Np.where On Multiple Variables

I have a data frame with: customer_id [1,2,3,4,5,6,7,8,9,10] feature1 [0,0,1,1,0,0,1,1,0,0] feature2 [1,0,1,0,1,0,1,0,1,0] feature3 [0,0,1,0,0,0,1,0,0,0] Using this I want to crea

Solution 1:

I think you need numpy.select - it select first True values and all another are not important:

m1 = df['feature1']==1 
m2 = df['feature2']==1    
m3 = df['feature3']==1 
df['new_var'] = np.select([m1, m2, m3], ['1', '2', '3'], default='4')

Sample:

customer_id = [1,2,3,4,5,6,7,8,9,10]
feature1 = [0,0,1,1,0,0,1,1,0,0]
feature2 = [1,0,1,0,1,0,1,0,1,0]
feature3  = [0,0,1,0,0,0,1,0,0,0]

df = pd.DataFrame({'customer_id':customer_id,
                   'feature1':feature1,
                   'feature2':feature2,
                   'feature3':feature3})

m1 = df['feature1']==1 
m2 = df['feature2']==1    
m3 = df['feature3']==1 
df['new_var'] = np.select([m1, m2, m3], ['1', '2', '3'], default='4')
print (df)
   customer_id  feature1  feature2  feature3 new_var
0            1         0         1         0       2
1            2         0         0         0       4
2            3         1         1         1       1
3            4         1         0         0       1
4            5         0         1         0       2
5            6         0         0         0       4
6            7         1         1         1       1
7            8         1         0         0       1
8            9         0         1         0       2
9           10         0         0         0       4

If in features only 1 and 0 is possible convert 0 to False and 1 to True:

m1 = df['feature1'].astype(bool)
m2 = df['feature2'].astype(bool)
m3 = df['feature3'].astype(bool)
df['new_var'] = np.select([m1, m2, m3], ['1', '2', '3'], default='4')
print (df)
   customer_id  feature1  feature2  feature3 new_var
0            1         0         1         0       2
1            2         0         0         0       4
2            3         1         1         1       1
3            4         1         0         0       1
4            5         0         1         0       2
5            6         0         0         0       4
6            7         1         1         1       1
7            8         1         0         0       1
8            9         0         1         0       2
9           10         0         0         0       4

Solution 2:

Try:

df['new_var']=np.where(df['feature3']==1, '3', '4')
df['new_var']=np.where(df['feature2']==1,'2', df['new_var'])
df['new_var']=np.where(df['feature1']==1, '1', df['new_var'])