yfinance下载的多级列名如何处理

Question

我有一个代码列表 (tickerStrings)，我必须一次下载所有代码。当我尝试使用 Pandas' read_csv 时，它不会读取 CSV file in the way it does when I download the data from yfinance.

我通常像这样通过自动收报机访问我的数据：data['AAPL'] 或 data['AAPL'].Close，但是当我从 CSV 文件读取数据时，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

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'

Answer 1

将所有代码下载到具有单级列的单个数据框中 headers

选项 1

下载单个股票行情数据时，return编辑的数据框列名称是单个级别，但没有行情列。
这将为每个代码下载数据，添加一个代码列，并根据所有需要的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

下载所有代码并拆开关卡
- group_by='Ticker' 将代码放在列名 level=0 处

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` csv 已存储 multi-level 列名称

如果您希望保留并读入具有 multi-level 列索引的文件，请使用以下代码，这会将数据框 return 恢复为原始形式。

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 multi-level 列名称

                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，它看起来像下面的例子，并产生一个数据框，就像你遇到问题一样。

,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 multi-level 列展平为一个级别并添加代码列

如果股票代码是列名称的 level=0（顶部）
- 使用group_by='Ticker'时

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

如果股票代码是列名称的 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

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单个数据帧

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])

Answer 2

另一个维护 pandas 数据框但删除不需要的数据的选项是将列索引从多索引更改为单个索引。由于您只关心 'Close' 列，因此第一步将丢弃其他列：

df = yf.download(...)
df = df[['Close']]

这很好，但是每列都有一个多索引，看起来像 (Close/AAPL) 或 (Close/MSFT) 等。您真正想要的只是代码。

df.columns = [col[1] for col in df.columns]

现在，如果您想将数据框拆分为每一列的单独数据框，您可以使用列表理解来完成此操作。

separated = [df.iloc[:,i] for i in range(len(df.columns))]

Answer 3

把它变成d[ticker]=df的dict:

df = yf.download(tickers, group_by="ticker")
d = {idx: gp.xs(idx, level=0, axis=1) for idx, gp in df.groupby(level=0, axis=1)}

Answer 4

使用下面的行写入和读取 CSV 文件。它们的格式与您从 yfinance API.

下载的格式完全相同

写入文件

data.to_csv('file_loc')

读取文件

data = pd.read_csv('file_loc', header=[0, 1], index_col=[0])

yfinance下载的多级列名如何处理

How to deal with multi-level column names downloaded with yfinance

python

dataframe

python-3.x

pandas

yfinance

将所有代码下载到具有单级列的单个数据框中 headers

选项 1

选项 2

读取 `yfinance` csv 已存储 multi-level 列名称

将 multi-level 列展平为一个级别并添加代码列

下载每个代码并将其保存到单独的文件

读入上一节保存的多个文件并创建单个数据帧

yfinance下载的多级列名如何处理

How to deal with multi-level column names downloaded with yfinance

python

dataframe

python-3.x

pandas

yfinance

将所有代码下载到具有单级列的单个数据框中 headers

选项 1

选项 2

读取 yfinance csv 已存储 multi-level 列名称

将 multi-level 列展平为一个级别并添加代码列

下载每个代码并将其保存到单独的文件

读入上一节保存的多个文件并创建单个数据帧

读取 `yfinance` csv 已存储 multi-level 列名称