如何处理通过yfinance下载的多级列名？
问题内容：
我有一个代码清单（tickerStrings）列表，可以一次下载所有代码。当我尝试使用pandas时，read_csv它不会像从yfinance下载数据时那样读取csv文件。
我通常通过代码这样的代码来访问我的数据：data['AAPL']或data['AAPL'].Close，但是当我从csv文件读取数据时，我不允许这样做。
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
我在尝试访问数据时遇到的错误：
Traceback (most recent call last):
File "getdata.py", line 49, in processData
avg = data[x].Close.mean()
AttributeError: 'Series' object has no attribute 'Close'
问题答案：
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 MSFT Open High Low Close Adj Close Volume Open High Low Close Adj Close VolumeDate
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 example -
data看起来像Open High Low Close Adj Close Volume tickerDate
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 pd
from pathlib import Path
# set the path to the files
p = Path('c:/path_to_files')
# find the files
files = list(p.glob('ticker_*.csv'))
# read the files into a dataframe
df_list = list()
for file in files:
df_list.append(pd.read_csv(file))
# combine dataframes
df = pd.concat(df_list)
