title: Convert incomplete 12h datetime-like strings into appropriate datetime type
body: <p>I've got a pandas Series containing datetime-like strings with 12h format, but without the <em>am/pm</em> abbreviations. It covers an entire month of data :</p>
  
  <pre><code>40    01/01/2017 11:51:00
  41    01/01/2017 11:51:05
  42    01/01/2017 11:55:05
  43    01/01/2017 11:55:10
  44    01/01/2017 11:59:30
  45    01/01/2017 11:59:35
  46    02/01/2017 12:00:05
  47    02/01/2017 12:00:10
  48    02/01/2017 12:13:20
  49    02/01/2017 12:13:25
  50    02/01/2017 12:24:50
  51    02/01/2017 12:24:55
  52    02/01/2017 12:33:30
  Name: TS, dtype: object
  (318621,) # shape
  </code></pre>
  
  <p>My goal is to convert it to datetime format, so as to obtain the appropriate unix timestamps values, and make comparisions/arithmetics with other datetime data with, this time, 24h format. So I already tried this :</p>
  
  <pre><code>pd.to_datetime(df.TS, format = '%d/%m/%Y %I:%M:%S') # %I for 12h format
  </code></pre>
  
  <p>Which outputs me :</p>
  
  <pre><code>64     2017-01-02 00:46:50
  65     2017-01-02 00:46:55
  66     2017-01-02 01:01:00
  67     2017-01-02 01:01:05
  68     2017-01-02 01:05:00
  </code></pre>
  
  <p>But the am/pm informations are not taken into account. I know that, as a rule, the am/pm first have to be specified in the strings, then one can use <em>dt.dt.strptime()</em> or <em>pd.to_datetime()</em> to parse them with the %p indicator. </p>
  
  <p>So I wanted to know if there's an other way to deal with this issue through <em>datetime</em> or <em>pandas</em> <em>datetime</em> modules ? Or, do I have to manualy add the abbreviations 'am/pm' before the parsing ?</p>