# 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>]