for循环和在Python中添加附加列groupby dataframe

下面的代码是我原来的方式

import pandas as pd
data = {'id':[1001,1001,1001,1001,1001,1001,1001,1001,1002,1002,1002,1002,1002,1002,1002,1002],
    'name':['Tom', 'Tom', 'Tom', 'Tom','Tom', 'Tom', 'Tom', 'Tom','Jack','Jack','Jack','Jack','Jack','Jack','Jack','Jack'],
    'team':['A','A', 'B', 'B', 'C','C', 'D', 'D','A','A', 'B', 'B', 'C','C', 'D', 'D',],
    'year':[2011,2011,2012,2012,2013,2013,2014,2014,2011,2011,2012,2012,2013,2013,2014,2014],
    'avg':[0.500,0.400,0.300,0.200,0.100,0.200,0.300,0.400,0.500,0.400,0.300,0.200,0.100,0.200,0.300,0.400]}

df = pd.DataFrame(data)

print (df)

team_names = [c for c in df['team'].value_counts().index]
team_names

for i in team_names:
    df[i+'_vs_avg_2011'] = df.loc[(df['team']==i)&(df['year']==2011)].groupby(['id','name'])['avg'].transform('mean')
    df[i+'_vs_avg_2012'] = df.loc[(df['team']==i)&(df['year']==2012)].groupby(['id','name'])['avg'].transform('mean')
    df[i+'_vs_avg_2013'] = df.loc[(df['team']==i)&(df['year']==2013)].groupby(['id','name'])['avg'].transform('mean')
    df[i+'_vs_avg_2014'] = df.loc[(df['team']==i)&(df['year']==2014)].groupby(['id','name'])['avg'].transform('mean')
    print(i)

对于循环部分 我试过了

ValueError:要解压缩的值太多（应为2个）

有没有办法简化或修复此代码？

我认为您可以在

多索引中使用带展平列的instaed循环，然后使用原始
数据帧
：

df1 = df.pivot_table(index=['id','name'],columns=['team','year'],values='avg', aggfunc='mean')
df1.columns = [f'{a}_vs_avg_{b}' for a, b in df1.columns]
print (df1)
           A_vs_avg_2011  B_vs_avg_2012  C_vs_avg_2013  D_vs_avg_2014
id   name                                                            
1001 Tom            0.45           0.25           0.15           0.35
1002 Jack           0.45           0.25           0.15           0.35

df = df.join(df1, on=['id','name'])
print (df)

从_到

的

年数是多少？抱歉，我刚刚添加了您的预期输出是什么？对于加入

数据帧，我得到了一个错误

列重叠但没有指定后缀：索引（['A_vs_avg_2011'、'B_vs_avg_2012'、'D_vs_avg_2014'、'C_vs_avg_2013']，dtype='object'）

@Yusufsn起初我也有错误，但是现在我可以得到正确的结果了。

df1 = df.pivot_table(index=['id','name'],columns=['team','year'],values='avg', aggfunc='mean')
df1.columns = [f'{a}_vs_avg_{b}' for a, b in df1.columns]
print (df1)
           A_vs_avg_2011  B_vs_avg_2012  C_vs_avg_2013  D_vs_avg_2014
id   name                                                            
1001 Tom            0.45           0.25           0.15           0.35
1002 Jack           0.45           0.25           0.15           0.35

df = df.join(df1, on=['id','name'])
print (df)