title: Pandas extends intervals in timeseries
body: <p>I have the following DataFrame:</p>
  <pre><code>    product     timestamp               count
  0   apple    2021-06-29 11:00:00-04:00  1023
  1   apple    2021-06-29 12:00:00-04:00  3790
  2   apple    2021-06-29 13:00:00-04:00  128
  3   apple    2021-06-29 14:00:00-04:00  0
  4   apple    2021-06-29 15:00:00-04:00  323
  5   apple    2021-06-29 16:00:00-04:00  4223
  6   apple    2021-06-29 17:00:00-04:00  1194
  4   orange   2021-06-29 15:00:00-04:00  23
  5   orange   2021-06-29 16:00:00-04:00  4289
  </code></pre>
  <p>As you can see for both the products (apple and orange) there is a count associated to an hour.
  For the apple we have data in the time interval <code>2021-06-29 11:00:00-04:00 - 2021-06-29 17:00:00-04:00</code> while for the orange we have data in the time interval <code>2021-06-29 15:00:00-04:00 - 2021-06-29 16:00:00-04:00</code>.</p>
  <p>I would like to extend the time interval so that for both product we have hourly data from the beginning to the end of the day, that is in the interval <code>2021-06-29 00:00:00-04:00 - 2021-06-29 23:00:00-04:00</code>.</p>
  <p>The result would be the following:</p>
  <pre><code>    product     timestamp               count
  0    apple    2021-06-29 00:00:00-04:00  0
  1    apple    2021-06-29 01:00:00-04:00  0
  2    apple    2021-06-29 02:00:00-04:00  0
  3    apple    2021-06-29 03:00:00-04:00  0
  4    apple    2021-06-29 04:00:00-04:00  0
  5    apple    2021-06-29 05:00:00-04:00  0
  6    apple    2021-06-29 06:00:00-04:00  0
  7    apple    2021-06-29 07:00:00-04:00  0
  8    apple    2021-06-29 08:00:00-04:00  0
  9    apple    2021-06-29 09:00:00-04:00  0
  10   apple    2021-06-29 10:00:00-04:00  0
  11   apple    2021-06-29 11:00:00-04:00  1023
  12   apple    2021-06-29 12:00:00-04:00  3790
  13   apple    2021-06-29 13:00:00-04:00  128
  14   apple    2021-06-29 14:00:00-04:00  0
  15   apple    2021-06-29 15:00:00-04:00  323
  16   apple    2021-06-29 16:00:00-04:00  4223
  17   apple    2021-06-29 17:00:00-04:00  1194
  18   apple    2021-06-29 18:00:00-04:00  0
  19   apple    2021-06-29 19:00:00-04:00  0
  20   apple    2021-06-29 20:00:00-04:00  0
  21   apple    2021-06-29 21:00:00-04:00  0
  22   apple    2021-06-29 22:00:00-04:00  0
  23   apple    2021-06-29 23:00:00-04:00  0
  24   orange    2021-06-29 00:00:00-04:00  0
  25   orange    2021-06-29 01:00:00-04:00  0
  26   orange    2021-06-29 02:00:00-04:00  0
  27   orange    2021-06-29 03:00:00-04:00  0
  28   orange    2021-06-29 04:00:00-04:00  0
  29   orange    2021-06-29 05:00:00-04:00  0
  30   orange    2021-06-29 06:00:00-04:00  0
  31   orange    2021-06-29 07:00:00-04:00  0
  32   orange    2021-06-29 08:00:00-04:00  0
  33   orange    2021-06-29 09:00:00-04:00  0
  34   orange    2021-06-29 10:00:00-04:00  0
  35   orange    2021-06-29 11:00:00-04:00  0
  36   orange    2021-06-29 12:00:00-04:00  0
  37   orange    2021-06-29 13:00:00-04:00  0
  38   orange    2021-06-29 14:00:00-04:00  0
  39   orange    2021-06-29 15:00:00-04:00  23
  40   orange    2021-06-29 16:00:00-04:00  4289
  41   orange    2021-06-29 17:00:00-04:00  0
  42   orange    2021-06-29 18:00:00-04:00  0
  43   orange    2021-06-29 19:00:00-04:00  0
  44   orange    2021-06-29 20:00:00-04:00  0
  45   orange    2021-06-29 21:00:00-04:00  0
  46   orange    2021-06-29 22:00:00-04:00  0
  47   orange    2021-06-29 23:00:00-04:00  0
  </code></pre>
  <p>For far I tried to reindex the DataFrame but I am doing something wrong.</p>
  <p>Thank you!</p>