parameters: (condition, x=None, y=None)
return_statement: return _mx_nd_np.where(condition, x, y)
docstring: where(condition, [x, y])
      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()``. The rest of this documentation
          covers only the case where all three arguments are provided.
  
      Parameters
      ----------
      condition : ndarray
          Where True, yield `x`, otherwise yield `y`.
      x, y : ndarray
          Values from which to choose. `x`, `y` and `condition` need to be
          broadcastable to some shape. `x` and `y` must have the same dtype.
  
      Returns
      -------
      out : ndarray
          An array with elements from `x` where `condition` is True, and elements
          from `y` elsewhere.
  
      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
      --------
      >>> 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:
  
      >>> cond = np.array([[True, False], [True, True]])
      >>> x = np.array([[1, 2], [3, 4]])
      >>> y = np.array([[9, 8], [7, 6]])
      >>> np.where(cond, x, y)
      array([[1., 8.],
             [3., 4.]])
  
      The shapes of x, y, and the condition are broadcast together:
  
      >>> x, y = onp.ogrid[:3, :4]
      >>> x = np.array(x)
      >>> y = np.array(y)
      >>> 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]], dtype=int64)
  
      >>> 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.]])
docstring_summary: where(condition, [x, y])
      Return elements chosen from `x` or `y` depending on `condition`.
docstring_tokens: where ( condition [ x y ] ) Return elements chosen from x or y depending on condition .
function: def where(condition, x=None, y=None):
      """where(condition, [x, y])
      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()``. The rest of this documentation
          covers only the case where all three arguments are provided.
  
      Parameters
      ----------
      condition : ndarray
          Where True, yield `x`, otherwise yield `y`.
      x, y : ndarray
          Values from which to choose. `x`, `y` and `condition` need to be
          broadcastable to some shape. `x` and `y` must have the same dtype.
  
      Returns
      -------
      out : ndarray
          An array with elements from `x` where `condition` is True, and elements
          from `y` elsewhere.
  
      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
      --------
      >>> 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:
  
      >>> cond = np.array([[True, False], [True, True]])
      >>> x = np.array([[1, 2], [3, 4]])
      >>> y = np.array([[9, 8], [7, 6]])
      >>> np.where(cond, x, y)
      array([[1., 8.],
             [3., 4.]])
  
      The shapes of x, y, and the condition are broadcast together:
  
      >>> x, y = onp.ogrid[:3, :4]
      >>> x = np.array(x)
      >>> y = np.array(y)
      >>> 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]], dtype=int64)
  
      >>> 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.]])
      """
      return _mx_nd_np.where(condition, x, y)
function_tokens: def where ( condition , x = None , y = None ) : return _mx_nd_np . where ( condition , x , y )