title: datetime groupby/resample month and day across different years (drop the year)
body: <p>I have looked at the resample/Timegrouper functionality in Pandas.  However, I'm trying to figure out how to use it for this specific case.  I want to do a seasonal analysis across a financial asset - let's say S&amp;P 500.  I want to know how the asset performs between any two custom dates on average across many years.  </p>
  
  <p>Example:  If I have a 10 year history of daily changes of S&amp;P 500 and I pick the date range between March 13th and March 23rd, then I want to know the average change for each date in my range across the last 10 years - i.e. average change on 3/13 each year for the last 10 years, and then for 3/14, 3/15 and so on until 3/23.  This means I need to groupby month and day and do an average of values across different years.  </p>
  
  <p>I can probably do this by creating 3 different columns for year, month, and day and then grouping by two of them, but I wonder if there are more elegant ways of doing this.</p>