Python Django：处理DataFrame并执行计算以在chart.js中使用

我正试着让我的头脑围绕着数据帧。我试图得到一个键值列表，其中包含日期和当天提供的所有股票的总价格

我在阅读数据帧时注意到，有些人提到不建议使用

iterrows（）

，应该在数据帧中进行计算。但是我没有看到我应该怎么做，没有一个列表可以传递给我的chart.js数据

代码

    data = yf.download(  # or pdr.get_data_yahoo(...
        # tickers list or string as well
        tickers = "AAPL GOOGL NIO"),

        # use "period" instead of start/end
        # valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
        # (optional, default is '1mo')
        period = "ytd",

        # fetch data by interval (including intraday if period < 60 days)
        # valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
        # (optional, default is '1d')
        interval = "1d",

        # group by ticker (to access via data['SPY'])
        # (optional, default is 'column')
        group_by = 'ticker',

        # adjust all OHLC automatically
        # (optional, default is False)
        auto_adjust = False,

        # download pre/post regular market hours data
        # (optional, default is False)
        prepost = False,

        # use threads for mass downloading? (True/False/Integer)
        # (optional, default is True)
        threads = True,

        # proxy URL scheme use use when downloading?
        # (optional, default is None)
        proxy = None
    )

    logger.info(data)

    for index, row in data.iterrows():
        logger.info(row)

                  GOOGL               ...        AAPL           
                   Open         High  ...   Adj Close     Volume
Date                                  ...                       
2020-01-02  1348.410034  1368.680054  ...   74.573036  135480400
2020-01-03  1348.000000  1373.750000  ...   73.848030  146322800
2020-01-06  1351.630005  1398.319946  ...   74.436470  118387200
2020-01-07  1400.459961  1403.500000  ...   74.086395  108872000
2020-01-08  1394.819946  1411.849976  ...   75.278160  132079200
...                 ...          ...  ...         ...        ...
2020-09-09  1548.900024  1558.719971  ...  117.320000  176940500
2020-09-10  1550.180054  1573.660034  ...  113.489998  182274400
2020-09-11  1528.150024  1538.699951  ...  112.000000  180860300
2020-09-14  1531.650024  1557.000000  ...  115.360001  140150100
2020-09-15  1527.890015  1550.989990  ...  115.540001  184110700


[178 rows x 18 columns]
GOOGL  Open         1.348410e+03
       High         1.368680e+03
       Low          1.346490e+03
       Close        1.368680e+03
       Adj Close    1.368680e+03
       Volume       1.363900e+06
NIO    Open         4.100000e+00
       High         4.100000e+00
       Low          3.610000e+00
       Close        3.720000e+00
       Adj Close    3.720000e+00
       Volume       1.037401e+08
AAPL   Open         7.406000e+01
       High         7.515000e+01
       Low          7.379750e+01
       Close        7.508750e+01
       Adj Close    7.457304e+01
       Volume       1.354804e+08
Name: 2020-01-02 00:00:00, dtype: float64
GOOGL  Open         1.348000e+03
       High         1.373750e+03
       Low          1.347320e+03
       Close        1.361520e+03
       Adj Close    1.361520e+03
       Volume       1.170400e+06
NIO    Open         3.500000e+00
       High         3.900000e+00
       Low          3.480000e+00
       Close        3.830000e+00
       Adj Close    3.830000e+00
       Volume       8.289240e+07
AAPL   Open         7.428750e+01
       High         7.514500e+01
       Low          7.412500e+01
       Close        7.435750e+01
       Adj Close    7.384803e+01
       Volume       1.463228e+08
....