Nested np.where

Question

I have the following dataframe:

S A
1 1
1 0
2 1
2 0

I wanted to create a new \'Result\' column that is calculated based on the val

Answer 1

You should use nested np.where. It is like sql case clause. But be careful when there is nan in the data.

df=pd.DataFrame({'S':[1,1,2,2],'A':[1,0,1,0]})
df['Result'] = np.where((df.S == 1) & (df.A == 1), 1,   #when... then
                 np.where((df.S == 1) & (df.A == 0), 0,  #when... then
                  np.where((df.S == 2) & (df.A == 1), 0,  #when... then
                    1)))                                  #else
df

output:

|   | S | A | Result |
|---|---|---|--------|
| 0 | 1 | 1 | 1      |
| 1 | 1 | 0 | 0      |
| 2 | 2 | 1 | 0      |
| 3 | 2 | 0 | 1      |