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python - 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)，我可以一次下载所有代码。当我尝试使用 pandas时，read_csv它不会像我从 yfinance 下载数据时那样读取 csv 文件。

我通常通过这样的股票代码访问我的数据：data['AAPL']或data['AAPL'].Close，但是当我从 csv 文件中读取数据时，它不允许我这样做。

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

尝试访问数据时出现的错误：

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

标签： pythonpython-3.xpandasdataframeyfinance

解决方案

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单个数据框。
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level=0列名的位置
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，这会将数据框返回到其原始形式。
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，它导致最终数据框具有多级列名
                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，并产生一个数据框，就像您遇到问题一样。
,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,,,,,,

将多级列展平为单级并添加一个股票行情

  • 如果股票代码是level=0列名的（顶部）
    • 什么时候group_by='Ticker'使用
df.stack(level=0).rename_axis(['Date', 'Ticker']).reset_index(level=1)
  • 如果股票代码是level=1（底部）列名
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 看起来像
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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