title: pandas group by hour sum by day
body: <p>I have a time series of a week, and I would like to sum my values over the days grouped per hour, I have already tried to exploit the groupby and the resample methods but without success.</p>
  
  <pre><code>df.resample('h').sum().plot()
  </code></pre>
  
  <p>and</p>
  
  <pre><code>times   = pd.DatetimeIndex(df.index)
  grouped = df.groupby([times.hour, times.day])
  </code></pre>
  
  <p>example of the time series<a href="https://i.stack.imgur.com/4Xt0k.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/4Xt0k.png" alt="enter image description here"></a></p>
  
  <p>EDIT here the example data which I plotted:</p>
  
  <pre><code>end,Count
  2019-11-21 00:00:00,323
  2019-11-21 01:00:00,210
  2019-11-21 02:00:00,207
  2019-11-21 03:00:00,181
  2019-11-21 04:00:00,231
  2019-11-21 05:00:00,334
  2019-11-21 06:00:00,574
  2019-11-21 07:00:00,1176
  2019-11-21 08:00:00,2297
  2019-11-21 09:00:00,3599
  2019-11-21 10:00:00,4084
  2019-11-21 11:00:00,3866
  2019-11-21 12:00:00,4207
  2019-11-21 13:00:00,4673
  2019-11-21 14:00:00,4925
  2019-11-21 15:00:00,5238
  2019-11-21 16:00:00,4972
  2019-11-21 17:00:00,5481
  2019-11-21 18:00:00,4574
  2019-11-21 19:00:00,3756
  2019-11-21 20:00:00,2668
  2019-11-21 21:00:00,926
  2019-11-21 22:00:00,594
  2019-11-21 23:00:00,497
  2019-11-22 00:00:00,337
  2019-11-22 01:00:00,277
  2019-11-22 02:00:00,242
  2019-11-22 03:00:00,243
  2019-11-22 04:00:00,328
  2019-11-22 05:00:00,388
  2019-11-22 06:00:00,560
  2019-11-22 07:00:00,1264
  2019-11-22 08:00:00,2674
  2019-11-22 09:00:00,4274
  2019-11-22 10:00:00,5226
  2019-11-22 11:00:00,5180
  2019-11-22 12:00:00,5170
  2019-11-22 13:00:00,4979
  2019-11-22 14:00:00,5320
  2019-11-22 15:00:00,5330
  2019-11-22 16:00:00,5506
  2019-11-22 17:00:00,5586
  2019-11-22 18:00:00,4886
  2019-11-22 19:00:00,4034
  2019-11-22 20:00:00,3733
  2019-11-22 21:00:00,2790
  2019-11-22 22:00:00,1505
  2019-11-22 23:00:00,751
  2019-11-23 00:00:00,416
  2019-11-23 01:00:00,273
  2019-11-23 02:00:00,195
  2019-11-23 03:00:00,220
  2019-11-23 04:00:00,269
  2019-11-23 05:00:00,292
  2019-11-23 06:00:00,403
  2019-11-23 07:00:00,957
  2019-11-23 08:00:00,2771
  2019-11-23 09:00:00,5316
  2019-11-23 10:00:00,7753
  2019-11-23 11:00:00,7822
  2019-11-23 12:00:00,8060
  2019-11-23 13:00:00,8455
  2019-11-23 14:00:00,8878
  2019-11-23 15:00:00,10267
  2019-11-23 16:00:00,10725
  2019-11-23 17:00:00,8563
  2019-11-23 18:00:00,5721
  2019-11-23 19:00:00,3743
  2019-11-23 20:00:00,2677
  2019-11-23 21:00:00,2098
  2019-11-23 22:00:00,975
  2019-11-23 23:00:00,661
  2019-11-24 00:00:00,469
  2019-11-24 01:00:00,207
  2019-11-24 02:00:00,191
  2019-11-24 03:00:00,176
  2019-11-24 04:00:00,226
  2019-11-24 05:00:00,168
  2019-11-24 06:00:00,238
  2019-11-24 07:00:00,558
  2019-11-24 08:00:00,1727
  2019-11-24 09:00:00,3751
  2019-11-24 10:00:00,5092
  2019-11-24 11:00:00,5653
  2019-11-24 12:00:00,4931
  2019-11-24 13:00:00,4926
  2019-11-24 14:00:00,5861
  2019-11-24 15:00:00,6462
  2019-11-24 16:00:00,7111
  2019-11-24 17:00:00,5697
  2019-11-24 18:00:00,3976
  2019-11-24 19:00:00,2602
  2019-11-24 20:00:00,1349
  2019-11-24 21:00:00,682
  2019-11-24 22:00:00,388
  2019-11-24 23:00:00,324
  2019-11-25 00:00:00,174
  2019-11-25 01:00:00,143
  2019-11-25 02:00:00,104
  2019-11-25 03:00:00,76
  2019-11-25 04:00:00,143
  2019-11-25 05:00:00,238
  2019-11-25 06:00:00,321
  2019-11-25 07:00:00,530
  2019-11-25 08:00:00,882
  2019-11-25 09:00:00,1739
  2019-11-25 10:00:00,2648
  2019-11-25 11:00:00,2788
  2019-11-25 12:00:00,2797
  2019-11-25 13:00:00,2653
  2019-11-25 14:00:00,2814
  2019-11-25 15:00:00,3128
  2019-11-25 16:00:00,3034
  2019-11-25 17:00:00,2761
  2019-11-25 18:00:00,2420
  2019-11-25 19:00:00,1846
  2019-11-25 20:00:00,1321
  2019-11-25 21:00:00,516
  2019-11-25 22:00:00,389
  2019-11-25 23:00:00,270
  2019-11-26 00:00:00,179
  2019-11-26 01:00:00,113
  2019-11-26 02:00:00,85
  2019-11-26 03:00:00,108
  2019-11-26 04:00:00,137
  2019-11-26 05:00:00,212
  2019-11-26 06:00:00,392
  2019-11-26 07:00:00,458
  2019-11-26 08:00:00,629
  2019-11-26 09:00:00,1224
  2019-11-26 10:00:00,1527
  2019-11-26 11:00:00,1548
  2019-11-26 12:00:00,1625
  2019-11-26 13:00:00,1502
  2019-11-26 14:00:00,1492
  2019-11-26 15:00:00,1477
  2019-11-26 16:00:00,1498
  2019-11-26 17:00:00,1480
  2019-11-26 18:00:00,1098
  2019-11-26 19:00:00,1000
  2019-11-26 20:00:00,595
  2019-11-26 21:00:00,244
  2019-11-26 22:00:00,175
  2019-11-26 23:00:00,116
  2019-11-27 00:00:00,26
  2019-11-27 01:00:00,25
  2019-11-27 02:00:00,72
  2019-11-27 09:00:00,1756
  2019-11-27 10:00:00,4008
  2019-11-27 11:00:00,3803
  2019-11-27 12:00:00,4139
  2019-11-27 13:00:00,4607
  2019-11-27 14:00:00,5072
  2019-11-27 15:00:00,4908
  2019-11-27 16:00:00,5401
  2019-11-27 17:00:00,5329
  2019-11-27 18:00:00,4340
  2019-11-27 19:00:00,4048
  2019-11-27 20:00:00,2747
  2019-11-27 21:00:00,890
  2019-11-27 22:00:00,549
  2019-11-27 23:00:00,396
  </code></pre>
  
  <p>any ideas? I would like not to iterate with a loop</p>
  
  <p>I would like to obtain a single plot that represents the sum of the time interval over the other days like this
  (This is just the plot over one day)</p>
  
  <p><a href="https://i.stack.imgur.com/k2t6Y.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/k2t6Y.png" alt="enter image description here"></a></p>
  
  <p>Sample data:</p>
  
  <pre><code>print (df)
                       rand  Count
  end                             
  2019-11-21 00:00:00     1      8
  2019-11-21 01:00:00     1      5
  2019-11-25 12:00:00     1      7
  2019-11-25 13:00:00     1      2
  2019-11-25 13:00:00     0      3
  2019-11-25 22:00:00     0      5
  2019-11-26 04:00:00     1      7
  2019-11-26 11:00:00     0      5
  2019-11-27 01:00:00     1      8
  2019-11-27 01:00:00     0     25
  2019-11-27 02:00:00     1     11
  </code></pre>