Numpy where to get array elements based on condition checking

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 THEN 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.

Example Using Pandas DataFrame

Use our sample student DataFrame. We will add one column status which will store pass or fail value for each student based on the mark they scored. Pass mark is 60.

import pandas as pd 
import numpy as np 
df= pd.read_csv('D:\\my_data\\student.csv') # DataFrame from csv file data
df['status']=np.where(df['mark']>=60,'Pass','Fail')
print(df)

We can use nested WHERE to distribute GRADE to each student based on the mark they scored.

df['grade']=np.where(df['mark'] >=80,'A',
            np.where(df['mark']>=70,'B',
            np.where(df['mark']>=50,'C','D') ))

Students have appered in different subject exams. Here is the input DataFrame.

   Name Subject_1  Mark_1  Subject_2  Mark_2
0  Alex   Science      30  Chemistry      40
1   Ron    Social      90       Math      80
2  Ravi   History      10    Physics      60
3  King   English     100  Geography      90

Arrange the subjects in alphabetical order for each student without any change in marks. The output should be like this.

   Name  Subject_1 Mark_1  Subject_2 Mark_2
0  Alex  Chemistry     40    Science     30
1   Ron       Math     80     Social     90
2  Ravi    History     10    Physics     60
3  King    English    100  Geography     90

Solution ???

import pandas as pd 
import numpy as np
my_dict={'Name':['Alex','Ron','Ravi','King'],
         'Subject_1':['Science','Social','History','English'],
         'Mark_1':[30,90,10,100],
         'Subject_2':['Chemistry','Math','Physics','Geography'],
	     'Mark_2':[40,80,60,90]}
df = pd.DataFrame(data=my_dict)         
print(df)
#
df['Subject_1'],df['Mark_1'],df['Subject_2'],df['Mark_2']=np.where(df['Subject_2']>df['Subject_1'],
(df['Subject_1'],df['Mark_1'],df['Subject_2'],df['Mark_2']),
(df['Subject_2'],df['Mark_2'],df['Subject_1'],df['Mark_1']))
print(df)

Numpy eye() ones() bincount() linspace()

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Subhendu Mohapatra

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Numpy where()

Examples : Updating data

Nested np.where()

Example Using Pandas DataFrame

Subhendu Mohapatra