Pandas Inconsistent date-time format
<p>I started using pandas library about a fortnight back. Learning the new features. I would appreciate help on the following problem.</p>
<p>I have a column with dates in mixed format. These are the 2 formats present</p>
<ol>
<li><code>mm/dd/yyyy</code></li>
<li><code>dd/mm/yyyy</code></li>
</ol>
<p>An extract from the dataset :-</p>
<pre><code>Dates  
6/5/2016  
7/5/2016  
7/5/2016  
7/5/2016  
9/5/2016  
9/5/2016  
9/5/2016  
9/5/2016  
5/13/2016  
5/14/2016  
5/14/2016  
</code></pre>
<p>I am struggling to convert these to a common format. I tried using pandas's 'to_datetime'. It does not work.
I am also not sure how will using regular expressions help in this case.</p>
<p>Another piece of information. The dates are in sorted order. Can something be done using info.</p>
<h3>EDIT1:</h3>
<p>I understand that it is impossible to distinguish between 6/4/2016 and 5/6/2016, if we look at them alone. However, I was hoping that the fact the dates are in ascending order and the actual dataset is spread for over a year, there would be a method to make sense of it. Is anyone aware of a function which can make sense of the format given the fact that the dates are in ascending order ?</p>
<p>EDIT2:
Sample of 2 months :- April and May 2016. Please note that there is no pattern. So please do not suggest any solution based on patterns of the data below.</p>
<blockquote>
<p>4/1/2016
4/1/2016
4/3/2016
4/3/2016
3/4/2016
4/4/2016
4/4/2016
4/5/2016
4/5/2016
4/7/2016
4/7/2016
4/8/2016
4/8/2016
4/14/2016
4/16/2016
6/4/2016
7/4/2016
8/4/2016
11/4/2016
11/4/2016
11/4/2016
11/4/2016
11/4/2016
12/4/2016
12/4/2016
12/4/2016
13/4/2016
13/4/2016
13/4/2016
13/4/2016
14/04/2016
15/4/2016
16/4/2016
16/4/2016
18/4/2016
18/4/2016
19/4/2016
19/4/2016
20/4/2016
20/4/2016
21/4/2016
21/4/2016
21/4/2016
22/4/2016
23/4/2016
23/4/2016
25/4/2016
25/4/2016
26/4/2016
26/4/2016
26/4/2016
26/4/2016
26/4/2016
26/4/2016
29/4/2016
29/4/2016
29/4/2016
30/4/2016
2/5/2016
2/5/2016
3/5/2016
3/5/2016
3/5/2016
3/5/2016
4/5/2016
5/4/2016
5/4/2016
5/4/2016
6/5/2016
6/5/2016
7/5/2016
7/5/2016
7/5/2016
9/5/2016
9/5/2016
9/5/2016
9/5/2016
10/5/2016
10/5/2016
11/5/2016
11/5/2016
12/5/2016
5/13/2016
5/14/2016
5/14/2016
5/15/2016
5/16/2016
5/16/2016
5/16/2016
5/16/2016
5/16/2016
5/16/2016
5/16/2016
5/17/2016
5/17/2016
5/18/2016
5/18/2016
5/19/2016
5/19/2016
5/20/2016
5/20/2016
5/20/2016
5/20/2016
5/20/2016
5/21/2016
5/23/2016
5/23/2016
5/23/2016
5/23/2016
5/23/2016
5/23/2016
5/24/2016
5/24/2016
5/25/2016
5/26/2016
5/26/2016
5/26/2016
5/27/2016
5/27/2016
5/27/2016
5/27/2016
5/27/2016
5/27/2016
5/27/2016
5/28/2016
5/30/2016
5/30/2016</p>
</blockquote>