python - 如何使用 apply 来实现这个功能

Question

我正在尝试使用 apply 来评估赫芬达尔指数。我通过将数据帧转换为 numpy 矩阵来完成此操作。事实上，函数 evalHerfindahlIndex 运行良好，它评估每行 Herfindahl 索引的正确值。但是，当我尝试使用相同的函数(evalHerfindahlIndexForDF)来使用 apply 时，我收到一个非常奇怪的错误:

ValueError: ("No axis named 1 for object type <class 'pandas.core.series.Series'>", 'occurred at index A')

整个代码是这样的:

import pandas as pd
import numpy as np
import datetime


def evalHerfindahlIndex(x):
    soma=np.sum(x,axis=1)
    y=np.empty(np.shape(x))
    for line in range(len(soma)):
        y[line,:]=np.power(x[line,:]/soma[line],2.0)
    hhi=np.sum(y,axis=1)    
    return hhi

def evalHerfindahlIndexForDF(x):
    soma=x.sum(axis=1)

def creatingDataFrame():

    dateList=[]
    dateList.append(datetime.date(2002,1,1))
    dateList.append(datetime.date(2002,2,1))
    dateList.append(datetime.date(2002,1,1))
    dateList.append(datetime.date(2002,1,1))
    dateList.append(datetime.date(2002,2,1))
    raw_data = {'Date': dateList,            
                'Company': ['A', 'B', 'B', 'C' , 'C'],                
                'var1': [10, 20, 30, 40 , 50]}

    df = pd.DataFrame(raw_data, columns = ['Date','Company', 'var1'])
    df.loc[1, 'var1'] = np.nan
    return df


if __name__=="__main__":
    df=creatingDataFrame()
    print(df)
    dfPivot=df.pivot(index='Date', columns='Company', values='var1')
    #print(dfPivot)
    dfPivot=dfPivot.fillna(0)
    dfPivot['Date']=dfPivot.index

    listOfCompanies=list(set(df['Company']))
    Pivot=dfPivot.as_matrix(columns=listOfCompanies)
    print(evalHerfindahlIndex(Pivot))
    print(dfPivot)

    print(dfPivot[listOfCompanies].apply(evalHerfindahlIndexForDF))

我使用的数据框是 dfPivot:

Company        A     B     C        Date
Date                                    
2002-01-01  10.0  30.0  40.0  2002-01-01
2002-02-01   0.0   0.0  50.0  2002-02-01

使用 evalHerfindahlIndex 评估的 Herfindahl 指数的正确值为:

[0.40625 1.     ]

我想将其作为数据框 dfPivot 的额外列返回。

Answer 1

考虑更新您的方法，然后通过将数组返回专门转换为 pandas Series 来更新您的调用

def evalHerfindahlIndex(df):
    x = df.as_matrix(columns = listOfCompanies)          # MOVE MATRIX OPERATION WITHIN FCT        
    soma = np.sum(x,axis = 1)
    y = np.empty(np.shape(x))
    for line in range(len(soma)):
        y[line,:] = np.power(x[line,:]/soma[line],2.0)
    hhi = pd.Series(np.sum(y,axis = 1))                  # CONVERT TO SERIES

    return hhi

...

if __name__=="__main__":
    df = creatingDataFrame()
    print(df)
    dfPivot = df.pivot(index = 'Date', columns = 'Company', values = 'var1')
    #print(dfPivot)
    dfPivot = dfPivot.fillna(0)
    dfPivot['Date'] = dfPivot.index

    # ASSIGN SERIES VALUES (.values to IGNORE INDEX)
    dfPivot['HE_Result'] = evalHerfindahlIndex(dfPivot).values   

    # OUTPUT  
    print(evalHerfindahlIndex(dfPivot))
    # 0    0.40625
    # 1    1.00000
    # dtype: float64

    print(dfPivot)
    # Company        A     B     C        Date  HE_Result
    # Date                                               
    # 2002-01-01  10.0  30.0  40.0  2002-01-01    0.40625
    # 2002-02-01   0.0   0.0  50.0  2002-02-01    1.00000