title: How to get DataFrame.pct_change to calculate monthly change on daily price data?
body: <p>I know that it is possible to offset with the <code>periods</code> argument, but how would one go about return-izing daily price data that is spread throughout a month (trading days, for example)?</p>
  
  <p>Example data is:</p>
  
  <pre><code>In [1]: df.AAPL
  2009-01-02 16:00:00    90.36
  2009-01-05 16:00:00    94.18
  2009-01-06 16:00:00    92.62
  2009-01-07 16:00:00    90.62
  2009-01-08 16:00:00    92.30
  2009-01-09 16:00:00    90.19
  2009-01-12 16:00:00    88.28
  2009-01-13 16:00:00    87.34
  2009-01-14 16:00:00    84.97
  2009-01-15 16:00:00    83.02
  2009-01-16 16:00:00    81.98
  2009-01-20 16:00:00    77.87
  2009-01-21 16:00:00    82.48
  2009-01-22 16:00:00    87.98
  2009-01-23 16:00:00    87.98
  ...
  2009-12-10 16:00:00    195.59
  2009-12-11 16:00:00    193.84
  2009-12-14 16:00:00    196.14
  2009-12-15 16:00:00    193.34
  2009-12-16 16:00:00    194.20
  2009-12-17 16:00:00    191.04
  2009-12-18 16:00:00    194.59
  2009-12-21 16:00:00    197.38
  2009-12-22 16:00:00    199.50
  2009-12-23 16:00:00    201.24
  2009-12-24 16:00:00    208.15
  2009-12-28 16:00:00    210.71
  2009-12-29 16:00:00    208.21
  2009-12-30 16:00:00    210.74
  2009-12-31 16:00:00    209.83
  Name: AAPL, Length: 252
  </code></pre>
  
  <p>As you can see, simply offsetting by 30 would not produce correct results, as there are gaps in the timestamp data, not every month is 30 days, etc.  I know there must be an easy way to do this using pandas.</p>