In this article we will understand np.where, np.select with some examples.

numpy.where

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

This function returns x if the condition is true else it returns y

Example 1: Given a one-dimensional array from (0,9) if elements are less than 5 the element should be the same else multiply the element by 10.

import numpy as np 
a = np.arange(10)
np.where(a < 5,a,10*a)
Out[1]: array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

**Example 2: **Given two 2-D arrays obtain an array with respect to condition.

np.where([[True,False],[True,False]],[[1,2],[3,4]],[[45,52],[78,96]])
Out[3]: 
array([[ 1, 52],
       [ 3, 96]])

If the condition is true we take element from x else from y.

**Example 3: **given a 2-d matrix if the value in the ix if less than 4, The value should be the same else return the value as -1.

np.where(a < 4,a,-1)
Out[8]: 
array([[ 0,  1,  2],
       [ 0,  2, -1],
       [ 0,  3, -1]])

#machine-learning #artificial-intelligence #python #numpy #matrix