title: Python regularise irregular time series with linear interpolation
body: <p>I have a time series in pandas that looks like this:</p>
  
  <pre><code>                     Values
  1992-08-27 07:46:48    28.0  
  1992-08-27 08:00:48    28.2  
  1992-08-27 08:33:48    28.4  
  1992-08-27 08:43:48    28.8  
  1992-08-27 08:48:48    29.0  
  1992-08-27 08:51:48    29.2  
  1992-08-27 08:53:48    29.6  
  1992-08-27 08:56:48    29.8  
  1992-08-27 09:03:48    30.0
  </code></pre>
  
  <p>I would like to resample it to a regular time series with 15 min times steps where the values are linearly interpolated. Basically I would like to get:</p>
  
  <pre><code>                     Values
  1992-08-27 08:00:00    28.2  
  1992-08-27 08:15:00    28.3  
  1992-08-27 08:30:00    28.4  
  1992-08-27 08:45:00    28.8  
  1992-08-27 09:00:00    29.9
  </code></pre>
  
  <p>However using the resample method (df.resample('15Min')) from Pandas I get:</p>
  
  <pre><code>                     Values
  1992-08-27 08:00:00   28.20  
  1992-08-27 08:15:00     NaN  
  1992-08-27 08:30:00   28.60  
  1992-08-27 08:45:00   29.40  
  1992-08-27 09:00:00   30.00  
  </code></pre>
  
  <p>I have tried the resample method with different 'how' and 'fill_method' parameters but never got exactly the results I wanted. Am I using the wrong method?</p>
  
  <p>I figure this is a fairly simple query, but I have searched the web for a while and couldn't find an answer.</p>
  
  <p>Thanks in advance for any help I can get.</p>