numpy.where — NumPy v2.3.dev0 Manual (original) (raw)

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 fornp.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:

conditionarray_like, bool

Where True, yield x, otherwise yield y.

x, yarray_like

Values from which to choose. x, y and condition need to be broadcastable to some shape.

Returns:

outndarray

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)]

Examples

import numpy as np 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]])