title: Pandas: Generate Sequential Timestamp with Jump
body: <p>I have a df with the following index</p>
  
  <pre><code>df.index
  &gt;&gt;&gt; [2010-01-04 10:00:00, ..., 2010-12-31 16:00:00]
  </code></pre>
  
  <p>The main column is <code>volume</code>. </p>
  
  <p>In the timestamp sequence, weekends and some other weekdays are not present. I want to resample my time index to have the aggregate sum of volume per minute. So I do the following:</p>
  
  <p><code>df = df.resample('60S', how=sum)</code></p>
  
  <p>There are some missing minutes. In other words, there are minutes where there are no trades. I want to include these missing minutes and add a 0 to the column <code>volume</code>. 
  To solve this, I would usually do something like:</p>
  
  <pre><code>new_range = pd.date_range('20110104 09:30:00','20111231 16:00:00',
                            freq='60s')+df.index
  df = df.reindex(new_range)
  df = df.between_time(start_time='10:00', end_time='16:00') # time interval per day that I want
  df = df.fillna(0)
  </code></pre>
  
  <p>But now I am stuck with unwanted dates like the weekends and some other days. How can I get rid of the dates that were not originally in my timestamp index? </p>