title: Pandas time series index from list of dictionaries
body: <p>I'm trying to use the pandas library for time series analysis in python.
  My data is now stored as a list of dictionaries:</p>
  
  <pre><code>mydata = [
  {
      'date': datetime.date(2013, 1, 1),
      'snow_depth': 1.0,
  }, {
      'date': datetime.date(2013, 1, 2),
      'snow_depth': 2.5,
  }, {
      'date': datetime.date(2013, 1, 3),
      'snow_depth': 8.0,
  },
  ]
  </code></pre>
  
  <p>I use the following command to get a DataFrame:</p>
  
  <pre><code>df = pd.DataFrame(mydata).set_index('date')
  </code></pre>
  
  <p>But the index is not recognized as a DateTimeIndex, but only as an object:</p>
  
  <pre><code>df.index
  </code></pre>
  
  <p>returns: <code>Index([2013-01-01, 2013-01-02, 2013-01-03], dtype='object')
  </code></p>
  
  <p>So, I can't do some of the time series operations in Pandas, like aggregate by month and so on. When I run <code>df.index</code> I'm expecting to get something like: </p>
  
  <pre><code>&lt;class 'pandas.tseries.index.DatetimeIndex'&gt;
  [2013-01-01, ..., 2013-01-03]
  Length: 3, Freq: D, Timezone: None
  </code></pre>
  
  <p>How do I create the DataFrame from the list when I require the index to be a <code>DateTimeIndex</code>?</p>