Numpy where to get array elements based on condition checking

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Numpy

numpy.where(condition to check, x, y)

Return x or y as elements based on condition check.

`condition`	array_like, bool
`x,y`	x is returned if condition is True, y otherwise

Examples : Updating data

We will create an array by using arange(). We will multiply each element by 3 if they are even numbers.

import numpy as np
ar=np.arange(6) #[0 1 2 3 4 5]
ar=np.where(ar%2==0,ar*3,ar)
print(ar)

Output ( updated the same array with new data )

[ 0  1  6  3 12  5]

Fill all elements by np.NaN if they are divisible by 5

import numpy as np
ar=np.arange(15) 
ar=np.where(ar%5==0,np.NaN,ar)
print(ar)

output

[nan  1.  2.  3.  4. nan  6.  7.  8.  9. nan 11. 12. 13. 14.]

returns positions of elements where condition is True

import numpy as np
ar=np.array([12,2,7,1,9,3,11]) 
ar=np.where(ar>5)
print(ar)

Output ( Position of the elements where numbers are more than 5 )

(array([0, 2, 4, 6]),)

Using and to combine two conditions. Returns the position of elements satisfying the condition.

import numpy as np
ar=np.array([12,2,7,1,9,3,11]) 
ar=np.where((ar > 5) & (ar < 10))
print(ar)
print(ar[0][1])

Output

(array([2, 4]),)
4

Using OR to combine two conditions

import numpy as np
ar=np.array([12,2,7,1,4,3,11]) 
ar=np.where((ar > 5) | (ar %2==0))
print(ar)

Output

(array([0, 1, 2, 4, 6]),)

Using multidimensional arrays

np.where([[True, False], [True, True]],
         [[5, 2], [13, 42]],[[9, 18], [73, 16]])

Output

array([[ 5, 18],
       [13, 42]])

Nested np.where()

We can use nested np.where() condition checks ( like we do for case when condition checking in other languages). We will keep another np.where() when our first np.where() condition returns false.

Here is a solution we used to assign some numbers to another column ( allowed ) based on the value at dept column.

my_data['allowed']=np.where(my_data['dept']=='mktg',50,
                np.where(my_data['dept']=='production',65,
                 np.where(my_data['dept']=='planning',45,np.nan)))

This is the part of a solution of Exercise No 3-4 , read the full exercise to understand the requirement.

Here my_data['allowed'] is assigned value of 50 if the my_data['dept'] column is equal to mktg, similarly this value is 65 for production and 45 for planning.

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Numpy where()

Examples : Updating data

Nested np.where()