将所有代码下载到具有单个级别列标题的单个数据帧中
选项1
- 下载单个股票行情收录器数据时，返回的数据框列名称是单个级别，但没有股票行情栏。
这将下载每个行情自动收录器的数据，添加行情自动收录器列，并根据所有所需的行情自动收录器创建单个数据框。
import yfinance as yf
import pandas as pdtickerStrings = [‘AAPL’, ‘MSFT’]
df_list = list()
for ticker in tickerStrings:
data = yf.download(ticker, group_by=”Ticker”, period=‘2d’)
data[‘ticker’] = ticker # add this column becasue 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
下载所有股票并取消堆叠
group_by='Ticker'
将代码置入level=0
列名称
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
已存储有多级列名称的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，这会将数据帧恢复为原始格式。
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
代码清单，这将导致最终数据帧具有多级列名AAPL MSFTOpen 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将其保存到csv后，它看起来像下面的示例，并导致出现数据框，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,,,,,,
将多级列展平为单个级，然后添加行情栏
如果股票代号
level=0
在列名的顶部- 什么时候
group_by='Ticker'
使用
df.stack(level=0).rename_axis([‘Date’, ‘Ticker’]).reset_index(level=1)
- 什么时候
如果股票代号
level=1
在列名的（底部）df.stack(level=1).rename_axis([‘Date’, ‘Ticker’]).reset_index(level=1)
下载每个股票行情并将其保存到单独的文件中
我建议分别下载并保存每个行情收录器，如下所示：
import yfinance as yf
import pandas as pdtickerStrings = [‘AAPL’, ‘MSFT’]
for ticker in tickerStrings:
data = yf.download(ticker, group_by=”Ticker”, period=prd, interval=intv)
data[‘ticker’] = ticker # add this column becasue the dataframe doesn’t contain a column with the ticker
data.to_csv(f’ticker_{ticker}.csv’) # ticker_AAPL.csv for exampledata
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
读入上一部分保存的多个文件并创建一个数据框
import pandas as pdfrom pathlib import Path# set the path to the filesp = Path('c:/path_to_files')# find the filesfiles = list(p.glob('ticker_*.csv'))# read the files into a dataframedf_list = list()for file in files: df_list.append(pd.read_csv(file))# combine dataframesdf = pd.concat(df_list)
