title: Pandas dataframe: Fill in interpolated date values per category
body: <p>I'm working in Python 3 with a Pandas DataFrame. It has columns for Category, Date and Value. For each category, I want to add rows with the missing days, such the value is linearly interpolated.</p>
  <p>To create the minimal example, I use the following code</p>
  <pre><code>df = pd.DataFrame({
  'cat':['A', 'A', 'A', 'A', 'B', 'B', 'B'],
  'date': ['2021-1-1', '2021-1-4', '2021-1-5', '2021-1-7', '2021-11-1', '2021-11-2', '2021-11-5'],
  'value': [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 9.0]})
  
  df['cat'] = df['cat'].astype('category')
  df['date'] = df['date'].astype('datetime64')
  </code></pre>
  <p>Which gives the following dataframe</p>
  <pre><code>cat date          value
  A   2021-01-01    1.0
  A   2021-01-04    2.0
  A   2021-01-05    3.0
  A   2021-01-07    4.0
  B   2021-11-01    5.0
  B   2021-11-02    6.0
  B   2021-11-05    9.0
  </code></pre>
  <p>I would like the output to be like this example, Where I used '&lt;' to indicate the newly inserted rows</p>
  <pre><code>cat date          value
  A   2021-01-01    1.0
  A   2021-01-02    1.333  &lt;
  A   2021-01-03    1.667  &lt;
  A   2021-01-04    2.0
  A   2021-01-05    3.0
  A   2021-01-06    3.5    &lt;
  A   2021-01-07    4.0
  B   2021-11-01    5.0
  B   2021-11-02    6.0
  B   2021-11-03    7.0    &lt;
  B   2021-11-04    8.0    &lt;
  B   2021-11-05    9.0
  </code></pre>
  <p>In the actual problem, I don't want the weekend-days (Saturdays and Sundays), but I've stated the problem like above to prevent adding extra layers (I can easily filter the weekend-days out later if needed). However, not including them in the first place may allow for more efficient code, so I'd thought I'd mention this snag as well. Thanks for any help!</p>