Python Pandas平均值和加權平均值

Question

我是python熊貓的新手。 任何幫助都感激不盡

這是我的原始數據：

         Feed  Close  Sector  Market_Cap
Date
2015-09-18 A   5.60  Property  50    
2015-09-21 A   5.60  Property  20    
2015-09-23 A   5.60  Property  30    
2015-09-18 ABC 0.67  Property  50    
2015-09-21 ABC 0.66  Property  80     
2015-09-18 DA  0.67  Mining    65    
2015-09-21 KK  1.66  Mining    80    

我想得到的是：

1創建一個新的列調用均值以計算每個提要的平均市值。

2查找加權平均值。

This is what I want:
         Feed  Close  Sector   Market_Cap   Mean   Sector_WeightedAvg
Date
2015-09-18 A   5.60  Property  50           33.33      33.33/(33.33+65) 
2015-09-21 A   5.60  Property  20           33.33      33.33/(33.33+65)
2015-09-23 A   5.60  Property  30           33.33      33.33/(33.33+65)
2015-09-18 ABC 0.67  Property  50           65         65/(33.33+65)
2015-09-21 ABC 0.66  Property  80           65         65/(33.33+65) 
2015-09-18 DA  0.67  Mining    65           62         62/(62+80)
2015-09-21 KK  1.66  Mining    80           80         80/(62+80)

這是我目前獲得的NaN均值代碼：

df3= pd.DataFrame(df3)
df3['Mean'] = df3.groupby(by=['Sector'])[ Market_Cap].mean()  

         Feed  Close  Sector   Market_Cap   Mean   
Date
2015-09-18 A   5.60  Property  50           NaN       
2015-09-21 A   5.60  Property  20           NaN      
2015-09-23 A   5.60  Property  30           NaN      
2015-09-18 ABC 0.67  Property  50           NaN             

對於加權平均代碼：

df2['WeightedAverage'] =df3[ Market_Cap].value /df3['Mean'].value

我得到了錯誤：

AttributeError：“系列”對象沒有屬性“值”

Answer 1

IIUC可以使用transform和mean 。

Weighted Average是柱Mean通過柱的唯一值的總和除以Mean和df3是由列組Sector 。

print df3
          Feed  Close    Sector  Market_Cap
Date                                        
2015-09-18    A   5.60  Property          50
2015-09-21    A   5.60  Property          20
2015-09-23    A   5.60  Property          30
2015-09-18  ABC   0.67  Property          50
2015-09-21  ABC   0.66  Property          80
2015-09-18   DA   0.67    Mining          65
2015-09-21   KK   1.66    Mining          80

df3['Mean'] = df3.groupby(by=['Feed'])['Market_Cap'].transform('mean')   
df3['WeightedAverage'] = df3['Mean'] / df3.groupby(by=['Sector'])[ 'Mean'].transform(lambda x: sum(x.unique())) 
print df3
           Feed  Close    Sector  Market_Cap       Mean  WeightedAverage
Date                                                                    
2015-09-18    A   5.60  Property          50  33.333333         0.338983
2015-09-21    A   5.60  Property          20  33.333333         0.338983
2015-09-23    A   5.60  Property          30  33.333333         0.338983
2015-09-18  ABC   0.67  Property          50  65.000000         0.661017
2015-09-21  ABC   0.66  Property          80  65.000000         0.661017
2015-09-18   DA   0.67    Mining          65  65.000000         0.448276
2015-09-21   KK   1.66    Mining          80  80.000000         0.551724

Answer 2

嘗試組合transform（'sum'），表示

In [5]: df
Out[5]: 
   Close Feed  Market_Cap    Sector
0   5.60    A          50  Property
1   5.60    A          20  Property
2   5.60    A          30  Property
3   0.67  ABC          50  Property
4   0.66  ABC          80  Property
5   0.67   DA          65    Mining
6   1.66   KK          80    Mining

In [6]: g = df.groupby(['Sector', 'Feed'])

..

In [7]: c = g.Market_Cap.mean()

In [8]: c
Out[8]: 
Sector    Feed
Mining    DA      65.000000
          KK      80.000000
Property  A       33.333333
          ABC     65.000000
Name: Market_Cap, dtype: float64

In [9]: d = c.groupby(level=0).transform('sum')

In [10]: d
Out[10]: 
Sector    Feed
Mining    DA      145.000000
          KK      145.000000
Property  A        98.333333
          ABC      98.333333
dtype: float64

..

In [11]: df['Mean'] = df.apply(lambda x: c[x.Sector, x.Feed], axis=1)

In [12]: df['Weighted_Avg'] = df.apply(lambda x: c[x.Sector, x.Feed] / d[x.Sector, x.Feed], axis=1)

In [13]: df
Out[13]: 
   Close Feed  Market_Cap    Sector       Mean  Weighted_Avg
0   5.60    A          50  Property  33.333333      0.338983
1   5.60    A          20  Property  33.333333      0.338983
2   5.60    A          30  Property  33.333333      0.338983
3   0.67  ABC          50  Property  65.000000      0.661017
4   0.66  ABC          80  Property  65.000000      0.661017
5   0.67   DA          65    Mining  65.000000      0.448276
6   1.66   KK          80    Mining  80.000000      0.551724