title: How to use pandas.to_datetime with "strange" strings formats
body: <p>I'm trying to convert a pandas dataframe's column from string type to datetime, using pandas.to_datetime.
  The col's values have this format:</p>
  
  <blockquote>
    <p>Q1'17</p>
  </blockquote>
  
  <p>where 'Q' is the quarter of the year and '17' is the year 2017.</p>
  
  <p>I also have null's values in that column (I can remove the entire row where the date is empty).</p>
  
  <p>I tried to solve it eliminating the quarter and considering only the year, but I could not convert, for example, 16 to 2016 in this way:</p>
  
  <pre><code>df_cpu["Launch_Date"] = [str(x) for x in df_cpu["Launch_Date"]]
  df_cpu["Launch_Date"] = [x[3:5] for x in df_cpu["Launch_Date"]]
  df_cpu["Launch_Date"] = [int(x) for x in df_cpu["Launch_Date"]]
  df_cpu["Launch_Date"] = pd.to_datetime(df_cpu["Launch_Date"],    format('%Y'))
  </code></pre>
  
  <p>Even this way does not work:</p>
  
  <pre><code>df_cpu["Launch_Date"] = pd.to_datetime(df_cpu["Launch_Date"], format('Q*\'%Y'))
  </code></pre>
  
  <p>Because I obtain this error: </p>
  
  <blockquote>
    <p>ValueError: ('Unknown string format:', "Q3'16")</p>
  </blockquote>
  
  <p>How should I solve this problem?</p>
  
  <p>I'd like to have the following kind of output: if per example the value is "Q1'16" I'd like to have "01-01-2016".</p>