python - Pandas 数据框中多列的加权平均值

Question

我有一个如下所示的数据框

Class|  Student|    V1| V2| V3| wb

A|      Max|        10| 12| 14| 1

A|      Ann|        9|  6|  7|  0.9

B|      Tom|        6|  7|  10| 0.3

B|      Dick|       3|  8|  7|  0.7

C|      Dibs|       5|  2|  3|  0.8

C|      Mock|       6|  4|  3|  0.6

D|      Sunny|      3|  4|  5|  0.9

D|      Lock|       8|  3|  6|  1

我想计算按类别分组的 V1、V2、V3 的加权平均值结果应该如下所示

Class  V1_M  V2_M V3_M

A   9  8   3

B   5  3   3

C   4  4   3

到目前为止，我可以分离每列的数据框。但我感觉效率很低

这是 1 个变量的代码

import pandas as pd
import numpy as np

def wtdavg(frame, var, wb):
  d = frame[var]
  w = frame[wb]
  return (d * w).sum() / w.sum()

df = pd.read_csv('Sample.csv')
Matrix = df.groupby(['Class']).apply(wtdavg,var='V2',wb='wb')
print(Matrix)

我是一个新手，有 1 周的 pandas 经验。提前致谢。

最大

Answer 1

#use apply to calculate weighted mean for alll 3 columns in one go.
df2 = df.groupby('Class').apply(lambda x: pd.Series([sum(x.V1*x.wb)/sum(x.wb), sum(x.V2*x.wb)/sum(x.wb), sum(x.V3*x.wb)/sum(x.wb)]))
#rename columns
df2.columns=['V1_M','V2_M','V3_M']

df2
Out[858]: 
           V1_M      V2_M       V3_M
Class                               
A      9.526316  9.157895  10.684211
B      3.900000  7.700000   7.900000
C      5.428571  2.857143   3.000000
D      5.631579  3.473684   5.526316

更新(值列的动态列表，即 var_cols)

#put all your variable names in a list (can be copied over from df.columns)
var_cols = ['V1', 'V2', 'V3']
df2 = df.groupby('Class').apply(lambda x: pd.Series([sum(x[v] * x.wb) / sum(x.wb) for v in var_cols]))
df2.columns = [e+'_M' for e in var_cols]
           V1_M      V2_M       V3_M
Class                               
A      9.526316  9.157895  10.684211
B      3.900000  7.700000   7.900000
C      5.428571  2.857143   3.000000
D      5.631579  3.473684   5.526316