Normally, we could use the resample method of a pandas DataFrame. Supposing we wanted to calculate the average monthly return, we could run df.log_rtn.resample('M').mean().

For the resample method, we can use any built-in aggregate functions of pandas, such as mean, sum, min, and max. However, our case is a bit more complex, so we defined a helper function called realized_volatility, and replicated the behavior of resample by using a combination of groupby, Grouper, and apply.

We presented the most basic visualization of the results (please refer to the next recipe for information about visualizing time series).