python之从 Pandas 数据框中抑制或删除名为 'index' 的列

我正在尝试从三个父(或源)数据框(每个都从一个 .csv 文件创建)创建一个数据框，但是当将生成的数据框写入文件或在屏幕上打印时，显示名为“索引”的列.我如何抑制/删除它们？

三个“父”数据框:

df1 ... 
   fname  lname employer  score1  score2  score3 
0  Alice  Adams      IMB    -1.0     2.5    -0.2 
1  Alice  Brown      MFS     2.2    -7.9     3.7 
2  Alice   Curt      OCR     2.6    -1.2    -0.7 
 
df2 ... 
   fname  lname employer  score1  score2  score3 
0  Alice  Adams      IMB     3.0     0.1    -2.9 
1  Alice  Brown      MFS    -2.1     2.6    -1.0 
2  Alice   Curt      OCR     3.1     1.9    -0.1 
 
df3 ... 
   fname  lname employer  score1  score2  score3 
0  Alice  Adams      IMB    -1.0    -2.1     0.1 
1  Alice  Brown      MFS     3.2    -0.9     5.1 
2  Alice   Curt      OCR    -1.1    -1.2    -1.9 

经过一系列操作后，我得到了这个:

   fname  lname  index employer  score1  index employer  score3  index employer  score1  index employer  score3  index employer  score1  index employer  score3 
0  Alice  Adams      0      IMB    -1.0      2      OCR    -0.7      1      MFS    -2.1      0      IMB    -2.9      2      OCR    -1.1      2      OCR    -1.9 
1  Alice  Brown      1      MFS     2.2      0      IMB    -0.2      0      IMB     3.0      1      MFS    -1.0      0      IMB    -1.0      0      IMB     0.1 
2  Alice   Curt      2      OCR     2.6      1      MFS     3.7      2      OCR     3.1      2      OCR    -0.1      1      MFS     3.2      1      MFS     5.1 

我在找什么:
去掉名为“index”的列。

我有一个 MWE，我从中粘贴了上述结果。如果您想在此处查看源 .csv 和 .py 文件，请告诉我。

附录
发布源 .csv 文件和 .py 脚本:

A.csv ...   
fname,lname,employer,score1,score2,score3 
Alice,Adams,IMB,-1.0,2.5,-0.2 
Alice,Brown,MFS,2.2,-7.9,3.7 
Alice,Curt,OCR,2.6,-1.2,-0.7 
 
B.csv ... 
fname,lname,employer,score1,score2,score3 
Alice,Adams,IMB,3.0,0.1,-2.9 
Alice,Brown,MFS,-2.1,2.6,-1.0 
Alice,Curt,OCR,3.1,1.9,-0.1 
 
C.csv ... 
fname,lname,employer,score1,score2,score3 
Alice,Adams,IMB,-1.0,-2.1,0.1 
Alice,Brown,MFS,3.2,-0.9,5.1 
Alice,Curt,OCR,-1.1,-1.2,-1.9 

现在，.py 脚本...

# -*- coding: utf-8 -*- 
 
import fnmatch 
import os 
import matplotlib.pyplot as plt 
import pandas as pd 
 
pd.set_option('display.max_columns', None) 
 
Datasets = ['A', 'B', 'C'] 
bigDF = pd.DataFrame() 
 
for fname in Datasets: 
    if fname == 'A': 
        csvdf = pd.read_csv(fname+'.csv') 
        csvdfBUa = csvdf[['fname', 'lname']] 
        csvdfBUb = csvdf[['employer', 'score1']] 
        csvdfBUb = csvdfBUb.sort(['score1'], ascending=[1]) 
        csvdfBUb = csvdfBUb.reset_index() 
        csvdfBUc = csvdf[['employer', 'score3']] 
        csvdfBUc = csvdfBUc.sort(['score3'], ascending=[1]) 
        csvdfBUc = csvdfBUc.reset_index() 
        csvdfBU = pd.concat([csvdfBUa, csvdfBUb, csvdfBUc], axis=1, ignore_index=False) 
        print csvdf 
        if len(bigDF.index) < 1: 
            bigDF = csvdfBU 
        else: 
            bigDF = pd.concat([bigDF, csvdfBU], axis=1, ignore_index=False) 
    elif fname == 'B': 
        csvdf = pd.read_csv(fname+'.csv') 
        csvdfAFb = csvdf[['employer', 'score1']] 
        csvdfAFb = csvdfAFb.sort(['score1'], ascending=[1]) 
        csvdfAFb = csvdfAFb.reset_index() 
        csvdfAFc = csvdf[['employer', 'score3']] 
        csvdfAFc = csvdfAFc.sort(['score3'], ascending=[1]) 
        csvdfAFc = csvdfAFc.reset_index() 
        csvdfAF = pd.concat([csvdfAFb, csvdfAFc], axis=1, ignore_index=False) 
        print csvdf 
        if len(bigDF.index) < 1: 
            bigDF = csvdfAF 
        else: 
            bigDF = pd.concat([bigDF, csvdfAF], axis=1, ignore_index=False) 
    elif fname == 'C': 
        csvdf = pd.read_csv(fname+'.csv') 
        csvdfGAb = csvdf[['employer', 'score1']] 
        csvdfGAb = csvdfGAb.sort(['score1'], ascending=[1]) 
        csvdfGAb = csvdfGAb.reset_index() 
        csvdfGAc = csvdf[['employer', 'score3']] 
        csvdfGAc = csvdfGAc.sort(['score3'], ascending=[1]) 
        csvdfGAc = csvdfGAc.reset_index() 
        csvdfGA = pd.concat([csvdfGAb, csvdfGAc], axis=1, ignore_index=False) 
        print csvdf 
        if len(bigDF.index) < 1: 
            bigDF = csvdfGA 
        else: 
            bigDF = pd.concat([bigDF, csvdfGA], axis=1, ignore_index=False) 
 
print bigDF 

请您参考如下方法:

您可以使用以下方法删除“索引”列:

del df['index'] 

注意:我怀疑您首先可以避免这种情况...