title: Finding average data per hour based on the day of the week to simulate data for missing days
body: <p>I have a set of hourly data taken from 07-Feb-19 to 17-Feb-19:</p>
  
  <pre><code>                             t     v_amm     v_alc     v_no2
  0    2019-02-07 08:00:00+00:00  0.320000  0.344000  1.612000
  1    2019-02-07 09:00:00+00:00  0.322889  0.391778  1.580889
  2    2019-02-07 10:00:00+00:00  0.209375  0.325208  2.371250
  ...
  251  2019-02-17 19:00:00+00:00  1.082041  0.652041  0.967143
  252  2019-02-17 20:00:00+00:00  0.936923  0.598654  1.048077
  253  2019-02-17 21:00:00+00:00  0.652553  0.499574  1.184894
  </code></pre>
  
  <p>and another similar set of hourly data taken from 01-Mar-19 to 11-Mar-19:</p>
  
  <pre><code>                            t     v_amm     v_alc     v_no2
  0   2019-03-01 00:00:00+00:00  0.428222  0.384444  1.288222
  1   2019-03-01 01:00:00+00:00  0.398600  0.359600  1.325800
  2   2019-03-01 02:00:00+00:00  0.365682  0.352273  1.360000
  ...
  244 2019-03-11 04:00:00+00:00  0.444048  0.415238  1.265000
  245 2019-03-11 05:00:00+00:00  0.590698  0.591395  1.156977
  246 2019-03-11 06:00:00+00:00  0.497872  0.465319  1.228298
  </code></pre>
  
  <p>However, there is <strong>no data available between 17-Feb-19 and 01-Mar-19</strong>.
  Hence, I'd like to find the hourly average data <strong>based on the day of the week to simulate the missing hourly data</strong> between 17-Feb-19 and 01-Mar-19. </p>
  
  <p>In other words, using all the hourly data from the same day of the week and find the average for each hour for that day. Expected output for 17-Feb-19 to 01-Mar-19 is something like:</p>
  
  <pre><code>                             t     v_amm     v_alc     v_no2
  0    2019-02-17 22:00:00+00:00  1.082041  0.652041  0.967143
  1    2019-02-17 23:00:00+00:00  0.936923  0.598654  1.048077
  2    2019-02-18 00:00:00+00:00  0.652553  0.499574  1.184894
  ...
  250  2019-02-29 21:00:00+00:00  0.428222  0.384444  1.288222
  251  2019-02-29 22:00:00+00:00  0.398600  0.359600  1.325800
  252  2019-02-29 23:00:00+00:00  0.365682  0.352273  1.360000
  </code></pre>
  
  <p>Does anyone know how to obtain this in pandas?</p>