8

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'
4

4 回答 4

23

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])
于 2020-07-27T02:18:54.753 回答
1

把它变成一个字典d[ticker]=df：

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)}
于 2021-04-07T16:09:57.793 回答
0

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“关闭”列，因此第一步将丢弃其他列：

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

这很好，但每列都有一个多索引，看起来像 (Close/AAPL) 或 (Close/MSFT) 等。你真正想要的只是股票代码。

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执行此操作。

separated = [df.iloc[:,i] for i in range(len(df.columns))]
于 2021-03-15T16:12:50.313 回答
0

使用下面的行来写入和读取 csv。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

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

于 2021-10-28T18:11:05.823 回答