python - 如何只为pandas中IsB列不为空的记录设置IsA？

Question

我有一个 pandas DataFrame。如何仅为 data 中具有 IsB(而非 null)的记录设置列 IsA？对于 IsB 为 null 的记录，IsA 应为 null。

data['IsA'] = (data['IsB'] == True) & (data['IsC'] == False) & (data['IsD'] == False) & (data['IsE'] == False)

| ID | IsA    | IsB    | IsC | IsD | IsE |
|----|--------|--------|-----|-----|-----|
| 1  | 0      | 1      | 0   | 1   | 0   |
| 2  | 1      | 1      | 0   | 0   | 0   |
| 3  | <null> | <null> | 0   | 0   | 0   |

Answer 1

我认为需要isna和 notna用于检查 NaN，缺少 numpy.where 的值对于设置NaN:

谢谢@coldspeed 的想法:

data['IsA'] = data[['IsC', 'IsD', 'IsE']].notnull().all(1) & data['IsB'].isnull()

编辑:

print (data)
   ID  IsA  IsB  IsC  IsD  IsE
0   1  0.0  1.0    0    1    0
1   2  1.0  NaN    0    0    0
2   3  NaN  NaN    0    0    0
3   4  1.0  NaN    1    0    1


mask = (data['IsB'] == True) & (data['IsC'] == False) & 
       (data['IsD'] == False) & (data['IsE'] == False)
#simplify
#mask = (data['IsB'] == True) & data[['IsC', 'IsD', 'IsE']].eq(False).all(1)
data['IsA'] = np.where(data['IsB'].isna(),np.nan, mask)
print (data)
   ID  IsA  IsB  IsC  IsD  IsE
0   1  0.0  1.0    0    1    0
1   2  1.0  1.0    0    0    0
2   3  NaN  NaN    0    0    0
3   4  NaN  NaN    1    0    1