dask.array.Array.partitions

dask.array.Array.partitions¶

property Array.partitions¶

Slice an array by partitions. Alias of dask array .blocks attribute.

This alias allows you to write agnostic code that works with both dask arrays and dask dataframes.

This returns a Blockview object that provides an array-like interface to the blocks of a dask array. Numpy-style indexing of a Blockview object returns a selection of blocks as a new dask array.

You can index array.blocks like a numpy array of shape equal to the number of blocks in each dimension, (available as array.blocks.size). The dimensionality of the output array matches the dimension of this array, even if integer indices are passed. Slicing with np.newaxis or multiple lists is not supported.

Returns

An instance of da.array.Blockview

Examples

>>> import dask.array as da
>>> x = da.arange(8, chunks=2)
>>> x.partitions.shape # aliases x.numblocks
(4,)
>>> x.partitions[0].compute()
array([0, 1])
>>> x.partitions[:3].compute()
array([0, 1, 2, 3, 4, 5])
>>> x.partitions[::2].compute()
array([0, 1, 4, 5])
>>> x.partitions[[-1, 0]].compute()
array([6, 7, 0, 1])
>>> x.partitions.ravel() 
[dask.array<blocks, shape=(2,), dtype=int64, chunksize=(2,), chunktype=numpy.ndarray>,
 dask.array<blocks, shape=(2,), dtype=int64, chunksize=(2,), chunktype=numpy.ndarray>,
 dask.array<blocks, shape=(2,), dtype=int64, chunksize=(2,), chunktype=numpy.ndarray>,
 dask.array<blocks, shape=(2,), dtype=int64, chunksize=(2,), chunktype=numpy.ndarray>]

dask.array.Array.numblocks

dask.array.Array.persist