mxnet.np.where¶
-
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.
y (x,) – 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 – An array with elements from x where condition is True, and elements from y elsewhere.
- Return type
ndarray
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.]])
Did this page help you?
Yes
No
Thanks for your feedback!