title: Access Pandas Data Frame row with index value
body: <p>I have a very simple Pandas Data Frame with one index (of type <code>TimedeltaIndex</code>) and one column named <code>TotalVolume</code>.</p>
  
  <pre><code>&gt;&gt; print(df)
          TotalVolume
  ...
  09:00:00  143846.153846
  09:05:00   84353.846154
  09:10:00   46946.153846
  09:15:00   46765.384615
  09:20:00   53076.923077
  09:25:00   31642.307692
  09:30:00   48269.230769
  ...
  </code></pre>
  
  <p>I would like to be able to query this dictionary with <code>09:00:00</code> for example and get <code>143846.153846</code>. For information this is the structure of the index:</p>
  
  <pre><code>&gt;&gt; print(df.index)
  TimedeltaIndex(['07:00:00', '07:05:00', '07:10:00', '07:15:00', '07:20:00', '07:25:00', '07:30:00', '07:35:00', '07:40:00', '07:45:00', '07:50:00', '07:55:00', '08:00:00', '08:05:00', '08:10:00', '08:15:00', '08:20:00', '08:25:00', '08:30:00', '08:35:00', '08:40:00', '08:45:00', '08:50:00', '08:55:00', '09:00:00', '09:05:00', '09:10:00', '09:15:00', '09:20:00', '09:25:00', '09:30:00', '09:35:00', '09:40:00', '09:45:00', '09:50:00', '09:55:00', '10:00:00', '10:05:00', '10:10:00', '10:15:00', '10:20:00', '10:25:00', '10:30:00', '10:35:00', '10:40:00', '10:45:00', '10:50:00', '10:55:00', '11:00:00', '11:05:00', '11:10:00', '11:15:00', '11:20:00', '11:25:00', '11:30:00', '11:35:00', '11:40:00', '11:45:00', '11:50:00', '11:55:00', '12:00:00', '12:05:00', '12:10:00', '12:15:00', '12:20:00', '12:25:00', '12:30:00', '12:35:00', '12:40:00', '12:45:00', '12:50:00', '12:55:00', '13:00:00', '13:05:00', '13:10:00', '13:15:00', '13:20:00', '13:25:00', '13:30:00', '13:35:00', '13:40:00', '13:45:00',
              '13:50:00', '13:55:00', '14:00:00', '14:05:00', '14:10:00', '14:15:00', '14:20:00', '14:25:00', '14:30:00', '14:35:00', '14:40:00', '14:45:00', '14:50:00', '14:55:00', '15:00:00'],
             dtype='timedelta64[ns]', freq=None)
  </code></pre>
  
  <p>When I do,</p>
  
  <pre><code>print(df['09:00:00'])
  </code></pre>
  
  <p>I have</p>
  
  <pre><code>        TotalVolume
   09:00:00  143846.153846
   09:05:00   84353.846154
   09:10:00   46946.153846
   09:15:00   46765.384615
   09:20:00   53076.923077
   09:25:00   31642.307692
   09:30:00   48269.230769
   09:35:00   35715.384615
   09:40:00   38576.923077
   09:45:00   37211.538462
   09:50:00   41803.846154
   09:55:00   37503.846154
  </code></pre>
  
  <p>It seems like the filter is not working as I would like. It works correctly for <code>09:05:00</code> though.</p>
  
  <p>What is the most pandatonic way to do it?</p>