在pd.to_numeric之前使用Series.str.replace将,转换为.然后您可以使用groupby.agg

agg_df = (df.assign(Amount = pd.to_numeric(df['Amount'].str.replace(',','.'),
                                           errors = 'coerce'))
            .groupby('ID').Amount.agg(['mean','sum']))
print(agg_df)
#if you want change the type of Amount previously
#df['Amount'] =pd.to_numeric(df['Amount'].str.replace(',','.'),errors = 'coerce')
#agg_df = df.groupby('ID').Amount.agg(['mean','sum']))

     mean    sum
ID              
0   31.65  31.65
1   31.65  31.65
2   31.65  31.65
3   31.65  31.65
4   31.65  31.65

如果你想聚合到初始化的框架，使用GroupBy.transform：

groups = pd.to_numeric(df['Amount'].str.replace(',','.'),errors = 'coerce').groupby(df['ID'])
#if you want change the type of Amount previously
#df['Amount'] =pd.to_numeric(df['Amount'].str.replace(',','.'),errors = 'coerce')
#groups = df.groupby('ID')['Amount']
df['mean'] = groups.transform('mean')
df['sum'] = groups.transform('sum')
print(df)
   ID  customer   Month Amount   mean    sum  
0   0        26  201707  31,65  31.65  31.65  
1   1        26  201708  31,65  31.65  31.65  
2   2        26  201709  31,65  31.65  31.65  
3   3        26  201710  31,65  31.65  31.65  
4   4        26  201711  31,65  31.65  31.65

更简单的语法可用吗？接受答案的第一部分没有问题：

df['Amount'] = pd.to_numeric(df['Amount'].str.replace(',','.'))

关于分组，pivot允许更简单的语法：

pd.pivot_table(data = df,
               values = ['Amount'],
               index = ['ID customer'],
               aggfunc = [np.sum, np.mean])

输出量：

sum   mean
             Amount Amount
ID customer               
026          158.25  31.65