如何处理用yfinance下载的多级列名

问题描述

我有一个代码列表 (tickerStrings)，我可以一次性下载.当我尝试使用 pandas 的 read_csv 时，它不会像我从 yfinance 下载数据时那样读取 csv 文件.

I have a list of tickers (tickerStrings) that I to download all at once. When I try to use pandas' read_csv it doesn't read the csv file in the way it does when I download the data from yfinance.

我通常通过如下代码访问我的数据:data['AAPL'] 或 data['AAPL'].Close，但是当我从它不允许我这样做的 csv 文件.

I usually access my data by ticker like this: data['AAPL'] or data['AAPL'].Close, but when I read the data from the csv file it does not let me do that.

if path.exists(data_file):
    data = pd.read_csv(data_file, low_memory=False)
    data = pd.DataFrame(data)
    print(data.head())
else:
    data = yf.download(tickerStrings, group_by="Ticker", period=prd, interval=intv)
    data.to_csv(data_file)

这是打印输出:

                  Unnamed: 0                 OLN               OLN.1               OLN.2               OLN.3  ...                 W.1                 W.2                 W.3                 W.4     W.5
0                        NaN                Open                High                 Low               Close  ...                High                 Low               Close           Adj Close  Volume
1                   Datetime                 NaN                 NaN                 NaN                 NaN  ...                 NaN                 NaN                 NaN                 NaN     NaN
2  2020-06-25 09:30:00-04:00    11.1899995803833  11.220000267028809  11.010000228881836  11.079999923706055  ...   201.2899932861328   197.3000030517578  197.36000061035156  197.36000061035156  112156
3  2020-06-25 09:45:00-04:00  11.130000114440918  11.260000228881836  11.100000381469727   11.15999984741211  ...  200.48570251464844  196.47999572753906  199.74000549316406  199.74000549316406   83943
4  2020-06-25 10:00:00-04:00  11.170000076293945  11.220000267028809  11.119999885559082  11.170000076293945  ...  200.49000549316406  198.19000244140625   200.4149932861328   200.4149932861328   88771

我在尝试访问数据时遇到的错误:

The error I get when trying to access the data:

Traceback (most recent call last):
File "getdata.py", line 49, in processData
    avg = data[x].Close.mean()
AttributeError: 'Series' object has no attribute 'Close'

推荐答案

将所有代码下载到具有单级列标题的单个数据框中

选项 1

• 下载单个股票代码数据时，返回的数据框列名称是单个级别，但没有代码列.
• 这将为每个代码下载数据，添加代码列，并从所有所需代码创建一个数据框.

import yfinance as yf
import pandas as pd

tickerStrings = ['AAPL', 'MSFT']
df_list = list()
for ticker in tickerStrings:
    data = yf.download(ticker, group_by="Ticker", period='2d')
    data['ticker'] = ticker  # add this column because the dataframe doesn't contain a column with the ticker
    df_list.append(data)

# combine all dataframes into a single dataframe
df = pd.concat(df_list)

# save to csv
df.to_csv('ticker.csv')

选项 2

• 下载所有代码并取消堆叠级别
  • group_by='Ticker' 将代码放在列名的level=0

  Option 2

  • Download all the tickers and unstack the levels
    • group_by='Ticker' puts the ticker at level=0 of the column name

    tickerStrings = ['AAPL', 'MSFT']
    df = yf.download(tickerStrings, group_by='Ticker', period='2d')
    df = df.stack(level=0).rename_axis(['Date', 'Ticker']).reset_index(level=1)
    

    ---

    读取 yfinance 已存储多级列名的 csv

    • 如果您希望保留并读取具有多级列索引的文件，请使用以下代码，该代码会将数据框恢复为其原始形式.

    ---

    Read yfinance csv already stored with multi-level column names

    • If you wish to keep, and read in a file with a multi-level column index, use the following code, which will return the dataframe to its original form.

    df = pd.read_csv('test.csv', header=[0, 1])
    df.drop([0], axis=0, inplace=True)  # drop this row because it only has one column with Date in it
    df[('Unnamed: 0_level_0', 'Unnamed: 0_level_1')] = pd.to_datetime(df[('Unnamed: 0_level_0', 'Unnamed: 0_level_1')], format='%Y-%m-%d')  # convert the first column to a datetime
    df.set_index(('Unnamed: 0_level_0', 'Unnamed: 0_level_1'), inplace=True)  # set the first column as the index
    df.index.name = None  # rename the index
    

    • 问题是，tickerStrings 是一个代码列表，这会导致最终数据帧具有多级列名
    • The issue is, tickerStrings is a list of tickers, which results in a final dataframe with multi-level column names

                    AAPL                                                    MSFT                                
                    Open      High       Low     Close Adj Close     Volume Open High Low Close Adj Close Volume
    Date                                                                                                        
    1980-12-12  0.513393  0.515625  0.513393  0.513393  0.405683  117258400  NaN  NaN NaN   NaN       NaN    NaN
    1980-12-15  0.488839  0.488839  0.486607  0.486607  0.384517   43971200  NaN  NaN NaN   NaN       NaN    NaN
    1980-12-16  0.453125  0.453125  0.450893  0.450893  0.356296   26432000  NaN  NaN NaN   NaN       NaN    NaN
    1980-12-17  0.462054  0.464286  0.462054  0.462054  0.365115   21610400  NaN  NaN NaN   NaN       NaN    NaN
    1980-12-18  0.475446  0.477679  0.475446  0.475446  0.375698   18362400  NaN  NaN NaN   NaN       NaN    NaN
    

    • 当它被保存到 csv 时，它看起来像下面的示例，并产生一个你遇到问题的数据框.

    ,AAPL,AAPL,AAPL,AAPL,AAPL,AAPL,MSFT,MSFT,MSFT,MSFT,MSFT,MSFT
    ,Open,High,Low,Close,Adj Close,Volume,Open,High,Low,Close,Adj Close,Volume
    Date,,,,,,,,,,,,
    1980-12-12,0.5133928656578064,0.515625,0.5133928656578064,0.5133928656578064,0.40568336844444275,117258400,,,,,,
    1980-12-15,0.4888392984867096,0.4888392984867096,0.4866071343421936,0.4866071343421936,0.3845173120498657,43971200,,,,,,
    1980-12-16,0.453125,0.453125,0.4508928656578064,0.4508928656578064,0.3562958240509033,26432000,,,,,,
    

    ---

    将多级列扁平化为单级并添加一个ticker列

    • 如果股票代码是列名的 level=0(顶部)
      • 当使用 group_by='Ticker' 时

      ---

      Flatten multi-level columns into a single level and add a ticker column

      • If the ticker symbol is level=0 (top) of the column names
        • When group_by='Ticker' is used

        df.stack(level=0).rename_axis(['Date', 'Ticker']).reset_index(level=1)
        

        • 如果股票代码是列名的level=1(底部)

        df.stack(level=1).rename_axis(['Date', 'Ticker']).reset_index(level=1)
        

        ---

        下载每个代码并将其保存到单独的文件中

        • 我建议单独下载并保存每个代码，如下所示:

        import yfinance as yf
        import pandas as pd
        
        tickerStrings = ['AAPL', 'MSFT']
        for ticker in tickerStrings:
            data = yf.download(ticker, group_by="Ticker", period=prd, interval=intv)
            data['ticker'] = ticker  # add this column because the dataframe doesn't contain a column with the ticker
            data.to_csv(f'ticker_{ticker}.csv')  # ticker_AAPL.csv for example
        

        • data 看起来像

                        Open      High       Low     Close  Adj Close      Volume ticker
        Date                                                                            
        1986-03-13  0.088542  0.101562  0.088542  0.097222   0.062205  1031788800   MSFT
        1986-03-14  0.097222  0.102431  0.097222  0.100694   0.064427   308160000   MSFT
        1986-03-17  0.100694  0.103299  0.100694  0.102431   0.065537   133171200   MSFT
        1986-03-18  0.102431  0.103299  0.098958  0.099826   0.063871    67766400   MSFT
        1986-03-19  0.099826  0.100694  0.097222  0.098090   0.062760    47894400   MSFT
        

        • 生成的 csv 将如下所示

        Date,Open,High,Low,Close,Adj Close,Volume,ticker
        1986-03-13,0.0885416641831398,0.1015625,0.0885416641831398,0.0972222238779068,0.0622050017118454,1031788800,MSFT
        1986-03-14,0.0972222238779068,0.1024305522441864,0.0972222238779068,0.1006944477558136,0.06442664563655853,308160000,MSFT
        1986-03-17,0.1006944477558136,0.1032986119389534,0.1006944477558136,0.1024305522441864,0.0655374601483345,133171200,MSFT
        1986-03-18,0.1024305522441864,0.1032986119389534,0.0989583358168602,0.0998263880610466,0.06387123465538025,67766400,MSFT
        1986-03-19,0.0998263880610466,0.1006944477558136,0.0972222238779068,0.0980902761220932,0.06276042759418488,47894400,MSFT
        

        读入上一节保存的多个文件并创建一个数据框

        import pandas as pd
        from pathlib import Path
        
        # set the path to the files
        p = Path('c:/path_to_files')
        
        # find the files; this is a generator, not a list
        files = p.glob('ticker_*.csv')
        
        # read the files into a dataframe
        df = pd.concat([pd.read_csv(file) for file in files])
        

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