title: pandas - Use datetime.time objects as a dtype
body: <p>I am reading an Excel file that has the following structure:</p>
  
  <pre><code>    A               B 
  2015-09-05       15:05:32
  2015-09-05       19:05:02
  </code></pre>
  
  <p>I am reading this file using</p>
  
  <pre><code>df = pd.ExcelFile(filename).parse(..)
  </code></pre>
  
  <p>When I look at the <code>dtype</code>, of this DataFrame, I can see that the dates are parsed properly as <code>datetime64</code> objects, but the times are not:</p>
  
  <pre><code>&gt;&gt;&gt; df.dtypes
       A         datetime64[ns]
       B         object
  </code></pre>
  
  <p>What's odd is that, when I look at the content of the <code>B</code>, I can see that they are all <code>datetime.time</code> objects</p>
  
  <pre><code>[s for s in main_df['B'].tolist() if type(s) is not datetime.time]
  # There are no values that are *not* datetime.time objects
  </code></pre>
  
  <p>I'd like to convert this <code>B</code> column to something that I can use more readily. For instance, I'd like to use a <code>MultiIndex</code> with first the day, and then the time (so that I can group and aggregate). Or I'd like to join the two so that I have a single column that's the full date.</p>
  
  <p>But at this point, I'm stuck. I tried converting them to <code>datetime</code>:</p>
  
  <pre><code>main_df['B'] = main_df['B'].astype('datetime64')
  ValueError: Could not convert object to NumPy datetime
  </code></pre>
  
  <p>Any ideas?</p>