Seperate multihead-columns dataframe in pandas-CodePudding

Please help divide this multi-frame pandas into separates, that's code

import datetime as dt
import pandas as pd
import pandas_datareader.data as web
pd.set_option('display.max_columns', 500)
stocks=["1.HK","2.HK"]
start_time = dt.datetime(2022, 7, 1)
end_time = dt.datetime.now().date().isoformat()
df = web.DataReader(stocks, 'stooq', start_time, end_time)
print(df)

here is data retrieve

Attributes  Close          High           Low          Open          Volume  \
Symbols      2.HK   1.HK   2.HK   1.HK   2.HK   1.HK   2.HK   1.HK     2.HK   
Date                                                                          
2022-07-08  66.30  53.20  66.30  53.30  65.80  52.70  66.00  53.10  2495792   
2022-07-07  65.95  52.80  65.95  52.80  65.25  52.15  65.50  52.50  3668691   
2022-07-06  65.60  52.65  65.65  53.05  65.00  52.30  65.10  52.85  4684631   
2022-07-05  65.95  53.15  66.00  54.20  65.60  53.00  65.90  53.70  2059494   
2022-07-04  65.40  53.30  65.80  53.50  64.80  52.55  65.65  52.70  2732110   

Attributes           
Symbols        1.HK  
Date                 
2022-07-08  2837712  
2022-07-07  2426217  
2022-07-06  3301780  
2022-07-05  2887622  
2022-07-04  3258099

I want to get separate frames (pd.dataframe) also it would be best to set columns names as: Close, High, Low, Open, Volume; and Date as a index. df_1HK


Date        Close   High    Low   Open   Volume                                           
2022-07-08  53.20  53.30  52.70  53.10  2837712
2022-07-07  52.80  52.80  52.15  52.50  2426217
2022-07-06  52.65  53.05  52.30  52.85  3301780
2022-07-05  53.15  54.20  53.00  53.70  2887622
2022-07-04  53.30  53.50  52.55  52.70  3258099

And there df_2HK

Date        Close   High    Low   Open   Volume
2022-07-08  66.30  66.30  65.80  66.00  2495792
2022-07-07  65.95  65.95  65.25  65.50  3668691
2022-07-06  65.60  65.65  65.00  65.10  4684631
2022-07-05  65.95  66.00  65.60  65.90  2059494
2022-07-04  65.40  65.80  64.80  65.65  2732110

Thank You!

CodePudding user response：

`DataFrame.xs`

df1 = df.xs('1.HK', level=1, axis=1)
df2 = df.xs('2.HK', level=1, axis=1)

Or if you have multiple stocks then you can use a dictionary to store the corresponding dataframes

d = {s: df.xs(s, level=1, axis=1) for s in stocks}

>>> df1
Attributes  Close   High    Low   Open   Volume
Date                                           
2022-07-08  53.20  53.30  52.70  53.10  2837712
2022-07-07  52.80  52.80  52.15  52.50  2426217
2022-07-06  52.65  53.05  52.30  52.85  3301780
2022-07-05  53.15  54.20  53.00  53.70  2887622
2022-07-04  53.30  53.50  52.55  52.70  3258099


>>> df2
Attributes  Close   High    Low   Open   Volume
Date                                           
2022-07-08  66.30  66.30  65.80  66.00  2495792
2022-07-07  65.95  65.95  65.25  65.50  3668691
2022-07-06  65.60  65.65  65.00  65.10  4684631
2022-07-05  65.95  66.00  65.60  65.90  2059494
2022-07-04  65.40  65.80  64.80  65.65  2732110