title: Finding skipped index time steps and filling the values in a Pandas DataFrame
body: <p>I am working with a pandas <code>DataFrame</code> that has an index that skips one or more time steps which in my case is one or more hours. I want to know if there is a way to find these time step skips and possibly insert these missing time steps.</p>
  
  <p>Example of what I have:</p>
  
  <pre class="lang-py prettyprint-override"><code>[In]: df
  [Out]:
                      point_value
  Timestamp   
  2016-01-01 00:00:00 2550.63
  2016-01-01 01:00:00 2535.97
  2016-01-01 02:00:00 2538.25
  2016-01-01 04:00:00 2548.63
  2016-01-01 05:00:00 2555.16
  </code></pre>
  
  <p>Example of what I am looking for:</p>
  
  <pre class="lang-py prettyprint-override"><code>[In]: df
  [Out]:
                      point_value
  Timestamp   
  2016-01-01 02:00:00 2538.25
  2016-01-01 04:00:00 2548.63
  
  </code></pre>
  
  <p>Ideally after finding these time step gaps I'd want to fill them with the time steps missing as such:</p>
  
  <pre class="lang-py prettyprint-override"><code>[In]: df
  [Out]:
                      point_value
  Timestamp   
  2016-01-01 00:00:00 2550.63
  2016-01-01 01:00:00 2535.97
  2016-01-01 02:00:00 2538.25
  2016-01-01 03:00:00 NaN
  2016-01-01 04:00:00 2548.63
  2016-01-01 05:00:00 2555.16
  </code></pre>
  
  <p>I have searched on stack overflow and can't seem to find something that pertains to the index itself. If this is a duplicated question then I will be happy to take it down and find the result.  Thanks for the help in advance.</p>