dask.array.asanyarray(a, dtype=None, order=None, *, like=None, inline_array=False)[source]

Convert the input to a dask array.

Subclasses of np.ndarray will be passed through as chunks unchanged.


Input data, in any form that can be converted to a dask array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays.

dtypedata-type, optional

By default, the data-type is inferred from the input data.

order{‘C’, ‘F’, ‘A’, ‘K’}, optional

Memory layout. ‘A’ and ‘K’ depend on the order of input array a. ‘C’ row-major (C-style), ‘F’ column-major (Fortran-style) memory representation. ‘A’ (any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise ‘K’ (keep) preserve input order. Defaults to ‘C’.

like: array-like

Reference object to allow the creation of Dask arrays with chunks that are not NumPy arrays. If an array-like passed in as like supports the __array_function__ protocol, the chunk type of the resulting array will be definde by it. In this case, it ensures the creation of a Dask array compatible with that passed in via this argument. If like is a Dask array, the chunk type of the resulting array will be defined by the chunk type of like. Requires NumPy 1.20.0 or higher.


Whether to inline the array in the resulting dask graph. For more information, see the documentation for dask.array.from_array().

outdask array

Dask array interpretation of a.


>>> import dask.array as da
>>> import numpy as np
>>> x = np.arange(3)
>>> da.asanyarray(x)
dask.array<array, shape=(3,), dtype=int64, chunksize=(3,), chunktype=numpy.ndarray>
>>> y = [[1, 2, 3], [4, 5, 6]]
>>> da.asanyarray(y)
dask.array<array, shape=(2, 3), dtype=int64, chunksize=(2, 3), chunktype=numpy.ndarray>