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# numpy.where

`numpy.``where`(condition[, x, y])

  Return elements, either from x or y, depending on condition.

  If only condition is given, return `condition.nonzero()`.

  Parameters: | condition : array_like, bool When True, yield x, otherwise yield y. x, y : array_like, optional Values from which to choose. x and y need to have the same shape as condition.
  Returns: | out : ndarray or tuple of ndarrays If both x and y are specified, the output array contains elements of x where condition is True, and elements from y elsewhere. If only condition is given, return the tuple `condition.nonzero()`, the indices where condition is True.

  See also

  `nonzero`, `choose`

  Notes

  If x and y are given and input arrays are 1-D, `where` is equivalent to:

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

  Examples

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

      >>> np.where([[0, 1], [1, 0]])
      (array([0, 1]), array([1, 0]))

      >>> x = np.arange(9.).reshape(3, 3)
      >>> np.where( x > 5 )
      (array([2, 2, 2]), array([0, 1, 2]))
      >>> x[np.where( x > 3.0 )]               # Note: result is 1D.
      array([ 4.,  5.,  6.,  7.,  8.])
      >>> np.where(x < 5, x, -1)               # Note: broadcasting.
      array([[ 0.,  1.,  2.],
             [ 3.,  4., -1.],
             [-1., -1., -1.]])

  Find the indices of elements of x that are in goodvalues.

      >>> goodvalues = [3, 4, 7]
      >>> ix = np.in1d(x.ravel(), goodvalues).reshape(x.shape)
      >>> ix
      array([[False, False, False],
             [ True,  True, False],
             [False,  True, False]], dtype=bool)
      >>> np.where(ix)
      (array([1, 1, 2]), array([0, 1, 1]))