title: Pandas: Standardize irregular time intervals
body: <p>I'm wondering if Pandas has some built-in functionality to take random time intervals (roughly hours) and convert them to standardized hours.  Code example and non-working attempt:</p>
  
  <pre><code>import pandas as pd
  
  df = pd.DataFrame({'start': ['2018-09-04 01:12', '2018-09-04 02:11'], 
                     'end'  : ['2018-09-04 02:10','2018-09-04 03:20'], 
                     'val'  : [500, 600]})[['start','end','val']]
  
  df[['start','end']] = df[['start','end']].apply(pd.to_datetime)
  </code></pre>
  
  <p>Gives us: </p>
  
  <pre><code>           start               end  val
  2018-09-04 01:12  2018-09-04 02:10  500
  2018-09-04 02:11  2018-09-04 03:20  600
  </code></pre>
  
  <p>and:</p>
  
  <pre><code>df = df.resample('1H', on = 'start', ).reset_index()
  </code></pre>
  
  <p>would ideally (but doesn't) yield:</p>
  
  <pre><code>           start               end     val
  2018-09-04 01:00  2018-09-04 01:59  406.78
  2018-09-04 02:00  2018-09-04 02:59  513.22
  2018-09-04 03:00  2018-09-04 03:59  180.00
  </code></pre>
  
  <p>I could code some hack to make this work, but figured Pandas would have some simple function that does this.</p>