根据熊猫中的条件交换列值

Question

我想按条件重新定位列。如果国家是“日本”，我需要重新定位 last_name 和 first_name 。

df = pd.DataFrame([['France','Kylian', 'Mbappe'],
               ['Japan','Hiroyuki', 'Tajima'],
               ['Japan','Shiji', 'Kagawa'],
               ['England','Harry', 'Kane'],
               ['Japan','Yuya', 'Ohsako'],
               ['Portuguese','Cristiano', 'Ronaldo']],
              columns=['country', 'first_name', 'last_name'])

当前输出为

      country first_name last_name
0      France     Kylian    Mbappe
1       Japan   Hiroyuki    Tajima
2       Japan      Shiji    kagawa
3     England      Harry      Kane
4       Japan       Yuya    Ohsako
5  Portuguese  Cristiano   Ronaldo

我想让它跟随。

      country first_name last_name
0      France     Kylian    Mbappe
1       Japan     Tajima  Hiroyuki
2       Japan     Kagawa    Shinji
3     England      Harry      Kane
4       Japan     Ohsako      Yuya
5  Portuguese  Cristiano   Ronaldo

任何的想法？

Answer 1

### check below   


 df['first_name'],df['last_name']=np.where(df['country']=='Japan',(df['last_name'],df['first_name']),(df['first_name'],df['last_name']))

输出：

   country      first_name     last_name
0   France      Kylian         Mbappe
1   Japan       Tajima         Hiroyuki
2   Japan       Kagawa         Shiji
3   England     Harry          Kane
4   Japan       Ohsako         Yuya
5   Portuguese  Cristiano  Ronaldo

Answer 2

loc对于“国家”与“日本”匹配的行，使用并交换“first_name”和“last_name”值。

m = df['country'] == 'Japan'

df.loc[m, ['first_name', 'last_name']] = (
    df.loc[m, ['last_name', 'first_name']].values)
df    

      country first_name last_name
0  France      Kylian     Mbappe  
1  Japan       Tajima     Hiroyuki
2  Japan       Kagawa     Shiji   
3  England     Harry      Kane    
4  Japan       Ohsako     Yuya    
5  Portuguese  Cristiano  Ronaldo 

使用rename和的另一种选择update：

mp = {'first_name': 'last_name', 'last_name': 'first_name'}
df.update(df.loc[m].rename(mp, axis=1))
df

      country first_name last_name
0  France      Kylian     Mbappe  
1  Japan       Tajima     Hiroyuki
2  Japan       Kagawa     Shiji   
3  England     Harry      Kane    
4  Japan       Ohsako     Yuya    
5  Portuguese  Cristiano  Ronaldo