# dask.array.where

dask.array.where(condition, [x, y, ]/)

  This docstring was copied from numpy.where.

  Some inconsistencies with the Dask version may exist.

  Return elements chosen from x or y depending on condition.

  Note

  When only condition is provided, this function is a shorthand for `np.asarray(condition).nonzero()`. Using nonzero directly should be preferred, as it behaves correctly for subclasses. The rest of this documentation covers only the case where all three arguments are provided.

  Parameters:
    condition array_like, bool

      Where True, yield x, otherwise yield y.

    x, y array_like

      Values from which to choose. x, y and condition need to be broadcastable to some shape.

  Returns:
    out ndarray

      An array with elements from x where condition is True, and elements from y elsewhere.

  See also

  `choose`
  `nonzero`

    The function that is called when x and y are omitted

  Notes

  If all the arrays are 1-D, where is equivalent to:

      [xv if c else yv
       for c, xv, yv in zip(condition, x, y)]

  Examples

      >>> import numpy as np
      >>> a = np.arange(10)
      >>> a
      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
      >>> np.where(a < 5, a, 10*a)
      array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

  This can be used on multidimensional arrays too:

      >>> np.where([[True, False], [True, True]],
      ...          [[1, 2], [3, 4]],
      ...          [[9, 8], [7, 6]])
      array([[1, 8],
             [3, 4]])

  The shapes of x, y, and the condition are broadcast together:

      >>> x, y = np.ogrid[:3, :4]
      >>> np.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
      array([[10,  0,  0,  0],
             [10, 11,  1,  1],
             [10, 11, 12,  2]])

      >>> a = np.array([[0, 1, 2],
      ...               [0, 2, 4],
      ...               [0, 3, 6]])
      >>> np.where(a < 4, a, -1)  # -1 is broadcast
      array([[ 0,  1,  2],
             [ 0,  2, -1],
             [ 0,  3, -1]])