title: Specifying date format when converting with pandas.to_datetime
body: <p>I have data in a csv file with dates stored as strings in a standard UK format - <code>%d/%m/%Y</code> - meaning they look like:</p>
  
  <pre><code>12/01/2012
  30/01/2012
  </code></pre>
  
  <p>The examples above represent 12 January 2012 and 30 January 2012.</p>
  
  <p>When I import this data with pandas version 0.11.0 I applied the following transformation:</p>
  
  <pre><code>import pandas as pd
  ...
  cpts.Date = cpts.Date.apply(pd.to_datetime)
  </code></pre>
  
  <p>but it converted dates inconsistently. To use my existing example, 12/01/2012 would convert as a datetime object representing 1 December 2012 but 30/01/2012 converts as 30 January 2012, which is what I want.</p>
  
  <p>After looking at <a href="https://stackoverflow.com/questions/15929861/pandas-to-datetime-inconsistent-time-string-format">this question</a> I tried:</p>
  
  <pre><code>cpts.Date = cpts.Date.apply(pd.to_datetime, format='%d/%m/%Y')
  </code></pre>
  
  <p>but the results are exactly the same. The <a href="https://github.com/pydata/pandas/blob/v0.11.0/pandas/tseries/tools.py" rel="noreferrer">source code</a> suggests I'm doing things right so I'm at a loss. Does anyone know what I'm doing wrong?</p>