where(condition, [x, y], /)
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.
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)]
Values from which to choose. x, y and :None:None:`condition` need to be broadcastable to some shape.
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`.
nonzero
The function that is called when x and y are omitted
>>> 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],See :
... [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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