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where(condition, [x, y], /)
note

When only :None:None:`condition` is provided, this function is a shorthand for np.asarray(condition).nonzero() . Using :None:None:`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.

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)]

Parameters

condition : array_like, bool

Where True, yield x, otherwise yield y.

x, y : array_like

Values from which to choose. x, y and :None:None:`condition` need to be broadcastable to some shape.

Returns

out : ndarray

An array with elements from x where :None:None:`condition` is True, and elements from y elsewhere.

Return elements chosen from x or y depending on :None:None:`condition`.

See Also

choose
nonzero

The function that is called when x and y are omitted

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]])
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