title: pd.read_csv not correctly parsing date/month field when set parse_date = ['column name']
body: <p>I ran in to this bug while trying to parse the few dates through parse_dates of <code>pandas.read_csv()</code>. In the following code snippet, I'm trying to parse dates that have format <code>dd/mm/yy</code> which is resulting me an improper conversion. For some cases, the date field is considered as month and vice versa.</p>
  
  <p>To keep it simple, for some cases <code>dd/mm/yy</code> get converted to <code>yyyy-dd-mm</code> instead of <code>yyyy-mm-dd</code>.</p>
  
  <p>Case 1:</p>
  
  <pre><code>  04/10/96 is parsed as 1996-04-10, which is wrong.
  </code></pre>
  
  <p>Case 2:</p>
  
  <pre><code>  15/07/97 is parsed as 1997-07-15, which is correct.
  </code></pre>
  
  <p>Case 3:</p>
  
  <pre><code>  10/12/97 is parsed as 1997-10-12, which is wrong.
  </code></pre>
  
  <p>Code Sample</p>
  
  <pre><code>import pandas as pd
  
  df = pd.read_csv('date_time.csv') 
  print 'Data in csv:'
  print df
  print df['start_date'].dtypes
  
  print '----------------------------------------------'
  
  df = pd.read_csv('date_time.csv', parse_dates = ['start_date'])
  print 'Data after parsing:'
  print df
  print df['start_date'].dtypes
  </code></pre>
  
  <p>Current Output</p>
  
  <pre><code>----------------------
  Data in csv:
  ----------------------
    start_date
  0   04/10/96
  1   15/07/97
  2   10/12/97
  3   06/03/99
  4     //1994
  5   /02/1967
  object
  ----------------------
  Data after parsing:
  ----------------------
     start_date
  0 1996-04-10
  1 1997-07-15
  2 1997-10-12
  3 1999-06-03
  4 1994-01-01
  5 1967-02-01
  datetime64[ns]
  </code></pre>
  
  <p>Expected Output</p>
  
  <pre><code>----------------------
  Data in csv:
  ----------------------
     start_date
  0   04/10/96
  1   15/07/97
  2   10/12/97
  3   06/03/99
  4     //1994
  5   /02/1967
  object
  ----------------------
  Data after parsing:
  ----------------------
    start_date
  
  0 1996-10-04
  1 1997-07-15
  2 1997-12-10
  3 1999-03-06
  4 1994-01-01
  5 1967-02-01
  datetime64[ns]
  </code></pre>
  
  <p>More Comments:</p>
  
  <p>I could use <code>date_parser</code> or <code>pandas.to_datetime()</code> to specify the proper format for date. But in my case, I have few date fields like <code>['//1997', '/02/1967']</code> for which I need to convert <code>['01/01/1997','01/02/1967']</code>. The <code>parse_dates</code> helps me in converting those type of date fields to the expected format without making me to write extra line of code.</p>
  
  <p>Is there any solution for this?</p>
  
  <p>Bug Link @GitHub: <a href="https://github.com/pydata/pandas/issues/13063" rel="nofollow">https://github.com/pydata/pandas/issues/13063</a></p>