python - pandas:在每行中设置 group_by 值

Question

我从互联网服务获得具有以下结构的 pandas 数据框:

from yfinance module

                      DXCM                                               
                      Open        High         Low       Close   Volume   
    Date                                                                  
    2020-01-30  237.850006  239.770004  232.000000  238.929993   459600   
    2020-01-31  238.820007  247.339996  238.009995  240.750000  1000100   
    2020-02-03  241.039993  243.839996  236.449997  237.089996   637700   
    2020-02-04  238.910004  245.259995  237.669998  243.699997   685500   


                      SPY                                                 
                      Open        High         Low       Close     Volume  
    Date                                                                   
    2020-01-30  324.359985  327.910004  323.540009  327.679993   75491800  
    2020-01-31  327.000000  327.170013  320.730011  321.730011  113845600  
    2020-02-03  323.350006  326.160004  323.220001  324.119995   69242300  
    2020-02-04  328.070007  330.010010  327.720001  329.059998   62573200

DXCM 和 SPY 按值分组 = 股票代码

我需要按值分组，即每行中的 SPY(= 股票代码)。但我找不到正确的代码。

我使用以下代码获取此数据框:

data = yf.download(ticker, start=von, end=bis,
                   group_by="ticker",interval= '1d', auto_adjust=True) 

如果我通过参数消除组，情况会更糟。

请帮忙。

爱德华

Answer 1

试试这个代码:

import yfinance as yf

data = yf.download('DXCM SPY', start="2017-01-01", end="2017-04-30",
                   interval= '1d', auto_adjust=True)
data = data.T.unstack().T
data.index.names = ['Date', 'Ticker']

结果的head()是:

                        Close        High         Low        Open      Volume
Date       Ticker                                                            
2017-01-03 DXCM     58.250000   59.650002   57.759998   59.500000   1429300.0
           SPY     212.796539  213.353941  211.511672  212.607577  91366500.0
2017-01-04 DXCM     60.720001   61.150002   57.680000   58.299999   1811600.0
           SPY     214.062500  214.223107  213.146087  213.155529  78744400.0
2017-01-05 DXCM     62.660000   64.550003   60.529999   60.889999   1641400.0