title: Pandas: convert datetimeindex to periodindex?
body: <p>I have a bunch of data from users logging events, and the data is of type 0, 1, or 2. These all hence form irregular time series. I have brought them into <code>pandas Series</code>, one for each data type, with a data time index. I have also resampled with <code>ffill</code> and applying <code>asfreq</code> to my data types. </p>
  
  <p>However, I would like to be able to calculate cross correlation functions and run regressions. It seems like the easiest way to get there would be to convert my Series indices to <code>period indices</code> so that even if times are not exactly the same, they will be in the same period and so will share an index. <strong>How can I convert my date time indices to period indices?</strong></p>
  
  <p>Here's what my series look like:</p>
  
  <p>Series 0:</p>
  
  <pre><code>2014-10-30 19:27:04.330    160
  2014-10-30 22:10:32.682     88
  2014-10-31 00:43:43.806    128
  2014-10-31 03:40:07.164     89
  2014-10-31 11:51:03.867     66
  2014-10-31 17:58:01.835     84
  2014-10-31 20:56:20.770    104
  2014-10-31 21:58:41.153     82
  2014-10-31 23:26:51.695     95
  dtype: float64
  </code></pre>
  
  <p>Series 1:</p>
  
  <pre><code>2016-02-06 00:11:00.910     50
  2016-02-07 05:06:21.677     30
  2016-02-07 21:26:05.312     26
  2016-02-10 18:14:03.762    100
  2016-02-11 00:56:34.635     20
  2016-02-12 03:20:44.296     15
  2016-02-12 18:41:54.061     90
  2016-02-13 17:08:00.000     25
  2016-02-13 19:26:01.988     40
  dtype: float64
  </code></pre>
  
  <p>Series 2 looks similar. Series 0 starts substantially earlier than the others, but all three do have overlapping dates.</p>
  
  <p>Thanks for suggestions!</p>