Flatten a Multi-Index DataFrame

Resource

Whilst a multi-index datafame can be useful for complex data orginisation, for example across multiple markets and time periods. Flattening the data has it’s own advantages; making for easier data manipulation, merging with other datasets and visulising. Here is a quick example.

# Import
import pandas as pd
import yfinance as yf

# Fetch data
#S&P 500 Vanguard - VOO
#S&P 500 Black Rock - BSPIX
tickers = ['VOO','VTWO']
df = yf.download(tickers,  start='2019-01-01', end='2024-10-11')

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print(df.columns)

MultiIndex([('Adj Close',  'VOO'),
            ('Adj Close', 'VTWO'),
            (    'Close',  'VOO'),
            (    'Close', 'VTWO'),
            (     'High',  'VOO'),
            (     'High', 'VTWO'),
            (      'Low',  'VOO'),
            (      'Low', 'VTWO'),
            (     'Open',  'VOO'),
            (     'Open', 'VTWO'),
            (   'Volume',  'VOO'),
            (   'Volume', 'VTWO')],
           names=['Price', 'Ticker'])

# Completely flatten the DataFrame
df_flattened = df.stack(future_stack=True).reset_index()

print(df_flattened.columns)

Index(['Date', 'Ticker', 'Adj Close', 'Close', 'High', 'Low', 'Open',
       'Volume'],
      dtype='object', name='Price')