检查相同的行并在pandas中有条件地创建新列

Question

我有像这样的数据框

df = pd.DataFrame({'User':['101','101','101','102','102','101','101','102','102','102'],'Country':['India','Japan','India','Brazil','Japan','UK','Austria','Japan','Singapore','UK'],
               'Name':['RN','TN','AP','AP','TN','TN','TS','RN','TN','AP']})

如果用户和国家/地区相同，我想将名称列值组合在其他列中，如下所示

Answer 1

你可以让

df['Name_E'] = df.groupby(['User', 'Country']).Name.transform(lambda x: str.join(', ', x))

Answer 2

你需要：

res = df.merge(df.groupby(['User', 'Country'])['Name'].unique().reset_index().rename(columns={'Name':'Name_E'}), on=['Country', 'User'])
res['Name_E'] = res['Name_E'].apply(lambda x: ",".join(x))

输出：

   User    Country Name Name_E
0  101      India   RN  RN,AP
1  101      India   AP  RN,AP
2  101      Japan   TN     TN
3  102     Brazil   AP     AP
4  102      Japan   TN  TN,RN
5  102      Japan   RN  TN,RN
6  101         UK   TN     TN
7  101    Austria   TS     TS
8  102  Singapore   TN     TN
9  102         UK   AP     AP

Answer 3

使用 groupby

transform

df['all_names'] = df.groupby(['Country', 'User']).Name.transform(lambda x: ','.join(set(x)))

     Country Name User all_names
0      India   RN  101     AP,RN
1      Japan   TN  101        TN
2      India   AP  101     AP,RN
3     Brazil   AP  102        AP
4      Japan   TN  102     TN,RN
5         UK   TN  101        TN
6    Austria   TS  101        TS
7      Japan   RN  102     TN,RN
8  Singapore   TN  102        TN
9         UK   AP  102        AP