title: Pandas read_csv parse_dates=true missing out date column
body: <p>I have a CSV file (example below), that I'm trying to load into a dataframe and have pandas automatically parse the dates.</p>
  
  <pre><code>"http://www.example.com","http://example.com","test",2016-06-16,2016-02-21,4
  </code></pre>
  
  <p>When I load this file specifying the columns to be parsed, they are successfully loaded as datetimes:</p>
  
  <pre><code>df = pd.read_csv(inputfile, parse_dates=[3,4])
  </code></pre>
  
  <p>However I don't know that these dates will always be columns 3 &amp; 4, so I wanted it to attempt to parse each column and see if it's a date, my understanding from the <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow noreferrer">pandas docs</a>, was this is accomplished by:</p>
  
  <pre><code>df = pd.read_csv(inputfile, parse_dates=True)
  </code></pre>
  
  <p>However this loads columns 3 &amp; 4 as objects. Presumably I have misunderstood this. Is there a correct way to do this? Do I need to load the dataframe and then try to convert each column to a date?</p>
  
  <p>(I'm running Canopy with Python 2.7.11 --  64-bit on Windows 10)</p>