title: Interpolating Month rows to Day rows
body: <p>"""SO."""
  I have data for a store's revenue each month. How do I create daily rows for each month?</p>
  
  <pre><code>df = pd.DataFrame({
      'date': ['2019-01-01','2019-01-01','2019-01-01','2019-02-01',
          '2019-02-01','2019-02-01','2019-03-01','2019-03-01','2019-03-01'],
      'Company': ['Store A', 'Store B', 'Store C', 'Store A', 'Store B', 
          'Store C','Store A', 'Store B', 'Store C'],
      'Monthly Revenue': [200, 800, 400, 400, 300, 600, 400, 400, 900]
  })
  
  print(df)
  
           date  Company  Monthly Revenue
  0  2019-01-01  Store A              200
  1  2019-01-01  Store B              800
  2  2019-01-01  Store C              400
  3  2019-02-01  Store A              400
  4  2019-02-01  Store B              300
  5  2019-02-01  Store C              600
  6  2019-03-01  Store A              400
  7  2019-03-01  Store B              400
  8  2019-03-01  Store C              900
  </code></pre>
  
  <p>Desired Result:</p>
  
  <pre><code>         date  Company  Monthly Revenue
  0  2019-01-01  Store A              200
  1  2019-01-02  Store A              200
  2  2019-01-03  Store A              200
  3  2019-01-04  Store A              200
  ........
  30  2019-01-31 Store A              200
  31  2019-02-01 Store A              400
  32  2019-02-02 Store A              400
  32  2019-02-03 Store A              400
  </code></pre>
  
  <p>Notice each month has a specific number of days. All of my data is monthly through January to December, so the correct number of daily rows must match the number of days in a month (31 rows for Jan, 28 rows in Feb, 31 rows in March, etc).</p>