基于前后行去重DataFrame值：替换特定重复项为None

初始数据定义 #

import pandas as pd
d = {"a":["Residency", "Citizenship", "Citizenship","Residency",
          "Citizenship","Citizenship","Citizenship","Residency"],
     "b":["UK","UK","UK","UK","UK","UK","UK","UK"]}
df = pd.DataFrame(d)

初始数据预览 #

a   b
0    Residency  UK
1  Citizenship  UK
2  Citizenship  UK
3    Residency  UK
4  Citizenship  UK
5  Citizenship  UK
6  Citizenship  UK
7    Residency  UK

需求说明 #

当a列的前一行值不为Residency时，将当前行的Citizenship和UK替换为None，需保留DataFrame中其他所有列的原有数据不变。

实现代码 #

# 构建筛选条件：前一行a列不是Residency，且当前行a列是Citizenship
mask = (df['a'].shift() != 'Residency') & (df['a'] == 'Citizenship')

# 对符合条件的行，将a、b列赋值为None
df.loc[mask, ['a', 'b']] = None

最终结果 #

a     b
0    Residency    UK
1  Citizenship    UK
2         None  None
3    Residency    UK
4  Citizenship    UK
5         None  None
6         None  None
7    Residency    UK

内容的提问来源于stack exchange，提问作者nzskra