numpy.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()
. 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, y：array_like
Values from which to choose. x, y and condition need to be broadcastable to some shape.
Returns：
out：ndarray
An array with elements from x where condition is True, and elements from y elsewhere.
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)] 複制
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]) 複制
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]]) 複制
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]]) 複制
>>> 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]]) 複制