# dask.array.nonzero

dask.array.nonzero(a)

  Return the indices of the elements that are non-zero.

  This docstring was copied from numpy.nonzero.

  Some inconsistencies with the Dask version may exist.

  Returns a tuple of arrays, one for each dimension of a, containing the indices of the non-zero elements in that dimension. The values in a are always tested and returned in row-major, C-style order.

  To group the indices by element, rather than dimension, use argwhere, which returns a row for each non-zero element.

  Note

  When called on a zero-d array or scalar, `nonzero(a)` is treated as `nonzero(atleast_1d(a))`.

  Deprecated since version 1.17.0: Use atleast_1d explicitly if this behavior is deliberate.

  Parameters:
    a array_like

      Input array.

  Returns:
    tuple_of_arrays tuple

      Indices of elements that are non-zero.

  See also

  `flatnonzero`

    Return indices that are non-zero in the flattened version of the input array.

  `ndarray.nonzero`

    Equivalent ndarray method.

  `count_nonzero`

    Counts the number of non-zero elements in the input array.

  Notes

  While the nonzero values can be obtained with `a[nonzero(a)]`, it is recommended to use `x[x.astype(bool)]` or `x[x != 0]` instead, which will correctly handle 0-d arrays.

  Examples

      >>> import numpy as np
      >>> x = np.array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])
      >>> x
      array([[3, 0, 0],
             [0, 4, 0],
             [5, 6, 0]])
      >>> np.nonzero(x)
      (array([0, 1, 2, 2]), array([0, 1, 0, 1]))

      >>> x[np.nonzero(x)]
      array([3, 4, 5, 6])
      >>> np.transpose(np.nonzero(x))
      array([[0, 0],
             [1, 1],
             [2, 0],
             [2, 1]])

  A common use for `nonzero` is to find the indices of an array, where a condition is True. Given an array a, the condition a > 3 is a boolean array and since False is interpreted as 0, np.nonzero(a > 3) yields the indices of the a where the condition is true.

      >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
      >>> a > 3
      array([[False, False, False],
             [ True,  True,  True],
             [ True,  True,  True]])
      >>> np.nonzero(a > 3)
      (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

  Using this result to index a is equivalent to using the mask directly:

      >>> a[np.nonzero(a > 3)]
      array([4, 5, 6, 7, 8, 9])
      >>> a[a > 3]  # prefer this spelling
      array([4, 5, 6, 7, 8, 9])

  `nonzero` can also be called as a method of the array.

      >>> (a > 3).nonzero()
      (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))