title: Resample only first two bars of every day in a pandas time series dataframe
body: <p>I've got a time series data-set that the first two bars of everyday should be re-sample as one.</p>
  
  <p>Since this is not a regular re-sample request (resample just two bar in particular time, 09:15:00 and 09:16:00 only), any ideas can solve this problem?</p>
  
  <pre class="lang-py prettyprint-override"><code>Datetime            Open    High    Low Close   Vol
  2019-07-26 09:15:00 28397.0 28397.0 28397.0 28397.0 205.0
  2019-07-26 09:16:00 28393.0 28418.0 28382.0 28414.0 2083.0
  2019-07-26 09:17:00 28414.0 28423.0 28393.0 28402.0 988.0
  2019-07-26 09:18:00 28401.0 28401.0 28382.0 28386.0 1138.0
  2019-07-26 09:19:00 28386.0 28400.0 28374.0 28399.0 623.0
  2019-07-26 09:20:00 28398.0 28398.0 28378.0 28381.0 426.0
  .
  .
  .
  2019-07-29 09:15:00 28265.0 28265.0 28265.0 28265.0 197.0
  2019-07-29 09:16:00 28264.0 28285.0 28238.0 28265.0 1737.0
  2019-07-29 09:17:00 28265.0 28274.0 28258.0 28259.0 841.0
  2019-07-29 09:18:00 28258.0 28314.0 28257.0 28312.0 1065.0
  2019-07-29 09:19:00 28313.0 28317.0 28294.0 28295.0 1066.0
  2019-07-29 09:20:00 28294.0 28297.0 28286.0 28287.0 325.0
  </code></pre>
  
  <p>Using for-loop may be the last resolve, since iterating dataframe is quite slow.</p>
  
  <p>Expected outcome:</p>
  
  <pre class="lang-py prettyprint-override"><code>Datetime            Open    High    Low Close   Vol
  2019-07-26 09:16:00 28397.0 28418.0 28382.0 28414.0 2288.0
  2019-07-26 09:17:00 28414.0 28423.0 28393.0 28402.0 988.0
  2019-07-26 09:18:00 28401.0 28401.0 28382.0 28386.0 1138.0
  2019-07-26 09:19:00 28386.0 28400.0 28374.0 28399.0 623.0
  2019-07-26 09:20:00 28398.0 28398.0 28378.0 28381.0 426.0
  .
  .
  .
  2019-07-29 09:16:00 28265.0 28285.0 28238.0 28265.0 1934.0
  2019-07-29 09:17:00 28265.0 28274.0 28258.0 28259.0 841.0
  2019-07-29 09:18:00 28258.0 28314.0 28257.0 28312.0 1065.0
  2019-07-29 09:19:00 28313.0 28317.0 28294.0 28295.0 1066.0
  2019-07-29 09:20:00 28294.0 28297.0 28286.0 28287.0 325.0
  </code></pre>