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

Question

I have a list of tickers (tickerStrings) that I have to download all at once. When I try to use Pandas' read_csv it doesn't read the CSV file in the way it does when I download the data from yfinance.

I usually access my data by ticker like this: data['AAPL'] or data['AAPL'].Close, but when I read the data from the CSV file it does not let me do that.

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)

Here's the print output:

                  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

The error I get when trying to access the data:

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

Download all tickers into single dataframe with single level column headers

Option 1

• When downloading single stock ticker data, the returned dataframe column names are a single level, but don't have a ticker column.
• This will download data for each ticker, add a ticker column, and create a single dataframe from all desired tickers.

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')

Option 2

• Download all the tickers and unstack the levels
  • group_by='Ticker' puts the ticker at level=0 of the column name

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)

---

Read yfinance csv already stored with multi-level column names

• If you wish to keep, and read in a file with a multi-level column index, use the following code, which will return the dataframe to its original form.

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

• The issue is, tickerStrings is a list of tickers, which results in a final dataframe with multi-level column names

                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

• When this is saved to a csv, it looks like the following example, and results in a dataframe like you're having issues with.

,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,,,,,,

---

Flatten multi-level columns into a single level and add a ticker column

• If the ticker symbol is level=0 (top) of the column names
  • When group_by='Ticker' is used

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

• If the ticker symbol is level=1 (bottom) of the column names

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

---

Download each ticker and save it to a separate file

• I recommend downloading and saving each ticker individually, which would look something like the following:

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 will look like

                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

• the resulting csv will look like

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

Read in multiple files saved with the previous section and create a single dataframe

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

To turn it into a dict of 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)}