Python fill zeros in a timeseries dataframe

Question

I have a list of dates and a dataframe. Now the dataframe has an id column and other values that are not consistent for all dates. I want to fill zeros in all columns for the ids and dates where there is no data. Let me show you by example:

    date     id     clicks    conv    rev
  2019-01-21 234      34        1     10
  2019-01-21 235      32        0     0
  2019-01-24 234      56        2     20
  2019-01-23 235      23        3     30

date list is like this:

    [2019-01-01, 2019-01-02,2019-01-03 ....2019-02-28]

What I want is to add zeros for all the missing dates in the dataframe for all ids. So the resultant df should look like:

    date     id     clicks    conv    rev
  2019-01-01 234      0         0     0
  2019-01-01 235      0         0     0
     .                .         .     .
     .                .         .     .

  2019-01-21 234      34        1     10
  2019-01-21 235      32        0     0
  2019-01-22 234      0         0     0
  2019-01-22 235      0         0     0
  2019-01-23 234      0         0     0
  2019-01-23 235      0         0     0
  2019-01-24 234      56        2     20
  2019-01-23 235      23        3     30
      .               .         .      .
  2019-02-28  0       0         0      0

Answer 1

With set_index + reindex from the cartesian product of values. Here I'll create the dates with pd.date_range to save some typing, and ensure dates are datetime

import pandas as pd

df['date'] = pd.to_datetime(df.date)
my_dates = pd.date_range('2019-01-01', '2019-02-28', freq='D')

idx = pd.MultiIndex.from_product([my_dates, df.id.unique()], names=['date', 'id'])
df = df.set_index(['date', 'id']).reindex(idx).fillna(0).reset_index()

Output: df

          date     id  clicks  conv   rev
0   2019-01-01    234     0.0   0.0   0.0
1   2019-01-01    235     0.0   0.0   0.0
...
45  2019-01-23    235    23.0   3.0  30.0
46  2019-01-24    234    56.0   2.0  20.0
47  2019-01-24    235     0.0   0.0   0.0
...
115 2019-02-27    235     0.0   0.0   0.0
116 2019-02-28    234     0.0   0.0   0.0
117 2019-02-28    235     0.0   0.0   0.0