nan, 5, 6], ... "C": [1, np.nan, 2, np.nan], "D":["a","b","c","d"]}) >>> display(df)
A
B
C
D
0
nan
3
1
a
1
2
nan
nan
b
2
2
5
2
c
3
3
6
nan
d
>>> df.isna() A B C D 0 True False False False 1 False True True False 2 False False False False 3 False False True False (4 rows x 4 columns)
- classmethod load(prefix_path, file_format='INFER')[source]¶
Load dataframe from file. file_format needed for consistency with other load functions.
- Parameters:
prefix_path (str) – The prefix path for the data.
file_format (string, default = "INFER")
- Returns:
A dataframe loaded from the prefix_path.
- Return type:
Examples
To store data in <my_dir>/my_data_LOCALE0000, use “<my_dir>/my_data” as the prefix.
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'hdf5_output','my_data') >>> Path(my_path).mkdir(parents=True, exist_ok=True) >>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)}) >>> df.save(my_path, file_type="distribute") >>> df.load(my_path)
A
B
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
- memory_usage(index=True, unit='B') arkouda.series.Series[source]¶
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of the index.
- Parameters:
index (bool, default True) – Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If
index=True, the memory usage of the index is the first item in the output.unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.
- Returns:
A Series whose index is the original column names and whose values is the memory usage of each column in bytes.
- Return type:
See also
arkouda.pdarrayclass.nbytes,arkouda.index.Index.memory_usage,arkouda.index.MultiIndex.memory_usage,arkouda.series.Series.memory_usageExamples
>>> import arkouda as ak >>> ak.connect() >>> dtypes = [ak.int64, ak.float64, ak.bool] >>> data = dict([(str(t), ak.ones(5000, dtype=ak.int64).astype(t)) for t in dtypes]) >>> df = ak.DataFrame(data) >>> display(df.head())
int64
float64
bool
0
1
1
True
1
1
1
True
2
1
1
True
3
1
1
True
4
1
1
True
>>> df.memory_usage()
0
Index
40000
int64
40000
float64
40000
bool
5000
>>> df.memory_usage(index=False)
0
int64
40000
float64
40000
bool
5000
>>> df.memory_usage(unit="KB")
0
Index
39.0625
int64
39.0625
float64
39.0625
bool
4.88281
To get the approximate total memory usage:
>>> df.memory_usage(index=True).sum()
- memory_usage_info(unit='GB')[source]¶
A formatted string representation of the size of this DataFrame.
- Parameters:
unit (str, default = "GB") – Unit to return. One of {‘KB’, ‘MB’, ‘GB’}.
- Returns:
A string representation of the number of bytes used by this DataFrame in [unit]s.
- Return type:
str
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': ak.arange(1000), 'col2': ak.arange(1000)}) >>> df.memory_usage_info() '0.00 GB'
>>> df.memory_usage_info(unit="KB") '15 KB'
- merge(right: DataFrame, on: str | List[str] | None = None, how: str = 'inner', left_suffix: str = '_x', right_suffix: str = '_y', convert_ints: bool = True, sort: bool = True) DataFrame[source]¶
Merge Arkouda DataFrames with a database-style join. The resulting dataframe contains rows from both DataFrames as specified by the merge condition (based on the “how” and “on” parameters).
Based on pandas merge functionality. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
- Parameters:
right (DataFrame) – The Right DataFrame to be joined.
on (Optional[Union[str, List[str]]] = None) – The name or list of names of the DataFrame column(s) to join on. If on is None, this defaults to the intersection of the columns in both DataFrames.
how ({"inner", "left", "right}, default = "inner") – The merge condition. Must be “inner”, “left”, or “right”.
left_suffix (str, default = "_x") – A string indicating the suffix to add to columns from the left dataframe for overlapping column names in both left and right. Defaults to “_x”. Only used when how is “inner”.
right_suffix (str, default = "_y") – A string indicating the suffix to add to columns from the right dataframe for overlapping column names in both left and right. Defaults to “_y”. Only used when how is “inner”.
convert_ints (bool = True) – If True, convert columns with missing int values (due to the join) to float64. This is to match pandas. If False, do not convert the column dtypes. This has no effect when how = “inner”.
sort (bool = True) – If True, DataFrame is returned sorted by “on”. Otherwise, the DataFrame is not sorted.
- Returns:
Joined Arkouda DataFrame.
- Return type:
Note
Multiple column joins are only supported for integer columns.
Examples
>>> import arkouda as ak >>> ak.connect() >>> left_df = ak.DataFrame({'col1': ak.arange(5), 'col2': -1 * ak.arange(5)}) >>> display(left_df)
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
>>> right_df = ak.DataFrame({'col1': 2 * ak.arange(5), 'col2': 2 * ak.arange(5)}) >>> display(right_df)
col1
col2
0
0
0
1
2
2
2
4
4
3
6
6
4
8
8
>>> left_df.merge(right_df, on = "col1")
col1
col2_x
col2_y
0
0
0
0
1
2
-2
2
2
4
-4
4
>>> left_df.merge(right_df, on = "col1", how = "left")
col1
col2_y
col2_x
0
0
0
0
1
1
nan
-1
2
2
2
-2
3
3
nan
-3
4
4
4
-4
>>> left_df.merge(right_df, on = "col1", how = "right")
col1
col2_x
col2_y
0
0
0
0
1
2
-2
2
2
4
-4
4
3
6
nan
6
4
8
nan
8
>>> left_df.merge(right_df, on = "col1", how = "outer")
col1
col2_y
col2_x
0
0
0
0
1
1
nan
-1
2
2
2
-2
3
3
nan
-3
4
4
4
-4
5
6
6
nan
6
8
8
nan
- notna() DataFrame[source]¶
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA. numpy.NaN values get mapped to False values.
- Returns:
Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> import numpy as np >>> df = ak.DataFrame({"A": [np.nan, 2, 2, 3], "B": [3, np.nan, 5, 6], ... "C": [1, np.nan, 2, np.nan], "D":["a","b","c","d"]}) >>> display(df)
A
B
C
D
0
nan
3
1
a
1
2
nan
nan
b
2
2
5
2
c
3
3
6
nan
d
>>> df.notna() A B C D 0 False True True True 1 True False False True 2 True True True True 3 True True False True (4 rows x 4 columns)
- classmethod read_csv(filename: str, col_delim: str = ',')[source]¶
Read the columns of a CSV file into an Arkouda DataFrame. If the file contains the appropriately formatted header, typed data will be returned. Otherwise, all data will be returned as a Strings objects.
- Parameters:
filename (str) – Filename to read data from.
col_delim (str, default=",") – The delimiter for columns within the data.
- Returns:
Arkouda DataFrame containing the columns from the CSV file.
- Return type:
- Raises:
ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server.
See also
Notes
CSV format is not currently supported by load/load_all operations.
The column delimiter is expected to be the same for column names and data.
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”\n”) at this time.
Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing
bytes as uint(8).
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'csv_output','my_data') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_csv(my_path) >>> df2 = DataFrame.read_csv(my_path + "_LOCALE0000") >>> display(df2)
A
B
0
1
3
1
2
4
- register(user_defined_name: str) DataFrame[source]¶
Register this DataFrame object and underlying components with the Arkouda server.
- Parameters:
user_defined_name (str) – User defined name the DataFrame is to be registered under. This will be the root name for underlying components.
- Returns:
The same DataFrame which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different DataFrames with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str.
RegistrationError – If the server was unable to register the DataFrame with the user_defined_name.
See also
unregister,attach,unregister_dataframe_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
Any changes made to a DataFrame object after registering with the server may not be reflected in attached copies.
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister() >>> df.is_registered() False
- rename(mapper: Callable | Dict | None = None, index: Callable | Dict | None = None, column: Callable | Dict | None = None, axis: str | int = 0, inplace: bool = False) DataFrame | None[source]¶
Rename indexes or columns according to a mapping.
- Parameters:
mapper (callable or dict-like, Optional) – Function or dictionary mapping existing values to new values. Nonexistent names will not raise an error. Uses the value of axis to determine if renaming column or index
column (callable or dict-like, Optional) – Function or dictionary mapping existing column names to new column names. Nonexistent names will not raise an error. When this is set, axis is ignored.
index (callable or dict-like, Optional) – Function or dictionary mapping existing index names to new index names. Nonexistent names will not raise an error. When this is set, axis is ignored.
axis (int or str, default=0) – Indicates which axis to perform the rename. 0/”index” - Indexes 1/”column” - Columns
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.
- Returns:
DateFrame when inplace=False; None when inplace=True.
- Return type:
arkouda.dataframe.DataFrame or None
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({"A": ak.array([1, 2, 3]), "B": ak.array([4, 5, 6])}) >>> display(df)
A
B
0
1
4
1
2
5
2
3
6
Rename columns using a mapping:
>>> df.rename(column={'A':'a', 'B':'c'})
a
c
0
1
4
1
2
5
2
3
6
Rename indexes using a mapping:
>>> df.rename(index={0:99, 2:11})
A
B
0
1
4
1
2
5
2
3
6
Rename using an axis style parameter:
>>> df.rename(str.lower, axis='column')
a
b
0
1
4
1
2
5
2
3
6
- reset_index(size: int | None = None, inplace: bool = False) None | DataFrame[source]¶
Set the index to an integer range.
Useful if this dataframe is the result of a slice operation from another dataframe, or if you have permuted the rows and no longer need to keep that ordering on the rows.
- Parameters:
size (int, optional) – If size is passed, do not attempt to determine size based on existing column sizes. Assume caller handles consistency correctly.
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.
- Returns:
DateFrame when inplace=False; None when inplace=True.
- Return type:
arkouda.dataframe.DataFrame or None
Note
Pandas adds a column ‘index’ to indicate the original index. Arkouda does not currently support this behavior.
Example
>>> df = ak.DataFrame({"A": ak.array([1, 2, 3]), "B": ak.array([4, 5, 6])}) >>> display(df)
A
B
0
1
4
1
2
5
2
3
6
>>> perm_df = df[ak.array([0,2,1])] >>> display(perm_df)
A
B
0
1
4
1
3
6
2
2
5
>>> perm_df.reset_index()
A
B
0
1
4
1
3
6
2
2
5
- sample(n=5)[source]¶
Return a random sample of n rows.
- Parameters:
n (int, default=5) – Number of rows to return.
- Returns:
The sampled n rows of the DataFrame.
- Return type:
Example
>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)}) >>> display(df)
A
B
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
Random output of size 3:
>>> df.sample(n=3)
A
B
0
0
0
1
1
-1
2
4
-4
- save(path, index=False, columns=None, file_format='HDF5', file_type='distribute', compression: str | None = None)[source]¶
DEPRECATED Save DataFrame to disk, preserving column names.
- Parameters:
path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (list, default=None) – List of columns to include in the file. If None, writes out all columns.
file_format (str, default='HDF5') – ‘HDF5’ or ‘Parquet’. Defaults to ‘HDF5’
file_type (str, default=distribute) – “single” or “distribute” If single, will right a single file to locale 0.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Compression type. Only used for Parquet
Notes
This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'hdf5_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)}) >>> df.save(my_path + '/my_data', file_type="single") >>> df.load(my_path + '/my_data')
A
B
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
- sort_index(ascending=True)[source]¶
Sort the DataFrame by indexed columns.
Note: Fails on sort order of arkouda.strings.Strings columns when multiple columns being sorted.
- Parameters:
ascending (bool, default = True) – Sort values in ascending (default) or descending order.
Example
>>> df = ak.DataFrame({'col1': [1.1, 3.1, 2.1], 'col2': [6, 5, 4]}, ... index = Index(ak.array([2,0,1]), name="idx"))
>>> display(df)
idx
col1
col2
0
1.1
6
1
3.1
5
2
2.1
4
>>> df.sort_index()
idx
col1
col2
0
3.1
5
1
2.1
4
2
1.1
6
- sort_values(by=None, ascending=True)[source]¶
Sort the DataFrame by one or more columns.
If no column is specified, all columns are used.
Note: Fails on order of arkouda.strings.Strings columns when multiple columns being sorted.
- Parameters:
by (str or list/tuple of str, default = None) – The name(s) of the column(s) to sort by.
ascending (bool, default = True) – Sort values in ascending (default) or descending order.
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': [2, 2, 1], 'col2': [3, 4, 3], 'col3':[5, 6, 7]}) >>> display(df)
col1
col2
col3
0
2
3
5
1
2
4
6
2
1
3
7
>>> df.sort_values()
col1
col2
col3
0
1
3
7
1
2
3
5
2
2
4
6
>>> df.sort_values("col3")
col1
col2
col3
0
1
3
7
1
2
3
5
2
2
4
6
- tail(n=5)[source]¶
Return the last n rows.
This function returns the last n rows for the dataframe. It is useful for quickly testing if your object has the right type of data in it.
- Parameters:
n (int, default=5) – Number of rows to select.
- Returns:
The last n rows of the DataFrame.
- Return type:
See also
arkouda.dataframe.headExamples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({'col1': ak.arange(10), 'col2': -1 * ak.arange(10)}) >>> display(df)
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
5
5
-5
6
6
-6
7
7
-7
8
8
-8
9
9
-9
>>> df.tail()
col1
col2
0
5
-5
1
6
-6
2
7
-7
3
8
-8
4
9
-9
>>> df.tail(n=2)
col1
col2
0
8
-8
1
9
-9
- to_csv(path: str, index: bool = False, columns: List[str] | None = None, col_delim: str = ',', overwrite: bool = False)[source]¶
Writes DataFrame to CSV file(s). File will contain a column for each column in the DataFrame. All CSV Files written by Arkouda include a header denoting data types of the columns. Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing bytes as uint(8).
- Parameters:
path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
index (bool, default=False) – If True, the index of the DataFrame will be written to the file as a column.
columns (list of str (Optional)) – Column names to assign when writing data.
col_delim (str, default=",") – Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, default=False) – If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
- Return type:
None
- Raises:
ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server.
Notes
CSV format is not currently supported by load/load_all operations.
The column delimiter is expected to be the same for column names and data.
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”\n”) at this time.
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'csv_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_csv(my_path + "/my_data") >>> df2 = DataFrame.read_csv(my_path + "/my_data" + "_LOCALE0000") >>> display(df2)
A
B
0
1
3
1
2
4
- to_hdf(path, index=False, columns=None, file_type='distribute')[source]¶
Save DataFrame to disk as hdf5, preserving column names.
- Parameters:
path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (List, default = None) – List of columns to include in the file. If None, writes out all columns.
file_type (str (single | distribute), default=distribute) – Whether to save to a single file or distribute across Locales.
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray.
Notes
This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.
See also
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'hdf_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_hdf(my_path + "/my_data") >>> df.load(my_path + "/my_data")
A
B
0
1
3
1
2
4
- to_markdown(mode='wt', index=True, tablefmt='grid', storage_options=None, **kwargs)[source]¶
Print DataFrame in Markdown-friendly format.
- Parameters:
mode (str, optional) – Mode in which file is opened, “wt” by default.
index (bool, optional, default True) – Add index (row) labels.
tablefmt (str = "grid") – Table format to call from tablulate: https://pypi.org/project/tabulate/
storage_options (dict, optional) – Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a non-fsspec URL. See the fsspec and backend storage implementation docs for the set of allowed keys and values.
**kwargs – These parameters will be passed to tabulate.
Note
This function should only be called on small DataFrames as it calls pandas.DataFrame.to_markdown: https://pandas.pydata.org/pandas-docs/version/1.2.4/reference/api/pandas.DataFrame.to_markdown.html
Examples
>>> import arkouda as ak >>> ak.connect() >>> df = ak.DataFrame({"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}) >>> print(df.to_markdown()) +----+------------+------------+ | | animal_1 | animal_2 | +====+============+============+ | 0 | elk | dog | +----+------------+------------+ | 1 | pig | quetzal | +----+------------+------------+
Suppress the index:
>>> print(df.to_markdown(index = False)) +------------+------------+ | animal_1 | animal_2 | +============+============+ | elk | dog | +------------+------------+ | pig | quetzal | +------------+------------+
- to_pandas(datalimit=maxTransferBytes, retain_index=False)[source]¶
Send this DataFrame to a pandas DataFrame.
- Parameters:
datalimit (int, default=arkouda.client.maxTransferBytes) – The maximum number size, in megabytes to transfer. The requested DataFrame will be converted to a pandas DataFrame only if the estimated size of the DataFrame does not exceed this value.
retain_index (bool, default=False) – Normally, to_pandas() creates a new range index object. If you want to keep the index column, set this to True.
- Returns:
The result of converting this DataFrame to a pandas DataFrame.
- Return type:
pandas.DataFrame
Examples
>>> import arkouda as ak >>> ak.connect() >>> ak_df = ak.DataFrame({"A": ak.arange(2), "B": -1 * ak.arange(2)}) >>> type(ak_df) arkouda.dataframe.DataFrame >>> display(ak_df)
A
B
0
0
0
1
1
-1
>>> import pandas as pd >>> pd_df = ak_df.to_pandas() >>> type(pd_df) pandas.core.frame.DataFrame >>> display(pd_df)
A
B
0
0
0
1
1
-1
- to_parquet(path, index=False, columns=None, compression: str | None = None, convert_categoricals: bool = False)[source]¶
Save DataFrame to disk as parquet, preserving column names.
- Parameters:
path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (list) – List of columns to include in the file. If None, writes out all columns.
compression (str (Optional), default=None) – Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4
convert_categoricals (bool, default=False) – Parquet requires all columns to be the same size and Categoricals don’t satisfy that requirement. If set, write the equivalent Strings in place of any Categorical columns.
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'parquet_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_parquet(my_path + "/my_data") >>> df.load(my_path + "/my_data")
B
A
0
3
1
1
4
2
- transfer(hostname, port)[source]¶
Sends a DataFrame to a different Arkouda server.
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the DataFrame is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Returns:
A message indicating a complete transfer.
- Return type:
str
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unregister()[source]¶
Unregister this DataFrame object in the arkouda server which was previously registered using register() and/or attached to using attach().
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister.
See also
register,attach,unregister_dataframe_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister() >>> df.is_registered() False
- static unregister_dataframe_by_name(user_defined_name: str) str[source]¶
Function to unregister DataFrame object by name which was registered with the arkouda server via register().
- Parameters:
user_defined_name (str) – Name under which the DataFrame object was registered.
- Raises:
TypeError – If user_defined_name is not a string.
RegistrationError – If there is an issue attempting to unregister any underlying components.
See also
Example
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) >>> df.register("my_table_name") >>> df.attach("my_table_name") >>> df.is_registered() True >>> df.unregister_dataframe_by_name("my_table_name") >>> df.is_registered() False
- update_hdf(prefix_path: str, index=False, columns=None, repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this dataframe. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (List, default=None) – List of columns to include in the file. If None, writes out all columns.
repack (bool, default=True) – HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Returns:
Success message if successful.
- Return type:
str
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray.
Notes
- If file does not contain File_Format attribute to indicate how it was saved,
the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added.
Examples
>>> import arkouda as ak >>> ak.connect() >>> import os.path >>> from pathlib import Path >>> my_path = os.path.join(os.getcwd(), 'hdf_output') >>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]}) >>> df.to_hdf(my_path + "/my_data") >>> df.load(my_path + "/my_data")
A
B
0
1
3
1
2
4
>>> df2 = ak.DataFrame({"A":[5,6],"B":[7,8]}) >>> df2.update_hdf(my_path + "/my_data") >>> df.load(my_path + "/my_data")
A
B
0
5
7
1
6
8
- class arkouda.Datetime(pda, unit: str = _BASE_UNIT)[source]¶
Bases:
_AbstractBaseTimeRepresents a date and/or time.
Datetime is the Arkouda analog to pandas DatetimeIndex and other timeseries data types.
- Parameters:
pda (int64 pdarray, pd.DatetimeIndex, pd.Series, or np.datetime64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.
Possible values:
’weeks’ or ‘w’
’days’ or ‘d’
’hours’ or ‘h’
’minutes’, ‘m’, or ‘t’
’seconds’ or ‘s’
’milliseconds’, ‘ms’, or ‘l’
’microseconds’, ‘us’, or ‘u’
’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers
Notes
The
.valuesattribute is always in nanoseconds with int64 dtype.- property date¶
- property day¶
- property day_of_week¶
- property day_of_year¶
- property dayofweek¶
- property dayofyear¶
- property hour¶
- property is_leap_year¶
- property microsecond¶
- property millisecond¶
- property minute¶
- property month¶
- property nanosecond¶
- property second¶
- property week¶
- property weekday¶
- property weekofyear¶
- property year¶
- special_objType = 'Datetime'¶
- supported_opeq¶
- supported_with_datetime¶
- supported_with_pdarray¶
- supported_with_r_datetime¶
- supported_with_r_pdarray¶
- supported_with_r_timedelta¶
- supported_with_timedelta¶
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- register(user_defined_name)[source]¶
Register this Datetime object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Datetime is to be registered under, this will be the root name for underlying components
- Returns:
The same Datetime which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Datetimes with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Datetimes with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- to_pandas()[source]¶
Convert array to a pandas DatetimeIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.
See also
to_ndarray
- unregister()[source]¶
Unregister this Datetime object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- class arkouda.Datetime(pda, unit: str = _BASE_UNIT)[source]¶
Bases:
_AbstractBaseTimeRepresents a date and/or time.
Datetime is the Arkouda analog to pandas DatetimeIndex and other timeseries data types.
- Parameters:
pda (int64 pdarray, pd.DatetimeIndex, pd.Series, or np.datetime64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.
Possible values:
’weeks’ or ‘w’
’days’ or ‘d’
’hours’ or ‘h’
’minutes’, ‘m’, or ‘t’
’seconds’ or ‘s’
’milliseconds’, ‘ms’, or ‘l’
’microseconds’, ‘us’, or ‘u’
’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers
Notes
The
.valuesattribute is always in nanoseconds with int64 dtype.- property date¶
- property day¶
- property day_of_week¶
- property day_of_year¶
- property dayofweek¶
- property dayofyear¶
- property hour¶
- property is_leap_year¶
- property microsecond¶
- property millisecond¶
- property minute¶
- property month¶
- property nanosecond¶
- property second¶
- property week¶
- property weekday¶
- property weekofyear¶
- property year¶
- special_objType = 'Datetime'¶
- supported_opeq¶
- supported_with_datetime¶
- supported_with_pdarray¶
- supported_with_r_datetime¶
- supported_with_r_pdarray¶
- supported_with_r_timedelta¶
- supported_with_timedelta¶
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- register(user_defined_name)[source]¶
Register this Datetime object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Datetime is to be registered under, this will be the root name for underlying components
- Returns:
The same Datetime which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Datetimes with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Datetimes with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- to_pandas()[source]¶
Convert array to a pandas DatetimeIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.
See also
to_ndarray
- unregister()[source]¶
Unregister this Datetime object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- class arkouda.Datetime(pda, unit: str = _BASE_UNIT)[source]¶
Bases:
_AbstractBaseTimeRepresents a date and/or time.
Datetime is the Arkouda analog to pandas DatetimeIndex and other timeseries data types.
- Parameters:
pda (int64 pdarray, pd.DatetimeIndex, pd.Series, or np.datetime64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.
Possible values:
’weeks’ or ‘w’
’days’ or ‘d’
’hours’ or ‘h’
’minutes’, ‘m’, or ‘t’
’seconds’ or ‘s’
’milliseconds’, ‘ms’, or ‘l’
’microseconds’, ‘us’, or ‘u’
’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers
Notes
The
.valuesattribute is always in nanoseconds with int64 dtype.- property date¶
- property day¶
- property day_of_week¶
- property day_of_year¶
- property dayofweek¶
- property dayofyear¶
- property hour¶
- property is_leap_year¶
- property microsecond¶
- property millisecond¶
- property minute¶
- property month¶
- property nanosecond¶
- property second¶
- property week¶
- property weekday¶
- property weekofyear¶
- property year¶
- special_objType = 'Datetime'¶
- supported_opeq¶
- supported_with_datetime¶
- supported_with_pdarray¶
- supported_with_r_datetime¶
- supported_with_r_pdarray¶
- supported_with_r_timedelta¶
- supported_with_timedelta¶
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- register(user_defined_name)[source]¶
Register this Datetime object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Datetime is to be registered under, this will be the root name for underlying components
- Returns:
The same Datetime which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Datetimes with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Datetimes with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- to_pandas()[source]¶
Convert array to a pandas DatetimeIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.
See also
to_ndarray
- unregister()[source]¶
Unregister this Datetime object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- class arkouda.DatetimeAccessor(series)[source]¶
Bases:
Properties
- class arkouda.DiffAggregate(gb, series)[source]¶
A column in a GroupBy that has been differenced. Aggregation operations can be done on the result.
- gb¶
GroupBy object, where the aggregation keys are values of column(s) of a dataframe.
- values¶
A column to compute the difference on.
- Type:
arkouda.series.Series.
- class arkouda.ErrorMode[source]¶
Bases:
enum.EnumGeneric enumeration.
Derive from this class to define new enumerations.
- ignore = 'ignore'¶
- return_validity = 'return_validity'¶
- strict = 'strict'¶
- class arkouda.Fields(values, names, MSB_left=True, pad='-', separator='', show_int=True)[source]¶
Bases:
BitVectorAn integer-backed representation of a set of named binary fields, e.g. flags.
- Parameters:
values (pdarray or Strings) – The array of field values. If (u)int64, the values are used as-is for the binary representation of fields. If Strings, the values are converted to binary according to the mapping defined by the names and MSB_left arguments.
names (str or sequence of str) – The names of the fields, in order. A string will be treated as a list of single-character field names. Multi-character field names are allowed, but must be passed as a list or tuple and user must specify a separator.
MSB_left (bool) – Controls how field names are mapped to binary values. If True (default), the left-most field name corresponds to the most significant bit in the binary representation. If False, the left-most field name corresponds to the least significant bit.
pad (str) – Character to display when field is not present. Use empty string if no padding is desired.
separator (str) – Substring that separates fields. Used to parse input values (if ak.Strings) and to display output.
show_int (bool) – If True (default), display the integer value of the binary fields in output.
- Returns:
fields – The array of field values
- Return type:
Notes
This class is a thin wrapper around pdarray that mostly affects how values are displayed to the user. Operators and methods will typically treat this class like an int64 pdarray.
- arkouda.GROUPBY_REDUCTION_TYPES¶
- class arkouda.Generator(name_dict=None, seed=None, state=1)[source]¶
Generatorexposes a number of methods for generating random numbers drawn from a variety of probability distributions. In addition to the distribution-specific arguments, each method takes a keyword argument size that defaults toNone. If size isNone, then a single value is generated and returned. If size is an integer, then a 1-D array filled with generated values is returned.- Parameters:
seed (int) – Seed to allow for reproducible random number generation.
name_dict (dict) – Dictionary mapping the server side names associated with the generators for each dtype.
state (int) – The current state we are in the random number generation stream. This information makes it so calls to any dtype generator function affects the stream of random numbers for the other generators. This mimics the behavior we see in numpy
See also
default_rngRecommended constructor for Generator.
- integers(low, high=None, size=None, dtype=akint64, endpoint=False)[source]¶
Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive).
Return random integers from the “discrete uniform” distribution of the specified dtype. If high is None (the default), then results are from 0 to low.
- Parameters:
low (numeric_scalars) – Lowest (signed) integers to be drawn from the distribution (unless high=None, in which case this parameter is 0 and this value is used for high).
high (numeric_scalars) – If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None)
size (numeric_scalars) – Output shape. Default is None, in which case a single value is returned.
dtype (dtype, optional) – Desired dtype of the result. The default value is ak.int64.
endpoint (bool, optional) – If true, sample from the interval [low, high] instead of the default [low, high). Defaults to False
- Returns:
Values drawn uniformly from the specified range having the desired dtype, or a single such random int if size not provided.
- Return type:
pdarray, numeric_scalar
Examples
>>> rng = ak.random.default_rng() >>> rng.integers(5, 20, 10) array([15, 13, 10, 8, 5, 18, 16, 14, 7, 13]) # random >>> rng.integers(5, size=10) array([2, 4, 0, 0, 0, 3, 1, 5, 5, 3]) # random
- random(size=None)[source]¶
Return random floats in the half-open interval [0.0, 1.0).
Results are from the uniform distribution over the stated interval.
- Parameters:
size (numeric_scalars, optional) – Output shape. Default is None, in which case a single value is returned.
- Returns:
Pdarray of random floats (unless size=None, in which case a single float is returned).
- Return type:
Notes
To sample over [a,b), use uniform or multiply the output of random by (b - a) and add a:
(b - a) * random() + aSee also
Examples
>>> rng = ak.random.default_rng() >>> rng.random() 0.47108547995356098 # random >>> rng.random(3) array([0.055256829926011691, 0.62511314008006458, 0.16400145561571539]) # random
- shuffle(x)[source]¶
Randomly shuffle a pdarray in place.
- Parameters:
x (pdarray) – shuffle the elements of x randomly in place
- Return type:
None
- standard_normal(size=None)[source]¶
Draw samples from a standard Normal distribution (mean=0, stdev=1).
- Parameters:
size (numeric_scalars, optional) – Output shape. Default is None, in which case a single value is returned.
- Returns:
Pdarray of floats (unless size=None, in which case a single float is returned).
- Return type:
Notes
For random samples from \(N(\mu, \sigma^2)\), use:
(sigma * standard_normal(size)) + muExamples
>>> rng = ak.random.default_rng() >>> rng.standard_normal() 2.1923875335537315 # random >>> rng.standard_normal(3) array([0.8797352989638163, -0.7085325853376141, 0.021728052940979934]) # random
- uniform(low=0.0, high=1.0, size=None)[source]¶
Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval [low, high). In other words, any value within the given interval is equally likely to be drawn by uniform.
- Parameters:
low (float, optional) – Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.
high (float, optional) – Upper boundary of the output interval. All values generated will be less than high. high must be greater than or equal to low. The default value is 1.0.
size (numeric_scalars, optional) – Output shape. Default is None, in which case a single value is returned.
- Returns:
Pdarray of floats (unless size=None, in which case a single float is returned).
- Return type:
Examples
>>> rng = ak.random.default_rng() >>> rng.uniform(-1, 1, 3) array([0.030785499755523249, 0.08505865366367038, -0.38552048588998722]) # random
- class arkouda.GroupBy(keys: groupable | None = None, assume_sorted: bool = False, dropna: bool = True, **kwargs)[source]¶
Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.
- Parameters:
keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)
- nkeys¶
The number of key arrays (columns)
- Type:
int
- unique_keys¶
The unique values of the keys array(s), in grouped order
- Type:
(list of) pdarray, Strings, or Categorical
- ngroups¶
The length of the unique_keys array(s), i.e. number of groups
- Type:
int
- logger¶
Used for all logging operations
- Type:
ArkoudaLogger
- dropna¶
If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Type:
bool (default=True)
- Raises:
TypeError – Raised if keys is a pdarray with a dtype other than int64
Notes
Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.
For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:
a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.
(Optional) a .group() method that returns the permutation that groups the array
If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.
- Reductions¶
- objType = 'GroupBy'¶
- AND(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise AND of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with AND
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- OR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise OR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with OR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- XOR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise XOR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with XOR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- aggregate(values: groupable, operator: str, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, groupable][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.
- Parameters:
values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype
Examples
>>> keys = ak.arange(0, 10) >>> vals = ak.linspace(-1, 1, 10) >>> g = ak.GroupBy(keys) >>> g.aggregate(vals, 'sum') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768, -0.55555555555555536, -0.33333333333333348, -0.11111111111111116, 0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768, 1])) >>> g.aggregate(vals, 'min') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779, -0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116, 0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))
- all(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “and”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- any(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “or”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
- argmax(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmax
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmax(b) (array([2, 3, 4]), array([9, 3, 2]))
- argmin(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmin
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmin(b) (array([2, 3, 4]), array([5, 4, 2]))
- static attach(user_defined_name: str) GroupBy[source]¶
Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which GroupBy object was registered under
- Returns:
The GroupBy object created by re-attaching to the corresponding server components
- Return type:
- Raises:
RegistrationError – if user_defined_name is not registered
See also
register,is_registered,unregister,unregister_groupby_by_name
- broadcast(values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, permute: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Fill each group’s segment with a constant value.
- Parameters:
- Returns:
The broadcasted values
- Return type:
- Raises:
TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment
Notes
This function is a sparse analog of
np.broadcast. If a GroupBy object represents a sparse matrix (tensor), then this function takes a (dense) column vector and replicates each value to the non-zero elements in the corresponding row.Examples
>>> a = ak.array([0, 1, 0, 1, 0]) >>> values = ak.array([3, 5]) >>> g = ak.GroupBy(a) # By default, result is in original order >>> g.broadcast(values) array([3, 5, 3, 5, 3]) # With permute=False, result is in grouped order >>> g.broadcast(values, permute=False) array([3, 3, 3, 5, 5] >>> a = ak.randint(1,5,10) >>> a array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> g.broadcast(counts > 2) array([True False True True True False True True False False]) >>> g.broadcast(counts == 3) array([True False True True True False True True False False]) >>> g.broadcast(counts < 4) array([True True True True True True True True True True])
- static build_from_components(user_defined_name: str | None = None, **kwargs) GroupBy[source]¶
function to build a new GroupBy object from component keys and permutation.
- Parameters:
user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”
- Returns:
The GroupBy object created by using the given components
- Return type:
- count() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
Notes
This alias is an alias of “size”.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- first(values: groupable_element_type) Tuple[groupable, groupable_element_type][source]¶
First value in each group.
- Parameters:
values (pdarray-like) – The values from which to take the first of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group
- is_registered() bool[source]¶
Return True if the object is contained in the registry
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mismatch of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- max(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.max(b) (array([2, 3, 4]), array([4, 4, 3]))
- mean(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.
- Parameters:
values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.mean(b) (array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))
- median(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.
- Parameters:
values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,9) >>> a array([4 1 4 3 2 2 2 3 3]) >>> g = ak.GroupBy(a) >>> g.keys array([4 1 4 3 2 2 2 3 3]) >>> b = ak.linspace(-5,5,9) >>> b array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5]) >>> g.median(b) (array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))
- min(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.min(b) (array([2, 3, 4]), array([1, 1, 3]))
- mode(values: groupable) Tuple[groupable, groupable][source]¶
Most common value in each group. If a group is multi-modal, return the modal value that occurs first.
- Parameters:
values ((list of) pdarray-like) – The values from which to take the mode of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group
- nunique(values: groupable) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.
- Parameters:
values (pdarray, int64) – The values to group and find unique values
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype
Examples
>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> data array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> labels ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g = ak.GroupBy(labels) >>> g.keys ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g.nunique(data) array([1,2,3,4]), array([2, 2, 3, 1]) # Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4 # Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4 # Group (3,3,3) has values [3,4,1] -> 3 unique values # Group (4) has values [4] -> 1 unique value
- prod(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.
- Parameters:
values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.prod(b) (array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))
- register(user_defined_name: str) GroupBy[source]¶
Register this GroupBy object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components
- Returns:
The same GroupBy which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different GroupBys with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name
See also
unregister,attach,unregister_groupby_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- size() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
See also
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.size() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- std(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.
- Parameters:
values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e.,
std = sqrt(mean((x - x.mean())**2)).The average squared deviation is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of the infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even withddof=1, it will not be an unbiased estimate of the standard deviation per se.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.std(b) (array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))
- sum(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.
- Parameters:
values (pdarray) – The values to group and sum
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance
skipna (bool) – boolean which determines if NANs should be skipped
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The grouped sum of a boolean
pdarrayreturns integers.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.sum(b) (array([2, 3, 4]), array([8, 14, 6]))
- to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶
Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Returns:
None
GroupBy is not currently supported by Parquet
- unique(values: groupable)[source]¶
Return the set of unique values in each group, as a SegArray.
- Parameters:
values ((list of) pdarray-like) – The values to unique
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group
- Raises:
TypeError – Raised if values is or contains Strings or Categorical
- unregister()[source]¶
Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_groupby_by_name(user_defined_name: str) None[source]¶
Function to unregister GroupBy object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the GroupBy object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- var(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.
- Parameters:
values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The variance is the average of the squared deviations from the mean, i.e.,
var = mean((x - x.mean())**2).The mean is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of a hypothetical infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.var(b) (array([2 3 4]), array([2.333333333333333 1.2 0]))
- class arkouda.GroupBy(keys: groupable | None = None, assume_sorted: bool = False, dropna: bool = True, **kwargs)[source]¶
Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.
- Parameters:
keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)
- nkeys¶
The number of key arrays (columns)
- Type:
int
- unique_keys¶
The unique values of the keys array(s), in grouped order
- Type:
(list of) pdarray, Strings, or Categorical
- ngroups¶
The length of the unique_keys array(s), i.e. number of groups
- Type:
int
- logger¶
Used for all logging operations
- Type:
ArkoudaLogger
- dropna¶
If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Type:
bool (default=True)
- Raises:
TypeError – Raised if keys is a pdarray with a dtype other than int64
Notes
Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.
For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:
a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.
(Optional) a .group() method that returns the permutation that groups the array
If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.
- Reductions¶
- objType = 'GroupBy'¶
- AND(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise AND of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with AND
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- OR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise OR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with OR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- XOR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise XOR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with XOR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- aggregate(values: groupable, operator: str, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, groupable][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.
- Parameters:
values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype
Examples
>>> keys = ak.arange(0, 10) >>> vals = ak.linspace(-1, 1, 10) >>> g = ak.GroupBy(keys) >>> g.aggregate(vals, 'sum') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768, -0.55555555555555536, -0.33333333333333348, -0.11111111111111116, 0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768, 1])) >>> g.aggregate(vals, 'min') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779, -0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116, 0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))
- all(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “and”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- any(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “or”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
- argmax(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmax
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmax(b) (array([2, 3, 4]), array([9, 3, 2]))
- argmin(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmin
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmin(b) (array([2, 3, 4]), array([5, 4, 2]))
- static attach(user_defined_name: str) GroupBy[source]¶
Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which GroupBy object was registered under
- Returns:
The GroupBy object created by re-attaching to the corresponding server components
- Return type:
- Raises:
RegistrationError – if user_defined_name is not registered
See also
register,is_registered,unregister,unregister_groupby_by_name
- broadcast(values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, permute: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Fill each group’s segment with a constant value.
- Parameters:
- Returns:
The broadcasted values
- Return type:
- Raises:
TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment
Notes
This function is a sparse analog of
np.broadcast. If a GroupBy object represents a sparse matrix (tensor), then this function takes a (dense) column vector and replicates each value to the non-zero elements in the corresponding row.Examples
>>> a = ak.array([0, 1, 0, 1, 0]) >>> values = ak.array([3, 5]) >>> g = ak.GroupBy(a) # By default, result is in original order >>> g.broadcast(values) array([3, 5, 3, 5, 3]) # With permute=False, result is in grouped order >>> g.broadcast(values, permute=False) array([3, 3, 3, 5, 5] >>> a = ak.randint(1,5,10) >>> a array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> g.broadcast(counts > 2) array([True False True True True False True True False False]) >>> g.broadcast(counts == 3) array([True False True True True False True True False False]) >>> g.broadcast(counts < 4) array([True True True True True True True True True True])
- static build_from_components(user_defined_name: str | None = None, **kwargs) GroupBy[source]¶
function to build a new GroupBy object from component keys and permutation.
- Parameters:
user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”
- Returns:
The GroupBy object created by using the given components
- Return type:
- count() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
Notes
This alias is an alias of “size”.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- first(values: groupable_element_type) Tuple[groupable, groupable_element_type][source]¶
First value in each group.
- Parameters:
values (pdarray-like) – The values from which to take the first of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group
- is_registered() bool[source]¶
Return True if the object is contained in the registry
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mismatch of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- max(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.max(b) (array([2, 3, 4]), array([4, 4, 3]))
- mean(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.
- Parameters:
values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.mean(b) (array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))
- median(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.
- Parameters:
values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,9) >>> a array([4 1 4 3 2 2 2 3 3]) >>> g = ak.GroupBy(a) >>> g.keys array([4 1 4 3 2 2 2 3 3]) >>> b = ak.linspace(-5,5,9) >>> b array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5]) >>> g.median(b) (array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))
- min(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.min(b) (array([2, 3, 4]), array([1, 1, 3]))
- mode(values: groupable) Tuple[groupable, groupable][source]¶
Most common value in each group. If a group is multi-modal, return the modal value that occurs first.
- Parameters:
values ((list of) pdarray-like) – The values from which to take the mode of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group
- nunique(values: groupable) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.
- Parameters:
values (pdarray, int64) – The values to group and find unique values
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype
Examples
>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> data array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> labels ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g = ak.GroupBy(labels) >>> g.keys ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g.nunique(data) array([1,2,3,4]), array([2, 2, 3, 1]) # Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4 # Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4 # Group (3,3,3) has values [3,4,1] -> 3 unique values # Group (4) has values [4] -> 1 unique value
- prod(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.
- Parameters:
values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.prod(b) (array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))
- register(user_defined_name: str) GroupBy[source]¶
Register this GroupBy object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components
- Returns:
The same GroupBy which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different GroupBys with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name
See also
unregister,attach,unregister_groupby_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- size() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
See also
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.size() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- std(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.
- Parameters:
values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e.,
std = sqrt(mean((x - x.mean())**2)).The average squared deviation is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of the infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even withddof=1, it will not be an unbiased estimate of the standard deviation per se.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.std(b) (array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))
- sum(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.
- Parameters:
values (pdarray) – The values to group and sum
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance
skipna (bool) – boolean which determines if NANs should be skipped
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The grouped sum of a boolean
pdarrayreturns integers.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.sum(b) (array([2, 3, 4]), array([8, 14, 6]))
- to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶
Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Returns:
None
GroupBy is not currently supported by Parquet
- unique(values: groupable)[source]¶
Return the set of unique values in each group, as a SegArray.
- Parameters:
values ((list of) pdarray-like) – The values to unique
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group
- Raises:
TypeError – Raised if values is or contains Strings or Categorical
- unregister()[source]¶
Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_groupby_by_name(user_defined_name: str) None[source]¶
Function to unregister GroupBy object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the GroupBy object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- var(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.
- Parameters:
values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The variance is the average of the squared deviations from the mean, i.e.,
var = mean((x - x.mean())**2).The mean is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of a hypothetical infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.var(b) (array([2 3 4]), array([2.333333333333333 1.2 0]))
- class arkouda.GroupBy(keys: groupable | None = None, assume_sorted: bool = False, dropna: bool = True, **kwargs)[source]¶
Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.
- Parameters:
keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)
- nkeys¶
The number of key arrays (columns)
- Type:
int
- unique_keys¶
The unique values of the keys array(s), in grouped order
- Type:
(list of) pdarray, Strings, or Categorical
- ngroups¶
The length of the unique_keys array(s), i.e. number of groups
- Type:
int
- logger¶
Used for all logging operations
- Type:
ArkoudaLogger
- dropna¶
If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Type:
bool (default=True)
- Raises:
TypeError – Raised if keys is a pdarray with a dtype other than int64
Notes
Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.
For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:
a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.
(Optional) a .group() method that returns the permutation that groups the array
If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.
- Reductions¶
- objType = 'GroupBy'¶
- AND(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise AND of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with AND
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- OR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise OR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with OR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- XOR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise XOR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with XOR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- aggregate(values: groupable, operator: str, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, groupable][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.
- Parameters:
values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype
Examples
>>> keys = ak.arange(0, 10) >>> vals = ak.linspace(-1, 1, 10) >>> g = ak.GroupBy(keys) >>> g.aggregate(vals, 'sum') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768, -0.55555555555555536, -0.33333333333333348, -0.11111111111111116, 0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768, 1])) >>> g.aggregate(vals, 'min') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779, -0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116, 0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))
- all(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “and”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- any(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “or”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
- argmax(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmax
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmax(b) (array([2, 3, 4]), array([9, 3, 2]))
- argmin(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmin
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmin(b) (array([2, 3, 4]), array([5, 4, 2]))
- static attach(user_defined_name: str) GroupBy[source]¶
Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which GroupBy object was registered under
- Returns:
The GroupBy object created by re-attaching to the corresponding server components
- Return type:
- Raises:
RegistrationError – if user_defined_name is not registered
See also
register,is_registered,unregister,unregister_groupby_by_name
- broadcast(values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, permute: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Fill each group’s segment with a constant value.
- Parameters:
- Returns:
The broadcasted values
- Return type:
- Raises:
TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment
Notes
This function is a sparse analog of
np.broadcast. If a GroupBy object represents a sparse matrix (tensor), then this function takes a (dense) column vector and replicates each value to the non-zero elements in the corresponding row.Examples
>>> a = ak.array([0, 1, 0, 1, 0]) >>> values = ak.array([3, 5]) >>> g = ak.GroupBy(a) # By default, result is in original order >>> g.broadcast(values) array([3, 5, 3, 5, 3]) # With permute=False, result is in grouped order >>> g.broadcast(values, permute=False) array([3, 3, 3, 5, 5] >>> a = ak.randint(1,5,10) >>> a array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> g.broadcast(counts > 2) array([True False True True True False True True False False]) >>> g.broadcast(counts == 3) array([True False True True True False True True False False]) >>> g.broadcast(counts < 4) array([True True True True True True True True True True])
- static build_from_components(user_defined_name: str | None = None, **kwargs) GroupBy[source]¶
function to build a new GroupBy object from component keys and permutation.
- Parameters:
user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”
- Returns:
The GroupBy object created by using the given components
- Return type:
- count() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
Notes
This alias is an alias of “size”.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- first(values: groupable_element_type) Tuple[groupable, groupable_element_type][source]¶
First value in each group.
- Parameters:
values (pdarray-like) – The values from which to take the first of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group
- is_registered() bool[source]¶
Return True if the object is contained in the registry
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mismatch of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- max(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.max(b) (array([2, 3, 4]), array([4, 4, 3]))
- mean(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.
- Parameters:
values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.mean(b) (array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))
- median(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.
- Parameters:
values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,9) >>> a array([4 1 4 3 2 2 2 3 3]) >>> g = ak.GroupBy(a) >>> g.keys array([4 1 4 3 2 2 2 3 3]) >>> b = ak.linspace(-5,5,9) >>> b array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5]) >>> g.median(b) (array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))
- min(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.min(b) (array([2, 3, 4]), array([1, 1, 3]))
- mode(values: groupable) Tuple[groupable, groupable][source]¶
Most common value in each group. If a group is multi-modal, return the modal value that occurs first.
- Parameters:
values ((list of) pdarray-like) – The values from which to take the mode of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group
- nunique(values: groupable) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.
- Parameters:
values (pdarray, int64) – The values to group and find unique values
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype
Examples
>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> data array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> labels ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g = ak.GroupBy(labels) >>> g.keys ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g.nunique(data) array([1,2,3,4]), array([2, 2, 3, 1]) # Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4 # Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4 # Group (3,3,3) has values [3,4,1] -> 3 unique values # Group (4) has values [4] -> 1 unique value
- prod(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.
- Parameters:
values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.prod(b) (array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))
- register(user_defined_name: str) GroupBy[source]¶
Register this GroupBy object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components
- Returns:
The same GroupBy which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different GroupBys with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name
See also
unregister,attach,unregister_groupby_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- size() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
See also
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.size() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- std(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.
- Parameters:
values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e.,
std = sqrt(mean((x - x.mean())**2)).The average squared deviation is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of the infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even withddof=1, it will not be an unbiased estimate of the standard deviation per se.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.std(b) (array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))
- sum(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.
- Parameters:
values (pdarray) – The values to group and sum
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance
skipna (bool) – boolean which determines if NANs should be skipped
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The grouped sum of a boolean
pdarrayreturns integers.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.sum(b) (array([2, 3, 4]), array([8, 14, 6]))
- to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶
Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Returns:
None
GroupBy is not currently supported by Parquet
- unique(values: groupable)[source]¶
Return the set of unique values in each group, as a SegArray.
- Parameters:
values ((list of) pdarray-like) – The values to unique
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group
- Raises:
TypeError – Raised if values is or contains Strings or Categorical
- unregister()[source]¶
Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_groupby_by_name(user_defined_name: str) None[source]¶
Function to unregister GroupBy object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the GroupBy object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- var(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.
- Parameters:
values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The variance is the average of the squared deviations from the mean, i.e.,
var = mean((x - x.mean())**2).The mean is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of a hypothetical infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.var(b) (array([2 3 4]), array([2.333333333333333 1.2 0]))
- class arkouda.GroupBy(keys: groupable | None = None, assume_sorted: bool = False, dropna: bool = True, **kwargs)[source]¶
Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.
- Parameters:
keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)
- nkeys¶
The number of key arrays (columns)
- Type:
int
- unique_keys¶
The unique values of the keys array(s), in grouped order
- Type:
(list of) pdarray, Strings, or Categorical
- ngroups¶
The length of the unique_keys array(s), i.e. number of groups
- Type:
int
- logger¶
Used for all logging operations
- Type:
ArkoudaLogger
- dropna¶
If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Type:
bool (default=True)
- Raises:
TypeError – Raised if keys is a pdarray with a dtype other than int64
Notes
Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.
For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:
a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.
(Optional) a .group() method that returns the permutation that groups the array
If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.
- Reductions¶
- objType = 'GroupBy'¶
- AND(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise AND of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with AND
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- OR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise OR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with OR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- XOR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise XOR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with XOR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- aggregate(values: groupable, operator: str, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, groupable][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.
- Parameters:
values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype
Examples
>>> keys = ak.arange(0, 10) >>> vals = ak.linspace(-1, 1, 10) >>> g = ak.GroupBy(keys) >>> g.aggregate(vals, 'sum') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768, -0.55555555555555536, -0.33333333333333348, -0.11111111111111116, 0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768, 1])) >>> g.aggregate(vals, 'min') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779, -0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116, 0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))
- all(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “and”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- any(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “or”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
- argmax(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmax
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmax(b) (array([2, 3, 4]), array([9, 3, 2]))
- argmin(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmin
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmin(b) (array([2, 3, 4]), array([5, 4, 2]))
- static attach(user_defined_name: str) GroupBy[source]¶
Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which GroupBy object was registered under
- Returns:
The GroupBy object created by re-attaching to the corresponding server components
- Return type:
- Raises:
RegistrationError – if user_defined_name is not registered
See also
register,is_registered,unregister,unregister_groupby_by_name
- broadcast(values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, permute: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Fill each group’s segment with a constant value.
- Parameters:
- Returns:
The broadcasted values
- Return type:
- Raises:
TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment
Notes
This function is a sparse analog of
np.broadcast. If a GroupBy object represents a sparse matrix (tensor), then this function takes a (dense) column vector and replicates each value to the non-zero elements in the corresponding row.Examples
>>> a = ak.array([0, 1, 0, 1, 0]) >>> values = ak.array([3, 5]) >>> g = ak.GroupBy(a) # By default, result is in original order >>> g.broadcast(values) array([3, 5, 3, 5, 3]) # With permute=False, result is in grouped order >>> g.broadcast(values, permute=False) array([3, 3, 3, 5, 5] >>> a = ak.randint(1,5,10) >>> a array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> g.broadcast(counts > 2) array([True False True True True False True True False False]) >>> g.broadcast(counts == 3) array([True False True True True False True True False False]) >>> g.broadcast(counts < 4) array([True True True True True True True True True True])
- static build_from_components(user_defined_name: str | None = None, **kwargs) GroupBy[source]¶
function to build a new GroupBy object from component keys and permutation.
- Parameters:
user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”
- Returns:
The GroupBy object created by using the given components
- Return type:
- count() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
Notes
This alias is an alias of “size”.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- first(values: groupable_element_type) Tuple[groupable, groupable_element_type][source]¶
First value in each group.
- Parameters:
values (pdarray-like) – The values from which to take the first of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group
- is_registered() bool[source]¶
Return True if the object is contained in the registry
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mismatch of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- max(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.max(b) (array([2, 3, 4]), array([4, 4, 3]))
- mean(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.
- Parameters:
values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.mean(b) (array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))
- median(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.
- Parameters:
values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,9) >>> a array([4 1 4 3 2 2 2 3 3]) >>> g = ak.GroupBy(a) >>> g.keys array([4 1 4 3 2 2 2 3 3]) >>> b = ak.linspace(-5,5,9) >>> b array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5]) >>> g.median(b) (array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))
- min(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.min(b) (array([2, 3, 4]), array([1, 1, 3]))
- mode(values: groupable) Tuple[groupable, groupable][source]¶
Most common value in each group. If a group is multi-modal, return the modal value that occurs first.
- Parameters:
values ((list of) pdarray-like) – The values from which to take the mode of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group
- nunique(values: groupable) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.
- Parameters:
values (pdarray, int64) – The values to group and find unique values
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype
Examples
>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> data array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> labels ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g = ak.GroupBy(labels) >>> g.keys ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g.nunique(data) array([1,2,3,4]), array([2, 2, 3, 1]) # Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4 # Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4 # Group (3,3,3) has values [3,4,1] -> 3 unique values # Group (4) has values [4] -> 1 unique value
- prod(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.
- Parameters:
values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.prod(b) (array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))
- register(user_defined_name: str) GroupBy[source]¶
Register this GroupBy object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components
- Returns:
The same GroupBy which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different GroupBys with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name
See also
unregister,attach,unregister_groupby_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- size() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
See also
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.size() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- std(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.
- Parameters:
values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e.,
std = sqrt(mean((x - x.mean())**2)).The average squared deviation is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of the infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even withddof=1, it will not be an unbiased estimate of the standard deviation per se.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.std(b) (array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))
- sum(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.
- Parameters:
values (pdarray) – The values to group and sum
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance
skipna (bool) – boolean which determines if NANs should be skipped
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The grouped sum of a boolean
pdarrayreturns integers.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.sum(b) (array([2, 3, 4]), array([8, 14, 6]))
- to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶
Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Returns:
None
GroupBy is not currently supported by Parquet
- unique(values: groupable)[source]¶
Return the set of unique values in each group, as a SegArray.
- Parameters:
values ((list of) pdarray-like) – The values to unique
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group
- Raises:
TypeError – Raised if values is or contains Strings or Categorical
- unregister()[source]¶
Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_groupby_by_name(user_defined_name: str) None[source]¶
Function to unregister GroupBy object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the GroupBy object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- var(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.
- Parameters:
values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The variance is the average of the squared deviations from the mean, i.e.,
var = mean((x - x.mean())**2).The mean is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of a hypothetical infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.var(b) (array([2 3 4]), array([2.333333333333333 1.2 0]))
- class arkouda.GroupBy(keys: groupable | None = None, assume_sorted: bool = False, dropna: bool = True, **kwargs)[source]¶
Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.
- Parameters:
keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)
- nkeys¶
The number of key arrays (columns)
- Type:
int
- unique_keys¶
The unique values of the keys array(s), in grouped order
- Type:
(list of) pdarray, Strings, or Categorical
- ngroups¶
The length of the unique_keys array(s), i.e. number of groups
- Type:
int
- logger¶
Used for all logging operations
- Type:
ArkoudaLogger
- dropna¶
If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Type:
bool (default=True)
- Raises:
TypeError – Raised if keys is a pdarray with a dtype other than int64
Notes
Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.
For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:
a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.
(Optional) a .group() method that returns the permutation that groups the array
If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.
- Reductions¶
- objType = 'GroupBy'¶
- AND(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise AND of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with AND
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- OR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise OR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with OR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- XOR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise XOR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with XOR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- aggregate(values: groupable, operator: str, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, groupable][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.
- Parameters:
values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype
Examples
>>> keys = ak.arange(0, 10) >>> vals = ak.linspace(-1, 1, 10) >>> g = ak.GroupBy(keys) >>> g.aggregate(vals, 'sum') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768, -0.55555555555555536, -0.33333333333333348, -0.11111111111111116, 0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768, 1])) >>> g.aggregate(vals, 'min') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779, -0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116, 0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))
- all(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “and”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- any(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “or”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
- argmax(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmax
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmax(b) (array([2, 3, 4]), array([9, 3, 2]))
- argmin(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmin
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmin(b) (array([2, 3, 4]), array([5, 4, 2]))
- static attach(user_defined_name: str) GroupBy[source]¶
Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which GroupBy object was registered under
- Returns:
The GroupBy object created by re-attaching to the corresponding server components
- Return type:
- Raises:
RegistrationError – if user_defined_name is not registered
See also
register,is_registered,unregister,unregister_groupby_by_name
- broadcast(values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, permute: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Fill each group’s segment with a constant value.
- Parameters:
- Returns:
The broadcasted values
- Return type:
- Raises:
TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment
Notes
This function is a sparse analog of
np.broadcast. If a GroupBy object represents a sparse matrix (tensor), then this function takes a (dense) column vector and replicates each value to the non-zero elements in the corresponding row.Examples
>>> a = ak.array([0, 1, 0, 1, 0]) >>> values = ak.array([3, 5]) >>> g = ak.GroupBy(a) # By default, result is in original order >>> g.broadcast(values) array([3, 5, 3, 5, 3]) # With permute=False, result is in grouped order >>> g.broadcast(values, permute=False) array([3, 3, 3, 5, 5] >>> a = ak.randint(1,5,10) >>> a array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> g.broadcast(counts > 2) array([True False True True True False True True False False]) >>> g.broadcast(counts == 3) array([True False True True True False True True False False]) >>> g.broadcast(counts < 4) array([True True True True True True True True True True])
- static build_from_components(user_defined_name: str | None = None, **kwargs) GroupBy[source]¶
function to build a new GroupBy object from component keys and permutation.
- Parameters:
user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”
- Returns:
The GroupBy object created by using the given components
- Return type:
- count() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
Notes
This alias is an alias of “size”.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- first(values: groupable_element_type) Tuple[groupable, groupable_element_type][source]¶
First value in each group.
- Parameters:
values (pdarray-like) – The values from which to take the first of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group
- is_registered() bool[source]¶
Return True if the object is contained in the registry
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mismatch of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- max(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.max(b) (array([2, 3, 4]), array([4, 4, 3]))
- mean(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.
- Parameters:
values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.mean(b) (array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))
- median(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.
- Parameters:
values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,9) >>> a array([4 1 4 3 2 2 2 3 3]) >>> g = ak.GroupBy(a) >>> g.keys array([4 1 4 3 2 2 2 3 3]) >>> b = ak.linspace(-5,5,9) >>> b array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5]) >>> g.median(b) (array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))
- min(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.min(b) (array([2, 3, 4]), array([1, 1, 3]))
- mode(values: groupable) Tuple[groupable, groupable][source]¶
Most common value in each group. If a group is multi-modal, return the modal value that occurs first.
- Parameters:
values ((list of) pdarray-like) – The values from which to take the mode of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group
- nunique(values: groupable) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.
- Parameters:
values (pdarray, int64) – The values to group and find unique values
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype
Examples
>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> data array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> labels ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g = ak.GroupBy(labels) >>> g.keys ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g.nunique(data) array([1,2,3,4]), array([2, 2, 3, 1]) # Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4 # Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4 # Group (3,3,3) has values [3,4,1] -> 3 unique values # Group (4) has values [4] -> 1 unique value
- prod(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.
- Parameters:
values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.prod(b) (array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))
- register(user_defined_name: str) GroupBy[source]¶
Register this GroupBy object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components
- Returns:
The same GroupBy which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different GroupBys with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name
See also
unregister,attach,unregister_groupby_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- size() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
See also
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.size() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- std(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.
- Parameters:
values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e.,
std = sqrt(mean((x - x.mean())**2)).The average squared deviation is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of the infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even withddof=1, it will not be an unbiased estimate of the standard deviation per se.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.std(b) (array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))
- sum(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.
- Parameters:
values (pdarray) – The values to group and sum
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance
skipna (bool) – boolean which determines if NANs should be skipped
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The grouped sum of a boolean
pdarrayreturns integers.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.sum(b) (array([2, 3, 4]), array([8, 14, 6]))
- to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶
Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Returns:
None
GroupBy is not currently supported by Parquet
- unique(values: groupable)[source]¶
Return the set of unique values in each group, as a SegArray.
- Parameters:
values ((list of) pdarray-like) – The values to unique
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group
- Raises:
TypeError – Raised if values is or contains Strings or Categorical
- unregister()[source]¶
Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_groupby_by_name(user_defined_name: str) None[source]¶
Function to unregister GroupBy object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the GroupBy object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- var(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.
- Parameters:
values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The variance is the average of the squared deviations from the mean, i.e.,
var = mean((x - x.mean())**2).The mean is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of a hypothetical infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.var(b) (array([2 3 4]), array([2.333333333333333 1.2 0]))
- class arkouda.GroupBy(keys: groupable | None = None, assume_sorted: bool = False, dropna: bool = True, **kwargs)[source]¶
Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.
- Parameters:
keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)
- nkeys¶
The number of key arrays (columns)
- Type:
int
- unique_keys¶
The unique values of the keys array(s), in grouped order
- Type:
(list of) pdarray, Strings, or Categorical
- ngroups¶
The length of the unique_keys array(s), i.e. number of groups
- Type:
int
- logger¶
Used for all logging operations
- Type:
ArkoudaLogger
- dropna¶
If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.
- Type:
bool (default=True)
- Raises:
TypeError – Raised if keys is a pdarray with a dtype other than int64
Notes
Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.
For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:
a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.
(Optional) a .group() method that returns the permutation that groups the array
If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.
- Reductions¶
- objType = 'GroupBy'¶
- AND(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise AND of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with AND
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- OR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise OR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with OR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- XOR(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Bitwise XOR of values in each segment.
Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.
- Parameters:
values (pdarray, int64) – The values to group and reduce with XOR
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- aggregate(values: groupable, operator: str, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, groupable][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.
- Parameters:
values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype
Examples
>>> keys = ak.arange(0, 10) >>> vals = ak.linspace(-1, 1, 10) >>> g = ak.GroupBy(keys) >>> g.aggregate(vals, 'sum') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768, -0.55555555555555536, -0.33333333333333348, -0.11111111111111116, 0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768, 1])) >>> g.aggregate(vals, 'min') (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779, -0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116, 0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))
- all(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “and”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype
- any(values: arkouda.pdarrayclass.pdarray) Tuple[arkouda.pdarrayclass.pdarray | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.
- Parameters:
values (pdarray, bool) – The values to group and reduce with “or”
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
- argmax(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmax
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmax(b) (array([2, 3, 4]), array([9, 3, 2]))
- argmin(values: arkouda.pdarrayclass.pdarray) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find argmin
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype
Notes
The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.argmin(b) (array([2, 3, 4]), array([5, 4, 2]))
- static attach(user_defined_name: str) GroupBy[source]¶
Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which GroupBy object was registered under
- Returns:
The GroupBy object created by re-attaching to the corresponding server components
- Return type:
- Raises:
RegistrationError – if user_defined_name is not registered
See also
register,is_registered,unregister,unregister_groupby_by_name
- broadcast(values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, permute: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Fill each group’s segment with a constant value.
- Parameters:
- Returns:
The broadcasted values
- Return type:
- Raises:
TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment
Notes
This function is a sparse analog of
np.broadcast. If a GroupBy object represents a sparse matrix (tensor), then this function takes a (dense) column vector and replicates each value to the non-zero elements in the corresponding row.Examples
>>> a = ak.array([0, 1, 0, 1, 0]) >>> values = ak.array([3, 5]) >>> g = ak.GroupBy(a) # By default, result is in original order >>> g.broadcast(values) array([3, 5, 3, 5, 3]) # With permute=False, result is in grouped order >>> g.broadcast(values, permute=False) array([3, 3, 3, 5, 5] >>> a = ak.randint(1,5,10) >>> a array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> g.broadcast(counts > 2) array([True False True True True False True True False False]) >>> g.broadcast(counts == 3) array([True False True True True False True True False False]) >>> g.broadcast(counts < 4) array([True True True True True True True True True True])
- static build_from_components(user_defined_name: str | None = None, **kwargs) GroupBy[source]¶
function to build a new GroupBy object from component keys and permutation.
- Parameters:
user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”
- Returns:
The GroupBy object created by using the given components
- Return type:
- count() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
Notes
This alias is an alias of “size”.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.count() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- first(values: groupable_element_type) Tuple[groupable, groupable_element_type][source]¶
First value in each group.
- Parameters:
values (pdarray-like) – The values from which to take the first of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group
- is_registered() bool[source]¶
Return True if the object is contained in the registry
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mismatch of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- max(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.max(b) (array([2, 3, 4]), array([4, 4, 3]))
- mean(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.
- Parameters:
values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.mean(b) (array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))
- median(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.
- Parameters:
values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,9) >>> a array([4 1 4 3 2 2 2 3 3]) >>> g = ak.GroupBy(a) >>> g.keys array([4 1 4 3 2 2 2 3 3]) >>> b = ak.linspace(-5,5,9) >>> b array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5]) >>> g.median(b) (array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))
- min(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.
- Parameters:
values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.min(b) (array([2, 3, 4]), array([1, 1, 3]))
- mode(values: groupable) Tuple[groupable, groupable][source]¶
Most common value in each group. If a group is multi-modal, return the modal value that occurs first.
- Parameters:
values ((list of) pdarray-like) – The values from which to take the mode of each group
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group
- nunique(values: groupable) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.
- Parameters:
values (pdarray, int64) – The values to group and find unique values
- Returns:
unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype
Examples
>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> data array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4]) >>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> labels ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g = ak.GroupBy(labels) >>> g.keys ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4]) >>> g.nunique(data) array([1,2,3,4]), array([2, 2, 3, 1]) # Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4 # Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4 # Group (3,3,3) has values [3,4,1] -> 3 unique values # Group (4) has values [4] -> 1 unique value
- prod(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.
- Parameters:
values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype
Notes
The return dtype is always float64.
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.prod(b) (array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))
- register(user_defined_name: str) GroupBy[source]¶
Register this GroupBy object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components
- Returns:
The same GroupBy which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different GroupBys with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name
See also
unregister,attach,unregister_groupby_by_name,is_registeredNotes
Objects registered with the server are immune to deletion until they are unregistered.
- size() Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).
- Parameters:
none
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears
See also
Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4]) >>> g = ak.GroupBy(a) >>> keys,counts = g.size() >>> keys array([1, 2, 3, 4]) >>> counts array([1, 2, 4, 3])
- std(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.
- Parameters:
values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The standard deviation is the square root of the average of the squared deviations from the mean, i.e.,
std = sqrt(mean((x - x.mean())**2)).The average squared deviation is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of the infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even withddof=1, it will not be an unbiased estimate of the standard deviation per se.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.std(b) (array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))
- sum(values: arkouda.pdarrayclass.pdarray, skipna: bool = True) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.
- Parameters:
values (pdarray) – The values to group and sum
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance
skipna (bool) – boolean which determines if NANs should be skipped
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The grouped sum of a boolean
pdarrayreturns integers.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.sum(b) (array([2, 3, 4]), array([8, 14, 6]))
- to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶
Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Returns:
None
GroupBy is not currently supported by Parquet
- unique(values: groupable)[source]¶
Return the set of unique values in each group, as a SegArray.
- Parameters:
values ((list of) pdarray-like) – The values to unique
- Returns:
unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group
- Raises:
TypeError – Raised if values is or contains Strings or Categorical
- unregister()[source]¶
Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- static unregister_groupby_by_name(user_defined_name: str) None[source]¶
Function to unregister GroupBy object by name which was registered with the arkouda server via register()
- Parameters:
user_defined_name (str) – Name under which the GroupBy object was registered
- Raises:
TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components
See also
- var(values: arkouda.pdarrayclass.pdarray, skipna: bool = True, ddof: arkouda.dtypes.int_scalars = 1) Tuple[groupable, arkouda.pdarrayclass.pdarray][source]¶
Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.
- Parameters:
values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance
- Raises:
TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
Notes
The return dtype is always float64.
The variance is the average of the squared deviations from the mean, i.e.,
var = mean((x - x.mean())**2).The mean is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of a hypothetical infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables.Examples
>>> a = ak.randint(1,5,10) >>> a array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> g = ak.GroupBy(a) >>> g.keys array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2]) >>> b = ak.randint(1,5,10) >>> b array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4]) >>> g.var(b) (array([2 3 4]), array([2.333333333333333 1.2 0]))
- class arkouda.IPv4(values)[source]¶
Bases:
arkouda.pdarrayclass.pdarrayRepresent integers as IPv4 addresses.
- Parameters:
values (pdarray, int64) – The integer IP addresses
- Returns:
The same IP addresses
- Return type:
Notes
This class is a thin wrapper around pdarray that mostly affects how values are displayed to the user. Operators and methods will typically treat this class like an int64 pdarray.
- special_objType = 'IPv4'¶
- normalize(x)[source]¶
Take in an IP address as a string, integer, or IPAddress object, and convert it to an integer.
- register(user_defined_name)[source]¶
Register this IPv4 object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the IPv4 is to be registered under, this will be the root name for underlying components
- Returns:
The same IPv4 which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different IPv4s with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the IPv4 with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- class arkouda.Index(values: List | arkouda.pdarrayclass.pdarray | arkouda.Strings | arkouda.Categorical | pandas.Index | Index, name: str | None = None, allow_list=False, max_list_size=1000)[source]¶
- property index¶
This is maintained to support older code
- property is_unique¶
Property indicating if all values in the index are unique :rtype: bool - True if all values are unique, False otherwise.
- property shape¶
- objType = 'Index'¶
Sequence used for indexing and alignment.
The basic object storing axis labels for all DataFrame objects.
- Parameters:
values (List, pdarray, Strings, Categorical, pandas.Index, or Index)
name (str, default=None) – Name to be stored in the index.
False (allow_list =) – If False, list values will be converted to a pdarray. If True, list values will remain as a list, provided the data length is less than max_list_size.
- :paramIf False, list values will be converted to a pdarray.
If True, list values will remain as a list, provided the data length is less than max_list_size.
- Parameters:
1000 (max_list_size =) – This is the maximum allowed data length for the values to be stored as a list object.
- Raises:
ValueError – Raised if allow_list=True and the size of values is > max_list_size.
See also
Examples
>>> ak.Index([1, 2, 3]) Index(array([1 2 3]), dtype='int64')
>>> ak.Index(list('abc')) Index(array(['a', 'b', 'c']), dtype='<U0')
>>> ak.Index([1, 2, 3], allow_list=True) Index([1, 2, 3], dtype='int64')
- is_registered()[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- map(arg: dict | arkouda.series.Series) Index[source]¶
Map values of Index according to an input mapping.
- Parameters:
arg (dict or Series) – The mapping correspondence.
- Returns:
A new index with the values transformed by the mapping correspondence.
- Return type:
- Raises:
TypeError – Raised if arg is not of type dict or arkouda.Series. Raised if index values not of type pdarray, Categorical, or Strings.
Examples
>>> import arkouda as ak >>> ak.connect() >>> idx = ak.Index(ak.array([2, 3, 2, 3, 4])) >>> display(idx) Index(array([2 3 2 3 4]), dtype='int64') >>> idx.map({4: 25.0, 2: 30.0, 1: 7.0, 3: 5.0}) Index(array([30.00000000000000000 5.00000000000000000 30.00000000000000000 5.00000000000000000 25.00000000000000000]), dtype='float64') >>> s2 = ak.Series(ak.array(["a","b","c","d"]), index = ak.array([4,2,1,3])) >>> idx.map(s2) Index(array(['b', 'b', 'd', 'd', 'a']), dtype='<U0')
- memory_usage(unit='B')[source]¶
Return the memory usage of the Index values.
- Parameters:
unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.
- Returns:
Bytes of memory consumed.
- Return type:
int
See also
arkouda.pdarrayclass.nbytes,arkouda.index.MultiIndex.memory_usage,arkouda.series.Series.memory_usage,arkouda.dataframe.DataFrame.memory_usageExamples
>>> import arkouda as ak >>> ak.connect() >>> idx = Index(ak.array([1, 2, 3])) >>> idx.memory_usage() 24
- register(user_defined_name)[source]¶
Register this Index object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Index is to be registered under, this will be the root name for underlying components
- Returns:
The same Index which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Indexes with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Index with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- save(prefix_path: str, dataset: str = 'index', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') str[source]¶
DEPRECATED Save the index to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string. Raised if the Index values are a list.
See also
save_all,load,read,to_parquet,to_hdfNotes
The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. Previously all files saved in Parquet format were saved with a.parquetfile extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used. The file I/O does not rely on the extension to determine the file format.
- set_dtype(dtype)[source]¶
Change the data type of the index
Currently only aku.ip_address and ak.array are supported.
- to_csv(prefix_path: str, dataset: str = 'index', col_delim: str = ',', overwrite: bool = False)[source]¶
Write Index to CSV file(s). File will contain a single column with the pdarray data. All CSV Files written by Arkouda include a header denoting data types of the columns.
- prefix_path: str
The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
- dataset: str
Column name to save the pdarray under. Defaults to “array”.
- col_delim: str
Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
- overwrite: bool
Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
str reponse message
- ValueError
Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
- RuntimeError
Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
- TypeError
Raised if we receive an unknown arkouda_type returned from the server. Raised if the Index values are a list.
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (`
`) at this time.
- to_hdf(prefix_path: str, dataset: str = 'index', mode: str = 'truncate', file_type: str = 'distribute') str[source]¶
Save the Index to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
TypeError – Raised if the Index values are a list.
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.
- to_parquet(prefix_path: str, dataset: str = 'index', mode: str = 'truncate', compression: str | None = None)[source]¶
Save the Index to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
TypeError – Raised if the Index values are a list.
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.
- unregister()[source]¶
Unregister this Index object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- update_hdf(prefix_path: str, dataset: str = 'index', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this Index object. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the index
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data
- arkouda.LEN_SUFFIX = '_lengths'¶
- class arkouda.LogLevel[source]¶
Bases:
enum.EnumGeneric enumeration.
Derive from this class to define new enumerations.
- CRITICAL = 'CRITICAL'¶
- DEBUG = 'DEBUG'¶
- ERROR = 'ERROR'¶
- INFO = 'INFO'¶
- WARN = 'WARN'¶
- class arkouda.MultiIndex(values, name=None, names=None)[source]¶
Bases:
Index- property index¶
This is maintained to support older code
- objType = 'MultiIndex'¶
- is_registered()[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- memory_usage(unit='B')[source]¶
Return the memory usage of the MultiIndex values.
- Parameters:
unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.
- Returns:
Bytes of memory consumed.
- Return type:
int
See also
arkouda.pdarrayclass.nbytes,arkouda.index.Index.memory_usage,arkouda.series.Series.memory_usage,arkouda.dataframe.DataFrame.memory_usageExamples
>>> import arkouda as ak >>> ak.connect() >>> m = ak.index.MultiIndex([ak.array([1,2,3]),ak.array([4,5,6])]) >>> m.memory_usage() 48
- register(user_defined_name)[source]¶
Register this Index object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Index is to be registered under, this will be the root name for underlying components
- Returns:
The same Index which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Indexes with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Index with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- set_dtype(dtype)[source]¶
Change the data type of the index
Currently only aku.ip_address and ak.array are supported.
- to_hdf(prefix_path: str, dataset: str = 'index', mode: str = 'truncate', file_type: str = 'distribute') str[source]¶
Save the Index to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray.
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.
- unregister()[source]¶
Unregister this Index object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- update_hdf(prefix_path: str, dataset: str = 'index', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this Index object. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the index
TypeError – Raised if the Index values are a list.
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data
- exception arkouda.NonUniqueError[source]¶
Bases:
ValueErrorInappropriate argument value (of correct type).
- class arkouda.Power_divergenceResult[source]¶
Bases:
namedtuple('Power_divergenceResult', ('statistic','pvalue'))The results of a power divergence statistical test.
- statistic¶
- Type:
numpy.float64
- pvalue¶
- Type:
numpy.float64
- arkouda.RegisteredSymbols = '__RegisteredSymbols__'¶
- exception arkouda.RegistrationError[source]¶
Bases:
ExceptionError/Exception used when the Arkouda Server cannot register an object
- exception arkouda.RegistrationError[source]¶
Bases:
ExceptionError/Exception used when the Arkouda Server cannot register an object
- exception arkouda.RegistrationError[source]¶
Bases:
ExceptionError/Exception used when the Arkouda Server cannot register an object
- exception arkouda.RegistrationError[source]¶
Bases:
ExceptionError/Exception used when the Arkouda Server cannot register an object
- exception arkouda.RegistrationError[source]¶
Bases:
ExceptionError/Exception used when the Arkouda Server cannot register an object
- class arkouda.Row(dict=None, /, **kwargs)[source]¶
Bases:
collections.UserDictThis class is useful for printing and working with individual rows of a of an aku.DataFrame.
- arkouda.SEG_SUFFIX = '_segments'¶
- arkouda.ScalarDTypes¶
- class arkouda.SegArray(segments, values, lengths=None, grouping=None)[source]¶
- property grouping¶
- property non_empty¶
- objType = 'SegArray'¶
- append(other, axis=0)[source]¶
Append other to self, either vertically (axis=0, length of resulting SegArray increases), or horizontally (axis=1, each sub-array of other appends to the corresponding sub-array of self).
- Parameters:
other (SegArray) – Array of sub-arrays to append
axis (0 or 1) – Whether to append vertically (0) or horizontally (1). If axis=1, other must be same size as self.
- Returns:
axis=0: New SegArray containing all sub-arrays axis=1: New SegArray of same length, with pairs of sub-arrays concatenated
- Return type:
- classmethod attach(user_defined_name)[source]¶
Using the defined name, attach to a SegArray that has been registered to the Symbol Table
- Parameters:
user_defined_name (str) – user defined name which the SegArray object was registered under
- Returns:
The resulting SegArray
- Return type:
- Raises:
RuntimeError – Raised if the server could not attach to the SegArray object
See also
- classmethod concat(x, axis=0, ordered=True)[source]¶
Concatenate a sequence of SegArrays
- Parameters:
x (sequence of SegArray) – The SegArrays to concatenate
axis (0 or 1) – Select vertical (0) or horizontal (1) concatenation. If axis=1, all SegArrays must have same size.
ordered (bool) – Must be True. This option is present for compatibility only, because unordered concatenation is not yet supported.
- Returns:
The input arrays joined into one SegArray
- Return type:
- classmethod from_multi_array(m)[source]¶
Construct a SegArray from a list of columns. This essentially transposes the input, resulting in an array of rows.
- classmethod from_parts(segments, values, lengths=None, grouping=None) SegArray[source]¶
DEPRECATED Construct a SegArray object from its parts
- Parameters:
- Returns:
Data structure representing an array whose elements are variable-length arrays.
- Return type:
Notes
Keyword args ‘lengths’ and ‘grouping’ are not user-facing. They are used by the attach method.
- get_jth(j, return_origins=True, compressed=False, default=0)[source]¶
Select the j-th element of each sub-array, where possible.
- Parameters:
j (int) – The index of the value to get from each sub-array. If j is negative, it counts backwards from the end of each sub-array.
return_origins (bool) – If True, return a logical index indicating where j is in bounds
compressed (bool) – If False, return array is same size as self, with default value where j is out of bounds. If True, the return array only contains values where j is in bounds.
default (scalar) – When compressed=False, the value to return when j is out of bounds for the sub-array
- Returns:
val (pdarray) – compressed=False: The j-th value of each sub-array where j is in bounds and the default value where j is out of bounds. compressed=True: The j-th values of only the sub-arrays where j is in bounds
origin_indices (pdarray, bool) – A Boolean array that is True where j is in bounds for the sub-array.
Notes
If values are Strings, only the compressed format is supported.
- get_length_n(n, return_origins=True)[source]¶
Return all sub-arrays of length n, as a list of columns.
- Parameters:
n (int) – Length of sub-arrays to select
return_origins (bool) – Return a logical index indicating which sub-arrays are length n
- Returns:
columns (list of pdarray) – An n-long list of pdarray, where each row is one of the n-long sub-arrays from the SegArray. The number of rows is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Array of bool for each element of the SegArray, True where sub-array has length n.
- get_ngrams(n, return_origins=True)[source]¶
Return all n-grams from all sub-arrays.
- Parameters:
n (int) – Length of n-gram
return_origins (bool) – If True, return an int64 array indicating which sub-array each returned n-gram came from.
- Returns:
ngrams (list of pdarray) – An n-long list of pdarrays, essentially a table where each row is an n-gram.
origin_indices (pdarray, int) – The index of the sub-array from which the corresponding n-gram originated
- get_prefixes(n, return_origins=True, proper=True)[source]¶
Return all sub-array prefixes of length n (for sub-arrays that are at least n+1 long)
- Parameters:
n (int) – Length of suffix
return_origins (bool) – If True, return a logical index indicating which sub-arrays were long enough to return an n-prefix
proper (bool) – If True, only return proper prefixes, i.e. from sub-arrays that are at least n+1 long. If False, allow the entire sub-array to be returned as a prefix.
- Returns:
prefixes (list of pdarray) – An n-long list of pdarrays, essentially a table where each row is an n-prefix. The number of rows is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the sub-array was long enough to return an n-suffix, False otherwise.
- get_suffixes(n, return_origins=True, proper=True)[source]¶
Return the n-long suffix of each sub-array, where possible
- Parameters:
n (int) – Length of suffix
return_origins (bool) – If True, return a logical index indicating which sub-arrays were long enough to return an n-suffix
proper (bool) – If True, only return proper suffixes, i.e. from sub-arrays that are at least n+1 long. If False, allow the entire sub-array to be returned as a suffix.
- Returns:
suffixes (list of pdarray) – An n-long list of pdarrays, essentially a table where each row is an n-suffix. The number of rows is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the sub-array was long enough to return an n-suffix, False otherwise.
- hash() Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Compute a 128-bit hash of each segment.
- intersect(other)[source]¶
Computes the intersection of 2 SegArrays.
- Parameters:
other (SegArray) – SegArray to compute against
- Returns:
Segments are the 1d intersections of the segments of self and other
- Return type:
See also
Examples
>>> a = [1, 2, 3, 1, 4] >>> b = [3, 1, 4, 5] >>> c = [1, 3, 3, 5] >>> d = [2, 2, 4] >>> seg_a = ak.segarray(ak.array([0, len(a)]), ak.array(a+b)) >>> seg_b = ak.segarray(ak.array([0, len(c)]), ak.array(c+d)) >>> seg_a.intersect(seg_b) SegArray([ [1, 3], [4] ])
- is_registered() bool[source]¶
Checks if the name of the SegArray object is registered in the Symbol Table
- Returns:
True if SegArray is registered, false if not
- Return type:
bool
See also
- classmethod load(prefix_path, dataset='segarray', segment_name='segments', value_name='values')[source]¶
- classmethod read_hdf(prefix_path, dataset='segarray')[source]¶
Load a saved SegArray from HDF5. All arguments must match what was supplied to SegArray.save()
- Parameters:
prefix_path (str) – Directory and filename prefix
dataset (str) – Name prefix for saved data within the HDF5 files
- Return type:
- register(user_defined_name)[source]¶
Register this SegArray object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name which this SegArray object will be registered under
- Returns:
The same SegArray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different SegArrays with the same name.
- Return type:
- Raises:
RegistrationError – Raised if the server could not register the SegArray object
Notes
Objects registered with the server are immune to deletion until they are unregistered.
See also
- remove_repeats(return_multiplicity=False)[source]¶
Condense sequences of repeated values within a sub-array to a single value.
- Parameters:
return_multiplicity (bool) – If True, also return the number of times each value was repeated.
- Returns:
norepeats (SegArray) – Sub-arrays with runs of repeated values replaced with single value
multiplicity (SegArray) – If return_multiplicity=True, this array contains the number of times each value in the returned SegArray was repeated in the original SegArray.
- save(prefix_path, dataset='segarray', mode='truncate', file_type='distribute')[source]¶
DEPRECATED Save the SegArray to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.
- set_jth(i, j, v)[source]¶
Set the j-th element of each sub-array in a subset.
- Parameters:
- Raises:
ValueError – If j is out of bounds in any of the sub-arrays specified by i.
- setdiff(other)[source]¶
Computes the set difference of 2 SegArrays.
- Parameters:
other (SegArray) – SegArray to compute against
- Returns:
Segments are the 1d set difference of the segments of self and other
- Return type:
See also
Examples
>>> a = [1, 2, 3, 1, 4] >>> b = [3, 1, 4, 5] >>> c = [1, 3, 3, 5] >>> d = [2, 2, 4] >>> seg_a = ak.segarray(ak.array([0, len(a)]), ak.array(a+b)) >>> seg_b = ak.segarray(ak.array([0, len(c)]), ak.array(c+d)) >>> seg_a.setdiff(seg_b) SegArray([ [2, 4], [1, 3, 5] ])
- setxor(other)[source]¶
Computes the symmetric difference of 2 SegArrays.
- Parameters:
other (SegArray) – SegArray to compute against
- Returns:
Segments are the 1d symmetric difference of the segments of self and other
- Return type:
See also
Examples
>>> a = [1, 2, 3, 1, 4] >>> b = [3, 1, 4, 5] >>> c = [1, 3, 3, 5] >>> d = [2, 2, 4] >>> seg_a = ak.segarray(ak.array([0, len(a)]), ak.array(a+b)) >>> seg_b = ak.segarray(ak.array([0, len(c)]), ak.array(c+d)) >>> seg_a.setxor(seg_b) SegArray([ [2, 4, 5], [1, 3, 5, 2] ])
- to_hdf(prefix_path, dataset='segarray', mode='truncate', file_type='distribute')[source]¶
Save the SegArray to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
None
See also
- to_list()[source]¶
Convert the segarray into a list containing sub-arrays
- Returns:
A list with the same sub-arrays (also list) as this segarray
- Return type:
list
See also
Examples
>>> segarr = ak.SegArray(ak.array([0, 4, 7]), ak.arange(12)) >>> segarr.to_list() [[0, 1, 2, 3], [4, 5, 6], [7, 8, 9, 10, 11]] >>> type(segarr.to_list()) list
- to_ndarray()[source]¶
Convert the array into a numpy.ndarray containing sub-arrays
- Returns:
A numpy ndarray with the same sub-arrays (also numpy.ndarray) as this array
- Return type:
np.ndarray
Examples
>>> segarr = ak.SegArray(ak.array([0, 4, 7]), ak.arange(12)) >>> segarr.to_ndarray() array([array([1, 2, 3, 4]), array([5, 6, 7]), array([8, 9, 10, 11, 12])]) >>> type(segarr.to_ndarray()) numpy.ndarray
- to_parquet(prefix_path, dataset='segarray', mode: str = 'truncate', compression: str | None = None)[source]¶
Save the SegArray object to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the object to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: Deprecated.
Parameter kept to maintain functionality of other calls. Only Truncate supported. By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – If write mode is not Truncate.
Notes
Append mode for Parquet has been deprecated. It was not implemented for SegArray.
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a Segmented Array to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the Segmented Array is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- union(other)[source]¶
Computes the union of 2 SegArrays.
- Parameters:
other (SegArray) – SegArray to compute against
- Returns:
Segments are the 1d union of the segments of self and other
- Return type:
See also
Examples
>>> a = [1, 2, 3, 1, 4] >>> b = [3, 1, 4, 5] >>> c = [1, 3, 3, 5] >>> d = [2, 2, 4] >>> seg_a = ak.segarray(ak.array([0, len(a)]), ak.array(a+b)) >>> seg_b = ak.segarray(ak.array([0, len(c)]), ak.array(c+d)) >>> seg_a.union(seg_b) SegArray([ [1, 2, 3, 4, 5], [1, 2, 3, 4, 5] ])
- unregister()[source]¶
Unregister this SegArray object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not unregister the SegArray object from the Symbol Table
Notes
Objects registered with the server are immune to deletion until they are unregistered.
See also
- static unregister_segarray_by_name(user_defined_name)[source]¶
Using the defined name, remove the registered SegArray object from the Symbol Table
- Parameters:
user_defined_name (str) – user defined name which the SegArray object was registered under
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not unregister the SegArray object from the Symbol Table
See also
- update_hdf(prefix_path: str, dataset: str = 'segarray', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this SegArray object. If the dataset does not exist it is added.
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the SegArray
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data
- class arkouda.Series(data: Tuple | List | arkouda.groupbyclass.groupable_element_type, name=None, index: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | Tuple | List | arkouda.index.Index | None = None)[source]¶
One-dimensional arkouda array with axis labels.
- Parameters:
- Raises:
TypeError – Raised if index is not a pdarray or Strings object Raised if data is not a pdarray, Strings, or Categorical object
ValueError – Raised if the index size does not match data size
Notes
The Series class accepts either positional arguments or keyword arguments. If entering positional arguments,
- 2 arguments entered:
argument 1 - data argument 2 - index
- 1 argument entered:
argument 1 - data
If entering 1 positional argument, it is assumed that this is the data argument. If only ‘data’ argument is passed in, Index will automatically be generated. If entering keywords,
‘data’ (see Parameters) ‘index’ (optional) must match size of ‘data’
- property at: _LocIndexer¶
Accesses entries of a Series by label
- property iat: _iLocIndexer¶
Accesses entries of a Series by position
- Parameters:
key (int) – The positions or container of positions to access entries for
- property iloc: _iLocIndexer¶
Accesses entries of a Series by position
- Parameters:
key (int) – The positions or container of positions to access entries for
- property loc: _LocIndexer¶
Accesses entries of a Series by label
- property shape¶
- dt¶
- objType = 'Series'¶
- str_acc¶
- static attach(label: str, nkeys: int = 1) Series[source]¶
DEPRECATED Retrieve a series registered with arkouda
- Parameters:
label (name used to register the series)
nkeys (number of keys, if a multi-index was registerd)
- static concat(arrays: List, axis: int = 0, index_labels: List[str] | None = None, value_labels: List[str] | None = None) arkouda.dataframe.DataFrame | Series[source]¶
Concatenate in arkouda a list of arkouda Series or grouped arkouda arrays horizontally or vertically. If a list of grouped arkouda arrays is passed they are converted to a series. Each grouping is a 2-tuple with the first item being the key(s) and the second being the value. If horizontal, each series or grouping must have the same length and the same index. The index of the series is converted to a column in the dataframe. If it is a multi-index,each level is converted to a column.
- Parameters:
arrays (The list of series/groupings to concat.)
axis (Whether or not to do a verticle (axis=0) or horizontal (axis=1) concatenation)
index_labels (column names(s) to label the index.)
value_labels (column names to label values of each series.)
- Returns:
axis=0 (an arkouda series.)
axis=1 (an arkouda dataframe.)
- diff() Series[source]¶
Diffs consecutive values of the series.
Returns a new series with the same index and length. First value is set to NaN.
- fillna(value) Series[source]¶
Fill NA/NaN values using the specified method.
- Parameters:
value (scalar, Series, or pdarray) – Value to use to fill holes (e.g. 0), alternately a Series of values specifying which value to use for each index. Values not in the Series will not be filled. This value cannot be a list.
- Returns:
Object with missing values filled.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda import Series
>>> data = ak.Series([1, np.nan, 3, np.nan, 5]) >>> data
0
0
1
1
nan
2
3
3
nan
4
5
>>> fill_values1 = ak.ones(5) >>> data.fillna(fill_values1)
0
0
1
1
1
2
3
3
1
4
5
>>> fill_values2 = Series(ak.ones(5)) >>> data.fillna(fill_values2)
0
0
1
1
1
2
3
3
1
4
5
>>> fill_values3 = 100.0 >>> data.fillna(fill_values3)
0
0
1
1
100
2
3
3
100
4
5
- classmethod from_return_msg(repMsg: str) Series[source]¶
Return a Series instance pointing to components created by the arkouda server. The user should not call this function directly.
- Parameters:
repMsg (str) –
delimited string containing the values and indexes
- Returns:
A Series representing a set of pdarray components on the server
- Return type:
- Raises:
RuntimeError – Raised if a server-side error is thrown in the process of creating the Series instance
- has_repeat_labels() bool[source]¶
Returns whether the Series has any labels that appear more than once
- hasnans() bool[source]¶
Return True if there are any NaNs.
- Return type:
bool
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda import Series >>> import numpy as np
>>> s = ak.Series(ak.array([1, 2, 3, np.nan])) >>> s
>>> s.hasnans True
- is_registered() bool[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- isin(lst: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | List) Series[source]¶
Find series elements whose values are in the specified list
Input¶
Either a python list or an arkouda array.
- rtype:
Arkouda boolean which is true for elements that are in the list and false otherwise.
- isna() Series[source]¶
Detect missing values.
Return a boolean same-sized object indicating if the values are NA. NA values, such as numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ‘’ are not considered NA values.
- Returns:
Mask of bool values for each element in Series that indicates whether an element is an NA value.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda import Series >>> import numpy as np
>>> s = Series(ak.array([1, 2, np.nan]), index = ak.array([1, 2, 4])) >>> s.isna()
0
1
False
2
False
4
True
- isnull() Series[source]¶
Series.isnull is an alias for Series.isna.
Detect missing values.
Return a boolean same-sized object indicating if the values are NA. NA values, such as numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ‘’ are not considered NA values.
- Returns:
Mask of bool values for each element in Series that indicates whether an element is an NA value.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda import Series >>> import numpy as np
>>> s = Series(ak.array([1, 2, np.nan]), index = ak.array([1, 2, 4])) >>> s.isnull()
0
1
False
2
False
4
True
- locate(key: int | arkouda.pdarrayclass.pdarray | arkouda.index.Index | Series | List | Tuple) Series[source]¶
Lookup values by index label
The input can be a scalar, a list of scalers, or a list of lists (if the series has a MultiIndex). As a special case, if a Series is used as the key, the series labels are preserved with its values use as the key.
Keys will be turned into arkouda arrays as needed.
- Return type:
A Series containing the values corresponding to the key.
- map(arg: dict | arkouda.Series) arkouda.Series[source]¶
Map values of Series according to an input mapping.
- Parameters:
arg (dict or Series) – The mapping correspondence.
- Returns:
A new series with the same index as the caller. When the input Series has Categorical values, the return Series will have Strings values. Otherwise, the return type will match the input type.
- Return type:
- Raises:
TypeError – Raised if arg is not of type dict or arkouda.Series. Raised if series values not of type pdarray, Categorical, or Strings.
Examples
>>> import arkouda as ak >>> ak.connect() >>> s = ak.Series(ak.array([2, 3, 2, 3, 4])) >>> display(s)
0
0
2
1
3
2
2
3
3
4
4
>>> s.map({4: 25.0, 2: 30.0, 1: 7.0, 3: 5.0})
0
0
30.0
1
5.0
2
30.0
3
5.0
4
25.0
>>> s2 = ak.Series(ak.array(["a","b","c","d"]), index = ak.array([4,2,1,3])) >>> s.map(s2)
0
0
b
1
b
2
d
3
d
4
a
- memory_usage(index: bool = True, unit='B') int[source]¶
Return the memory usage of the Series.
The memory usage can optionally include the contribution of the index.
- Parameters:
index (bool, default True) – Specifies whether to include the memory usage of the Series index.
unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.
- Returns:
Bytes of memory consumed.
- Return type:
int
See also
arkouda.pdarrayclass.nbytes,arkouda.index.Index.memory_usage,arkouda.series.Series.memory_usage,arkouda.dataframe.DataFrame.memory_usageExamples
>>> from arkouda.series import Series >>> s = ak.Series(ak.arange(3)) >>> s.memory_usage() 48
Not including the index gives the size of the rest of the data, which is necessarily smaller:
>>> s.memory_usage(index=False) 24
Select the units:
>>> s = ak.Series(ak.arange(3000)) >>> s.memory_usage(unit="KB") 46.875
- notna() Series[source]¶
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings ‘’ are not considered NA values. NA values, such as numpy.NaN, get mapped to False values.
- Returns:
Mask of bool values for each element in Series that indicates whether an element is not an NA value.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda import Series >>> import numpy as np
>>> s = Series(ak.array([1, 2, np.nan]), index = ak.array([1, 2, 4])) >>> s.notna()
0
1
True
2
True
4
False
- notnull() Series[source]¶
Series.notnull is an alias for Series.notna.
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings ‘’ are not considered NA values. NA values, such as numpy.NaN, get mapped to False values.
- Returns:
Mask of bool values for each element in Series that indicates whether an element is not an NA value.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda import Series >>> import numpy as np
>>> s = Series(ak.array([1, 2, np.nan]), index = ak.array([1, 2, 4])) >>> s.notnull()
0
1
True
2
True
4
False
- static pdconcat(arrays: List, axis: int = 0, labels: arkouda.strings.Strings | None = None) pandas.Series | pandas.DataFrame[source]¶
Concatenate a list of arkouda Series or grouped arkouda arrays, returning a PANDAS object.
If a list of grouped arkouda arrays is passed they are converted to a series. Each grouping is a 2-tuple with the first item being the key(s) and the second being the value.
If horizontal, each series or grouping must have the same length and the same index. The index of the series is converted to a column in the dataframe. If it is a multi-index,each level is converted to a column.
- Parameters:
arrays (The list of series/groupings to concat.)
axis (Whether or not to do a verticle (axis=0) or horizontal (axis=1) concatenation)
labels (names to give the columns of the data frame.)
- Returns:
axis=0 (a local PANDAS series)
axis=1 (a local PANDAS dataframe)
- register(user_defined_name: str)[source]¶
Register this Series object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the Series is to be registered under, this will be the root name for underlying components
- Returns:
The same Series which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Series with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Series with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- sort_index(ascending: bool = True) Series[source]¶
Sort the series by its index
- Parameters:
ascending (bool) – Sort values in ascending (default) or descending order.
- Return type:
A new Series sorted.
- sort_values(ascending: bool = True) Series[source]¶
Sort the series numerically
- Parameters:
ascending (bool) – Sort values in ascending (default) or descending order.
- Return type:
A new Series sorted smallest to largest
- to_dataframe(index_labels: List[str] | None = None, value_label: str | None = None) arkouda.dataframe.DataFrame[source]¶
Converts series to an arkouda data frame
- Parameters:
index_labels (column names(s) to label the index.)
value_label (column name to label values.)
- Return type:
An arkouda dataframe.
- to_markdown(mode='wt', index=True, tablefmt='grid', storage_options=None, **kwargs)[source]¶
Print Series in Markdown-friendly format.
- Parameters:
mode (str, optional) – Mode in which file is opened, “wt” by default.
index (bool, optional, default True) – Add index (row) labels.
tablefmt (str = "grid") – Table format to call from tablulate: https://pypi.org/project/tabulate/
storage_options (dict, optional) – Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a non-fsspec URL. See the fsspec and backend storage implementation docs for the set of allowed keys and values.
**kwargs – These parameters will be passed to tabulate.
Note
This function should only be called on small Series as it calls pandas.Series.to_markdown: https://pandas.pydata.org/docs/reference/api/pandas.Series.to_markdown.html
Examples
>>> import arkouda as ak >>> ak.connect() >>> s = ak.Series(["elk", "pig", "dog", "quetzal"], name="animal") >>> print(s.to_markdown()) | | animal | |---:|:---------| | 0 | elk | | 1 | pig | | 2 | dog | | 3 | quetzal |
Output markdown with a tabulate option.
>>> print(s.to_markdown(tablefmt="grid")) +----+----------+ | | animal | +====+==========+ | 0 | elk | +----+----------+ | 1 | pig | +----+----------+ | 2 | dog | +----+----------+ | 3 | quetzal | +----+----------+
- topn(n: int = 10) Series[source]¶
Return the top values of the series
- Parameters:
n (Number of values to return)
- Return type:
A new Series with the top values
- unregister()[source]¶
Unregister this Series object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- validate_key(key: Series | arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical | List | supported_scalars) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical | supported_scalars[source]¶
Validates type requirements for keys when reading or writing the Series. Also converts list and tuple arguments into pdarrays.
- Parameters:
key (Series, pdarray, Strings, Categorical, List, supported_scalars) – The key or container of keys that might be used to index into the Series.
- Return type:
The validated key(s), with lists and tuples converted to pdarrays
- Raises:
TypeError – Raised if keys are not boolean values or the type of the labels Raised if key is not one of the supported types
KeyError – Raised if container of keys has keys not present in the Series
IndexError – Raised if the length of a boolean key array is different from the Series
- validate_val(val: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | supported_scalars | List) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | supported_scalars[source]¶
Validates type requirements for values being written into the Series. Also converts list and tuple arguments into pdarrays.
- Parameters:
val (pdarray, Strings, list, supported_scalars) – The value or container of values that might be assigned into the Series.
- Return type:
The validated value, with lists converted to pdarrays
- Raises:
TypeError –
- Raised if val is not the same type or a container with elements
of the same time as the Series
Raised if val is a string or Strings type. Raised if val is not one of the supported types
- class arkouda.StringAccessor(series)[source]¶
Bases:
Properties
- class arkouda.Timedelta(pda, unit: str = _BASE_UNIT)[source]¶
Bases:
_AbstractBaseTimeRepresents a duration, the difference between two dates or times.
Timedelta is the Arkouda equivalent of pandas.TimedeltaIndex.
- Parameters:
pda (int64 pdarray, pd.TimedeltaIndex, pd.Series, or np.timedelta64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.
Possible values:
’weeks’ or ‘w’
’days’ or ‘d’
’hours’ or ‘h’
’minutes’, ‘m’, or ‘t’
’seconds’ or ‘s’
’milliseconds’, ‘ms’, or ‘l’
’microseconds’, ‘us’, or ‘u’
’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers
Notes
The
.valuesattribute is always in nanoseconds with int64 dtype.- property components¶
- property days¶
- property microseconds¶
- property nanoseconds¶
- property seconds¶
- special_objType = 'Timedelta'¶
- supported_opeq¶
- supported_with_datetime¶
- supported_with_pdarray¶
- supported_with_r_datetime¶
- supported_with_r_pdarray¶
- supported_with_r_timedelta¶
- supported_with_timedelta¶
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- register(user_defined_name)[source]¶
Register this Timedelta object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the timedelta is to be registered under, this will be the root name for underlying components
- Returns:
The same Timedelta which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Timedeltas with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the timedelta with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- std(ddof: arkouda.dtypes.int_scalars = 0)[source]¶
Returns the standard deviation as a pd.Timedelta object
- to_pandas()[source]¶
Convert array to a pandas TimedeltaIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.
See also
to_ndarray
- unregister()[source]¶
Unregister this timedelta object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- class arkouda.Timedelta(pda, unit: str = _BASE_UNIT)[source]¶
Bases:
_AbstractBaseTimeRepresents a duration, the difference between two dates or times.
Timedelta is the Arkouda equivalent of pandas.TimedeltaIndex.
- Parameters:
pda (int64 pdarray, pd.TimedeltaIndex, pd.Series, or np.timedelta64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.
Possible values:
’weeks’ or ‘w’
’days’ or ‘d’
’hours’ or ‘h’
’minutes’, ‘m’, or ‘t’
’seconds’ or ‘s’
’milliseconds’, ‘ms’, or ‘l’
’microseconds’, ‘us’, or ‘u’
’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers
Notes
The
.valuesattribute is always in nanoseconds with int64 dtype.- property components¶
- property days¶
- property microseconds¶
- property nanoseconds¶
- property seconds¶
- special_objType = 'Timedelta'¶
- supported_opeq¶
- supported_with_datetime¶
- supported_with_pdarray¶
- supported_with_r_datetime¶
- supported_with_r_pdarray¶
- supported_with_r_timedelta¶
- supported_with_timedelta¶
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry or is a component of a registered object.
- Returns:
Indicates if the object is contained in the registry
- Return type:
numpy.bool
- Raises:
RegistrationError – Raised if there’s a server-side error or a mis-match of registered components
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- register(user_defined_name)[source]¶
Register this Timedelta object and underlying components with the Arkouda server
- Parameters:
user_defined_name (str) – user defined name the timedelta is to be registered under, this will be the root name for underlying components
- Returns:
The same Timedelta which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Timedeltas with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the timedelta with the user_defined_name
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- std(ddof: arkouda.dtypes.int_scalars = 0)[source]¶
Returns the standard deviation as a pd.Timedelta object
- to_pandas()[source]¶
Convert array to a pandas TimedeltaIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.
See also
to_ndarray
- unregister()[source]¶
Unregister this timedelta object in the arkouda server which was previously registered using register() and/or attached to using attach()
- Raises:
RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister
See also
Notes
Objects registered with the server are immune to deletion until they are unregistered.
- arkouda.VAL_SUFFIX = '_values'¶
- arkouda.abs(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise absolute value of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing absolute values of the input array elements
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.abs(ak.arange(-5,-1)) array([5, 4, 3, 2])
>>> ak.abs(ak.linspace(-5,-1,5)) array([5, 4, 3, 2, 1])
- arkouda.akabs(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray¶
Return the element-wise absolute value of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing absolute values of the input array elements
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.abs(ak.arange(-5,-1)) array([5, 4, 3, 2])
>>> ak.abs(ak.linspace(-5,-1,5)) array([5, 4, 3, 2, 1])
- arkouda.akbool¶
- arkouda.akbool¶
- arkouda.akcast(pda: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, dt: numpy.dtype | type | str | arkouda.dtypes.BigInt, errors: ErrorMode = ErrorMode.strict) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical | Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray]¶
Cast an array to another dtype.
- Parameters:
dt (np.dtype, type, or str) – The target dtype to cast values to
errors ({strict, ignore, return_validity}) –
Controls how errors are handled when casting strings to a numeric type (ignored for casts from numeric types).
strict: raise RuntimeError if any string cannot be converted
- ignore: never raise an error. Uninterpretable strings get
converted to NaN (float64), -2**63 (int64), zero (uint64 and uint8), or False (bool)
return_validity: in addition to returning the same output as “ignore”, also return a bool array indicating where the cast was successful.
- Returns:
pdarray or Strings – Array of values cast to desired dtype
[validity (pdarray(bool)]) – If errors=”return_validity” and input is Strings, a second array is returned with True where the cast succeeded and False where it failed.
Notes
The cast is performed according to Chapel’s casting rules and is NOT safe from overflows or underflows. The user must ensure that the target dtype has the precision and capacity to hold the desired result.
Examples
>>> ak.cast(ak.linspace(1.0,5.0,5), dt=ak.int64) array([1, 2, 3, 4, 5])
>>> ak.cast(ak.arange(0,5), dt=ak.float64).dtype dtype('float64')
>>> ak.cast(ak.arange(0,5), dt=ak.bool) array([False, True, True, True, True])
>>> ak.cast(ak.linspace(0,4,5), dt=ak.bool) array([False, True, True, True, True])
- arkouda.akcast(pda: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, dt: numpy.dtype | type | str | arkouda.dtypes.BigInt, errors: ErrorMode = ErrorMode.strict) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical | Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray]¶
Cast an array to another dtype.
- Parameters:
dt (np.dtype, type, or str) – The target dtype to cast values to
errors ({strict, ignore, return_validity}) –
Controls how errors are handled when casting strings to a numeric type (ignored for casts from numeric types).
strict: raise RuntimeError if any string cannot be converted
- ignore: never raise an error. Uninterpretable strings get
converted to NaN (float64), -2**63 (int64), zero (uint64 and uint8), or False (bool)
return_validity: in addition to returning the same output as “ignore”, also return a bool array indicating where the cast was successful.
- Returns:
pdarray or Strings – Array of values cast to desired dtype
[validity (pdarray(bool)]) – If errors=”return_validity” and input is Strings, a second array is returned with True where the cast succeeded and False where it failed.
Notes
The cast is performed according to Chapel’s casting rules and is NOT safe from overflows or underflows. The user must ensure that the target dtype has the precision and capacity to hold the desired result.
Examples
>>> ak.cast(ak.linspace(1.0,5.0,5), dt=ak.int64) array([1, 2, 3, 4, 5])
>>> ak.cast(ak.arange(0,5), dt=ak.float64).dtype dtype('float64')
>>> ak.cast(ak.arange(0,5), dt=ak.bool) array([False, True, True, True, True])
>>> ak.cast(ak.linspace(0,4,5), dt=ak.bool) array([False, True, True, True, True])
- arkouda.akfloat64¶
- arkouda.akfloat64¶
- arkouda.akint64¶
- arkouda.akint64¶
- arkouda.akint64¶
- arkouda.akuint64¶
- arkouda.akuint64¶
- arkouda.akuint64¶
- arkouda.align(*args)[source]¶
Map multiple arrays of sparse identifiers to a common 0-up index.
- Parameters:
*args (pdarrays or sequences of pdarrays) – Arrays to map to dense index
- Returns:
aligned – Arrays with values replaced by 0-up indices
- Return type:
list of pdarrays
- arkouda.all(pda: pdarray) numpy.bool_[source]¶
Return True iff all elements of the array evaluate to True.
- Parameters:
pda (pdarray) – The pdarray instance to be evaluated
- Returns:
Indicates if all pdarray elements evaluate to True
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.all_scalars¶
The DType enum defines the supported Arkouda data types in string form.
- arkouda.any(pda: pdarray) numpy.bool_[source]¶
Return True iff any element of the array evaluates to True.
- Parameters:
pda (pdarray) – The pdarray instance to be evaluated
- Returns:
Indicates if 1..n pdarray elements evaluate to True
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.arange(*args, **kwargs) arkouda.pdarrayclass.pdarray[source]¶
arange([start,] stop[, stride,] dtype=int64)
Create a pdarray of consecutive integers within the interval [start, stop). If only one arg is given then arg is the stop parameter. If two args are given, then the first arg is start and second is stop. If three args are given, then the first arg is start, second is stop, third is stride.
The return value is cast to type dtype
- Parameters:
start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Integers from start (inclusive) to stop (exclusive) by stride
- Return type:
pdarray, dtype
- Raises:
TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0
Notes
Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.
Examples
>>> ak.arange(0, 5, 1) array([0, 1, 2, 3, 4])
>>> ak.arange(5, 0, -1) array([5, 4, 3, 2, 1])
>>> ak.arange(0, 10, 2) array([0, 2, 4, 6, 8])
>>> ak.arange(-5, -10, -1) array([-5, -6, -7, -8, -9])
- arkouda.arange(*args, **kwargs) arkouda.pdarrayclass.pdarray[source]¶
arange([start,] stop[, stride,] dtype=int64)
Create a pdarray of consecutive integers within the interval [start, stop). If only one arg is given then arg is the stop parameter. If two args are given, then the first arg is start and second is stop. If three args are given, then the first arg is start, second is stop, third is stride.
The return value is cast to type dtype
- Parameters:
start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Integers from start (inclusive) to stop (exclusive) by stride
- Return type:
pdarray, dtype
- Raises:
TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0
Notes
Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.
Examples
>>> ak.arange(0, 5, 1) array([0, 1, 2, 3, 4])
>>> ak.arange(5, 0, -1) array([5, 4, 3, 2, 1])
>>> ak.arange(0, 10, 2) array([0, 2, 4, 6, 8])
>>> ak.arange(-5, -10, -1) array([-5, -6, -7, -8, -9])
- arkouda.arange(*args, **kwargs) arkouda.pdarrayclass.pdarray[source]¶
arange([start,] stop[, stride,] dtype=int64)
Create a pdarray of consecutive integers within the interval [start, stop). If only one arg is given then arg is the stop parameter. If two args are given, then the first arg is start and second is stop. If three args are given, then the first arg is start, second is stop, third is stride.
The return value is cast to type dtype
- Parameters:
start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Integers from start (inclusive) to stop (exclusive) by stride
- Return type:
pdarray, dtype
- Raises:
TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0
Notes
Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.
Examples
>>> ak.arange(0, 5, 1) array([0, 1, 2, 3, 4])
>>> ak.arange(5, 0, -1) array([5, 4, 3, 2, 1])
>>> ak.arange(0, 10, 2) array([0, 2, 4, 6, 8])
>>> ak.arange(-5, -10, -1) array([-5, -6, -7, -8, -9])
- arkouda.arange(*args, **kwargs) arkouda.pdarrayclass.pdarray[source]¶
arange([start,] stop[, stride,] dtype=int64)
Create a pdarray of consecutive integers within the interval [start, stop). If only one arg is given then arg is the stop parameter. If two args are given, then the first arg is start and second is stop. If three args are given, then the first arg is start, second is stop, third is stride.
The return value is cast to type dtype
- Parameters:
start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Integers from start (inclusive) to stop (exclusive) by stride
- Return type:
pdarray, dtype
- Raises:
TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0
Notes
Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.
Examples
>>> ak.arange(0, 5, 1) array([0, 1, 2, 3, 4])
>>> ak.arange(5, 0, -1) array([5, 4, 3, 2, 1])
>>> ak.arange(0, 10, 2) array([0, 2, 4, 6, 8])
>>> ak.arange(-5, -10, -1) array([-5, -6, -7, -8, -9])
- arkouda.arange(*args, **kwargs) arkouda.pdarrayclass.pdarray[source]¶
arange([start,] stop[, stride,] dtype=int64)
Create a pdarray of consecutive integers within the interval [start, stop). If only one arg is given then arg is the stop parameter. If two args are given, then the first arg is start and second is stop. If three args are given, then the first arg is start, second is stop, third is stride.
The return value is cast to type dtype
- Parameters:
start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Integers from start (inclusive) to stop (exclusive) by stride
- Return type:
pdarray, dtype
- Raises:
TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0
Notes
Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.
Examples
>>> ak.arange(0, 5, 1) array([0, 1, 2, 3, 4])
>>> ak.arange(5, 0, -1) array([5, 4, 3, 2, 1])
>>> ak.arange(0, 10, 2) array([0, 2, 4, 6, 8])
>>> ak.arange(-5, -10, -1) array([-5, -6, -7, -8, -9])
- arkouda.arange(*args, **kwargs) arkouda.pdarrayclass.pdarray[source]¶
arange([start,] stop[, stride,] dtype=int64)
Create a pdarray of consecutive integers within the interval [start, stop). If only one arg is given then arg is the stop parameter. If two args are given, then the first arg is start and second is stop. If three args are given, then the first arg is start, second is stop, third is stride.
The return value is cast to type dtype
- Parameters:
start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Integers from start (inclusive) to stop (exclusive) by stride
- Return type:
pdarray, dtype
- Raises:
TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0
Notes
Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.
Examples
>>> ak.arange(0, 5, 1) array([0, 1, 2, 3, 4])
>>> ak.arange(5, 0, -1) array([5, 4, 3, 2, 1])
>>> ak.arange(0, 10, 2) array([0, 2, 4, 6, 8])
>>> ak.arange(-5, -10, -1) array([-5, -6, -7, -8, -9])
- arkouda.arccos(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise inverse cosine of the array. The result is between 0 and pi.
- Parameters:
- Returns:
A pdarray containing inverse cosine for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.arccosh(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise inverse hyperbolic cosine of the array.
- Parameters:
- Returns:
A pdarray containing inverse hyperbolic cosine for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.arcsin(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise inverse sine of the array. The result is between -pi/2 and pi/2.
- Parameters:
- Returns:
A pdarray containing inverse sine for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.arcsinh(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise inverse hyperbolic sine of the array.
- Parameters:
- Returns:
A pdarray containing inverse hyperbolic sine for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.arctan(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise inverse tangent of the array. The result is between -pi/2 and pi/2.
- Parameters:
- Returns:
A pdarray containing inverse tangent for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.arctan2(num: arkouda.pdarrayclass.pdarray | arkouda.dtypes.numeric_scalars, denom: arkouda.pdarrayclass.pdarray | arkouda.dtypes.numeric_scalars, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise inverse tangent of the array pair. The result chosen is the signed angle in radians between the ray ending at the origin and passing through the point (1,0), and the ray ending at the origin and passing through the point (denom, num). The result is between -pi and pi.
- Parameters:
num (Union[numeric_scalars, pdarray]) – Numerator of the arctan2 argument.
denom (Union[numeric_scalars, pdarray]) – Denominator of the arctan2 argument.
where (Boolean or pdarray) – This condition is broadcast over the input. At locations where the condition is True, the inverse tangent will be applied to the corresponding values. Elsewhere, it will retain its original value. Default set to True.
- Returns:
A pdarray containing inverse tangent for each corresponding element pair of the original pdarray, using the signed values or the numerator and denominator to get proper placement on unit circle.
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.arctanh(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise inverse hyperbolic tangent of the array.
- Parameters:
- Returns:
A pdarray containing inverse hyperbolic tangent for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameters are not a pdarray or numeric scalar.
- arkouda.argmax(pda: pdarray) numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array max value.
- Parameters:
pda (pdarray) – Values for which to calculate the argmax
- Returns:
The index of the argmax calculated from the pda
- Return type:
Union[np.int64, np.uint64]
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.argmaxk(pda: pdarray, k: arkouda.dtypes.int_scalars) pdarray[source]¶
Find the indices corresponding to the k maximum values of an array.
Returns the largest k values of an array, sorted
- Parameters:
pda (pdarray) – Input array.
k (int_scalars) – The desired count of indices corresponding to maxmum array values
- Returns:
The indices of the maximum k values from the pda, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty or k < 1
Notes
This call is equivalent in value to:
ak.argsort(a)[k:]
and generally outperforms this operation.
This reduction will see a significant drop in performance as k grows beyond a certain value. This value is system dependent, but generally about a k of 5 million is where performance degradation has been observed.
Examples
>>> A = ak.array([10,5,1,3,7,2,9,0]) >>> ak.argmaxk(A, 3) array([4, 6, 0]) >>> ak.argmaxk(A, 4) array([1, 4, 6, 0])
- arkouda.argmin(pda: pdarray) numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array min value.
- Parameters:
pda (pdarray) – Values for which to calculate the argmin
- Returns:
The index of the argmin calculated from the pda
- Return type:
Union[np.int64, np.uint64]
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.argmink(pda: pdarray, k: arkouda.dtypes.int_scalars) pdarray[source]¶
Finds the indices corresponding to the k minimum values of an array.
- Parameters:
pda (pdarray) – Input array.
k (int_scalars) – The desired count of indices corresponding to minimum array values
- Returns:
The indices of the minimum k values from the pda, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty or k < 1
Notes
This call is equivalent in value to:
ak.argsort(a)[:k]
and generally outperforms this operation.
This reduction will see a significant drop in performance as k grows beyond a certain value. This value is system dependent, but generally about a k of 5 million is where performance degradation has been observed.
Examples
>>> A = ak.array([10,5,1,3,7,2,9,0]) >>> ak.argmink(A, 3) array([7, 2, 5]) >>> ak.argmink(A, 4) array([7, 2, 5, 3])
- arkouda.argsort(pda: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD, axis: arkouda.dtypes.int_scalars = 0) arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that sorts the array.
- Parameters:
pda (pdarray or Strings or Categorical) – The array to sort (int64, uint64, or float64)
- Returns:
The indices such that
pda[indices]is sorted- Return type:
pdarray, int64
- Raises:
TypeError – Raised if the parameter is other than a pdarray or Strings
See also
Notes
Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.
Examples
>>> a = ak.randint(0, 10, 10) >>> perm = ak.argsort(a) >>> a[perm] array([0, 1, 1, 3, 4, 5, 7, 8, 8, 9])
- arkouda.argsort(pda: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD, axis: arkouda.dtypes.int_scalars = 0) arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that sorts the array.
- Parameters:
pda (pdarray or Strings or Categorical) – The array to sort (int64, uint64, or float64)
- Returns:
The indices such that
pda[indices]is sorted- Return type:
pdarray, int64
- Raises:
TypeError – Raised if the parameter is other than a pdarray or Strings
See also
Notes
Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.
Examples
>>> a = ak.randint(0, 10, 10) >>> perm = ak.argsort(a) >>> a[perm] array([0, 1, 1, 3, 4, 5, 7, 8, 8, 9])
- arkouda.argsort(pda: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD, axis: arkouda.dtypes.int_scalars = 0) arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that sorts the array.
- Parameters:
pda (pdarray or Strings or Categorical) – The array to sort (int64, uint64, or float64)
- Returns:
The indices such that
pda[indices]is sorted- Return type:
pdarray, int64
- Raises:
TypeError – Raised if the parameter is other than a pdarray or Strings
See also
Notes
Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.
Examples
>>> a = ak.randint(0, 10, 10) >>> perm = ak.argsort(a) >>> a[perm] array([0, 1, 1, 3, 4, 5, 7, 8, 8, 9])
- arkouda.array(a: arkouda.pdarrayclass.pdarray | numpy.ndarray | Iterable, dtype: numpy.dtype | type | str | None = None, max_bits: int = -1) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Convert a Python or Numpy Iterable to a pdarray or Strings object, sending the corresponding data to the arkouda server.
- Parameters:
a (Union[pdarray, np.ndarray]) – Rank-1 array of a supported dtype
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
A pdarray instance stored on arkouda server or Strings instance, which is composed of two pdarrays stored on arkouda server
- Return type:
- Raises:
TypeError – Raised if a is not a pdarray, np.ndarray, or Python Iterable such as a list, array, tuple, or deque
RuntimeError – Raised if a is not one-dimensional, nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if the returned message is malformed or does not contain the fields required to generate the array.
See also
Notes
The number of bytes in the input array cannot exceed ak.client.maxTransferBytes, otherwise a RuntimeError will be raised. This is to protect the user from overwhelming the connection between the Python client and the arkouda server, under the assumption that it is a low-bandwidth connection. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but should proceed with caution.
If the pdrray or ndarray is of type U, this method is called twice recursively to create the Strings object and the two corresponding pdarrays for string bytes and offsets, respectively.
Examples
>>> ak.array(np.arange(1,10)) array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> ak.array(range(1,10)) array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> strings = ak.array([f'string {i}' for i in range(0,5)]) >>> type(strings) <class 'arkouda.strings.Strings'>
- arkouda.array(a: arkouda.pdarrayclass.pdarray | numpy.ndarray | Iterable, dtype: numpy.dtype | type | str | None = None, max_bits: int = -1) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Convert a Python or Numpy Iterable to a pdarray or Strings object, sending the corresponding data to the arkouda server.
- Parameters:
a (Union[pdarray, np.ndarray]) – Rank-1 array of a supported dtype
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
A pdarray instance stored on arkouda server or Strings instance, which is composed of two pdarrays stored on arkouda server
- Return type:
- Raises:
TypeError – Raised if a is not a pdarray, np.ndarray, or Python Iterable such as a list, array, tuple, or deque
RuntimeError – Raised if a is not one-dimensional, nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if the returned message is malformed or does not contain the fields required to generate the array.
See also
Notes
The number of bytes in the input array cannot exceed ak.client.maxTransferBytes, otherwise a RuntimeError will be raised. This is to protect the user from overwhelming the connection between the Python client and the arkouda server, under the assumption that it is a low-bandwidth connection. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but should proceed with caution.
If the pdrray or ndarray is of type U, this method is called twice recursively to create the Strings object and the two corresponding pdarrays for string bytes and offsets, respectively.
Examples
>>> ak.array(np.arange(1,10)) array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> ak.array(range(1,10)) array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> strings = ak.array([f'string {i}' for i in range(0,5)]) >>> type(strings) <class 'arkouda.strings.Strings'>
- arkouda.array(a: arkouda.pdarrayclass.pdarray | numpy.ndarray | Iterable, dtype: numpy.dtype | type | str | None = None, max_bits: int = -1) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Convert a Python or Numpy Iterable to a pdarray or Strings object, sending the corresponding data to the arkouda server.
- Parameters:
a (Union[pdarray, np.ndarray]) – Rank-1 array of a supported dtype
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
A pdarray instance stored on arkouda server or Strings instance, which is composed of two pdarrays stored on arkouda server
- Return type:
- Raises:
TypeError – Raised if a is not a pdarray, np.ndarray, or Python Iterable such as a list, array, tuple, or deque
RuntimeError – Raised if a is not one-dimensional, nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if the returned message is malformed or does not contain the fields required to generate the array.
See also
Notes
The number of bytes in the input array cannot exceed ak.client.maxTransferBytes, otherwise a RuntimeError will be raised. This is to protect the user from overwhelming the connection between the Python client and the arkouda server, under the assumption that it is a low-bandwidth connection. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but should proceed with caution.
If the pdrray or ndarray is of type U, this method is called twice recursively to create the Strings object and the two corresponding pdarrays for string bytes and offsets, respectively.
Examples
>>> ak.array(np.arange(1,10)) array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> ak.array(range(1,10)) array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> strings = ak.array([f'string {i}' for i in range(0,5)]) >>> type(strings) <class 'arkouda.strings.Strings'>
- arkouda.array(a: arkouda.pdarrayclass.pdarray | numpy.ndarray | Iterable, dtype: numpy.dtype | type | str | None = None, max_bits: int = -1) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Convert a Python or Numpy Iterable to a pdarray or Strings object, sending the corresponding data to the arkouda server.
- Parameters:
a (Union[pdarray, np.ndarray]) – Rank-1 array of a supported dtype
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
A pdarray instance stored on arkouda server or Strings instance, which is composed of two pdarrays stored on arkouda server
- Return type:
- Raises:
TypeError – Raised if a is not a pdarray, np.ndarray, or Python Iterable such as a list, array, tuple, or deque
RuntimeError – Raised if a is not one-dimensional, nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if the returned message is malformed or does not contain the fields required to generate the array.
See also
Notes
The number of bytes in the input array cannot exceed ak.client.maxTransferBytes, otherwise a RuntimeError will be raised. This is to protect the user from overwhelming the connection between the Python client and the arkouda server, under the assumption that it is a low-bandwidth connection. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but should proceed with caution.
If the pdrray or ndarray is of type U, this method is called twice recursively to create the Strings object and the two corresponding pdarrays for string bytes and offsets, respectively.
Examples
>>> ak.array(np.arange(1,10)) array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> ak.array(range(1,10)) array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> strings = ak.array([f'string {i}' for i in range(0,5)]) >>> type(strings) <class 'arkouda.strings.Strings'>
- arkouda.attach_all(names: list)[source]¶
Attach to all objects registered with the names provide
- Parameters:
names (list) – List of names to attach to
- Return type:
dict
- arkouda.attach_pdarray(user_defined_name: str) pdarray[source]¶
class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which array was registered under
- Returns:
pdarray which is bound to the corresponding server side component which was registered with user_defined_name
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
See also
attach,register,unregister,is_registered,unregister_pdarray_by_name,list_registryNotes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.attach_pdarray("my_zeros") >>> # ...other work... >>> b.unregister()
- arkouda.bigint¶
- arkouda.bigint¶
- arkouda.bigint_from_uint_arrays(arrays, max_bits=-1)[source]¶
Create a bigint pdarray from an iterable of uint pdarrays. The first item in arrays will be the highest 64 bits and the last item will be the lowest 64 bits.
- Parameters:
arrays (Sequence[pdarray]) – An iterable of uint pdarrays used to construct the bigint pdarray. The first item in arrays will be the highest 64 bits and the last item will be the lowest 64 bits.
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
bigint pdarray constructed from uint arrays
- Return type:
- Raises:
TypeError – Raised if any pdarray in arrays has a dtype other than uint or if the pdarrays are not the same size.
RuntimeError – Raised if there is a server-side error thrown
See also
Examples
>>> a = ak.bigint_from_uint_arrays([ak.ones(5, dtype=ak.uint64), ak.arange(5, dtype=ak.uint64)]) >>> a array(["18446744073709551616" "18446744073709551617" "18446744073709551618" "18446744073709551619" "18446744073709551620"])
>>> a.dtype dtype(bigint)
>>> all(a[i] == 2**64 + i for i in range(5)) True
- arkouda.bitType¶
- arkouda.bitType¶
- arkouda.bool¶
- arkouda.bool_scalars¶
- arkouda.broadcast(segments: arkouda.pdarrayclass.pdarray, values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, size: int | numpy.int64 | numpy.uint64 = -1, permutation: arkouda.pdarrayclass.pdarray | None = None)[source]¶
Broadcast a dense column vector to the rows of a sparse matrix or grouped array.
- Parameters:
segments (pdarray, int64) – Offsets of the start of each row in the sparse matrix or grouped array. Must be sorted in ascending order.
values (pdarray, Strings) – The values to broadcast, one per row (or group)
size (int) – The total number of nonzeros in the matrix. If permutation is given, this argument is ignored and the size is inferred from the permutation array.
permutation (pdarray, int64) – The permutation to go from the original ordering of nonzeros to the ordering grouped by row. To broadcast values back to the original ordering, this permutation will be inverted. If no permutation is supplied, it is assumed that the original nonzeros were already grouped by row. In this case, the size argument must be given.
- Returns:
The broadcast values, one per nonzero
- Return type:
- Raises:
ValueError –
If segments and values are different sizes
If segments are empty
If number of nonzeros (either user-specified or inferred from permutation) is less than one
Examples
>>> # Define a sparse matrix with 3 rows and 7 nonzeros >>> row_starts = ak.array([0, 2, 5]) >>> nnz = 7 # Broadcast the row number to each nonzero element >>> row_number = ak.arange(3) >>> ak.broadcast(row_starts, row_number, nnz) array([0 0 1 1 1 2 2]) # If the original nonzeros were in reverse order... >>> permutation = ak.arange(6, -1, -1) >>> ak.broadcast(row_starts, row_number, permutation=permutation) array([2 2 1 1 1 0 0])
- arkouda.broadcast(segments: arkouda.pdarrayclass.pdarray, values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, size: int | numpy.int64 | numpy.uint64 = -1, permutation: arkouda.pdarrayclass.pdarray | None = None)[source]¶
Broadcast a dense column vector to the rows of a sparse matrix or grouped array.
- Parameters:
segments (pdarray, int64) – Offsets of the start of each row in the sparse matrix or grouped array. Must be sorted in ascending order.
values (pdarray, Strings) – The values to broadcast, one per row (or group)
size (int) – The total number of nonzeros in the matrix. If permutation is given, this argument is ignored and the size is inferred from the permutation array.
permutation (pdarray, int64) – The permutation to go from the original ordering of nonzeros to the ordering grouped by row. To broadcast values back to the original ordering, this permutation will be inverted. If no permutation is supplied, it is assumed that the original nonzeros were already grouped by row. In this case, the size argument must be given.
- Returns:
The broadcast values, one per nonzero
- Return type:
- Raises:
ValueError –
If segments and values are different sizes
If segments are empty
If number of nonzeros (either user-specified or inferred from permutation) is less than one
Examples
>>> # Define a sparse matrix with 3 rows and 7 nonzeros >>> row_starts = ak.array([0, 2, 5]) >>> nnz = 7 # Broadcast the row number to each nonzero element >>> row_number = ak.arange(3) >>> ak.broadcast(row_starts, row_number, nnz) array([0 0 1 1 1 2 2]) # If the original nonzeros were in reverse order... >>> permutation = ak.arange(6, -1, -1) >>> ak.broadcast(row_starts, row_number, permutation=permutation) array([2 2 1 1 1 0 0])
- arkouda.broadcast(segments: arkouda.pdarrayclass.pdarray, values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, size: int | numpy.int64 | numpy.uint64 = -1, permutation: arkouda.pdarrayclass.pdarray | None = None)[source]¶
Broadcast a dense column vector to the rows of a sparse matrix or grouped array.
- Parameters:
segments (pdarray, int64) – Offsets of the start of each row in the sparse matrix or grouped array. Must be sorted in ascending order.
values (pdarray, Strings) – The values to broadcast, one per row (or group)
size (int) – The total number of nonzeros in the matrix. If permutation is given, this argument is ignored and the size is inferred from the permutation array.
permutation (pdarray, int64) – The permutation to go from the original ordering of nonzeros to the ordering grouped by row. To broadcast values back to the original ordering, this permutation will be inverted. If no permutation is supplied, it is assumed that the original nonzeros were already grouped by row. In this case, the size argument must be given.
- Returns:
The broadcast values, one per nonzero
- Return type:
- Raises:
ValueError –
If segments and values are different sizes
If segments are empty
If number of nonzeros (either user-specified or inferred from permutation) is less than one
Examples
>>> # Define a sparse matrix with 3 rows and 7 nonzeros >>> row_starts = ak.array([0, 2, 5]) >>> nnz = 7 # Broadcast the row number to each nonzero element >>> row_number = ak.arange(3) >>> ak.broadcast(row_starts, row_number, nnz) array([0 0 1 1 1 2 2]) # If the original nonzeros were in reverse order... >>> permutation = ak.arange(6, -1, -1) >>> ak.broadcast(row_starts, row_number, permutation=permutation) array([2 2 1 1 1 0 0])
- arkouda.broadcast(segments: arkouda.pdarrayclass.pdarray, values: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, size: int | numpy.int64 | numpy.uint64 = -1, permutation: arkouda.pdarrayclass.pdarray | None = None)[source]¶
Broadcast a dense column vector to the rows of a sparse matrix or grouped array.
- Parameters:
segments (pdarray, int64) – Offsets of the start of each row in the sparse matrix or grouped array. Must be sorted in ascending order.
values (pdarray, Strings) – The values to broadcast, one per row (or group)
size (int) – The total number of nonzeros in the matrix. If permutation is given, this argument is ignored and the size is inferred from the permutation array.
permutation (pdarray, int64) – The permutation to go from the original ordering of nonzeros to the ordering grouped by row. To broadcast values back to the original ordering, this permutation will be inverted. If no permutation is supplied, it is assumed that the original nonzeros were already grouped by row. In this case, the size argument must be given.
- Returns:
The broadcast values, one per nonzero
- Return type:
- Raises:
ValueError –
If segments and values are different sizes
If segments are empty
If number of nonzeros (either user-specified or inferred from permutation) is less than one
Examples
>>> # Define a sparse matrix with 3 rows and 7 nonzeros >>> row_starts = ak.array([0, 2, 5]) >>> nnz = 7 # Broadcast the row number to each nonzero element >>> row_number = ak.arange(3) >>> ak.broadcast(row_starts, row_number, nnz) array([0 0 1 1 1 2 2]) # If the original nonzeros were in reverse order... >>> permutation = ak.arange(6, -1, -1) >>> ak.broadcast(row_starts, row_number, permutation=permutation) array([2 2 1 1 1 0 0])
- arkouda.broadcast_dims(sa: Sequence[int], sb: Sequence[int]) Tuple[int, Ellipsis][source]¶
Algorithm to determine shape of broadcasted PD array given two array shapes
see: https://data-apis.org/array-api/latest/API_specification/broadcasting.html#algorithm
- arkouda.broadcast_to_shape(pda: pdarray, shape: Tuple[int, Ellipsis]) pdarray[source]¶
expand an array’s rank to the specified shape using broadcasting
- arkouda.cast(pda: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, dt: numpy.dtype | type | str | arkouda.dtypes.BigInt, errors: ErrorMode = ErrorMode.strict) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical | Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Cast an array to another dtype.
- Parameters:
dt (np.dtype, type, or str) – The target dtype to cast values to
errors ({strict, ignore, return_validity}) –
Controls how errors are handled when casting strings to a numeric type (ignored for casts from numeric types).
strict: raise RuntimeError if any string cannot be converted
- ignore: never raise an error. Uninterpretable strings get
converted to NaN (float64), -2**63 (int64), zero (uint64 and uint8), or False (bool)
return_validity: in addition to returning the same output as “ignore”, also return a bool array indicating where the cast was successful.
- Returns:
pdarray or Strings – Array of values cast to desired dtype
[validity (pdarray(bool)]) – If errors=”return_validity” and input is Strings, a second array is returned with True where the cast succeeded and False where it failed.
Notes
The cast is performed according to Chapel’s casting rules and is NOT safe from overflows or underflows. The user must ensure that the target dtype has the precision and capacity to hold the desired result.
Examples
>>> ak.cast(ak.linspace(1.0,5.0,5), dt=ak.int64) array([1, 2, 3, 4, 5])
>>> ak.cast(ak.arange(0,5), dt=ak.float64).dtype dtype('float64')
>>> ak.cast(ak.arange(0,5), dt=ak.bool) array([False, True, True, True, True])
>>> ak.cast(ak.linspace(0,4,5), dt=ak.bool) array([False, True, True, True, True])
- arkouda.cast(pda: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, dt: numpy.dtype | type | str | arkouda.dtypes.BigInt, errors: ErrorMode = ErrorMode.strict) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical | Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Cast an array to another dtype.
- Parameters:
dt (np.dtype, type, or str) – The target dtype to cast values to
errors ({strict, ignore, return_validity}) –
Controls how errors are handled when casting strings to a numeric type (ignored for casts from numeric types).
strict: raise RuntimeError if any string cannot be converted
- ignore: never raise an error. Uninterpretable strings get
converted to NaN (float64), -2**63 (int64), zero (uint64 and uint8), or False (bool)
return_validity: in addition to returning the same output as “ignore”, also return a bool array indicating where the cast was successful.
- Returns:
pdarray or Strings – Array of values cast to desired dtype
[validity (pdarray(bool)]) – If errors=”return_validity” and input is Strings, a second array is returned with True where the cast succeeded and False where it failed.
Notes
The cast is performed according to Chapel’s casting rules and is NOT safe from overflows or underflows. The user must ensure that the target dtype has the precision and capacity to hold the desired result.
Examples
>>> ak.cast(ak.linspace(1.0,5.0,5), dt=ak.int64) array([1, 2, 3, 4, 5])
>>> ak.cast(ak.arange(0,5), dt=ak.float64).dtype dtype('float64')
>>> ak.cast(ak.arange(0,5), dt=ak.bool) array([False, True, True, True, True])
>>> ak.cast(ak.linspace(0,4,5), dt=ak.bool) array([False, True, True, True, True])
- arkouda.ceil(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise ceiling of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing ceiling values of the input array elements
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.ceil(ak.linspace(1.1,5.5,5)) array([2, 3, 4, 5, 6])
- arkouda.check_np_dtype(dt: numpy.dtype | BigInt) None[source]¶
Assert that numpy dtype dt is one of the dtypes supported by arkouda, otherwise raise TypeError.
- Raises:
TypeError – Raised if the dtype is not in supported dtypes or if dt is not a np.dtype
- arkouda.chisquare(f_obs, f_exp=None, ddof=0)[source]¶
Computes the chi square statistic and p-value.
- Parameters:
- Return type:
arkouda.akstats.Power_divergenceResult
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda.akstats import chisquare >>> chisquare(ak.array([10, 20, 30, 10]), ak.array([10, 30, 20, 10])) Power_divergenceResult(statistic=8.333333333333334, pvalue=0.03960235520756414)
See also
scipy.stats.chisquare,arkouda.akstats.power_divergenceReferences
[1] “Chi-squared test”, https://en.wikipedia.org/wiki/Chi-squared_test
[2] “scipy.stats.chisquare”, https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html
- arkouda.clear() None[source]¶
Send a clear message to clear all unregistered data from the server symbol table
- Return type:
None
- Raises:
RuntimeError – Raised if there is a server-side error in executing clear request
- arkouda.clip(pda: arkouda.pdarrayclass.pdarray, lo: arkouda.dtypes.numeric_scalars | arkouda.pdarrayclass.pdarray, hi: arkouda.dtypes.numeric_scalars | arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Clip (limit) the values in an array to a given range [lo,hi]
Given an array a, values outside the range are clipped to the range edges, such that all elements lie in the range.
There is no check to enforce that lo < hi. If lo > hi, the corresponding value of the array will be set to hi.
If lo or hi (or both) are pdarrays, the check is by pairwise elements. See examples.
- Parameters:
pda (pdarray, int64 or float64) – the array of values to clip
lo (scalar or pdarray, int64 or float64) – the lower value of the clipping range
hi (scalar or pdarray, int64 or float64) – the higher value of the clipping range
pdarrays (If lo or hi (or both) are) – See examples.
elements. (the check is by pairwise) – See examples.
- Returns:
- A pdarray matching pda, except that element x remains x if lo <= x <= hi,
or becomes lo if x < lo, or becomes hi if x > hi.
- Return type:
Examples
>>> a = ak.array([1,2,3,4,5,6,7,8,9,10]) >>> ak.clip(a,3,8) array([3,3,3,4,5,6,7,8,8,8]) >>> ak.clip(a,3,8.0) array([3.00000000000000000 3.00000000000000000 3.00000000000000000 4.00000000000000000 5.00000000000000000 6.00000000000000000 7.00000000000000000 8.00000000000000000 8.00000000000000000 8.00000000000000000]) >>> ak.clip(a,None,7) array([1,2,3,4,5,6,7,7,7,7]) >>> ak.clip(a,5,None) array([5,5,5,5,5,6,7,8,9,10]) >>> ak.clip(a,None,None) ValueError : either min or max must be supplied >>> ak.clip(a,ak.array([2,2,3,3,8,8,5,5,6,6],8)) array([2,2,3,4,8,8,7,8,8,8]) >>> ak.clip(a,4,ak.array([10,9,8,7,6,5,5,5,5,5])) array([4,4,4,4,5,5,5,5,5,5])
Notes
Either lo or hi may be None, but not both. If lo > hi, all x = hi. If all inputs are int64, output is int64, but if any input is float64, output is float64.
- Raises:
ValueError – Raised if both lo and hi are None
- arkouda.clz(pda: pdarray) pdarray[source]¶
Count leading zeros for each integer in an array.
- Parameters:
pda (pdarray, int64, uint64, bigint) – Input array (must be integral).
- Returns:
lz – The number of leading zeros of each element.
- Return type:
- Raises:
TypeError – If input array is not int64, uint64, or bigint
Examples
>>> A = ak.arange(10) >>> ak.clz(A) array([64, 63, 62, 62, 61, 61, 61, 61, 60, 60])
- arkouda.coargsort(arrays: Sequence[arkouda.strings.Strings | arkouda.pdarrayclass.pdarray | arkouda.categorical.Categorical], algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD) arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that groups the rows (left-to-right), if the input arrays are treated as columns. The permutation sorts numeric columns, but not strings/Categoricals – strings/Categoricals are grouped, but not ordered.
- Parameters:
arrays (Sequence[Union[Strings, pdarray, Categorical]]) – The columns (int64, uint64, float64, Strings, or Categorical) to sort by row
- Returns:
The indices that permute the rows to grouped order
- Return type:
pdarray, int64
- Raises:
ValueError – Raised if the pdarrays are not of the same size or if the parameter is not an Iterable containing pdarrays, Strings, or Categoricals
See also
Notes
Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive. Starts with the last array and moves forward. This sort operates directly on numeric types, but for Strings, it operates on a hash. Thus, while grouping of equivalent strings is guaranteed, lexicographic ordering of the groups is not. For Categoricals, coargsort sorts based on Categorical.codes which guarantees grouping of equivalent categories but not lexicographic ordering of those groups.
Examples
>>> a = ak.array([0, 1, 0, 1]) >>> b = ak.array([1, 1, 0, 0]) >>> perm = ak.coargsort([a, b]) >>> perm array([2, 0, 3, 1]) >>> a[perm] array([0, 0, 1, 1]) >>> b[perm] array([0, 1, 0, 1])
- arkouda.coargsort(arrays: Sequence[arkouda.strings.Strings | arkouda.pdarrayclass.pdarray | arkouda.categorical.Categorical], algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD) arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that groups the rows (left-to-right), if the input arrays are treated as columns. The permutation sorts numeric columns, but not strings/Categoricals – strings/Categoricals are grouped, but not ordered.
- Parameters:
arrays (Sequence[Union[Strings, pdarray, Categorical]]) – The columns (int64, uint64, float64, Strings, or Categorical) to sort by row
- Returns:
The indices that permute the rows to grouped order
- Return type:
pdarray, int64
- Raises:
ValueError – Raised if the pdarrays are not of the same size or if the parameter is not an Iterable containing pdarrays, Strings, or Categoricals
See also
Notes
Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive. Starts with the last array and moves forward. This sort operates directly on numeric types, but for Strings, it operates on a hash. Thus, while grouping of equivalent strings is guaranteed, lexicographic ordering of the groups is not. For Categoricals, coargsort sorts based on Categorical.codes which guarantees grouping of equivalent categories but not lexicographic ordering of those groups.
Examples
>>> a = ak.array([0, 1, 0, 1]) >>> b = ak.array([1, 1, 0, 0]) >>> perm = ak.coargsort([a, b]) >>> perm array([2, 0, 3, 1]) >>> a[perm] array([0, 0, 1, 1]) >>> b[perm] array([0, 1, 0, 1])
- arkouda.coargsort(arrays: Sequence[arkouda.strings.Strings | arkouda.pdarrayclass.pdarray | arkouda.categorical.Categorical], algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD) arkouda.pdarrayclass.pdarray[source]¶
Return the permutation that groups the rows (left-to-right), if the input arrays are treated as columns. The permutation sorts numeric columns, but not strings/Categoricals – strings/Categoricals are grouped, but not ordered.
- Parameters:
arrays (Sequence[Union[Strings, pdarray, Categorical]]) – The columns (int64, uint64, float64, Strings, or Categorical) to sort by row
- Returns:
The indices that permute the rows to grouped order
- Return type:
pdarray, int64
- Raises:
ValueError – Raised if the pdarrays are not of the same size or if the parameter is not an Iterable containing pdarrays, Strings, or Categoricals
See also
Notes
Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive. Starts with the last array and moves forward. This sort operates directly on numeric types, but for Strings, it operates on a hash. Thus, while grouping of equivalent strings is guaranteed, lexicographic ordering of the groups is not. For Categoricals, coargsort sorts based on Categorical.codes which guarantees grouping of equivalent categories but not lexicographic ordering of those groups.
Examples
>>> a = ak.array([0, 1, 0, 1]) >>> b = ak.array([1, 1, 0, 0]) >>> perm = ak.coargsort([a, b]) >>> perm array([2, 0, 3, 1]) >>> a[perm] array([0, 0, 1, 1]) >>> b[perm] array([0, 1, 0, 1])
- arkouda.complex128¶
- arkouda.complex64¶
- arkouda.compute_join_size(a: arkouda.pdarrayclass.pdarray, b: arkouda.pdarrayclass.pdarray) Tuple[int, int][source]¶
Compute the internal size of a hypothetical join between a and b. Returns both the number of elements and number of bytes required for the join.
- arkouda.concatenate(arrays: Sequence[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | Categorical], ordered: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | Categorical[source]¶
Concatenate a list or tuple of
pdarrayorStringsobjects into onepdarrayorStringsobject, respectively.- Parameters:
arrays (Sequence[Union[pdarray,Strings,Categorical]]) – The arrays to concatenate. Must all have same dtype.
ordered (bool) – If True (default), the arrays will be appended in the order given. If False, array data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.
- Returns:
Single pdarray or Strings object containing all values, returned in the original order
- Return type:
Union[pdarray,Strings,Categorical]
- Raises:
ValueError – Raised if arrays is empty or if 1..n pdarrays have differing dtypes
TypeError – Raised if arrays is not a pdarrays or Strings python Sequence such as a list or tuple
RuntimeError – Raised if 1..n array elements are dtypes for which concatenate has not been implemented.
Examples
>>> ak.concatenate([ak.array([1, 2, 3]), ak.array([4, 5, 6])]) array([1, 2, 3, 4, 5, 6])
>>> ak.concatenate([ak.array([True,False,True]),ak.array([False,True,True])]) array([True, False, True, False, True, True])
>>> ak.concatenate([ak.array(['one','two']),ak.array(['three','four','five'])]) array(['one', 'two', 'three', 'four', 'five'])
- arkouda.concatenate(arrays: Sequence[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | Categorical], ordered: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | Categorical[source]¶
Concatenate a list or tuple of
pdarrayorStringsobjects into onepdarrayorStringsobject, respectively.- Parameters:
arrays (Sequence[Union[pdarray,Strings,Categorical]]) – The arrays to concatenate. Must all have same dtype.
ordered (bool) – If True (default), the arrays will be appended in the order given. If False, array data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.
- Returns:
Single pdarray or Strings object containing all values, returned in the original order
- Return type:
Union[pdarray,Strings,Categorical]
- Raises:
ValueError – Raised if arrays is empty or if 1..n pdarrays have differing dtypes
TypeError – Raised if arrays is not a pdarrays or Strings python Sequence such as a list or tuple
RuntimeError – Raised if 1..n array elements are dtypes for which concatenate has not been implemented.
Examples
>>> ak.concatenate([ak.array([1, 2, 3]), ak.array([4, 5, 6])]) array([1, 2, 3, 4, 5, 6])
>>> ak.concatenate([ak.array([True,False,True]),ak.array([False,True,True])]) array([True, False, True, False, True, True])
>>> ak.concatenate([ak.array(['one','two']),ak.array(['three','four','five'])]) array(['one', 'two', 'three', 'four', 'five'])
- arkouda.concatenate(arrays: Sequence[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | Categorical], ordered: bool = True) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | Categorical[source]¶
Concatenate a list or tuple of
pdarrayorStringsobjects into onepdarrayorStringsobject, respectively.- Parameters:
arrays (Sequence[Union[pdarray,Strings,Categorical]]) – The arrays to concatenate. Must all have same dtype.
ordered (bool) – If True (default), the arrays will be appended in the order given. If False, array data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.
- Returns:
Single pdarray or Strings object containing all values, returned in the original order
- Return type:
Union[pdarray,Strings,Categorical]
- Raises:
ValueError – Raised if arrays is empty or if 1..n pdarrays have differing dtypes
TypeError – Raised if arrays is not a pdarrays or Strings python Sequence such as a list or tuple
RuntimeError – Raised if 1..n array elements are dtypes for which concatenate has not been implemented.
Examples
>>> ak.concatenate([ak.array([1, 2, 3]), ak.array([4, 5, 6])]) array([1, 2, 3, 4, 5, 6])
>>> ak.concatenate([ak.array([True,False,True]),ak.array([False,True,True])]) array([True, False, True, False, True, True])
>>> ak.concatenate([ak.array(['one','two']),ak.array(['three','four','five'])]) array(['one', 'two', 'three', 'four', 'five'])
- arkouda.corr(x: pdarray, y: pdarray) numpy.float64[source]¶
Return the correlation between x and y
- Parameters:
- Returns:
The scalar correlation of the two pdarrays
- Return type:
np.float64
- Raises:
TypeError – Raised if x or y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
Notes
The correlation is calculated by cov(x, y) / (x.std(ddof=1) * y.std(ddof=1))
- arkouda.cos(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise cosine of the array.
- Parameters:
- Returns:
A pdarray containing cosine for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.cosh(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise hyperbolic cosine of the array.
- Parameters:
- Returns:
A pdarray containing hyperbolic cosine for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.cov(x: pdarray, y: pdarray) numpy.float64[source]¶
Return the covariance of x and y
- Parameters:
- Returns:
The scalar covariance of the two pdarrays
- Return type:
np.float64
- Raises:
TypeError – Raised if x or y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
Notes
The covariance is calculated by
cov = ((x - x.mean()) * (y - y.mean())).sum() / (x.size - 1).
- arkouda.create_pdarray(repMsg: str, max_bits=None) pdarray[source]¶
Return a pdarray instance pointing to an array created by the arkouda server. The user should not call this function directly.
- Parameters:
repMsg (str) – space-delimited string containing the pdarray name, datatype, size dimension, shape,and itemsize
- Returns:
A pdarray with the same attributes and data as the pdarray; on GPU
- Return type:
- Raises:
ValueError – If there’s an error in parsing the repMsg parameter into the six values needed to create the pdarray instance
RuntimeError – Raised if a server-side error is thrown in the process of creating the pdarray instance
- arkouda.create_pdarray(repMsg: str, max_bits=None) pdarray[source]¶
Return a pdarray instance pointing to an array created by the arkouda server. The user should not call this function directly.
- Parameters:
repMsg (str) – space-delimited string containing the pdarray name, datatype, size dimension, shape,and itemsize
- Returns:
A pdarray with the same attributes and data as the pdarray; on GPU
- Return type:
- Raises:
ValueError – If there’s an error in parsing the repMsg parameter into the six values needed to create the pdarray instance
RuntimeError – Raised if a server-side error is thrown in the process of creating the pdarray instance
- arkouda.create_pdarray(repMsg: str, max_bits=None) pdarray[source]¶
Return a pdarray instance pointing to an array created by the arkouda server. The user should not call this function directly.
- Parameters:
repMsg (str) – space-delimited string containing the pdarray name, datatype, size dimension, shape,and itemsize
- Returns:
A pdarray with the same attributes and data as the pdarray; on GPU
- Return type:
- Raises:
ValueError – If there’s an error in parsing the repMsg parameter into the six values needed to create the pdarray instance
RuntimeError – Raised if a server-side error is thrown in the process of creating the pdarray instance
- arkouda.create_pdarray(repMsg: str, max_bits=None) pdarray[source]¶
Return a pdarray instance pointing to an array created by the arkouda server. The user should not call this function directly.
- Parameters:
repMsg (str) – space-delimited string containing the pdarray name, datatype, size dimension, shape,and itemsize
- Returns:
A pdarray with the same attributes and data as the pdarray; on GPU
- Return type:
- Raises:
ValueError – If there’s an error in parsing the repMsg parameter into the six values needed to create the pdarray instance
RuntimeError – Raised if a server-side error is thrown in the process of creating the pdarray instance
- arkouda.ctz(pda: pdarray) pdarray[source]¶
Count trailing zeros for each integer in an array.
- Parameters:
pda (pdarray, int64, uint64, bigint) – Input array (must be integral).
- Returns:
lz – The number of trailing zeros of each element.
- Return type:
Notes
ctz(0) is defined to be zero.
- Raises:
TypeError – If input array is not int64, uint64, or bigint
Examples
>>> A = ak.arange(10) >>> ak.ctz(A) array([0, 0, 1, 0, 2, 0, 1, 0, 3, 0])
- arkouda.cumprod(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the cumulative product over the array.
The product is inclusive, such that the
ith element of the result is the product of elements up to and includingi.- Parameters:
pda (pdarray)
- Returns:
A pdarray containing cumulative products for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.cumprod(ak.arange(1,5)) array([1, 2, 6, 24]))
>>> ak.cumprod(ak.uniform(5,1.0,5.0)) array([1.5728783400481925, 7.0472855509390593, 33.78523998586553, 134.05309592737584, 450.21589865655358])
- arkouda.cumsum(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the cumulative sum over the array.
The sum is inclusive, such that the
ith element of the result is the sum of elements up to and includingi.- Parameters:
pda (pdarray)
- Returns:
A pdarray containing cumulative sums for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.cumsum(ak.arange([1,5])) array([1, 3, 6])
>>> ak.cumsum(ak.uniform(5,1.0,5.0)) array([3.1598310770203937, 5.4110385860243131, 9.1622479306453748, 12.710615785506533, 13.945880905466208])
>>> ak.cumsum(ak.randint(0, 1, 5, dtype=ak.bool)) array([0, 1, 1, 2, 3])
- arkouda.cumsum(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the cumulative sum over the array.
The sum is inclusive, such that the
ith element of the result is the sum of elements up to and includingi.- Parameters:
pda (pdarray)
- Returns:
A pdarray containing cumulative sums for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.cumsum(ak.arange([1,5])) array([1, 3, 6])
>>> ak.cumsum(ak.uniform(5,1.0,5.0)) array([3.1598310770203937, 5.4110385860243131, 9.1622479306453748, 12.710615785506533, 13.945880905466208])
>>> ak.cumsum(ak.randint(0, 1, 5, dtype=ak.bool)) array([0, 1, 1, 2, 3])
- arkouda.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, inclusive='both', **kwargs)[source]¶
Creates a fixed frequency Datetime range. Alias for
ak.Datetime(pd.date_range(args)). Subject to size limit imposed by client.maxTransferBytes.- Parameters:
start (str or datetime-like, optional) – Left bound for generating dates.
end (str or datetime-like, optional) – Right bound for generating dates.
periods (int, optional) – Number of periods to generate.
freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’. See timeseries.offset_aliases for a list of frequency aliases.
tz (str or tzinfo, optional) – Time zone name for returning localized DatetimeIndex, for example ‘Asia/Hong_Kong’. By default, the resulting DatetimeIndex is timezone-naive.
normalize (bool, default False) – Normalize start/end dates to midnight before generating date range.
name (str, default None) – Name of the resulting DatetimeIndex.
closed ({None, 'left', 'right'}, optional) – Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None, the default). Deprecated
inclusive ({"both", "neither", "left", "right"}, default "both") – Include boundaries. Whether to set each bound as closed or open.
**kwargs – For compatibility. Has no effect on the result.
- Returns:
rng
- Return type:
DatetimeIndex
Notes
Of the four parameters
start,end,periods, andfreq, exactly three must be specified. Iffreqis omitted, the resultingDatetimeIndexwill haveperiodslinearly spaced elements betweenstartandend(closed on both sides).To learn more about the frequency strings, please see this link.
- arkouda.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, inclusive='both', **kwargs)[source]¶
Creates a fixed frequency Datetime range. Alias for
ak.Datetime(pd.date_range(args)). Subject to size limit imposed by client.maxTransferBytes.- Parameters:
start (str or datetime-like, optional) – Left bound for generating dates.
end (str or datetime-like, optional) – Right bound for generating dates.
periods (int, optional) – Number of periods to generate.
freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’. See timeseries.offset_aliases for a list of frequency aliases.
tz (str or tzinfo, optional) – Time zone name for returning localized DatetimeIndex, for example ‘Asia/Hong_Kong’. By default, the resulting DatetimeIndex is timezone-naive.
normalize (bool, default False) – Normalize start/end dates to midnight before generating date range.
name (str, default None) – Name of the resulting DatetimeIndex.
closed ({None, 'left', 'right'}, optional) – Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None, the default). Deprecated
inclusive ({"both", "neither", "left", "right"}, default "both") – Include boundaries. Whether to set each bound as closed or open.
**kwargs – For compatibility. Has no effect on the result.
- Returns:
rng
- Return type:
DatetimeIndex
Notes
Of the four parameters
start,end,periods, andfreq, exactly three must be specified. Iffreqis omitted, the resultingDatetimeIndexwill haveperiodslinearly spaced elements betweenstartandend(closed on both sides).To learn more about the frequency strings, please see this link.
- arkouda.deg2rad(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Converts angles element-wise from degrees to radians.
- Parameters:
- Returns:
A pdarray containing an angle converted to radians, from degrees, for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.disableVerbose(logLevel: LogLevel = LogLevel.INFO) None[source]¶
Disables verbose logging (DEBUG log level) for all ArkoudaLoggers, setting the log level for each to the logLevel parameter
- Parameters:
logLevel (LogLevel) – The new log level, defaultts to LogLevel.INFO
- Raises:
TypeError – Raised if logLevel is not a LogLevel enum
- arkouda.divmod(x: arkouda.dtypes.numeric_scalars | pdarray, y: arkouda.dtypes.numeric_scalars | pdarray, where: bool | pdarray = True) Tuple[pdarray, pdarray][source]¶
- Parameters:
x (numeric_scalars(float_scalars, int_scalars) or pdarray) – The dividend array, the values that will be the numerator of the floordivision and will be acted on by the bases for modular division.
y (numeric_scalars(float_scalars, int_scalars) or pdarray) – The divisor array, the values that will be the denominator of the division and will be the bases for the modular division.
where (Boolean or pdarray) – This condition is broadcast over the input. At locations where the condition is True, the corresponding value will be divided using floor and modular division. Elsewhere, it will retain its original value. Default set to True.
- Returns:
Returns a tuple that contains quotient and remainder of the division
- Return type:
- Raises:
TypeError – At least one entry must be a pdarray
ValueError – If both inputs are both pdarrays, their size must match
ZeroDivisionError – No entry in y is allowed to be 0, to prevent division by zero
Notes
The div is calculated by x // y The mod is calculated by x % y
Examples
>>> x = ak.arange(5, 10) >>> y = ak.array([2, 1, 4, 5, 8]) >>> ak.divmod(x,y) (array([2 6 1 1 1]), array([1 0 3 3 1])) >>> ak.divmod(x,y, x % 2 == 0) (array([5 6 7 1 9]), array([5 0 7 3 9]))
- arkouda.dot(pda1: numpy.int64 | numpy.float64 | numpy.uint64 | pdarray, pda2: numpy.int64 | numpy.float64 | numpy.uint64 | pdarray) numpy.int64 | numpy.float64 | numpy.uint64 | pdarray[source]¶
Returns the sum of the elementwise product of two arrays of the same size (the dot product) or the product of a singleton element and an array.
- Parameters:
- Returns:
The sum of the elementwise product pda1 and pda2 or the product of a singleton element and an array.
- Return type:
Union[numeric_scalars, pdarray]
- Raises:
ValueError – Raised if the size of pda1 is not the same as pda2
Examples
>>> x = ak.array([2, 3]) >>> y = ak.array([4, 5]) >>> ak.dot(x,y) 23 >>> ak.dot(x,2) array([4 6])
- arkouda.enableVerbose() None[source]¶
Enables verbose logging (DEBUG log level) for all ArkoudaLoggers
- arkouda.exp(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise exponential of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing exponential values of the input array elements
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.exp(ak.arange(1,5)) array([2.7182818284590451, 7.3890560989306504, 20.085536923187668, 54.598150033144236])
>>> ak.exp(ak.uniform(5,1.0,5.0)) array([11.84010843172504, 46.454368507659211, 5.5571769623557188, 33.494295836924771, 13.478894913238722])
- arkouda.expm1(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise exponential of the array minus one.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing exponential values of the input array elements minus one
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.exp1m(ak.arange(1,5)) array([1.7182818284590451, 6.3890560989306504, 19.085536923187668, 53.598150033144236])
>>> ak.exp1m(ak.uniform(5,1.0,5.0)) array([10.84010843172504, 45.454368507659211, 4.5571769623557188, 32.494295836924771, 12.478894913238722])
- arkouda.export(read_path: str, dataset_name: str = 'ak_data', write_file: str | None = None, return_obj: bool = True, index: bool = False)[source]¶
Export data from Arkouda file (Parquet/HDF5) to Pandas object or file formatted to be readable by Pandas
- Parameters:
read_path (str) – path to file where arkouda data is stored.
dataset_name (str) – name to store dataset under
index (bool) – Default False. When True, maintain the indexes loaded from the pandas file
write_file (str, optional) – path to file to write pandas formatted data to. Only write the file if this is set
return_obj (bool, optional) – Default True. When True return the Pandas DataFrame object, otherwise return None
- Raises:
RuntimeError –
Unsupported file type
- Returns:
When return_obj=True
- Return type:
pd.DataFrame
See also
pandas.DataFrame.to_parquet,pandas.DataFrame.to_hdf,pandas.DataFrame.read_parquet,pandas.DataFrame.read_hdf,ak.import_dataNotes
If Arkouda file is exported for pandas, the format will not change. This mean parquet files will remain parquet and hdf5 will remain hdf5.
Export can only be performed from hdf5 or parquet files written by Arkouda. The result will be the same file type, but formatted to be read by Pandas.
- arkouda.find(query, space)[source]¶
Return indices of query items in a search list of items (-1 if not found).
- Parameters:
query ((sequence of) array-like) – The items to search for. If multiple arrays, each “row” is an item.
space ((sequence of) array-like) – The set of items in which to search. Must have same shape/dtype as query.
- Returns:
indices – For each item in query, its index in space or -1 if not found.
- Return type:
pdarray, int64
- arkouda.float32¶
- arkouda.float64¶
- arkouda.float_scalars¶
- arkouda.floor(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise floor of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing floor values of the input array elements
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.floor(ak.linspace(1.1,5.5,5)) array([1, 2, 3, 4, 5])
- arkouda.fmod(dividend: pdarray | arkouda.dtypes.numeric_scalars, divisor: pdarray | arkouda.dtypes.numeric_scalars) pdarray[source]¶
Returns the element-wise remainder of division.
It is equivalent to np.fmod, the remainder has the same sign as the dividend.
- Parameters:
- Returns:
Returns an array that contains the element-wise remainder of division.
- Return type:
- arkouda.from_series(series: pandas.Series, dtype: type | str | None = None) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Converts a Pandas Series to an Arkouda pdarray or Strings object. If dtype is None, the dtype is inferred from the Pandas Series. Otherwise, the dtype parameter is set if the dtype of the Pandas Series is to be overridden or is unknown (for example, in situations where the Series dtype is object).
- Parameters:
series (Pandas Series) – The Pandas Series with a dtype of bool, float64, int64, or string
dtype (Optional[type]) – The valid dtype types are np.bool, np.float64, np.int64, and np.str
- Return type:
- Raises:
TypeError – Raised if series is not a Pandas Series object
ValueError – Raised if the Series dtype is not bool, float64, int64, string, datetime, or timedelta
Examples
>>> ak.from_series(pd.Series(np.random.randint(0,10,5))) array([9, 0, 4, 7, 9])
>>> ak.from_series(pd.Series(['1', '2', '3', '4', '5']),dtype=np.int64) array([1, 2, 3, 4, 5])
>>> ak.from_series(pd.Series(np.random.uniform(low=0.0,high=1.0,size=3))) array([0.57600036956445599, 0.41619265571741659, 0.6615356693784662])
>>> ak.from_series(pd.Series(['0.57600036956445599', '0.41619265571741659', '0.6615356693784662']), dtype=np.float64) array([0.57600036956445599, 0.41619265571741659, 0.6615356693784662])
>>> ak.from_series(pd.Series(np.random.choice([True, False],size=5))) array([True, False, True, True, True])
>>> ak.from_series(pd.Series(['True', 'False', 'False', 'True', 'True']), dtype=np.bool) array([True, True, True, True, True])
>>> ak.from_series(pd.Series(['a', 'b', 'c', 'd', 'e'], dtype="string")) array(['a', 'b', 'c', 'd', 'e'])
>>> ak.from_series(pd.Series(['a', 'b', 'c', 'd', 'e']),dtype=np.str) array(['a', 'b', 'c', 'd', 'e'])
>>> ak.from_series(pd.Series(pd.to_datetime(['1/1/2018', np.datetime64('2018-01-01')]))) array([1514764800000000000, 1514764800000000000])
Notes
The supported datatypes are bool, float64, int64, string, and datetime64[ns]. The data type is either inferred from the the Series or is set via the dtype parameter.
Series of datetime or timedelta are converted to Arkouda arrays of dtype int64 (nanoseconds)
A Pandas Series containing strings has a dtype of object. Arkouda assumes the Series contains strings and sets the dtype to str
- arkouda.from_series(series: pandas.Series, dtype: type | str | None = None) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Converts a Pandas Series to an Arkouda pdarray or Strings object. If dtype is None, the dtype is inferred from the Pandas Series. Otherwise, the dtype parameter is set if the dtype of the Pandas Series is to be overridden or is unknown (for example, in situations where the Series dtype is object).
- Parameters:
series (Pandas Series) – The Pandas Series with a dtype of bool, float64, int64, or string
dtype (Optional[type]) – The valid dtype types are np.bool, np.float64, np.int64, and np.str
- Return type:
- Raises:
TypeError – Raised if series is not a Pandas Series object
ValueError – Raised if the Series dtype is not bool, float64, int64, string, datetime, or timedelta
Examples
>>> ak.from_series(pd.Series(np.random.randint(0,10,5))) array([9, 0, 4, 7, 9])
>>> ak.from_series(pd.Series(['1', '2', '3', '4', '5']),dtype=np.int64) array([1, 2, 3, 4, 5])
>>> ak.from_series(pd.Series(np.random.uniform(low=0.0,high=1.0,size=3))) array([0.57600036956445599, 0.41619265571741659, 0.6615356693784662])
>>> ak.from_series(pd.Series(['0.57600036956445599', '0.41619265571741659', '0.6615356693784662']), dtype=np.float64) array([0.57600036956445599, 0.41619265571741659, 0.6615356693784662])
>>> ak.from_series(pd.Series(np.random.choice([True, False],size=5))) array([True, False, True, True, True])
>>> ak.from_series(pd.Series(['True', 'False', 'False', 'True', 'True']), dtype=np.bool) array([True, True, True, True, True])
>>> ak.from_series(pd.Series(['a', 'b', 'c', 'd', 'e'], dtype="string")) array(['a', 'b', 'c', 'd', 'e'])
>>> ak.from_series(pd.Series(['a', 'b', 'c', 'd', 'e']),dtype=np.str) array(['a', 'b', 'c', 'd', 'e'])
>>> ak.from_series(pd.Series(pd.to_datetime(['1/1/2018', np.datetime64('2018-01-01')]))) array([1514764800000000000, 1514764800000000000])
Notes
The supported datatypes are bool, float64, int64, string, and datetime64[ns]. The data type is either inferred from the the Series or is set via the dtype parameter.
Series of datetime or timedelta are converted to Arkouda arrays of dtype int64 (nanoseconds)
A Pandas Series containing strings has a dtype of object. Arkouda assumes the Series contains strings and sets the dtype to str
- arkouda.full(size: arkouda.dtypes.int_scalars | str, fill_value: arkouda.dtypes.numeric_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Create a pdarray filled with fill_value.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
fill_value (int_scalars) – Value with which the array will be filled
dtype (all_scalars) – Resulting array type, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
array of the requested size and dtype filled with fill_value
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
Examples
>>> ak.full(5, 7, dtype=ak.int64) array([7, 7, 7, 7, 7])
>>> ak.full(5, 9, dtype=ak.float64) array([9, 9, 9, 9, 9])
>>> ak.full(5, 5, dtype=ak.bool) array([True, True, True, True, True])
- arkouda.full(size: arkouda.dtypes.int_scalars | str, fill_value: arkouda.dtypes.numeric_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings[source]¶
Create a pdarray filled with fill_value.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
fill_value (int_scalars) – Value with which the array will be filled
dtype (all_scalars) – Resulting array type, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
array of the requested size and dtype filled with fill_value
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
Examples
>>> ak.full(5, 7, dtype=ak.int64) array([7, 7, 7, 7, 7])
>>> ak.full(5, 9, dtype=ak.float64) array([9, 9, 9, 9, 9])
>>> ak.full(5, 5, dtype=ak.bool) array([True, True, True, True, True])
- arkouda.full_like(pda: arkouda.pdarrayclass.pdarray, fill_value: arkouda.dtypes.numeric_scalars) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with fill_value of the same size and dtype as an existing pdarray.
- Parameters:
pda (pdarray) – Array to use for size and dtype
fill_value (int_scalars) – Value with which the array will be filled
- Returns:
Equivalent to ak.full(pda.size, fill_value, pda.dtype)
- Return type:
- Raises:
TypeError – Raised if the pda parameter is not a pdarray.
See also
Notes
Logic for generating the pdarray is delegated to the ak.full method. Accordingly, the supported dtypes match are defined by the ak.full method.
Examples
>>> full = ak.full(5, 7, dtype=ak.int64) >>> ak.full_like(full) array([7, 7, 7, 7, 7])
>>> full = ak.full(5, 9, dtype=ak.float64) >>> ak.full_like(full) array([9, 9, 9, 9, 9])
>>> full = ak.full(5, 5, dtype=ak.bool) >>> ak.full_like(full) array([True, True, True, True, True])
- arkouda.gen_ranges(starts, ends, stride=1, return_lengths=False)[source]¶
Generate a segmented array of variable-length, contiguous ranges between pairs of start- and end-points.
- Parameters:
- Returns:
segments (pdarray, int64) – The starting index of each range in the resulting array
ranges (pdarray, int64) – The actual ranges, flattened into a single array
lengths (pdarray, int64) – The lengths of each segment. Only returned if return_lengths=True.
- arkouda.gen_ranges(starts, ends, stride=1, return_lengths=False)[source]¶
Generate a segmented array of variable-length, contiguous ranges between pairs of start- and end-points.
- Parameters:
- Returns:
segments (pdarray, int64) – The starting index of each range in the resulting array
ranges (pdarray, int64) – The actual ranges, flattened into a single array
lengths (pdarray, int64) – The lengths of each segment. Only returned if return_lengths=True.
- arkouda.getArkoudaLogger(name: str, handlers: List[logging.Handler] | None = None, logFormat: str | None = ArkoudaLogger.DEFAULT_LOG_FORMAT, logLevel: LogLevel | None = None) ArkoudaLogger[source]¶
A convenience method for instantiating an ArkoudaLogger that retrieves the logging level from the ARKOUDA_LOG_LEVEL env variable
- Parameters:
name (str) – The name of the ArkoudaLogger
handlers (List[Handler]) – A list of logging.Handler objects, if None, a list consisting of one StreamHandler named ‘console-handler’ is generated and configured
logFormat (str) – The format for log messages, defaults to the following format: ‘[%(name)s] Line %(lineno)d %(levelname)s: %(message)s’
- Return type:
ArkoudaLogger
- Raises:
TypeError – Raised if either name or logFormat is not a str object or if handlers is not a list of str objects
Notes
Important note: if a list of 1..n logging.Handler objects is passed in, and dynamic changes to 1..n handlers is desired, set a name for each Handler object as follows: handler.name = <desired name>, which will enable retrieval and updates for the specified handler.
- arkouda.get_byteorder(dt: numpy.dtype) str[source]¶
Get a concrete byteorder (turns ‘=’ into ‘<’ or ‘>’)
- arkouda.get_columns(filenames: str | List[str], col_delim: str = ',', allow_errors: bool = False) List[str][source]¶
Get a list of column names from CSV file(s).
- arkouda.get_datasets(filenames: str | List[str], allow_errors: bool = False, column_delim: str = ',', read_nested: bool = True) List[str][source]¶
Get the names of the datasets in the provide files
- Parameters:
filenames (str or List[str]) – Name of the file/s from which to return datasets
allow_errors (bool) – Default: False Whether or not to allow errors while accessing datasets
column_delim (str) – Column delimiter to be used if dataset is CSV. Otherwise, unused.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Only used for Parquet Files.
- Return type:
List[str] of names of the datasets
- Raises:
RuntimeError –
If no datasets are returned
Notes
This function currently supports HDF5 and Parquet formats.
Future updates to Parquet will deprecate this functionality on that format,
but similar support will be added for Parquet at that time. - If a list of files is provided, only the datasets in the first file will be returned
See also
- arkouda.get_filetype(filenames: str | List[str]) str[source]¶
Get the type of a file accessible to the server. Supported file types and possible return strings are ‘HDF5’ and ‘Parquet’.
- Parameters:
filenames (Union[str, List[str]]) – A file or list of files visible to the arkouda server
- Returns:
Type of the file returned as a string, either ‘HDF5’, ‘Parquet’ or ‘CSV
- Return type:
str
- Raises:
ValueError – Raised if filename is empty or contains only whitespace
Notes
When list provided, it is assumed that all files are the same type
CSV Files without the Arkouda Header are not supported
See also
- arkouda.get_null_indices(filenames: str | List[str], datasets: str | List[str] | None = None) arkouda.pdarrayclass.pdarray | Mapping[str, arkouda.pdarrayclass.pdarray][source]¶
Get null indices of a string column in a Parquet file.
- Parameters:
filenames (list or str) – Either a list of filenames or shell expression
datasets (list or str or None) – (List of) name(s) of dataset(s) to read. Each dataset must be a string column. There is no default value for this function, the datasets to be read must be specified.
- Returns:
For a single dataset returns an Arkouda pdarray and for multiple datasets
returns a dictionary of Arkouda pdarrays – Dictionary of {datasetName: pdarray}
- Raises:
RuntimeError – Raised if one or more of the specified files cannot be opened.
TypeError – Raised if we receive an unknown arkouda_type returned from the server
See also
- arkouda.hash(pda: arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | SegArray | Categorical | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | SegArray | Categorical], full: bool = True) Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray] | arkouda.pdarrayclass.pdarray[source]¶
Return an element-wise hash of the array or list of arrays.
- Parameters:
pda (Union[pdarray, Strings, Segarray, Categorical],) – List[Union[pdarray, Strings, Segarray, Categorical]]]
full (bool) – This is only used when a single pdarray is passed into hash By default, a 128-bit hash is computed and returned as two int64 arrays. If full=False, then a 64-bit hash is computed and returned as a single int64 array.
- Returns:
If full=True or a list of pdarrays is passed, a 2-tuple of pdarrays containing the high and low 64 bits of each hash, respectively. If full=False and a single pdarray is passed, a single pdarray containing a 64-bit hash
- Return type:
hashes
- Raises:
TypeError – Raised if the parameter is not a pdarray
Notes
In the case of a single pdarray being passed, this function uses the SIPhash algorithm, which can output either a 64-bit or 128-bit hash. However, the 64-bit hash runs a significant risk of collisions when applied to more than a few million unique values. Unless the number of unique values is known to be small, the 128-bit hash is strongly recommended.
Note that this hash should not be used for security, or for any cryptographic application. Not only is SIPhash not intended for such uses, but this implementation employs a fixed key for the hash, which makes it possible for an adversary with control over input to engineer collisions.
In the case of a list of pdrrays, Strings, Categoricals, or Segarrays being passed, a non-linear function must be applied to each array since hashes of subsequent arrays cannot be simply XORed because equivalent values will cancel each other out, hence we do a rotation by the ordinal of the array.
- arkouda.hist_all(ak_df: arkouda.dataframe.DataFrame, cols: list = [])[source]¶
Create a grid plot histogramming all numeric columns in ak dataframe
- Parameters:
ak_df (ak.DataFrame) – Full Arkouda DataFrame containing data to be visualized
cols (list) – (Optional) A specified list of columns to be plotted
Notes
This function displays the plot.
Examples
>>> import arkouda as ak >>> from arkouda.plotting import hist_all >>> ak_df = ak.DataFrame({"a": ak.array(np.random.randn(100)), "b": ak.array(np.random.randn(100)), "c": ak.array(np.random.randn(100)), "d": ak.array(np.random.randn(100)) }) >>> hist_all(ak_df)
- arkouda.histogram(pda: arkouda.pdarrayclass.pdarray, bins: arkouda.dtypes.int_scalars = 10) Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Compute a histogram of evenly spaced bins over the range of an array.
- Parameters:
pda (pdarray) – The values to histogram
bins (int_scalars) – The number of equal-size bins to use (default: 10)
- Returns:
Bin edges and The number of values present in each bin
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray or if bins is not an int.
ValueError – Raised if bins < 1
NotImplementedError – Raised if pdarray dtype is bool or uint8
See also
Notes
The bins are evenly spaced in the interval [pda.min(), pda.max()].
Examples
>>> import matplotlib.pyplot as plt >>> A = ak.arange(0, 10, 1) >>> nbins = 3 >>> h, b = ak.histogram(A, bins=nbins) >>> h array([3, 3, 4]) >>> b array([0., 3., 6., 9.])
# To plot, export the left edges and the histogram to NumPy >>> plt.plot(b.to_ndarray()[::-1], h.to_ndarray())
- arkouda.histogram(pda: arkouda.pdarrayclass.pdarray, bins: arkouda.dtypes.int_scalars = 10) Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Compute a histogram of evenly spaced bins over the range of an array.
- Parameters:
pda (pdarray) – The values to histogram
bins (int_scalars) – The number of equal-size bins to use (default: 10)
- Returns:
Bin edges and The number of values present in each bin
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray or if bins is not an int.
ValueError – Raised if bins < 1
NotImplementedError – Raised if pdarray dtype is bool or uint8
See also
Notes
The bins are evenly spaced in the interval [pda.min(), pda.max()].
Examples
>>> import matplotlib.pyplot as plt >>> A = ak.arange(0, 10, 1) >>> nbins = 3 >>> h, b = ak.histogram(A, bins=nbins) >>> h array([3, 3, 4]) >>> b array([0., 3., 6., 9.])
# To plot, export the left edges and the histogram to NumPy >>> plt.plot(b.to_ndarray()[::-1], h.to_ndarray())
- arkouda.histogram2d(x: arkouda.pdarrayclass.pdarray, y: arkouda.pdarrayclass.pdarray, bins: arkouda.dtypes.int_scalars | Sequence[arkouda.dtypes.int_scalars] = 10) Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Compute the bi-dimensional histogram of two data samples with evenly spaced bins
- Parameters:
x (pdarray) – A pdarray containing the x coordinates of the points to be histogrammed.
y (pdarray) – A pdarray containing the y coordinates of the points to be histogrammed.
bins (int_scalars or [int, int] = 10) – The number of equal-size bins to use. If int, the number of bins for the two dimensions (nx=ny=bins). If [int, int], the number of bins in each dimension (nx, ny = bins). Defaults to 10
- Returns:
hist (ArrayView, shape(nx, ny)) – The bi-dimensional histogram of samples x and y. Values in x are histogrammed along the first dimension and values in y are histogrammed along the second dimension.
x_edges (pdarray) – The bin edges along the first dimension.
y_edges (pdarray) – The bin edges along the second dimension.
- Raises:
TypeError – Raised if x or y parameters are not pdarrays or if bins is not an int or (int, int).
ValueError – Raised if bins < 1
NotImplementedError – Raised if pdarray dtype is bool or uint8
See also
Notes
The x bins are evenly spaced in the interval [x.min(), x.max()] and y bins are evenly spaced in the interval [y.min(), y.max()].
Examples
>>> x = ak.arange(0, 10, 1) >>> y = ak.arange(9, -1, -1) >>> nbins = 3 >>> h, x_edges, y_edges = ak.histogram2d(x, y, bins=nbins) >>> h array([[0, 0, 3], [0, 2, 1], [3, 1, 0]]) >>> x_edges array([0.0 3.0 6.0 9.0]) >>> x_edges array([0.0 3.0 6.0 9.0])
- arkouda.histogramdd(sample: Sequence[arkouda.pdarrayclass.pdarray], bins: arkouda.dtypes.int_scalars | Sequence[arkouda.dtypes.int_scalars] = 10) Tuple[arkouda.pdarrayclass.pdarray, Sequence[arkouda.pdarrayclass.pdarray]][source]¶
Compute the multidimensional histogram of data in sample with evenly spaced bins.
- Parameters:
sample (Sequence[pdarray]) – A sequence of pdarrays containing the coordinates of the points to be histogrammed.
bins (int_scalars or Sequence[int_scalars] = 10) – The number of equal-size bins to use. If int, the number of bins for all dimensions (nx=ny=…=bins). If [int, int, …], the number of bins in each dimension (nx, ny, … = bins). Defaults to 10
- Returns:
hist (ArrayView, shape(nx, ny, …, nd)) – The multidimensional histogram of pdarrays in sample. Values in first pdarray are histogrammed along the first dimension. Values in second pdarray are histogrammed along the second dimension and so on.
edges (List[pdarray]) – A list of pdarrays containing the bin edges for each dimension.
- Raises:
ValueError – Raised if bins < 1
NotImplementedError – Raised if pdarray dtype is bool or uint8
See also
Notes
The bins for each dimension, m, are evenly spaced in the interval [m.min(), m.max()]
Examples
>>> x = ak.arange(0, 10, 1) >>> y = ak.arange(9, -1, -1) >>> z = ak.where(x % 2 == 0, x, y) >>> h, edges = ak.histogramdd((x, y,z), bins=(2,2,5)) >>> h array([[[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]],
- [[1, 1, 1, 1, 1],
[0, 0, 0, 0, 0]]])
>>> edges [array([0.0 4.5 9.0]), array([0.0 4.5 9.0]), array([0.0 1.6 3.2 4.8 6.4 8.0])]
- arkouda.import_data(read_path: str, write_file: str | None = None, return_obj: bool = True, index: bool = False)[source]¶
Import data from a file saved by Pandas (HDF5/Parquet) to Arkouda object and/or a file formatted to be read by Arkouda.
- Parameters:
read_path (str) – path to file where pandas data is stored. This can be glob expression for parquet formats.
write_file (str, optional) – path to file to write arkouda formatted data to. Only write file if provided
return_obj (bool, optional) – Default True. When True return the Arkouda DataFrame object, otherwise return None
index (bool, optional) – Default False. When True, maintain the indexes loaded from the pandas file
- Raises:
RuntimeWarning –
Export attempted on Parquet file. Arkouda formatted Parquet files are readable by pandas.
RuntimeError –
Unsupported file type
- Returns:
When return_obj=True
- Return type:
pd.DataFrame
See also
pandas.DataFrame.to_parquet,pandas.DataFrame.to_hdf,pandas.DataFrame.read_parquet,pandas.DataFrame.read_hdf,ak.exportNotes
Import can only be performed from hdf5 or parquet files written by pandas.
- arkouda.in1d(pda1: arkouda.groupbyclass.groupable, pda2: arkouda.groupbyclass.groupable, assume_unique: bool = False, symmetric: bool = False, invert: bool = False) arkouda.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶
Test whether each element of a 1-D array is also present in a second array.
Returns a boolean array the same length as pda1 that is True where an element of pda1 is in pda2 and False otherwise.
Support multi-level – test membership of rows of a in the set of rows of b.
- Parameters:
a (list of pdarrays, pdarray, Strings, or Categorical) – Rows are elements for which to test membership in b
b (list of pdarrays, pdarray, Strings, or Categorical) – Rows are elements of the set in which to test membership
assume_unique (bool) – If true, assume rows of a and b are each unique and sorted. By default, sort and unique them explicitly.
symmetric (bool) – Return in1d(pda1, pda2), in1d(pda2, pda1) when pda1 and 2 are single items.
invert (bool, optional) – If True, the values in the returned array are inverted (that is, False where an element of pda1 is in pda2 and True otherwise). Default is False.
ak.in1d(a, b, invert=True)is equivalent to (but is faster than)~ak.in1d(a, b).
- Return type:
True for each row in a that is contained in b
Return Type¶
pdarray, bool
Notes
Only works for pdarrays of int64 dtype, float64, Strings, or Categorical
- arkouda.in1d(pda1: arkouda.groupbyclass.groupable, pda2: arkouda.groupbyclass.groupable, assume_unique: bool = False, symmetric: bool = False, invert: bool = False) arkouda.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶
Test whether each element of a 1-D array is also present in a second array.
Returns a boolean array the same length as pda1 that is True where an element of pda1 is in pda2 and False otherwise.
Support multi-level – test membership of rows of a in the set of rows of b.
- Parameters:
a (list of pdarrays, pdarray, Strings, or Categorical) – Rows are elements for which to test membership in b
b (list of pdarrays, pdarray, Strings, or Categorical) – Rows are elements of the set in which to test membership
assume_unique (bool) – If true, assume rows of a and b are each unique and sorted. By default, sort and unique them explicitly.
symmetric (bool) – Return in1d(pda1, pda2), in1d(pda2, pda1) when pda1 and 2 are single items.
invert (bool, optional) – If True, the values in the returned array are inverted (that is, False where an element of pda1 is in pda2 and True otherwise). Default is False.
ak.in1d(a, b, invert=True)is equivalent to (but is faster than)~ak.in1d(a, b).
- Return type:
True for each row in a that is contained in b
Return Type¶
pdarray, bool
Notes
Only works for pdarrays of int64 dtype, float64, Strings, or Categorical
- arkouda.in1d(pda1: arkouda.groupbyclass.groupable, pda2: arkouda.groupbyclass.groupable, assume_unique: bool = False, symmetric: bool = False, invert: bool = False) arkouda.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶
Test whether each element of a 1-D array is also present in a second array.
Returns a boolean array the same length as pda1 that is True where an element of pda1 is in pda2 and False otherwise.
Support multi-level – test membership of rows of a in the set of rows of b.
- Parameters:
a (list of pdarrays, pdarray, Strings, or Categorical) – Rows are elements for which to test membership in b
b (list of pdarrays, pdarray, Strings, or Categorical) – Rows are elements of the set in which to test membership
assume_unique (bool) – If true, assume rows of a and b are each unique and sorted. By default, sort and unique them explicitly.
symmetric (bool) – Return in1d(pda1, pda2), in1d(pda2, pda1) when pda1 and 2 are single items.
invert (bool, optional) – If True, the values in the returned array are inverted (that is, False where an element of pda1 is in pda2 and True otherwise). Default is False.
ak.in1d(a, b, invert=True)is equivalent to (but is faster than)~ak.in1d(a, b).
- Return type:
True for each row in a that is contained in b
Return Type¶
pdarray, bool
Notes
Only works for pdarrays of int64 dtype, float64, Strings, or Categorical
- arkouda.in1d_intervals(vals, intervals, symmetric=False)[source]¶
Test each value for membership in any of a set of half-open (pythonic) intervals.
- Parameters:
vals (pdarray(int, float)) – Values to test for membership in intervals
intervals (2-tuple of pdarrays) – Non-overlapping, half-open intervals, as a tuple of (lower_bounds_inclusive, upper_bounds_exclusive)
symmetric (bool) – If True, also return boolean pdarray indicating which intervals contained one or more query values.
- Returns:
pdarray(bool) – Array of same length as <vals>, True if corresponding value is included in any of the ranges defined by (low[i], high[i]) inclusive.
pdarray(bool) (if symmetric=True) – Array of same length as number of intervals, True if corresponding interval contains any of the values in <vals>.
Notes
- First return array is equivalent to the following:
((vals >= intervals[0][0]) & (vals < intervals[1][0])) | ((vals >= intervals[0][1]) & (vals < intervals[1][1])) | … ((vals >= intervals[0][-1]) & (vals < intervals[1][-1]))
But much faster when testing many ranges.
- Second (optional) return array is equivalent to:
((intervals[0] <= vals[0]) & (intervals[1] > vals[0])) | ((intervals[0] <= vals[1]) & (intervals[1] > vals[1])) | … ((intervals[0] <= vals[-1]) & (intervals[1] > vals[-1]))
But much faster when vals is non-trivial size.
- arkouda.indexof1d(keys: arkouda.groupbyclass.groupable, arr: arkouda.groupbyclass.groupable) arkouda.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶
Returns an integer array of the index values where the values of the first array appear in the second.
- Parameters:
keys (pdarray or Strings or Categorical) – Input array of values to find the indices of in arr.
arr (pdarray or Strings or Categorical) – The values to search.
- Returns:
The indices of the values of keys in arr.
- Return type:
pdarray, int
- Raises:
TypeError – Raised if either keys or arr is not a pdarray, Strings, or Categorical object
RuntimeError – Raised if the dtype of either array is not supported
- arkouda.information(names: List[str] | str = RegisteredSymbols) str[source]¶
Returns JSON formatted string containing information about the objects in names
- Parameters:
names (Union[List[str], str]) – names is either the name of an object or list of names of objects to retrieve info if names is ak.AllSymbols, retrieves info for all symbols in the symbol table if names is ak.RegisteredSymbols, retrieves info for all symbols in the registry
- Returns:
JSON formatted string containing a list of information for each object in names
- Return type:
str
- Raises:
RuntimeError – Raised if a server-side error is thrown in the process of retrieving information about the objects in names
- arkouda.int16¶
- arkouda.int32¶
- arkouda.int64¶
- arkouda.int64¶
- arkouda.int8¶
- arkouda.intTypes¶
- arkouda.intTypes¶
- arkouda.intTypes¶
- arkouda.int_scalars¶
- arkouda.int_scalars¶
- arkouda.int_scalars¶
- arkouda.intersect(a, b, positions=True, unique=False)[source]¶
Find the intersection of two arkouda arrays.
This function can be especially useful when positions=True so that the caller gets the indices of values present in both arrays.
- Parameters:
positions (bool, default=True) – Return tuple of boolean pdarrays that indicate positions in a and b of the intersection values.
unique (bool, default=False) – If the number of distinct values in a (and b) is equal to the size of a (and b), there is a more efficient method to compute the intersection.
- Returns:
The indices of a and b where any element occurs at least once in both arrays.
- Return type:
(arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray) or arkouda.pdarrayclass.pdarray
Examples
>>> import arkouda as ak >>> ak.connect() >>> a = ak.arange(10) >>> print(a) [0 1 2 3 4 5 6 7 8 9]
>>> b = 2 * ak.arange(10) >>> print(b) [0 2 4 6 8 10 12 14 16 18]
>>> intersect(a,b, positions=True) (array([True False True False True False True False True False]), array([True True True True True False False False False False]))
>>> intersect(a,b, positions=False) array([0 2 4 6 8])
- arkouda.intersect1d(pda1: arkouda.groupbyclass.groupable, pda2: arkouda.groupbyclass.groupable, assume_unique: bool = False) arkouda.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶
Find the intersection of two arrays.
Return the sorted, unique values that are in both of the input arrays.
- Parameters:
pda1 (pdarray/Sequence[pdarray, Strings, Categorical]) – Input array/Sequence of groupable objects
pda2 (pdarray/List) – Input array/sequence of groupable objects
assume_unique (bool) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
- Returns:
Sorted 1D array/List of sorted pdarrays of common and unique elements.
- Return type:
pdarray/groupable
- Raises:
TypeError – Raised if either pda1 or pda2 is not a pdarray
RuntimeError – Raised if the dtype of either pdarray is not supported
See also
Notes
ak.intersect1d is not supported for bool or float64 pdarrays
Examples
>>> # 1D Example >>> ak.intersect1d([1, 3, 4, 3], [3, 1, 2, 1]) array([1, 3]) # Multi-Array Example >>> a = ak.arange(5) >>> b = ak.array([1, 5, 3, 4, 2]) >>> c = ak.array([1, 4, 3, 2, 5]) >>> d = ak.array([1, 2, 3, 5, 4]) >>> multia = [a, a, a] >>> multib = [b, c, d] >>> ak.intersect1d(multia, multib) [array([1, 3]), array([1, 3]), array([1, 3])]
- arkouda.interval_lookup(keys, values, arguments, fillvalue=-1, tiebreak=None, hierarchical=False)[source]¶
Apply a function defined over intervals to an array of arguments.
- Parameters:
keys (2-tuple of (sequences of) pdarrays) – Tuple of closed intervals expressed as (lower_bounds_inclusive, upper_bounds_inclusive). Must have same dtype(s) as vals.
values (pdarray) – Function value to return for each entry in keys.
arguments ((sequences of) pdarray) – Values to search for in intervals. If multiple arrays, each “row” is an item.
fillvalue (scalar) – Default value to return when argument is not in any interval.
tiebreak ((optional) pdarray, numeric) – When an argument is present in more than one key interval, the interval with the lowest tiebreak value will be chosen. If no tiebreak is given, the first valid key interval will be chosen.
- Returns:
Value of function corresponding to the keys interval containing each argument, or fillvalue if argument not in any interval.
- Return type:
- arkouda.intx(a, b)[source]¶
Find all the rows that are in both dataframes. Columns should be in identical order.
Note: does not work for columns of floating point values, but does work for Strings, pdarrays of int64 type, and Categorical should work.
Examples
>>> import arkouda as ak >>> ak.connect() >>> a = ak.DataFrame({'a':ak.arange(5),'b': 2* ak.arange(5)}) >>> display(a)
a
b
0
0
0
1
1
2
2
2
4
3
3
6
4
4
8
>>> b = ak.DataFrame({'a':ak.arange(5),'b':ak.array([0,3,4,7,8])}) >>> display(b)
a
b
0
0
0
1
1
3
2
2
4
3
3
7
4
4
8
>>> intx(a,b) >>> intersect_df = a[intx(a,b)] >>> display(intersect_df)
a
b
0
0
0
1
2
4
2
4
8
- arkouda.invert_permutation(perm)[source]¶
Find the inverse of a permutation array.
- Parameters:
perm (pdarray) – The permutation array.
- Returns:
The inverse of the permutation array.
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda.index import Index >>> i = Index(ak.array([1,2,0,5,4])) >>> perm = i.argsort() >>> print(perm) [2 0 1 4 3] >>> invert_permutation(perm) array([1 2 0 4 3])
- arkouda.ip_address(values)[source]¶
Convert values to an Arkouda array of IP addresses.
- Parameters:
values (list-like, integer pdarray, or IPv4) – The integer IP addresses or IPv4 object.
- Returns:
The same IP addresses as an Arkouda array
- Return type:
Notes
This helper is intended to help future proof changes made to accomodate IPv6 and to prevent errors if a user inadvertently casts a IPv4 instead of a int64 pdarray. It can also be used for importing Python lists of IP addresses into Arkouda.
- arkouda.is_cosorted(arrays)[source]¶
Return True iff the arrays are cosorted, i.e., if the arrays were columns in a table then the rows are sorted.
- Parameters:
arrays (list-like of pdarrays) – Arrays to check for cosortedness
- Returns:
True iff arrays are cosorted.
- Return type:
bool
- Raises:
ValueError – Raised if arrays are not the same length
TypeError – Raised if arrays is not a list-like of pdarrays
- arkouda.is_ipv4(ip: arkouda.pdarrayclass.pdarray | IPv4, ip2: arkouda.pdarrayclass.pdarray | None = None) arkouda.pdarrayclass.pdarray[source]¶
Indicate which values are ipv4 when passed data containing IPv4 and IPv6 values.
- Parameters:
- Return type:
pdarray of bools indicating which indexes are IPv4.
See also
ak.is_ipv6
- arkouda.is_ipv6(ip: arkouda.pdarrayclass.pdarray | IPv4, ip2: arkouda.pdarrayclass.pdarray | None = None) arkouda.pdarrayclass.pdarray[source]¶
Indicate which values are ipv6 when passed data containing IPv4 and IPv6 values.
- Parameters:
- Return type:
pdarray of bools indicating which indexes are IPv6.
See also
ak.is_ipv4
- arkouda.is_registered(name: str, as_component: bool = False) bool[source]¶
Determine if the name provided is associated with a registered Object
- Parameters:
name (str) – The name to check for in the registry
as_component (bool) – Default: False When True, the name will be checked to determine if it is registered as a component of a registered object
- Return type:
bool
- arkouda.is_sorted(pda: pdarray) numpy.bool_[source]¶
Return True iff the array is monotonically non-decreasing.
- Parameters:
pda (pdarray) – The pdarray instance to be evaluated
- Returns:
Indicates if the array is monotonically non-decreasing
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.is_sorted(pda: pdarray) numpy.bool_[source]¶
Return True iff the array is monotonically non-decreasing.
- Parameters:
pda (pdarray) – The pdarray instance to be evaluated
- Returns:
Indicates if the array is monotonically non-decreasing
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.isfinite(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise isfinite check applied to the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing boolean values indicating whether the input array elements are finite
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
RuntimeError – if the underlying pdarray is not float-based
Examples
>>> ak.isfinite(ak.array[1.0, 2.0, ak.inf]) array([True, True, False])
- arkouda.isinf(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise isinf check applied to the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing boolean values indicating whether the input array elements are infinite
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
RuntimeError – if the underlying pdarray is not float-based
Examples
>>> ak.isinf(ak.array[1.0, 2.0, ak.inf]) array([False, False, True])
- arkouda.isnan(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise isnan check applied to the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing boolean values indicating whether the input array elements are NaN
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
RuntimeError – if the underlying pdarray is not float-based
Examples
>>> ak.isnan(ak.array[1.0, 2.0, 1.0 / 0.0]) array([False, False, True])
- arkouda.isnan(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise isnan check applied to the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing boolean values indicating whether the input array elements are NaN
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
RuntimeError – if the underlying pdarray is not float-based
Examples
>>> ak.isnan(ak.array[1.0, 2.0, 1.0 / 0.0]) array([False, False, True])
- arkouda.join_on_eq_with_dt(a1: arkouda.pdarrayclass.pdarray, a2: arkouda.pdarrayclass.pdarray, t1: arkouda.pdarrayclass.pdarray, t2: arkouda.pdarrayclass.pdarray, dt: int | numpy.int64, pred: str, result_limit: int | numpy.int64 = 1000) Tuple[arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray][source]¶
Performs an inner-join on equality between two integer arrays where the time-window predicate is also true
- Parameters:
a1 (pdarray, int64) – pdarray to be joined
a2 (pdarray, int64) – pdarray to be joined
t1 (pdarray) – timestamps in millis corresponding to the a1 pdarray
t2 (pdarray) – timestamps in millis corresponding to the a2 pdarray
dt (Union[int,np.int64]) – time delta
pred (str) – time window predicate
result_limit (Union[int,np.int64]) – size limit for returned result
- Returns:
result_array_one (pdarray, int64) – a1 indices where a1 == a2
result_array_one (pdarray, int64) – a2 indices where a2 == a1
- Raises:
TypeError – Raised if a1, a2, t1, or t2 is not a pdarray, or if dt or result_limit is not an int
ValueError – if a1, a2, t1, or t2 dtype is not int64, pred is not ‘true_dt’, ‘abs_dt’, or ‘pos_dt’, or result_limit is < 0
- arkouda.left_align(left, right)[source]¶
Map two arrays of sparse identifiers to the 0-up index set implied by the left array, discarding values from right that do not appear in left.
- arkouda.linspace(start: arkouda.dtypes.numeric_scalars, stop: arkouda.dtypes.numeric_scalars, length: arkouda.dtypes.int_scalars) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray of linearly-spaced floats in a closed interval.
- Parameters:
start (numeric_scalars) – Start of interval (inclusive)
stop (numeric_scalars) – End of interval (inclusive)
length (int_scalars) – Number of points
- Returns:
Array of evenly spaced float values along the interval
- Return type:
pdarray, float64
- Raises:
TypeError – Raised if start or stop is not a float or int or if length is not an int
See also
Notes
If that start is greater than stop, the pdarray values are generated in descending order.
Examples
>>> ak.linspace(0, 1, 5) array([0, 0.25, 0.5, 0.75, 1])
>>> ak.linspace(start=1, stop=0, length=5) array([1, 0.75, 0.5, 0.25, 0])
>>> ak.linspace(start=-5, stop=0, length=5) array([-5, -3.75, -2.5, -1.25, 0])
- arkouda.list_registry(detailed: bool = False)[source]¶
Return a list containing the names of all registered objects
- Parameters:
detailed (bool) – Default = False Return details of registry objects. Currently includes object type for any objects
- Returns:
Dict containing keys “Components” and “Objects”.
- Return type:
dict
- Raises:
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.list_symbol_table() List[str][source]¶
Return a list containing the names of all objects in the symbol table
- Parameters:
None
- Returns:
List of all object names in the symbol table
- Return type:
list
- Raises:
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.load(path_prefix: str, file_format: str = 'INFER', dataset: str = 'array', calc_string_offsets: bool = False, column_delim: str = ',') arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index | Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index][source]¶
Load a pdarray previously saved with
pdarray.save().- Parameters:
path_prefix (str) – Filename prefix used to save the original pdarray
file_format (str) – ‘INFER’, ‘HDF5’ or ‘Parquet’. Defaults to ‘INFER’. Used to indicate the file type being loaded. If INFER, this will be detected during processing
dataset (str) – Dataset name where the pdarray was saved, defaults to ‘array’
calc_string_offsets (bool) – If True the server will ignore Segmented Strings ‘offsets’ array and derive it from the null-byte terminators. Defaults to False currently
column_delim (str) – Column delimiter to be used if dataset is CSV. Otherwise, unused.
- Returns:
The pdarray or Strings that was previously saved
- Return type:
- Raises:
TypeError – Raised if either path_prefix or dataset is not a str
ValueError – Raised if invalid file_format or if the dataset is not present in all hdf5 files or if the path_prefix does not correspond to files accessible to Arkouda
RuntimeError – Raised if the hdf5 files are present but there is an error in opening one or more of them
See also
Notes
If you have a previously saved Parquet file that is raising a FileNotFound error, try loading it with a .parquet appended to the prefix_path. Parquet files were previously ALWAYS stored with a
.parquetextension.ak.load does not support loading a single file. For loading single HDF5 files without the _LOCALE#### suffix please use ak.read().
CSV files without the Arkouda Header are not supported.
Examples
>>> # Loading from file without extension >>> obj = ak.load('path/prefix') Loads the array from numLocales files with the name ``cwd/path/name_prefix_LOCALE####``. The file type is inferred during processing.
>>> # Loading with an extension (HDF5) >>> obj = ak.load('path/prefix.test') Loads the object from numLocales files with the name ``cwd/path/name_prefix_LOCALE####.test`` where #### is replaced by each locale numbers. Because filetype is inferred during processing, the extension is not required to be a specific format.
- arkouda.load_all(path_prefix: str, file_format: str = 'INFER', column_delim: str = ',', read_nested=True) Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.categorical.Categorical][source]¶
Load multiple pdarrays, Strings, SegArrays, or Categoricals previously saved with
save_all().- Parameters:
path_prefix (str) – Filename prefix used to save the original pdarray
file_format (str) – ‘INFER’, ‘HDF5’, ‘Parquet’, or ‘CSV’. Defaults to ‘INFER’. Indicates the format being loaded. When ‘INFER’ the processing will detect the format Defaults to ‘INFER’
column_delim (str) – Column delimiter to be used if dataset is CSV. Otherwise, unused.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Parquet files only
- Returns:
Dictionary of {datsetName: Union[pdarray, Strings, SegArray, Categorical]} with the previously saved pdarrays, Strings, SegArrays, or Categoricals
- Return type:
Mapping[str, Union[pdarray, Strings, SegArray, Categorical]]
- Raises:
TypeError: – Raised if path_prefix is not a str
ValueError – Raised if file_format/extension is encountered that is not hdf5 or parquet or if all datasets are not present in all hdf5/parquet files or if the path_prefix does not correspond to files accessible to Arkouda
RuntimeError – Raised if the hdf5 files are present but there is an error in opening one or more of them
See also
to_parquet,to_hdf,load,readNotes
This function has been updated to determine the file extension based on the file format variable
This function will be deprecated when glob flags are added to read_* methods
CSV files without the Arkouda Header are not supported.
- arkouda.log(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise natural log of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing natural log values of the input array elements
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Notes
Logarithms with other bases can be computed as follows:
Examples
>>> A = ak.array([1, 10, 100]) # Natural log >>> ak.log(A) array([0, 2.3025850929940459, 4.6051701859880918]) # Log base 10 >>> ak.log(A) / np.log(10) array([0, 1, 2]) # Log base 2 >>> ak.log(A) / np.log(2) array([0, 3.3219280948873626, 6.6438561897747253])
- arkouda.log10(x: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise base 10 log of the array.
- Parameters:
x (pdarray) – array to compute on
- Return type:
pdarray contain values of the base 10 log
- arkouda.log1p(x: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise natural log of one plus the array.
- Parameters:
x (pdarray) – array to compute on
- Return type:
pdarray contain values of the natural log of one plus the array
- arkouda.log2(x: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise base 2 log of the array.
- Parameters:
x (pdarray) – array to compute on
- Return type:
pdarray contain values of the base 2 log
- arkouda.lookup(keys, values, arguments, fillvalue=-1)[source]¶
Apply the function defined by the mapping keys –> values to arguments.
- Parameters:
keys ((sequence of) array-like) – The domain of the function. Entries must be unique (if a sequence of arrays is given, each row is treated as a tuple-valued entry).
values (pdarray) – The range of the function. Must be same length as keys.
arguments ((sequence of) array-like) – The arguments on which to evaluate the function. Must have same dtype (or tuple of dtypes, for a sequence) as keys.
fillvalue (scalar) – The default value to return for arguments not in keys.
- Returns:
evaluated – The result of evaluating the function over arguments.
- Return type:
Notes
While the values cannot be Strings (or other complex objects), the same result can be achieved by passing an arange as the values, then using the return as indices into the desired object.
Examples
# Lookup numbers by two-word name >>> keys1 = ak.array([‘twenty’ for _ in range(5)]) >>> keys2 = ak.array([‘one’, ‘two’, ‘three’, ‘four’, ‘five’]) >>> values = ak.array([21, 22, 23, 24, 25]) >>> args1 = ak.array([‘twenty’, ‘thirty’, ‘twenty’]) >>> args2 = ak.array([‘four’, ‘two’, ‘two’]) >>> aku.lookup([keys1, keys2], values, [args1, args2]) array([24, -1, 22])
# Other direction requires an intermediate index >>> revkeys = values >>> revindices = ak.arange(values.size) >>> revargs = ak.array([24, 21, 22]) >>> idx = aku.lookup(revkeys, revindices, revargs) >>> keys1[idx], keys2[idx] (array([‘twenty’, ‘twenty’, ‘twenty’]), array([‘four’, ‘one’, ‘two’]))
- arkouda.ls(filename: str, col_delim: str = ',', read_nested: bool = True) List[str][source]¶
This function calls the h5ls utility on a HDF5 file visible to the arkouda server or calls a function that imitates the result of h5ls on a Parquet file.
- Parameters:
filename (str) – The name of the file to pass to the server
col_delim (str) – The delimiter used to separate columns if the file is a csv
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Only used for Parquet files.
- Returns:
The string output of the datasets from the server
- Return type:
str
- Raises:
TypeError – Raised if filename is not a str
ValueError – Raised if filename is empty or contains only whitespace
RuntimeError – Raised if error occurs in executing ls on an HDF5 file
Notes –
- This will need to be updated because Parquet will not technically support this when we update.
Similar functionality will be added for Parquet in the future
For CSV files without headers, please use ls_csv
See also
- arkouda.ls_csv(filename: str, col_delim: str = ',') List[str][source]¶
Used for identifying the datasets within a file when a CSV does not have a header.
- Parameters:
filename (str) – The name of the file to pass to the server
col_delim (str) – The delimiter used to separate columns if the file is a csv
- Returns:
The string output of the datasets from the server
- Return type:
str
See also
- arkouda.max(pda: pdarray) arkouda.dtypes.numpy_scalars[source]¶
Return the maximum value of the array.
- Parameters:
pda (pdarray) – Values for which to calculate the max
- Returns:
The max calculated from the pda
- Return type:
numpy_scalars
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.maxk(pda: pdarray, k: arkouda.dtypes.int_scalars) pdarray[source]¶
Find the k maximum values of an array.
Returns the largest k values of an array, sorted
- Parameters:
pda (pdarray) – Input array.
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty or k < 1
Notes
This call is equivalent in value to:
a[ak.argsort(a)[k:]]
and generally outperforms this operation.
This reduction will see a significant drop in performance as k grows beyond a certain value. This value is system dependent, but generally about a k of 5 million is where performance degredation has been observed.
Examples
>>> A = ak.array([10,5,1,3,7,2,9,0]) >>> ak.maxk(A, 3) array([7, 9, 10]) >>> ak.maxk(A, 4) array([5, 7, 9, 10])
- arkouda.mean(pda: pdarray) numpy.float64[source]¶
Return the mean of the array.
- Parameters:
pda (pdarray) – Values for which to calculate the mean
- Returns:
The mean calculated from the pda sum and size
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.merge(left: DataFrame, right: DataFrame, on: str | List[str] | None = None, how: str = 'inner', left_suffix: str = '_x', right_suffix: str = '_y', convert_ints: bool = True, sort: bool = True) DataFrame[source]¶
Merge Arkouda DataFrames with a database-style join. The resulting dataframe contains rows from both DataFrames as specified by the merge condition (based on the “how” and “on” parameters).
Based on pandas merge functionality. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html
- Parameters:
left (DataFrame) – The Left DataFrame to be joined.
right (DataFrame) – The Right DataFrame to be joined.
on (Optional[Union[str, List[str]]] = None) – The name or list of names of the DataFrame column(s) to join on. If on is None, this defaults to the intersection of the columns in both DataFrames.
how (str, default = "inner") – The merge condition. Must be one of “inner”, “left”, “right”, or “outer”.
left_suffix (str, default = "_x") – A string indicating the suffix to add to columns from the left dataframe for overlapping column names in both left and right. Defaults to “_x”. Only used when how is “inner”.
right_suffix (str, default = "_y") – A string indicating the suffix to add to columns from the right dataframe for overlapping column names in both left and right. Defaults to “_y”. Only used when how is “inner”.
convert_ints (bool = True) – If True, convert columns with missing int values (due to the join) to float64. This is to match pandas. If False, do not convert the column dtypes. This has no effect when how = “inner”.
sort (bool = True) – If True, DataFrame is returned sorted by “on”. Otherwise, the DataFrame is not sorted.
- Returns:
Joined Arkouda DataFrame.
- Return type:
Note
Multiple column joins are only supported for integer columns.
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda import merge >>> left_df = ak.DataFrame({'col1': ak.arange(5), 'col2': -1 * ak.arange(5)}) >>> display(left_df)
col1
col2
0
0
0
1
1
-1
2
2
-2
3
3
-3
4
4
-4
>>> right_df = ak.DataFrame({'col1': 2 * ak.arange(5), 'col2': 2 * ak.arange(5)}) >>> display(right_df)
col1
col2
0
0
0
1
2
2
2
4
4
3
6
6
4
8
8
>>> merge(left_df, right_df, on = "col1")
col1
col2_x
col2_y
0
0
0
0
1
2
-2
2
2
4
-4
4
>>> merge(left_df, right_df, on = "col1", how = "left")
col1
col2_y
col2_x
0
0
0
0
1
1
nan
-1
2
2
2
-2
3
3
nan
-3
4
4
4
-4
>>> merge(left_df, right_df, on = "col1", how = "right")
col1
col2_x
col2_y
0
0
0
0
1
2
-2
2
2
4
-4
4
3
6
nan
6
4
8
nan
8
>>> merge(left_df, right_df, on = "col1", how = "outer")
col1
col2_y
col2_x
0
0
0
0
1
1
nan
-1
2
2
2
-2
3
3
nan
-3
4
4
4
-4
5
6
6
nan
6
8
8
nan
- arkouda.min(pda: pdarray) arkouda.dtypes.numpy_scalars[source]¶
Return the minimum value of the array.
- Parameters:
pda (pdarray) – Values for which to calculate the min
- Returns:
The min calculated from the pda
- Return type:
numpy_scalars
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.mink(pda: pdarray, k: arkouda.dtypes.int_scalars) pdarray[source]¶
Find the k minimum values of an array.
Returns the smallest k values of an array, sorted
- Parameters:
pda (pdarray) – Input array.
k (int_scalars) – The desired count of minimum values to be returned by the output.
- Returns:
The minimum k values from pda, sorted
- Return type:
- Raises:
TypeError – Raised if pda is not a pdarray
ValueError – Raised if the pda is empty or k < 1
Notes
This call is equivalent in value to:
a[ak.argsort(a)[:k]]
and generally outperforms this operation.
This reduction will see a significant drop in performance as k grows beyond a certain value. This value is system dependent, but generally about a k of 5 million is where performance degredation has been observed.
Examples
>>> A = ak.array([10,5,1,3,7,2,9,0]) >>> ak.mink(A, 3) array([0, 1, 2]) >>> ak.mink(A, 4) array([0, 1, 2, 3])
- arkouda.mod(dividend, divisor) pdarray[source]¶
Returns the element-wise remainder of division.
Computes the remainder complementary to the floor_divide function. It is equivalent to np.mod, the remainder has the same sign as the divisor.
- Parameters:
dividend – The array being acted on by the bases for the modular division.
divisor – The array that will be the bases for the modular division.
- Returns:
Returns an array that contains the element-wise remainder of division.
- Return type:
- arkouda.numeric_scalars¶
- arkouda.numpy_scalars¶
- arkouda.ones(size: arkouda.dtypes.int_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with ones.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
dtype (Union[float64, int64, bool]) – Resulting array type, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Ones of the requested size and dtype
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
Examples
>>> ak.ones(5, dtype=ak.int64) array([1, 1, 1, 1, 1])
>>> ak.ones(5, dtype=ak.float64) array([1, 1, 1, 1, 1])
>>> ak.ones(5, dtype=ak.bool) array([True, True, True, True, True])
- arkouda.ones(size: arkouda.dtypes.int_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with ones.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
dtype (Union[float64, int64, bool]) – Resulting array type, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Ones of the requested size and dtype
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
Examples
>>> ak.ones(5, dtype=ak.int64) array([1, 1, 1, 1, 1])
>>> ak.ones(5, dtype=ak.float64) array([1, 1, 1, 1, 1])
>>> ak.ones(5, dtype=ak.bool) array([True, True, True, True, True])
- arkouda.ones(size: arkouda.dtypes.int_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with ones.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
dtype (Union[float64, int64, bool]) – Resulting array type, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Ones of the requested size and dtype
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
Examples
>>> ak.ones(5, dtype=ak.int64) array([1, 1, 1, 1, 1])
>>> ak.ones(5, dtype=ak.float64) array([1, 1, 1, 1, 1])
>>> ak.ones(5, dtype=ak.bool) array([True, True, True, True, True])
- arkouda.ones(size: arkouda.dtypes.int_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with ones.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
dtype (Union[float64, int64, bool]) – Resulting array type, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Ones of the requested size and dtype
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
Examples
>>> ak.ones(5, dtype=ak.int64) array([1, 1, 1, 1, 1])
>>> ak.ones(5, dtype=ak.float64) array([1, 1, 1, 1, 1])
>>> ak.ones(5, dtype=ak.bool) array([True, True, True, True, True])
- arkouda.ones_like(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Create a one-filled pdarray of the same size and dtype as an existing pdarray.
- Parameters:
pda (pdarray) – Array to use for size and dtype
- Returns:
Equivalent to ak.ones(pda.size, pda.dtype)
- Return type:
- Raises:
TypeError – Raised if the pda parameter is not a pdarray.
See also
Notes
Logic for generating the pdarray is delegated to the ak.ones method. Accordingly, the supported dtypes match are defined by the ak.ones method.
Examples
>>> ones = ak.ones(5, dtype=ak.int64) >>> ak.ones_like(ones) array([1, 1, 1, 1, 1])
>>> ones = ak.ones(5, dtype=ak.float64) >>> ak.ones_like(ones) array([1, 1, 1, 1, 1])
>>> ones = ak.ones(5, dtype=ak.bool) >>> ak.ones_like(ones) array([True, True, True, True, True])
- arkouda.parity(pda: pdarray) pdarray[source]¶
Find the bit parity (XOR of all bits) for each integer in an array.
- Parameters:
pda (pdarray, int64, uint64, bigint) – Input array (must be integral).
- Returns:
parity – The parity of each element: 0 if even number of bits set, 1 if odd.
- Return type:
- Raises:
TypeError – If input array is not int64, uint64, or bigint
Examples
>>> A = ak.arange(10) >>> ak.parity(A) array([0, 1, 1, 0, 1, 0, 0, 1, 1, 0])
- class arkouda.pdarray(name: str, mydtype: numpy.dtype | str, size: arkouda.dtypes.int_scalars, ndim: arkouda.dtypes.int_scalars, shape: Sequence[int], itemsize: arkouda.dtypes.int_scalars, max_bits: int | None = None)[source]¶
The basic arkouda array class. This class contains only the attributies of the array; the data resides on the arkouda server. When a server operation results in a new array, arkouda will create a pdarray instance that points to the array data on the server. As such, the user should not initialize pdarray instances directly.
- name¶
The server-side identifier for the array
- Type:
str
- dtype¶
The element type of the array
- Type:
dtype
- size¶
The number of elements in the array
- Type:
int_scalars
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
int_scalars
- shape¶
A list or tuple containing the sizes of each dimension of the array
- Type:
Sequence[int]
- itemsize¶
The size in bytes of each element
- Type:
int_scalars
- property max_bits¶
- property nbytes¶
The size of the pdarray in bytes.
- Returns:
The size of the pdarray in bytes.
- Return type:
int
- BinOps¶
- OpEqOps¶
- objType = 'pdarray'¶
- argmax() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array max value.
- argmaxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Finds the indices corresponding to the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- argmin() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array min value
- argmink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- astype(dtype) pdarray[source]¶
Cast values of pdarray to provided dtype
- Parameters:
dtype (np.dtype or str) – Dtype to cast to
- Returns:
An arkouda pdarray with values converted to the specified data type
- Return type:
ak.pdarray
Notes
This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.
- static attach(user_defined_name: str) pdarray[source]¶
class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which array was registered under
- Returns:
pdarray which is bound to the corresponding server side component which was registered with user_defined_name
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- bigint_to_uint_arrays() List[pdarray][source]¶
Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Returns:
A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Return type:
List[pdarrays]
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> a = ak.arange(2**64, 2**64 + 5) >>> a array(["18446744073709551616" "18446744073709551617" "18446744073709551618" "18446744073709551619" "18446744073709551620"])
>>> a.bigint_to_uint_arrays() [array([1 1 1 1 1]), array([0 1 2 3 4])]
- corr(y: pdarray) numpy.float64[source]¶
Compute the correlation between self and y using pearson correlation coefficient.
- Parameters:
y (pdarray) – Other pdarray used to calculate correlation
- Returns:
The scalar correlation of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- cov(y: pdarray) numpy.float64[source]¶
Compute the covariance between self and y.
- Parameters:
y (pdarray) – Other pdarray used to calculate covariance
- Returns:
The scalar covariance of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- fill(value: arkouda.dtypes.numeric_scalars) None[source]¶
Fill the array (in place) with a constant value.
- Parameters:
value (numeric_scalars)
- Raises:
TypeError – Raised if value is not an int, int64, float, or float64
- format_other(other) str[source]¶
Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.
- Parameters:
other (object) – The scalar to be cast to the pdarray.dtype
- Return type:
string representation of np.dtype corresponding to the other parameter
- Raises:
TypeError – Raised if the other parameter cannot be converted to Numpy dtype
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry
- Parameters:
None
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RuntimeError – Raised if there’s a server-side error thrown
Note
This will return True if the object is registered itself or as a component of another object
- is_sorted() numpy.bool_[source]¶
Return True iff the array is monotonically non-decreasing.
- Parameters:
None
- Returns:
Indicates if the array is monotonically non-decreasing
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- maxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- mink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- popcount() pdarray[source]¶
Find the population (number of bits set) in each element. See ak.popcount.
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- prod() numpy.float64[source]¶
Return the product of all elements in the array. Return value is always a np.float64 or np.int64.
- register(user_defined_name: str) pdarray[source]¶
Register this pdarray with a user defined name in the arkouda server so it can be attached to later using pdarray.attach() This is an in-place operation, registering a pdarray more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one pdarray at a time.
- Parameters:
user_defined_name (str) – user defined name array is to be registered under
- Returns:
The same pdarray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different pdarrays with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.
See also
attach,unregister,is_registered,list_registry,unregister_pdarray_by_nameNotes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- reshape(*shape, order='row_major')[source]¶
Gives a new shape to an array without changing its data.
- Parameters:
shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
order (str {'row_major' | 'C' | 'column_major' | 'F'}) – Read the elements of the pdarray in this index order By default, read the elements in row_major or C-like order where the last index changes the fastest If ‘column_major’ or ‘F’, read the elements in column_major or Fortran-like order where the first index changes the fastest
- Returns:
An arrayview object with the data from the array but with the new shape
- Return type:
- save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') str[source]¶
DEPRECATED Save the pdarray to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. HDF5 support single files, in which case the file name will only be that provided. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string
See also
save_all,load,read,to_parquet,to_hdfNotes
The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. Previously all files saved in Parquet format were saved with a.parquetfile extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.save('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.save('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving with an extension (Parquet) >>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet') Saves the array in numLocales Parquet files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- slice_bits(low, high) pdarray[source]¶
Returns a pdarray containing only bits from low to high of self.
This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)
- Parameters:
low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)
- Returns:
A new pdarray containing the bits of self from low to high
- Return type:
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> p = ak.array([2**65 + (2**64 - 1)]) >>> bin(p[0]) '0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0]) '0b10'
- std(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the standard deviation. See
arkouda.stdfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
The scalar standard deviation of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶
Write pdarray to CSV file(s). File will contain a single column with the pdarray data. All CSV Files written by Arkouda include a header denoting data types of the columns.
- prefix_path: str
The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
- dataset: str
Column name to save the pdarray under. Defaults to “array”.
- col_delim: str
Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
- overwrite: bool
Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
str reponse message
- ValueError
Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
- RuntimeError
Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
- TypeError
Raised if we receive an unknown arkouda_type returned from the server
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (`
`) at this time.
- to_cuda()[source]¶
Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.
- Returns:
A Numba ndarray with the same attributes and data as the pdarray; on GPU
- Return type:
numba.DeviceNDArray
- Raises:
ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_cuda() array([0, 1, 2, 3, 4])
>>> type(a.to_cuda()) numpy.devicendarray
- to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') str[source]¶
Save the pdarray to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_hdf('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_hdf('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving to a single file >>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single') Saves the array in to single hdf5 file on the root node. ``cwd/path/name_prefix.hdf5``
- to_list() List[source]¶
Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A list with the same data as the pdarray
- Return type:
list
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_list() [0, 1, 2, 3, 4]
>>> type(a.to_list()) list
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A numpy ndarray with the same attributes and data as the pdarray
- Return type:
np.ndarray
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_ndarray() array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray()) numpy.ndarray
- to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) str[source]¶
Save the pdarray to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_parquet('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_parqet('path/prefix.parquet', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a pdarray to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unregister() None[source]¶
Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not find the internal name/symbol to remove
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
- value_counts()[source]¶
Count the occurrences of the unique values of self.
- Returns:
unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs
Examples
>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts() (array([0, 2, 4]), array([3, 2, 1]))
- var(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the variance. See
arkouda.varfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
The scalar variance of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
ValueError – Raised if the ddof >= pdarray size
RuntimeError – Raised if there’s a server-side error thrown
- class arkouda.pdarray(name: str, mydtype: numpy.dtype | str, size: arkouda.dtypes.int_scalars, ndim: arkouda.dtypes.int_scalars, shape: Sequence[int], itemsize: arkouda.dtypes.int_scalars, max_bits: int | None = None)[source]¶
The basic arkouda array class. This class contains only the attributies of the array; the data resides on the arkouda server. When a server operation results in a new array, arkouda will create a pdarray instance that points to the array data on the server. As such, the user should not initialize pdarray instances directly.
- name¶
The server-side identifier for the array
- Type:
str
- dtype¶
The element type of the array
- Type:
dtype
- size¶
The number of elements in the array
- Type:
int_scalars
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
int_scalars
- shape¶
A list or tuple containing the sizes of each dimension of the array
- Type:
Sequence[int]
- itemsize¶
The size in bytes of each element
- Type:
int_scalars
- property max_bits¶
- property nbytes¶
The size of the pdarray in bytes.
- Returns:
The size of the pdarray in bytes.
- Return type:
int
- BinOps¶
- OpEqOps¶
- objType = 'pdarray'¶
- argmax() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array max value.
- argmaxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Finds the indices corresponding to the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- argmin() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array min value
- argmink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- astype(dtype) pdarray[source]¶
Cast values of pdarray to provided dtype
- Parameters:
dtype (np.dtype or str) – Dtype to cast to
- Returns:
An arkouda pdarray with values converted to the specified data type
- Return type:
ak.pdarray
Notes
This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.
- static attach(user_defined_name: str) pdarray[source]¶
class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which array was registered under
- Returns:
pdarray which is bound to the corresponding server side component which was registered with user_defined_name
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- bigint_to_uint_arrays() List[pdarray][source]¶
Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Returns:
A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Return type:
List[pdarrays]
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> a = ak.arange(2**64, 2**64 + 5) >>> a array(["18446744073709551616" "18446744073709551617" "18446744073709551618" "18446744073709551619" "18446744073709551620"])
>>> a.bigint_to_uint_arrays() [array([1 1 1 1 1]), array([0 1 2 3 4])]
- corr(y: pdarray) numpy.float64[source]¶
Compute the correlation between self and y using pearson correlation coefficient.
- Parameters:
y (pdarray) – Other pdarray used to calculate correlation
- Returns:
The scalar correlation of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- cov(y: pdarray) numpy.float64[source]¶
Compute the covariance between self and y.
- Parameters:
y (pdarray) – Other pdarray used to calculate covariance
- Returns:
The scalar covariance of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- fill(value: arkouda.dtypes.numeric_scalars) None[source]¶
Fill the array (in place) with a constant value.
- Parameters:
value (numeric_scalars)
- Raises:
TypeError – Raised if value is not an int, int64, float, or float64
- format_other(other) str[source]¶
Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.
- Parameters:
other (object) – The scalar to be cast to the pdarray.dtype
- Return type:
string representation of np.dtype corresponding to the other parameter
- Raises:
TypeError – Raised if the other parameter cannot be converted to Numpy dtype
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry
- Parameters:
None
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RuntimeError – Raised if there’s a server-side error thrown
Note
This will return True if the object is registered itself or as a component of another object
- is_sorted() numpy.bool_[source]¶
Return True iff the array is monotonically non-decreasing.
- Parameters:
None
- Returns:
Indicates if the array is monotonically non-decreasing
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- maxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- mink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- popcount() pdarray[source]¶
Find the population (number of bits set) in each element. See ak.popcount.
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- prod() numpy.float64[source]¶
Return the product of all elements in the array. Return value is always a np.float64 or np.int64.
- register(user_defined_name: str) pdarray[source]¶
Register this pdarray with a user defined name in the arkouda server so it can be attached to later using pdarray.attach() This is an in-place operation, registering a pdarray more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one pdarray at a time.
- Parameters:
user_defined_name (str) – user defined name array is to be registered under
- Returns:
The same pdarray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different pdarrays with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.
See also
attach,unregister,is_registered,list_registry,unregister_pdarray_by_nameNotes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- reshape(*shape, order='row_major')[source]¶
Gives a new shape to an array without changing its data.
- Parameters:
shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
order (str {'row_major' | 'C' | 'column_major' | 'F'}) – Read the elements of the pdarray in this index order By default, read the elements in row_major or C-like order where the last index changes the fastest If ‘column_major’ or ‘F’, read the elements in column_major or Fortran-like order where the first index changes the fastest
- Returns:
An arrayview object with the data from the array but with the new shape
- Return type:
- save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') str[source]¶
DEPRECATED Save the pdarray to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. HDF5 support single files, in which case the file name will only be that provided. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string
See also
save_all,load,read,to_parquet,to_hdfNotes
The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. Previously all files saved in Parquet format were saved with a.parquetfile extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.save('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.save('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving with an extension (Parquet) >>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet') Saves the array in numLocales Parquet files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- slice_bits(low, high) pdarray[source]¶
Returns a pdarray containing only bits from low to high of self.
This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)
- Parameters:
low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)
- Returns:
A new pdarray containing the bits of self from low to high
- Return type:
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> p = ak.array([2**65 + (2**64 - 1)]) >>> bin(p[0]) '0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0]) '0b10'
- std(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the standard deviation. See
arkouda.stdfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
The scalar standard deviation of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶
Write pdarray to CSV file(s). File will contain a single column with the pdarray data. All CSV Files written by Arkouda include a header denoting data types of the columns.
- prefix_path: str
The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
- dataset: str
Column name to save the pdarray under. Defaults to “array”.
- col_delim: str
Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
- overwrite: bool
Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
str reponse message
- ValueError
Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
- RuntimeError
Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
- TypeError
Raised if we receive an unknown arkouda_type returned from the server
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (`
`) at this time.
- to_cuda()[source]¶
Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.
- Returns:
A Numba ndarray with the same attributes and data as the pdarray; on GPU
- Return type:
numba.DeviceNDArray
- Raises:
ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_cuda() array([0, 1, 2, 3, 4])
>>> type(a.to_cuda()) numpy.devicendarray
- to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') str[source]¶
Save the pdarray to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_hdf('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_hdf('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving to a single file >>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single') Saves the array in to single hdf5 file on the root node. ``cwd/path/name_prefix.hdf5``
- to_list() List[source]¶
Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A list with the same data as the pdarray
- Return type:
list
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_list() [0, 1, 2, 3, 4]
>>> type(a.to_list()) list
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A numpy ndarray with the same attributes and data as the pdarray
- Return type:
np.ndarray
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_ndarray() array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray()) numpy.ndarray
- to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) str[source]¶
Save the pdarray to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_parquet('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_parqet('path/prefix.parquet', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a pdarray to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unregister() None[source]¶
Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not find the internal name/symbol to remove
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
- value_counts()[source]¶
Count the occurrences of the unique values of self.
- Returns:
unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs
Examples
>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts() (array([0, 2, 4]), array([3, 2, 1]))
- var(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the variance. See
arkouda.varfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
The scalar variance of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
ValueError – Raised if the ddof >= pdarray size
RuntimeError – Raised if there’s a server-side error thrown
- class arkouda.pdarray(name: str, mydtype: numpy.dtype | str, size: arkouda.dtypes.int_scalars, ndim: arkouda.dtypes.int_scalars, shape: Sequence[int], itemsize: arkouda.dtypes.int_scalars, max_bits: int | None = None)[source]¶
The basic arkouda array class. This class contains only the attributies of the array; the data resides on the arkouda server. When a server operation results in a new array, arkouda will create a pdarray instance that points to the array data on the server. As such, the user should not initialize pdarray instances directly.
- name¶
The server-side identifier for the array
- Type:
str
- dtype¶
The element type of the array
- Type:
dtype
- size¶
The number of elements in the array
- Type:
int_scalars
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
int_scalars
- shape¶
A list or tuple containing the sizes of each dimension of the array
- Type:
Sequence[int]
- itemsize¶
The size in bytes of each element
- Type:
int_scalars
- property max_bits¶
- property nbytes¶
The size of the pdarray in bytes.
- Returns:
The size of the pdarray in bytes.
- Return type:
int
- BinOps¶
- OpEqOps¶
- objType = 'pdarray'¶
- argmax() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array max value.
- argmaxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Finds the indices corresponding to the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- argmin() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array min value
- argmink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- astype(dtype) pdarray[source]¶
Cast values of pdarray to provided dtype
- Parameters:
dtype (np.dtype or str) – Dtype to cast to
- Returns:
An arkouda pdarray with values converted to the specified data type
- Return type:
ak.pdarray
Notes
This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.
- static attach(user_defined_name: str) pdarray[source]¶
class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which array was registered under
- Returns:
pdarray which is bound to the corresponding server side component which was registered with user_defined_name
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- bigint_to_uint_arrays() List[pdarray][source]¶
Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Returns:
A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Return type:
List[pdarrays]
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> a = ak.arange(2**64, 2**64 + 5) >>> a array(["18446744073709551616" "18446744073709551617" "18446744073709551618" "18446744073709551619" "18446744073709551620"])
>>> a.bigint_to_uint_arrays() [array([1 1 1 1 1]), array([0 1 2 3 4])]
- corr(y: pdarray) numpy.float64[source]¶
Compute the correlation between self and y using pearson correlation coefficient.
- Parameters:
y (pdarray) – Other pdarray used to calculate correlation
- Returns:
The scalar correlation of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- cov(y: pdarray) numpy.float64[source]¶
Compute the covariance between self and y.
- Parameters:
y (pdarray) – Other pdarray used to calculate covariance
- Returns:
The scalar covariance of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- fill(value: arkouda.dtypes.numeric_scalars) None[source]¶
Fill the array (in place) with a constant value.
- Parameters:
value (numeric_scalars)
- Raises:
TypeError – Raised if value is not an int, int64, float, or float64
- format_other(other) str[source]¶
Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.
- Parameters:
other (object) – The scalar to be cast to the pdarray.dtype
- Return type:
string representation of np.dtype corresponding to the other parameter
- Raises:
TypeError – Raised if the other parameter cannot be converted to Numpy dtype
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry
- Parameters:
None
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RuntimeError – Raised if there’s a server-side error thrown
Note
This will return True if the object is registered itself or as a component of another object
- is_sorted() numpy.bool_[source]¶
Return True iff the array is monotonically non-decreasing.
- Parameters:
None
- Returns:
Indicates if the array is monotonically non-decreasing
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- maxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- mink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- popcount() pdarray[source]¶
Find the population (number of bits set) in each element. See ak.popcount.
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- prod() numpy.float64[source]¶
Return the product of all elements in the array. Return value is always a np.float64 or np.int64.
- register(user_defined_name: str) pdarray[source]¶
Register this pdarray with a user defined name in the arkouda server so it can be attached to later using pdarray.attach() This is an in-place operation, registering a pdarray more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one pdarray at a time.
- Parameters:
user_defined_name (str) – user defined name array is to be registered under
- Returns:
The same pdarray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different pdarrays with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.
See also
attach,unregister,is_registered,list_registry,unregister_pdarray_by_nameNotes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- reshape(*shape, order='row_major')[source]¶
Gives a new shape to an array without changing its data.
- Parameters:
shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
order (str {'row_major' | 'C' | 'column_major' | 'F'}) – Read the elements of the pdarray in this index order By default, read the elements in row_major or C-like order where the last index changes the fastest If ‘column_major’ or ‘F’, read the elements in column_major or Fortran-like order where the first index changes the fastest
- Returns:
An arrayview object with the data from the array but with the new shape
- Return type:
- save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') str[source]¶
DEPRECATED Save the pdarray to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. HDF5 support single files, in which case the file name will only be that provided. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string
See also
save_all,load,read,to_parquet,to_hdfNotes
The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. Previously all files saved in Parquet format were saved with a.parquetfile extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.save('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.save('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving with an extension (Parquet) >>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet') Saves the array in numLocales Parquet files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- slice_bits(low, high) pdarray[source]¶
Returns a pdarray containing only bits from low to high of self.
This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)
- Parameters:
low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)
- Returns:
A new pdarray containing the bits of self from low to high
- Return type:
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> p = ak.array([2**65 + (2**64 - 1)]) >>> bin(p[0]) '0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0]) '0b10'
- std(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the standard deviation. See
arkouda.stdfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
The scalar standard deviation of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶
Write pdarray to CSV file(s). File will contain a single column with the pdarray data. All CSV Files written by Arkouda include a header denoting data types of the columns.
- prefix_path: str
The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
- dataset: str
Column name to save the pdarray under. Defaults to “array”.
- col_delim: str
Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
- overwrite: bool
Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
str reponse message
- ValueError
Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
- RuntimeError
Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
- TypeError
Raised if we receive an unknown arkouda_type returned from the server
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (`
`) at this time.
- to_cuda()[source]¶
Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.
- Returns:
A Numba ndarray with the same attributes and data as the pdarray; on GPU
- Return type:
numba.DeviceNDArray
- Raises:
ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_cuda() array([0, 1, 2, 3, 4])
>>> type(a.to_cuda()) numpy.devicendarray
- to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') str[source]¶
Save the pdarray to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_hdf('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_hdf('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving to a single file >>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single') Saves the array in to single hdf5 file on the root node. ``cwd/path/name_prefix.hdf5``
- to_list() List[source]¶
Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A list with the same data as the pdarray
- Return type:
list
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_list() [0, 1, 2, 3, 4]
>>> type(a.to_list()) list
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A numpy ndarray with the same attributes and data as the pdarray
- Return type:
np.ndarray
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_ndarray() array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray()) numpy.ndarray
- to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) str[source]¶
Save the pdarray to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_parquet('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_parqet('path/prefix.parquet', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a pdarray to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unregister() None[source]¶
Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not find the internal name/symbol to remove
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
- value_counts()[source]¶
Count the occurrences of the unique values of self.
- Returns:
unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs
Examples
>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts() (array([0, 2, 4]), array([3, 2, 1]))
- var(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the variance. See
arkouda.varfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
The scalar variance of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
ValueError – Raised if the ddof >= pdarray size
RuntimeError – Raised if there’s a server-side error thrown
- class arkouda.pdarray(name: str, mydtype: numpy.dtype | str, size: arkouda.dtypes.int_scalars, ndim: arkouda.dtypes.int_scalars, shape: Sequence[int], itemsize: arkouda.dtypes.int_scalars, max_bits: int | None = None)[source]¶
The basic arkouda array class. This class contains only the attributies of the array; the data resides on the arkouda server. When a server operation results in a new array, arkouda will create a pdarray instance that points to the array data on the server. As such, the user should not initialize pdarray instances directly.
- name¶
The server-side identifier for the array
- Type:
str
- dtype¶
The element type of the array
- Type:
dtype
- size¶
The number of elements in the array
- Type:
int_scalars
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
int_scalars
- shape¶
A list or tuple containing the sizes of each dimension of the array
- Type:
Sequence[int]
- itemsize¶
The size in bytes of each element
- Type:
int_scalars
- property max_bits¶
- property nbytes¶
The size of the pdarray in bytes.
- Returns:
The size of the pdarray in bytes.
- Return type:
int
- BinOps¶
- OpEqOps¶
- objType = 'pdarray'¶
- argmax() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array max value.
- argmaxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Finds the indices corresponding to the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- argmin() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array min value
- argmink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- astype(dtype) pdarray[source]¶
Cast values of pdarray to provided dtype
- Parameters:
dtype (np.dtype or str) – Dtype to cast to
- Returns:
An arkouda pdarray with values converted to the specified data type
- Return type:
ak.pdarray
Notes
This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.
- static attach(user_defined_name: str) pdarray[source]¶
class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which array was registered under
- Returns:
pdarray which is bound to the corresponding server side component which was registered with user_defined_name
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- bigint_to_uint_arrays() List[pdarray][source]¶
Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Returns:
A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Return type:
List[pdarrays]
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> a = ak.arange(2**64, 2**64 + 5) >>> a array(["18446744073709551616" "18446744073709551617" "18446744073709551618" "18446744073709551619" "18446744073709551620"])
>>> a.bigint_to_uint_arrays() [array([1 1 1 1 1]), array([0 1 2 3 4])]
- corr(y: pdarray) numpy.float64[source]¶
Compute the correlation between self and y using pearson correlation coefficient.
- Parameters:
y (pdarray) – Other pdarray used to calculate correlation
- Returns:
The scalar correlation of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- cov(y: pdarray) numpy.float64[source]¶
Compute the covariance between self and y.
- Parameters:
y (pdarray) – Other pdarray used to calculate covariance
- Returns:
The scalar covariance of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- fill(value: arkouda.dtypes.numeric_scalars) None[source]¶
Fill the array (in place) with a constant value.
- Parameters:
value (numeric_scalars)
- Raises:
TypeError – Raised if value is not an int, int64, float, or float64
- format_other(other) str[source]¶
Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.
- Parameters:
other (object) – The scalar to be cast to the pdarray.dtype
- Return type:
string representation of np.dtype corresponding to the other parameter
- Raises:
TypeError – Raised if the other parameter cannot be converted to Numpy dtype
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry
- Parameters:
None
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RuntimeError – Raised if there’s a server-side error thrown
Note
This will return True if the object is registered itself or as a component of another object
- is_sorted() numpy.bool_[source]¶
Return True iff the array is monotonically non-decreasing.
- Parameters:
None
- Returns:
Indicates if the array is monotonically non-decreasing
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- maxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- mink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- popcount() pdarray[source]¶
Find the population (number of bits set) in each element. See ak.popcount.
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- prod() numpy.float64[source]¶
Return the product of all elements in the array. Return value is always a np.float64 or np.int64.
- register(user_defined_name: str) pdarray[source]¶
Register this pdarray with a user defined name in the arkouda server so it can be attached to later using pdarray.attach() This is an in-place operation, registering a pdarray more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one pdarray at a time.
- Parameters:
user_defined_name (str) – user defined name array is to be registered under
- Returns:
The same pdarray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different pdarrays with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.
See also
attach,unregister,is_registered,list_registry,unregister_pdarray_by_nameNotes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- reshape(*shape, order='row_major')[source]¶
Gives a new shape to an array without changing its data.
- Parameters:
shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
order (str {'row_major' | 'C' | 'column_major' | 'F'}) – Read the elements of the pdarray in this index order By default, read the elements in row_major or C-like order where the last index changes the fastest If ‘column_major’ or ‘F’, read the elements in column_major or Fortran-like order where the first index changes the fastest
- Returns:
An arrayview object with the data from the array but with the new shape
- Return type:
- save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') str[source]¶
DEPRECATED Save the pdarray to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. HDF5 support single files, in which case the file name will only be that provided. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string
See also
save_all,load,read,to_parquet,to_hdfNotes
The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. Previously all files saved in Parquet format were saved with a.parquetfile extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.save('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.save('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving with an extension (Parquet) >>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet') Saves the array in numLocales Parquet files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- slice_bits(low, high) pdarray[source]¶
Returns a pdarray containing only bits from low to high of self.
This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)
- Parameters:
low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)
- Returns:
A new pdarray containing the bits of self from low to high
- Return type:
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> p = ak.array([2**65 + (2**64 - 1)]) >>> bin(p[0]) '0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0]) '0b10'
- std(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the standard deviation. See
arkouda.stdfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
The scalar standard deviation of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶
Write pdarray to CSV file(s). File will contain a single column with the pdarray data. All CSV Files written by Arkouda include a header denoting data types of the columns.
- prefix_path: str
The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
- dataset: str
Column name to save the pdarray under. Defaults to “array”.
- col_delim: str
Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
- overwrite: bool
Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
str reponse message
- ValueError
Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
- RuntimeError
Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
- TypeError
Raised if we receive an unknown arkouda_type returned from the server
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (`
`) at this time.
- to_cuda()[source]¶
Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.
- Returns:
A Numba ndarray with the same attributes and data as the pdarray; on GPU
- Return type:
numba.DeviceNDArray
- Raises:
ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_cuda() array([0, 1, 2, 3, 4])
>>> type(a.to_cuda()) numpy.devicendarray
- to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') str[source]¶
Save the pdarray to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_hdf('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_hdf('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving to a single file >>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single') Saves the array in to single hdf5 file on the root node. ``cwd/path/name_prefix.hdf5``
- to_list() List[source]¶
Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A list with the same data as the pdarray
- Return type:
list
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_list() [0, 1, 2, 3, 4]
>>> type(a.to_list()) list
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A numpy ndarray with the same attributes and data as the pdarray
- Return type:
np.ndarray
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_ndarray() array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray()) numpy.ndarray
- to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) str[source]¶
Save the pdarray to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_parquet('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_parqet('path/prefix.parquet', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a pdarray to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unregister() None[source]¶
Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not find the internal name/symbol to remove
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
- value_counts()[source]¶
Count the occurrences of the unique values of self.
- Returns:
unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs
Examples
>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts() (array([0, 2, 4]), array([3, 2, 1]))
- var(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the variance. See
arkouda.varfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
The scalar variance of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
ValueError – Raised if the ddof >= pdarray size
RuntimeError – Raised if there’s a server-side error thrown
- class arkouda.pdarray(name: str, mydtype: numpy.dtype | str, size: arkouda.dtypes.int_scalars, ndim: arkouda.dtypes.int_scalars, shape: Sequence[int], itemsize: arkouda.dtypes.int_scalars, max_bits: int | None = None)[source]¶
The basic arkouda array class. This class contains only the attributies of the array; the data resides on the arkouda server. When a server operation results in a new array, arkouda will create a pdarray instance that points to the array data on the server. As such, the user should not initialize pdarray instances directly.
- name¶
The server-side identifier for the array
- Type:
str
- dtype¶
The element type of the array
- Type:
dtype
- size¶
The number of elements in the array
- Type:
int_scalars
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
int_scalars
- shape¶
A list or tuple containing the sizes of each dimension of the array
- Type:
Sequence[int]
- itemsize¶
The size in bytes of each element
- Type:
int_scalars
- property max_bits¶
- property nbytes¶
The size of the pdarray in bytes.
- Returns:
The size of the pdarray in bytes.
- Return type:
int
- BinOps¶
- OpEqOps¶
- objType = 'pdarray'¶
- argmax() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array max value.
- argmaxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Finds the indices corresponding to the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- argmin() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array min value
- argmink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- astype(dtype) pdarray[source]¶
Cast values of pdarray to provided dtype
- Parameters:
dtype (np.dtype or str) – Dtype to cast to
- Returns:
An arkouda pdarray with values converted to the specified data type
- Return type:
ak.pdarray
Notes
This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.
- static attach(user_defined_name: str) pdarray[source]¶
class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which array was registered under
- Returns:
pdarray which is bound to the corresponding server side component which was registered with user_defined_name
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- bigint_to_uint_arrays() List[pdarray][source]¶
Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Returns:
A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Return type:
List[pdarrays]
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> a = ak.arange(2**64, 2**64 + 5) >>> a array(["18446744073709551616" "18446744073709551617" "18446744073709551618" "18446744073709551619" "18446744073709551620"])
>>> a.bigint_to_uint_arrays() [array([1 1 1 1 1]), array([0 1 2 3 4])]
- corr(y: pdarray) numpy.float64[source]¶
Compute the correlation between self and y using pearson correlation coefficient.
- Parameters:
y (pdarray) – Other pdarray used to calculate correlation
- Returns:
The scalar correlation of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- cov(y: pdarray) numpy.float64[source]¶
Compute the covariance between self and y.
- Parameters:
y (pdarray) – Other pdarray used to calculate covariance
- Returns:
The scalar covariance of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- fill(value: arkouda.dtypes.numeric_scalars) None[source]¶
Fill the array (in place) with a constant value.
- Parameters:
value (numeric_scalars)
- Raises:
TypeError – Raised if value is not an int, int64, float, or float64
- format_other(other) str[source]¶
Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.
- Parameters:
other (object) – The scalar to be cast to the pdarray.dtype
- Return type:
string representation of np.dtype corresponding to the other parameter
- Raises:
TypeError – Raised if the other parameter cannot be converted to Numpy dtype
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry
- Parameters:
None
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RuntimeError – Raised if there’s a server-side error thrown
Note
This will return True if the object is registered itself or as a component of another object
- is_sorted() numpy.bool_[source]¶
Return True iff the array is monotonically non-decreasing.
- Parameters:
None
- Returns:
Indicates if the array is monotonically non-decreasing
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- maxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- mink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- popcount() pdarray[source]¶
Find the population (number of bits set) in each element. See ak.popcount.
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- prod() numpy.float64[source]¶
Return the product of all elements in the array. Return value is always a np.float64 or np.int64.
- register(user_defined_name: str) pdarray[source]¶
Register this pdarray with a user defined name in the arkouda server so it can be attached to later using pdarray.attach() This is an in-place operation, registering a pdarray more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one pdarray at a time.
- Parameters:
user_defined_name (str) – user defined name array is to be registered under
- Returns:
The same pdarray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different pdarrays with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.
See also
attach,unregister,is_registered,list_registry,unregister_pdarray_by_nameNotes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- reshape(*shape, order='row_major')[source]¶
Gives a new shape to an array without changing its data.
- Parameters:
shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
order (str {'row_major' | 'C' | 'column_major' | 'F'}) – Read the elements of the pdarray in this index order By default, read the elements in row_major or C-like order where the last index changes the fastest If ‘column_major’ or ‘F’, read the elements in column_major or Fortran-like order where the first index changes the fastest
- Returns:
An arrayview object with the data from the array but with the new shape
- Return type:
- save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') str[source]¶
DEPRECATED Save the pdarray to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. HDF5 support single files, in which case the file name will only be that provided. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string
See also
save_all,load,read,to_parquet,to_hdfNotes
The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. Previously all files saved in Parquet format were saved with a.parquetfile extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.save('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.save('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving with an extension (Parquet) >>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet') Saves the array in numLocales Parquet files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- slice_bits(low, high) pdarray[source]¶
Returns a pdarray containing only bits from low to high of self.
This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)
- Parameters:
low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)
- Returns:
A new pdarray containing the bits of self from low to high
- Return type:
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> p = ak.array([2**65 + (2**64 - 1)]) >>> bin(p[0]) '0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0]) '0b10'
- std(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the standard deviation. See
arkouda.stdfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
The scalar standard deviation of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶
Write pdarray to CSV file(s). File will contain a single column with the pdarray data. All CSV Files written by Arkouda include a header denoting data types of the columns.
- prefix_path: str
The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
- dataset: str
Column name to save the pdarray under. Defaults to “array”.
- col_delim: str
Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
- overwrite: bool
Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
str reponse message
- ValueError
Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
- RuntimeError
Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
- TypeError
Raised if we receive an unknown arkouda_type returned from the server
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (`
`) at this time.
- to_cuda()[source]¶
Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.
- Returns:
A Numba ndarray with the same attributes and data as the pdarray; on GPU
- Return type:
numba.DeviceNDArray
- Raises:
ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_cuda() array([0, 1, 2, 3, 4])
>>> type(a.to_cuda()) numpy.devicendarray
- to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') str[source]¶
Save the pdarray to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_hdf('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_hdf('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving to a single file >>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single') Saves the array in to single hdf5 file on the root node. ``cwd/path/name_prefix.hdf5``
- to_list() List[source]¶
Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A list with the same data as the pdarray
- Return type:
list
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_list() [0, 1, 2, 3, 4]
>>> type(a.to_list()) list
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A numpy ndarray with the same attributes and data as the pdarray
- Return type:
np.ndarray
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_ndarray() array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray()) numpy.ndarray
- to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) str[source]¶
Save the pdarray to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_parquet('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_parqet('path/prefix.parquet', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a pdarray to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unregister() None[source]¶
Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not find the internal name/symbol to remove
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
- value_counts()[source]¶
Count the occurrences of the unique values of self.
- Returns:
unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs
Examples
>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts() (array([0, 2, 4]), array([3, 2, 1]))
- var(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the variance. See
arkouda.varfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
The scalar variance of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
ValueError – Raised if the ddof >= pdarray size
RuntimeError – Raised if there’s a server-side error thrown
- class arkouda.pdarray(name: str, mydtype: numpy.dtype | str, size: arkouda.dtypes.int_scalars, ndim: arkouda.dtypes.int_scalars, shape: Sequence[int], itemsize: arkouda.dtypes.int_scalars, max_bits: int | None = None)[source]¶
The basic arkouda array class. This class contains only the attributies of the array; the data resides on the arkouda server. When a server operation results in a new array, arkouda will create a pdarray instance that points to the array data on the server. As such, the user should not initialize pdarray instances directly.
- name¶
The server-side identifier for the array
- Type:
str
- dtype¶
The element type of the array
- Type:
dtype
- size¶
The number of elements in the array
- Type:
int_scalars
- ndim¶
The rank of the array (currently only rank 1 arrays supported)
- Type:
int_scalars
- shape¶
A list or tuple containing the sizes of each dimension of the array
- Type:
Sequence[int]
- itemsize¶
The size in bytes of each element
- Type:
int_scalars
- property max_bits¶
- property nbytes¶
The size of the pdarray in bytes.
- Returns:
The size of the pdarray in bytes.
- Return type:
int
- BinOps¶
- OpEqOps¶
- objType = 'pdarray'¶
- argmax() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array max value.
- argmaxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Finds the indices corresponding to the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values, sorted
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- argmin() numpy.int64 | numpy.uint64[source]¶
Return the index of the first occurrence of the array min value
- argmink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
Indices corresponding to the maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- astype(dtype) pdarray[source]¶
Cast values of pdarray to provided dtype
- Parameters:
dtype (np.dtype or str) – Dtype to cast to
- Returns:
An arkouda pdarray with values converted to the specified data type
- Return type:
ak.pdarray
Notes
This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.
- static attach(user_defined_name: str) pdarray[source]¶
class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()
- Parameters:
user_defined_name (str) – user defined name which array was registered under
- Returns:
pdarray which is bound to the corresponding server side component which was registered with user_defined_name
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- bigint_to_uint_arrays() List[pdarray][source]¶
Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Returns:
A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
- Return type:
List[pdarrays]
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> a = ak.arange(2**64, 2**64 + 5) >>> a array(["18446744073709551616" "18446744073709551617" "18446744073709551618" "18446744073709551619" "18446744073709551620"])
>>> a.bigint_to_uint_arrays() [array([1 1 1 1 1]), array([0 1 2 3 4])]
- corr(y: pdarray) numpy.float64[source]¶
Compute the correlation between self and y using pearson correlation coefficient.
- Parameters:
y (pdarray) – Other pdarray used to calculate correlation
- Returns:
The scalar correlation of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- cov(y: pdarray) numpy.float64[source]¶
Compute the covariance between self and y.
- Parameters:
y (pdarray) – Other pdarray used to calculate covariance
- Returns:
The scalar covariance of the two arrays
- Return type:
np.float64
- Raises:
TypeError – Raised if y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- fill(value: arkouda.dtypes.numeric_scalars) None[source]¶
Fill the array (in place) with a constant value.
- Parameters:
value (numeric_scalars)
- Raises:
TypeError – Raised if value is not an int, int64, float, or float64
- format_other(other) str[source]¶
Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.
- Parameters:
other (object) – The scalar to be cast to the pdarray.dtype
- Return type:
string representation of np.dtype corresponding to the other parameter
- Raises:
TypeError – Raised if the other parameter cannot be converted to Numpy dtype
- info() str[source]¶
Returns a JSON formatted string containing information about all components of self
- Parameters:
None
- Returns:
JSON string containing information about all components of self
- Return type:
str
- is_registered() numpy.bool_[source]¶
Return True iff the object is contained in the registry
- Parameters:
None
- Returns:
Indicates if the object is contained in the registry
- Return type:
bool
- Raises:
RuntimeError – Raised if there’s a server-side error thrown
Note
This will return True if the object is registered itself or as a component of another object
- is_sorted() numpy.bool_[source]¶
Return True iff the array is monotonically non-decreasing.
- Parameters:
None
- Returns:
Indicates if the array is monotonically non-decreasing
- Return type:
bool
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- maxk(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the maximum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- mink(k: arkouda.dtypes.int_scalars) pdarray[source]¶
Compute the minimum “k” values.
- Parameters:
k (int_scalars) – The desired count of maximum values to be returned by the output.
- Returns:
The maximum k values from pda
- Return type:
pdarray, int
- Raises:
TypeError – Raised if pda is not a pdarray
- popcount() pdarray[source]¶
Find the population (number of bits set) in each element. See ak.popcount.
- pretty_print_info() None[source]¶
Prints information about all components of self in a human readable format
- Parameters:
None
- Return type:
None
- prod() numpy.float64[source]¶
Return the product of all elements in the array. Return value is always a np.float64 or np.int64.
- register(user_defined_name: str) pdarray[source]¶
Register this pdarray with a user defined name in the arkouda server so it can be attached to later using pdarray.attach() This is an in-place operation, registering a pdarray more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one pdarray at a time.
- Parameters:
user_defined_name (str) – user defined name array is to be registered under
- Returns:
The same pdarray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different pdarrays with the same name.
- Return type:
- Raises:
TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.
See also
attach,unregister,is_registered,list_registry,unregister_pdarray_by_nameNotes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- reshape(*shape, order='row_major')[source]¶
Gives a new shape to an array without changing its data.
- Parameters:
shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
order (str {'row_major' | 'C' | 'column_major' | 'F'}) – Read the elements of the pdarray in this index order By default, read the elements in row_major or C-like order where the last index changes the fastest If ‘column_major’ or ‘F’, read the elements in column_major or Fortran-like order where the first index changes the fastest
- Returns:
An arrayview object with the data from the array but with the new shape
- Return type:
- save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') str[source]¶
DEPRECATED Save the pdarray to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. HDF5 support single files, in which case the file name will only be that provided. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string
See also
save_all,load,read,to_parquet,to_hdfNotes
The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. Previously all files saved in Parquet format were saved with a.parquetfile extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.save('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.save('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving with an extension (Parquet) >>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet') Saves the array in numLocales Parquet files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- slice_bits(low, high) pdarray[source]¶
Returns a pdarray containing only bits from low to high of self.
This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)
- Parameters:
low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)
- Returns:
A new pdarray containing the bits of self from low to high
- Return type:
- Raises:
RuntimeError – Raised if there is a server-side error thrown
Examples
>>> p = ak.array([2**65 + (2**64 - 1)]) >>> bin(p[0]) '0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0]) '0b10'
- std(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the standard deviation. See
arkouda.stdfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
The scalar standard deviation of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶
Write pdarray to CSV file(s). File will contain a single column with the pdarray data. All CSV Files written by Arkouda include a header denoting data types of the columns.
- prefix_path: str
The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
- dataset: str
Column name to save the pdarray under. Defaults to “array”.
- col_delim: str
Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
- overwrite: bool
Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
str reponse message
- ValueError
Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
- RuntimeError
Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
- TypeError
Raised if we receive an unknown arkouda_type returned from the server
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (`
`) at this time.
- to_cuda()[source]¶
Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.
- Returns:
A Numba ndarray with the same attributes and data as the pdarray; on GPU
- Return type:
numba.DeviceNDArray
- Raises:
ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_cuda() array([0, 1, 2, 3, 4])
>>> type(a.to_cuda()) numpy.devicendarray
- to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') str[source]¶
Save the pdarray to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_hdf('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_hdf('path/prefix.h5', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number >>> # Saving to a single file >>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single') Saves the array in to single hdf5 file on the root node. ``cwd/path/name_prefix.hdf5``
- to_list() List[source]¶
Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A list with the same data as the pdarray
- Return type:
list
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.See also
Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_list() [0, 1, 2, 3, 4]
>>> type(a.to_list()) list
- to_ndarray() numpy.ndarray[source]¶
Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.
- Returns:
A numpy ndarray with the same attributes and data as the pdarray
- Return type:
np.ndarray
- Raises:
RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes
Notes
The number of bytes in the array cannot exceed
client.maxTransferBytes, otherwise aRuntimeErrorwill be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.Examples
>>> a = ak.arange(0, 5, 1) >>> a.to_ndarray() array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray()) numpy.ndarray
- to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) str[source]¶
Save the pdarray to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
- Return type:
string message indicating result of save operation
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
The prefix_path must be visible to the arkouda server and the user must
have write permission. - Output files have names of the form
<prefix_path>_LOCALE<i>, where<i>ranges from 0 tonumLocalesfor file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, aRuntimeErrorwill result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.Examples
>>> a = ak.arange(25) >>> # Saving without an extension >>> a.to_parquet('path/prefix', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####`` >>> # Saving with an extension (HDF5) >>> a.to_parqet('path/prefix.parquet', dataset='array') Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number
- transfer(hostname: str, port: arkouda.dtypes.int_scalars)[source]¶
Sends a pdarray to a different Arkouda server
- Parameters:
hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().
- Return type:
A message indicating a complete transfer
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- unregister() None[source]¶
Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not find the internal name/symbol to remove
Notes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.pdarray.attach("my_zeros") >>> # ...other work... >>> b.unregister()
- update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶
Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added
- Parameters:
prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Return type:
str - success message if successful
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
- value_counts()[source]¶
Count the occurrences of the unique values of self.
- Returns:
unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs
Examples
>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts() (array([0, 2, 4]), array([3, 2, 1]))
- var(ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Compute the variance. See
arkouda.varfor details.- Parameters:
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
The scalar variance of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
ValueError – Raised if the ddof >= pdarray size
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.plot_dist(b, h, log=True, xlabel=None, newfig=True)[source]¶
Plot the distribution and cumulative distribution of histogram Data
- Parameters:
b (np.ndarray) – Bin edges
h (np.ndarray) – Histogram data
log (bool) – use log to scale y
xlabel (str) – Label for the x axis of the graph
newfig (bool) – Generate a new figure or not
Notes
This function does not return or display the plot. A user must have matplotlib imported in addition to arkouda to display plots. This could be updated to return the object or have a flag to show the resulting plots. See Examples Below.
Examples
>>> import arkouda as ak >>> from matplotlib import pyplot as plt >>> b, h = ak.histogram(ak.arange(10), 3) >>> ak.plot_dist(b, h.to_ndarray()) >>> # to show the plot >>> plt.show()
- arkouda.popcount(pda: pdarray) pdarray[source]¶
Find the population (number of bits set) for each integer in an array.
- Parameters:
pda (pdarray, int64, uint64, bigint) – Input array (must be integral).
- Returns:
population – The number of bits set (1) in each element
- Return type:
- Raises:
TypeError – If input array is not int64, uint64, or bigint
Examples
>>> A = ak.arange(10) >>> ak.popcount(A) array([0, 1, 1, 2, 1, 2, 2, 3, 1, 2])
- arkouda.power(pda: pdarray, pwr: int | float | pdarray, where: bool | pdarray = True) pdarray[source]¶
Raises an array to a power. If where is given, the operation will only take place in the positions where the where condition is True.
Note: Our implementation of the where argument deviates from numpy. The difference in behavior occurs at positions where the where argument contains a False. In numpy, these position will have uninitialized memory (which can contain anything and will vary between runs). We have chosen to instead return the value of the original array in these positions.
- Parameters:
pda (pdarray) – A pdarray of values that will be raised to a power (pwr)
pwr (integer, float, or pdarray) – The power(s) that pda is raised to
where (Boolean or pdarray) – This condition is broadcast over the input. At locations where the condition is True, the corresponding value will be raised to the respective power. Elsewhere, it will retain its original value. Default set to True.
- Returns:
pdarray
Returns a pdarray of values raised to a power, under the boolean where condition.
Examples
>>> a = ak.arange(5) >>> ak.power(a, 3) array([0, 1, 8, 27, 64]) >>> ak.power(a), 3, a % 2 == 0) array([0, 1, 8, 3, 64])
- arkouda.power_divergence(f_obs, f_exp=None, ddof=0, lambda_=None)[source]¶
Computes the power divergence statistic and p-value.
- Parameters:
f_obs (pdarray) – The observed frequency.
f_exp (pdarray, default = None) – The expected frequency.
ddof (int) – The delta degrees of freedom.
lambda (string, default = "pearson") –
The power in the Cressie-Read power divergence statistic. Allowed values: “pearson”, “log-likelihood”, “freeman-tukey”, “mod-log-likelihood”, “neyman”, “cressie-read”
Powers correspond as follows:
”pearson”: 1
”log-likelihood”: 0
”freeman-tukey”: -0.5
”mod-log-likelihood”: -1
”neyman”: -2
”cressie-read”: 2 / 3
- Return type:
arkouda.akstats.Power_divergenceResult
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda.akstats import power_divergence >>> x = ak.array([10, 20, 30, 10]) >>> y = ak.array([10, 30, 20, 10]) >>> power_divergence(x, y, lambda_="pearson") Power_divergenceResult(statistic=8.333333333333334, pvalue=0.03960235520756414) >>> power_divergence(x, y, lambda_="log-likelihood") Power_divergenceResult(statistic=8.109302162163285, pvalue=0.04380595350226197)
See also
scipy.stats.power_divergence,arkouda.akstats.chisquareNotes
This is a modified version of scipy.stats.power_divergence [2] in order to scale using arkouda pdarrays.
References
[1] “scipy.stats.power_divergence”, https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.power_divergence.html
[2] Scipy contributors (2024) scipy (Version v1.12.0) [Source code]. https://github.com/scipy/scipy
- arkouda.pretty_print_information(names: List[str] | str = RegisteredSymbols) None[source]¶
Prints verbose information for each object in names in a human readable format
- Parameters:
names (Union[List[str], str]) – names is either the name of an object or list of names of objects to retrieve info if names is ak.AllSymbols, retrieves info for all symbols in the symbol table if names is ak.RegisteredSymbols, retrieves info for all symbols in the registry
- Return type:
None
- Raises:
RuntimeError – Raised if a server-side error is thrown in the process of retrieving information about the objects in names
- arkouda.prod(pda: pdarray) numpy.float64[source]¶
Return the product of all elements in the array. Return value is always a np.float64 or np.int64
- Parameters:
pda (pdarray) – Values for which to calculate the product
- Returns:
The product calculated from the pda
- Return type:
numpy_scalars
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.rad2deg(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Converts angles element-wise from radians to degrees.
- Parameters:
- Returns:
A pdarray containing an angle converted to degrees, from radians, for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.randint(low: arkouda.dtypes.numeric_scalars, high: arkouda.dtypes.numeric_scalars, size: arkouda.dtypes.int_scalars | Tuple[arkouda.dtypes.int_scalars, Ellipsis] = 1, dtype=akint64, seed: arkouda.dtypes.int_scalars | None = None) arkouda.pdarrayclass.pdarray[source]¶
Generate a pdarray of randomized int, float, or bool values in a specified range bounded by the low and high parameters.
- Parameters:
low (numeric_scalars) – The low value (inclusive) of the range
high (numeric_scalars) – The high value (exclusive for int, inclusive for float) of the range
size (int_scalars) – The length of the returned array
dtype (Union[int64, float64, bool]) – The dtype of the array
seed (int_scalars, optional) – Index for where to pull the first returned value
- Returns:
Values drawn uniformly from the specified range having the desired dtype
- Return type:
- Raises:
TypeError – Raised if dtype.name not in DTypes, size is not an int, low or high is not an int or float, or seed is not an int
ValueError – Raised if size < 0 or if high < low
Notes
Calling randint with dtype=float64 will result in uniform non-integral floating point values.
Ranges >= 2**64 in size is undefined behavior because it exceeds the maximum value that can be stored on the server (uint64)
Examples
>>> ak.randint(0, 10, 5) array([5, 7, 4, 8, 3])
>>> ak.randint(0, 1, 3, dtype=ak.float64) array([0.92176432277231968, 0.083130710959903542, 0.68894208386667544])
>>> ak.randint(0, 1, 5, dtype=ak.bool) array([True, False, True, True, True])
>>> ak.randint(1, 5, 10, seed=2) array([4, 3, 1, 3, 4, 4, 2, 4, 3, 2])
>>> ak.randint(1, 5, 3, dtype=ak.float64, seed=2) array([2.9160772326374946, 4.353429832157099, 4.5392023718621486])
>>> ak.randint(1, 5, 10, dtype=ak.bool, seed=2) array([False, True, True, True, True, False, True, True, True, True])
- arkouda.randint(low: arkouda.dtypes.numeric_scalars, high: arkouda.dtypes.numeric_scalars, size: arkouda.dtypes.int_scalars | Tuple[arkouda.dtypes.int_scalars, Ellipsis] = 1, dtype=akint64, seed: arkouda.dtypes.int_scalars | None = None) arkouda.pdarrayclass.pdarray[source]¶
Generate a pdarray of randomized int, float, or bool values in a specified range bounded by the low and high parameters.
- Parameters:
low (numeric_scalars) – The low value (inclusive) of the range
high (numeric_scalars) – The high value (exclusive for int, inclusive for float) of the range
size (int_scalars) – The length of the returned array
dtype (Union[int64, float64, bool]) – The dtype of the array
seed (int_scalars, optional) – Seed to allow for reproducible random number generation
- Returns:
Values drawn uniformly from the specified range having the desired dtype
- Return type:
- Raises:
TypeError – Raised if dtype.name not in DTypes, size is not an int, low or high is not an int or float, or seed is not an int
ValueError – Raised if size < 0 or if high < low
Notes
Calling randint with dtype=float64 will result in uniform non-integral floating point values.
Ranges >= 2**64 in size is undefined behavior because it exceeds the maximum value that can be stored on the server (uint64)
Examples
>>> ak.randint(0, 10, 5) array([5, 7, 4, 8, 3])
>>> ak.randint(0, 1, 3, dtype=ak.float64) array([0.92176432277231968, 0.083130710959903542, 0.68894208386667544])
>>> ak.randint(0, 1, 5, dtype=ak.bool) array([True, False, True, True, True])
>>> ak.randint(1, 5, 10, seed=2) array([4, 3, 1, 3, 4, 4, 2, 4, 3, 2])
>>> ak.randint(1, 5, 3, dtype=ak.float64, seed=2) array([2.9160772326374946, 4.353429832157099, 4.5392023718621486])
>>> ak.randint(1, 5, 10, dtype=ak.bool, seed=2) array([False, True, True, True, True, False, True, True, True, True])
- arkouda.random_strings_lognormal(logmean: arkouda.dtypes.numeric_scalars, logstd: arkouda.dtypes.numeric_scalars, size: arkouda.dtypes.int_scalars, characters: str = 'uppercase', seed: arkouda.dtypes.int_scalars | None = None) arkouda.strings.Strings[source]¶
Generate random strings with log-normally distributed lengths and with characters drawn from a specified set.
- Parameters:
logmean (numeric_scalars) – The log-mean of the length distribution
logstd (numeric_scalars) – The log-standard-deviation of the length distribution
size (int_scalars) – The number of strings to generate
characters ((uppercase, lowercase, numeric, printable, binary)) – The set of characters to draw from
seed (int_scalars, optional) – Value used to initialize the random number generator
- Returns:
The Strings object encapsulating a pdarray of random strings
- Return type:
- Raises:
TypeError – Raised if logmean is neither a float nor a int, logstd is not a float, size is not an int, or if characters is not a str
ValueError – Raised if logstd <= 0 or size < 0
See also
Notes
The lengths of the generated strings are distributed $Lognormal(mu, sigma^2)$, with \(\mu = logmean\) and \(\sigma = logstd\). Thus, the strings will have an average length of \(exp(\mu + 0.5*\sigma^2)\), a minimum length of zero, and a heavy tail towards longer strings.
Examples
>>> ak.random_strings_lognormal(2, 0.25, 5, seed=1) array(['TVKJTE', 'ABOCORHFM', 'LUDMMGTB', 'KWOQNPHZ', 'VSXRRL'])
>>> ak.random_strings_lognormal(2, 0.25, 5, seed=1, characters='printable') array(['+5"fp-', ']3Q4kC~HF', '=F=`,IE!', 'DjkBa'9(', '5oZ1)='])
- arkouda.random_strings_uniform(minlen: arkouda.dtypes.int_scalars, maxlen: arkouda.dtypes.int_scalars, size: arkouda.dtypes.int_scalars, characters: str = 'uppercase', seed: None | arkouda.dtypes.int_scalars = None) arkouda.strings.Strings[source]¶
Generate random strings with lengths uniformly distributed between minlen and maxlen, and with characters drawn from a specified set.
- Parameters:
minlen (int_scalars) – The minimum allowed length of string
maxlen (int_scalars) – The maximum allowed length of string
size (int_scalars) – The number of strings to generate
characters ((uppercase, lowercase, numeric, printable, binary)) – The set of characters to draw from
seed (Union[None, int_scalars], optional) – Value used to initialize the random number generator
- Returns:
The array of random strings
- Return type:
- Raises:
ValueError – Raised if minlen < 0, maxlen < minlen, or size < 0
See also
Examples
>>> ak.random_strings_uniform(minlen=1, maxlen=5, seed=1, size=5) array(['TVKJ', 'EWAB', 'CO', 'HFMD', 'U'])
>>> ak.random_strings_uniform(minlen=1, maxlen=5, seed=1, size=5, ... characters='printable') array(['+5"f', '-P]3', '4k', '~HFF', 'F'])
- arkouda.read(filenames: str | List[str], datasets: str | List[str] | None = None, iterative: bool = False, strictTypes: bool = True, allow_errors: bool = False, calc_string_offsets=False, column_delim: str = ',', read_nested: bool = True, has_non_float_nulls: bool = False) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index | Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index][source]¶
Read datasets from files. File Type is determined automatically.
- Parameters:
filenames (list or str) – Either a list of filenames or shell expression
datasets (list or str or None) – (List of) name(s) of dataset(s) to read (default: all available)
iterative (bool) – Iterative (True) or Single (False) function call(s) to server
strictTypes (bool) – If True (default), require all dtypes of a given dataset to have the same precision and sign. If False, allow dtypes of different precision and sign across different files. For example, if one file contains a uint32 dataset and another contains an int64 dataset with the same name, the contents of both will be read into an int64 pdarray.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
calc_string_offsets (bool) – Default False, if True this will tell the server to calculate the offsets/segments array on the server versus loading them from HDF5 files. In the future this option may be set to True as the default.
column_delim (str) – Column delimiter to be used if dataset is CSV. Otherwise, unused.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Ignored if datasets is not None Parquet Files only.
has_non_float_nulls (bool) – Default False. This flag must be set to True to read non-float parquet columns that contain null values.
- Returns:
For a single dataset returns an Arkouda pdarray, Arkouda Strings, Arkouda Segarrays,
or Arkouda ArrayViews. For multiple datasets returns a dictionary of Arkouda pdarrays,
Arkouda Strings, Arkouda Segarrays, or Arkouda ArrayViews. – Dictionary of {datasetName: pdarray, String, SegArray, or ArrayView}
- Raises:
RuntimeError – If invalid filetype is detected
See also
Notes
If filenames is a string, it is interpreted as a shell expression (a single filename is a valid expression, so it will work) and is expanded with glob to read all matching files.
If iterative == True each dataset name and file names are passed to the server as independent sequential strings while if iterative == False all dataset names and file names are passed to the server in a single string.
If datasets is None, infer the names of datasets from the first file and read all of them. Use
get_datasetsto show the names of datasets to HDF5/Parquet files.CSV files without the Arkouda Header are not supported.
Examples
Read with file Extension >>> x = ak.read(‘path/name_prefix.h5’) # load HDF5 - processing determines file type not extension Read without file Extension >>> x = ak.read(‘path/name_prefix.parquet’) # load Parquet Read Glob Expression >>> x = ak.read(‘path/name_prefix*’) # Reads HDF5
- arkouda.read_csv(filenames: str | List[str], datasets: str | List[str] | None = None, column_delim: str = ',', allow_errors: bool = False) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index | Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index][source]¶
Read CSV file(s) into Arkouda objects. If more than one dataset is found, the objects will be returned in a dictionary mapping the dataset name to the Arkouda object containing the data. If the file contains the appropriately formatted header, typed data will be returned. Otherwise, all data will be returned as a Strings object.
- Parameters:
filenames (str or List[str]) – The filenames to read data from
datasets (str or List[str] (Optional)) – names of the datasets to read. When None, all datasets will be read.
column_delim (str) – The delimiter for column names and data. Defaults to “,”.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
- Returns:
pdarray, Strings or Mapping {dset_name
- Return type:
obj} where obj is a pdarray or Strings.
- Raises:
ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server
See also
Notes
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (
\n) at this time.Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing bytes as uint(8).
- arkouda.read_hdf(filenames: str | List[str], datasets: str | List[str] | None = None, iterative: bool = False, strict_types: bool = True, allow_errors: bool = False, calc_string_offsets: bool = False, tag_data=False) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index | Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index][source]¶
Read Arkouda objects from HDF5 file/s
- Parameters:
filenames (str, List[str]) – Filename/s to read objects from
datasets (Optional str, List[str]) – datasets to read from the provided files
iterative (bool) – Iterative (True) or Single (False) function call(s) to server
strict_types (bool) – If True (default), require all dtypes of a given dataset to have the same precision and sign. If False, allow dtypes of different precision and sign across different files. For example, if one file contains a uint32 dataset and another contains an int64 dataset with the same name, the contents of both will be read into an int64 pdarray.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
calc_string_offsets (bool) – Default False, if True this will tell the server to calculate the offsets/segments array on the server versus loading them from HDF5 files. In the future this option may be set to True as the default.
tagData (bool) – Default False, if True tag the data with the code associated with the filename that the data was pulled from.
- Returns:
For a single dataset returns an Arkouda pdarray, Arkouda Strings, Arkouda Segarrays,
or Arkouda ArrayViews. For multiple datasets returns a dictionary of Arkouda pdarrays,
Arkouda Strings, Arkouda Segarrays, or Arkouda ArrayViews. – Dictionary of {datasetName: pdarray, String, SegArray, or ArrayView}
- Raises:
ValueError – Raised if all datasets are not present in all hdf5 files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server
Notes
If filenames is a string, it is interpreted as a shell expression (a single filename is a valid expression, so it will work) and is expanded with glob to read all matching files.
If iterative == True each dataset name and file names are passed to the server as independent sequential strings while if iterative == False all dataset names and file names are passed to the server in a single string.
If datasets is None, infer the names of datasets from the first file and read all of them. Use
get_datasetsto show the names of datasets to HDF5 files.See also
Examples
>>> # Read with file Extension >>> x = ak.read_hdf('path/name_prefix.h5') # load HDF5 # Read Glob Expression >>> x = ak.read_hdf('path/name_prefix*') # Reads HDF5
- arkouda.read_parquet(filenames: str | List[str], datasets: str | List[str] | None = None, iterative: bool = False, strict_types: bool = True, allow_errors: bool = False, tag_data: bool = False, read_nested: bool = True, has_non_float_nulls: bool = False) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index | Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.timeclass.Datetime | arkouda.timeclass.Timedelta | arkouda.index.Index][source]¶
Read Arkouda objects from Parquet file/s
- Parameters:
filenames (str, List[str]) – Filename/s to read objects from
datasets (Optional str, List[str]) – datasets to read from the provided files
iterative (bool) – Iterative (True) or Single (False) function call(s) to server
strict_types (bool) – If True (default), require all dtypes of a given dataset to have the same precision and sign. If False, allow dtypes of different precision and sign across different files. For example, if one file contains a uint32 dataset and another contains an int64 dataset with the same name, the contents of both will be read into an int64 pdarray.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
tagData (bool) – Default False, if True tag the data with the code associated with the filename that the data was pulled from.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. If datasets is not None, this will be ignored.
has_non_float_nulls (bool) – Default False. This flag must be set to True to read non-float parquet columns that contain null values.
- Returns:
For a single dataset returns an Arkouda pdarray, Arkouda Strings, or Arkouda ArrayView object
and for multiple datasets returns a dictionary of Arkouda pdarrays,
Arkouda Strings or Arkouda ArrayView. – Dictionary of {datasetName: pdarray or String}
- Raises:
ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server
Notes
If filenames is a string, it is interpreted as a shell expression (a single filename is a valid expression, so it will work) and is expanded with glob to read all matching files.
If iterative == True each dataset name and file names are passed to the server as independent sequential strings while if iterative == False all dataset names and file names are passed to the server in a single string.
If datasets is None, infer the names of datasets from the first file and read all of them. Use
get_datasetsto show the names of datasets to Parquet files.Parquet always recomputes offsets at this time This will need to be updated once parquets workflow is updated
See also
Examples
Read without file Extension >>> x = ak.read_parquet(‘path/name_prefix.parquet’) # load Parquet Read Glob Expression >>> x = ak.read_parquet(‘path/name_prefix*’) # Reads Parquet
- arkouda.read_tagged_data(filenames: str | List[str], datasets: str | List[str] | None = None, strictTypes: bool = True, allow_errors: bool = False, calc_string_offsets=False, read_nested: bool = True, has_non_float_nulls: bool = False)[source]¶
Read datasets from files and tag each record to the file it was read from. File Type is determined automatically.
- Parameters:
filenames (list or str) – Either a list of filenames or shell expression
datasets (list or str or None) – (List of) name(s) of dataset(s) to read (default: all available)
strictTypes (bool) – If True (default), require all dtypes of a given dataset to have the same precision and sign. If False, allow dtypes of different precision and sign across different files. For example, if one file contains a uint32 dataset and another contains an int64 dataset with the same name, the contents of both will be read into an int64 pdarray.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
calc_string_offsets (bool) – Default False, if True this will tell the server to calculate the offsets/segments array on the server versus loading them from HDF5 files. In the future this option may be set to True as the default.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Ignored if datasets is not None Parquet Files only.
has_non_float_nulls (bool) – Default False. This flag must be set to True to read non-float parquet columns that contain null values.
Notes
Not currently supported for Categorical or GroupBy datasets
Examples
Read files and return data with tagging corresponding to the Categorical returned cat.codes will link the codes in data to the filename. Data will contain the code Filename_Codes >>> data, cat = ak.read_tagged_data(‘path/name’) >>> data {‘Filname_Codes’: array([0 3 6 9 12]), ‘col_name’: array([0 0 0 1])}
- arkouda.receive(hostname: str, port)[source]¶
Receive a pdarray sent by pdarray.transfer().
- Parameters:
hostname (str) – The hostname of the pdarray that sent the array
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to pdarray.transfer().
- Returns:
The pdarray sent from the sending server to the current receiving server.
- Return type:
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- arkouda.receive_dataframe(hostname: str, port)[source]¶
Receive a pdarray sent by dataframe.transfer().
- Parameters:
hostname (str) – The hostname of the dataframe that sent the array
port (int_scalars) – The port to send the dataframe over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to pdarray.send_array().
- Returns:
The dataframe sent from the sending server to the current receiving server.
- Return type:
- Raises:
ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype
- arkouda.register_all(data: dict)[source]¶
Register all objects in the provided dictionary
- Parameters:
data (dict) – Maps name to register the object to the object. For example, {“MyArray”: ak.array([0, 1, 2])
- Return type:
None
- arkouda.resolve_scalar_dtype(val: object) str[source]¶
Try to infer what dtype arkouda_server should treat val as.
- arkouda.restore(filename)[source]¶
Return data saved using ak.snapshot
- Parameters:
filename (str)
read (Name used to create snapshot to be)
- Return type:
Dict
Notes
Unlike other save/load methods using snapshot restore will save DataFrames alongside other objects in HDF5. Thus, they are returned within the dictionary as a dataframe.
- arkouda.right_align(left, right)[source]¶
Map two arrays of sparse values to the 0-up index set implied by the right array, discarding values from left that do not appear in right.
- Parameters:
- Returns:
keep (pdarray, bool) – Logical index of left-hand values that survived
aligned ((pdarray, pdarray)) – Left and right arrays with values replaced by 0-up indices
- arkouda.rotl(x, rot) pdarray[source]¶
Rotate bits of <x> to the left by <rot>.
- Parameters:
- Returns:
rotated – The rotated elements of x.
- Return type:
pdarray(int64/uint64)
- Raises:
TypeError – If input array is not int64 or uint64
Examples
>>> A = ak.arange(10) >>> ak.rotl(A, A) array([0, 2, 8, 24, 64, 160, 384, 896, 2048, 4608])
- arkouda.rotr(x, rot) pdarray[source]¶
Rotate bits of <x> to the left by <rot>.
- Parameters:
- Returns:
rotated – The rotated elements of x.
- Return type:
pdarray(int64/uint64)
- Raises:
TypeError – If input array is not int64 or uint64
Examples
>>> A = ak.arange(10) >>> ak.rotr(1024 * A, A) array([0, 512, 512, 384, 256, 160, 96, 56, 32, 18])
- arkouda.round(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise rounding of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing input array elements rounded to the nearest integer
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.round(ak.array([1.1, 2.5, 3.14159])) array([1, 3, 3])
- arkouda.save_all(columns: Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView] | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView], prefix_path: str, names: List[str] | None = None, file_format='HDF5', mode: str = 'truncate', file_type: str = 'distribute', compression: str | None = None) None[source]¶
DEPRECATED Save multiple named pdarrays to HDF5/Parquet files. :param columns: Collection of arrays to save :type columns: dict or list of pdarrays :param prefix_path: Directory and filename prefix for output files :type prefix_path: str :param names: Dataset names for the pdarrays :type names: list of str :param file_format: ‘HDF5’ or ‘Parquet’. Defaults to hdf5 :type file_format: str :param mode: By default, truncate (overwrite) the output files if they exist.
If ‘append’, attempt to create new dataset in existing files.
- Parameters:
file_type (str ("single" | "distribute")) – Default: distribute Single writes the dataset to a single file Distribute writes the dataset to a file per locale Only used with HDF5
compression (str (None | "snappy" | "gzip" | "brotli" | "zstd" | "lz4")) – Optional Select the compression to use with Parquet files. Only used with Parquet.
- Return type:
None
- Raises:
ValueError – Raised if (1) the lengths of columns and values differ or (2) the mode is not ‘truncate’ or ‘append’
See also
save,load_all,to_parquet,to_hdfNotes
Creates one file per locale containing that locale’s chunk of each pdarray. If columns is a dictionary, the keys are used as the HDF5 dataset names. Otherwise, if no names are supplied, 0-up integers are used. By default, any existing files at path_prefix will be overwritten, unless the user specifies the ‘append’ mode, in which case arkouda will attempt to add <columns> as new datasets to existing files. If the wrong number of files is present or dataset names already exist, a RuntimeError is raised.
Examples
>>> a = ak.arange(25) >>> b = ak.arange(25) >>> # Save with mapping defining dataset names >>> ak.save_all({'a': a, 'b': b}, 'path/name_prefix', file_format='Parquet') >>> # Save using names instead of mapping >>> ak.save_all([a, b], 'path/name_prefix', names=['a', 'b'], file_format='Parquet')
- arkouda.search_intervals(vals, intervals, tiebreak=None, hierarchical=True)[source]¶
Given an array of query vals and non-overlapping, closed intervals, return the index of the best (see tiebreak) interval containing each query value, or -1 if not present in any interval.
- Parameters:
vals ((sequence of) pdarray(int, uint, float)) – Values to search for in intervals. If multiple arrays, each “row” is an item.
intervals (2-tuple of (sequences of) pdarrays) – Non-overlapping, half-open intervals, as a tuple of (lower_bounds_inclusive, upper_bounds_exclusive) Must have same dtype(s) as vals.
tiebreak ((optional) pdarray, numeric) – When a value is present in more than one interval, the interval with the lowest tiebreak value will be chosen. If no tiebreak is given, the first containing interval will be chosen.
hierarchical (boolean) – When True, sequences of pdarrays will be treated as components specifying a single dimension (i.e. hierarchical) When False, sequences of pdarrays will be specifying multi-dimensional intervals
- Returns:
idx – Index of interval containing each query value, or -1 if not found
- Return type:
pdarray(int64)
Notes
- The return idx satisfies the following condition:
present = idx > -1 ((intervals[0][idx[present]] <= vals[present]) &
(intervals[1][idx[present]] >= vals[present])).all()
Examples
>>> starts = (ak.array([0, 5]), ak.array([0, 11])) >>> ends = (ak.array([5, 9]), ak.array([10, 20])) >>> vals = (ak.array([0, 0, 2, 5, 5, 6, 6, 9]), ak.array([0, 20, 1, 5, 15, 0, 12, 30])) >>> ak.search_intervals(vals, (starts, ends), hierarchical=False) array([0 -1 0 0 1 -1 1 -1]) >>> ak.search_intervals(vals, (starts, ends)) array([0 0 0 0 1 1 1 -1]) >>> bi_starts = ak.bigint_from_uint_arrays([ak.cast(a, ak.uint64) for a in starts]) >>> bi_ends = ak.bigint_from_uint_arrays([ak.cast(a, ak.uint64) for a in ends]) >>> bi_vals = ak.bigint_from_uint_arrays([ak.cast(a, ak.uint64) for a in vals]) >>> bi_starts, bi_ends, bi_vals (array(["0" "92233720368547758091"]), array(["92233720368547758090" "166020696663385964564"]), array(["0" "20" "36893488147419103233" "92233720368547758085" "92233720368547758095" "110680464442257309696" "110680464442257309708" "166020696663385964574"])) >>> ak.search_intervals(bi_vals, (bi_starts, bi_ends)) array([0 0 0 0 1 1 1 -1])
- arkouda.segarray(segments: arkouda.pdarrayclass.pdarray, values: arkouda.pdarrayclass.pdarray, lengths=None, grouping=None)[source]¶
Alias for the from_parts function. Prevents user from needing to call ak.SegArray constructor DEPRECATED
- arkouda.setdiff1d(pda1: arkouda.groupbyclass.groupable, pda2: arkouda.groupbyclass.groupable, assume_unique: bool = False) arkouda.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶
Find the set difference of two arrays.
Return the sorted, unique values in pda1 that are not in pda2.
- Parameters:
pda1 (pdarray/Sequence[pdarray, Strings, Categorical]) – Input array/Sequence of groupable objects
pda2 (pdarray/List) – Input array/sequence of groupable objects
assume_unique (bool) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
- Returns:
Sorted 1D array/List of sorted pdarrays of values in pda1 that are not in pda2.
- Return type:
pdarray/groupable
- Raises:
TypeError – Raised if either pda1 or pda2 is not a pdarray
RuntimeError – Raised if the dtype of either pdarray is not supported
See also
Notes
ak.setdiff1d is not supported for bool or float64 pdarrays
Examples
>>> a = ak.array([1, 2, 3, 2, 4, 1]) >>> b = ak.array([3, 4, 5, 6]) >>> ak.setdiff1d(a, b) array([1, 2]) #Multi-Array Example >>> a = ak.arange(1, 6) >>> b = ak.array([1, 5, 3, 4, 2]) >>> c = ak.array([1, 4, 3, 2, 5]) >>> d = ak.array([1, 2, 3, 5, 4]) >>> multia = [a, a, a] >>> multib = [b, c, d] >>> ak.setdiff1d(multia, multib) [array([2, 4, 5]), array([2, 4, 5]), array([2, 4, 5])]
- arkouda.setxor1d(pda1: arkouda.groupbyclass.groupable, pda2: arkouda.groupbyclass.groupable, assume_unique: bool = False) arkouda.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶
Find the set exclusive-or (symmetric difference) of two arrays.
Return the sorted, unique values that are in only one (not both) of the input arrays.
- Parameters:
pda1 (pdarray/Sequence[pdarray, Strings, Categorical]) – Input array/Sequence of groupable objects
pda2 (pdarray/List) – Input array/sequence of groupable objects
assume_unique (bool) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
- Returns:
Sorted 1D array/List of sorted pdarrays of unique values that are in only one of the input arrays.
- Return type:
pdarray/groupable
- Raises:
TypeError – Raised if either pda1 or pda2 is not a pdarray
RuntimeError – Raised if the dtype of either pdarray is not supported
Notes
ak.setxor1d is not supported for bool or float64 pdarrays
Examples
>>> a = ak.array([1, 2, 3, 2, 4]) >>> b = ak.array([2, 3, 5, 7, 5]) >>> ak.setxor1d(a,b) array([1, 4, 5, 7]) #Multi-Array Example >>> a = ak.arange(1, 6) >>> b = ak.array([1, 5, 3, 4, 2]) >>> c = ak.array([1, 4, 3, 2, 5]) >>> d = ak.array([1, 2, 3, 5, 4]) >>> multia = [a, a, a] >>> multib = [b, c, d] >>> ak.setxor1d(multia, multib) [array([2, 2, 4, 4, 5, 5]), array([2, 5, 2, 4, 4, 5]), array([2, 4, 5, 4, 2, 5])]
- arkouda.sign(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise sign of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing sign values of the input array elements
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.sign(ak.array([-10, -5, 0, 5, 10])) array([-1, -1, 0, 1, 1])
- arkouda.sin(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise sine of the array.
- Parameters:
- Returns:
A pdarray containing sin for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.sinh(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise hyperbolic sine of the array.
- Parameters:
- Returns:
A pdarray containing hyperbolic sine for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.skew(pda: pdarray, bias: bool = True) numpy.float64[source]¶
Computes the sample skewness of an array. Skewness > 0 means there’s greater weight in the right tail of the distribution. Skewness < 0 means there’s greater weight in the left tail of the distribution. Skewness == 0 means the data is normally distributed. Based on the scipy.stats.skew function.
- Parameters:
pda (pdarray) – A pdarray of values that will be calculated to find the skew
bias (bool, optional) – If False, then the calculations are corrected for statistical bias.
- Returns:
The skew of all elements in the array
- Return type:
np.float64
Examples: >>> a = ak.array([1, 1, 1, 5, 10]) >>> ak.skew(a) 0.9442193396379163
- arkouda.snapshot(filename)[source]¶
Create a snapshot of the current Arkouda namespace. All currently accessible variables containing Arkouda objects will be written to an HDF5 file.
Unlike other save/load functions, this maintains the integrity of dataframes.
Current Variable names are used as the dataset name when saving.
- Parameters:
filename (str)
file (Name to use when storing)
- Return type:
None
See also
ak.restore
- arkouda.sort(pda: arkouda.pdarrayclass.pdarray, algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD) arkouda.pdarrayclass.pdarray[source]¶
Return a sorted copy of the array. Only sorts numeric arrays; for Strings, use argsort.
- Parameters:
pda (pdarray or Categorical) – The array to sort (int64, uint64, or float64)
- Returns:
The sorted copy of pda
- Return type:
pdarray, int64, uint64, or float64
- Raises:
TypeError – Raised if the parameter is not a pdarray
ValueError – Raised if sort attempted on a pdarray with an unsupported dtype such as bool
See also
Notes
Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.
Examples
>>> a = ak.randint(0, 10, 10) >>> sorted = ak.sort(a) >>> a array([0, 1, 1, 3, 4, 5, 7, 8, 8, 9])
- arkouda.sqrt(pda: pdarray, where: bool | pdarray = True) pdarray[source]¶
Takes the square root of array. If where is given, the operation will only take place in the positions where the where condition is True.
- Parameters:
- Returns:
pdarray
Returns a pdarray of square rooted values, under the boolean where condition.
Examples: >>> a = ak.arange(5) >>> ak.sqrt(a) array([0 1 1.4142135623730951 1.7320508075688772 2]) >>> ak.sqrt(a, ak.sqrt([True, True, False, False, True])) array([0, 1, 2, 3, 2])
- arkouda.square(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise square of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing square values of the input array elements
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.square(ak.arange(1,5)) array([1, 4, 9, 16])
- arkouda.standard_normal(size: arkouda.dtypes.int_scalars, seed: None | arkouda.dtypes.int_scalars = None) arkouda.pdarrayclass.pdarray[source]¶
Draw real numbers from the standard normal distribution.
- Parameters:
size (int_scalars) – The number of samples to draw (size of the returned array)
seed (int_scalars) – Value used to initialize the random number generator
- Returns:
The array of random numbers
- Return type:
pdarray, float64
- Raises:
TypeError – Raised if size is not an int
ValueError – Raised if size < 0
See also
Notes
For random samples from \(N(\mu, \sigma^2)\), use:
(sigma * standard_normal(size)) + muExamples
>>> ak.standard_normal(3,1) array([-0.68586185091150265, 1.1723810583573375, 0.567584107142031])
- arkouda.standard_normal(size: arkouda.dtypes.int_scalars, seed: None | arkouda.dtypes.int_scalars = None) arkouda.pdarrayclass.pdarray[source]¶
Draw real numbers from the standard normal distribution.
- Parameters:
size (int_scalars) – The number of samples to draw (size of the returned array)
seed (int_scalars) – Value used to initialize the random number generator
- Returns:
The array of random numbers
- Return type:
pdarray, float64
- Raises:
TypeError – Raised if size is not an int
ValueError – Raised if size < 0
See also
Notes
For random samples from \(N(\mu, \sigma^2)\), use:
(sigma * standard_normal(size)) + muExamples
>>> ak.standard_normal(3,1) array([-0.68586185091150265, 1.1723810583573375, 0.567584107142031])
- arkouda.std(pda: pdarray, ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Return the standard deviation of values in the array. The standard deviation is implemented as the square root of the variance.
- Parameters:
pda (pdarray) – values for which to calculate the standard deviation
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std
- Returns:
The scalar standard deviation of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance or ddof is not an integer
ValueError – Raised if ddof is an integer < 0
RuntimeError – Raised if there’s a server-side error thrown
Notes
The standard deviation is the square root of the average of the squared deviations from the mean, i.e.,
std = sqrt(mean((x - x.mean())**2)).The average squared deviation is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of the infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even withddof=1, it will not be an unbiased estimate of the standard deviation per se.
- arkouda.str_¶
- arkouda.str_¶
- arkouda.str_scalars¶
- arkouda.sum(pda: pdarray) numpy.float64[source]¶
Return the sum of all elements in the array.
- Parameters:
pda (pdarray) – Values for which to calculate the sum
- Returns:
The sum of all elements in the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown
- arkouda.tan(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise tangent of the array.
- Parameters:
- Returns:
A pdarray containing tangent for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.tanh(pda: arkouda.pdarrayclass.pdarray, where: bool | arkouda.pdarrayclass.pdarray = True) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise hyperbolic tangent of the array.
- Parameters:
- Returns:
A pdarray containing hyperbolic tangent for each element of the original pdarray
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
- arkouda.timedelta_range(start=None, end=None, periods=None, freq=None, name=None, closed=None, **kwargs)[source]¶
Return a fixed frequency TimedeltaIndex, with day as the default frequency. Alias for
ak.Timedelta(pd.timedelta_range(args)). Subject to size limit imposed by client.maxTransferBytes.- Parameters:
start (str or timedelta-like, default None) – Left bound for generating timedeltas.
end (str or timedelta-like, default None) – Right bound for generating timedeltas.
periods (int, default None) – Number of periods to generate.
freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’.
name (str, default None) – Name of the resulting TimedeltaIndex.
closed (str, default None) – Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None).
- Returns:
rng
- Return type:
TimedeltaIndex
Notes
Of the four parameters
start,end,periods, andfreq, exactly three must be specified. Iffreqis omitted, the resultingTimedeltaIndexwill haveperiodslinearly spaced elements betweenstartandend(closed on both sides).To learn more about the frequency strings, please see this link.
- arkouda.timedelta_range(start=None, end=None, periods=None, freq=None, name=None, closed=None, **kwargs)[source]¶
Return a fixed frequency TimedeltaIndex, with day as the default frequency. Alias for
ak.Timedelta(pd.timedelta_range(args)). Subject to size limit imposed by client.maxTransferBytes.- Parameters:
start (str or timedelta-like, default None) – Left bound for generating timedeltas.
end (str or timedelta-like, default None) – Right bound for generating timedeltas.
periods (int, default None) – Number of periods to generate.
freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’.
name (str, default None) – Name of the resulting TimedeltaIndex.
closed (str, default None) – Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None).
- Returns:
rng
- Return type:
TimedeltaIndex
Notes
Of the four parameters
start,end,periods, andfreq, exactly three must be specified. Iffreqis omitted, the resultingTimedeltaIndexwill haveperiodslinearly spaced elements betweenstartandend(closed on both sides).To learn more about the frequency strings, please see this link.
- arkouda.to_csv(columns: Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings] | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings], prefix_path: str, names: List[str] | None = None, col_delim: str = ',', overwrite: bool = False)[source]¶
Write Arkouda object(s) to CSV file(s). All CSV Files written by Arkouda include a header denoting data types of the columns.
- Parameters:
columns (Mapping[str, pdarray] or List[pdarray]) – The objects to be written to CSV file. If a mapping is used and names is None the keys of the mapping will be used as the dataset names.
prefix_path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
names (List[str] (Optional)) – names of dataset to be written. Order should correspond to the order of data provided in columns.
col_delim (str) – Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool) – Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.
- Return type:
None
- Raises:
ValueError – Raised if any datasets are present in all csv files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server
See also
Notes
CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (
\n) at this time.Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing bytes as uint(8).
- arkouda.to_hdf(columns: Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView] | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView], prefix_path: str, names: List[str] | None = None, mode: str = 'truncate', file_type: str = 'distribute') None[source]¶
Save multiple named pdarrays to HDF5 files.
- Parameters:
columns (dict or list of pdarrays) – Collection of arrays to save
prefix_path (str) – Directory and filename prefix for output files
names (list of str) – Dataset names for the pdarrays
mode ({'truncate' | 'append'}) – By default, truncate (overwrite) the output files if they exist. If ‘append’, attempt to create new dataset in existing files.
file_type (str ("single" | "distribute")) – Default: distribute Single writes the dataset to a single file Distribute writes the dataset to a file per locale
- Return type:
None
- Raises:
ValueError – Raised if (1) the lengths of columns and values differ or (2) the mode is not ‘truncate’ or ‘append’
RuntimeError – Raised if a server-side error is thrown saving the pdarray
See also
Notes
Creates one file per locale containing that locale’s chunk of each pdarray. If columns is a dictionary, the keys are used as the HDF5 dataset names. Otherwise, if no names are supplied, 0-up integers are used. By default, any existing files at path_prefix will be overwritten, unless the user specifies the ‘append’ mode, in which case arkouda will attempt to add <columns> as new datasets to existing files. If the wrong number of files is present or dataset names already exist, a RuntimeError is raised.
Examples
>>> a = ak.arange(25) >>> b = ak.arange(25)
>>> # Save with mapping defining dataset names >>> ak.to_hdf({'a': a, 'b': b}, 'path/name_prefix')
>>> # Save using names instead of mapping >>> ak.to_hdf([a, b], 'path/name_prefix', names=['a', 'b'])
- arkouda.to_parquet(columns: Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView] | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView], prefix_path: str, names: List[str] | None = None, mode: str = 'truncate', compression: str | None = None, convert_categoricals: bool = False) None[source]¶
Save multiple named pdarrays to Parquet files.
- Parameters:
columns (dict or list of pdarrays) – Collection of arrays to save
prefix_path (str) – Directory and filename prefix for output files
names (list of str) – Dataset names for the pdarrays
mode ({'truncate' | 'append'}) – By default, truncate (overwrite) the output files if they exist. If ‘append’, attempt to create new dataset in existing files. ‘append’ is deprecated, please use the multi-column write
compression (str (Optional)) –
- Default None
Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4
- convert_categoricals: bool
Defaults to False Parquet requires all columns to be the same size and Categoricals don’t satisfy that requirement. if set, write the equivalent Strings in place of any Categorical columns.
- Return type:
None
- Raises:
ValueError – Raised if (1) the lengths of columns and values differ or (2) the mode is not ‘truncate’ or ‘append’
RuntimeError – Raised if a server-side error is thrown saving the pdarray
Notes
Creates one file per locale containing that locale’s chunk of each pdarray. If columns is a dictionary, the keys are used as the Parquet column names. Otherwise, if no names are supplied, 0-up integers are used. By default, any existing files at path_prefix will be overwritten, unless the user specifies the ‘append’ mode, in which case arkouda will attempt to add <columns> as new datasets to existing files. If the wrong number of files is present or dataset names already exist, a RuntimeError is raised.
Examples
>>> a = ak.arange(25) >>> b = ak.arange(25)
>>> # Save with mapping defining dataset names >>> ak.to_parquet({'a': a, 'b': b}, 'path/name_prefix')
>>> # Save using names instead of mapping >>> ak.to_parquet([a, b], 'path/name_prefix', names=['a', 'b'])
- arkouda.translate_np_dtype(dt) Tuple[str, int][source]¶
Split numpy dtype dt into its kind and byte size, raising TypeError for unsupported dtypes.
- Raises:
TypeError – Raised if the dtype is not in supported dtypes or if dt is not a np.dtype
- arkouda.trunc(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Return the element-wise truncation of the array.
- Parameters:
pda (pdarray)
- Returns:
A pdarray containing input array elements truncated to the nearest integer
- Return type:
- Raises:
TypeError – Raised if the parameter is not a pdarray
Examples
>>> ak.trunc(ak.array([1.1, 2.5, 3.14159])) array([1, 2, 3])
- arkouda.uint16¶
- arkouda.uint32¶
- arkouda.uint64¶
- arkouda.uint8¶
- arkouda.uniform(size: arkouda.dtypes.int_scalars, low: arkouda.dtypes.numeric_scalars = float(0.0), high: arkouda.dtypes.numeric_scalars = 1.0, seed: None | arkouda.dtypes.int_scalars = None) arkouda.pdarrayclass.pdarray[source]¶
Generate a pdarray with uniformly distributed random float values in a specified range.
- Parameters:
low (float_scalars) – The low value (inclusive) of the range, defaults to 0.0
high (float_scalars) – The high value (inclusive) of the range, defaults to 1.0
size (int_scalars) – The length of the returned array
seed (int_scalars, optional) – Value used to initialize the random number generator
- Returns:
Values drawn uniformly from the specified range
- Return type:
pdarray, float64
- Raises:
TypeError – Raised if dtype.name not in DTypes, size is not an int, or if either low or high is not an int or float
ValueError – Raised if size < 0 or if high < low
Notes
The logic for uniform is delegated to the ak.randint method which is invoked with a dtype of float64
Examples
>>> ak.uniform(3) array([0.92176432277231968, 0.083130710959903542, 0.68894208386667544])
>>> ak.uniform(size=3,low=0,high=5,seed=0) array([0.30013431967121934, 0.47383036230759112, 1.0441791878997098])
- arkouda.uniform(size: arkouda.dtypes.int_scalars, low: arkouda.dtypes.numeric_scalars = float(0.0), high: arkouda.dtypes.numeric_scalars = 1.0, seed: None | arkouda.dtypes.int_scalars = None) arkouda.pdarrayclass.pdarray[source]¶
Generate a pdarray with uniformly distributed random float values in a specified range.
- Parameters:
low (float_scalars) – The low value (inclusive) of the range, defaults to 0.0
high (float_scalars) – The high value (inclusive) of the range, defaults to 1.0
size (int_scalars) – The length of the returned array
seed (int_scalars, optional) – Value used to initialize the random number generator
- Returns:
Values drawn uniformly from the specified range
- Return type:
pdarray, float64
- Raises:
TypeError – Raised if dtype.name not in DTypes, size is not an int, or if either low or high is not an int or float
ValueError – Raised if size < 0 or if high < low
Notes
The logic for uniform is delegated to the ak.randint method which is invoked with a dtype of float64
Examples
>>> ak.uniform(3) array([0.92176432277231968, 0.083130710959903542, 0.68894208386667544])
>>> ak.uniform(size=3,low=0,high=5,seed=0) array([0.30013431967121934, 0.47383036230759112, 1.0441791878997098])
- arkouda.union1d(pda1: arkouda.groupbyclass.groupable, pda2: arkouda.groupbyclass.groupable) arkouda.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶
Find the union of two arrays/List of Arrays.
Return the unique, sorted array of values that are in either of the two input arrays.
- Parameters:
pda1 (pdarray/Sequence[pdarray, Strings, Categorical]) – Input array/Sequence of groupable objects
pda2 (pdarray/List) – Input array/sequence of groupable objects
- Returns:
Unique, sorted union of the input arrays.
- Return type:
pdarray/groupable
- Raises:
TypeError – Raised if either pda1 or pda2 is not a pdarray
RuntimeError – Raised if the dtype of either array is not supported
See also
Notes
ak.union1d is not supported for bool or float64 pdarrays
Examples
>>> # 1D Example >>> ak.union1d(ak.array([-1, 0, 1]), ak.array([-2, 0, 2])) array([-2, -1, 0, 1, 2]) #Multi-Array Example >>> a = ak.arange(1, 6) >>> b = ak.array([1, 5, 3, 4, 2]) >>> c = ak.array([1, 4, 3, 2, 5]) >>> d = ak.array([1, 2, 3, 5, 4]) >>> multia = [a, a, a] >>> multib = [b, c, d] >>> ak.union1d(multia, multib) [array[1, 2, 2, 3, 4, 4, 5, 5], array[1, 2, 5, 3, 2, 4, 4, 5], array[1, 2, 4, 3, 5, 4, 2, 5]]
- arkouda.unique(pda: groupable, return_groups: bool = False, assume_sorted: bool = False, return_indices: bool = False) groupable | Tuple[groupable, arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray, int][source]¶
Find the unique elements of an array.
Returns the unique elements of an array, sorted if the values are integers. There is an optional output in addition to the unique elements: the number of times each unique value comes up in the input array.
- Parameters:
pda ((list of) pdarray, Strings, or Categorical) – Input array.
return_groups (bool, optional) – If True, also return grouping information for the array.
return_indices (bool, optional) – Only applicable if return_groups is True. If True, return unique key indices along with other groups
assume_sorted (bool, optional) – If True, assume pda is sorted and skip sorting step
- Returns:
unique ((list of) pdarray, Strings, or Categorical) – The unique values. If input dtype is int64, return values will be sorted.
permutation (pdarray, optional) – Permutation that groups equivalent values together (only when return_groups=True)
segments (pdarray, optional) – The offset of each group in the permuted array (only when return_groups=True)
- Raises:
TypeError – Raised if pda is not a pdarray or Strings object
RuntimeError – Raised if the pdarray or Strings dtype is unsupported
Notes
For integer arrays, this function checks to see whether pda is sorted and, if so, whether it is already unique. This step can save considerable computation. Otherwise, this function will sort pda.
Examples
>>> A = ak.array([3, 2, 1, 1, 2, 3]) >>> ak.unique(A) array([1, 2, 3])
- arkouda.unique(pda: groupable, return_groups: bool = False, assume_sorted: bool = False, return_indices: bool = False) groupable | Tuple[groupable, arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray, int][source]¶
Find the unique elements of an array.
Returns the unique elements of an array, sorted if the values are integers. There is an optional output in addition to the unique elements: the number of times each unique value comes up in the input array.
- Parameters:
pda ((list of) pdarray, Strings, or Categorical) – Input array.
return_groups (bool, optional) – If True, also return grouping information for the array.
return_indices (bool, optional) – Only applicable if return_groups is True. If True, return unique key indices along with other groups
assume_sorted (bool, optional) – If True, assume pda is sorted and skip sorting step
- Returns:
unique ((list of) pdarray, Strings, or Categorical) – The unique values. If input dtype is int64, return values will be sorted.
permutation (pdarray, optional) – Permutation that groups equivalent values together (only when return_groups=True)
segments (pdarray, optional) – The offset of each group in the permuted array (only when return_groups=True)
- Raises:
TypeError – Raised if pda is not a pdarray or Strings object
RuntimeError – Raised if the pdarray or Strings dtype is unsupported
Notes
For integer arrays, this function checks to see whether pda is sorted and, if so, whether it is already unique. This step can save considerable computation. Otherwise, this function will sort pda.
Examples
>>> A = ak.array([3, 2, 1, 1, 2, 3]) >>> ak.unique(A) array([1, 2, 3])
- arkouda.unique(pda: groupable, return_groups: bool = False, assume_sorted: bool = False, return_indices: bool = False) groupable | Tuple[groupable, arkouda.pdarrayclass.pdarray, arkouda.pdarrayclass.pdarray, int][source]¶
Find the unique elements of an array.
Returns the unique elements of an array, sorted if the values are integers. There is an optional output in addition to the unique elements: the number of times each unique value comes up in the input array.
- Parameters:
pda ((list of) pdarray, Strings, or Categorical) – Input array.
return_groups (bool, optional) – If True, also return grouping information for the array.
return_indices (bool, optional) – Only applicable if return_groups is True. If True, return unique key indices along with other groups
assume_sorted (bool, optional) – If True, assume pda is sorted and skip sorting step
- Returns:
unique ((list of) pdarray, Strings, or Categorical) – The unique values. If input dtype is int64, return values will be sorted.
permutation (pdarray, optional) – Permutation that groups equivalent values together (only when return_groups=True)
segments (pdarray, optional) – The offset of each group in the permuted array (only when return_groups=True)
- Raises:
TypeError – Raised if pda is not a pdarray or Strings object
RuntimeError – Raised if the pdarray or Strings dtype is unsupported
Notes
For integer arrays, this function checks to see whether pda is sorted and, if so, whether it is already unique. This step can save considerable computation. Otherwise, this function will sort pda.
Examples
>>> A = ak.array([3, 2, 1, 1, 2, 3]) >>> ak.unique(A) array([1, 2, 3])
- arkouda.unregister_all(names: list)[source]¶
Unregister all names provided
- Parameters:
names (list) – List of names used to register objects to be unregistered
- Return type:
None
- arkouda.unregister_pdarray_by_name(user_defined_name: str) None[source]¶
Unregister a named pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach_pdarray()
- Parameters:
user_defined_name (str) – user defined name which array was registered under
- Return type:
None
- Raises:
RuntimeError – Raised if the server could not find the internal name/symbol to remove
See also
register,unregister,is_registered,list_registry,attachNotes
Registered names/pdarrays in the server are immune to deletion until they are unregistered.
Examples
>>> a = zeros(100) >>> a.register("my_zeros") >>> # potentially disconnect from server and reconnect to server >>> b = ak.attach_pdarray("my_zeros") >>> # ...other work... >>> ak.unregister_pdarray_by_name(b)
- arkouda.update_hdf(columns: Mapping[str, arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView] | List[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.segarray.SegArray | arkouda.array_view.ArrayView], prefix_path: str, names: List[str] | None = None, repack: bool = True)[source]¶
Overwrite the datasets with name appearing in names or keys in columns if columns is a dictionary
- Parameters:
columns (dict or list of pdarrays) – Collection of arrays to save
prefix_path (str) – Directory and filename prefix for output files
names (list of str) – Dataset names for the pdarrays
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.
- Raises:
RuntimeError – Raised if a server-side error is thrown saving the datasets
Notes
If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the datasets provided do not exist, they will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data
This workflow is slightly different from to_hdf to prevent reading and creating a copy of the file for each dataset
- arkouda.value_counts(pda: arkouda.pdarrayclass.pdarray) Categorical | Tuple[arkouda.pdarrayclass.pdarray | arkouda.strings.Strings, arkouda.pdarrayclass.pdarray | None][source]¶
Count the occurrences of the unique values of an array.
- Parameters:
pda (pdarray, int64) – The array of values to count
- Returns:
unique_values (pdarray, int64 or Strings) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs
- Raises:
TypeError – Raised if the parameter is not a pdarray
Notes
This function differs from
histogram()in that it only returns counts for values that are present, leaving out empty “bins”. This function delegates all logic to the unique() method where the return_counts parameter is set to True.Examples
>>> A = ak.array([2, 0, 2, 4, 0, 0]) >>> ak.value_counts(A) (array([0, 2, 4]), array([3, 2, 1]))
- arkouda.var(pda: pdarray, ddof: arkouda.dtypes.int_scalars = 0) numpy.float64[source]¶
Return the variance of values in the array.
- Parameters:
pda (pdarray) – Values for which to calculate the variance
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var
- Returns:
The scalar variance of the array
- Return type:
np.float64
- Raises:
TypeError – Raised if pda is not a pdarray instance
ValueError – Raised if the ddof >= pdarray size
RuntimeError – Raised if there’s a server-side error thrown
Notes
The variance is the average of the squared deviations from the mean, i.e.,
var = mean((x - x.mean())**2).The mean is normally calculated as
x.sum() / N, whereN = len(x). If, however, ddof is specified, the divisorN - ddofis used instead. In standard statistical practice,ddof=1provides an unbiased estimator of the variance of a hypothetical infinite population.ddof=0provides a maximum likelihood estimate of the variance for normally distributed variables.
- arkouda.where(condition: arkouda.pdarrayclass.pdarray, A: str | arkouda.dtypes.numeric_scalars | arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, B: str | arkouda.dtypes.numeric_scalars | arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical[source]¶
Returns an array with elements chosen from A and B based upon a conditioning array. As is the case with numpy.where, the return array consists of values from the first array (A) where the conditioning array elements are True and from the second array (B) where the conditioning array elements are False.
- Parameters:
condition (pdarray) – Used to choose values from A or B
A (Union[numeric_scalars, str, pdarray, Strings, Categorical]) – Value(s) used when condition is True
B (Union[numeric_scalars, str, pdarray, Strings, Categorical]) – Value(s) used when condition is False
- Returns:
Values chosen from A where the condition is True and B where the condition is False
- Return type:
- Raises:
TypeError – Raised if the condition object is not a pdarray, if A or B is not an int, np.int64, float, np.float64, pdarray, str, Strings, Categorical if pdarray dtypes are not supported or do not match, or multiple condition clauses (see Notes section) are applied
ValueError – Raised if the shapes of the condition, A, and B pdarrays are unequal
Examples
>>> a1 = ak.arange(1,10) >>> a2 = ak.ones(9, dtype=np.int64) >>> cond = a1 < 5 >>> ak.where(cond,a1,a2) array([1, 2, 3, 4, 1, 1, 1, 1, 1])
>>> a1 = ak.arange(1,10) >>> a2 = ak.ones(9, dtype=np.int64) >>> cond = a1 == 5 >>> ak.where(cond,a1,a2) array([1, 1, 1, 1, 5, 1, 1, 1, 1])
>>> a1 = ak.arange(1,10) >>> a2 = 10 >>> cond = a1 < 5 >>> ak.where(cond,a1,a2) array([1, 2, 3, 4, 10, 10, 10, 10, 10])
>>> s1 = ak.array([f'str {i}' for i in range(10)]) >>> s2 = 'str 21' >>> cond = (ak.arange(10) % 2 == 0) >>> ak.where(cond,s1,s2) array(['str 0', 'str 21', 'str 2', 'str 21', 'str 4', 'str 21', 'str 6', 'str 21', 'str 8','str 21'])
>>> c1 = ak.Categorical(ak.array([f'str {i}' for i in range(10)])) >>> c2 = ak.Categorical(ak.array([f'str {i}' for i in range(9, -1, -1)])) >>> cond = (ak.arange(10) % 2 == 0) >>> ak.where(cond,c1,c2) array(['str 0', 'str 8', 'str 2', 'str 6', 'str 4', 'str 4', 'str 6', 'str 2', 'str 8', 'str 0'])
Notes
A and B must have the same dtype and only one conditional clause is supported e.g., n < 5, n > 1, which is supported in numpy is not currently supported in Arkouda
- arkouda.where(condition: arkouda.pdarrayclass.pdarray, A: str | arkouda.dtypes.numeric_scalars | arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, B: str | arkouda.dtypes.numeric_scalars | arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical[source]¶
Returns an array with elements chosen from A and B based upon a conditioning array. As is the case with numpy.where, the return array consists of values from the first array (A) where the conditioning array elements are True and from the second array (B) where the conditioning array elements are False.
- Parameters:
condition (pdarray) – Used to choose values from A or B
A (Union[numeric_scalars, str, pdarray, Strings, Categorical]) – Value(s) used when condition is True
B (Union[numeric_scalars, str, pdarray, Strings, Categorical]) – Value(s) used when condition is False
- Returns:
Values chosen from A where the condition is True and B where the condition is False
- Return type:
- Raises:
TypeError – Raised if the condition object is not a pdarray, if A or B is not an int, np.int64, float, np.float64, pdarray, str, Strings, Categorical if pdarray dtypes are not supported or do not match, or multiple condition clauses (see Notes section) are applied
ValueError – Raised if the shapes of the condition, A, and B pdarrays are unequal
Examples
>>> a1 = ak.arange(1,10) >>> a2 = ak.ones(9, dtype=np.int64) >>> cond = a1 < 5 >>> ak.where(cond,a1,a2) array([1, 2, 3, 4, 1, 1, 1, 1, 1])
>>> a1 = ak.arange(1,10) >>> a2 = ak.ones(9, dtype=np.int64) >>> cond = a1 == 5 >>> ak.where(cond,a1,a2) array([1, 1, 1, 1, 5, 1, 1, 1, 1])
>>> a1 = ak.arange(1,10) >>> a2 = 10 >>> cond = a1 < 5 >>> ak.where(cond,a1,a2) array([1, 2, 3, 4, 10, 10, 10, 10, 10])
>>> s1 = ak.array([f'str {i}' for i in range(10)]) >>> s2 = 'str 21' >>> cond = (ak.arange(10) % 2 == 0) >>> ak.where(cond,s1,s2) array(['str 0', 'str 21', 'str 2', 'str 21', 'str 4', 'str 21', 'str 6', 'str 21', 'str 8','str 21'])
>>> c1 = ak.Categorical(ak.array([f'str {i}' for i in range(10)])) >>> c2 = ak.Categorical(ak.array([f'str {i}' for i in range(9, -1, -1)])) >>> cond = (ak.arange(10) % 2 == 0) >>> ak.where(cond,c1,c2) array(['str 0', 'str 8', 'str 2', 'str 6', 'str 4', 'str 4', 'str 6', 'str 2', 'str 8', 'str 0'])
Notes
A and B must have the same dtype and only one conditional clause is supported e.g., n < 5, n > 1, which is supported in numpy is not currently supported in Arkouda
- arkouda.where(condition: arkouda.pdarrayclass.pdarray, A: str | arkouda.dtypes.numeric_scalars | arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical, B: str | arkouda.dtypes.numeric_scalars | arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical) arkouda.pdarrayclass.pdarray | arkouda.strings.Strings | arkouda.categorical.Categorical[source]¶
Returns an array with elements chosen from A and B based upon a conditioning array. As is the case with numpy.where, the return array consists of values from the first array (A) where the conditioning array elements are True and from the second array (B) where the conditioning array elements are False.
- Parameters:
condition (pdarray) – Used to choose values from A or B
A (Union[numeric_scalars, str, pdarray, Strings, Categorical]) – Value(s) used when condition is True
B (Union[numeric_scalars, str, pdarray, Strings, Categorical]) – Value(s) used when condition is False
- Returns:
Values chosen from A where the condition is True and B where the condition is False
- Return type:
- Raises:
TypeError – Raised if the condition object is not a pdarray, if A or B is not an int, np.int64, float, np.float64, pdarray, str, Strings, Categorical if pdarray dtypes are not supported or do not match, or multiple condition clauses (see Notes section) are applied
ValueError – Raised if the shapes of the condition, A, and B pdarrays are unequal
Examples
>>> a1 = ak.arange(1,10) >>> a2 = ak.ones(9, dtype=np.int64) >>> cond = a1 < 5 >>> ak.where(cond,a1,a2) array([1, 2, 3, 4, 1, 1, 1, 1, 1])
>>> a1 = ak.arange(1,10) >>> a2 = ak.ones(9, dtype=np.int64) >>> cond = a1 == 5 >>> ak.where(cond,a1,a2) array([1, 1, 1, 1, 5, 1, 1, 1, 1])
>>> a1 = ak.arange(1,10) >>> a2 = 10 >>> cond = a1 < 5 >>> ak.where(cond,a1,a2) array([1, 2, 3, 4, 10, 10, 10, 10, 10])
>>> s1 = ak.array([f'str {i}' for i in range(10)]) >>> s2 = 'str 21' >>> cond = (ak.arange(10) % 2 == 0) >>> ak.where(cond,s1,s2) array(['str 0', 'str 21', 'str 2', 'str 21', 'str 4', 'str 21', 'str 6', 'str 21', 'str 8','str 21'])
>>> c1 = ak.Categorical(ak.array([f'str {i}' for i in range(10)])) >>> c2 = ak.Categorical(ak.array([f'str {i}' for i in range(9, -1, -1)])) >>> cond = (ak.arange(10) % 2 == 0) >>> ak.where(cond,c1,c2) array(['str 0', 'str 8', 'str 2', 'str 6', 'str 4', 'str 4', 'str 6', 'str 2', 'str 8', 'str 0'])
Notes
A and B must have the same dtype and only one conditional clause is supported e.g., n < 5, n > 1, which is supported in numpy is not currently supported in Arkouda
- arkouda.write_log(log_msg: str, tag: str = 'ClientGeneratedLog', log_lvl: LogLevel = LogLevel.INFO)[source]¶
Allows the user to write custom logs.
- Parameters:
log_msg (str) – The message to be added to the server log
tag (str) – The tag to use in the log. This takes the place of the server function name. Allows for easy identification of custom logs. Defaults to “ClientGeneratedLog”
log_lvl (LogLevel) – The type of log to be written Defaults to LogLevel.INFO
See also
- arkouda.xlogy(x: arkouda.pdarrayclass.pdarray | numpy.float64, y: arkouda.pdarrayclass.pdarray)[source]¶
Computes x * log(y).
- Parameters:
- Return type:
Examples
>>> import arkouda as ak >>> ak.connect() >>> from arkouda.scipy.special import xlogy >>> xlogy( ak.array([1, 2, 3, 4]), ak.array([5,6,7,8])) array([1.6094379124341003 3.5835189384561099 5.8377304471659395 8.317766166719343]) >>> xlogy( 5.0, ak.array([1, 2, 3, 4])) array([0.00000000000000000 3.4657359027997265 5.4930614433405491 6.9314718055994531])
- arkouda.zeros(size: arkouda.dtypes.int_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with zeros.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
dtype (all_scalars) – Type of resulting array, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Zeros of the requested size and dtype
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
See also
Examples
>>> ak.zeros(5, dtype=ak.int64) array([0, 0, 0, 0, 0])
>>> ak.zeros(5, dtype=ak.float64) array([0, 0, 0, 0, 0])
>>> ak.zeros(5, dtype=ak.bool) array([False, False, False, False, False])
- arkouda.zeros(size: arkouda.dtypes.int_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with zeros.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
dtype (all_scalars) – Type of resulting array, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Zeros of the requested size and dtype
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
See also
Examples
>>> ak.zeros(5, dtype=ak.int64) array([0, 0, 0, 0, 0])
>>> ak.zeros(5, dtype=ak.float64) array([0, 0, 0, 0, 0])
>>> ak.zeros(5, dtype=ak.bool) array([False, False, False, False, False])
- arkouda.zeros(size: arkouda.dtypes.int_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with zeros.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
dtype (all_scalars) – Type of resulting array, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Zeros of the requested size and dtype
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
See also
Examples
>>> ak.zeros(5, dtype=ak.int64) array([0, 0, 0, 0, 0])
>>> ak.zeros(5, dtype=ak.float64) array([0, 0, 0, 0, 0])
>>> ak.zeros(5, dtype=ak.bool) array([False, False, False, False, False])
- arkouda.zeros(size: arkouda.dtypes.int_scalars | str, dtype: numpy.dtype | type | str | arkouda.dtypes.BigInt = float64, max_bits: int | None = None) arkouda.pdarrayclass.pdarray[source]¶
Create a pdarray filled with zeros.
- Parameters:
size (int_scalars) – Size of the array (only rank-1 arrays supported)
dtype (all_scalars) – Type of resulting array, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays
- Returns:
Zeros of the requested size and dtype
- Return type:
- Raises:
TypeError – Raised if the supplied dtype is not supported or if the size parameter is neither an int nor a str that is parseable to an int.
See also
Examples
>>> ak.zeros(5, dtype=ak.int64) array([0, 0, 0, 0, 0])
>>> ak.zeros(5, dtype=ak.float64) array([0, 0, 0, 0, 0])
>>> ak.zeros(5, dtype=ak.bool) array([False, False, False, False, False])
- arkouda.zeros_like(pda: arkouda.pdarrayclass.pdarray) arkouda.pdarrayclass.pdarray[source]¶
Create a zero-filled pdarray of the same size and dtype as an existing pdarray.
- Parameters:
pda (pdarray) – Array to use for size and dtype
- Returns:
Equivalent to ak.zeros(pda.size, pda.dtype)
- Return type:
- Raises:
TypeError – Raised if the pda parameter is not a pdarray.
Examples
>>> zeros = ak.zeros(5, dtype=ak.int64) >>> ak.zeros_like(zeros) array([0, 0, 0, 0, 0])
>>> zeros = ak.zeros(5, dtype=ak.float64) >>> ak.zeros_like(zeros) array([0, 0, 0, 0, 0])
>>> zeros = ak.zeros(5, dtype=ak.bool) >>> ak.zeros_like(zeros) array([False, False, False, False, False])