title: Pandas read_csv not recognizing ISO8601 as datetime dtype
body: <p>Currently I am using pandas to read a csv file into a <code>DataFrame</code>, using the first column as the index. The first column is in ISO 8601 format, so according to the documentation for <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.io.parsers.read_csv.html" rel="noreferrer">read_csv</a>, it should be recognized as a datetime:</p>
  
  <pre><code>In [1]: import pandas as pd
  
  In [2]: df = pd.read_csv('data.csv', index_col=0)
  
  In [3]: print df.head()
                          U     V     Z    Ubar    Udir
  2014-11-01 00:00:00  0.73 -0.81  0.46  1.0904  317.97
  2014-11-01 01:00:00  1.26 -1.50  0.32  1.9590  319.97
  2014-11-01 02:00:00  1.50 -1.80  0.13  2.3431  320.19
  2014-11-01 03:00:00  1.39 -1.65  0.03  2.1575  319.89
  2014-11-01 04:00:00  0.94 -1.08 -0.03  1.4318  318.96
  </code></pre>
  
  <p>However, when querying the index dtype, it returns 'object':</p>
  
  <pre><code>In [4]: print df.index.dtype
  object
  </code></pre>
  
  <p>I then have to manually convert it to datetime dtype:</p>
  
  <pre><code>In [5]: df.index = pd.to_datetime(df.index)
  
  In [6]: print df.index.dtype
  datetime64[ns]
  </code></pre>
  
  <p>Is there any way to automatically have the index set to datetime dtype when calling <code>read_csv()</code>?</p>