set two columns as the index in a pandas dataframe for time series analysis

Question

In the case of weather or stock market data, temperatures and stock prices are both measured at multiple stations or stock tickers for any given date.

Therefore what is the most effective way to set an index which contains two fields?

For weather: the weather_station and then Date

For Stock Data: the stock_code and then Date

Setting the index in this way would allow filtering such as:

• stock_df["code"]["start_date":"end_date"]
• weather_df["station"]["start_date":"end_date"]

Answer 1

As mentioned by Anton you need to use MultiIndex as follows:

stock_df.index = pd.MultiIndex.from_arrays(stock_df[['code', 'date']].values.T, names=['idx1', 'idx2'])

weather_df.index = pd.MultiIndex.from_arrays(weather_df[['station', 'date']].values.T, names=['idx1', 'idx2'])

Answer 2

That functionality currently exists. Please refer to the documentation for more examples.

stock_df = pd.DataFrame({'symbol': ['AAPL', 'AAPL', 'F', 'F', 'F'], 
                         'date': ['2016-1-1', '2016-1-2', '2016-1-1', '2016-1-2', '2016-1-3'], 
                         'price': [100., 101, 50, 47.5, 49]}).set_index(['symbol', 'date'])

>>> stock_df
                 price
symbol date           
AAPL   2016-1-1  100.0
       2016-1-2  101.0
F      2016-1-1   50.0
       2016-1-2   47.5
       2016-1-3   49.0

>>> stock_df.loc['AAPL']
          price
date           
2016-1-1    100
2016-1-2    101

>>> stock_df.loc['AAPL', '2016-1-2']
price    101
Name: (AAPL, 2016-1-2), dtype: float64