嘗試包括在Python

Question

在Pnadas數據幀的基於列的輸入文件名我有具有以下結構的幾個CSV文件：

  Erster Hoch Tief Schlusskurs Stuecke  Volumen 
Datum                 
14.02.2017 151.55 152.35 151.05  152.25 110.043 16.687.376 
13.02.2017 149.85 152.20 149.25  151.25  415.76 62.835.200 
10.02.2017 149.00 150.05 148.65  149.40 473.664 70.746.088 
09.02.2017 144.75 148.45 144.35  148.00 642.175 94.348.392 


      Erster  Hoch  Tief Schlusskurs Stuecke Volumen 
Datum                
14.02.2017 111.454 111.776 111.454  111.776  44 4.918 
13.02.2017 110.570 110.989 110.570  110.989  122 13.535 
10.02.2017 109.796 110.705 109.796  110.705  0  0 
09.02.2017 107.993 108.750 107.993  108.750  496 53.933 

基於文件名的命名都是不同的：

wkn_A1EWWW_historic.csv 
wkn_A0YAQA_historic.csv 

我想有以下輸出：

Date  wkn  Open High low  Close pieced  Volume 

14.02.2017 A1EWWW 151.55 152.35 151.05 152.25 110.043 16.687.376 
13.02.2017 A1EWWW 149.85 152.20 149.25 151.25  415.76 62.835.200 
10.02.2017 A1EWWW 149.00 150.05 148.65 149.40 473.664 70.746.088 
09.02.2017 A1EWWW 144.75 148.45 144.35 148.00 642.175 94.348.392 

Date  wkn  Open High low  Close pieced  Volume 
14.02.2017 A0YAQA 111.454 111.776 111.454  111.776  44 4.918 
13.02.2017 A0YAQA 110.570 110.989 110.570  110.989  122 13.535 
10.02.2017 A0YAQA 109.796 110.705 109.796  110.705  0  0 
09.02.2017 A0YAQA 107.993 108.750 107.993  108.750  496 53.933 

的代碼如下所示：

import pandas as pd 

wkn_list_dummy = {'A0YAQA','A1EWWW'} 

for w_list in wkn_list_dummy: 
    url = 'C:/wkn_'+str(w_list)+'_historic.csv' 
    df = pd.read_csv(url, encoding='cp1252', sep=';', decimal=',', index_col=0) 
    print(df) 

我嘗試了使用融化但它不工作。

Answer 1

您可以通過它分配一個值，將其添加列：

df['new_column'] = 'string' 

一起：

import pandas as pd 

wkn_list_dummy = {'A0YAQA','A1EWWW'} 

final_df = pd.DataFrame() 
for w_list in wkn_list_dummy: 
    url = 'C:/wkn_'+str(w_list)+'_historic.csv' 
    df = pd.read_csv(url, encoding='cp1252', sep=';', decimal=',', index_col=0) 
    df['wkn'] = w_list 
    final_df = final_df.append(df) 
final_df.reset_index(inplace=True) 
print(final_df)