python - 如何将列添加到多索引数据帧？

Question

我有一个如下所示的多索引数据框:

       ACA FP Equity            UCG IM Equity            
          LAST PRICE     VOLUME    LAST PRICE      VOLUME
date                                                         
2010-01-04        12.825  5879617.0       15.0292  10844639.0
2010-01-05        13.020  6928587.0       14.8092  16456228.0
2010-01-06        13.250  5290631.0       14.6834  10446450.0
2010-01-07        13.255  5328586.0       15.0292  31900341.0
2010-01-08        13.470  7160295.0       15.1707  40750768.0

如果我想在数据框中为每个股权添加第三列，语法是什么？例如:

df['ACA FP Equity']['PriceVolume'] = df['ACA FP Equity']['LAST PRICE']*3

但我想为每个 Assets 都这样做，而不是手动添加每个 Assets 。

提前致谢。

Answer 1

如果您需要所有 LAST PRICE 列乘以 3 使用 slicers 选择它们并重命名列名称:

idx = pd.IndexSlice
df1 = df.loc[:, idx[:, 'LAST PRICE']].rename(columns={'LAST PRICE':'PriceVolume'}) * 3
print (df1)
           ACA FP Equity UCG IM Equity
             PriceVolume   PriceVolume
2010-01-04        38.475       45.0876
2010-01-05        39.060       44.4276
2010-01-06        39.750       44.0502
2010-01-07        39.765       45.0876
2010-01-08        40.410       45.5121

然后你需要 concat 输出:

print (pd.concat([df,df1], axis=1))
           ACA FP Equity            UCG IM Equity             ACA FP Equity  \
              LAST PRICE     VOLUME    LAST PRICE      VOLUME   PriceVolume   
2010-01-04        12.825  5879617.0       15.0292  10844639.0        38.475   
2010-01-05        13.020  6928587.0       14.8092  16456228.0        39.060   
2010-01-06        13.250  5290631.0       14.6834  10446450.0        39.750   
2010-01-07        13.255  5328586.0       15.0292  31900341.0        39.765   
2010-01-08        13.470  7160295.0       15.1707  40750768.0        40.410   

           UCG IM Equity  
             PriceVolume  
2010-01-04       45.0876  
2010-01-05       44.4276  
2010-01-06       44.0502  
2010-01-07       45.0876  
2010-01-08       45.5121  

另一种不使用 concat 的解决方案是从 selected_df 的列创建元组，然后分配输出:

idx = pd.IndexSlice
selected_df = df.loc[:, idx[:, 'LAST PRICE']]

new_cols = [(x, 'PriceVolume') for x in selected_df.columns.levels[0]]
print (new_cols)
[('ACA FP Equity', 'PriceVolume'), ('UCG IM Equity', 'PriceVolume')]

df[new_cols] = selected_df * 3
print(df)
           ACA FP Equity            UCG IM Equity             ACA FP Equity  \
              LAST PRICE     VOLUME    LAST PRICE      VOLUME   PriceVolume   
2010-01-04        12.825  5879617.0       15.0292  10844639.0        38.475   
2010-01-05        13.020  6928587.0       14.8092  16456228.0        39.060   
2010-01-06        13.250  5290631.0       14.6834  10446450.0        39.750   
2010-01-07        13.255  5328586.0       15.0292  31900341.0        39.765   
2010-01-08        13.470  7160295.0       15.1707  40750768.0        40.410   

           UCG IM Equity  
             PriceVolume  
2010-01-04       45.0876  
2010-01-05       44.4276  
2010-01-06       44.0502  
2010-01-07       45.0876  
2010-01-08       45.5121