title: Python pandas calculating average price within 15 minute time frame
body: <p>I have a dateframe that contains datetime and price.</p>
  
  <p>Here is a sample chosen at random</p>
  
  <pre><code>In [2]: df
  Out[2]: 
            price           datetime
  239035    5.05 2016-04-14 14:13:27
  1771224   5.67 2016-08-30 14:19:47
  2859140   4.00 2016-12-05 20:57:01
  1311384   7.71 2016-07-08 18:16:22
  141709    4.19 2016-04-07 13:30:00
  2802527   3.94 2016-11-30 15:36:11
  1411955   7.27 2016-07-20 13:55:20
  2215987   4.87 2016-10-07 19:56:13
  </code></pre>
  
  <p>The datetime is accurate to the second.</p>
  
  <p>I want to calculate the average price every 15 minutes starting at 9:00am and ending at 4:30pm, and store the new data into a new dataframe.</p>
  
  <p>I could do it the old fashion way, make a list of all the 15 minute time intervals within 9am-4:30pm for each date, and iterate through each row of the CSV file, check its time and dump it into the appropriate bucket. Then find the average value for each bucket in each day.</p>
  
  <p>But I was wondering if there is a nicer way to do this in panda. if not I'll just brute force my way through it...</p>