numpy.where(condition[, x, y])

Return elements chosen from x or y counting on condition.

Note

When only condition is provided, this function may be a shorthand for np.asarray(condition).nonzero(). Using nonzero directly should be preferred, because it behaves correctly for subclasses. the remainder 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 settle on . x, y and condition got to be broadcastable to some shape.

Returns:

out : ndarray

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

The function that’s 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]])