title: Converting different date time formats to MM/DD/YYYY format in pandas dataframe
body: <p>I have a date column in a <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html" rel="nofollow noreferrer"><code>pandas.DataFrame</code></a> in various date time formats and stored as list object, like the following:</p>
  
  <pre><code>            date
  1    [May 23rd, 2011]
  2    [January 1st, 2010]
      ...
  99   [Apr. 15, 2008]
  100  [07-11-2013]
      ...
  256  [9/01/1995]
  257  [04/15/2000]
  258  [11/22/68]
      ...
  360  [12/1997]
  361  [08/2002]
       ...
  463  [2014]
  464  [2016]
  </code></pre>
  
  <p>For the sake of convenience, I want to convert them all to <code>MM/DD/YYYY</code> format. It doesn't seem possible to use regex replace() function to do this, since one cannot execute this operation over list objects. Also, to use strptime() for each cell will be too time-consuming. </p>
  
  <p>What will be the easier way to convert them all to the desired <code>MM/DD/YYYY</code> format? I found it very hard to do this on list objects within a dataframe.</p>
  
  <p>Note: for cell values of the form <code>[YYYY]</code> (e.g., <code>[2014]</code> and <code>[2016]</code>), I will assume they are the first day of that year (i.e., January 1, 1968) and for cell values such as <code>[08/2002]</code> (or <code>[8/2002]</code>), I will assume they the first day of the month of that year (i.e., August 1, 2002).</p>