title: pandas time series average monthly volume
body: <p>I have csv time series data of once per day date, and cumulative sale.  Silimar to this</p>
  
  <pre><code>01-01-2010 12:10:10      50.00
  01-02-2010 12:10:10      80.00
  01-03-2010 12:10:10      110.00
  .
  . for each dat of 2010
  .
  01-01-2011 12:10:10      2311.00
  01-02-2011 12:10:10      2345.00
  01-03-2011 12:10:10      2445.00
  .
  . for each dat of 2011
  .
  
  and so on.  
  </code></pre>
  
  <p>I am looking to get the monthly sale (max - min) for each month in each year.   Therefore for past 5 years, I will have 5 Jan values (max - min), 5 Feb values (max - min) ... and so on</p>
  
  <p>once I have those, I next get the (5 years avg) for Jan, 5 years avg for Feb .. and so on.</p>
  
  <p>Right now, I do this by slicing the original df [year/month] and then do the averaging over the specific month of the year.</p>
  
  <p>I am looking to use time series resample() approach, but I am currently stuck at telling PD to sample monthly (max - min) for each month in [past 10 years from today]. and then chain in a .mean()</p>
  
  <p>Any advice on an efficient way to do this with resample() would be appreciated.</p>