title: How to get pandas.read_csv() to infer datetime and timedelta types from CSV file columns?
body: <p><a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html" rel="noreferrer">pandas.read_csv()</a> infers the types of columns, but I can't get it to infer any datetime or timedelta type (e.g. <code>datetime64</code>, <code>timedelta64</code>) for columns whose values seem like obvious datetimes and time deltas.</p>
  
  <p>Here's an example CSV file:</p>
  
  <pre><code>datetime,timedelta,integer,number,boolean,string
  20111230 00:00:00,one hour,10,1.6,True,Foobar
  </code></pre>
  
  <p>And some code to read it with pandas:</p>
  
  <pre><code>dataframe = pandas.read_csv(path)
  </code></pre>
  
  <p>The types of the columns on that dataframe come out as object, object, int, float, bool, object. They're all as I would expect except the first two columns, which I want to be datetime and timedelta.</p>
  
  <p>Is it possible to get pandas to automatically detect datetime and timedelta columns?</p>
  
  <p>(I don't want to have to tell pandas which columns are datetimes and timedeltas or tell it the formats, I want it to try and detect them automatically like it does for into, float and bool columns.)</p>