xorbits.numpy.where#

xorbits.numpy.where(condition, [x, y, ]/)[source]#

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

Returns

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

Return type

ndarray

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

>>> 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]])

This docstring was copied from numpy.