arkouda.array_api¶
Submodules¶
- arkouda.array_api.array_object
- arkouda.array_api.creation_functions
- arkouda.array_api.data_type_functions
- arkouda.array_api.elementwise_functions
- arkouda.array_api.indexing_functions
- arkouda.array_api.linalg
- arkouda.array_api.manipulation_functions
- arkouda.array_api.searching_functions
- arkouda.array_api.set_functions
- arkouda.array_api.sorting_functions
- arkouda.array_api.statistical_functions
- arkouda.array_api.utility_functions
Classes¶
n-d array object for the array API namespace. |
Package Contents¶
- class arkouda.array_api.Array[source]¶
n-d array object for the array API namespace.
See the docstring of
np.ndarray
for more information.This is a wrapper around numpy.ndarray that restricts the usage to only those things that are required by the array API namespace. Note, attributes on this object that start with a single underscore are not part of the API specification and should only be used internally. This object should not be constructed directly. Rather, use one of the creation functions, such as asarray().
- chunk_info(/) List[List[int]] [source]¶
Get a list of indices indicating how the array is chunked across Locales (compute nodes). Although Arkouda arrays don’t have a notion of chunking, like Dask arrays for example, it can be useful to know how the array is distributed across locales in order to write/read data to/from a chunked format like Zarr.
Returns a nested list of integers, where the outer list corresponds to dimensions, and the inner lists correspond to locales. The value at [d][l] is the global array index where locale l’s local subdomain along the d-th dimension begins.
For example, calling this function on a 100x40 2D array stored across 4 locales could return: [[0, 50], [0, 20]], indicating that the 4 “chunks” start at indices 0 and 50 in the first dimension, and 0 and 20 in the second dimension.
- property device: arkouda.array_api._typing.Device¶
- property dtype: arkouda.array_api._typing.Dtype¶
- item()[source]¶
Get the scalar value from a 0-dimensional array.
Raises a ValueError if the array has more than one element.
- property ndim: int¶
- property shape: Tuple[int, Ellipsis]¶
- property size: int¶
- to_ndarray()[source]¶
Convert the array to a numpy ndarray
This involves copying the data from the server to the client, and thus will fail if the array is too large (see:
maxTransferBytes()
)