arkouda ¶

arkouda.dataframe.DataFrameGroupBy or arkouda.groupbyclass.GroupBy

Notes

Objects registered with the server are immune to deletion until they are unregistered.

registered_name = None¶

reverse = False¶

special_objType = 'BitVector'¶

to_list()[source]¶: Export data to a list of string-formatted bit vectors.

to_ndarray()[source]¶: Export data to a numpy array of string-formatted bit vectors.

values¶

width = 64¶

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.BoolDType¶

Bases: numpy.dtype

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.ByteDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.BytesDType¶

Bases: numpy.dtype

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.CLongDoubleDType¶

Bases: numpy.dtypes._ComplexAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.CachedAccessor(name: str, accessor)[source]¶

Custom property-like object.

A descriptor for caching accessors.

Parameters:

name (str) – Namespace that will be accessed under, e.g. df.foo.
accessor (cls) – Class with the extension methods.

Notes

For accessor, The class’s __init__ method assumes that one of Series, DataFrame or Index as the single argument data.

class arkouda.Complex128DType¶

Bases: numpy.dtypes._ComplexAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Complex64DType¶

Bases: numpy.dtypes._ComplexAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.DType[source]¶

BIGINT(*args, **kwargs)¶

BOOL(*args, **kwargs)¶

COMPLEX128(*args, **kwargs)¶

COMPLEX64(*args, **kwargs)¶

FLOAT(*args, **kwargs)¶

FLOAT32(*args, **kwargs)¶

FLOAT64(*args, **kwargs)¶

INT(*args, **kwargs)¶

INT16(*args, **kwargs)¶

INT32(*args, **kwargs)¶

INT64(*args, **kwargs)¶

INT8(*args, **kwargs)¶

STR(*args, **kwargs)¶

UINT(*args, **kwargs)¶

UINT16(*args, **kwargs)¶

UINT32(*args, **kwargs)¶

UINT64(*args, **kwargs)¶

UINT8(*args, **kwargs)¶

name(*args, **kwargs)¶: The name of the Enum member.

value(*args, **kwargs)¶: The value of the Enum member.

class arkouda.DTypeObjects¶

Build an immutable unordered collection of unique elements.

copy()¶: Return a shallow copy of a set.

difference(*others)¶: Return a new set with elements in the set that are not in the others.

intersection(*others)¶: Return a new set with elements common to the set and all others.

isdisjoint(other, /)¶: Return True if two sets have a null intersection.

issubset(other, /)¶: Report whether another set contains this set.

issuperset(other, /)¶: Report whether this set contains another set.

symmetric_difference(other, /)¶: Return a new set with elements in either the set or other but not both.

union(*others)¶: Return a new set with elements from the set and all others.

class arkouda.DTypes¶

Build an immutable unordered collection of unique elements.

copy()¶: Return a shallow copy of a set.

difference(*others)¶: Return a new set with elements in the set that are not in the others.

intersection(*others)¶: Return a new set with elements common to the set and all others.

isdisjoint(other, /)¶: Return True if two sets have a null intersection.

issubset(other, /)¶: Report whether another set contains this set.

issuperset(other, /)¶: Report whether this set contains another set.

symmetric_difference(other, /)¶: Return a new set with elements in either the set or other but not both.

union(*others)¶: Return a new set with elements from the set and all others.

class arkouda.DataFrame(dict=None, /, **kwargs)[source]¶

Bases: collections.UserDict

A 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

>>> import arkouda as ak
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

GroupBy(keys, use_series=False, as_index=True, dropna=True) → DataFrameGroupBy | GroupBy_class[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.DataFrameGroupBy 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.DataFrameGroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.

Return type:

See also

arkouda.GroupBy

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

GroupBy_class(keys: groupable | None = None, assume_sorted: bool = False, dropna: bool = True, **kwargs)¶

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

permutation¶

The permutation that sorts the keys array(s) by value (row)

Type:: pdarray

unique_keys¶

The unique values of the keys array(s), in grouped order

Type:: pdarray, Strings, or Categorical

ngroups¶

The length of the unique_keys array(s), i.e. number of groups

Type:: int_scalars

segments¶

The start index of each group in the grouped array(s)

Type:: pdarray

logger¶

Used for all logging operations

Type:: ArkoudaLogger

dropna[source]¶

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. The default is True

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.

all(axis=0) → 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.pandas.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) → 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.pandas.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.

See also

sort

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:

arkouda.numpy.pdarrayclass.pdarray

See also

coargsort

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

assign(**kwargs) → DataFrame[source]¶

Assign new columns to a DataFrame.

Return a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters:: **kwargs (dict of {str: callable or Series}) – The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.
Returns:: A new DataFrame with the new columns in addition to all the existing columns.
Return type:: DataFrame

Notes

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Examples

>>> import arkouda as ak
>>> df = ak.DataFrame({'temp_c': [17.0, 25.0]},
...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15

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:: arkouda.numpy.pdarrayclass.pdarray

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])
>>>

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:: arkouda.index.Index

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')

concat(items, ordered=True)[source]¶: Essentially an append, but different formatting.

corr() → DataFrame[source]¶

Return new DataFrame with pairwise correlation of columns.

Returns:: Arkouda DataFrame containing correlation matrix of all columns.
Return type:: DataFrame
Raises:: RuntimeError – Raised if there’s a server-side error thrown.

See also

pdarray.corr

Notes

Generate 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) → 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

GroupBy.count

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 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:

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]¶

Drop 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) – If True, the resulting axis will be labeled 0, 1, …, n - 1.

Returns:

DataFrame with NA entries dropped from it.

Return type:

arkouda.dataframe.DataFrameGroupBy or arkouda.groupbyclass.GroupBy

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

property dtypes: DataFrame¶

The dtypes of the dataframe.

Returns:: dtypes – The dtypes of the dataframe.
Return type:: arkouda.pandas.row.Row

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: DataFrame¶

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

filter_by_range(keys, low=1, high=None)[source]¶

Filter rows by the size of groups defined on one or more columns.

Group the DataFrame by the specified keys, compute the count of each group, and return a boolean mask indicating which rows belong to groups whose sizes fall within the inclusive range [low, high].

Parameters:

keys (str or list of str) – Column name or list of column names to group by.
low (int, default=1) – Minimum group size (inclusive). Must be >= 0.
high (int or None, default=None) – Maximum group size (inclusive). If None, no upper bound is applied.

Returns:

A boolean mask array of length equal to the number of rows in the DataFrame, where True indicates the row’s group size is between low and high.

Return type:

pdarray of bool

Raises:

ValueError – If low is negative, or if high is not None and high < low.
TypeError – If keys is not a string or list of strings.

Examples

>>> import arkouda as ak
>>> 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

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:: DataFrame

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

from_return_msg(rep_msg)[source]¶

Create a DataFrame object from an arkouda server response message.

Parameters:: rep_msg (string) – Server response message used to create a DataFrame.
Return type:: DataFrame

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.DataFrameGroupBy 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.DataFrameGroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.

Return type:

See also

arkouda.GroupBy

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:: DataFrame

See also

tail

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

property index¶

The index of the dataframe.

Returns:: The index of the dataframe.
Return type:: arkouda.index.Index or arkouda.index.MultiIndex

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¶

Return 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)"

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

register, attach, unregister, unregister_dataframe_by_name

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: pdarray | Dict | 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:: DataFrame

See also

ak.Series.isin

Notes

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:: DataFrame

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)

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

>>> import arkouda as ak
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') → 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:

Examples

>>> 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]¶

Return 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:: DataFrame

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)

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to ‘utf-8’. errors defaults to ‘strict’.

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

to_csv

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]¶

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:

arkouda.pandas.series.Series

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_registered

Notes

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 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:

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:

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) → DataFrame[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:: DataFrame

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

property shape: DataFrame¶

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: DataFrame¶

Return 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

sort_index(ascending=True)[source]¶

Sort the DataFrame by indexed columns.

Note: Fails on sort order of arkouda.numpy.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.numpy.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

apply_permutation

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:: DataFrame

See also

arkouda.dataframe.head

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.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]¶

Write 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.

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.

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

to_parquet, load

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=1073741824, 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.

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

to_hdf, load

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]¶

Send 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.

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_registered

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

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

update_nrows()[source]¶: Compute the number of rows on the arkouda server and updates the size parameter.

class arkouda.DataFrameGroupBy[source]¶

A DataFrame that has been grouped by a subset of columns.

Parameters:

gb_key_names (str or list(str), default=None) – The column name(s) associated with the aggregated columns.
as_index (bool, default=True) – If True, interpret aggregated column as index (only implemented for single dimensional aggregates). Otherwise, treat aggregated column as a dataframe column.

gb¶

GroupBy object, where the aggregation keys are values of column(s) of a dataframe, usually in preparation for aggregating with respect to the other columns.

Type:: GroupBy

df¶

The dataframe containing the original data.

Type:: DataFrame

gb_key_names¶

The column name(s) associated with the aggregated columns.

Type:: Union[str, List[str]]

as_index¶

If True the grouped values of the aggregation keys will be treated as an index. Defaults to True.

Type:: bool

all(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

any(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

argmax(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

argmin(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

broadcast(x, permute=True)[source]¶

Fill each group’s segment with a constant value.

Parameters:

x (Series or pdarray) – The values to put in each group’s segment.
permute (bool, default=True) – If True (default), permute broadcast values back to the ordering of the original array on which GroupBy was called. If False, the broadcast values are grouped by value.

Returns:

A Series with the Index of the original frame and the values of the broadcast.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda.dataframe import DataFrameGroupBy
>>> df = ak.DataFrame({"A":[1,2,2,3],"B":[3,4,5,6]})

	A	B
0	1	3
1	2	4
2	2	5
3	3	6

>>> gb = df.groupby("A")
>>> x = ak.array([10,11,12])
>>> s = DataFrameGroupBy.broadcast(gb, x)
>>> df["C"] = s.values
>>> display(df)

	A	B	C
0	1	3	10
1	2	4	11
2	2	5	11
3	3	6	12

count(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

diff(colname)[source]¶

Create a difference aggregate for the given column.

For each group, the difference between successive values is calculated. Aggregate operations (mean,min,max,std,var) can be done on the results.

Parameters:: colname (str) – Name of the column to compute the difference on.
Returns:: Object containing the differences, which can be aggregated.
Return type:: DiffAggregate

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[1,2,2,2,3,3],"B":[3,9,11,27,86,100]})
>>> display(df)

	A	B
0	1	3
1	2	9
2	2	11
3	2	27
4	3	86
5	3	100

>>> gb = df.groupby("A")
>>> gb.diff("B").values
array([nan nan 2.00000000000000000 16.00000000000000000 nan 14.00000000000000000])

first(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

head(n: int = 5, sort_index: bool = True) → DataFrame[source]¶

Return the first n rows from each group.

Parameters:

n (int, optional, default = 5) – Maximum number of rows to return for each group. If the number of rows in a group is less than n, all the values from that group will be returned.
sort_index (bool, default = True) – If true, return the DataFrame with indices sorted.

Return type:

Examples

>>> import arkouda as ak
>>> df = ak.DataFrame({"a":ak.arange(10) %3 , "b":ak.arange(10)})

	a	b
0	0	0
1	1	1
2	2	2
3	0	3
4	1	4
5	2	5
6	0	6
7	1	7
8	2	8
9	0	9

>>> df.groupby("a").head(2)

	a	b
0	0	0
1	0	3
2	1	1
3	1	4
4	2	2
5	2	5

max(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

mean(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

median(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

min(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

mode(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

nunique(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

prod(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

sample(n=None, frac=None, replace=False, weights=None, random_state=None)[source]¶

Return a random sample from each group.

You can either specify the number of elements or the fraction of elements to be sampled. random_state can be used for reproducibility

Parameters:

n (int, optional) – Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
frac (float, optional) – Fraction of items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the same row more than once.
weights (pdarray, optional) – Default None results in equal probability weighting. If passed a pdarray, then values must have the same length as the underlying DataFrame and will be used as sampling probabilities after normalization within each group. Weights must be non-negative with at least one positive element within each group.
random_state (int or ak.random.Generator, optional) – If int, seed for random number generator. If ak.random.Generator, use as given.

Returns:

A new DataFrame containing items randomly sampled from each group sorted according to the grouped columns.

Return type:

arkouda.dataframe.DataFrame or arkouda.pandas.series.Series

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[3,1,2,1,2,3],"B":[3,4,5,6,7,8]})
>>> display(df)
+----+-----+-----+
|    |   A |   B |
+====+=====+=====+
|  0 |   3 |   3 |
+----+-----+-----+
|  1 |   1 |   4 |
+----+-----+-----+
|  2 |   2 |   5 |
+----+-----+-----+
|  3 |   1 |   6 |
+----+-----+-----+
|  4 |   2 |   7 |
+----+-----+-----+
|  5 |   3 |   8 |
+----+-----+-----+

>>> df.groupby("A").sample(random_state=6)

	A	B
3	1	6
4	2	7
5	3	8

>>> df.groupby("A").sample(frac=0.5, random_state=3, weights=ak.array([1,1,1,0,0,0]))

	A	B
1	1	4
2	2	5
0	3	3

>>> df.groupby("A").sample(n=3, replace=True, random_state=ak.random.default_rng(7))
+----+-----+-----+
|    |   A |   B |
+====+=====+=====+
|  1 |   1 |   4 |
+----+-----+-----+
|  3 |   1 |   6 |
+----+-----+-----+
|  1 |   1 |   4 |
+----+-----+-----+
|  4 |   2 |   7 |
+----+-----+-----+
|  4 |   2 |   7 |
+----+-----+-----+
|  4 |   2 |   7 |
+----+-----+-----+
|  0 |   3 |   3 |
+----+-----+-----+
|  5 |   3 |   8 |
+----+-----+-----+
|  5 |   3 |   8 |
+----+-----+-----+

size(as_series=None, sort_index=True)[source]¶

Compute the size of each value as the total number of rows, including NaN values.

Parameters:

as_series (bool, default=None) – Indicates whether to return arkouda.dataframe.DataFrame (if as_series = False) or arkouda.pandas.series.Series (if as_series = True)
sort_index (bool, default=True) – If True, results will be returned with index values sorted in ascending order.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[1,2,2,3],"B":[3,4,5,6]})
>>> display(df)

	A	B
0	1	3
1	2	4
2	2	5
3	3	6

>>> df.groupby("A").size(as_series = False)

	size
0	1
1	2
2	1

std(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

sum(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

tail(n: int = 5, sort_index: bool = True) → DataFrame[source]¶

Return the last n rows from each group.

Parameters:

n (int, optional, default = 5) – Maximum number of rows to return for each group. If the number of rows in a group is less than n, all the rows from that group will be returned.
sort_index (bool, default = True) – If true, return the DataFrame with indices sorted.

Return type:

Examples

>>> import arkouda as ak
>>> df = ak.DataFrame({"a":ak.arange(10) %3 , "b":ak.arange(10)})

	a	b
0	0	0
1	1	1
2	2	2
3	0	3
4	1	4
5	2	5
6	0	6
7	1	7
8	2	8
9	0	9

>>> df.groupby("a").tail(2)

	a	b
0	0	6
1	0	9
2	1	4
3	1	7
4	2	5
5	2	8

unique(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

var(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

xor(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: DataFrame

class arkouda.DateTime64DType¶

Bases: numpy.dtype

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Datetime(pda, unit: str = _BASE_UNIT)[source]¶

Bases: _AbstractBaseTime

Represents 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 .values attribute 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¶

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

Notes

Objects registered with the server are immune to deletion until they are unregistered.

isocalendar()[source]¶

property microsecond¶

property millisecond¶

property minute¶

property month¶

property nanosecond¶

register(user_defined_name)[source]¶

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:

Datetime

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.

property second¶

special_objType = 'Datetime'¶

sum()[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numeric_scalars if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.sum(ak.array([1,2,3,4,5]))
np.int64(15)
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
np.float64(17.5)
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

supported_opeq¶

supported_with_datetime¶

supported_with_pdarray¶

supported_with_r_datetime¶

supported_with_r_pdarray¶

supported_with_r_timedelta¶

supported_with_timedelta¶

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.

property week¶

property weekday¶

property weekofyear¶

property year¶

class arkouda.DatetimeAccessor(series)[source]¶

Bases: Properties

series¶

class arkouda.DiffAggregate[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.

Type:: GroupBy

values¶

A column to compute the difference on.

Type:: Series

all()¶

any()¶

argmax()¶

argmin()¶

count()¶

first()¶

max()¶

mean()¶

median()¶

min()¶

mode()¶

nunique()¶

prod()¶

std()¶

sum()¶

unique()¶

var()¶

xor()¶

class arkouda.ErrorMode(*args, **kwds)[source]¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:
```
>>> Color.RED
<Color.RED: 1>
```
value lookup:
```
>>> Color(1)
<Color.RED: 1>
```
name lookup:
```
>>> Color['RED']
<Color.RED: 1>
```

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

ignore = 'ignore'¶

return_validity = 'return_validity'¶

strict = 'strict'¶

class arkouda.False_¶

Bases: numpy.generic

Boolean type (True or False), stored as a byte.

Warning

The bool type is not a subclass of the int_ type (the bool is not even a number type). This is different than Python’s default implementation of bool as a sub-class of int.

Character code:

'?'

class arkouda.Fields(values, names, MSB_left=True, pad='-', separator='', show_int=True)[source]¶

Bases: BitVector

An 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:

Fields

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.

MSB_left = True¶

format(x)[source]¶: Format a single binary value as a string of named fields.

name = None¶

names¶

namewidth¶

opeq(other, op)[source]¶

pad¶

padchar = '-'¶

separator = ''¶

show_int = True¶

class arkouda.Float16DType¶

Bases: numpy.dtypes._FloatAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Float32DType¶

Bases: numpy.dtypes._FloatAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Float64DType¶

Bases: numpy.dtypes._FloatAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.GROUPBY_REDUCTION_TYPES¶

Build an immutable unordered collection of unique elements.

copy()¶: Return a shallow copy of a set.

difference(*others)¶: Return a new set with elements in the set that are not in the others.

intersection(*others)¶: Return a new set with elements common to the set and all others.

isdisjoint(other, /)¶: Return True if two sets have a null intersection.

issubset(other, /)¶: Report whether another set contains this set.

issuperset(other, /)¶: Report whether this set contains another set.

symmetric_difference(other, /)¶: Return a new set with elements in either the set or other but not both.

union(*others)¶: Return a new set with elements from the set and all others.

class arkouda.GroupBy[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

permutation¶

The permutation that sorts the keys array(s) by value (row)

Type:: pdarray

unique_keys¶

The unique values of the keys array(s), in grouped order

Type:: pdarray, Strings, or Categorical

ngroups¶

The length of the unique_keys array(s), i.e. number of groups

Type:: int_scalars

segments¶

The start index of each group in the grouped array(s)

Type:: pdarray

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. The default is True

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.

AND(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise AND of values in each segment.

Group another array of values and perform a bitwise AND reduction on each group.

Group using the permutation stored in the GroupBy instance.

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
resultpdarray, int64: Bitwise AND of values in segments corresponding to keys

Return type:

Tuple[Union[pdarray, List[Union[pdarray, Strings]]], pdarray]

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: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise OR of values in each segment.

Group another array of values and perform a bitwise OR reduction on each group.

Group using the permutation stored in the GroupBy instance.

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
resultpdarray, int64: Bitwise OR of values in segments corresponding to keys

Return type:

Tuple[Union[pdarray, List[Union[pdarray, Strings]]], pdarray]

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

Reductions(*args, **kwargs)¶: Build an immutable unordered collection of unique elements.

XOR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise XOR of values in each segment.

Group another array of values and perform a bitwise XOR reduction on each group.

Group using the permutation stored in the GroupBy instance.

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
resultpdarray, int64: Bitwise XOR of values in segments corresponding to keys

Return type:

Tuple[Union[pdarray, List[Union[pdarray, Strings]]], pdarray]

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: int_scalars = 1) → Tuple[groupable, groupable][source]¶

Group another array of values and apply a reduction to each group’s values.

Group using the permutation stored in the GroupBy instance.

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_keysgroupable: The unique keys, in grouped order
aggregatesgroupable: One aggregate value per unique key in the GroupBy instance

Return type:

Tuple[groupable, groupable]

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

>>> import arkouda as ak
>>> keys = ak.arange(0, 5)
>>> vals = ak.linspace(-1, 1, 5)
>>> g = ak.GroupBy(keys)
>>> g.aggregate(vals, 'sum')
(array([0 1 2 3 4]),
 array([-1.00000000000000000 -0.5 0.00000000000000000 0.5 1.00000000000000000]))
>>> g.aggregate(vals, 'min')
(array([0 1 2 3 4]),
 array([-1.00000000000000000 -0.5 0.00000000000000000 0.5 1.00000000000000000]))

all(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Group another array of values and perform an “and” reduction on each group.

Group using the permutation stored in the GroupBy instance.

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_anypdarray, bool: One bool per unique key in the GroupBy instance

Return type:

Tuple[Union[pdarray, List[Union[pdarray, Strings]]], pdarray]

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: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Group another array of values and perform an “or” reduction on each group.

Group using the permutation stored in the GroupBy instance.

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_anypdarray, bool: One bool per unique key in the GroupBy instance

Return type:

Tuple[Union[pdarray, List[Union[pdarray, Strings]]], pdarray]

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: pdarray) → Tuple[groupable, pdarray][source]¶

Group another array of values and return the location of the first maximum of each group.

Group using the permutation stored in the GroupBy instance.

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_argmaximapdarray, int64: One index per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.argmax(b)
(array([1 2 3 4]), array([4 0 9 1]))

argmin(values: pdarray) → Tuple[groupable, pdarray][source]¶

Group another array of values and return the location of the first minimum of each group.

Group using the permutation stored in the GroupBy instance.

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_argminimapdarray, int64: One index per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.argmin(b)
(array([1 2 3 4]), array([4 0 9 1]))

broadcast(values: pdarray | Strings, permute: bool = True) → pdarray | Strings[source]¶

Fill each group’s segment with a constant value.

Parameters:

values (pdarray, Strings) – The values to put in each group’s segment
permute (bool) – If True (default), permute broadcast values back to the ordering of the original array on which GroupBy was called. If False, the broadcast values are grouped by value.

Returns:

The broadcasted values

Return type:

pdarray, Strings

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

>>> import arkouda as ak
>>> 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, seed=1) >>> a array([2 4 4 2 1 4 1 2 4 3]) >>> g = ak.GroupBy(a) >>> keys,counts = g.size() >>> g.broadcast(counts > 2) array([True True True True False True False True True False]) >>> g.broadcast(counts == 3) array([True False False True False False False True False False]) >>> g.broadcast(counts < 4) array([True False False True True False True True False True])

build_from_components(user_defined_name: str | None = None, **kwargs) → GroupBy[source]¶

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:

GroupBy

count(values: pdarray) → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group.

NaN values will be excluded from the total.

Parameters:

values (pdarray) – The values to be count by group (excluding NaN values).

Returns:

unique_keys(list of) pdarray or Strings: The unique keys, in grouped order
countspdarray, int64: The number of times each unique key appears (excluding NaN values).

Return type:

List[pdarray|Strings], pdarray|int64

Examples

>>> import arkouda as ak
>>> a = ak.array([1, 0, -1, 1, 0, -1])
>>> a
array([1 0 -1 1 0 -1])
>>> b = ak.array([1, np.nan, -1, np.nan, np.nan, -1], dtype = "float64")
>>> b
array([1.00000000000000000 nan -1.00000000000000000 nan nan -1.00000000000000000])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.count(b)
>>> keys
array([-1 0 1])
>>> counts
array([2 0 1])

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
resultpdarray-like: The first value of each group

Return type:

Tuple[groupable, groupable_element_type]

from_return_msg(rep_msg)[source]¶

head(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the first n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys(list of) pdarray-like: The unique keys, in grouped order
resultpdarray-like: The first n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Return type:

Tuple[groupable, groupable_element_type]

Examples

>>> import arkouda as ak
>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.head(v, 2, return_indices=True)
>>> _, values = g.head(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([0 3 1 4 2 5])
>>> values
array([0 3 1 4 2 5])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.head(v2, 2, return_indices=True)
>>> _, values2 = g.head(v2, 2, return_indices=False)
>>> idx2
array([0 3 1 4 2 5])
>>> values2
array([0 -6 -2 -8 -4 -10])

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

register, attach, unregister, unregister_groupby_by_name

Notes

Objects registered with the server are immune to deletion until they are unregistered.

max(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Group another array of values and return the maximum of each group’s values.

Group using the permutation stored in the GroupBy instance.

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_maximapdarray: One maximum per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.max(b)
(array([1 2 3 4]), array([1 2 3 4]))

mean(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Group another array of values and compute the mean of each group’s values.

Group using the permutation stored in the GroupBy instance.

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_meanspdarray, float64: One mean value per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.mean(b)
(array([1 2 3 4]),
array([1.00000000000000000 2.00000000000000000 3.00000000000000000 4.00000000000000000]))

median(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Group another array of values and compute the median of each group’s values.

Group using the permutation stored in the GroupBy instance.

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_medianspdarray, float64: One median value per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 9, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4])
>>> b = ak.linspace(-5, 5, 9)
>>> b
array([-5.00000000000000000 -3.75 -2.5 -1.25 0.00000000000000000
    1.25 2.5 3.75 5.00000000000000000])
>>> g.median(b)
(array([1 2 4]), array([1.25 -1.25 -0.625]))

min(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Group another array of values and return the minimum of each group’s values.

Group using the permutation stored in the GroupBy instance.

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_minimapdarray: One minimum per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.min(b)
(array([1 2 3 4]), array([1 2 3 4]))

mode(values: groupable) → Tuple[groupable, groupable][source]¶

Return the 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

Return type:

Tuple[groupable, groupable]

nunique(values: groupable) → Tuple[groupable, pdarray][source]¶

Group another array of values and return the number of unique values in each group.

Group using the permutation stored in the GroupBy instance.

Parameters:

values (pdarray, int64) – The values to group and find unique values

Returns:

unique_keysgroupable: The unique keys, in grouped order
group_nuniquegroupable: Number of unique values per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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

>>> import arkouda as ak
>>> 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
array([1 1 1 2 2 2 3 3 3 4])
>>> g = ak.GroupBy(labels)
>>> g.keys
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

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

prod(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Group another array of values and compute the product of each group’s values.

Group using the permutation stored in the GroupBy instance.

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_productspdarray, float64: One product per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.prod(b)
(array([1 2 3 4]),
array([1.00000000000000000 7.9999999999999982 3.0000000000000004 255.99999999999994]))

register(user_defined_name: str) → GroupBy[source]¶

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:

GroupBy

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_registered

Notes

Objects registered with the server are immune to deletion until they are unregistered.

sample(values: groupable, n=None, frac=None, replace=False, weights=None, random_state=None, return_indices=False, permute_samples=False)[source]¶

Return a random sample from each group.

You can either specify the number of elements or the fraction of elements to be sampled. random_state can be used for reproducibility

Parameters:

values ((list of) pdarray-like) – The values from which to sample, according to their group membership.
n (int, optional) – Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
frac (float, optional) – Fraction of items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the value more than once.
weights (pdarray, optional) – Default None results in equal probability weighting. If passed a pdarray, then values must have the same length as the groupby keys and will be used as sampling probabilities after normalization within each group. Weights must be non-negative with at least one positive element within each group.
random_state (int or ak.random.Generator, optional) – If int, seed for random number generator. If ak.random.Generator, use as given.
return_indices (bool, default False) – if True, return the indices of the sampled values. Otherwise, return the sample values.
permute_samples (bool, default False) – if True, return permute the samples according to group Otherwise, keep samples in original order.

Returns:

if return_indices is True, return the indices of the sampled values. Otherwise, return the sample values.

Return type:

size() → Tuple[groupable, 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
countspdarray, int64: The number of times each unique key appears

Return type:

List[pdarray|Strings], pdarray|int64

See also

count

Examples

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> keys
array([1 2 3 4])
>>> counts
array([2 3 1 4])

std(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Group another array of values and compute the standard deviation of each group’s values.

Group using the permutation stored in the GroupBy instance.

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_stdspdarray, float64: One std value per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of the infinite population. ddof=0 provides 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 with ddof=1, it will not be an unbiased estimate of the standard deviation per se.

Examples

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.std(b)
(array([1 2 3 4]), array([0.00000000000000000 0.00000000000000000 nan 0.00000000000000000]))

sum(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Group another array of values and sum each group’s values.

Group using the permutation stored in the GroupBy instance.

Parameters:

values (pdarray) – The values to group and sum
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_sumspdarray: One sum per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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 pdarray returns integers.

Examples

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.sum(b)
(array([1 2 3 4]), array([2 6 3 16]))

tail(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the last n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys(list of) pdarray-like: The unique keys, in grouped order
resultpdarray-like: The last n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Return type:

Tuple[groupable, groupable_element_type]

Examples

>>> import arkouda as ak
>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.tail(v, 2, return_indices=True)
>>> _, values = g.tail(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([6 9 4 7 5 8])
>>> values
array([6 9 4 7 5 8])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.tail(v2, 2, return_indices=True)
>>> _, values2 = g.tail(v2, 2, return_indices=False)
>>> idx2
array([6 9 4 7 5 8])
>>> values2
array([-12 -18 -8 -14 -10 -16])

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.

Notes

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

Return type:

(list of) pdarray-like, (list of) SegArray

Raises:

TypeError – Raised if values is or contains Strings or Categorical

unregister()[source]¶

Unregister this GroupBy object.

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

register, attach, unregister_groupby_by_name, is_registered

Notes

Objects registered with the server are immune to deletion until they are unregistered.

update_hdf(prefix_path: str, dataset: str = 'groupby', repack: bool = True)[source]¶

var(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Group another array of values and compute the variance of each group’s values.

Group using the permutation stored in the GroupBy instance.

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_varspdarray, float64: One var value per unique key in the GroupBy instance

Return type:

Tuple[groupable, pdarray]

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, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of a hypothetical infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables.

Examples

>>> import arkouda as ak
>>> a = ak.randint(1, 5, 10, seed=1)
>>> a
array([2 4 4 2 1 4 1 2 4 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([2 4 4 2 1 4 1 2 4 3])
>>> b = ak.randint(1, 5, 10, seed=1)
>>> b
array([2 4 4 2 1 4 1 2 4 3])
>>> g.var(b)
(array([1 2 3 4]), array([0.00000000000000000 0.00000000000000000 nan 0.00000000000000000]))

class arkouda.IPv4(values)[source]¶

Bases: arkouda.numpy.pdarrayclass.pdarray

Represent integers as IPv4 addresses.

Parameters:: values (pdarray, int64) – The integer IP addresses
Returns:: The same IP addresses
Return type:: IPv4

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.

export_uint()[source]¶

format(x)[source]¶: Format a single integer IP address as a string.

normalize(x)[source]¶

Normalize IP adress.

Take in an IP address as a string, integer, or IPAddress object, and convert it to an integer.

opeq(other, op)[source]¶

register(user_defined_name)[source]¶

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:

IPv4

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.

special_objType = 'IPv4'¶

to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute')[source]¶: Override of the pdarray to_hdf to store the special object type.

to_list()[source]¶: Export array as a list of integers.

to_ndarray()[source]¶: Export array as a numpy array of integers.

update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶: Override the pdarray implementation so that the special object type will be used.

values¶

class arkouda.Int16DType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Int32DType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Int64DType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Int8DType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.IntDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.LogLevel(*args, **kwds)[source]¶

Bases: enum.Enum

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

attribute access:
```
>>> Color.RED
<Color.RED: 1>
```
value lookup:
```
>>> Color(1)
<Color.RED: 1>
```
name lookup:
```
>>> Color['RED']
<Color.RED: 1>
```

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

CRITICAL = 'CRITICAL'¶

DEBUG = 'DEBUG'¶

ERROR = 'ERROR'¶

INFO = 'INFO'¶

WARN = 'WARN'¶

class arkouda.LongDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.LongDoubleDType¶

Bases: numpy.dtypes._FloatAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.LongLongDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.NUMBER_FORMAT_STRINGS¶

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:: d = {} for k, v in iterable:

d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs: in the keyword argument list. For example: dict(one=1, two=2)

clear()¶: Remove all items from the dict.

copy()¶: Return a shallow copy of the dict.

fromkeys(iterable, value=None, /)¶: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)¶: Return the value for key if key is in the dictionary, else default.

items()¶: Return a set-like object providing a view on the dict’s items.

keys()¶: Return a set-like object providing a view on the dict’s keys.

pop(*args, **kwargs)¶

D.pop(k[,d]) -> v, remove specified key and return the corresponding value.

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()¶

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)¶

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update(*args, **kwargs)¶: D.update([E, ]**F) -> None. Update D from mapping/iterable E and F. If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()¶: Return an object providing a view on the dict’s values.

exception arkouda.NonUniqueError[source]¶

Bases: ValueError

Inappropriate argument value (of correct type).

class arkouda.NumericDTypes¶

Build an immutable unordered collection of unique elements.

copy()¶: Return a shallow copy of a set.

difference(*others)¶: Return a new set with elements in the set that are not in the others.

intersection(*others)¶: Return a new set with elements common to the set and all others.

isdisjoint(other, /)¶: Return True if two sets have a null intersection.

issubset(other, /)¶: Report whether another set contains this set.

issuperset(other, /)¶: Report whether this set contains another set.

symmetric_difference(other, /)¶: Return a new set with elements in either the set or other but not both.

union(*others)¶: Return a new set with elements from the set and all others.

class arkouda.ObjectDType¶

Bases: numpy.dtype

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Power_divergenceResult[source]¶

Bases: Power_divergenceResult

The results of a power divergence statistical test.

statistic¶

Type:: float64

pvalue¶

Type:: float64

class arkouda.Properties[source]¶

arkouda.RegisteredSymbols = '__RegisteredSymbols__'¶

exception arkouda.RegistrationError[source]¶

Bases: Exception

Error/Exception used when the Arkouda Server cannot register an object

class arkouda.Row(dict=None, /, **kwargs)[source]¶

Bases: collections.UserDict

A dictionary‐like representation of a single row in an Arkouda DataFrame.

Wraps the column→value mapping for one row and provides convenient ASCII and HTML formatting for display.

Parameters:: data (dict) – Mapping of column names to their corresponding values for this row.

Examples

>>> import arkouda as ak
>>> from arkouda.row import Row
>>> df = ak.DataFrame({'x': ak.array([10, 20]), 'y': ak.array(['a', 'b'])})

Suppose df[0] returns {‘x’: 10, ‘y’: ‘a’} >>> row = Row({‘x’: 10, ‘y’: ‘a’}) >>> print(row) keys values —- —— x 10 y a

class arkouda.ScalarDTypes¶

Build an immutable unordered collection of unique elements.

copy()¶: Return a shallow copy of a set.

difference(*others)¶: Return a new set with elements in the set that are not in the others.

intersection(*others)¶: Return a new set with elements common to the set and all others.

isdisjoint(other, /)¶: Return True if two sets have a null intersection.

issubset(other, /)¶: Report whether another set contains this set.

issuperset(other, /)¶: Report whether this set contains another set.

symmetric_difference(other, /)¶: Return a new set with elements in either the set or other but not both.

union(*others)¶: Return a new set with elements from the set and all others.

class arkouda.ScalarType¶

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable’s items.

If the argument is a tuple, the return value is the same object.

count(value, /)¶: Return number of occurrences of value.

index(value, start=0, stop=9223372036854775807, /)¶

Return first index of value.

Raises ValueError if the value is not present.

class arkouda.SegArray(segments, values, lengths=None, grouping=None)[source]¶

AND(x=None)[source]¶

OR(x=None)[source]¶

XOR(x=None)[source]¶

aggregate(op, x=None)[source]¶

all(x=None)[source]¶

any(x=None)[source]¶

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:

append_single(x, prepend=False)[source]¶

Append a single value to each sub-array.

Parameters:: x (pdarray or scalar) – Single value to append to each sub-array
Returns:: Copy of original SegArray with values from x appended to each sub-array
Return type:: SegArray

argmax(x=None)[source]¶

argmin(x=None)[source]¶

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:

copy()[source]¶: Return a deep copy.

dtype¶

filter(filter, discard_empty: bool = False)[source]¶

Filter values out of the SegArray object

Parameters:

filter (pdarray, list, or value) – The value/s to be filtered out of the SegArray
discard_empty (bool) – Defaults to False. When True, empty segments are removed from the return 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.

Parameters:: m (list of pdarray or Strings) – List of columns, the rows of which will form the sub-arrays of the output
Returns:: Array of rows of input
Return type:: SegArray

classmethod from_return_msg(rep_msg) → SegArray[source]¶

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:

valpdarray: 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_indicespdarray, bool: A Boolean array that is True where j is in bounds for the sub-array.

Return type:

pdarray, pdarray|bool

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:

columnslist 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_indicespdarray, bool: Array of bool for each element of the SegArray, True where sub-array has length n.

Return type:

List of pdarray, pdarray|bool

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:

ngramslist of pdarray: An n-long list of pdarrays, essentially a table where each row is an n-gram.
origin_indicespdarray, int: The index of the sub-array from which the corresponding n-gram originated

Return type:

pdarray, pdarray|int

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:

prefixeslist 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_indicespdarray, bool: Boolean array that is True where the sub-array was long enough to return an n-suffix, False otherwise.

Return type:

List of pdarray, pdarray|bool

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:

suffixeslist 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_indicespdarray, bool: Boolean array that is True where the sub-array was long enough to return an n-suffix, False otherwise.

Return type:

List of pdarray, pdarray|bool

property grouping¶

hash() → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Compute a 128-bit hash of each segment.

Returns:: A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
Return type:: Tuple[pdarray,pdarray]

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:: SegArray

See also

pdarraysetops.intersect1d

Examples

>>> import arkouda as ak
>>> 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]¶

Check 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

register, unregister, attach

logger¶

max(x=None)[source]¶

mean(x=None)[source]¶

min(x=None)[source]¶

property nbytes¶

The size of the segarray in bytes.

Returns:: The size of the segarray in bytes.
Return type:: int

property non_empty¶

nunique(x=None)[source]¶

objType = 'SegArray'¶

prepend_single(x)[source]¶

prod(x=None)[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]¶

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:: SegArray
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

registered_name: str | None = None¶

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:

norepeatsSegArray: Sub-arrays with runs of repeated values replaced with single value
multiplicitySegArray: If return_multiplicity=True, this array contains the number of times each value in the returned SegArray was repeated in the original SegArray.

Return type:

Segarray, Segarray

segments¶

set_jth(i, j, v)[source]¶

Set the j-th element of each sub-array in a subset.

Parameters:

i (pdarray, int) – Indices of sub-arrays to set j-th element
j (int) – Index of value to set in each sub-array. If j is negative, it counts backwards from the end of the sub-array.
v (pdarray or scalar) – The value(s) to set. If v is a pdarray, it must have same length as i.

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:: SegArray

See also

pdarraysetops.setdiff1d

Examples

>>> import arkouda as ak
>>> 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:: SegArray

See also

pdarraysetops.setxor1d

Examples

>>> import arkouda as ak
>>> 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 2 3 5]
])

size¶

sum(x=None)[source]¶

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

load

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

to_ndarray

Examples

>>> import arkouda as ak
>>> 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())
<class '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

See also

array, to_list

Examples

>>> import arkouda as ak
>>> segarr = ak.SegArray(ak.array([0, 4, 7]), ak.arange(12))
>>> segarr.to_ndarray()
array([array([0, 1, 2, 3]), array([4, 5, 6]), array([ 7,  8,  9, 10, 11])],
  dtype=object)
>>> type(segarr.to_ndarray())
<class '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:

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, where  ranges from 0 to numLocales for 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, a RuntimeError will 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.numpy.dtypes.int_scalars)[source]¶

Send 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:: SegArray

See also

pdarraysetops.union1d

Examples

>>> import arkouda as ak
>>> 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]
])

unique(x=None)[source]¶

Return sub-arrays of unique values.

Parameters:: x (pdarray) – The values to unique, per group. By default, the values of this SegArray’s sub-arrays.
Returns:: Same number of sub-arrays as original SegArray, but elements in sub-array are unique and in sorted order.
Return type:: SegArray

unregister()[source]¶

Unregister this SegArray object in the arkouda server which was previously registered using register() and/or attached to using attach()

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

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.

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

valsize¶

values¶

One-dimensional arkouda array with axis labels.

Parameters:

index (pdarray, Strings) – an array of indices associated with the data array. If empty, it will default to a range of ints whose size match the size of the data. optional
data (Tuple, List, groupable_element_type, Series, SegArray) – a 1D array. Must not be None.

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’

add(b: Series) → Series[source]¶

property at: _LocIndexer¶

Accesses entries of a Series by label.

Returns:: An indexer for label-based access to Series entries.
Return type:: _LocIndexer

static concat(arrays: List, axis: int = 0, index_labels: List[str] | None = None, value_labels: List[str] | None = None, ordered: bool = False) → arkouda.dataframe.DataFrame | Series[source]¶

Concatenate a list of Arkouda Series or grouped arrays horizontally or vertically.

If a list of grouped Arkouda arrays is passed, they are converted to Series. Each grouping is a 2-tuple where the first item is the key(s) and the second is the value. If concatenating horizontally (axis=1), all series/groupings must have the same length and the same index. The index is converted to a column in the resulting DataFrame; if it’s a MultiIndex, each level is converted to a separate column.

Parameters:

arrays (List) – A list of Series or groupings (tuples of index and values) to concatenate.
axis (int, default=0) – The axis to concatenate along: - 0 = vertical (stack series into one) - 1 = horizontal (align by index and produce a DataFrame)
index_labels (List of str or None, optional) – Column name(s) to label the index when axis=1.
value_labels (List of str or None, optional) – Column names to label the values of each Series.
ordered (bool, default=False) – Unused parameter. Reserved for future support of deterministic vs. performance-optimized concatenation.

Returns:

If axis=0: a new Series
If axis=1: a new DataFrame

Return type:

Series or 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.

dt¶

property dtype: numpy.dtype¶

fillna(value: supported_scalars | Series | arkouda.numpy.pdarrayclass.pdarray) → Series[source]¶

Fill NA/NaN values using the specified method.

Parameters:: value (supported_scalars, 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:: Series

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda import Series

>>> data = ak.Series([1, np.nan, 3, np.nan, 5])
>>> data

	0
0	1.0
1	nan
2	3.0
3	nan
4	5.0

>>> fill_values1 = ak.ones(5)
>>> data.fillna(fill_values1)

	0
0	1.0
1	1.0
2	3.0
3	1.0
4	5.0

>>> fill_values2 = Series(ak.ones(5))
>>> data.fillna(fill_values2)

	0
0	1.0
1	1.0
2	3.0
3	1.0
4	5.0

>>> fill_values3 = 100.0
>>> data.fillna(fill_values3)

	0
0	1.0
1	100.0
2	3.0
3	100.0
4	5.0

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]¶: Return whether the Series has any labels that appear more than once.

hasnans() → arkouda.numpy.dtypes.bool_scalars[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

	0
0	1.0
1	2.0
2	3.0
3	nan

>>> s.hasnans()
True

head(n: int = 10) → Series[source]¶: Return the first n values of the series

property iat: _iLocIndexer¶

Accesses entries of a Series by position.

Returns:: An indexer for position-based access to a single element.
Return type:: _iLocIndexer

property iloc: _iLocIndexer¶

Accesses entries of a Series by position.

Returns:: An indexer for position-based access to Series entries.
Return type:: _iLocIndexer

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:: bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

register, attach, unregister

Notes

Objects registered with the server are immune to deletion until they are unregistered.

isin(lst: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | List) → Series[source]¶

Find Series elements whose values are in the specified list.

Parameters:: lst (pdarray, Strings, or List) – Either a Python list or an Arkouda array to check membership against.
Returns:: A Series of booleans that is True for elements found in the list, and False otherwise.
Return type:: Series

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:: Series

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.

Returns:: Mask of bool values for each element in Series that indicates whether an element is an NA value.
Return type:: Series

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

property loc: _LocIndexer¶

Accesses entries of a Series by label.

Returns:: An indexer for label-based access to Series entries.
Return type:: _LocIndexer

Lookup values by index label.

Parameters:

key (int, pdarray, Index, Series, List, or Tuple) –

The key or keys to look up. This can be: - A scalar - A list of scalars - A list of lists (for MultiIndex) - A Series (in which case labels are preserved, and its values are used as keys)

Keys will be converted to Arkouda arrays as needed.

Returns:

A Series containing the values corresponding to the key.

Return type:

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:: Series
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]))
>>> 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	d
2	b
3	d
4	a

memory_usage(index: bool = True, unit: Literal['B', 'KB', 'MB', 'GB'] = '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 ({"B", "KB", "MB", "GB"}, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.

Returns:

Bytes of memory consumed.

Return type:

int

See also

arkouda.numpy.pdarrayclass.nbytes, arkouda.Index.memory_usage, arkouda.pandas.series.Series.memory_usage, arkouda.datafame.DataFrame.memory_usage

Examples

>>> import arkouda as ak
>>> 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

property ndim: int¶

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:: Series

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.

Returns:: Mask of bool values for each element in Series that indicates whether an element is not an NA value.
Return type:: Series

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

objType = 'Series'¶

static pdconcat(arrays: List, axis: int = 0, labels: arkouda.numpy.strings.Strings | None = None) → pandas.Series | pandas.DataFrame[source]¶

Concatenate a list of Arkouda Series or grouped arrays, returning a local pandas object.

If a list of grouped Arkouda arrays is passed, they are converted to Series. Each grouping is a 2-tuple with the first item being the key(s) and the second the value.

If axis=1 (horizontal), each Series or grouping must have the same length and the same index. The index is converted to a column in the resulting DataFrame. If it is a MultiIndex, each level is converted to a separate column.

Parameters:

arrays (List) – A list of Series or groupings (tuples of index and values) to concatenate.
axis (int, default=0) – The axis along which to concatenate: - 0 = vertical (stack into a Series) - 1 = horizontal (align by index into a DataFrame)
labels (Strings or None, optional) – Names to assign to the resulting columns in the DataFrame.

Returns:

If axis=0: a local pandas Series
If axis=1: a local pandas DataFrame

Return type:

Series or DataFrame

register(user_defined_name: str)[source]¶

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.

registered_name: str | None = None¶

property shape: Tuple[int]¶

size¶

sort_index(ascending: bool = True) → Series[source]¶

Sort the Series by its index.

Parameters:: ascending (bool, default=True) – Whether to sort the index in ascending (default) or descending order.
Returns:: A new Series sorted by index.
Return type:: Series

sort_values(ascending: bool = True) → Series[source]¶

Sort the Series by its values.

Parameters:: ascending (bool, default=True) – Whether to sort values in ascending (default) or descending order.
Returns:: A new Series sorted by its values.
Return type:: Series

str_acc¶

tail(n: int = 10) → Series[source]¶: Return the last n values of the series

to_dataframe(index_labels: List[str] | None = None, value_label: str | None = None) → arkouda.dataframe.DataFrame[source]¶

Convert the Series to an Arkouda DataFrame.

Parameters:

index_labels (list of str or None, optional) – Column name(s) to label the index.
value_label (str or None, optional) – Column name to label the values.

Returns:

An Arkouda DataFrame representing the Series.

Return type:

to_list() → list[source]¶

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  |
+----+----------+

to_ndarray() → numpy.ndarray[source]¶

to_pandas() → pandas.Series[source]¶: Convert the series to a local PANDAS series

topn(n: int = 10) → Series[source]¶

Return the top values of the Series.

Parameters:: n (int, default=10) – Number of values to return. The default of 10 returns the top 10 values.
Returns:: A new Series containing the top n values.
Return type:: Series

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 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, or SegArray) – 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 type requirements for values being written into the Series. Also converts list and tuple arguments into pdarrays.

Parameters:

val (pdarray, Strings, supported_scalars, or List) – 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

value_counts(sort: bool = True) → Series[source]¶

Return a Series containing counts of unique values.

Parameters:: sort (bool, default=True) – Whether to sort the result by count in descending order. If False, the order of the results is not guaranteed.
Returns:: A Series where the index contains the unique values and the values are their counts in the original Series.
Return type:: Series

class arkouda.SeriesDTypes¶

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:: d = {} for k, v in iterable:

d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs: in the keyword argument list. For example: dict(one=1, two=2)

clear()¶: Remove all items from the dict.

copy()¶: Return a shallow copy of the dict.

fromkeys(iterable, value=None, /)¶: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)¶: Return the value for key if key is in the dictionary, else default.

items()¶: Return a set-like object providing a view on the dict’s items.

keys()¶: Return a set-like object providing a view on the dict’s keys.

pop(*args, **kwargs)¶

D.pop(k[,d]) -> v, remove specified key and return the corresponding value.

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()¶

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)¶

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update(*args, **kwargs)¶: D.update([E, ]**F) -> None. Update D from mapping/iterable E and F. If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()¶: Return an object providing a view on the dict’s values.

class arkouda.ShortDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

arkouda.SortingAlgorithm¶

class arkouda.StrDType¶

Bases: numpy.dtype

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.StringAccessor(series)[source]¶

Bases: Properties

series¶

class arkouda.Strings(strings_pdarray: arkouda.numpy.pdarrayclass.pdarray, bytes_size: arkouda.numpy.dtypes.int_scalars)[source]¶

Represents an array of strings whose data resides on the arkouda server. The user should not call this class directly; rather its instances are created by other arkouda functions.

entry¶

Encapsulation of a Segmented Strings array contained on the arkouda server. This is a composite of

offsets array: starting indices for each string

bytes array: raw bytes of all strings joined by nulls

Type:: pdarray

size¶

The number of strings in the array

Type:: int_scalars

nbytes¶

The total number of bytes in all strings

Type:: int_scalars

ndim¶

The rank of the array (currently only rank 1 arrays supported)

Type:: int_scalars

shape¶

The sizes of each dimension of the array

Type:: tuple

dtype¶

The dtype is ak.str_

Type:: type

logger¶

Used for all logging operations

Type:: ArkoudaLogger

Notes

Strings is composed of two pdarrays: (1) offsets, which contains the starting indices for each string and (2) bytes, which contains the raw bytes of all strings, delimited by nulls.

BinOps¶

astype(dtype: numpy.dtype | str) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Cast values of Strings object 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:: pdarray

Notes

This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.

cached_regex_patterns() → List[source]¶: Returns the regex patterns for which Match objects have been cached

capitalize() → Strings[source]¶

Return a new Strings from the original replaced with the first letter capitilzed and the remaining letters lowercase.

Returns:: Strings from the original replaced with the capitalized equivalent.
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown.

See also

Strings.lower, String.upper, String.title

Examples

>>> import arkouda as ak
>>> strings = ak.array([f'StrINgS aRe Here {i}' for i in range(5)])
>>> strings
array(['StrINgS aRe Here 0', 'StrINgS aRe Here 1', 'StrINgS aRe Here 2', 'StrINgS aRe Here 3', 'StrINgS aRe Here 4'])
>>> strings.title()
array(['Strings Are Here 0', 'Strings Are Here 1', 'Strings Are Here 2', 'Strings Are Here 3', 'Strings Are Here 4'])

static concatenate_uniquely(strings: List[Strings]) → Strings[source]¶

Concatenates a list of Strings into a single Strings object containing only unique strings. Order may not be preserved.

Parameters:: strings (List[Strings]) – List of segmented string objects to concatenate.
Returns:: A new Strings object containing the unique values.
Return type:: Strings

contains(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element contains the given substring.

Parameters:

substr (bytes or str_scalars) – The substring in the form of string or byte array to search for
regex (bool, default=False) – 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:

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

Examples

>>> import arkouda as ak
>>> strings = ak.array([f'{i} string {i}' for i in range(1, 6)])
>>> strings
array(['1 string 1', '2 string 2', '3 string 3', '4 string 4', '5 string 5'])
>>> strings.contains('string')
array([True True True True True])
>>> strings.contains('string \d', regex=True)
array([True True True True True])

decode(fromEncoding: str, toEncoding: str = 'UTF-8') → Strings[source]¶

Return a new strings object in fromEncoding, expecting that the current Strings is encoded in toEncoding

Parameters:

fromEncoding (str) – The current encoding of the strings object
toEncoding (str, default="UTF-8") – The encoding that the strings will be converted to, default to UTF-8

Returns:

A new Strings object in toEncoding

Return type:

Raises:

RuntimeError – Raised if there is a server-side error thrown

property dtype: numpy.dtype¶: Return the dtype object of the underlying data.

encode(toEncoding: str, fromEncoding: str = 'UTF-8') → Strings[source]¶

Return a new strings object in toEncoding, expecting that the current Strings is encoded in fromEncoding

Parameters:

toEncoding (str) – The encoding that the strings will be converted to
fromEncoding (str, default="UTF-8") – The current encoding of the strings object, default to UTF-8

Returns:

A new Strings object in toEncoding

Return type:

Raises:

RuntimeError – Raised if there is a server-side error thrown

endswith(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element ends with the given substring.

Parameters:

substr (bytes or str_scalars) – The suffix to search for
regex (bool, default=False) – 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:

Strings.findall, Strings.match

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

Examples

>>> import arkouda as ak
>>> strings_start = ak.array([f'{i} string' for i in range(1,6)])
>>> strings_start
array(['1 string', '2 string', '3 string', '4 string', '5 string'])
>>> strings_start.endswith('ing')
array([True True True True True])
>>> strings_end = ak.array([f'string {i}' for i in range(1, 6)])
>>> strings_end
array(['string 1', 'string 2', 'string 3', 'string 4', 'string 5'])
>>> strings_end.endswith('ing \d', regex = True)
array([True True True True True])

entry: arkouda.numpy.pdarrayclass.pdarray¶

equals(other: Any) → arkouda.numpy.dtypes.bool_scalars[source]¶

Whether Strings are the same size and all entries are equal.

Parameters:: other (Any) – object to compare.
Returns:: True if the Strings are the same, o.w. False.
Return type:: bool_scalars

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> s = ak.array(["a", "b", "c"])
>>> s_cpy = ak.array(["a", "b", "c"])
>>> s.equals(s_cpy)
np.True_
>>> s2 = ak.array(["a", "x", "c"])
>>> s.equals(s2)
np.False_

find_locations(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Finds pattern matches and returns pdarrays containing the number, start postitions, and lengths of matches

Parameters:

pattern (bytes or str_scalars) – The regex pattern used to find matches

Returns:

pdarray, int64: For each original string, the number of pattern matches
pdarray, int64: The start positons of pattern matches
pdarray, int64: The lengths of pattern matches

Return type:

Tuple[pdarray, pdarray, pdarray]

Raises:

TypeError – Raised if the pattern parameter is not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> import arkouda as ak
>>> strings = ak.array([f'{i} string {i}' for i in range(1, 6)])
>>> num_matches, starts, lens = strings.find_locations('\d')
>>> num_matches
array([2 2 2 2 2])
>>> starts
array([0 9 0 9 0 9 0 9 0 9])
>>> lens
array([1 1 1 1 1 1 1 1 1 1])

findall(pattern: bytes | arkouda.numpy.dtypes.str_scalars, return_match_origins: bool = False) → Strings | Tuple[source]¶

Return a new Strings containg all non-overlapping matches of pattern

Parameters:

pattern (bytes or str_scalars) – Regex used to find matches
return_match_origins (bool, default=False) – If True, return a pdarray containing the index of the original string each pattern match is from

Returns:

Strings: Strings object containing only pattern matches
pdarray, int64 (optional): The index of the original string each pattern match is from

Return type:

Union[Strings, Tuple]

Raises:

TypeError – Raised if the pattern parameter is not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.find_locations

Examples

>>> import arkouda as ak
>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.findall('_+', return_match_origins=True)
(array(['_', '___', '____', '__', '___', '____', '___']), array([0 0 1 3 3 3 3]))

flatten() → Strings[source]¶

Return a copy of the array collapsed into one dimension.

Return type:: A copy of the input array, flattened to one dimension.

Note

As multidimensional Strings are currently supported, flatten on a Strings object will always return itself.

static from_parts(offset_attrib: arkouda.numpy.pdarrayclass.pdarray | str, bytes_attrib: arkouda.numpy.pdarrayclass.pdarray | str) → Strings[source]¶

Assemble a Strings object from separate offset and bytes arrays.

This factory method constructs a segmented Strings array by sending two separate components—offsets and values—to the Arkouda server and instructing it to assemble them into a single Strings object. Use this when offsets and byte data are created or transported independently.

Parameters:

offset_attrib (pdarray or str) – The array of starting positions for each string, or a string expression that can be passed to create_pdarray to build it.
bytes_attrib (pdarray or str) – The array of raw byte values (e.g., uint8 character codes), or a string expression that can be passed to create_pdarray to build it.

Returns:

A Strings object representing the assembled segmented strings array on the Arkouda server.

Return type:

Strings.islower, Strings.isupper

Raises:

RuntimeError – If conversion of offset_attrib or bytes_attrib to pdarray fails, or if the server is unable to assemble the parts into a Strings.

Notes

Both inputs can be existing pdarray instances or arguments suitable for create_pdarray.
Internally uses the CMD_ASSEMBLE command to merge offsets and values.

static from_return_msg(rep_msg: str) → Strings[source]¶

Create a Strings object from an Arkouda server response message.

Parse the server’s response descriptor and construct a Strings array with its underlying pdarray and total byte size.

Parameters:: rep_msg (str) – Server response message of the form: ` created <name> <type> <size> <ndim> <shape> <itemsize>+... bytes.size <total_bytes> ` For example: ` "created foo Strings 3 1 (3,) 8+created bytes.size 24" `
Returns:: A Strings object representing the segmented strings array on the server, initialized with the returned pdarray and byte-size metadata.
Return type:: Strings
Raises:: RuntimeError – If the response message cannot be parsed or does not match the expected format.

Examples

>>> import arkouda as ak

# Example response message (typically from generic_msg) >>> rep_msg = “created foo Strings 3 1 (3,) 8+created bytes.size 24” >>> s = ak.Strings.from_return_msg(rep_msg) >>> isinstance(s, ak.Strings) True

fullmatch(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Return a match object where elements match only if the whole string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match only if the whole string matches the regular expression pattern
Return type:: Match

Examples

>>> import arkouda as ak
>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.fullmatch('_+')
<ak.Match object: matched=False; matched=True, span=(0, 4); matched=False;
matched=False; matched=False>

get_bytes() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Getter for the bytes component (uint8 pdarray) of this Strings.

Returns:: Pdarray of bytes of the string accessed
Return type:: pdarray

Example

>>> import arkouda as ak
>>> x = ak.array(['one', 'two', 'three'])
>>> x.get_bytes()
array([111 110 101 0 116 119 111 0 116 104 114 101 101 0])

get_lengths() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the length of each string in the array.

Returns:: The length of each string
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

get_offsets() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Getter for the offsets component (int64 pdarray) of this Strings.

Returns:: Pdarray of offsets of the string accessed
Return type:: pdarray

Example

>>> import arkouda as ak
>>> x = ak.array(['one', 'two', 'three'])
>>> x.get_offsets()
array([0 4 8])

get_prefixes(n: arkouda.numpy.dtypes.int_scalars, return_origins: bool = True, proper: bool = True) → Strings | Tuple[Strings, arkouda.numpy.pdarrayclass.pdarray][source]¶

Return the n-long prefix of each string, where possible

Parameters:

n (int_scalars) – Length of prefix
return_origins (bool, default=True) – If True, return a logical index indicating which strings were long enough to return an n-prefix
proper (bool, default=True) – If True, only return proper prefixes, i.e. from strings that are at least n+1 long. If False, allow the entire string to be returned as a prefix.

Returns:

prefixesStrings: The array of n-character prefixes; the number of elements is the number of True values in the returned mask.
origin_indicespdarray, bool: Boolean array that is True where the string was long enough to return an n-character prefix, False otherwise.

Return type:

Union[Strings, Tuple[Strings, pdarray]]

get_suffixes(n: arkouda.numpy.dtypes.int_scalars, return_origins: bool = True, proper: bool = True) → Strings | Tuple[Strings, arkouda.numpy.pdarrayclass.pdarray][source]¶

Return the n-long suffix of each string, where possible

Parameters:

n (int_scalars) – Length of suffix
return_origins (bool, default=True) – If True, return a logical index indicating which strings were long enough to return an n-suffix
proper (bool, default=True) – If True, only return proper suffixes, i.e. from strings that are at least n+1 long. If False, allow the entire string to be returned as a suffix.

Returns:

suffixesStrings: The array of n-character suffixes; the number of elements is the number of True values in the returned mask.
origin_indicespdarray, bool: Boolean array that is True where the string was long enough to return an n-character suffix, False otherwise.

Return type:

Union[Strings, Tuple[Strings, pdarray]]

group() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the permutation that groups the array, placing equivalent strings together. All instances of the same string 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:: pdarray

See also

GroupBy, unique

Notes

If the arkouda server is compiled with “-sSegmentedString.useHash=true”, then arkouda uses 128-bit hash values to group strings, rather than sorting the strings directly. This method is fast, but the resulting permutation merely groups equivalent strings and does not sort them. If the “useHash” parameter is false, then a full sort is performed.

Raises:: RuntimeError – Raised if there is a server-side error in executing group request or creating the pdarray encapsulating the return message

hash() → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Compute a 128-bit hash of each string.

Returns:: A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
Return type:: Tuple[pdarray,pdarray]

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.

property inferred_type: str¶: Return a string of the type inferred from the values.

info() → str[source]¶

Return a JSON formatted string containing information about all components of self

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

isalnum() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i of the Strings is alphanumeric.

Returns:: True for elements that are alphanumeric, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> import arkouda as ak
>>> not_alnum = ak.array([f'%Strings {i}' for i in range(3)])
>>> alnum = ak.array([f'Strings{i}' for i in range(3)])
>>> strings = ak.concatenate([not_alnum, alnum])
>>> strings
array(['%Strings 0', '%Strings 1', '%Strings 2', 'Strings0', 'Strings1', 'Strings2'])
>>> strings.isalnum()
array([False False False True True True])

isalpha() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i of the Strings is alphabetic. This means there is at least one character, and all the characters are alphabetic.

Returns:: True for elements that are alphabetic, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> import arkouda as ak
>>> not_alpha = ak.array([f'%Strings {i}' for i in range(3)])
>>> alpha = ak.array(['StringA','StringB','StringC'])
>>> strings = ak.concatenate([not_alpha, alpha])
>>> strings
array(['%Strings 0', '%Strings 1', '%Strings 2', 'StringA', 'StringB', 'StringC'])
>>> strings.isalpha()
array([False False False True True True])

isdecimal() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i of the Strings has all decimal characters.

Returns:: True for elements that are decimals, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.isdigit

Examples

>>> import arkouda as ak
>>> not_decimal = ak.array([f'Strings {i}' for i in range(3)])
>>> decimal = ak.array([f'12{i}' for i in range(3)])
>>> strings = ak.concatenate([not_decimal, decimal])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '120', '121', '122'])
>>> strings.isdecimal()
array([False False False True True True])

Special Character Examples

>>> special_strings = ak.array(["3.14", "0", "²", "2³₇", "2³x₇"])
>>> special_strings
array(['3.14', '0', '²', '2³₇', '2³x₇'])
>>> special_strings.isdecimal()
array([False True False False False])

isdigit() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i of the Strings has all digit characters.

Returns:: True for elements that are digits, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> import arkouda as ak
>>> not_digit = ak.array([f'Strings {i}' for i in range(3)])
>>> digit = ak.array([f'12{i}' for i in range(3)])
>>> strings = ak.concatenate([not_digit, digit])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '120', '121', '122'])
>>> strings.isdigit()
array([False False False True True True])

Special Character Examples

>>> special_strings = ak.array(["3.14", "0", "²", "2³₇", "2³x₇"])
>>> special_strings
array(['3.14', '0', '²', '2³₇', '2³x₇'])
>>> special_strings.isdigit()
array([False True True True False])

isempty() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i of the Strings is empty.

True for elements that are the empty string, False otherwise

Returns:: True for elements that are digits, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> import arkouda as ak
>>> not_empty = ak.array([f'Strings {i}' for i in range(3)])
>>> empty = ak.array(['' for i in range(3)])
>>> strings = ak.concatenate([not_empty, empty])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '', '', ''])
>>> strings.isempty()
array([False False False True True True])

islower() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i of the Strings is entirely lowercase

Returns:: True for elements that are entirely lowercase, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.isupper

Examples

>>> import arkouda as ak
>>> lower = ak.array([f'strings {i}' for i in range(3)])
>>> upper = ak.array([f'STRINGS {i}' for i in range(3)])
>>> strings = ak.concatenate([lower, upper])
>>> strings
array(['strings 0', 'strings 1', 'strings 2', 'STRINGS 0', 'STRINGS 1', 'STRINGS 2'])
>>> strings.islower()
array([True True True False False False])

isspace() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i has all whitespace characters (‘ ’, ‘\t’, ‘\n’, ‘\v’, ‘\f’, ‘\r’).

Returns:: True for elements that are whitespace, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> import arkouda as ak
>>> not_space = ak.array([f'Strings {i}' for i in range(3)])
>>> space = ak.array([' ', '\t', '\n', '\v', '\f', '\r', ' \t\n\v\f\r'])
>>> strings = ak.concatenate([not_space, space])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', ' ', 'u0009', 'n', 'u000B', 'u000C', 'u000D', ' u0009nu000Bu000Cu000D'])
>>> strings.isspace()
array([False False False True True True True True True True])

istitle() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i of the Strings is titlecase

Returns:: True for elements that are titlecase, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> import arkouda as ak
>>> mixed = ak.array([f'sTrINgs {i}' for i in range(3)])
>>> title = ak.array([f'Strings {i}' for i in range(3)])
>>> strings = ak.concatenate([mixed, title])
>>> strings
array(['sTrINgs 0', 'sTrINgs 1', 'sTrINgs 2', 'Strings 0', 'Strings 1', 'Strings 2'])
>>> strings.istitle()
array([False False False True True True])

isupper() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a boolean pdarray where index i indicates whether string i of the Strings is entirely uppercase

Returns:: True for elements that are entirely uppercase, False otherwise
Return type:: pdarray
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.islower

Examples

>>> import arkouda as ak
>>> lower = ak.array([f'strings {i}' for i in range(3)])
>>> upper = ak.array([f'STRINGS {i}' for i in range(3)])
>>> strings = ak.concatenate([lower, upper])
>>> strings
array(['strings 0', 'strings 1', 'strings 2', 'STRINGS 0', 'STRINGS 1', 'STRINGS 2'])
>>> strings.isupper()
array([False False False True True True])

logger: arkouda.logger.ArkoudaLogger¶

lower() → Strings[source]¶

Return a new Strings with all uppercase characters from the original replaced with their lowercase equivalent

Returns:: Strings with all uppercase characters from the original replaced with their lowercase equivalent
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.upper

Examples

>>> import arkouda as ak
>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.lower()
array(['strings 0', 'strings 1', 'strings 2', 'strings 3', 'strings 4'])

lstick(other: Strings, delimiter: bytes | arkouda.numpy.dtypes.str_scalars = '') → Strings[source]¶

Join the strings from another array onto the left of the strings of this array, optionally inserting a delimiter. Warning: This function is experimental and not guaranteed to work.

Parameters:

other (Strings) – The strings to join onto self’s strings
delimiter (bytes or str_scalars, default="") – String inserted between self and other

Returns:

The array of joined strings, as other + self

Return type:

Raises:

TypeError – Raised if the delimiter parameter is neither bytes nor a str or if the other parameter is not a Strings instance
RuntimeError – Raised if there is a server-side error thrown

See also

stick, peel, rpeel

Examples

>>> import arkouda as ak
>>> s = ak.array(['a', 'c', 'e'])
>>> t = ak.array(['b', 'd', 'f'])
>>> s.lstick(t, delimiter='.')
array(['b.a', 'd.c', 'f.e'])

match(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Return a match object where elements match only if the beginning of the string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match only if the beginning of the string matches the regular expression pattern
Return type:: Match

Examples

>>> import arkouda as ak
>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.match('_+')
<ak.Match object: matched=False; matched=True, span=(0, 4); matched=False;
matched=True, span=(0, 2); matched=False>

nbytes: arkouda.numpy.dtypes.int_scalars¶

ndim: arkouda.numpy.dtypes.int_scalars¶

objType = 'Strings'¶

peel(delimiter: bytes | arkouda.numpy.dtypes.str_scalars, times: arkouda.numpy.dtypes.int_scalars = 1, includeDelimiter: bool = False, keepPartial: bool = False, fromRight: bool = False, regex: bool = False) → Tuple[Strings, Strings][source]¶

Peel off one or more delimited fields from each string (similar to string.partition), returning two new arrays of strings. Warning: This function is experimental and not guaranteed to work.

Parameters:

delimiter (bytes or str_scalars) – The separator where the split will occur
times (int_scalars, default=1) – The number of times the delimiter is sought, i.e. skip over the first (times-1) delimiters
includeDelimiter (bool, default=False) – If true, append the delimiter to the end of the first return array. By default, it is prepended to the beginning of the second return array.
keepPartial (bool, default=False) – If true, a string that does not contain <times> instances of the delimiter will be returned in the first array. By default, such strings are returned in the second array.
fromRight (bool, default=False) – If true, peel from the right instead of the left (see also rpeel)
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

left: Strings: The field(s) peeled from the end of each string (unless fromRight is true)
right: Strings: The remainder of each string after peeling (unless fromRight is true)

Return type:

Tuple[Strings, Strings]

Raises:

TypeError – Raised if the delimiter parameter is not byte or str_scalars, if times is not int64, or if includeDelimiter, keepPartial, or fromRight is not bool
ValueError – Raised if times is < 1 or if delimiter is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

rpeel, stick, lstick

Examples

>>> import arkouda as ak
>>> s = ak.array(['a.b', 'c.d', 'e.f.g'])
>>> s.peel('.')
(array(['a', 'c', 'e']), array(['b', 'd', 'f.g']))
>>> s.peel('.', includeDelimiter=True)
(array(['a.', 'c.', 'e.']), array(['b', 'd', 'f.g']))
>>> s.peel('.', times=2)
(array(['', '', 'e.f']), array(['a.b', 'c.d', 'g']))
>>> s.peel('.', times=2, keepPartial=True)
(array(['a.b', 'c.d', 'e.f']), array(['', '', 'g']))

pretty_print_info() → None[source]¶: Print information about all components of self in a human readable format.

purge_cached_regex_patterns() → None[source]¶: purges cached regex patterns

regex_split(pattern: bytes | arkouda.numpy.dtypes.str_scalars, maxsplit: int = 0, return_segments: bool = False) → Strings | Tuple[source]¶

Return a new Strings split by the occurrences of pattern. If maxsplit is nonzero, at most maxsplit splits occur

Parameters:

pattern (bytes or str_scalars) – Regex used to split strings into substrings
maxsplit (int, default=0) – The max number of pattern match occurences in each element to split. The default maxsplit=0 splits on all occurences
return_segments (bool, default=False) – If True, return mapping of original strings to first substring in return array.

Returns:

Strings: Substrings with pattern matches removed
pdarray, int64 (optional): For each original string, the index of first corresponding substring in the return array

Return type:

Union[Strings, Tuple]

Examples

>>> import arkouda as ak
>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.regex_split('_+', maxsplit=2, return_segments=True)
(array(['1', '2', '', '', '', '3', '', '4', '5____6___7', '']), array([0 3 5 6 9]))

register(user_defined_name: str) → Strings[source]¶

Register this Strings object with a user defined name in the arkouda server so it can be attached to later using Strings.attach() This is an in-place operation, registering a Strings object more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one object at a time.

Parameters:

user_defined_name (str) – user defined name which the Strings object is to be registered under

Returns:

The same Strings object 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 objects 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 Strings object with the user_defined_name If the user is attempting to register more than one object with the same name, the former should be unregistered first to free up the registration name.

See also

attach, unregister

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

registered_name: str | None = None¶

rpeel(delimiter: bytes | arkouda.numpy.dtypes.str_scalars, times: arkouda.numpy.dtypes.int_scalars = 1, includeDelimiter: bool = False, keepPartial: bool = False, regex: bool = False) → Tuple[Strings, Strings][source]¶

Peel off one or more delimited fields from the end of each string (similar to string.rpartition), returning two new arrays of strings. Warning: This function is experimental and not guaranteed to work.

Parameters:

delimiter (bytes or str_scalars) – The separator where the split will occur
times (int_scalars, default=1) – The number of times the delimiter is sought, i.e. skip over the last (times-1) delimiters
includeDelimiter (bool, default=False) – If true, prepend the delimiter to the start of the first return array. By default, it is appended to the end of the second return array.
keepPartial (bool, default=False) – If true, a string that does not contain <times> instances of the delimiter will be returned in the second array. By default, such strings are returned in the first array.
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

left: Strings: The remainder of the string after peeling
right: Strings: The field(s) that were peeled from the right of each string

Return type:

Tuple[Strings, Strings]

Raises:

TypeError – Raised if the delimiter parameter is not bytes or str_scalars or if times is not int64
ValueError – Raised if times is < 1 or if delimiter is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

peel, stick, lstick

Examples

>>> import arkouda as ak
>>> s = ak.array(['a.b', 'c.d', 'e.f.g'])
>>> s.rpeel('.')
(array(['a', 'c', 'e.f']), array(['b', 'd', 'g']))

Compared against peel

>>> s.peel('.')
(array(['a', 'c', 'e']), array(['b', 'd', 'f.g']))

search(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Return a match object with the first location in each element where pattern produces a match. Elements match if any part of the string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match if any part of the string matches the regular expression pattern
Return type:: Match

Examples

>>> import arkouda as ak
>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.search('_+')
<ak.Match object: matched=True, span=(1, 2); matched=True, span=(0, 4);
matched=False; matched=True, span=(0, 2); matched=False>

shape: Tuple[int]¶

size: arkouda.numpy.dtypes.int_scalars¶

split(delimiter: str, return_segments: bool = False, regex: bool = False) → Strings | Tuple[source]¶

Unpack delimiter-joined substrings into a flat array.

Parameters:

delimiter (str) – Characters used to split strings into substrings
return_segments (bool, default=False) – If True, also return mapping of original strings to first substring in return array.
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

Strings: Flattened substrings with delimiters removed
pdarray, int64 (optional): For each original string, the index of first corresponding substring in the return array

Return type:

Union[Strings, Tuple]

See also

peel, rpeel

Examples

>>> import arkouda as ak
>>> orig = ak.array(['one|two', 'three|four|five', 'six'])
>>> orig.split('|')
array(['one', 'two', 'three', 'four', 'five', 'six'])
>>> flat, mapping = orig.split('|', return_segments=True)
>>> mapping
array([0 2 5])
>>> under = ak.array(['one_two', 'three_____four____five', 'six'])
>>> under_split, under_map = under.split('_+', return_segments=True, regex=True)
>>> under_split
array(['one', 'two', 'three', 'four', 'five', 'six'])
>>> under_map
array([0 2 5])

startswith(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element starts with the given substring.

Parameters:

substr (bytes or str_scalars) – The prefix to search for
regex (bool, default=False) – 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:

Strings.contains, Strings.endswith

Raises:

TypeError – Raised if the substr parameter is not a bytes ior str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> import arkouda as ak
>>> strings_end = ak.array([f'string {i}' for i in range(1, 6)])
>>> strings_end
array(['string 1', 'string 2', 'string 3', 'string 4', 'string 5'])
>>> strings_end.startswith('string')
array([True True True True True])
>>> strings_start = ak.array([f'{i} string' for i in range(1,6)])
>>> strings_start
array(['1 string', '2 string', '3 string', '4 string', '5 string'])
>>> strings_start.startswith('\d str', regex = True)
array([True True True True True])

stick(other: Strings, delimiter: bytes | arkouda.numpy.dtypes.str_scalars = '', toLeft: bool = False) → Strings[source]¶

Join the strings from another array onto one end of the strings of this array, optionally inserting a delimiter. Warning: This function is experimental and not guaranteed to work.

Parameters:

other (Strings) – The strings to join onto self’s strings
delimiter (bytes or str_scalars, default="") – String inserted between self and other
toLeft (bool, default=False) – If true, join other strings to the left of self. By default, other is joined to the right of self.

Returns:

The array of joined strings

Return type:

Raises:

TypeError – Raised if the delimiter parameter is not bytes or str_scalars or if the other parameter is not a Strings instance
ValueError – Raised if times is < 1
RuntimeError – Raised if there is a server-side error thrown

See also

lstick, peel, rpeel

Examples

>>> import arkouda as ak
>>> s = ak.array(['a', 'c', 'e'])
>>> t = ak.array(['b', 'd', 'f'])
>>> s.stick(t, delimiter='.')
array(['a.b', 'c.d', 'e.f'])

strip(chars: bytes | arkouda.numpy.dtypes.str_scalars | None = '') → Strings[source]¶

Return a new Strings object with all leading and trailing occurrences of characters contained in chars removed. The chars argument is a string specifying the set of characters to be removed. If omitted, the chars argument defaults to removing whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped.

Parameters:: chars (bytes or str_scalars, optional) – the set of characters to be removed
Returns:: Strings object with the leading and trailing characters matching the set of characters in the chars argument removed
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> import arkouda as ak
>>> strings = ak.array(['Strings ', '  StringS  ', 'StringS   '])
>>> s = strings.strip()
>>> s
array(['Strings', 'StringS', 'StringS'])

>>> strings = ak.array(['Strings 1', '1 StringS  ', '  1StringS  12 '])
>>> s = strings.strip(' 12')
>>> s
array(['Strings', 'StringS', 'StringS'])

sub(pattern: bytes | arkouda.numpy.dtypes.str_scalars, repl: bytes | arkouda.numpy.dtypes.str_scalars, count: int = 0) → Strings[source]¶

Return new Strings obtained by replacing non-overlapping occurrences of pattern with the replacement repl. If count is nonzero, at most count substitutions occur

Parameters:

pattern (bytes or str_scalars) – The regex to substitue
repl (bytes or str_scalars) – The substring to replace pattern matches with
count (int, default=0) – The max number of pattern match occurences in each element to replace. The default count=0 replaces all occurences of pattern with repl

Returns:

Strings with pattern matches replaced

Return type:

Raises:

TypeError – Raised if pattern or repl are not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.subn

Examples

>>> import arkouda as ak
>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.sub(pattern='_+', repl='-', count=2)
array(['1-2-', '-', '3', '-4-5____6___7', ''])

subn(pattern: bytes | arkouda.numpy.dtypes.str_scalars, repl: bytes | arkouda.numpy.dtypes.str_scalars, count: int = 0) → Tuple[Strings, arkouda.numpy.pdarrayclass.pdarray][source]¶

Perform the same operation as sub(), but return a tuple (new_Strings, number_of_substitions)

Parameters:

pattern (bytes or str_scalars) – The regex to substitue
repl (bytes or str_scalars) – The substring to replace pattern matches with
count (int, default=0) – The max number of pattern match occurences in each element to replace. The default count=0 replaces all occurences of pattern with repl

Returns:

Strings: Strings with pattern matches replaced
pdarray, int64: The number of substitutions made for each element of Strings

Return type:

Tuple[Strings, pdarray]

Raises:

TypeError – Raised if pattern or repl are not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.sub

Examples

>>> import arkouda as ak
>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.subn(pattern='_+', repl='-', count=2)
(array(['1-2-', '-', '3', '-4-5____6___7', '']), array([2 1 0 2 0]))

title() → Strings[source]¶

Return a new Strings from the original replaced with their titlecase equivalent.

Returns:: Strings from the original replaced with their titlecase equivalent.
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown.

See also

Strings.lower, String.upper

Examples

>>> import arkouda as ak
>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.title()
array(['Strings 0', 'Strings 1', 'Strings 2', 'Strings 3', 'Strings 4'])

to_csv(prefix_path: str, dataset: str = 'strings_array', col_delim: str = ',', overwrite: bool = False) → str[source]¶

Write Strings to CSV file(s). File will contain a single column with the Strings data. 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:

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, default="strings_array") – Column name to save the Strings under. Defaults to “strings_array”.
col_delim (str, default=",") – 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, default=False) – 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.

Returns:

response message

Return type:

str

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.

to_hdf(prefix_path: str, dataset: str = 'strings_array', mode: Literal['truncate', 'append'] = 'truncate', save_offsets: bool = True, file_type: Literal['single', 'distribute'] = 'distribute') → str[source]¶

Save the Strings object to HDF5. The object can be saved to a collection of files or single file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – The name of the Strings dataset to be written, defaults to strings_array
mode ({"truncate", "append"}, default = "truncate") – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Strings dataset within existing files.
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read.
file_type ({"single", "distribute"}, default = "distribute") – Default: Distribute Distribute the dataset over a file per locale. Single file will save the dataset to one file

Returns:

String message indicating result of save operation

Return type:

str

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

Parquet files do not store the segments, only the values.
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
the hdf5 group is named via the dataset parameter.
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, where  ranges from 0 to numLocales for 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, a RuntimeError will 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_hdf

to_list() → List[str][source]¶

Convert the SegString to a list, transferring data from the arkouda server to Python. If the SegString exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A list with the same strings as this SegString
Return type:: List[str]

Notes

The number of bytes in the array cannot exceed ak.client.maxTransferBytes, otherwise a RuntimeError will 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.

See also

to_ndarray

Examples

>>> import arkouda as ak
>>> a = ak.array(["hello", "my", "world"])
>>> a.to_list()
['hello', 'my', 'world']
>>> type(a.to_list())
<class 'list'>

to_ndarray() → numpy.ndarray[source]¶

Convert the array to a np.ndarray, transferring array data from the arkouda server to Python. If the array exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A numpy ndarray with the same strings as this array
Return type:: np.ndarray

Notes

See also

array, to_list

Examples

>>> import arkouda as ak
>>> a = ak.array(["hello", "my", "world"])
>>> a.to_ndarray()
array(['hello', 'my', 'world'], dtype='<U5')
>>> type(a.to_ndarray())
<class 'numpy.ndarray'>

to_parquet(prefix_path: str, dataset: str = 'strings_array', mode: Literal['truncate', 'append'] = 'truncate', compression: Literal['snappy', 'gzip', 'brotli', 'zstd', 'lz4'] | None = None) → str[source]¶

Save the Strings 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 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, default=”strings_array” :param mode: By default, truncate (overwrite) output files, if they exist.

If ‘append’, attempt to create new dataset in existing files.

Parameters:: compression ({"snappy", "gzip", "brotli", "zstd", "lz4"}, optional) – Sets the compression type used with Parquet files
Returns:: string message indicating result of save operation
Return type:: str
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, where  ranges from 0 to numLocales for 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, a RuntimeError will 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.numpy.dtypes.int_scalars) → str | memoryview[source]¶

Send a Strings object to a different Arkouda server.

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the Strings object 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() → None[source]¶

Unregister a Strings object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

See also

register, attach

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

update_hdf(prefix_path: str, dataset: str = 'strings_array', save_offsets: bool = True, repack: bool = True) → str[source]¶

Overwrite the dataset with the name provided with this Strings 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, default="strings_array") – Name of the dataset to create in files
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read.
repack (bool, default=True) – 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 Strings object

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

upper() → Strings[source]¶

Return a new Strings with all lowercase characters from the original replaced with their uppercase equivalent

Returns:: Strings with all lowercase characters from the original replaced with their uppercase equivalent
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.lower

Examples

>>> import arkouda as ak
>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.upper()
array(['STRINGS 0', 'STRINGS 1', 'STRINGS 2', 'STRINGS 3', 'STRINGS 4'])

class arkouda.TimeDelta64DType¶

Bases: numpy.dtype

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.Timedelta(pda, unit: str = _BASE_UNIT)[source]¶

Bases: _AbstractBaseTime

Represents 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 .values attribute is always in nanoseconds with int64 dtype.

abs()[source]¶: Absolute value of time interval.

property components¶

property days¶

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

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property microseconds¶

property nanoseconds¶

register(user_defined_name)[source]¶

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:

Timedelta

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.

property seconds¶

special_objType = 'Timedelta'¶

std(ddof: arkouda.numpy.dtypes.int_scalars = 0, axis: None | int | tuple | None = None, keepdims: bool | None = False)[source]¶: Returns the standard deviation as a pd.Timedelta object, with args compatible with ak.std

sum()[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numeric_scalars if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.sum(ak.array([1,2,3,4,5]))
np.int64(15)
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
np.float64(17.5)
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

supported_opeq¶

supported_with_datetime¶

supported_with_pdarray¶

supported_with_r_datetime¶

supported_with_r_pdarray¶

supported_with_r_timedelta¶

supported_with_timedelta¶

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

total_seconds()[source]¶

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.True_¶

Bases: numpy.generic

Boolean type (True or False), stored as a byte.

Warning

The bool type is not a subclass of the int_ type (the bool is not even a number type). This is different than Python’s default implementation of bool as a sub-class of int.

Character code:

'?'

class arkouda.UByteDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.UInt16DType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.UInt32DType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.UInt64DType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.UInt8DType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.UIntDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.ULongDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.ULongLongDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.UShortDType¶

Bases: numpy.dtypes._IntegerAbstractDType

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

class arkouda.VoidDType¶

Bases: numpy.dtype

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

arkouda.abs(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.abs(ak.arange(-5,-1))
array([5 4 3 2])

>>> ak.abs(ak.linspace(-5,-1,5))
array([5.00000000000000000 4.00000000000000000 3.00000000000000000
2.00000000000000000 1.00000000000000000])

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

class arkouda.all_scalars¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

arkouda.apply(arr: arkouda.numpy.pdarrayclass.pdarray, func: Callable | str, result_dtype: numpy.dtype | str | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Apply a python function to a pdarray.

The function should take one argument and return a new value. The function will then be called on each element in the pdarray.

Warning: This function is experimental and may not work as expected. Known limitations: - Any python modules used inside of the function must be installed on the server.

Parameters:

arr (pdarray) – The pdarray to which the function is applied
func (Union[Callable, str]) – The function to apply to the array. This can be a callable function or a string, but either way it should take a single argument and return a single value. If a string, it should be a lambda function that takes a single argument, e.g. “lambda x,: x+1”. Note the dangling comma after the argument, this is required for string functions.
result_dtype (Optional[Union[np.dtype, str]]) – The dtype of the resulting pdarray. If None, the dtype of the resulting pdarray will be the same as the input pdarray. If a string, it should be a valid numpy dtype string, e.g. “float64”. If a numpy dtype, it should be a valid numpy dtype object, e.g. np.float64. This is not supported for functions passed as strings.

Returns:

The pdarray resulting from applying the function to the input array

Return type:

Examples

>>> import arkouda as ak
>>> arr = ak.apply(ak.array([1, 2, 3]), lambda x: x+1)
>>> arr
array([2 3 4])

Or, >>> import math >>> arr = ak.randint(0, 10, 4, seed=1) >>> def times_pi(x): … return x*math.pi >>> arr = ak.apply(arr, times_pi, “float64”) >>> arr array([21.991148575128552 28.274333882308138 15.707963267948966 3.1415926535897931])

arkouda.arange(__arg1: arkouda.numpy.dtypes.int_scalars, *, dtype: numpy.dtype | type | arkouda.numpy.dtypes.bigint | None = None, max_bits: int | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

arkouda.arange(__arg1: arkouda.numpy.dtypes.int_scalars, __arg2: arkouda.numpy.dtypes.int_scalars, *, dtype: numpy.dtype | type | arkouda.numpy.dtypes.bigint | None = None, max_bits: int | None = None) → arkouda.numpy.pdarrayclass.pdarray

arkouda.arange(__arg1: arkouda.numpy.dtypes.int_scalars, __arg2: arkouda.numpy.dtypes.int_scalars, __arg3: arkouda.numpy.dtypes.int_scalars, *, dtype: numpy.dtype | type | arkouda.numpy.dtypes.bigint | None = None, max_bits: int | None = None) → arkouda.numpy.pdarrayclass.pdarray

arange([start,] stop[, step,] 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 step.

The return value is cast to type dtype

Parameters:

start (int_scalars, optional)
stop (int_scalars, optional)
step (int_scalars, optional) – if one of these three is supplied, it’s used as stop, and start = 0, step = 1 if two of them are supplied, start = start, stop = stop, step = 1 if all three are supplied, start = start, stop = stop, step = step
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 step

Return type:

Raises:

ValueError – Raised if none of start, stop, step was supplied
TypeError – Raised if start, stop, or step is not an int object
ZeroDivisionError – Raised if step == 0

See also

linspace, zeros, ones, randint

Notes

Negative steps result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.

Examples

>>> import arkouda as ak
>>> 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.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise inverse cosine of the array. The result is between 0 and pi.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the inverse cosine will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-0.7,0.5,4)
>>> ak.arccos(a)
array([2.3461938234056494 1.8754889808102941 1.4706289056333368 1.0471975511965979])

arkouda.arccosh(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise inverse hyperbolic cosine of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the inverse hyperbolic cosine will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(1,500,4)
>>> ak.arccosh(a)
array([0.00000000000000000 5.8131260830342795 6.5032874256927515 6.9077542789806374])

arkouda.arcsin(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise inverse sine of the array. The result is between -pi/2 and pi/2.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the inverse sine will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-0.7,0.5,4)
>>> ak.arcsin(a)
array([-0.77539749661075297 -0.30469265401539752 0.10016742116155977 0.52359877559829893])

arkouda.arcsinh(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise inverse hyperbolic sine of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the inverse hyperbolic sine will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-500,500,4)
>>> ak.arcsinh(a)
array([-6.9077562789806368 -5.8091519901925297 5.8091519901925297 6.9077562789806368])

arkouda.arctan(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise inverse tangent of the array. The result is between -pi/2 and pi/2.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the inverse tangent will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-10.7,10.5,4)
>>> ak.arctan(a)
array([-1.4776090650260174 -1.3022168962760161 1.2873750736468446 1.4758446204521403])

arkouda.arctan2(num: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.dtypes.numeric_scalars, denom: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.dtypes.numeric_scalars, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.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 (pdarray or numeric_scalars) – Numerator of the arctan2 argument.
denom (pdarray or numeric_scalars) – Denominator of the arctan2 argument.
where (bool or pdarray, default=True) – 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 any parameter fails the typechecking
Raised if any element of pdarrays num and denom is not a supported type
Raised if both num and denom are scalars
Raised if where is neither boolean nor a pdarray of boolean

Examples

>>> import arkouda as ak
>>> x = ak.array([1,-1,-1,1])
>>> y = ak.array([1,1,-1,-1])
>>> ak.arctan2(y,x)
array([0.78539816339744828 2.3561944901923448 -2.3561944901923448 -0.78539816339744828])

arkouda.arctanh(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise inverse hyperbolic tangent of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the inverse hyperbolic tangent will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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.

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-.999,.999,4)
>>> ak.arctanh(a)
array([-3.8002011672501994 -0.34619863713154242 0.34619863713154253 3.8002011672501994])

arkouda.argmaxk(pda: pdarray, k: arkouda.numpy.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:

Raises:

TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty, or pda.ndim > 1, 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

>>> import arkouda as ak
>>> 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.argmink(pda: pdarray, k: arkouda.numpy.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:

Raises:

TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty, or pda.ndim > 1, or k < 1

Notes

This call is equivalent in value to ak.argsort(a)[:k] and generally outperforms this operation.

Examples

>>> import arkouda as ak
>>> 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.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical, algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD, axis: arkouda.numpy.dtypes.int_scalars = 0) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the permutation that sorts the array.

Parameters:

pda (pdarray, Strings, or Categorical) – The array to sort (int64, uint64, or float64)
algorithm (SortingAlgorithm, default=SortingAlgorithm.RadixSortLSD) – The algorithm to be used for sorting the array.
axis (int_scalars, default=0) – The axis to sort over.

Returns:

The indices such that pda[indices] is sorted

Return type:

assert_arkouda_array_equal

Raises:

TypeError – Raised if the parameter is other than a pdarray, Strings or Categorical

See also

coargsort

Notes

Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.

Examples

>>> import arkouda as ak
>>> a = ak.randint(0, 10, 10, seed=1)
>>> a
array([7 9 5 1 4 1 8 5 5 0])

>>> perm = ak.argsort(a)
>>> a[perm]
array([0 1 1 4 5 5 5 7 8 9])

>>> ak.argsort(a, ak.sorting.SortingAlgorithm["RadixSortLSD"])
array([9 3 5 4 2 7 8 0 6 1])

>>> ak.argsort(a, ak.sorting.SortingAlgorithm["TwoArrayRadixSort"])
array([9 3 5 4 2 7 8 0 6 1])

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]) – 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:

pdarray or Strings

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 nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if a has rank is not in get_array_ranks(), or if the returned message is malformed or does not contain the fields required to generate the array.

See also

pdarray.to_ndarray

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

>>> import arkouda as ak
>>> 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.numpy.strings.Strings'>

arkouda.array_equal(pda_a: arkouda.numpy.pdarrayclass.pdarray, pda_b: arkouda.numpy.pdarrayclass.pdarray, equal_nan: bool = False) → bool[source]¶

Compares two pdarrays for equality. If neither array has any nan elements, then if all elements are pairwise equal, it returns True. If equal_Nan is False, then any nan element in either array gives a False return. If equal_Nan is True, then pairwise-corresponding nans are considered equal.

Parameters:

pda_a (pdarray)
pda_b (pdarray)
equal_nan (bool, default=False) – Determines how to handle nans

Returns:

With string data:

False if one array is type ak.str_ & the other isn’t, True if both are ak.str_ & they match.

With numeric data:

True if neither array has any nan elements, and all elements pairwise equal.

True if equal_Nan True, all non-nans pairwise equal & nans in pda_a correspond to nans in pda_b

False if equal_Nan False, & either array has any nan element.

Return type:

boolean

Examples

>>> import arkouda as ak
>>> a = ak.randint(0,10,10,dtype=ak.float64)
>>> b = a
>>> ak.array_equal(a,b)
True
>>> b[9] = np.nan
>>> ak.array_equal(a,b)
False
>>> a[9] = np.nan
>>> ak.array_equal(a,b)
False
>>> ak.array_equal(a,b,True)
True

arkouda.assert_almost_equal(left, right, rtol: float = 1e-05, atol: float = 1e-08, **kwargs) → None[source]¶

Check that the left and right objects are approximately equal.

By approximately equal, we refer to objects that are numbers or that contain numbers which may be equivalent to specific levels of precision.

Parameters:

left (object)
right (object)
rtol (float, default 1e-5) – Relative tolerance.
atol (float, default 1e-8) – Absolute tolerance.

Warning

This function cannot be used on pdarray of size > ak.client.maxTransferBytes because it converts pdarrays to numpy arrays and calls np.allclose.

arkouda.assert_almost_equivalent(left, right, rtol: float = 1e-05, atol: float = 1e-08) → None[source]¶

Check that the left and right objects are approximately equal.

By approximately equal, we refer to objects that are numbers or that contain numbers which may be equivalent to specific levels of precision.

If the objects are pandas or numpy objects, they are converted to arkouda objects. Then assert_almost_equal is applied to the result.

Parameters:

left (object)
right (object)
rtol (float, default 1e-5) – Relative tolerance.
atol (float, default 1e-8) – Absolute tolerance.

Warning

This function cannot be used on pdarray of size > ak.client.maxTransferBytes because it converts pdarrays to numpy arrays and calls np.allclose.

See also

assert_almost_equal

arkouda.assert_arkouda_array_equal(left: arkouda.pdarray | arkouda.Strings | arkouda.Categorical | arkouda.SegArray, right: arkouda.pdarray | arkouda.Strings | arkouda.Categorical | arkouda.SegArray, check_dtype: bool = True, err_msg=None, check_same=None, obj: str = 'pdarray', index_values=None) → None[source]¶

Check that ‘ak.pdarray’ or ‘ak.Strings’, ‘ak.Categorical’, or ‘ak.SegArray’ is equivalent.

Parameters:

left (arkouda.pdarray or arkouda.numpy.Strings or arkouda.Categorical or)
right (arkouda.pdarray or arkouda.numpy.Strings or arkouda.Categorical or)
arkouda.numpy.SegArray – The two arrays to be compared.
check_dtype (bool, default True) – Check dtype if both a and b are ak.pdarray.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'numpy array') – Specify object name being compared, internally used to show appropriate assertion message.
index_values (Index | arkouda.pdarray, default None) – optional index (shared by both left and right), used in output.

Check that ‘np.array’, ‘pd.Categorical’, ‘ak.pdarray’, ‘ak.Strings’, ‘ak.Categorical’, or ‘ak.SegArray’ is equivalent.

np.nparray’s and pd.Categorical’s will be converted to the arkouda equivalent. Then assert_arkouda_pdarray_equal will be applied to the result.

Parameters:

left (np.ndarray, pd.Categorical, arkouda.pdarray or arkouda.numpy.Strings or)
right (np.ndarray, pd.Categorical, arkouda.pdarray or arkouda.numpy.Strings or)
arkouda.Categorical – The two arrays to be compared.
check_dtype (bool, default True) – Check dtype if both a and b are ak.pdarray or np.ndarray.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'numpy array') – Specify object name being compared, internally used to show appropriate assertion message.
index_values (Index | arkouda.pdarray, default None) – optional index (shared by both left and right), used in output.

See also

arkouda.assert_arkouda_pdarray_equal(left: arkouda.pdarray, right: arkouda.pdarray, check_dtype: bool = True, err_msg=None, check_same=None, obj: str = 'pdarray', index_values=None) → None[source]¶

Check that the two ‘ak.pdarray’s are equivalent.

Parameters:

left (arkouda.pdarray) – The two arrays to be compared.
right (arkouda.pdarray) – The two arrays to be compared.
check_dtype (bool, default True) – Check dtype if both a and b are ak.pdarray.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'pdarray') – Specify object name being compared, internally used to show appropriate assertion message.
index_values (Index | arkouda.pdarray, default None) – optional index (shared by both left and right), used in output.

arkouda.assert_arkouda_segarray_equal(left: arkouda.SegArray, right: arkouda.SegArray, check_dtype: bool = True, err_msg=None, check_same=None, obj: str = 'segarray') → None[source]¶

Check that the two ‘ak.SegArray’s are equivalent.

Parameters:

left (arkouda.numpy.SegArray) – The two segarrays to be compared.
right (arkouda.numpy.SegArray) – The two segarrays to be compared.
check_dtype (bool, default True) – Check dtype if both a and b are ak.pdarray.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'pdarray') – Specify object name being compared, internally used to show appropriate assertion message.

arkouda.assert_arkouda_strings_equal(left, right, err_msg=None, check_same=None, obj: str = 'Strings', index_values=None) → None[source]¶

Check that ‘ak.Strings’ is equivalent.

Parameters:

left (arkouda.numpy.Strings) – The two Strings to be compared.
right (arkouda.numpy.Strings) – The two Strings to be compared.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'Strings') – Specify object name being compared, internally used to show appropriate assertion message.
index_values (Index | arkouda.pdarray, default None) – optional index (shared by both left and right), used in output.

arkouda.assert_attr_equal(attr: str, left, right, obj: str = 'Attributes') → None[source]¶

Check attributes are equal. Both objects must have attribute.

Parameters:

attr (str) – Attribute name being compared.
left (object)
right (object)
obj (str, default 'Attributes') – Specify object name being compared, internally used to show appropriate assertion message

arkouda.assert_categorical_equal(left, right, check_dtype: bool = True, check_category_order: bool = True, obj: str = 'Categorical') → None[source]¶

Test that Categoricals are equivalent.

Parameters:

left (Categorical)
right (Categorical)
check_dtype (bool, default True) – Check that integer dtype of the codes are the same.
check_category_order (bool, default True) – Whether the order of the categories should be compared, which implies identical integer codes. If False, only the resulting values are compared. The ordered attribute is checked regardless.
obj (str, default 'Categorical') – Specify object name being compared, internally used to show appropriate assertion message.

arkouda.assert_class_equal(left, right, exact: bool = True, obj: str = 'Input') → None[source]¶: Check classes are equal.

arkouda.assert_contains_all(iterable, dic) → None[source]¶: Assert that a dictionary contains all the elements of an iterable. :param iterable: :type iterable: iterable :param dic: :type dic: dict

arkouda.assert_copy(iter1, iter2, **eql_kwargs) → None[source]¶

Check that the elements are equal, but not the same object. (Does not check that items in sequences are also not the same object.)

Parameters:

iter1 (iterable) – Iterables that produce elements comparable with assert_almost_equal.
iter2 (iterable) – Iterables that produce elements comparable with assert_almost_equal.

arkouda.assert_dict_equal(left, right, compare_keys: bool = True) → None[source]¶

Assert that two dictionaries are equal. Values must be arkouda objects. :param left: The dictionaries to be compared. :type left: dict :param right: The dictionaries to be compared. :type right: dict :param compare_keys: Whether to compare the keys.

If False, only the values are compared.

arkouda.assert_equal(left, right, **kwargs) → None[source]¶

Wrapper for tm.assert_*_equal to dispatch to the appropriate test function.

Parameters:

left (Index, Series, DataFrame, or pdarray) – The two items to be compared.
right (Index, Series, DataFrame, or pdarray) – The two items to be compared.
**kwargs – All keyword arguments are passed through to the underlying assert method.

arkouda.assert_equivalent(left, right, **kwargs) → None[source]¶

Wrapper for tm.assert_*_equivalent to dispatch to the appropriate test function.

Parameters:

left (Index, pd.Index, Series, pd.Series, DataFrame, pd.DataFrame,)
right (Index, pd.Index, Series, pd.Series, DataFrame, pd.DataFrame,)
Strings – The two items to be compared.
Categorical – The two items to be compared.
pd.Categorical – The two items to be compared.
SegArray – The two items to be compared.
pdarray – The two items to be compared.
np.ndarray – The two items to be compared.

:param : The two items to be compared. :param **kwargs: All keyword arguments are passed through to the underlying assert method.

arkouda.assert_frame_equal(left: arkouda.DataFrame, right: arkouda.DataFrame, check_dtype: bool = True, check_index_type: bool = True, check_column_type: bool = True, check_frame_type: bool = True, check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_like: bool = False, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'DataFrame') → None[source]¶

Check that left and right DataFrame are equal.

This function is intended to compare two DataFrames and output any differences. It is mostly intended for use in unit tests. Additional parameters allow varying the strictness of the equality checks performed.

Parameters:

left (DataFrame) – First DataFrame to compare.
right (DataFrame) – Second DataFrame to compare.
check_dtype (bool, default True) – Whether to check the DataFrame dtype is identical.
check_index_type (bool, default = True) – Whether to check the Index class, dtype and inferred_type are identical.
check_column_type (bool or {'equiv'}, default 'equiv') – Whether to check the columns class, dtype and inferred_type are identical. Is passed as the exact argument of assert_index_equal().
check_frame_type (bool, default True) – Whether to check the DataFrame class is identical.
check_names (bool, default True) – Whether to check that the names attribute for both the index and column attributes of the DataFrame is identical.
check_exact (bool, default False) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_like (bool, default False) – If True, ignore the order of index & columns. Note: index labels must match their respective rows (same as in columns) - same labels must be with the same data.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'DataFrame') – Specify object name being compared, internally used to show appropriate assertion message.

See also

assert_series_equal: Equivalent method for asserting Series equality.

Examples

>>> import arkouda as ak
This example shows comparing two DataFrames that are equal
but with columns of differing dtypes.

>>> from arkouda.testing import assert_frame_equal
>>> df1 = ak.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> df2 = ak.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})

df1 equals itself.

>>> assert_frame_equal(df1, df1)

df1 differs from df2 as column ‘b’ is of a different type.

>>> assert_frame_equal(df1, df2)
Traceback (most recent call last):
...
AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different

Attribute “dtype” are different [left]: int64 [right]: float64

Ignore differing dtypes in columns with check_dtype.

>>> assert_frame_equal(df1, df2, check_dtype=False)

arkouda.assert_frame_equivalent(left: arkouda.DataFrame | pandas.DataFrame, right: arkouda.DataFrame | pandas.DataFrame, check_dtype: bool = True, check_index_type: bool = True, check_column_type: bool = True, check_frame_type: bool = True, check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_like: bool = False, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'DataFrame') → None[source]¶

Check that left and right DataFrame are equal.

pd.DataFrame’s will be converted to the arkouda equivalent. Then assert_frame_equal will be applied to the result.

Parameters:

left (DataFrame or pd.DataFrame) – First DataFrame to compare.
right (DataFrame or pd.DataFrame) – Second DataFrame to compare.
check_dtype (bool, default True) – Whether to check the DataFrame dtype is identical.
check_index_type (bool, default = True) – Whether to check the Index class, dtype and inferred_type are identical.
check_column_type (bool or {'equiv'}, default 'equiv') – Whether to check the columns class, dtype and inferred_type are identical. Is passed as the exact argument of assert_index_equal().
check_frame_type (bool, default True) – Whether to check the DataFrame class is identical.
check_names (bool, default True) – Whether to check that the names attribute for both the index and column attributes of the DataFrame is identical.
check_exact (bool, default False) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_like (bool, default False) – If True, ignore the order of index & columns. Note: index labels must match their respective rows (same as in columns) - same labels must be with the same data.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'DataFrame') – Specify object name being compared, internally used to show appropriate assertion message.

See also

assert_frame_equal

Examples

>>> import arkouda as ak
This example shows comparing two DataFrames that are equal
but with columns of differing dtypes.

>>> from arkouda.testing import assert_frame_equivalent
>>> import pandas as pd
>>> df1 = ak.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
>>> assert_frame_equivalent(df1, df1)

arkouda.assert_index_equal(left: arkouda.Index, right: arkouda.Index, exact: bool = True, check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_order: bool = True, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'Index') → None[source]¶

Check that left and right Index are equal.

Parameters:

left (Index)
right (Index)
exact (True) – Whether to check the Index class, dtype and inferred_type are identical.
check_names (bool, default True) – Whether to check the names attribute.
check_exact (bool, default True) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_order (bool, default True) – Whether to compare the order of index entries as well as their values. If True, both indexes must contain the same elements, in the same order. If False, both indexes must contain the same elements, but in any order.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'Index') – Specify object name being compared, internally used to show appropriate assertion message.

Examples

>>> import arkouda as ak
>>> from arkouda import testing as tm
>>> a = ak.Index([1, 2, 3])
>>> b = ak.Index([1, 2, 3])
>>> tm.assert_index_equal(a, b)

arkouda.assert_index_equivalent(left: arkouda.Index | pandas.Index, right: arkouda.Index | pandas.Index, exact: bool = True, check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_order: bool = True, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'Index') → None[source]¶

Check that left and right Index are equal.

If the objects are pandas.Index, they are converted to arkouda.Index. Then assert_almost_equal is applied to the result.

Parameters:

left (Index or pandas.Index)
right (Index or pandas.Index)
exact (True) – Whether to check the Index class, dtype and inferred_type are identical.
check_names (bool, default True) – Whether to check the names attribute.
check_exact (bool, default True) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_order (bool, default True) – Whether to compare the order of index entries as well as their values. If True, both indexes must contain the same elements, in the same order. If False, both indexes must contain the same elements, but in any order.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'Index') – Specify object name being compared, internally used to show appropriate assertion message.

See also

assert_index_equal

Examples

>>> import arkouda as ak
>>> from arkouda import testing as tm
>>> import pandas as pd
>>> a = ak.Index([1, 2, 3])
>>> b = pd.Index([1, 2, 3])
>>> tm.assert_index_equivalent(a, b)

arkouda.assert_is_sorted(seq) → None[source]¶: Assert that the sequence is sorted.

arkouda.assert_series_equal(left, right, check_dtype: bool = True, check_index_type: bool = True, check_series_type: bool = True, check_names: bool = True, check_exact: bool = False, check_categorical: bool = True, check_category_order: bool = True, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'Series', *, check_index: bool = True, check_like: bool = False) → None[source]¶

Check that left and right Series are equal.

Parameters:

left (Series)
right (Series)
check_dtype (bool, default True) – Whether to check the Series dtype is identical.
check_index_type (bool, default True) – Whether to check the Index class, dtype and inferred_type are identical.
check_series_type (bool, default True) – Whether to check the Series class is identical.
check_names (bool, default True) – Whether to check the Series and Index names attribute.
check_exact (bool, default False) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_category_order (bool, default True) – Whether to compare category order of internal Categoricals.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'Series') – Specify object name being compared, internally used to show appropriate assertion message.
check_index (bool, default True) – Whether to check index equivalence. If False, then compare only values.
check_like (bool, default False) – If True, ignore the order of the index. Must be False if check_index is False. Note: same labels must be with the same data.

Examples

>>> import arkouda as ak
>>> from arkouda import testing as tm
>>> a = ak.Series([1, 2, 3, 4])
>>> b = ak.Series([1, 2, 3, 4])
>>> tm.assert_series_equal(a, b)

arkouda.assert_series_equivalent(left: arkouda.Series | pandas.Series, right: arkouda.Series | pandas.Series, check_dtype: bool = True, check_index_type: bool = True, check_series_type: bool = True, check_names: bool = True, check_exact: bool = False, check_categorical: bool = True, check_category_order: bool = True, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'Series', *, check_index: bool = True, check_like: bool = False) → None[source]¶

Check that left and right Series are equal.

pd.Series’s will be converted to the arkouda equivalent. Then assert_series_equal will be applied to the result.

Parameters:

left (Series or pd.Series)
right (Series or pd.Series)
check_dtype (bool, default True) – Whether to check the Series dtype is identical.
check_index_type (bool, default True) – Whether to check the Index class, dtype and inferred_type are identical.
check_series_type (bool, default True) – Whether to check the Series class is identical.
check_names (bool, default True) – Whether to check the Series and Index names attribute.
check_exact (bool, default False) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_category_order (bool, default True) – Whether to compare category order of internal Categoricals.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'Series') – Specify object name being compared, internally used to show appropriate assertion message.
check_index (bool, default True) – Whether to check index equivalence. If False, then compare only values.
check_like (bool, default False) – If True, ignore the order of the index. Must be False if check_index is False. Note: same labels must be with the same data.

See also

assert_series_equal

Examples

>>> import arkouda as ak
>>> from arkouda import testing as tm
>>> import pandas as pd
>>> a = ak.Series([1, 2, 3, 4])
>>> b = pd.Series([1, 2, 3, 4])
>>> tm.assert_series_equivalent(a, b)

arkouda.attach(name: str)[source]¶

Attach a previously created Arkouda object by its registered name.

This function retrieves an Arkouda object (e.g., pdarray, DataFrame, Series, etc.) associated with a given name. It returns the corresponding object based on the type of object stored under that name.

Parameters:: name (str) – The name of the object to attach.
Returns:: The Arkouda object associated with the given name. The returned object could be of any supported type, such as pdarray, DataFrame, Series, etc.
Return type:: object
Raises:: ValueError – If the object type in the response message does not match any known types.

Examples

>>> import arkouda as ak

Attach an existing pdarray >>> obj = ak.array([1, 2, 3]) >>> registered_obj = obj.register(“my_array”) >>> arr = ak.attach(“my_array”) >>> print(arr) [1 2 3] >>> registered_obj.unregister()

arkouda.attach_all(names: list)[source]¶

Attach to all objects registered with the provided names.

This function returns a dictionary mapping each name in the input list to the corresponding Arkouda object retrieved using attach.

Parameters:: names (List of str) – A list of names corresponding to registered Arkouda objects.
Returns:: A dictionary mapping each name to the attached Arkouda object.
Return type:: dict

Examples

>>> import arkouda as ak
>>> data = { "arr1": ak.array([0, 1, 2]), "arr2": ak.array([3, 4, 5]) }
>>> ak.register_all(data)

Assuming “arr1” and “arr2” were previously registered >>> attached_objs = ak.attach_all([“arr1”, “arr2”]) >>> print(attached_objs[“arr1”]) [0 1 2] >>> print(type(attached_objs[“arr2”])) <class ‘arkouda.numpy.pdarrayclass.pdarray’> >>> ak.unregister_all([“arr1”, “arr2”])

arkouda.base_repr(number, base=2, padding=0)¶

Return a string representation of a number in the given base system.

Parameters:

number (int) – The value to convert. Positive and negative values are handled.
base (int, optional) – Convert number to the base number system. The valid range is 2-36, the default value is 2.
padding (int, optional) – Number of zeros padded on the left. Default is 0 (no padding).

Returns:

out – String representation of number in base system.

Return type:

str

See also

binary_repr: Faster version of base_repr for base 2.

Examples

>>> import numpy as np
>>> np.base_repr(5)
'101'
>>> np.base_repr(6, 5)
'11'
>>> np.base_repr(7, base=5, padding=3)
'00012'

>>> np.base_repr(10, base=16)
'A'
>>> np.base_repr(32, base=16)
'20'

class arkouda.bigint[source]¶

Datatype for representing integers of variable size.

May be used for integers that exceed 64 bits.

itemsize(*args, **kwargs)¶

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.__int__(). For floating-point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer literal in the given base. The literal can be preceded by ‘+’ or ‘-’ and be surrounded by whitespace. The base defaults to 10. Valid bases are 0 and 2-36. Base 0 means to interpret the base from the string as an integer literal. >>> int(‘0b100’, base=0) 4

name(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

ndim(*args, **kwargs)¶

int([x]) -> integer int(x, base=10) -> integer

Convert a number or string to an integer, or return 0 if no arguments are given. If x is a number, return x.__int__(). For floating-point numbers, this truncates towards zero.

shape(*args, **kwargs)¶

Built-in immutable sequence.

If no argument is given, the constructor returns an empty tuple. If iterable is specified the tuple is initialized from iterable’s items.

If the argument is a tuple, the return value is the same object.

type(x)[source]¶

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:

pdarray.bigint_to_uint_arrays

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

>>> import arkouda as ak
>>> 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.binary_repr(num, width=None)¶

Return the binary representation of the input number as a string.

For negative numbers, if width is not given, a minus sign is added to the front. If width is given, the two’s complement of the number is returned, with respect to that width.

In a two’s-complement system negative numbers are represented by the two’s complement of the absolute value. This is the most common method of representing signed integers on computers [1]_. A N-bit two’s-complement system can represent every integer in the range $-2^{N-1}$ to $+2^{N-1}-1$.

Parameters:

num (int) – Only an integer decimal number can be used.
width (int, optional) – The length of the returned string if num is positive, or the length of the two’s complement if num is negative, provided that width is at least a sufficient number of bits for num to be represented in the designated form. If the width value is insufficient, an error is raised.

Returns:

bin – Binary representation of num or two’s complement of num.

Return type:

str

See also

base_repr: Return a string representation of a number in the given base system.
bin: Python’s built-in binary representation generator of an integer.

Notes

binary_repr is equivalent to using base_repr with base 2, but about 25x faster.

References

Examples

>>> import numpy as np
>>> np.binary_repr(3)
'11'
>>> np.binary_repr(-3)
'-11'
>>> np.binary_repr(3, width=4)
'0011'

The two’s complement is returned when the input number is negative and width is specified:

>>> np.binary_repr(-3, width=3)
'101'
>>> np.binary_repr(-3, width=5)
'11101'

class arkouda.bitType¶

See: https://data-apis.org/array-api/latest/API_specification/broadcasting.html#algorithm

Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

Character code:: 'L'
Canonical name:: numpy.uint
Alias on this platform (Linux x86_64):: numpy.uint64: 64-bit unsigned integer (0 to 18_446_744_073_709_551_615).
Alias on this platform (Linux x86_64):: numpy.uintp: Unsigned integer large enough to fit pointer, compatible with C uintptr_t.

bit_count(/)¶

uint64.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint64(127).bit_count()
7

class arkouda.bool_scalars¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

arkouda.broadcast(segments: pdarray, values: pdarray | Strings, size: int | np.int64 | np.uint64 = -1, permutation: 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:

pdarray, Strings

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

>>> import arkouda as ak
>>>
# 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]¶

Determine the broadcasted shape of two arrays given their shapes.

This function implements the broadcasting rules from the Array API standard to compute the shape resulting from broadcasting two arrays together.

Parameters:

sa (Sequence[int]) – The shape of the first array.
sb (Sequence[int]) – The shape of the second array.

Returns:

The broadcasted shape resulting from combining sa and sb.

Return type:

Tuple[int, …]

Raises:

ValueError – If the shapes are not compatible for broadcasting.

Examples

>>> import arkouda as ak
>>> from arkouda.util import broadcast_dims
>>> broadcast_dims((5, 1), (1, 3))
(5, 3)

>>> broadcast_dims((4,), (3, 1))
(3, 4)

arkouda.broadcast_to_shape(pda: pdarray, shape: Tuple[int, Ellipsis]) → pdarray[source]¶

Create a “broadcasted” array (of rank ‘nd’) by copying an array into an array of the given shape.

E.g., given the following broadcast:

pda (3d array): 1 x 4 x 1

shape ( shape ): 7 x 4 x 2

Result (3d array): 7 x 4 x 2

When copying from a singleton dimension, the value is repeated along that dimension (e.g., pda’s 1st and 3rd above). For non singleton dimensions, the size of the two arrays must match, and the values are copied into the result array.

When prepending a new dimension to increase an array’s rank, the values from the other dimensions are repeated along the new dimension.

Parameters:

pda (pdarray) – the input to be broadcast
shape (tuple of int) – the shape to which pda is to be broadcast

Returns:

the result of the broadcast operation

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.arange(2).reshape(1,2,1)
>>> ak.broadcast_to_shape(a,(2,2,2))
array([array([array([0 0]) array([1 1])]) array([array([0 0]) array([1 1])])])
>>> a = ak.array([5,19]).reshape(1,2)
>>> ak.broadcast_to_shape(a,(2,2,2))
array([array([array([5 19]) array([5 19])]) array([array([5 19]) array([5 19])])])

Raises:: RuntimeError – raised if the pda can’t be broadcast to the given shape

class arkouda.byte¶

Signed integer type, compatible with C char.

Character code:

'b'

Canonical name:

numpy.byte

Alias on this platform (Linux x86_64):

numpy.int8: 8-bit signed integer (-128 to 127).

bit_count(/)¶

int8.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.int8(127).bit_count()
7
>>> np.int8(-127).bit_count()
7

class arkouda.bytes_¶

A byte string.

When used in arrays, this type strips trailing null bytes.

Character code:

'S'

T(*args, **kwargs)¶: Scalar attribute identical to the corresponding array attribute.

Please see ndarray.T.

all(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.all.

any(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.any.

argmax(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.argmax.

argmin(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.argmin.

argsort(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.argsort.

astype(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.astype.

base(*args, **kwargs)¶: Scalar attribute identical to the corresponding array attribute.

Please see ndarray.base.

byteswap(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.byteswap.

choose(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.choose.

clip(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.clip.

compress(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.compress.

conj(*args, **kwargs)¶

conjugate(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.conjugate.

copy(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.copy.

cumprod(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.cumprod.

cumsum(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.cumsum.

data(*args, **kwargs)¶: Pointer to start of data.

device(*args, **kwargs)¶

diagonal(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.diagonal.

dtype(*args, **kwargs)¶: Get array data-descriptor.

dump(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.dump.

dumps(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.dumps.

fill(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.fill.

flags(*args, **kwargs)¶: The integer value of flags.

flat(*args, **kwargs)¶: A 1-D view of the scalar.

flatten(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.flatten.

getfield(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.getfield.

imag(*args, **kwargs)¶: The imaginary part of the scalar.

item(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.item.

itemset(*args, **kwargs)¶

itemsize(*args, **kwargs)¶: The length of one element in bytes.

max(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.max.

mean(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.mean.

min(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.min.

nbytes(*args, **kwargs)¶

ndim(*args, **kwargs)¶: The number of array dimensions.

newbyteorder(*args, **kwargs)¶

nonzero(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.nonzero.

prod(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.prod.

ptp(*args, **kwargs)¶

put(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.put.

ravel(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.ravel.

real(*args, **kwargs)¶: The real part of the scalar.

repeat(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.repeat.

reshape(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.reshape.

resize(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.resize.

round(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.round.

searchsorted(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.searchsorted.

setfield(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.setfield.

setflags(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.setflags.

shape(*args, **kwargs)¶: Tuple of array dimensions.

size(*args, **kwargs)¶: The number of elements in the gentype.

sort(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.sort.

squeeze(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.squeeze.

std(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.std.

strides(*args, **kwargs)¶: Tuple of bytes steps in each dimension.

sum(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.sum.

swapaxes(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.swapaxes.

take(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.take.

to_device(*args, **kwargs)¶

tobytes(*args, **kwargs)¶

tofile(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.tofile.

tolist(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.tolist.

tostring(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.tostring.

trace(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.trace.

transpose(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.transpose.

var(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.var.

view(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.view.

arkouda.can_cast(from_, to) → bool[source]¶

Returns True if cast between data types can occur according to the casting rule.

Parameters:

from (dtype, dtype specifier, NumPy scalar, or pdarray) – Data type, NumPy scalar, or array to cast from.
to (dtype or dtype specifier) – Data type to cast to.

Returns:

True if cast can occur according to the casting rule.

Return type:

bool

arkouda.cast(pda: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical, dt: numpy.dtype | type | str | arkouda.numpy.dtypes.bigint, errors: ErrorMode = ErrorMode.strict) → arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical | Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Cast an array to another dtype.

Parameters:

pda (pdarray, Strings, or Categorical) – The array of values to cast
dt (np.dtype, type, str, or bigint) – The target dtype to cast values to
errors ({strict, ignore, return_validity}, default=ErrorMode.strict) –
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.
Default set to strict.

Returns:

pdarray or Strings: Array of values cast to desired dtype
[validitypdarray(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.

Return type:

Union[Union[pdarray, Strings, Categorical], Tuple[pdarray, pdarray]]

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

>>> import arkouda as ak
>>> 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])

class arkouda.cdouble¶

Bases: numpy.complexfloating

Complex number type composed of two double-precision floating-point

numbers, compatible with Python complex.

Character code:: 'D'
Canonical name:: numpy.cdouble
Alias on this platform (Linux x86_64):: numpy.complex128: Complex number type composed of 2 64-bit-precision floating-point numbers.

arkouda.ceil(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.ceil(ak.linspace(1.1,5.5,5))
array([2.00000000000000000 3.00000000000000000 4.00000000000000000
5.00000000000000000 6.00000000000000000])

arkouda.chisquare(f_obs, f_exp=None, ddof=0)[source]¶

Computes the chi square 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.

Return type:

arkouda.akstats.Power_divergenceResult

Examples

>>> import arkouda as ak
>>> from arkouda.scipy import chisquare
>>> chisquare(ak.array([10, 20, 30, 10]), ak.array([10, 30, 20, 10]))
Power_divergenceResult(statistic=np.float64(8.333333333333334),
    pvalue=np.float64(0.03960235520756414))

See also

scipy.stats.chisquare, arkouda.akstats.power_divergence

References

[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.

Raises:: RuntimeError – Raised if there is a server-side error in executing clear request

arkouda.clip(pda: arkouda.numpy.pdarrayclass.pdarray, lo: arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.pdarrayclass.pdarray, hi: arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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) – the array of values to clip
lo (numeric_scalars or pdarray) – the lower value of the clipping range
hi (numeric_scalars or pdarray) – the higher value of the clipping range If lo or hi (or both) are pdarrays, the check is by pairwise elements. 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

>>> import arkouda as ak
>>> 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

class arkouda.clongdouble¶

Bases: numpy.complexfloating

Complex number type composed of two extended-precision floating-point

numbers.

Character code:: 'G'
Alias on this platform (Linux x86_64):: numpy.complex256: Complex number type composed of 2 128-bit extended-precision floating-point numbers.

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:: The number of leading zeros of each element.
Return type:: pdarray
Raises:: TypeError – If input array is not int64, uint64, or bigint

Examples

>>> import arkouda as ak
>>> A = ak.arange(10)
>>> ak.clz(A)
array([64 63 62 62 61 61 61 61 60 60])

arkouda.coargsort(arrays: Sequence[arkouda.numpy.strings.Strings | arkouda.numpy.pdarrayclass.pdarray | arkouda.categorical.Categorical], algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD, ascending: bool = True) → arkouda.numpy.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 or Categoricals — those are grouped, not ordered.

Parameters:

arrays (Sequence of Strings, pdarray, or Categorical) – The columns (int64, uint64, float64, Strings, or Categorical) to sort by row.
algorithm (SortingAlgorithm, default=SortingAlgorithm.RadixSortLSD) – The algorithm to be used for sorting the arrays.
ascending (bool, default=True) – Whether to sort in ascending order. Ignored when arrays have ndim > 1.

Returns:

The indices that permute the rows into grouped order.

Return type:

Raises:

ValueError – If the inputs are not all the same size or not valid array types.

See also

argsort

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.

For Strings, sorting is based on a hash. This ensures grouping of identical strings, but not lexicographic order. For Categoricals, sorting is based on the internal codes.

Examples

>>> import arkouda as ak
>>> 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])

class arkouda.complex128¶

Bases: numpy.complexfloating

Complex number type composed of two double-precision floating-point

numbers, compatible with Python complex.

Character code:: 'D'
Canonical name:: numpy.cdouble
Alias on this platform (Linux x86_64):: numpy.complex128: Complex number type composed of 2 64-bit-precision floating-point numbers.

class arkouda.complex64¶

Bases: numpy.complexfloating

Complex number type composed of two single-precision floating-point

numbers.

Character code:: 'F'
Canonical name:: numpy.csingle
Alias on this platform (Linux x86_64):: numpy.complex64: Complex number type composed of 2 32-bit-precision floating-point numbers.

arkouda.compute_join_size(a: arkouda.numpy.pdarrayclass.pdarray, b: arkouda.numpy.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.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical], axis: int = 0, ordered: bool = True) → arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical | Sequence[arkouda.categorical.Categorical][source]¶

Concatenate a list or tuple of pdarray or Strings objects into one pdarray or Strings object, respectively.

Parameters:

arrays (Sequence[Union[pdarray,Strings,Categorical]]) – The arrays to concatenate. Must all have same dtype.
axis (int, default = 0) – The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Only for use with pdarray, and when ordered is True. Default is 0.
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 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 any array elements are dtypes for which concatenate has not been implemented.

Examples

>>> import arkouda as ak
>>> 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:

x (pdarray) – One of the pdarrays used to calculate correlation
y (pdarray) – One of the pdarrays used to calculate correlation

Returns:

The scalar correlation of the two pdarrays

Return type:

np.float64

Examples

>>> import arkouda as ak
>>> a = ak.arange(10)
>>> b = a + 1
>>> ak.corr(a,b)
np.float64(0.9999999999999998)
>>> a.corr(b)
np.float64(0.9999999999999998)

Raises:

TypeError – Raised if x or y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

See also

std, cov

Notes

The correlation is calculated by cov(x, y) / (x.std(ddof=1) * y.std(ddof=1))

arkouda.cos(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise cosine of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the cosine will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

Returns:

A pdarray containing cosine for each element of the original pdarray

Return type:

Raises:

TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-1.5,0.75,4)
>>> ak.cos(a)
array([0.070737201667702906 0.7316888688738209 1.00000000000000000 0.7316888688738209])

arkouda.cosh(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise hyperbolic cosine of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the hyperbolic cosine will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-0.9,0.7,4)
>>> ak.cosh(a)
array([1.4330863854487745 1.0679787433708894 1.0139210688781299 1.255169005630943])

arkouda.count_nonzero(pda: arkouda.numpy.pdarrayclass.pdarray) → numpy.int64[source]¶

Compute the nonzero count of a given array. 1D case only, for now.

Parameters:

pda (pdarray) – The input data, in pdarray form, numeric, bool, or str

Returns:

The nonzero count of the entire pdarray

Return type:

np.int64

Raises:

TypeError – Raised if the parameter is not a pdarray with numeric, bool, or str datatype
ValueError – Raised if sum applied to the pdarray doesn’t come back with a scalar

Examples

>>> import arkouda as ak
>>> pda = ak.array([0,4,7,8,1,3,5,2,-1])
>>> ak.count_nonzero(pda)
np.int64(8)
>>> pda = ak.array([False,True,False,True,False])
>>> ak.count_nonzero(pda)
np.int64(2)
>>> pda = ak.array(["hello","","there"])
>>> ak.count_nonzero(pda)
np.int64(2)

arkouda.cov(x: pdarray, y: pdarray) → numpy.float64[source]¶

Return the covariance of x and y

Parameters:

x (pdarray) – One of the pdarrays used to calculate covariance
y (pdarray) – One of the pdarrays used to calculate covariance

Returns:

The scalar covariance of the two pdarrays

Return type:

np.float64

Examples

>>> import arkouda as ak
>>> a = ak.arange(10)
>>> b = a + 1
>>> ak.cov(a,b)
np.float64(9.166666666666666)
>>> a.cov(b)
np.float64(9.166666666666666)

Raises:

TypeError – Raised if x or y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

See also

mean, var

Notes

The covariance is calculated by cov = ((x - x.mean()) * (y - y.mean())).sum() / (x.size - 1).

class arkouda.csingle¶

Bases: numpy.complexfloating

Complex number type composed of two single-precision floating-point

numbers.

Character code:: 'F'
Canonical name:: numpy.csingle
Alias on this platform (Linux x86_64):: numpy.complex64: Complex number type composed of 2 32-bit-precision floating-point numbers.

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:: The number of trailing zeros of each element.
Return type:: pdarray

Notes

ctz(0) is defined to be zero.

Raises:: TypeError – If input array is not int64, uint64, or bigint

Examples

>>> import arkouda as ak
>>> A = ak.arange(10)
>>> ak.ctz(A)
array([0 0 1 0 2 0 1 0 3 0])

arkouda.cumprod(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the cumulative product over the array.

The product is inclusive, such that the i th element of the result is the product of elements up to and including i.

Parameters:: pda (pdarray)
Returns:: A pdarray containing cumulative products for each element of the original pdarray
Return type:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.cumprod(ak.arange(1,5))
array([1 2 6 24])

>>> ak.cumprod(ak.uniform(5,1.0,5.0, seed=1))
array([4.1485937992669886 5.5470437965703221 22.201091353048209
    79.702126856955317 298.26551591732482])

arkouda.cumsum(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the cumulative sum over the array.

The sum is inclusive, such that the i th element of the result is the sum of elements up to and including i.

Parameters:: pda (pdarray)
Returns:: A pdarray containing cumulative sums for each element of the original pdarray
Return type:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.cumsum(ak.arange(1,5))
array([1 3 6 10])

>>> ak.cumsum(ak.uniform(5,1.0,5.0, seed=1))
array([4.1485937992669886 5.4856839230459 9.4880124005630435 13.078021809988414 16.820274716898091])

>>> ak.cumsum(ak.randint(0, 1, 5, dtype=ak.bool_, seed=1))
array([1 1 2 3 4])

arkouda.date_operators(cls)[source]¶

arkouda.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, inclusive='both', **kwargs)[source]¶

Create 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.
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, and freq, exactly three must be specified. If freq is omitted, the resulting DatetimeIndex will have periods linearly spaced elements between start and end (closed on both sides).

To learn more about the frequency strings, please see this link.

class arkouda.datetime64¶

Bases: numpy.generic

If created from a 64-bit integer, it represents an offset from

1970-01-01T00:00:00. If created from string, the string can be in ISO 8601 date or datetime format.

When parsing a string to create a datetime object, if the string contains a trailing timezone (A ‘Z’ or a timezone offset), the timezone will be dropped and a User Warning is given.

Datetime64 objects should be considered to be UTC and therefore have an offset of +0000.

>>> np.datetime64(10, 'Y')
np.datetime64('1980')
>>> np.datetime64('1980', 'Y')
np.datetime64('1980')
>>> np.datetime64(10, 'D')
np.datetime64('1970-01-11')

See arrays.datetime for more information.

Character code:: 'M'

arkouda.deg2rad(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Converts angles element-wise from degrees to radians.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the corresponding value will be converted from degrees to radians. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(0,359,4)
>>> ak.deg2rad(a)
array([0.00000000000000000 2.0885773382198809 4.1771546764397618 6.2657320146596422])

Return a copy of ‘arr’ with elements along the specified axis removed.

Parameters:

arr (pdarray) – The array to remove elements from
obj (slice, int, Sequence of int, Sequence of bool, or pdarray) – The indices to remove from ‘arr’. If obj is a pdarray, it must have an integer or bool dtype.
axis (Optional[int], optional) – The axis along which to remove elements. If None, the array will be flattened before removing elements. Defaults to None.

Returns:

A copy of ‘arr’ with elements removed

Return type:

Examples

>>> import arkouda as ak
>>> arr = ak.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
>>> arr
array([array([1 2 3 4]) array([5 6 7 8]) array([9 10 11 12])])
>>> ak.delete(arr, 1, 0)
array([array([1 2 3 4]) array([9 10 11 12])])

>>> ak.delete(arr, slice(0, 4, 2), 1)
array([array([2 4]) array([6 8]) array([10 12])])
>>> ak.delete(arr, [1, 3, 5], None)
array([1 3 5 7 8 9 10 11 12])

arkouda.diff(a: pdarray, n: int = 1, axis: int = -1, prepend=None, append=None) → pdarray[source]¶

Calculate the n-th discrete difference along the given axis.

The first difference is given by out[i] = a[i+1] - a[i] along the given axis, higher differences are calculated by using diff iteratively.

Parameters:

a (pdarray) – The array to calculate the difference
n (int, optional) – The order of the finite difference. Default is 1.
axis (int, optional) – The axis along which to calculate the difference. Default is the last axis.
prepend (pdarray, optional) – The pdarray to prepend to a along axis before calculating the difference.
append (pdarray, optional) – The pdarray to append to a along axis before calculating the difference.

Returns:

The n-th differences. The shape of the output is the same as a except along axis where the dimension is smaller by n. The type of the output is the same as the type of the difference between any two elements of a. This is the same as the type of a in most cases. A notable exception is datetime64, which results in a timedelta64 output array.

Return type:

Notes

Type is preserved for boolean arrays, so the result will contain False when consecutive elements are the same and True when they differ.

For unsigned integer arrays, the results will also be unsigned. This should not be surprising, as the result is consistent with calculating the difference directly.

If this is not desirable, then the array should be cast to a larger integer type first:

Examples

>>> import arkouda as ak
>>> a = ak.array([1, 2, 4, 7, 0])
>>> ak.diff(a)
array([1 2 3 -7])
>>> ak.diff(a, n=2)
array([1 1 -10])

>>> a = ak.array([[1, 3, 6, 10], [0, 5, 6, 8]])
>>> ak.diff(a)
array([array([2 3 4]) array([5 1 2])])
>>> ak.diff(a, axis=0)
array([array([-1 2 0 -2])])

arkouda.disableVerbose(logLevel: LogLevel = LogLevel.INFO) → None[source]¶

Disables verbose logging.

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.numpy.dtypes.numeric_scalars | pdarray, y: arkouda.numpy.dtypes.numeric_scalars | pdarray, where: arkouda.numpy.dtypes.bool_scalars | 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:

(pdarray, pdarray)

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

>>> import arkouda as ak
>>> 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]))

Computes dot product of two arrays.

If pda1 and pda2 are 1-D vectors of identical length, returns the conventional dot product.

If both pda1 and pda2 are scalars, returns their product.

If one of pda1, pda2 is a scalar, and the other a pdarray, returns the pdarray multiplied by the scalar.

If both pda1 and pda2 are 2-D arrays, returns the matrix multiplication.

If pda1 is M-D and pda2 is 1-D, returns a sum product over the last axis of pda1 and pda2.

If pda1 is M-D and pda2 is N-D, returns a sum product over the last axis of pda1 and the next-to-last axis of pda2, e.g.:

For example, If pda1 has rank (3,3,4) and pda2 has rank (4,2), then the result of ak.dot(pda1,pda2) has rank (3,3,2), and

result[i,j,k] = sum( pda1[i, j, :] * pda2[:, k] )

Parameters:

pda1 (Union[np.int64, np.float64, np.uint64, pdarray],)
pda2 (Union[np.int64, np.float64, np.uint64, pdarray],)

Returns:

as described above

Return type:

Union[numeric_scalars, pdarray]

Examples

>>> import arkouda as ak
>>> ak.dot(ak.array([1, 2, 3]),ak.array([4,5,6]))
np.int64(32)
>>> ak.dot(ak.array([1, 2, 3]),5)
array([5 10 15])
>>> ak.dot(5,ak.array([2, 3, 4]))
array([10 15 20])
>>> ak.dot(ak.arange(9).reshape(3,3),ak.arange(6).reshape(3,2))
array([array([10 13]) array([28 40]) array([46 67])])
>>> ak.dot(ak.arange(27).reshape(3,3,3),ak.array([2,3,4]))
array([array([11 38 65]) array([92 119 146]) array([173 200 227])])
>>> ak.dot(ak.arange(36).reshape(3,3,4),ak.arange(8).reshape(4,2))
array([array([array([28 34]) array([76 98]) array([124 162])]) array([array([172 226])
    array([220 290]) array([268 354])]) array([array([316 418]) array([364 482]) array([412 546])])])

Raises:: ValueError – Raised if either pdda1 or pda2 is not an allowed type, or if shapes are incompatible.

class arkouda.double¶

Bases: numpy.floating

Double-precision floating-point number type, compatible with Python

float and C double.

Character code:: 'd'
Canonical name:: numpy.double
Alias on this platform (Linux x86_64):: numpy.float64: 64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa.

as_integer_ratio(/)¶

double.as_integer_ratio() -> (int, int)

Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. Raise OverflowError on infinities and a ValueError on NaNs.
>>> np.double(10.0).as_integer_ratio()
(10, 1)
>>> np.double(0.0).as_integer_ratio()
(0, 1)
>>> np.double(-.25).as_integer_ratio()
(-1, 4)

fromhex(string, /)¶

Create a floating-point number from a hexadecimal string.

>>> float.fromhex('0x1.ffffp10')
2047.984375
>>> float.fromhex('-0x1p-1074')
-5e-324

hex(/)¶

Return a hexadecimal representation of a floating-point number.

>>> (-0.1).hex()
'-0x1.999999999999ap-4'
>>> 3.14159.hex()
'0x1.921f9f01b866ep+1'

is_integer(/)¶

double.is_integer() -> bool

Return True if the floating point number is finite with integral value, and False otherwise.

Added in version 1.22.
>>> np.double(-2.0).is_integer()
True
>>> np.double(3.2).is_integer()
False

arkouda.dtype(dtype)[source]¶

Create a data type object.

Parameters:: dtype (object) – Object to be converted to a data type object.
Return type:: type

arkouda.e: float¶

arkouda.enableVerbose() → None[source]¶: Enable verbose logging (DEBUG log level) for all ArkoudaLoggers.

arkouda.euler_gamma: float¶

arkouda.exp(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.exp(ak.arange(1,5))
array([2.7182818284590451 7.3890560989306504 20.085536923187668 54.598150033144236])

>>> ak.exp(ak.uniform(4, 1.0, 5.0, seed=1))
array([63.344862048230922 3.8079467144568273 54.725428723251447 36.234416869913829])

arkouda.expm1(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise exponential of the array minus one.

Parameters:: pda (pdarray)
Returns:: A pdarray containing e raised to each of the inputs, then subtracting one.
Return type:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.expm1(ak.arange(1,5))
array([1.7182818284590451 6.3890560989306504 19.085536923187668 53.598150033144236])

>>> ak.expm1(ak.uniform(5,1.0,5.0, seed=1))
array([62.344862048230922 2.8079467144568273 53.725428723251447
    35.234416869913829 41.192939934258227])

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 to pandas.

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_data

Notes

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.eye(rows: arkouda.numpy.dtypes.int_scalars, cols: arkouda.numpy.dtypes.int_scalars, diag: arkouda.numpy.dtypes.int_scalars = 0, dt: type = ak_float64) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a pdarray with zeros everywhere except along a diagonal, which is all ones. The matrix need not be square.

Parameters:

rows (int_scalars)
cols (int_scalars)
diag (int_scalars, default=0) –

if diag = 0, zeros start at element [0,0] and proceed along diagonal

if diag > 0, zeros start at element [0,diag] and proceed along diagonal

if diag < 0, zeros start at element [diag,0] and proceed along diagonal

etc. Default set to 0.
dt (type, default=ak_int64) – The data type of the elements in the matrix being returned. Default set to ak_int64

Returns:

an array of zeros with ones along the specified diagonal

Return type:

Examples

>>> import arkouda as ak
>>> ak.eye(rows=4,cols=4,diag=0,dt=ak.int64)
array([array([1 0 0 0]) array([0 1 0 0]) array([0 0 1 0]) array([0 0 0 1])])
>>> ak.eye(rows=3,cols=3,diag=1,dt=ak.float64)
array([array([0.00000000000000000 1.00000000000000000 0.00000000000000000])
array([0.00000000000000000 0.00000000000000000 1.00000000000000000])
array([0.00000000000000000 0.00000000000000000 0.00000000000000000])])
>>> ak.eye(rows=4,cols=4,diag=-1,dt=ak.bool_)
array([array([False False False False]) array([True False False False])
array([False True False False]) array([False False True False])])

Notes

if rows = cols and diag = 0, the result is an identity matrix Server returns an error if rank of pda < 2

arkouda.find(query, space, all_occurrences=False, remove_missing=False)[source]¶

Return indices of query items in a search list of items.

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.
all_occurrences (bool) – When duplicate terms are present in search space, if all_occurrences is True, return all occurrences found as a SegArray, otherwise return only the first occurrences as a pdarray. Defaults to only finding the first occurrence. Finding all occurrences is not yet supported on sequences of arrays
remove_missing (bool) – If all_occurrences is True, remove_missing is automatically enabled. If False, return -1 for any items in query not found in space. If True, remove these and only return indices of items that are found.

Returns:

indices – For each item in query, its index in space. If all_occurrences is False, the return will be a pdarray of the first index where each value in the query appears in the space. If all_occurrences is True, the return will be a SegArray containing every index where each value in the query appears in the space. If all_occurrences is True, remove_missing is automatically enabled. If remove_missing is True, exclude missing values, otherwise return -1.

Return type:

pdarray or SegArray

Examples

>>> import arkouda as ak
>>> select_from = ak.arange(10)
>>> arr1 = select_from[ak.randint(0, select_from.size, 20, seed=10)]
>>> arr2 = select_from[ak.randint(0, select_from.size, 20, seed=11)]

Remove some values to ensure we have some values which don’t appear in the search space

>>> arr2 = arr2[arr2 != 9]
>>> arr2 = arr2[arr2 != 3]

Find with defaults (all_occurrences and remove_missing both False)

>>> ak.find(arr1, arr2)
array([-1 -1 -1 0 1 -1 -1 -1 2 -1 5 -1 8 -1 5 -1 -1 11 5 0])

Set remove_missing to True, only difference from default is missing values are excluded

>>> ak.find(arr1, arr2, remove_missing=True)
array([0 1 2 5 8 5 11 5 0])

Set both remove_missing and all_occurrences to True, missing values will be empty segments

>>> ak.find(arr1, arr2, remove_missing=True, all_occurrences=True).to_list()
[[],
 [],
 [],
 [0, 4],
 [1, 3, 10],
 [],
 [],
 [],
 [2, 6, 12, 13],
 [],
 [5, 7],
 [],
 [8, 9, 14],
 [],
 [5, 7],
 [],
 [],
 [11, 15],
 [5, 7],
 [0, 4]]

class arkouda.finfo¶

finfo(dtype)

Machine limits for floating point types.

bits¶

The number of bits occupied by the type.

Type:: int

dtype¶

Returns the dtype for which finfo returns information. For complex input, the returned dtype is the associated float* dtype for its real and complex components.

Type:: dtype

eps¶

The difference between 1.0 and the next smallest representable float larger than 1.0. For example, for 64-bit binary floats in the IEEE-754 standard, eps = 2**-52, approximately 2.22e-16.

Type:: float

epsneg¶

The difference between 1.0 and the next smallest representable float less than 1.0. For example, for 64-bit binary floats in the IEEE-754 standard, epsneg = 2**-53, approximately 1.11e-16.

Type:: float

iexp¶

The number of bits in the exponent portion of the floating point representation.

Type:: int

machep¶

The exponent that yields eps.

Type:: int

max¶

The largest representable number.

Type:: floating point number of the appropriate type

maxexp¶

The smallest positive power of the base (2) that causes overflow.

Type:: int

min¶

The smallest representable number, typically -max.

Type:: floating point number of the appropriate type

minexp¶

The most negative power of the base (2) consistent with there being no leading 0’s in the mantissa.

Type:: int

negep¶

The exponent that yields epsneg.

Type:: int

nexp¶

The number of bits in the exponent including its sign and bias.

Type:: int

nmant¶

The number of bits in the mantissa.

Type:: int

precision¶

The approximate number of decimal digits to which this kind of float is precise.

Type:: int

resolution¶

The approximate decimal resolution of this type, i.e., 10**-precision.

Type:: floating point number of the appropriate type

tiny¶

An alias for smallest_normal, kept for backwards compatibility.

Type:: float

smallest_normal¶

The smallest positive floating point number with 1 as leading bit in the mantissa following IEEE-754 (see Notes).

Type:: float

smallest_subnormal¶

The smallest positive floating point number with 0 as leading bit in the mantissa following IEEE-754.

Type:: float

Parameters:: dtype (float, dtype, or instance) – Kind of floating point or complex floating point data-type about which to get information.

See also

iinfo: The equivalent for integer data types.
spacing: The distance between a value and the nearest adjacent number
nextafter: The next floating point value after x1 towards x2

Notes

For developers of NumPy: do not instantiate this at the module level. The initial calculation of these parameters is expensive and negatively impacts import times. These objects are cached, so calling finfo() repeatedly inside your functions is not a problem.

Note that smallest_normal is not actually the smallest positive representable value in a NumPy floating point type. As in the IEEE-754 standard [1]_, NumPy floating point types make use of subnormal numbers to fill the gap between 0 and smallest_normal. However, subnormal numbers may have significantly reduced precision [2].

This function can also be used for complex data types as well. If used, the output will be the same as the corresponding real float type (e.g. numpy.finfo(numpy.csingle) is the same as numpy.finfo(numpy.single)). However, the output is true for the real and imaginary components.

References

Examples

>>> import numpy as np
>>> np.finfo(np.float64).dtype
dtype('float64')
>>> np.finfo(np.complex64).dtype
dtype('float32')

property smallest_normal¶

Return the value for the smallest normal.

Returns:: smallest_normal – Value for the smallest normal.
Return type:: float
Warns:: UserWarning – If the calculated value for the smallest normal is requested for double-double.

property tiny¶

Return the value for tiny, alias of smallest_normal.

Returns:: tiny – Value for the smallest normal, alias of smallest_normal.
Return type:: float
Warns:: UserWarning – If the calculated value for the smallest normal is requested for double-double.

class arkouda.flexible¶

Bases: numpy.generic

Abstract base class of all scalar types without predefined length.: The actual size of these types depends on the specific numpy.dtype instantiation.

arkouda.flip(x: arkouda.numpy.pdarrayclass.pdarray, /, *, axis: int | Tuple[int, Ellipsis] | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

arkouda.flip(x: arkouda.numpy.strings.Strings, /, *, axis: int | Tuple[int, Ellipsis] | None = None) → arkouda.numpy.strings.Strings

arkouda.flip(x: arkouda.categorical.Categorical, /, *, axis: int | Tuple[int, Ellipsis] | None = None) → arkouda.categorical.Categorical

Reverse an array’s values along a particular axis or axes.

Parameters:

x (pdarray, Strings, or Categorical) –
Reverse the order of elements in an array along the given axis.

The shape of the array is preserved, but the elements are reordered.
axis (int or Tuple[int, ...], optional) – The axis or axes along which to flip the array. If None, flip the array along all axes.

Returns:

An array with the entries of axis reversed.

Return type:

pdarray, Strings, or Categorical

Note

This differs from numpy as it actually reverses the data, rather than presenting a view.

class arkouda.float16¶

Bases: numpy.floating

Half-precision floating-point number type.

Character code:

'e'

Canonical name:

numpy.half

Alias on this platform (Linux x86_64):

numpy.float16: 16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.

as_integer_ratio(/)¶

half.as_integer_ratio() -> (int, int)

Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. Raise OverflowError on infinities and a ValueError on NaNs.
>>> np.half(10.0).as_integer_ratio()
(10, 1)
>>> np.half(0.0).as_integer_ratio()
(0, 1)
>>> np.half(-.25).as_integer_ratio()
(-1, 4)

is_integer(/)¶

half.is_integer() -> bool

Return True if the floating point number is finite with integral value, and False otherwise.

Added in version 1.22.
>>> np.half(-2.0).is_integer()
True
>>> np.half(3.2).is_integer()
False

class arkouda.float32¶

Bases: numpy.floating

Single-precision floating-point number type, compatible with C float.

Character code:

'f'

Canonical name:

numpy.single

Alias on this platform (Linux x86_64):

numpy.float32: 32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa.

as_integer_ratio(/)¶

single.as_integer_ratio() -> (int, int)

Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. Raise OverflowError on infinities and a ValueError on NaNs.
>>> np.single(10.0).as_integer_ratio()
(10, 1)
>>> np.single(0.0).as_integer_ratio()
(0, 1)
>>> np.single(-.25).as_integer_ratio()
(-1, 4)

is_integer(/)¶

single.is_integer() -> bool

Return True if the floating point number is finite with integral value, and False otherwise.

Added in version 1.22.
>>> np.single(-2.0).is_integer()
True
>>> np.single(3.2).is_integer()
False

class arkouda.float64¶

Bases: numpy.floating

Double-precision floating-point number type, compatible with Python

float and C double.

Character code:: 'd'
Canonical name:: numpy.double
Alias on this platform (Linux x86_64):: numpy.float64: 64-bit precision floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa.

as_integer_ratio(/)¶

double.as_integer_ratio() -> (int, int)

Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. Raise OverflowError on infinities and a ValueError on NaNs.
>>> np.double(10.0).as_integer_ratio()
(10, 1)
>>> np.double(0.0).as_integer_ratio()
(0, 1)
>>> np.double(-.25).as_integer_ratio()
(-1, 4)

fromhex(string, /)¶

Create a floating-point number from a hexadecimal string.

>>> float.fromhex('0x1.ffffp10')
2047.984375
>>> float.fromhex('-0x1p-1074')
-5e-324

hex(/)¶

Return a hexadecimal representation of a floating-point number.

>>> (-0.1).hex()
'-0x1.999999999999ap-4'
>>> 3.14159.hex()
'0x1.921f9f01b866ep+1'

is_integer(/)¶

double.is_integer() -> bool

Return True if the floating point number is finite with integral value, and False otherwise.

Added in version 1.22.
>>> np.double(-2.0).is_integer()
True
>>> np.double(3.2).is_integer()
False

class arkouda.float_scalars¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

class arkouda.floating¶

Bases: numpy.inexact

Abstract base class of all floating-point scalar types.

arkouda.floor(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.floor(ak.linspace(1.1,5.5,5))
array([1.00000000000000000 2.00000000000000000 3.00000000000000000
4.00000000000000000 5.00000000000000000])

arkouda.fmod(dividend: pdarray | arkouda.numpy.dtypes.numeric_scalars, divisor: pdarray | arkouda.numpy.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:

dividend (numeric scalars or pdarray) – The array being acted on by the bases for the modular division.
divisor (numeric scalars or pdarray) – The array that will be the bases for the modular division.

Returns:

an array that contains the element-wise remainder of division.

Return type:

format_float_scientific

Raises:

TypeError – Raised if neither dividend nor divisor is a pdarray (at least one must be) or if any scalar or pdarray element is not one of int, uint, float, bigint

arkouda.format_float_positional(x, precision=None, unique=True, fractional=True, trim='k', sign=False, pad_left=None, pad_right=None, min_digits=None)¶

Format a floating-point scalar as a decimal string in positional notation.

Provides control over rounding, trimming and padding. Uses and assumes IEEE unbiased rounding. Uses the “Dragon4” algorithm.

Parameters:

x (python float or numpy floating scalar) – Value to format.
precision (non-negative integer or None, optional) – Maximum number of digits to print. May be None if unique is True, but must be an integer if unique is False.
unique (boolean, optional) – If True, use a digit-generation strategy which gives the shortest representation which uniquely identifies the floating-point number from other values of the same type, by judicious rounding. If precision is given fewer digits than necessary can be printed, or if min_digits is given more can be printed, in which cases the last digit is rounded with unbiased rounding. If False, digits are generated as if printing an infinite-precision value and stopping after precision digits, rounding the remaining value with unbiased rounding
fractional (boolean, optional) – If True, the cutoffs of precision and min_digits refer to the total number of digits after the decimal point, including leading zeros. If False, precision and min_digits refer to the total number of significant digits, before or after the decimal point, ignoring leading zeros.
trim (one of 'k', '.', '0', '-', optional) –
Controls post-processing trimming of trailing digits, as follows:
- ’k’ : keep trailing zeros, keep decimal point (no trimming)
- ’.’ : trim all trailing zeros, leave decimal point
- ’0’ : trim all but the zero before the decimal point. Insert the zero if it is missing.
- ’-’ : trim trailing zeros and any trailing decimal point
sign (boolean, optional) – Whether to show the sign for positive values.
pad_left (non-negative integer, optional) – Pad the left side of the string with whitespace until at least that many characters are to the left of the decimal point.
pad_right (non-negative integer, optional) – Pad the right side of the string with whitespace until at least that many characters are to the right of the decimal point.
min_digits (non-negative integer or None, optional) –
Minimum number of digits to print. Only has an effect if unique=True in which case additional digits past those necessary to uniquely identify the value may be printed, rounding the last additional digit.

Added in version 1.21.0.

Returns:

rep – The string representation of the floating point value

Return type:

string

See also

Examples

>>> import numpy as np
>>> np.format_float_positional(np.float32(np.pi))
'3.1415927'
>>> np.format_float_positional(np.float16(np.pi))
'3.14'
>>> np.format_float_positional(np.float16(0.3))
'0.3'
>>> np.format_float_positional(np.float16(0.3), unique=False, precision=10)
'0.3000488281'

arkouda.format_float_scientific(x, precision=None, unique=True, trim='k', sign=False, pad_left=None, exp_digits=None, min_digits=None)¶

Format a floating-point scalar as a decimal string in scientific notation.

Provides control over rounding, trimming and padding. Uses and assumes IEEE unbiased rounding. Uses the “Dragon4” algorithm.

Parameters:

x (python float or numpy floating scalar) – Value to format.
precision (non-negative integer or None, optional) – Maximum number of digits to print. May be None if unique is True, but must be an integer if unique is False.
unique (boolean, optional) – If True, use a digit-generation strategy which gives the shortest representation which uniquely identifies the floating-point number from other values of the same type, by judicious rounding. If precision is given fewer digits than necessary can be printed. If min_digits is given more can be printed, in which cases the last digit is rounded with unbiased rounding. If False, digits are generated as if printing an infinite-precision value and stopping after precision digits, rounding the remaining value with unbiased rounding
trim (one of 'k', '.', '0', '-', optional) –
Controls post-processing trimming of trailing digits, as follows:
- ’k’ : keep trailing zeros, keep decimal point (no trimming)
- ’.’ : trim all trailing zeros, leave decimal point
- ’0’ : trim all but the zero before the decimal point. Insert the zero if it is missing.
- ’-’ : trim trailing zeros and any trailing decimal point
sign (boolean, optional) – Whether to show the sign for positive values.
pad_left (non-negative integer, optional) – Pad the left side of the string with whitespace until at least that many characters are to the left of the decimal point.
exp_digits (non-negative integer, optional) – Pad the exponent with zeros until it contains at least this many digits. If omitted, the exponent will be at least 2 digits.
min_digits (non-negative integer or None, optional) –
Minimum number of digits to print. This only has an effect for unique=True. In that case more digits than necessary to uniquely identify the value may be printed and rounded unbiased.

Added in version 1.21.0.

Returns:

rep – The string representation of the floating point value

Return type:

string

See also

format_float_positional

Examples

>>> import numpy as np
>>> np.format_float_scientific(np.float32(np.pi))
'3.1415927e+00'
>>> s = np.float32(1.23e24)
>>> np.format_float_scientific(s, unique=False, precision=15)
'1.230000071797338e+24'
>>> np.format_float_scientific(s, exp_digits=4)
'1.23e+0024'

arkouda.from_series(series: pandas.Series, dtype: type | str | None = None) → arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.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:

Union[pdarray,Strings]

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

>>> import arkouda as ak
>>> np.random.seed(1701)
>>> ak.from_series(pd.Series(np.random.randint(0,10,5)))
array([4 3 3 5 0])

>>> ak.from_series(pd.Series(['1', '2', '3', '4', '5']),dtype=np.int64)
array([1 2 3 4 5])

>>> np.random.seed(1701)
>>> ak.from_series(pd.Series(np.random.uniform(low=0.0,high=1.0,size=3)))
array([0.089433234324597599 0.1153776854774361 0.51874393620990389])

>>> ak.from_series(
...     pd.Series([
...         '0.57600036956445599',
...         '0.41619265571741659',
...         '0.6615356693784662',
...     ]),
...     dtype=np.float64,
... )
array([0.57600036956445599 0.41619265571741659 0.6615356693784662])

>>> np.random.seed(1864)
>>> ak.from_series(pd.Series(np.random.choice([True, False],size=5)))
array([True True True False False])

>>> ak.from_series(pd.Series(['True', 'False', 'False', 'True', 'True']), dtype=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(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

Create a pdarray filled with fill_value.

Parameters:

size (int_scalars or tuple of int_scalars) – Size or shape of the array
fill_value (int_scalars or str) – 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:

pdarray or Strings

Raises:

TypeError – Raised if the supplied dtype is not supported
RuntimeError – Raised if the size parameter is neither an int nor a str that is parseable to an int.
ValueError – Raised if the rank of the given shape is not in get_array_ranks() or is empty Raised if max_bits is not NONE and ndim does not equal 1

See also

zeros, ones

Examples

>>> import arkouda as ak
>>> ak.full(5, 7, dtype=ak.int64)
array([7 7 7 7 7])

>>> ak.full(5, 9, dtype=ak.float64)
array([9.00000000000000000 9.00000000000000000 9.00000000000000000
       9.00000000000000000 9.00000000000000000])

>>> ak.full(5, 5, dtype=ak.bool_)
array([True True True True True])

arkouda.full_like(pda: arkouda.numpy.pdarrayclass.pdarray, fill_value: arkouda.numpy.dtypes.numeric_scalars) → arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings[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:

ones_like, zeros_like

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

>>> import arkouda as ak
>>> ak.full_like(ak.full(5,7,dtype=ak.int64),6)
array([6 6 6 6 6])

>>> ak.full_like(ak.full(7,9,dtype=ak.float64),10)
array([10.00000000000000000 10.00000000000000000 10.00000000000000000
       10.00000000000000000 10.00000000000000000 10.00000000000000000 10.00000000000000000])

>>> ak.full_like(ak.full(5,True,dtype=ak.bool_),False)
array([False False False False False])

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:

starts (pdarray, int64) – The start value of each range
ends (pdarray, int64) – The end value (exclusive) of each range
stride (int) – Difference between successive elements of each range
return_lengths (bool, optional) – Whether or not to return the lengths of each segment. Default False.

Returns:

segmentspdarray, int64: The starting index of each range in the resulting array
rangespdarray, int64: The actual ranges, flattened into a single array
lengthspdarray, int64: The lengths of each segment. Only returned if return_lengths=True.

Return type:

pdarray|int64, pdarray|int64, pdarray|int64

arkouda.get_byteorder(dt: np.dtype) → str[source]¶

Get a concrete byteorder (turns ‘=’ into ‘<’ or ‘>’) on the client.

Parameters:: dt (np.dtype) – The numpy dtype to determine the byteorder of.
Returns:: Returns “<” for little endian and “>” for big endian.
Return type:: str
Raises:: ValueError – Returned if sys.byteorder is not “little” or “big”

Examples

>>> import arkouda as ak
>>> ak.get_byteorder(ak.dtype(ak.int64))
'<'

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

ls

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

read_parquet, read_hdf

arkouda.get_null_indices(filenames: str | List[str], datasets: str | List[str] | None = None) → arkouda.numpy.pdarrayclass.pdarray | Mapping[str, arkouda.numpy.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:

Dictionary of {datasetName: pdarray}

Return type:

returns a dictionary of Arkouda pdarrays

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

get_datasets, ls

arkouda.get_server_byteorder() → str[source]¶

Get the server’s byteorder

Returns:: Returns “little” for little endian and “big” for big endian.
Return type:: str
Raises:: ValueError – Raised if Server byteorder is not ‘little’ or ‘big’

Examples

>>> import arkouda as ak
>>> ak.get_server_byteorder()
'little'

class arkouda.groupable¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

class arkouda.half¶

Bases: numpy.floating

Half-precision floating-point number type.

Character code:

'e'

Canonical name:

numpy.half

Alias on this platform (Linux x86_64):

numpy.float16: 16-bit-precision floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.

as_integer_ratio(/)¶

half.as_integer_ratio() -> (int, int)

Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. Raise OverflowError on infinities and a ValueError on NaNs.
>>> np.half(10.0).as_integer_ratio()
(10, 1)
>>> np.half(0.0).as_integer_ratio()
(0, 1)
>>> np.half(-.25).as_integer_ratio()
(-1, 4)

is_integer(/)¶

half.is_integer() -> bool

Return True if the floating point number is finite with integral value, and False otherwise.

Added in version 1.22.
>>> np.half(-2.0).is_integer()
True
>>> np.half(3.2).is_integer()
False

arkouda.hash(pda: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical], full: bool = True) → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray] | arkouda.numpy.pdarrayclass.pdarray[source]¶

Return an element-wise hash of the array or list of arrays.

Parameters:

pda (pdarray, Strings, SegArray, or Categorical or List of pdarray, Strings, SegArray, or Categorical)
full (bool, default=True) – 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

Examples

>>> import arkouda as ak
>>> a = ak.randint(0,65536,3,seed=8675309)
>>> ak.hash(a,full=False)
array([6132219720275344925 189443193828113335 14797568559700425150])
>>> ak.hash(a)
(array([12228890592923494910 17773622519799422780 16661993598191972647]),
    array([2936052102410048944 15730675498625067356 4746877828134486787]))

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
>>> import numpy as np
>>> 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.numpy.pdarrayclass.pdarray, bins: arkouda.numpy.dtypes.int_scalars = 10, range: Tuple[arkouda.numpy.dtypes.numeric_scalars, arkouda.numpy.dtypes.numeric_scalars] | None = None) → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.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, default=10) – The number of equal-size bins to use (default: 10)
range ((minVal, maxVal), optional) – The range of the values to count. Values outside of this range are dropped. By default, all values are counted.

Returns:

The number of values present in each bin and the bin edges

Return type:

(pdarray, Union[pdarray, int64 or float64])

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

value_counts, histogram2d

Notes

The bins are evenly spaced in the interval [pda.min(), pda.max()]. If range parameter is provided, the interval is [range[0], range[1]].

Examples

>>> import arkouda as ak
>>> 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([0.00000000000000000 3.00000000000000000 6.00000000000000000 9.00000000000000000])

To plot, export the left edges and the histogram to NumPy >>> b_np = b.to_ndarray() >>> import numpy as np >>> b_widths = np.diff(b_np) >>> plt.bar(b_np[:-1], h.to_ndarray(), width=b_widths, align=’edge’, edgecolor=’black’) <BarContainer object of 3 artists> >>> plt.show() # doctest: +SKIP

arkouda.histogram2d(x: arkouda.numpy.pdarrayclass.pdarray, y: arkouda.numpy.pdarrayclass.pdarray, bins: arkouda.numpy.dtypes.int_scalars | Sequence[arkouda.numpy.dtypes.int_scalars] = 10, range: Tuple[Tuple[arkouda.numpy.dtypes.numeric_scalars, arkouda.numpy.dtypes.numeric_scalars], Tuple[arkouda.numpy.dtypes.numeric_scalars, arkouda.numpy.dtypes.numeric_scalars]] | None = None) → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.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], default=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
range (((xMin, xMax), (yMin, yMax)), optional) – The ranges of the values in x and y to count. Values outside of these ranges are dropped. By default, all values are counted.

Returns:

histpdarray: 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_edgespdarray: The bin edges along the first dimension.
y_edgespdarray: The bin edges along the second dimension.

Return type:

Tuple[pdarray, pdarray, pdarray]

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

histogram

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()]. If range parameter is provided, the intervals are given by range[0] for x and range[1] for y..

Examples

>>> import arkouda as ak
>>> 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([array([0.00000000000000000 0.00000000000000000 3.00000000000000000])
       array([0.00000000000000000 2.00000000000000000 1.00000000000000000])
       array([3.00000000000000000 1.00000000000000000 0.00000000000000000])])
>>> x_edges
array([0.00000000000000000 3.00000000000000000 6.00000000000000000 9.00000000000000000])
>>> y_edges
array([0.00000000000000000 3.00000000000000000 6.00000000000000000 9.00000000000000000])

arkouda.histogramdd(sample: Sequence[arkouda.numpy.pdarrayclass.pdarray], bins: arkouda.numpy.dtypes.int_scalars | Sequence[arkouda.numpy.dtypes.int_scalars] = 10, range: Sequence[Tuple[arkouda.numpy.dtypes.numeric_scalars, arkouda.numpy.dtypes.numeric_scalars] | None] | None = None) → Tuple[arkouda.numpy.pdarrayclass.pdarray, Sequence[arkouda.numpy.pdarrayclass.pdarray]][source]¶

Compute the multidimensional histogram of data in sample with evenly spaced bins.

Parameters:

sample (Sequence of pdarray) – A sequence of pdarrays containing the coordinates of the points to be histogrammed.
bins (int_scalars or Sequence of int_scalars, default=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
range (Sequence[optional (minVal, maxVal)], optional) – The ranges of the values to count for each array in sample. Values outside of these ranges are dropped. By default, all values are counted.

Returns:

histpdarray: 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.
edgesList[pdarray]: A list of pdarrays containing the bin edges for each dimension.

Return type:

Tuple[pdarray, Sequence[pdarray]]

Raises:

ValueError – Raised if bins < 1
NotImplementedError – Raised if pdarray dtype is bool or uint8

See also

histogram

Notes

The bins for each dimension, m, are evenly spaced in the interval [m.min(), m.max()] or in the inverval determined by range[dimension], if provided.

Examples

>>> import arkouda as ak
>>> 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=(2,2,3))
>>> h
array([array([array([0.00000000000000000 0.00000000000000000 0.00000000000000000])
    array([2.00000000000000000 1.00000000000000000 2.00000000000000000])])
    array([array([2.00000000000000000 1.00000000000000000 2.00000000000000000])
    array([0.00000000000000000 0.00000000000000000 0.00000000000000000])])])
>>> edges
[array([0.00000000000000000 4.5 9.00000000000000000]),
    array([0.00000000000000000 4.5 9.00000000000000000]),
    array([0.00000000000000000 2.6666666666666665 5.333333333333333 8.00000000000000000])]

arkouda.hstack(tup: Sequence[arkouda.numpy.pdarrayclass.pdarray], *, dtype: str | type | None = None, casting: Literal['no', 'equiv', 'safe', 'same_kind', 'unsafe'] = 'same_kind') → arkouda.numpy.pdarrayclass.pdarray[source]¶

Stack arrays in sequence horizontally (column wise).

This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by hsplit.

This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.

Parameters:

tup (sequence of pdarray) – The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length. In the case of a single array_like input, it will be treated as a sequence of arrays; i.e., each element along the zeroth axis is treated as a separate array.
dtype (str or type, optional) – If provided, the destination array will have this type.
casting ({‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional) – Controls what kind of data casting may occur. Defaults to ‘same_kind’. Currently unused.

Returns:

The array formed by stacking the given arrays.

Return type:

See also

concatenate, stack, block, vstack, dstack, column_stack, hsplit, unstack

Examples

>>> import arkouda as ak
>>> a = ak.array([1, 2, 3])
>>> b = ak.array([4, 5, 6])
>>> ak.hstack((a, b))
array([1 2 3 4 5 6])
>>> a = ak.array([[1],[2],[3]])
>>> b = ak.array([[4],[5],[6]])
>>> ak.hstack((a, b))
array([array([1 4]) array([2 5]) array([3 6])])

class arkouda.iinfo¶

iinfo(type)

Machine limits for integer types.

bits¶

The number of bits occupied by the type.

Type:: int

dtype¶

Returns the dtype for which iinfo returns information.

Type:: dtype

min¶

The smallest integer expressible by the type.

Type:: int

max¶

The largest integer expressible by the type.

Type:: int

Parameters:: int_type (integer type, dtype, or instance) – The kind of integer data type to get information about.

See also

finfo: The equivalent for floating point data types.

Examples

With types:

>>> import numpy as np
>>> ii16 = np.iinfo(np.int16)
>>> ii16.min
-32768
>>> ii16.max
32767
>>> ii32 = np.iinfo(np.int32)
>>> ii32.min
-2147483648
>>> ii32.max
2147483647

With instances:

>>> ii32 = np.iinfo(np.int32(10))
>>> ii32.min
-2147483648
>>> ii32.max
2147483647

property max¶: Maximum value of given dtype.

property min¶: Minimum value of given dtype.

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).

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.export

Notes

Import can only be performed from hdf5 or parquet files written by pandas.

arkouda.in1d(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable, assume_unique: bool = False, symmetric: bool = False, invert: bool = False) → 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 A that is True where an element of A is in B and False otherwise.

Supports multi-level, i.e. test if rows of a are in the set of rows of b. But note that multi-dimensional pdarrays are not supported.

Parameters:

A (list of pdarrays, pdarray, Strings, or Categorical) – Entries will be tested for membership in B
B (list of pdarrays, pdarray, Strings, or Categorical) – The set of elements in which to test membership
assume_unique (bool, optional, defaults to False) – If true, assume rows of a and b are each unique and sorted. By default, sort and unique them explicitly.
symmetric (bool, optional, defaults to False) – Return in1d(A, B), in1d(B, A) when A and B are single items.
invert (bool, optional, defaults to False) – If True, the values in the returned array are inverted (that is, False where an element of A is in B and True otherwise). Default is False. ak.in1d(a, b, invert=True) is equivalent to (but is faster than) ~ak.in1d(a, b).

Returns:

True for each row in a that is contained in b

Return type:

groupable

Raises:

TypeError – Raised if either A or B is not a pdarray, Strings, or Categorical object, or if both are pdarrays and either has rank > 1, or if invert is not a bool
RuntimeError – Raised if the dtype of either array is not supported

Examples

>>> import arkouda as ak
>>> ak.in1d(ak.array([-1, 0, 1]), ak.array([-2, 0, 2]))
array([False True False])

>>> ak.in1d(ak.array(['one','two']),ak.array(['two', 'three','four','five']))
array([False True])

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 to ak.array([item in b for item in a]), but is much faster and scales to arbitrarily large a.

ak.in1d is not supported for bool or float64 pdarrays

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(query: arkouda.groupbyclass.groupable, space: arkouda.groupbyclass.groupable) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return indices of query items in a search list of items. Items not found will be excluded. When duplicate terms are present in search space return indices of all occurrences.

Parameters:

query ((sequence of) pdarray or Strings or Categorical) – The items to search for. If multiple arrays, each “row” is an item.
space ((sequence of) pdarray or Strings or Categorical) – The set of items in which to search. Must have same shape/dtype as query.

Returns:

For each item in query that is found in space, its index in space.

Return type:

Notes

This is an alias of ak.find(query, space, all_occurrences=True, remove_missing=True).values

Examples

>>> import arkouda as ak
>>> select_from = ak.arange(10)
>>> query = select_from[ak.randint(0, select_from.size, 20, seed=10)]
>>> space = select_from[ak.randint(0, select_from.size, 20, seed=11)]

remove some values to ensure that query has entries which don’t appear in space

>>> space = space[space != 9]
>>> space = space[space != 3]

>>> ak.indexof1d(query, space)
array([0 4 1 3 10 2 6 12 13 5 7 8 9 14 5 7 11 15 5 7 0 4])

Raises:

TypeError – Raised if either query or space is not a pdarray, Strings, or Categorical object
RuntimeError – Raised if the dtype of either array is not supported

class arkouda.inexact¶

Bases: numpy.number

Abstract base class of all numeric scalar types with a (potentially): inexact representation of the values in its range, such as floating-point numbers.

arkouda.inf: float¶

arkouda.information(names: List[str] | str = RegisteredSymbols) → str[source]¶

Return a 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

class arkouda.int16¶

Signed integer type, compatible with C short.

Character code:

'h'

Canonical name:

numpy.short

Alias on this platform (Linux x86_64):

numpy.int16: 16-bit signed integer (-32_768 to 32_767).

bit_count(/)¶

int16.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.int16(127).bit_count()
7
>>> np.int16(-127).bit_count()
7

class arkouda.int32¶

Signed integer type, compatible with C int.

Character code:

'i'

Canonical name:

numpy.intc

Alias on this platform (Linux x86_64):

numpy.int32: 32-bit signed integer (-2_147_483_648 to 2_147_483_647).

bit_count(/)¶

int32.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.int32(127).bit_count()
7
>>> np.int32(-127).bit_count()
7

class arkouda.int64¶

Default signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

Character code:: 'l'
Canonical name:: numpy.int_
Alias on this platform (Linux x86_64):: numpy.int64: 64-bit signed integer (-9_223_372_036_854_775_808 to 9_223_372_036_854_775_807).
Alias on this platform (Linux x86_64):: numpy.intp: Signed integer large enough to fit pointer, compatible with C intptr_t.

bit_count(/)¶

int64.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.int64(127).bit_count()
7
>>> np.int64(-127).bit_count()
7

class arkouda.int8¶

Signed integer type, compatible with C char.

Character code:

'b'

Canonical name:

numpy.byte

Alias on this platform (Linux x86_64):

numpy.int8: 8-bit signed integer (-128 to 127).

bit_count(/)¶

int8.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.int8(127).bit_count()
7
>>> np.int8(-127).bit_count()
7

class arkouda.intTypes¶

Build an immutable unordered collection of unique elements.

copy()¶: Return a shallow copy of a set.

difference(*others)¶: Return a new set with elements in the set that are not in the others.

intersection(*others)¶: Return a new set with elements common to the set and all others.

isdisjoint(other, /)¶: Return True if two sets have a null intersection.

issubset(other, /)¶: Report whether another set contains this set.

issuperset(other, /)¶: Report whether this set contains another set.

symmetric_difference(other, /)¶: Return a new set with elements in either the set or other but not both.

union(*others)¶: Return a new set with elements from the set and all others.

class arkouda.int_scalars¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

class arkouda.intc¶

Signed integer type, compatible with C int.

Character code:

'i'

Canonical name:

numpy.intc

Alias on this platform (Linux x86_64):

numpy.int32: 32-bit signed integer (-2_147_483_648 to 2_147_483_647).

bit_count(/)¶

int32.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.int32(127).bit_count()
7
>>> np.int32(-127).bit_count()
7

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:

a (Strings or pdarray) – An array of strings.
b (Strings or pdarray) – An array of strings.
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:

(arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray) or
arkouda.numpy.pdarrayclass.pdarray – The indices of a and b where any element occurs at least once in both arrays.

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(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable, assume_unique: bool = False) → arkouda.numpy.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:

A (list of pdarrays, pdarray, Strings, or Categorical)
B (list of pdarrays, pdarray, Strings, or Categorical)
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 A or B is not a groupable
RuntimeError – Raised if the dtype of either pdarray is not supported

Examples

>>> import arkouda as ak

1D Example >>> ak.intersect1d(ak.array([1, 3, 4, 3]), ak.array([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:

class arkouda.intp¶

Default signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

Character code:: 'l'
Canonical name:: numpy.int_
Alias on this platform (Linux x86_64):: numpy.int64: 64-bit signed integer (-9_223_372_036_854_775_808 to 9_223_372_036_854_775_807).
Alias on this platform (Linux x86_64):: numpy.intp: Signed integer large enough to fit pointer, compatible with C intptr_t.

bit_count(/)¶

int64.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.int64(127).bit_count()
7
>>> np.int64(-127).bit_count()
7

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:: arkouda.numpy.pdarrayclass.pdarray

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:: IPv4

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.isSupportedBool(num)[source]¶

Whether a scalar is an arkouda supported boolean dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported boolean dtype, else False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.isSupportedBool("True")
False
>>> ak.isSupportedBool(True)
True

arkouda.isSupportedDType(scalar: object) → bool[source]¶

Whether a scalar is an arkouda supported dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported dtype, else False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.isSupportedDType(ak.int64(64))
True
>>> ak.isSupportedDType(np.complex128(1+2j))
False

arkouda.isSupportedFloat(num)[source]¶

Whether a scalar is an arkouda supported float dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported float dtype, else False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.isSupportedFloat(56)
False
>>> ak.isSupportedFloat(56.7)
True

arkouda.isSupportedInt(num)[source]¶

Whether a scalar is an arkouda supported integer dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported integer dtype, else False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.isSupportedInt(79)
True
>>> ak.isSupportedInt(54.9)
False

arkouda.isSupportedNumber(num)[source]¶

Whether a scalar is an arkouda supported numeric dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported numeric dtype, else False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.isSupportedNumber(45.9)
True
>>> ak.isSupportedNumber("string")
False

arkouda.is_cosorted(arrays)[source]¶

Return True iff the arrays are cosorted.

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.numpy.pdarrayclass.pdarray | IPv4, ip2: arkouda.numpy.pdarrayclass.pdarray | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Indicate which values are ipv4 when passed data containing IPv4 and IPv6 values.

Parameters:

ip (pdarray (int64) or ak.IPv4)
in. (IPv4 value. High Bits of IPv6 if IPv6 is passed)
ip2 (pdarray (int64), Optional)
well. (Low Bits of IPv6. This is added for support when dealing with data that contains IPv6 as)

Return type:

pdarray of bools indicating which indexes are IPv4.

See also

ak.is_ipv6

arkouda.is_ipv6(ip: arkouda.numpy.pdarrayclass.pdarray | IPv4, ip2: arkouda.numpy.pdarrayclass.pdarray | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Indicate which values are ipv6 when passed data containing IPv4 and IPv6 values.

Parameters:

ip (pdarray (int64) or ak.IPv4)
IPv6. (High Bits of)
ip2 (pdarray (int64), Optional)
IPv6 (Low Bits of)

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 provided name is associated with a registered Arkouda object.

This function checks if the name is found in the registry of objects, and optionally checks if it is registered as a component of a registered object.

Parameters:

name (str) – The name to check for in the registry.
as_component (bool, default=False) – When True, the function checks if the name is registered as a component of a registered object (rather than as a standalone object).

Returns:

True if the name is found in the registry, False otherwise.

Return type:

bool

Raises:

KeyError – If the registry query encounters an issue (e.g., invalid registry data or access issues).

Examples

>>> import arkouda as ak

Check if a name is registered as an object >>> obj = ak.array([1, 2, 3]) >>> registered_obj = obj.register(“my_array”) >>> result = ak.is_registered(“my_array”) >>> print(result) True >>> registered_obj.unregister()

Check if a name is registered as a component >>> result = ak.is_registered(“my_component”, as_component=True) >>> print(result) False

arkouda.isfinite(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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

>>> import arkouda as ak
>>> ak.isfinite(ak.array([1.0, 2.0, ak.inf]))
array([True True False])

arkouda.isinf(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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 (positive or negative)

Return type:

Raises:

TypeError – Raised if the parameter is not a pdarray
RuntimeError – if the underlying pdarray is not float-based

Examples

>>> import arkouda as ak
>>> ak.isinf(ak.array([1.0, 2.0, ak.inf]))
array([False False True])

arkouda.isnan(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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

>>> import arkouda as ak
>>> ak.isnan(ak.array([1.0, 2.0, np.log(-1)]))
array([False False True])

arkouda.isscalar(element)¶

Returns True if the type of element is a scalar type.

Parameters:: element (any) – Input argument, can be of any type and shape.
Returns:: val – True if element is a scalar type, False if it is not.
Return type:: bool

See also

ndim: Get the number of dimensions of an array

Notes

If you need a stricter way to identify a numerical scalar, use isinstance(x, numbers.Number), as that returns False for most non-numerical elements such as strings.

In most cases np.ndim(x) == 0 should be used instead of this function, as that will also return true for 0d arrays. This is how numpy overloads functions in the style of the dx arguments to gradient and the bins argument to histogram. Some key differences:

x	`isscalar(x)`	`np.ndim(x) == 0`
PEP 3141 numeric objects (including builtins)	`True`	`True`
builtin string and buffer objects	`True`	`True`
other builtin objects, like pathlib.Path, Exception, the result of re.compile	`False`	`True`
third-party objects like matplotlib.figure.Figure	`False`	`True`
zero-dimensional numpy arrays	`False`	`True`
other numpy arrays	`False`	`False`
list, tuple, and other sequence objects	`False`	`False`

Examples

>>> import numpy as np

>>> np.isscalar(3.1)
True

>>> np.isscalar(np.array(3.1))
False

>>> np.isscalar([3.1])
False

>>> np.isscalar(False)
True

>>> np.isscalar('numpy')
True

NumPy supports PEP 3141 numbers:

>>> from fractions import Fraction
>>> np.isscalar(Fraction(5, 17))
True
>>> from numbers import Number
>>> np.isscalar(Number())
True

arkouda.issubdtype(arg1, arg2)¶

Returns True if first argument is a typecode lower/equal in type hierarchy.

This is like the builtin issubclass(), but for dtypes.

Parameters:

arg1 (dtype_like) – dtype or object coercible to one
arg2 (dtype_like) – dtype or object coercible to one

Returns:

out

Return type:

bool

See also

arrays.scalars: Overview of the numpy type hierarchy.

Examples

issubdtype can be used to check the type of arrays:

>>> ints = np.array([1, 2, 3], dtype=np.int32)
>>> np.issubdtype(ints.dtype, np.integer)
True
>>> np.issubdtype(ints.dtype, np.floating)
False

>>> floats = np.array([1, 2, 3], dtype=np.float32)
>>> np.issubdtype(floats.dtype, np.integer)
False
>>> np.issubdtype(floats.dtype, np.floating)
True

Similar types of different sizes are not subdtypes of each other:

>>> np.issubdtype(np.float64, np.float32)
False
>>> np.issubdtype(np.float32, np.float64)
False

but both are subtypes of floating:

>>> np.issubdtype(np.float64, np.floating)
True
>>> np.issubdtype(np.float32, np.floating)
True

For convenience, dtype-like objects are allowed too:

>>> np.issubdtype('S1', np.bytes_)
True
>>> np.issubdtype('i4', np.signedinteger)
True

arkouda.join_on_eq_with_dt(a1: arkouda.numpy.pdarrayclass.pdarray, a2: arkouda.numpy.pdarrayclass.pdarray, t1: arkouda.numpy.pdarrayclass.pdarray, t2: arkouda.numpy.pdarrayclass.pdarray, dt: int | numpy.int64, pred: str, result_limit: int | numpy.int64 = 1000) → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

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_onepdarray, int64: a1 indices where a1 == a2
result_array_onepdarray, int64: a2 indices where a2 == a1

Return type:

Tuple[pdarray, pdarray]

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.

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.numpy.dtypes.numeric_scalars, stop: arkouda.numpy.dtypes.numeric_scalars, length: arkouda.numpy.dtypes.int_scalars) → arkouda.numpy.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:

Raises:

TypeError – Raised if start or stop is not a float or int or if length is not an int

See also

arange

Notes

If that start is greater than stop, the pdarray values are generated in descending order.

Examples

>>> import arkouda as ak
>>> ak.linspace(0, 1, 5)
array([0.00000000000000000 0.25 0.5 0.75 1.00000000000000000])

>>> ak.linspace(start=1, stop=0, length=5)
array([1.00000000000000000 0.75 0.5 0.25 0.00000000000000000])

>>> ak.linspace(start=-5, stop=0, length=5)
array([-5.00000000000000000 -3.75 -2.5 -1.25 0.00000000000000000])

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 = ',') → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.numpy.timeclass.Datetime | arkouda.numpy.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:

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 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

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 .parquet extension.

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

>>> import arkouda as ak
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: bool = True) → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.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, read

Notes

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.load_checkpoint(name, path='.akdata')[source]¶

Load server’s state.

The server metadata must match the current configuration (e.g. same number of locales must be used).

Parameters:

name (str) – Name of the checkpoint. <path>/<name> must be a directory.
path (str) – The directory to save the checkpoint.

Returns:

The checkpoint name, which will be the same as the name argument.

Return type:

str

Examples

>>> import arkouda as ak
>>> arr = ak.zeros(10, int)
>>> arr[2] = 2
>>> arr[2]
2
>>> cp_name = ak.save_checkpoint()
>>> arr[2] = 3
>>> arr[2]
3
>>> ak.load_checkpoint(cp_name)
>>> arr[2]
2

See also

save_checkpoint

arkouda.log(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Notes

Logarithms with other bases can be computed as follows:

Examples

>>> import arkouda as ak
>>> A = ak.array([1, 10, 100])

Natural log >>> ak.log(A) array([0.00000000000000000 2.3025850929940459 4.6051701859880918])

Log base 10 >>> ak.log(A) / np.log(10) array([0.00000000000000000 1.00000000000000000 2.00000000000000000])

Log base 2 >>> ak.log(A) / np.log(2) array([0.00000000000000000 3.3219280948873626 6.6438561897747253])

arkouda.log10(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise base 10 log of the array.

Parameters:: pda (pdarray) – array to compute on
Returns:: pdarray containing base 10 log values of the input array elements
Return type:: pdarray

Examples

>>> import arkouda as ak
>>> a = ak.arange(1,5)
>>> ak.log10(a)
array([0.00000000000000000 0.3010299956639812 0.47712125471966244 0.6020599913279624])

arkouda.log1p(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise natural log of one plus the array.

Parameters:: pda (pdarray) – array to compute on
Returns:: pdarray containing natural log values of the input array elements, adding one before taking the log
Return type:: pdarray

Examples

>>> import arkouda as ak
>>> ak.log1p(ak.arange(1,5))
array([0.69314718055994529 1.0986122886681098 1.3862943611198906 1.6094379124341003])

arkouda.log2(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise base 2 log of the array.

Parameters:: pda (pdarray) – array to compute on
Returns:: pdarray containing base 2 log values of the input array elements
Return type:: pdarray

Examples

>>> import arkouda as ak
>>> a = ak.arange(1,5)
>>> ak.log2(a)
array([0.00000000000000000 1.00000000000000000 1.5849625007211561 2.00000000000000000])

class arkouda.longdouble¶

Bases: numpy.floating

Extended-precision floating-point number type, compatible with C

long double but not necessarily with IEEE 754 quadruple-precision.

Character code:: 'g'
Alias on this platform (Linux x86_64):: numpy.float128: 128-bit extended-precision floating-point number type.

as_integer_ratio(/)¶

longdouble.as_integer_ratio() -> (int, int)

Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. Raise OverflowError on infinities and a ValueError on NaNs.
>>> np.longdouble(10.0).as_integer_ratio()
(10, 1)
>>> np.longdouble(0.0).as_integer_ratio()
(0, 1)
>>> np.longdouble(-.25).as_integer_ratio()
(-1, 4)

is_integer(/)¶

longdouble.is_integer() -> bool

Return True if the floating point number is finite with integral value, and False otherwise.

Added in version 1.22.
>>> np.longdouble(-2.0).is_integer()
True
>>> np.longdouble(3.2).is_integer()
False

class arkouda.longlong¶

Signed integer type, compatible with C long long.

Character code:

'q'

bit_count(/)¶

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

>>> import arkouda as ak

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’]) >>> ak.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 = ak.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]¶

List the contents of an HDF5 or Parquet file on the Arkouda server.

This function invokes the HDF5 h5ls utility on a file visible to the Arkouda server, or simulates a similar listing for Parquet files. For CSV files without headers, see ls_csv.

Parameters:

filename (str) – Path to the file on the Arkouda server. Must be a non-empty string.
col_delim (str, default=",") – Delimiter to use when interpreting CSV files.
read_nested (bool, default=True) – If True, include nested Parquet columns (e.g., SegArray). If False, nested columns are ignored. Only applies to Parquet files.

Returns:

A list of lines describing each dataset or column in the file.

Return type:

List[str]

Raises:

TypeError – If filename is not a string.
ValueError – If filename is empty or contains only whitespace.
RuntimeError – If an error occurs when running h5ls or simulating the Parquet listing.

Notes

Parquet support is limited and may change in future releases.
Output lines mirror the format of the HDF5 h5ls output.
For CSV files lacking headers, use ls_csv.

See also

ls_csv: List the contents of CSV files without headers.

arkouda.ls_csv(filename: str, col_delim: str = ',') → List[str][source]¶

List 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

ls

arkouda.matmul(pdaLeft: arkouda.numpy.pdarrayclass.pdarray, pdaRight: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Compute the product of two matrices.

Parameters:

pdaLeft (pdarray)
pdaRight (pdarray)

Returns:

the matrix product pdaLeft x pdaRight

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.array([[1,2,3,4,5],[1,2,3,4,5]])
>>> b = ak.array([[1,1],[2,2],[3,3],[4,4],[5,5]])
>>> ak.matmul(a,b)
array([array([55 55]) array([55 55])])

>>> x = ak.array([[1,2,3],[1.1,2.1,3.1]])
>>> y = ak.array([[1,1,1],[0,2,2],[0,0,3]])
>>> ak.matmul(x,y)
array([array([1.00000000000000000 5.00000000000000000 14.00000000000000000])
array([1.1000000000000001 5.3000000000000007 14.600000000000001])])

Notes

Server returns an error if shapes of pdaLeft and pdaRight are incompatible with matrix multiplication.

arkouda.maxk(pda: pdarray, k: arkouda.numpy.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:

Raises:

TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty, or pda.ndim > 1, or k < 1

Notes

This call is equivalent in value to a[ak.argsort(a)[k:]]

and generally outperforms this operation.

Examples

>>> import arkouda as ak
>>> 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.median(pda: arkouda.numpy.pdarrayclass.pdarray) → numpy.float64[source]¶

Compute the median of a given array. 1d case only, for now.

Parameters:

pda (pdarray) – The input data, in pdarray form, numeric type or boolean

Returns:

The median of the entire pdarray

The array is sorted, and then if the number of elements is odd, the return value is the middle element. If even, then the mean of the two middle elements.

Return type:

np.float64

Examples

>>> import arkouda as ak
>>> pda = ak.array([0,4,7,8,1,3,5,2,-1])
>>> ak.median(pda)
np.float64(3.0)
>>> pda = ak.array([0,1,3,3,1,2,3,4,2,3])
>>> ak.median(pda)
np.float64(2.5)

arkouda.merge(left: DataFrame, right: DataFrame, on: str | List[str] | None = None, left_on: str | List[str] | None = None, right_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.
left_on (str or List of str, optional) – Column name or names to join on in the left DataFrame. If this is not None, then right_on must also not be None, and this will override on.
right_on (str or List of str, optional) – Column name or names to join on in the right DataFrame. If this is not None, then left_on must also not be None, and this will override on.
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_x	col2_y
0	0	0	0.0
1	1	-1	nan
2	2	-2	2.0
3	3	-3	nan
4	4	-4	4.0

>>> merge(left_df, right_df, on = "col1", how = "right")

	col1	col2_x	col2_y
0	0	0.0	0
1	2	-2.0	2
2	4	-4.0	4
3	6	nan	6
4	8	nan	8

>>> merge(left_df, right_df, on = "col1", how = "outer")

	col1	col2_x	col2_y
0	0	0.0	0.0
1	1	-1.0	nan
2	2	-2.0	2.0
3	3	-3.0	nan
4	4	-4.0	4.0
5	6	nan	6.0
6	8	nan	8.0

arkouda.mink(pda: pdarray, k: arkouda.numpy.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 pda.ndim > 1, or k < 1

Notes

This call is equivalent in value to a[ak.argsort(a)[:k]] and generally outperforms this operation.

Examples

>>> import arkouda as ak
>>> 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 – pdarray : The numeric scalar or pdarray being acted on by the bases for the modular division.
divisor – pdarray : The numeric scalar or pdarray that will be the bases for the modular division.

Returns:

an array that contains the element-wise remainder of division.

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
>>> b = ak.array([2,2,2,3,3,3,4,4,4,5,5,5,6,6,6,7,7,7,8,8])
>>> ak.mod(a,b)
array([1 0 1 1 2 0 3 0 1 0 1 2 1 2 3 2 3 4 3 4])

Raises:: ValueError – raised if shapes of dividend and divisor are incompatible

arkouda.nan: float¶

arkouda.newaxis: None¶

arkouda.nextafter(x1: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.dtypes.bigint, x2: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.dtypes.bigint) → arkouda.numpy.pdarrayclass.pdarray | float[source]¶

Return the next floating-point value after x1 towards x2, element-wise. Accuracy only guaranteed for 64 bit values.

Parameters:

x1 (pdarray, numeric_scalars, or bigint) – Values to find the next representable value of.
x2 (pdarray, numeric_scalars, or bigint) – The direction where to look for the next representable value of x1. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

Returns:

The next representable values of x1 in the direction of x2. This is a scalar if both x1 and x2 are scalars.

Return type:

pdarray or float

Examples

>>> import arkouda as ak
>>> eps = np.finfo(np.float64).eps
>>> ak.nextafter(1, 2) == 1 + eps
 np.True_
>>> a = ak.array([1, 2])
>>> b = ak.array([2, 1])
>>> ak.nextafter(a, b) == ak.array([eps + 1, 2 - eps])
array([True True])

class arkouda.number¶

Bases: numpy.generic

Abstract base class of all numeric scalar types.

class arkouda.numeric_and_bool_scalars¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

class arkouda.numeric_scalars¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

class arkouda.numpy_scalars¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

Create a pdarray filled with ones.

Parameters:

size (int_scalars or tuple of int_scalars) – Size or shape of the array
dtype (Union[float64, int64, bool]) – Resulting array type, default ak.float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays Included for consistency, as ones are all zeros ending on a one, regardless of max_bits

Returns:

Ones of the requested size or shape and dtype

Return type:

Raises:

TypeError – Raised if the supplied dtype is not supported
RuntimeError – Raised if the size parameter is neither an int nor a str that is parseable to an int.
ValueError – Raised if the rank of the given shape is not in get_array_ranks() or is empty

See also

zeros, ones_like

Examples

>>> import arkouda as ak
>>> ak.ones(5, dtype=ak.int64)
array([1 1 1 1 1])

>>> ak.ones(5, dtype=ak.float64)
array([1.00000000000000000 1.00000000000000000 1.00000000000000000
       1.00000000000000000 1.00000000000000000])

>>> ak.ones(5, dtype=ak.bool_)
array([True True True True True])

Notes

Logic for generating the pdarray is delegated to the ak.full method.

arkouda.ones_like(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the pda parameter is not a pdarray.

See also

ones, zeros_like

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

>>> import arkouda as ak
>>> ak.ones_like(ak.zeros(5,dtype=ak.int64))
array([1 1 1 1 1])

>>> ak.ones_like(ak.zeros(5,dtype=ak.float64))
array([1.00000000000000000 1.00000000000000000 1.00000000000000000
       1.00000000000000000 1.00000000000000000])

>>> ak.ones_like(ak.zeros(5,dtype=ak.bool_))
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:: The parity of each element: 0 if even number of bits set, 1 if odd.
Return type:: pdarray
Raises:: TypeError – If input array is not int64, uint64, or bigint

Examples

>>> import arkouda as ak
>>> 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, size: arkouda.numpy.dtypes.int_scalars, ndim: arkouda.numpy.dtypes.int_scalars, shape: Tuple[int, Ellipsis], itemsize: arkouda.numpy.dtypes.int_scalars, max_bits: int | None = None)[source]¶

The basic arkouda array class. This class contains only the attributes 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 dtype of the array

Type:: type

size¶

The number of elements in the array

Type:: int_scalars

ndim¶

The rank of the array

Type:: int_scalars

shape¶

A tuple containing the sizes of each dimension of the array

Type:: Tuple[int, …]

itemsize¶

The size in bytes of each element

Type:: int_scalars

BinOps¶

OpEqOps¶

all(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff all elements of the array along the given axis evaluate to True.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, pdarray if axis is supplied

Return type:

boolean or pdarray

Examples

>>> import arkouda as ak
>>> ak.all(ak.array([True,False,False]))
np.False_
>>> ak.all(ak.array([[True,True,False],[False,True,True]]),axis=0)
array([False True False])
>>> ak.all(ak.array([[True,True,True],[False,False,False]]),axis=0,keepdims=True)
array([array([False False False])])
>>> ak.all(ak.array([[True,True,True],[False,False,False]]),axis=1,keepdims=True)
array([array([True]) array([False])])
>>> ak.array([True,False,False]).all()
np.False_

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Notes

Works as a method of a pdarray (e.g. a.any()) or a standalone function (e.g. ak.all(a))

any(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff any element of the array along the given axis evaluates to True.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, else pdarray if axis is supplied

Return type:

boolean or pdarray

Examples

>>> import arkouda as ak
>>> ak.any(ak.array([True,False,False]))
np.True_
>>> ak.any(ak.array([[True,True,False],[False,True,True]]),axis=0)
array([True True True])
>>> ak.any(ak.array([[True,True,True],[False,False,False]]),axis=0,keepdims=True)
array([array([True True True])])
>>> ak.any(ak.array([[True,True,True],[False,False,False]]),axis=1,keepdims=True)
array([array([True]) array([False])])
>>> ak.array([True,False,False]).any()
np.True_

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Notes

Works as a method of a pdarray (e.g. a.any()) or a standalone function (e.g. ak.any(a))

argmax(axis: int | None | None = None, keepdims: bool = False) → numpy.int64 | numpy.uint64 | pdarray[source]¶

Return index of the first occurrence of the maximum along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

int64 or uint64 if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

int64, uint64 or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.argmax(ak.array([1,2,3,4,5]))
np.int64(4)
>>> ak.argmax(ak.array([5.5,4.5,3.5,2.5,1.5]))
np.int64(0)
>>> ak.array([[1,2,3],[5,4,3]]).argmax(axis=1)
array([2 0])

Notes

Works as a method of a pdarray (e.g. a.argmax()) or a standalone function (e.g. ak.argmax(a))

argmaxk(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Finds the indices corresponding to the k maximum values of an array. See arkouda.argmaxk for details.

argmin(axis: int | None | None = None, keepdims: bool = False) → numpy.int64 | numpy.uint64 | pdarray[source]¶

Return index of the first occurrence of the minimum along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

int64 or uint64 if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

int64, uint64 or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.argmin(ak.array([1,2,3,4,5]))
np.int64(0)
>>> ak.argmin(ak.array([5.5,4.5,3.5,2.5,1.5]))
np.int64(4)
>>> ak.array([[1,2,3],[5,4,3]]).argmin(axis=1)
array([0 2])

Notes

Works as a method of a pdarray (e.g. a.argmin()) or a standalone function (e.g. ak.argmin(a))

argmink(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Finds the indices corresponding to the k minimum values of an array. See arkouda.argmink for details.

astype(dtype) → pdarray[source]¶

Cast values of pdarray to provided dtype

Parameters:: dtype (np.dtype or str) – Dtype to cast to

Examples

>>> import arkouda as ak
>>> ak.array([1,2,3]).astype(ak.float64)
array([1.00000000000000000 2.00000000000000000 3.00000000000000000])
>>> ak.array([1.5,2.5]).astype(ak.int64)
array([1 2])
>>> ak.array([True,False]).astype(ak.int64)
array([1 0])

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.

bigint_to_uint_arrays() → List[pdarray][source]¶

Create 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

See also

pdarraycreation.bigint_from_uint_arrays

Examples

>>> import arkouda as ak
>>> 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])]

clz() → pdarray[source]¶: Count the number of leading zeros in each element. See ak.clz.

corr(y: pdarray) → numpy.float64[source]¶: Compute the correlation between self and y using pearson correlation coefficient. See arkouda.corr for details.

cov(y: pdarray) → numpy.float64[source]¶: Compute the covariance between self and y.

ctz() → pdarray[source]¶: Count the number of trailing zeros in each element. See ak.ctz.

dtype: numpy.dtype¶

equals(other) → arkouda.numpy.dtypes.bool_scalars[source]¶

Whether pdarrays are the same size and all entries are equal.

Parameters:: other (object) – object to compare.
Returns:: True if the pdarrays are the same, o.w. False.
Return type:: bool_scalars

Examples

>>> import arkouda as ak
>>> a = ak.array([1, 2, 3])
>>> a_cpy = ak.array([1, 2, 3])
>>> a.equals(a_cpy)
np.True_
>>> a2 = ak.array([1, 2, 5])
>>> a.equals(a2)
np.False_

fill(value: arkouda.numpy.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

flatten()[source]¶

Return a copy of the array collapsed into one dimension.

Return type:: A copy of the input array, flattened to one dimension.

Examples

>>> import arkouda as ak
>>> a = ak.array([[3,2,1],[2,3,1]])
>>> a.flatten()
array([3 2 1 2 3 1])

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

property inferred_type: str | None¶: Return a string of the type inferred from the values.

info() → str[source]¶

Return 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(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff the array (or given axis of the array) is monotonically non-decreasing.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, else pdarray if axis is supplied

Return type:

boolean or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.is_sorted(ak.array([1,2,3,4,5]))
np.True_
>>> ak.is_sorted(ak.array([5,4,3,2,1]))
np.False_
>>> ak.array([[1,2,3],[5,4,3]]).is_sorted(axis=1)
array([True False])

Notes

Works as a method of a pdarray (e.g. a.is_sorted()) or a standalone function (e.g. ak.is_sorted(a))

itemsize: arkouda.numpy.dtypes.int_scalars¶

max(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numeric_scalars | pdarray[source]¶

Return max of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numeric_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numeric_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.max(ak.array([1,2,3,4,5]))
np.int64(5)
>>> ak.max(ak.array([5.5,4.5,3.5,2.5,1.5]))
np.float64(5.5)
>>> ak.array([[1,2,3],[5,4,3]]).max(axis=1)
array([3 5])

Notes

Works as a method of a pdarray (e.g. a.max()) or a standalone function (e.g. ak.max(a))

property max_bits¶

maxk(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Compute the maximum “k” values. See arkouda.maxk for details.

mean(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numeric_scalars | pdarray[source]¶

Return the mean of the array.

Parameters:

pda (pdarray) – Values for which to calculate the mean
axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

The mean calculated from the pda sum and size, along the axis/axes if those are given.

Return type:

Union[np.float64, pdarray]

Examples

>>> import arkouda as ak
>>> a = ak.arange(10)
>>> ak.mean(a)
np.float64(4.5)
>>> a.mean()
np.float64(4.5)
>>> a = ak.arange(10).reshape(2,5)
>>> a.mean(axis=0)
array([2.5 3.5 4.5 5.5 6.5])
>>> ak.mean(a,axis=0)
array([2.5 3.5 4.5 5.5 6.5])
>>> a.mean(axis=1)
array([2.00000000000000000 7.00000000000000000])
>>> ak.mean(a,axis=1)
array([2.00000000000000000 7.00000000000000000])

Raises: Raised if pda is not a pdarray instance
RuntimeError: Raised if there’s a server-side error thrown

min(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numeric_scalars | pdarray[source]¶

Return min of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numeric_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numeric_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.min(ak.array([1,2,3,4,5]))
np.int64(1)
>>> ak.min(ak.array([5.5,4.5,3.5,2.5,1.5]))
np.float64(1.5)
>>> ak.array([[1,2,3],[5,4,3]]).min(axis=1)
array([1 3])

Notes

Works as a method of a pdarray (e.g. a.min()) or a standalone function (e.g. ak.min(a))

mink(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Compute the minimum “k” values. See arkouda.mink for details.

name: str¶

property nbytes¶

The size of the pdarray in bytes.

Returns:: The size of the pdarray in bytes.
Return type:: int

ndim: arkouda.numpy.dtypes.int_scalars¶

objType = 'pdarray'¶

opeq(other, op)[source]¶

parity() → pdarray[source]¶: Find the parity (XOR of all bits) in each element. See ak.parity.

popcount() → pdarray[source]¶: Find the population (number of bits set) in each element. See ak.popcount.

pretty_print_info() → None[source]¶: Print information about all components of self in a human readable format.

prod(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numeric_scalars | pdarray[source]¶

Return prod of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, defalt = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numeric_scalars if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.prod(ak.array([1,2,3,4,5]))
np.int64(120)
>>> ak.prod(ak.array([5.5,4.5,3.5,2.5,1.5]))
np.float64(324.84375)
>>> ak.array([[1,2,3],[5,4,3]]).prod(axis=1)
array([6 60])

Notes

Works as a method of a pdarray (e.g. a.prod()) or a standalone function (e.g. ak.prod(a))

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_name

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> import arkouda as ak
>>> a = ak.zeros(3)
>>> a.register("my_zeros")
array([0.00000000000000000 0.00000000000000000 0.00000000000000000])

potentially disconnect from server and reconnect to server >>> b = ak.attach(“my_zeros”) >>> b.unregister()

registered_name: str | None = None¶

reshape(*shape)[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.
Returns:: a pdarray with the same data, reshaped to the new shape
Return type:: pdarray

Examples

>>> import arkouda as ak
>>> a = ak.array([[3,2,1],[2,3,1]])
>>> a.reshape((3,2))
array([array([3 2]) array([1 2]) array([3 1])])
>>> a.reshape(3,2)
array([array([3 2]) array([1 2]) array([3 1])])
>>> a.reshape((6,1))
array([array([3]) array([2]) array([1]) array([2]) array([3]) array([1])])

Notes

only available as a method, not as a standalone function, i.e., a.reshape(compatibleShape) is valid, but ak.reshape(a,compatibleShape) is not.

rotl(other) → pdarray[source]¶: Rotate bits left by <other>.

rotr(other) → pdarray[source]¶: Rotate bits right by <other>.

property shape¶

Return the shape of an array.

Returns:: The elements of the shape tuple give the lengths of the corresponding array dimensions.
Return type:: tuple of int

size: arkouda.numpy.dtypes.int_scalars¶

slice_bits(low, high) → pdarray[source]¶

Return 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

>>> import arkouda as ak
>>> p = ak.array([2**65 + (2**64 - 1)])
>>> bin(p[0])
'0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0])
'0b10'
>>> a = ak.array([143,15])
>>> a.slice_bits(1,3)
array([7 7])
>>> a.slice_bits(4,9)
array([8 0])
>>> a.slice_bits(1,9)
array([71 7])

std(ddof: arkouda.numpy.dtypes.int_scalars = 0, axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool | None = False) → numpy.float64 | pdarray[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
axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

The scalar standard deviation of the array, or the standard deviation: along the axis/axes if supplied

Return type:

Union[np.float64, pdarray]

Examples

>>> import arkouda as ak
>>> a = ak.arange(10)
>>> ak.std(a)
np.float64(2.8722813232690143)
>>> a.std()
np.float64(2.8722813232690143)
>>> a = ak.arange(10).reshape(2,5)
>>> a.std(axis=0)
array([2.5 2.5 2.5 2.5 2.5])
>>> ak.std(a,axis=0)
array([2.5 2.5 2.5 2.5 2.5])
>>> a.std(axis=1)
array([1.4142135623730951 1.4142135623730951])
>>> ak.std(a,axis=1)
array([1.4142135623730951 1.4142135623730951])

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

See also

mean, var

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)).

sum(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numeric_scalars | pdarray[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numeric_scalars if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> import arkouda as ak
>>> ak.sum(ak.array([1,2,3,4,5]))
np.int64(15)
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
np.float64(17.5)
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶

Write pdarry 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.

Parameters:

prefix_path (str) – filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
dataset (str, defaults to "array") – column name to save the pdarray under.
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, existing files matching the provided path will be overwritten. if False and existing files are found, an error will be returned.

Returns:

response message

Return type:

str

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 – Raise if the server returns an unknown arkouda_type

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for all 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.

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 a RuntimeError will 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

array

Examples

>>> import arkouda as ak
>>> 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.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in 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:

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, where  ranges from 0 to numLocales for 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, a RuntimeError will result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

Examples

>>> import arkouda as ak
>>> a = ak.arange(25)

Saving without an extension >>> a.to_hdf(‘path/prefix’, dataset=’array’) # doctest: +SKIP 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’) # doctest: +SKIP 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’) # doctest: +SKIP Saves the array in to single hdf5 file on the root node. cwd/path/name_prefix.hdf5

to_list() → List[arkouda.numpy.dtypes.numeric_scalars][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[numeric_scalars]
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

See also

to_ndarray

Examples

>>> import arkouda as ak
>>> a = ak.arange(0, 5, 1)
>>> a.to_list()
[0, 1, 2, 3, 4]
>>> type(a.to_list())
<class '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

See also

array, to_list

Examples

>>> import arkouda as ak
>>> a = ak.arange(0, 5, 1)
>>> a.to_ndarray()
array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray())
<class '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.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
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

Examples

>>> import arkouda as ak
>>> a = ak.arange(25)

Saving without an extension >>> a.to_parquet(‘path/prefix’, dataset=’array’) # doctest: +SKIP 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’) # doctest: +SKIP 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.numpy.dtypes.int_scalars)[source]¶

Send 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()

Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

See also

register, unregister, is_registered, unregister_pdarray_by_name, list_registry

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> import arkouda as ak
>>> a = ak.zeros(3)
>>> a.register("my_zeros")
array([0.00000000000000000 0.00000000000000000 0.00000000000000000])

potentially disconnect from server and reconnect to server >>> b = ak.attach(“my_zeros”) >>> 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_valuespdarray: The unique values, sorted in ascending order
countspdarray, int64: The number of times the corresponding unique value occurs

Return type:

pdarray, pdarray|int64

Examples

>>> import arkouda as ak
>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts()
(array([0 2 4]), array([3 2 1]))

var(ddof: arkouda.numpy.dtypes.int_scalars = 0, axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool | None = False) → numpy.float64 | pdarray[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
axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

The scalar variance of the array, or the variance along the axis/axes if supplied

Return type:

Union[np.float64, pdarray]

Examples

>>> import arkouda as ak
>>> a = ak.arange(10)
>>> ak.var(a)
np.float64(8.25)
>>> a.var()
np.float64(8.25)
>>> a = ak.arange(10).reshape(2,5)
>>> a.var(axis=0)
array([6.25 6.25 6.25 6.25 6.25])
>>> ak.var(a,axis=0)
array([6.25 6.25 6.25 6.25 6.25])
>>> a.var(axis=1)
array([2.00000000000000000 2.00000000000000000])
>>> ak.var(a,axis=1)
array([2.00000000000000000 2.00000000000000000])

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

See also

mean, std

Notes

The variance is the average of the squared deviations from the mean, i.e., var = mean((x - x.mean())**2).

arkouda.percentile(a: arkouda.numpy.pdarrayclass.pdarray, q: arkouda.numpy.dtypes.numeric_scalars | Tuple[arkouda.numpy.dtypes.numeric_scalars] | numpy.ndarray | None = 0.5, axis: arkouda.numpy.dtypes.int_scalars | Tuple[arkouda.numpy.dtypes.int_scalars, Ellipsis] | None | None = None, method: str | None = 'linear', keepdims: bool = False) → arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.pdarrayclass.pdarray[source]¶

Compute the q-th percentile of the data along the specified axis.

Parameters:

a (pdarray) – data whose percentile will be computed
q (pdarray, Tuple, or np.ndarray) – a scalar, tuple, or np.ndarray of q values for the computation. All values must be in the range 0 <= q <= 100
axis (None, int scalar, or tuple of int scalars) – the axis or axes along which the percentiles are computed. The default is None, which computes the percenntile along a flattened version of the array.
method (string) – one of “inverted_cdf,” “averaged_inverted_cdf”, “closest_observation”, “interpolated_inverted_cdf”, “hazen”, “weibull”, “linear”, ‘median_unbiased”, “normal_unbiased”, “lower”,” higher”, “midpoint”
keepdims (bool) – True if the degenerate axes are to be retained after slicing, False if not

Returns:

If q is a scalar and axis is None, the result is a scalar. If q is a scalar and axis is supplied, the result is a pdarray of rank len(axis) less than the rank of a. If q is an array and axis is None, the result is a pdarray of shape q.shape If q is an array and axis is None, the result is a pdarray of rank q.ndim + pda.ndim - len(axis). However, there is an intermediate result which is of rank q.ndim + pda.ndim. If this is not in the compiled ranks, an error will be thrown even if the final result would be in the compiled ranks.

Return type:

pdarray or scalar

Notes

np.percentile also supports the method “nearest,” however its behavior does not match the numpy documentation, so it’s not supported here. np.percentile also allows for weighted inputs, but only for the method “inverted_cdf.” That also is not supported here.

Examples

>>> import arkouda as ak
>>> a = ak.array([[1,2,3,4,5],[1,2,3,4,5]])
>>> q = 70
>>> ak.percentile(a,q,axis=None,method="linear")
np.float64(4.0)
>>> ak.percentile(a,q,axis=1,method="lower")
array([3.00000000000000000 3.00000000000000000])
>>> q = np.array([40,60])
>>> ak.percentile(a,q,axis=None,method="weibull")
array([2.4000000000000004 3.5999999999999996])
>>> a = ak.array([[1,2],[5,3]])
>>> ak.percentile(a,q,axis=0,method="hazen")
array([array([2.2000000000000002 2.2999999999999998])
    array([3.7999999999999998 2.6999999999999997])])

Raises:: ValueError – Raised if scalar q or any value of array q is outside the range [0,100] Raised if the method is not one of the 12 supported methods. Raised if the result would have a rank not in the compiled ranks.

arkouda.pi: float¶

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)
>>> h = h[:-1]
>>> ak.plot_dist(b.to_ndarray(), h.to_ndarray())

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:: The number of bits set (1) in each element
Return type:: pdarray
Raises:: TypeError – If input array is not int64, uint64, or bigint

Examples

>>> import arkouda as ak
>>> 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: arkouda.numpy.dtypes.bool_scalars | 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:

a pdarray of values raised to a power, under the boolean where condition.

Return type:

Examples

>>> import arkouda as ak
>>> 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])

Raises:

TypeError – raised if pda is not a pdarray, or if pwe is not an int, float, or pdarray
ValueError – raised if pda and power are of incompatible dimensions

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
>>> from arkouda.scipy 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=np.float64(8.333333333333334),
    pvalue=np.float64(0.03960235520756414))
>>> power_divergence(x, y, lambda_="log-likelihood")
Power_divergenceResult(statistic=np.float64(8.109302162163285),
    pvalue=np.float64(0.04380595350226197))

See also

scipy.stats.power_divergence, arkouda.akstats.chisquare

Notes

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]¶

Print 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
Raises:: RuntimeError – Raised if a server-side error is thrown in the process of retrieving information about the objects in names

arkouda.promote_to_common_dtype(arrays: List[arkouda.numpy.pdarrayclass.pdarray]) → Tuple[Any, List[arkouda.numpy.pdarrayclass.pdarray]][source]¶

Promote a list of pdarrays to a common dtype.

Parameters:: arrays (List[pdarray]) – List of pdarrays to promote
Returns:: The common dtype of the pdarrays and the list of pdarrays promoted to that dtype
Return type:: dtype, List[pdarray]
Raises:: TypeError – Raised if any pdarray is a non-numeric type

See also

pdarray.promote_dtype

Examples

>>> import arkouda as ak
>>> a = ak.arange(5)
>>> b = ak.ones(5, dtype=ak.float64)
>>> dtype, promoted = ak.promote_to_common_dtype([a, b])
>>> dtype
dtype('float64')
>>> all(isinstance(p, ak.pdarray) and p.dtype == dtype for p in promoted)
True

arkouda.putmask(A: arkouda.numpy.pdarrayclass.pdarray, mask: arkouda.numpy.pdarrayclass.pdarray, Values: arkouda.numpy.pdarrayclass.pdarray) → None[source]¶

Overwrite elements of A with elements from B based upon a mask array. Similar to numpy.putmask, where mask = False, A retains its original value, but where mask = True, A is overwritten with the corresponding entry from Values.

This is similar to ak.where, except that (1) no new pdarray is created, and (2) Values does not have to be the same size as A and mask.

Parameters:

A (pdarray) – Value(s) used when mask is False (see Notes for allowed dtypes)
mask (pdarray) – Used to choose values from A or B, must be same size as A, and of type ak.bool_
Values (pdarray) – Value(s) used when mask is False (see Notes for allowed dtypes)

Examples

>>> import arkouda as ak
>>> a = ak.array(np.arange(10))
>>> ak.putmask (a,a>2,a**2)
>>> a
array([0 1 2 9 16 25 36 49 64 81])

>>> a = ak.array(np.arange(10))
>>> values = ak.array([3,2])
>>> ak.putmask (a,a>2,values)
>>> a
array([0 1 2 2 3 2 3 2 3 2])

Raises:: RuntimeError – Raised if mask is not same size as A, or if A.dtype and Values.dtype are not an allowed pair (see Notes for details).

Notes

A and mask must be the same size.  Values can be any size.
Allowed dtypes for A and Values conform to types accepted by numpy putmask.
If A is ak.float64, Values can be ak.float64, ak.int64, ak.uint64, ak.bool_.
If A is ak.int64, Values can be ak.int64 or ak.bool_.
If A is ak.uint64, Values can be ak.uint64, or ak.bool_.
If A is ak.bool_, Values must be ak.bool_.

Only one conditional clause is supported e.g., n < 5, n > 1.

multi-dim pdarrays are now implemented.

arkouda.quantile(a: arkouda.numpy.pdarrayclass.pdarray, q: arkouda.numpy.dtypes.numeric_scalars | Tuple[arkouda.numpy.dtypes.numeric_scalars] | numpy.ndarray | arkouda.numpy.pdarrayclass.pdarray | None = 0.5, axis: arkouda.numpy.dtypes.int_scalars | Tuple[arkouda.numpy.dtypes.int_scalars, Ellipsis] | None | None = None, method: str | None = 'linear', keepdims: bool = False) → arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.pdarrayclass.pdarray[source]¶

Compute the q-th quantile of the data along the specified axis.

Parameters:

a (pdarray) – data whose quantile will be computed
q (pdarray, Tuple, or np.ndarray) – a scalar, tuple, or np.ndarray of q values for the computation. All values must be in the range 0 <= q <= 1
axis (None, int scalar, or tuple of int scalars) – the axis or axes along which the quantiles are computed. The default is None, which computes the quantile along a flattened version of the array.
method (string) – one of “inverted_cdf,” “averaged_inverted_cdf”, “closest_observation”, “interpolated_inverted_cdf”, “hazen”, “weibull”, “linear”, ‘median_unbiased”, “normal_unbiased”, “lower”,” higher”, “midpoint”
keepdims (bool) – True if the degenerate axes are to be retained after slicing, False if not

Returns:

Return type:

pdarray or scalar

Notes

np.quantile also supports the method “nearest,” however its behavior does not match the numpy documentation, so it’s not supported here. np.quantile also allows for weighted inputs, but only for the method “inverted_cdf.” That also is not supported here.

Examples

>>> import arkouda as ak
>>> a = ak.array([[1,2,3,4,5],[1,2,3,4,5]])
>>> q = 0.7
>>> ak.quantile(a,q,axis=None,method="linear")
np.float64(4.0)
>>> ak.quantile(a,q,axis=1,method="lower")
array([3.00000000000000000 3.00000000000000000])
>>> q = np.array([0.4,0.6])
>>> ak.quantile(a,q,axis=None,method="weibull")
array([2.4000000000000004 3.5999999999999996])
>>> a = ak.array([[1,2],[5,3]])
>>> ak.quantile(a,q,axis=0,method="hazen")
array([array([2.2000000000000002 2.2999999999999998])
    array([3.7999999999999998 2.6999999999999997])])

Raises:: ValueError – Raised if scalar q or any value of array q is outside the range [0,1] Raised if the method is not one of the 12 supported methods. Raised if the result would have a rank not in the compiled ranks.

arkouda.rad2deg(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Converts angles element-wise from radians to degrees.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the corresponding value will be converted from radians to degrees. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(0,6.28,4)
>>> ak.rad2deg(a)
array([0.00000000000000000 119.93916511405233 239.87833022810466 359.81749534215703])

arkouda.randint(low: arkouda.numpy.dtypes.numeric_scalars, high: arkouda.numpy.dtypes.numeric_scalars, size: arkouda.numpy.dtypes.int_scalars | Tuple[arkouda.numpy.dtypes.int_scalars, Ellipsis] = 1, dtype=akint64, seed: arkouda.numpy.dtypes.int_scalars | None = None) → arkouda.numpy.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 or tuple of int_scalars) – The size or shape 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

>>> import arkouda as ak
>>> ak.randint(0, 10, 5, seed=1701)
array([6 5 1 6 3])

>>> ak.randint(0, 1, 3, seed=1701, dtype=ak.float64)
array([0.011410423448327005 0.73618171558685619 0.12367222192448891])

>>> ak.randint(0, 1, 5, seed=1701, dtype=ak.bool_)
array([False True False True False])

arkouda.random_strings_lognormal(logmean: arkouda.numpy.dtypes.numeric_scalars, logstd: arkouda.numpy.dtypes.numeric_scalars, size: arkouda.numpy.dtypes.int_scalars, characters: str = 'uppercase', seed: arkouda.numpy.dtypes.int_scalars | None = None) → arkouda.numpy.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:

random_strings_lognormal, randint

Raises:

TypeError – Raised if logmean is neither a float nor a int, logstd is not a float, seed is not an int, 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

>>> import arkouda as ak
>>> ak.random_strings_lognormal(2, 0.25, 5, seed=1)
array(['VWHJEX', 'BEBBXJHGM', 'RWOVKBUR', 'LNJCSDXD', 'NKEDQC'])

>>> ak.random_strings_lognormal(2, 0.25, 5, seed=1, characters='printable')
array(['eL96<O', ')o-GOe lR', ')PV yHf(', '._b3Yc&K', ',7Wjef'])

arkouda.random_strings_uniform(minlen: arkouda.numpy.dtypes.int_scalars, maxlen: arkouda.numpy.dtypes.int_scalars, size: arkouda.numpy.dtypes.int_scalars, characters: str = 'uppercase', seed: None | arkouda.numpy.dtypes.int_scalars = None) → arkouda.numpy.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:

random_strings_lognormal, randint

Raises:

ValueError – Raised if minlen < 0, maxlen < minlen, or size < 0

See also

Examples

>>> import arkouda as ak
>>> ak.random_strings_uniform(minlen=1, maxlen=5, seed=8675309, size=5)
array(['ECWO', 'WSS', 'TZG', 'RW', 'C'])

>>> ak.random_strings_uniform(minlen=1, maxlen=5, seed=8675309, size=5,
... characters='printable')
array(['2 .z', 'aom', '2d|', 'o(', 'M'])

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, fixed_len: int = -1) → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.numpy.timeclass.Datetime | arkouda.numpy.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.
fixed_len (int) – Default -1. This value can be set for reading Parquet string columns when the length of each string is known at runtime. This can allow for skipping byte calculation, which can have an impact on performance.

Returns:

Dictionary of {datasetName: pdarray, String, or SegArray}

Return type:

Returns a dictionary of Arkouda pdarrays, Arkouda Strings, or Arkouda Segarrays.

Raises:

RuntimeError – If invalid filetype is detected

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_datasets to show the names of datasets to HDF5/Parquet files.

CSV files without the Arkouda Header are not supported.

Examples

>>> import arkouda as ak
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

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:

Dictionary of {datasetName: pdarray, String, or SegArray}

Return type:

Returns a dictionary of Arkouda pdarrays, Arkouda Strings, or Arkouda Segarrays.

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

to_csv

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) → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.numpy.timeclass.Datetime | arkouda.numpy.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:

Dictionary of {datasetName: pdarray, String, SegArray}

Return type:

Returns a dictionary of Arkouda pdarrays, Arkouda Strings, or Arkouda Segarrays.

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 datasets is None, infer the names of datasets from the first file and read all of them. Use get_datasets to show the names of datasets to HDF5 files.

See also

read_tagged_data

Examples

>>> import arkouda as ak
>>>
# 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, fixed_len: int = -1) → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.numpy.timeclass.Datetime | arkouda.numpy.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.
fixed_len (int) – Default -1. This value can be set for reading Parquet string columns when the length of each string is known at runtime. This can allow for skipping byte calculation, which can have an impact on performance.

Returns:

Dictionary of {datasetName: pdarray, String, or SegArray}

Return type:

Returns a dictionary of Arkouda pdarrays, Arkouda Strings, or Arkouda Segarrays.

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 datasets is None, infer the names of datasets from the first file and read all of them. Use get_datasets to 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

read_tagged_data

Examples

>>> import arkouda as ak
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

>>> import arkouda as ak
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.read_zarr(store_path: str, ndim: int, dtype)[source]¶

Read a Zarr store from disk into a pdarray.

Supports multi-dimensional pdarrays of numeric types. To use this function, ensure you have installed the blosc dependency (make install-blosc) and have included ZarrMsg.chpl in the ServerModules.cfg file.

Parameters:

store_path (str) – The path to the Zarr store. The path must be to a directory that contains a .zarray file containing the Zarr store metadata.
ndim (int) – The number of dimensions in the array
dtype (str) – The data type of the array

Returns:

The pdarray read from the Zarr store.

Return type:

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]¶

This function iterates through the dictionary data, registering each object with its corresponding name. It is useful for batch registering multiple objects in Arkouda.

Parameters:: data (dict) – A dictionary that maps the name to register the object to the object itself. For example, {“MyArray”: ak.array([0, 1, 2])}.

Examples

>>> import arkouda as ak
>>> data = { "array1": ak.array([0, 1, 2]), "array2": ak.array([3, 4, 5]) }
>>> ak.register_all(data)

After calling this function, “array1” and “array2” are registered in Arkouda, and can be accessed by their names. >>> ak.unregister_all([“array1”, “array2”])

Repeat each element of an array after themselves

Parameters:

a (int, Sequence of int, or pdarray) – Input array.
repeats (int, Sequence of int, or pdarray) – The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis.
axis (int, optional) – The axis along which to repeat values. By default, use the flattened input array, and return a flat output array.

Returns:

Output array which has the same shape as a, except along the given axis.

Return type:

Examples

>>> import arkouda as ak
>>> ak.repeat(3, 4)
array([3 3 3 3])
>>> x = ak.array([[1,2],[3,4]])
>>> ak.repeat(x, 2)
array([1 1 2 2 3 3 4 4])
>>> ak.repeat(x, 3, axis=1)
array([array([1 1 1 2 2 2]) array([3 3 3 4 4 4])])
>>> ak.repeat(x, [1, 2], axis=0)
array([array([1 2]) array([3 4]) array([3 4])])

arkouda.resolve_scalar_dtype(val: object) → str[source]¶

Try to infer what dtype arkouda_server should treat val as.

Parameters:: val (object) – The object to determine the dtype of.
Returns:: The dtype name, if it can be resolved, otherwise the type (as str).
Return type:: str

Examples

>>> import arkouda as ak
>>> ak.resolve_scalar_dtype(1)
'int64'
>>> ak.resolve_scalar_dtype(2.0)
'float64'

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.result_type(*args: pdarray | np.dtype | type) → np.dtype | type[source]¶

Determine the promoted result dtype of inputs, including support for Arkouda’s bigint.

Determine the result dtype that would be returned by a NumPy-like operation on the provided input arguments, accounting for Arkouda’s extended types such as ak.bigint.

This function mimics numpy.result_type, with support for Arkouda types.

Parameters:: *args (Union[pdarray, np.dtype, type]) – One or more input objects. These can be NumPy arrays, dtypes, Python scalar types, or Arkouda pdarrays.
Returns:: The dtype (or equivalent Arkouda type) that results from applying type promotion rules to the inputs.
Return type:: Union[np.dtype, type]

Notes

This function is meant to be a drop-in replacement for numpy.result_type but includes logic to support Arkouda’s bigint types.

arkouda.right_align(left, right)[source]¶

Map two arrays of sparse values to the 0-up index.

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:

left (pdarray or a sequence of pdarrays) – Left-hand identifiers
right (pdarray or a sequence of pdarrays) – Right-hand identifiers that define the index

Returns:

keeppdarray, bool: Logical index of left-hand values that survived
aligned(pdarray, pdarray): Left and right arrays with values replaced by 0-up indices

Return type:

pdarray, (pdarray, pdarray)

arkouda.rotl(x, rot) → pdarray[source]¶

Rotate bits of <x> to the left by <rot>.

Parameters:

x (pdarray(int64/uint64) or integer) – Value(s) to rotate left.
rot (pdarray(int64/uint64) or integer) – Amount(s) to rotate by.

Returns:

The rotated elements of x.

Return type:

Raises:

TypeError – If input array is not int64 or uint64

Examples

>>> import arkouda as ak
>>> 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:

x (pdarray(int64/uint64) or integer) – Value(s) to rotate left.
rot (pdarray(int64/uint64) or integer) – Amount(s) to rotate by.

Returns:

The rotated elements of x.

Return type:

Raises:

TypeError – If input array is not int64 or uint64

Examples

>>> import arkouda as ak
>>> A = ak.arange(10)
>>> ak.rotr(1024 * A, A)
array([0 512 512 384 256 160 96 56 32 18])

arkouda.round(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.round(ak.array([1.1, 2.5, 3.14159]))
array([1.00000000000000000 3.00000000000000000 3.00000000000000000])

arkouda.save_checkpoint(name='', path='.akdata', mode: str = 'overwrite')[source]¶

Save the server’s state.

Records some metadata about the server, and saves all pdarrays into parquet files.

Parameters:

name (str) – Name of the checkpoint. The default will be the server session ID, which is typically in format id_<hash>_. A directory will be created in path with this name.
path (str) – The directory to save the checkpoint. If the directory doesn’t exist, it will be created. If it exists, a new directory for the checkpoint instance will be created inside this directory.
mode ({'overwrite' | 'preserve_previous' | 'error'}) – By default, overwrite the checkpoint files if they exist. If ‘preserve_previous’, an existing checkpoint with ‘name’ will be renamed to ‘name.prev’, overwriting ‘name.prev’ if it existed, before creating a new checkpoint with ‘name’. If ‘error’, an error will be raised if a checkpoint with the same name exists.

Notes

Only ``pdarray``s are saved. Other data structures will not be recorded. We expect to expand the coverage in the future.

Returns:: The checkpoint name, which will be the same as the name argument if it was passed.
Return type:: str

Examples

>>> import arkouda as ak
>>> arr = ak.zeros(10, int)
>>> arr[2] = 2
>>> arr[2]
2
>>> cp_name = ak.save_checkpoint()
>>> arr[2] = 3
>>> arr[2]
3
>>> ak.load_checkpoint(cp_name)
>>> arr[2]
2

See also

load_checkpoint

arkouda.scalar_array(value: arkouda.numpy.dtypes.numeric_scalars, dtype: numpy.dtype | type | str | arkouda.numpy.dtypes.bigint | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Create a pdarray from a single scalar value.

Parameters:: value (numeric_scalars) – Value to create pdarray from
Returns:: pdarray with a single element
Return type:: pdarray

Examples

>>> import arkouda as ak
>>> ak.scalar_array(5)
array([5])

>>> ak.scalar_array(7.0)
array([7.00000000000000000])

Raises:: RuntimeError – Raised if value cannot be cast as dtype

class arkouda.sctypeDict¶

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object’s

(key, value) pairs

dict(iterable) -> new dictionary initialized as if via:: d = {} for k, v in iterable:

d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs: in the keyword argument list. For example: dict(one=1, two=2)

clear()¶: Remove all items from the dict.

copy()¶: Return a shallow copy of the dict.

fromkeys(iterable, value=None, /)¶: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)¶: Return the value for key if key is in the dictionary, else default.

items()¶: Return a set-like object providing a view on the dict’s items.

keys()¶: Return a set-like object providing a view on the dict’s keys.

pop(*args, **kwargs)¶

D.pop(k[,d]) -> v, remove specified key and return the corresponding value.

If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()¶

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)¶

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update(*args, **kwargs)¶: D.update([E, ]**F) -> None. Update D from mapping/iterable E and F. If E is present and has a .keys() method, then does: for k in E.keys(): D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values()¶: Return an object providing a view on the dict’s values.

arkouda.search_intervals(vals, intervals, tiebreak=None, hierarchical=True)[source]¶

Return the index of the best interval containing each query value.

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

>>> import arkouda as ak
>>> 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.searchsorted(a: arkouda.numpy.pdarrayclass.pdarray, v: arkouda.numpy.dtypes.int_scalars | arkouda.numpy.dtypes.float64 | arkouda.numpy.dtypes.bigint | arkouda.numpy.pdarrayclass.pdarray, side: Literal['left', 'right'] = 'left') → int | arkouda.numpy.pdarrayclass.pdarray[source]¶

Find indices where elements should be inserted to maintain order.

Find the indices into a sorted array a such that, if the corresponding elements in v were inserted before the indices, the order of a would be preserved.

Parameters:

a (pdarray) – 1-D input array. Must be sorted in ascending order. sorter is not currently supported.
v (int_scalars, float64, bigint, or pdarray) – Values to insert into a. Can be a scalar or array-like.
side ({'left', 'right'}, default='left') – If ‘left’, the index of the first suitable location found is given. If ‘right’, return the last such index.

Returns:

indices – If v is an array, returns an array of insertion points with the same shape. If v is a scalar, returns a single integer index.

Return type:

int or pdarray

Raises:

ValueError – If a has more than one dimension.
TypeError – If a has an unsupported dtype (i.e., not int64, uint64, bigint, or float64). If the dtype of a and v does not match

Examples

>>> import arkouda as ak
>>> a = ak.array([11, 12, 13, 14, 15])
>>> ak.searchsorted(a, 13)
2
>>> ak.searchsorted(a, 13, side='right')
3
>>> v = ak.array([-10, 20, 12, 13])
>>> ak.searchsorted(a, v)
array([0 5 1 2])

arkouda.setdiff1d(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable, assume_unique: bool = False) → arkouda.numpy.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶

Find the set difference of two arrays.

Return the sorted, unique values in A that are not in B.

Parameters:

A (list of pdarrays, pdarray, Strings, or Categorical)
B (list of pdarrays, pdarray, Strings, or Categorical)
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 A that are not in B.

Return type:

pdarray/groupable

Raises:

TypeError – Raised if either A or B is not a pdarray
RuntimeError – Raised if the dtype of either pdarray is not supported

Notes

ak.setdiff1d is not supported for bool pdarrays

Examples

>>> import arkouda as ak
>>> 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(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable, assume_unique: bool = False) → arkouda.numpy.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:

A (list of pdarrays, pdarray, Strings, or Categorical)
B (list of pdarrays, pdarray, Strings, or Categorical)
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 A or B is not a groupable
RuntimeError – Raised if the dtype of either pdarray is not supported

Examples

>>> import arkouda as ak
>>> 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.shape(a: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.dtypes.all_scalars) → Tuple[source]¶

Return the shape of an array.

Parameters:: a (pdarray, Strings, or all_scalars) – Input array.
Returns:: The elements of the shape tuple give the lengths of the corresponding array dimensions.
Return type:: Tuple

Examples

>>> import arkouda as ak
>>> ak.shape(ak.eye(3,2))
(3, 2)
>>> ak.shape([[1, 3]])
(1, 2)
>>> ak.shape([0])
(1,)
>>> ak.shape(0)
()

class arkouda.short¶

Signed integer type, compatible with C short.

Character code:

'h'

Canonical name:

numpy.short

Alias on this platform (Linux x86_64):

numpy.int16: 16-bit signed integer (-32_768 to 32_767).

bit_count(/)¶

int16.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.int16(127).bit_count()
7
>>> np.int16(-127).bit_count()
7

arkouda.sign(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.sign(ak.array([-10, -5, 0, 5, 10]))
array([-1 -1 0 1 1])

class arkouda.signedinteger¶

Bases: numpy.integer

Abstract base class of all signed integer scalar types.

arkouda.sin(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise sine of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the sine will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

Returns:

A pdarray containing sin for each element of the original pdarray

Return type:

Raises:

TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-1.5,0.75,4)
>>> ak.sin(a)
array([-0.99749498660405445 -0.68163876002333412 0.00000000000000000 0.68163876002333412])

class arkouda.single¶

Bases: numpy.floating

Single-precision floating-point number type, compatible with C float.

Character code:

'f'

Canonical name:

numpy.single

Alias on this platform (Linux x86_64):

numpy.float32: 32-bit-precision floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa.

as_integer_ratio(/)¶

single.as_integer_ratio() -> (int, int)

Return a pair of integers, whose ratio is exactly equal to the original floating point number, and with a positive denominator. Raise OverflowError on infinities and a ValueError on NaNs.
>>> np.single(10.0).as_integer_ratio()
(10, 1)
>>> np.single(0.0).as_integer_ratio()
(0, 1)
>>> np.single(-.25).as_integer_ratio()
(-1, 4)

is_integer(/)¶

single.is_integer() -> bool

Return True if the floating point number is finite with integral value, and False otherwise.

Added in version 1.22.
>>> np.single(-2.0).is_integer()
True
>>> np.single(3.2).is_integer()
False

arkouda.sinh(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise hyperbolic sine of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the hyperbolic sine will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-0.9,0.7,4)
>>> ak.sinh(a)
array([-1.0265167257081753 -0.37493812328444776 0.16743934398751592 0.75858370183953339])

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)

See also

ak.restore

arkouda.sort(pda: arkouda.numpy.pdarrayclass.pdarray, algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD, axis: arkouda.numpy.dtypes.int_scalars = -1) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a sorted copy of the array. Only sorts numeric arrays; for Strings, use argsort.

Parameters:

pda (pdarray) – The array to sort (int64, uint64, or float64)
algorithm (SortingAlgorithm, default=SortingAlgorithm.RadixSortLSD) – The algorithm to be used for sorting the arrays.
axis (int_scalars, default=-1) – The axis to sort over. Setting to -1 means that it will sort over axis = ndim - 1.

Returns:

The sorted copy of pda

Return type:

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

argsort

Notes

Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.

Examples

>>> import arkouda as ak
>>> a = ak.randint(0, 10, 10, seed=1)
>>> a
array([7 9 5 1 4 1 8 5 5 0])
>>> sorted = ak.sort(a)
>>> sorted
array([0 1 1 4 5 5 5 7 8 9])

arkouda.sqrt(pda: pdarray, where: arkouda.numpy.dtypes.bool_scalars | 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:

pda (pdarray) – A pdarray of values the square roots of which will be computed
where (Boolean or pdarray) – This condition is broadcast over the input. At locations where the condition is True, the corresponding value will be square rooted. Elsewhere, it will retain its original value. Default set to True.

Returns:

a pdarray of square roots of the original values, or the original values themselves, subject to the boolean where condition.

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.arange(5)
>>> ak.sqrt(a)
array([0.00000000000000000 1.00000000000000000 1.4142135623730951
         1.7320508075688772 2.00000000000000000])
>>> ak.sqrt(a, ak.array([True, True, False, False, True]))
array([0.00000000000000000 1.00000000000000000 2.00000000000000000
         3.00000000000000000 2.00000000000000000])

Raises:: TypeError – raised if pda is not a pdarray of ak.int64 or ak.float64

Notes

Square roots of negative numbers are returned as nan.

arkouda.square(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.square(ak.arange(1,5))
array([1 4 9 16])

arkouda.squeeze(x: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.dtypes.bool_scalars, /, axis: None | int | Tuple[int, Ellipsis] = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Remove degenerate (size one) dimensions from an array.

Parameters:

x (pdarray) – The array to squeeze
axis (int or Tuple[int, ...]) – The axis or axes to squeeze (must have a size of one). If axis = None, all dimensions of size 1 will be squeezed.

Returns:

A copy of x with the dimensions specified in the axis argument removed.

Return type:

Examples

>>> import arkouda as ak
>>> x = ak.arange(10).reshape((1, 10, 1))
>>> x.shape
(1, 10, 1)
>>> ak.squeeze(x, axis=None).shape
(10,)
>>> ak.squeeze(x, axis=2).shape
(1, 10)
>>> ak.squeeze(x, axis=(0, 2)).shape
(10,)

arkouda.standard_normal(size: arkouda.numpy.dtypes.int_scalars, seed: None | arkouda.numpy.dtypes.int_scalars = None) → arkouda.numpy.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:

Raises:

TypeError – Raised if size is not an int
ValueError – Raised if size < 0

See also

randint

Notes

For random samples from $N(\mu, \sigma^2)$, use:

(sigma * standard_normal(size)) + mu

Examples

>>> import arkouda as ak
>>> ak.standard_normal(3,1)
array([-0.68586185091150265 1.1723810583573377 0.567584107142031])

class arkouda.str_¶

A unicode string.

This type strips trailing null codepoints.
>>> s = np.str_("abc\x00")
>>> s
'abc'
Unlike the builtin str, this supports the python:bufferobjects, exposing its contents as UCS4:
>>> m = memoryview(np.str_("abc"))
>>> m.format
'3w'
>>> m.tobytes()
b'a\x00\x00\x00b\x00\x00\x00c\x00\x00\x00'
Character code:

'U'

T(*args, **kwargs)¶: Scalar attribute identical to the corresponding array attribute.

Please see ndarray.T.

all(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.all.

any(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.any.

argmax(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.argmax.

argmin(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.argmin.

argsort(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.argsort.

astype(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.astype.

base(*args, **kwargs)¶: Scalar attribute identical to the corresponding array attribute.

Please see ndarray.base.

byteswap(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.byteswap.

choose(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.choose.

clip(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.clip.

compress(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.compress.

conj(*args, **kwargs)¶

conjugate(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.conjugate.

copy(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.copy.

cumprod(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.cumprod.

cumsum(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.cumsum.

data(*args, **kwargs)¶: Pointer to start of data.

device(*args, **kwargs)¶

diagonal(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.diagonal.

dtype(*args, **kwargs)¶: Get array data-descriptor.

dump(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.dump.

dumps(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.dumps.

fill(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.fill.

flags(*args, **kwargs)¶: The integer value of flags.

flat(*args, **kwargs)¶: A 1-D view of the scalar.

flatten(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.flatten.

getfield(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.getfield.

imag(*args, **kwargs)¶: The imaginary part of the scalar.

item(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.item.

itemset(*args, **kwargs)¶

itemsize(*args, **kwargs)¶: The length of one element in bytes.

max(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.max.

mean(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.mean.

min(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.min.

nbytes(*args, **kwargs)¶

ndim(*args, **kwargs)¶: The number of array dimensions.

newbyteorder(*args, **kwargs)¶

nonzero(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.nonzero.

prod(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.prod.

ptp(*args, **kwargs)¶

put(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.put.

ravel(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.ravel.

real(*args, **kwargs)¶: The real part of the scalar.

repeat(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.repeat.

reshape(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.reshape.

resize(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.resize.

round(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.round.

searchsorted(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.searchsorted.

setfield(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.setfield.

setflags(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.setflags.

shape(*args, **kwargs)¶: Tuple of array dimensions.

size(*args, **kwargs)¶: The number of elements in the gentype.

sort(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.sort.

squeeze(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.squeeze.

std(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.std.

strides(*args, **kwargs)¶: Tuple of bytes steps in each dimension.

sum(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.sum.

swapaxes(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.swapaxes.

take(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.take.

to_device(*args, **kwargs)¶

tobytes(*args, **kwargs)¶

tofile(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.tofile.

tolist(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.tolist.

tostring(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.tostring.

trace(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.trace.

transpose(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.transpose.

var(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.var.

view(*args, **kwargs)¶: Scalar method identical to the corresponding array attribute.

Please see ndarray.view.

class arkouda.str_scalars¶

Bases: _NotIterable

Mixin to prevent iteration, without being compatible with Iterable.

That is, we could do:

def __iter__(self): raise TypeError()

But this would make users of this mixin duck type-compatible with collections.abc.Iterable - isinstance(foo, Iterable) would be True.

Luckily, we can instead prevent iteration by setting __iter__ to None, which is treated specially.

copy_with(params)¶

arkouda.string_operators(cls)[source]¶

arkouda.take(a: arkouda.numpy.pdarrayclass.pdarray, indices: arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.pdarrayclass.pdarray, axis: int | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Take elements from an array along an axis.

When axis is not None, this function does the same thing as “fancy” indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis. A call such as np.take(arr, indices, axis=3) is equivalent to arr[:,:,:,indices,...].

Parameters:

a (pdarray) – The array from which to take elements
indices (numeric_scalars or pdarray) – The indices of the values to extract. Also allow scalars for indices.
axis (int, optional) – The axis over which to select values. By default, the flattened input array is used.

Returns:

The returned array has the same type as a.

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.array([4, 3, 5, 7, 6, 8])
>>> indices = [0, 1, 4]
>>> ak.take(a, indices)
array([4 3 6])

arkouda.tan(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise tangent of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the tangent will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

Returns:

A pdarray containing tangent for each element of the original pdarray

Return type:

Raises:

TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-1.5,0.75,4)
>>> ak.tan(a)
array([-14.101419947171719 -0.93159645994407247 0.00000000000000000 0.93159645994407247])

arkouda.tanh(pda: arkouda.numpy.pdarrayclass.pdarray, where: bool | arkouda.numpy.pdarrayclass.pdarray = True) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the element-wise hyperbolic tangent of the array.

Parameters:

pda (pdarray)
where (bool or pdarray, default=True) – This condition is broadcast over the input. At locations where the condition is True, the hyperbolic tangent will be applied to the corresponding value. Elsewhere, it will retain its original value. Default set to True.

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

Examples

>>> import arkouda as ak
>>> a = ak.linspace(-0.9,0.7,4)
>>> ak.tanh(a)
array([-0.71629787019902447 -0.35107264597890853 0.1651404129246293 0.60436777711716361])

arkouda.tile(A: arkouda.numpy.pdarrayclass.pdarray, /, reps: int | Tuple[int, Ellipsis]) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Construct an array by repeating A the number of times given by reps.

If reps has length d, the result will have dimension of max(d, A.ndim).

If A.ndim < d, A is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-D replication. If this is not the desired behavior, promote A to d-dimensions manually before calling this function.

If A.ndim > d, reps is promoted to A.ndim by prepending 1’s to it. Thus for an A of shape (2, 3, 4, 5), a reps of (2, 2) is treated as (1, 1, 2, 2).

Parameters:

A (pdarray) – The input pdarray to be tiled
reps (int or Tuple of int) – The number of repetitions of A along each axis.

Returns:

A new pdarray with the tiled data.

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.array([0, 1, 2])
>>> ak.tile(a, 2)
array([0 1 2 0 1 2])
>>> ak.tile(a, (2, 2))
array([array([0 1 2 0 1 2]) array([0 1 2 0 1 2])])
>>> ak.tile(a, (2, 1, 2))
array([array([array([0 1 2 0 1 2])]) array([array([0 1 2 0 1 2])])])

>>> b = ak.array([[1, 2], [3, 4]])
>>> ak.tile(b, 2)
array([array([1 2 1 2]) array([3 4 3 4])])
>>> ak.tile(b, (2, 1))
array([array([1 2]) array([3 4]) array([1 2]) array([3 4])])

>>> c = ak.array([1, 2, 3, 4])
>>> ak.tile(c, (4, 1))
array([array([1 2 3 4]) array([1 2 3 4]) array([1 2 3 4]) array([1 2 3 4])])

class arkouda.timedelta64¶

A timedelta stored as a 64-bit integer.

See arrays.datetime for more information.

Character code:

'm'

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, and freq, exactly three must be specified. If freq is omitted, the resulting TimedeltaIndex will have periods linearly spaced elements between start and end (closed on both sides).

To learn more about the frequency strings, please see this link.

arkouda.to_csv(columns: Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.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.

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

read_csv

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.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray], 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

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 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

>>> import arkouda as ak
>>> 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.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray], 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.

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

to_hdf, load, load_all, read

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 deleted (regardless of whether they would 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

>>> import arkouda as ak
>>> 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.to_zarr(store_path: str, arr: arkouda.numpy.pdarrayclass.pdarray, chunk_shape)[source]¶

Write a pdarray to disk as a Zarr store.

Parameters:

store_path (str) – The path at which Zarr store should be written
arr (pdarray) – The pdarray to be written to disk
chunk_shape (tuple) – The shape of the chunks to be used in the Zarr store

Raises:

ValueError – Raised if the number of dimensions in the chunk shape does not match the number of dimensions in the array or if the array is not a 32 or 64 bit numeric type

arkouda.transpose(pda: arkouda.numpy.pdarrayclass.pdarray, axes: Tuple[int, Ellipsis] | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Compute the transpose of a matrix.

Parameters:

pda (pdarray)
axes (Tuple[int,...] Optional, defaults to None) – If specified, must be a tuple which contains a permutation of the axes of pda.

Returns:

the transpose of the input matrix For a 1-D array, this is the original array. For a 2-D array, this is the standard matrix transpose. For an n-D array, if axes are given, their order indicates how the axes are permuted. If axes is None, the axes are reversed.

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.array([[1,2,3,4,5],[1,2,3,4,5]])
>>> ak.transpose(a)
array([array([1 1]) array([2 2]) array([3 3]) array([4 4]) array([5 5])])
>>> z = ak.array(np.arange(27).reshape(3,3,3))
>>> ak.transpose(z,axes=(1,0,2))
array([array([array([0 1 2]) array([9 10 11]) array([18 19 20])]) array([array([3 4 5])
  array([12 13 14]) array([21 22 23])]) array([array([6 7 8]) array([15 16 17]) array([24 25 26])])])

Raises:

ValueError – Raised if axes is not a legitimate permutation of the axes of pda
TypeError – Raised if pda is not a pdarray, or if axes is neither a tuple nor None

arkouda.tril(pda: arkouda.numpy.pdarrayclass.pdarray, diag: arkouda.numpy.dtypes.int_scalars = 0) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a copy of the pda with the upper triangle zeroed out

Parameters:

pda (pdarray)
diag (int_scalars, optional) –

if diag = 0, zeros start just above the main diagonal

if diag = 1, zeros start at the main diagonal

if diag = 2, zeros start just below the main diagonal

etc. Default set to 0.

Returns:

a copy of pda with zeros in the upper triangle

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.array([[1,2,3,4,5],[2,3,4,5,6],[3,4,5,6,7],[4,5,6,7,8],[5,6,7,8,9]])
>>> ak.tril(a,diag=4)
array([array([1 2 3 4 5]) array([2 3 4 5 6]) array([3 4 5 6 7])
array([4 5 6 7 8]) array([5 6 7 8 9])])
>>> ak.tril(a,diag=3)
array([array([1 2 3 4 0]) array([2 3 4 5 6]) array([3 4 5 6 7])
array([4 5 6 7 8]) array([5 6 7 8 9])])
>>> ak.tril(a,diag=2)
array([array([1 2 3 0 0]) array([2 3 4 5 0]) array([3 4 5 6 7])
array([4 5 6 7 8]) array([5 6 7 8 9])])
>>> ak.tril(a,diag=1)
array([array([1 2 0 0 0]) array([2 3 4 0 0]) array([3 4 5 6 0])
array([4 5 6 7 8]) array([5 6 7 8 9])])
>>> ak.tril(a,diag=0)
array([array([1 0 0 0 0]) array([2 3 0 0 0]) array([3 4 5 0 0])
array([4 5 6 7 0]) array([5 6 7 8 9])])

Notes

Server returns an error if rank of pda < 2

arkouda.triu(pda: arkouda.numpy.pdarrayclass.pdarray, diag: arkouda.numpy.dtypes.int_scalars = 0) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a copy of the pda with the lower triangle zeroed out

Parameters:

pda (pdarray)
diag (int_scalars, default=0) –

if diag = 0, zeros start just below the main diagonal

if diag = 1, zeros start at the main diagonal

if diag = 2, zeros start just above the main diagonal

etc. Default set to 0.

Returns:

a copy of pda with zeros in the lower triangle

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.array([[1,2,3,4,5],[2,3,4,5,6],[3,4,5,6,7],[4,5,6,7,8],[5,6,7,8,9]])
>>> ak.triu(a,diag=0)
array([array([1 2 3 4 5]) array([0 3 4 5 6]) array([0 0 5 6 7])
array([0 0 0 7 8]) array([0 0 0 0 9])])
>>> ak.triu(a,diag=1)
array([array([0 2 3 4 5]) array([0 0 4 5 6]) array([0 0 0 6 7])
array([0 0 0 0 8]) array([0 0 0 0 0])])
>>> ak.triu(a,diag=2)
array([array([0 0 3 4 5]) array([0 0 0 5 6]) array([0 0 0 0 7])
array([0 0 0 0 0]) array([0 0 0 0 0])])
>>> ak.triu(a,diag=3)
array([array([0 0 0 4 5]) array([0 0 0 0 6]) array([0 0 0 0 0])
array([0 0 0 0 0]) array([0 0 0 0 0])])
>>> ak.triu(a,diag=4)
array([array([0 0 0 0 5]) array([0 0 0 0 0]) array([0 0 0 0 0])
array([0 0 0 0 0]) array([0 0 0 0 0])])

Notes

Server returns an error if rank of pda < 2

arkouda.trunc(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.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:: pdarray
Raises:: TypeError – Raised if the parameter is not a pdarray

Examples

>>> import arkouda as ak
>>> ak.trunc(ak.array([1.1, 2.5, 3.14159]))
array([1.00000000000000000 2.00000000000000000 3.00000000000000000])

arkouda.typename(char)¶

Return a description for the given data type code.

Parameters:: char (str) – Data type code.
Returns:: out – Description of the input data type code.
Return type:: str

See also

dtype

Examples

>>> import numpy as np
>>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
...              'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
>>> for typechar in typechars:
...     print(typechar, ' : ', np.typename(typechar))
...
S1  :  character
?  :  bool
B  :  unsigned char
D  :  complex double precision
G  :  complex long double precision
F  :  complex single precision
I  :  unsigned integer
H  :  unsigned short
L  :  unsigned long integer
O  :  object
Q  :  unsigned long long integer
S  :  string
U  :  unicode
V  :  void
b  :  signed char
d  :  double precision
g  :  long precision
f  :  single precision
i  :  integer
h  :  short
l  :  long integer
q  :  long long integer

class arkouda.ubyte¶

Unsigned integer type, compatible with C unsigned char.

Character code:

'B'

Canonical name:

numpy.ubyte

Alias on this platform (Linux x86_64):

numpy.uint8: 8-bit unsigned integer (0 to 255).

bit_count(/)¶

uint8.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint8(127).bit_count()
7

class arkouda.uint¶

Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

Character code:: 'L'
Canonical name:: numpy.uint
Alias on this platform (Linux x86_64):: numpy.uint64: 64-bit unsigned integer (0 to 18_446_744_073_709_551_615).
Alias on this platform (Linux x86_64):: numpy.uintp: Unsigned integer large enough to fit pointer, compatible with C uintptr_t.

bit_count(/)¶

uint64.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint64(127).bit_count()
7

class arkouda.uint16¶

Unsigned integer type, compatible with C unsigned short.

Character code:

'H'

Canonical name:

numpy.ushort

Alias on this platform (Linux x86_64):

numpy.uint16: 16-bit unsigned integer (0 to 65_535).

bit_count(/)¶

uint16.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint16(127).bit_count()
7

class arkouda.uint32¶

Unsigned integer type, compatible with C unsigned int.

Character code:

'I'

Canonical name:

numpy.uintc

Alias on this platform (Linux x86_64):

numpy.uint32: 32-bit unsigned integer (0 to 4_294_967_295).

bit_count(/)¶

uint32.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint32(127).bit_count()
7

class arkouda.uint64¶

Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

Character code:: 'L'
Canonical name:: numpy.uint
Alias on this platform (Linux x86_64):: numpy.uint64: 64-bit unsigned integer (0 to 18_446_744_073_709_551_615).
Alias on this platform (Linux x86_64):: numpy.uintp: Unsigned integer large enough to fit pointer, compatible with C uintptr_t.

bit_count(/)¶

uint64.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint64(127).bit_count()
7

class arkouda.uint8¶

Unsigned integer type, compatible with C unsigned char.

Character code:

'B'

Canonical name:

numpy.ubyte

Alias on this platform (Linux x86_64):

numpy.uint8: 8-bit unsigned integer (0 to 255).

bit_count(/)¶

uint8.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint8(127).bit_count()
7

class arkouda.uintc¶

Unsigned integer type, compatible with C unsigned int.

Character code:

'I'

Canonical name:

numpy.uintc

Alias on this platform (Linux x86_64):

numpy.uint32: 32-bit unsigned integer (0 to 4_294_967_295).

bit_count(/)¶

uint32.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint32(127).bit_count()
7

class arkouda.uintp¶

Unsigned signed integer type, 64bit on 64bit systems and 32bit on 32bit

systems.

Character code:: 'L'
Canonical name:: numpy.uint
Alias on this platform (Linux x86_64):: numpy.uint64: 64-bit unsigned integer (0 to 18_446_744_073_709_551_615).
Alias on this platform (Linux x86_64):: numpy.uintp: Unsigned integer large enough to fit pointer, compatible with C uintptr_t.

bit_count(/)¶

uint64.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint64(127).bit_count()
7

class arkouda.ulonglong¶

Signed integer type, compatible with C unsigned long long.

Character code:

'Q'

bit_count(/)¶

arkouda.uniform(size: arkouda.numpy.dtypes.int_scalars, low: arkouda.numpy.dtypes.numeric_scalars = float(0.0), high: arkouda.numpy.dtypes.numeric_scalars = 1.0, seed: None | arkouda.numpy.dtypes.int_scalars = None) → arkouda.numpy.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:

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

>>> import arkouda as ak
>>> ak.uniform(3,seed=1701)
array([0.011410423448327005 0.73618171558685619 0.12367222192448891])

>>> ak.uniform(size=3,low=0,high=5,seed=0)
array([0.30013431967121934 0.47383036230759112 1.0441791878997098])

arkouda.union1d(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable) → 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:

A (list of pdarrays, pdarray, Strings, or Categorical)
B (list of pdarrays, pdarray, Strings, or Categorical)

Returns:

Unique, sorted union of the input arrays.

Return type:

groupable

Raises:

TypeError – Raised if either A or B is not a groupable
RuntimeError – Raised if the dtype of either input is not supported

Examples

>>> import arkouda as ak

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, pdarray, 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.
assume_sorted (bool, optional) – If True, assume pda is sorted and skip sorting step
return_indices (bool, optional) – Only applicable if return_groups is True. If True, return unique key indices along with other groups

Returns:

unique(list of) pdarray, Strings, or Categorical: The unique values. If input dtype is int64, return values will be sorted.
permutationpdarray, optional: Permutation that groups equivalent values together (only when return_groups=True)
segmentspdarray, optional: The offset of each group in the permuted array (only when return_groups=True)

Return type:

Union[groupable, Tuple[groupable, pdarray, pdarray, int]]

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

>>> import arkouda as ak
>>> A = ak.array([3, 2, 1, 1, 2, 3])
>>> ak.unique(A)
array([1 2 3])

arkouda.unregister(name: str) → str[source]¶

Unregister an Arkouda object by its name.

This function sends a request to unregister the Arkouda object associated with the specified name. It returns a response message indicating the success or failure of the operation.

Parameters:: name (str) – The name of the object to unregister.
Returns:: A message indicating the result of the unregister operation.
Return type:: str
Raises:: RuntimeError – If the object associated with the given name does not exist or cannot be unregistered.

Examples

>>> import arkouda as ak

Unregister an existing object >>> obj = ak.array([1, 2, 3]) >>> registered_obj = obj.register(“my_array”) >>> response = ak.unregister(“my_array”) >>> print(response) Unregistered PDARRAY my_array

arkouda.unregister_all(names: List[str])[source]¶

Unregister all Arkouda objects associated with the provided names.

This function iterates through the list of names, unregistering each corresponding object from the Arkouda server.

Parameters:: names (List of str) – A list of registered names corresponding to Arkouda objects that should be unregistered.

Examples

>>> import arkouda as ak
>>> data = { "array1": ak.array([0, 1, 2]), "array2": ak.array([3, 4, 5]) }
>>> ak.register_all(data)

After calling this function, “array1” and “array2” are registered in Arkouda, and can be accessed by their names. >>> ak.unregister_all([“array1”, “array2”])

“arr1” and “arr2” are now unregistered

class arkouda.unsignedinteger¶

Bases: numpy.integer

Abstract base class of all unsigned integer scalar types.

arkouda.unsqueeze(p)[source]¶

arkouda.update_hdf(columns: Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray], 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

class arkouda.ushort¶

Unsigned integer type, compatible with C unsigned short.

Character code:

'H'

Canonical name:

numpy.ushort

Alias on this platform (Linux x86_64):

numpy.uint16: 16-bit unsigned integer (0 to 65_535).

bit_count(/)¶

uint16.bit_count() -> int

Computes the number of 1-bits in the absolute value of the input. Analogous to the builtin int.bit_count or popcount in C++.
>>> np.uint16(127).bit_count()
7

arkouda.value_counts(pda: arkouda.numpy.pdarrayclass.pdarray) → tuple[arkouda.groupbyclass.groupable, arkouda.numpy.pdarrayclass.pdarray][source]¶

Count the occurrences of the unique values of an array.

Parameters:

pda (pdarray) – 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

See also

unique, histogram

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

>>> import arkouda as ak
>>> A = ak.array([2, 0, 2, 4, 0, 0])
>>> ak.value_counts(A)
(array([0 2 4]), array([3 2 1]))

arkouda.vecdot(x1: arkouda.numpy.pdarrayclass.pdarray, x2: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Compute the generalized dot product of two vectors along the given axis. Assumes that both tensors have already been broadcast to the same shape.

Parameters:

x1 (pdarray)
x2 (pdarray)

Returns:

x1 vecdot x2

Return type:

Examples

>>> import arkouda as ak
>>> a = ak.array([[1,2,3,4,5],[1,2,3,4,5]])
>>> b = ak.array([[2,2,2,2,2],[2,2,2,2,2]])
>>> ak.vecdot(a,b)
array([4 8 12 16 20])
>>> ak.vecdot(b,a)
array([4 8 12 16 20])

Raises:: ValueTypeError – Raised if x1 and x2 are not of matching shape or if rank of x1 < 2

class arkouda.void¶

Bases: numpy.flexible

np.void(length_or_data, /, dtype=None)

Create a new structured or unstructured void scalar.

length_or_dataint, array-like, bytes-like, object
One of multiple meanings (see notes). The length or bytes data of an unstructured void. Or alternatively, the data to be stored in the new scalar when dtype is provided. This can be an array-like, in which case an array may be returned.

dtypedtype, optional
If provided the dtype of the new scalar. This dtype must be “void” dtype (i.e. a structured or unstructured void, see also defining-structured-types).

Added in version 1.24.

For historical reasons and because void scalars can represent both arbitrary byte data and structured dtypes, the void constructor has three calling conventions:

np.void(5) creates a dtype="V5" scalar filled with five \0 bytes. The 5 can be a Python or NumPy integer.

np.void(b"bytes-like") creates a void scalar from the byte string. The dtype itemsize will match the byte string length, here "V10".

When a dtype= is passed the call is roughly the same as an array creation. However, a void scalar rather than array is returned.

Please see the examples which show all three different conventions.
>>> np.void(5)
np.void(b'\x00\x00\x00\x00\x00')
>>> np.void(b'abcd')
np.void(b'\x61\x62\x63\x64')
>>> np.void((3.2, b'eggs'), dtype="d,S5")
np.void((3.2, b'eggs'), dtype=[('f0', '<f8'), ('f1', 'S5')])
>>> np.void(3, dtype=[('x', np.int8), ('y', np.int8)])
np.void((3, 3), dtype=[('x', 'i1'), ('y', 'i1')])
Character code:

'V'

arkouda.vstack(tup: Sequence[arkouda.numpy.pdarrayclass.pdarray], *, dtype: str | type | None = None, casting: Literal['no', 'equiv', 'safe', 'same_kind', 'unsafe'] = 'same_kind') → arkouda.numpy.pdarrayclass.pdarray[source]¶

Stack arrays in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.

Parameters:

tup (sequence of pdarray) – The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length. In the case of a single array_like input, it will be treated as a sequence of arrays; i.e., each element along the zeroth axis is treated as a separate array.
dtype (str or type, optional) – If provided, the destination array will have this dtype.
casting ({"no", "equiv", "safe", "same_kind", "unsafe"], optional) – Controls what kind of data casting may occur. Defaults to ‘same_kind’. Currently unused.

Returns:

The array formed by stacking the given arrays, will be at least 2-D.

Return type:

See also

concatenate, stack, block, hstack, dstack, column_stack, hsplit, unstack

Examples

>>> import arkouda as ak
>>> a = ak.array([1, 2, 3])
>>> b = ak.array([4, 5, 6])
>>> ak.vstack((a, b))
array([array([1 2 3]) array([4 5 6])])

>>> a = ak.array([[1],[2],[3]])
>>> b = ak.array([[4],[5],[6]])
>>> ak.vstack((a, b))
array([array([1]) array([2]) array([3]) array([4]) array([5]) array([6])])

arkouda.where(condition: arkouda.numpy.pdarrayclass.pdarray, A: str | arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical, B: str | arkouda.numpy.dtypes.numeric_scalars | arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical) → arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical[source]¶

Return 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 (str, numeric_scalars, pdarray, Strings, or Categorical) – Value(s) used when condition is True
B (str, numeric_scalars, pdarray, Strings, or 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:

arkouda.numpy.pdarrayclass.pdarray

Raises:

TypeError – Raised if the condition object is not a pdarray, if A or B is not an int, np.int64, float, np.float64, bool, 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

>>> import arkouda as ak
>>> 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]¶

Allow 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

LogLevel

arkouda.xlogy(x: arkouda.numpy.pdarrayclass.pdarray | numpy.float64, y: arkouda.numpy.pdarrayclass.pdarray)[source]¶

Computes x * log(y).

Parameters:

x (pdarray or np.float64) – x must have a datatype that is castable to float64
y (pdarray)

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.zero_up(vals)[source]¶

Map an array of sparse values to 0-up indices.

Parameters:: vals (pdarray) – Array to map to dense index
Returns:: aligned – Array with values replaced by 0-up indices
Return type:: pdarray

Create a pdarray filled with zeros.

Parameters:

size (int_scalars or tuple of int_scalars) – Size or shape of the array
dtype (all_scalars) – Type of resulting array, default ak.float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays Included for consistency, as zeros are represented as all zeros, regardless of the value of max_bits

Returns:

Zeros of the requested size or shape and dtype

Return type: