title: Removing decimal seconds from time format data in DataFrame
body: <p>I've got two large df's from two xlsx spreadsheets and would like to merge them 'on' time stamp ['Time'] data. </p>
  
  <p>The problem is that one data set has recorded time to decimal seconds and the other to whole seconds. This means that the two data set ['Time'] keys never match up... </p>
  
  <pre><code>df1     Time                                 df2     Time
  0       00:07:53.755000                      0       00:07:53
  1       00:07:54.096000                      1       00:07:54
  2       00:07:55.097000                      2       00:07:55
  3       00:07:56.099000                      3       00:07:56
  4       00:07:57.002000                      4       00:07:57
  5       00:07:58.012000                      5       00:07:58
  </code></pre>
  
  <p>I've tried modifying time formats in Excel but it always retains the millisecond value because of its fractional 24 hour thing. I need to remove the decimal seconds from df1 (or just get them to match somehow!) to allow matching with row data from df2 and am hoping there is a much simpler way to do this in python?</p>
  
  <p>Thanks to any and all advice!</p>