Python pandas - Efficiently apply function over rolling window by group with missing dates

Note: I already know the answer to this question, i am posting it just because i couldn't find proper answer on stack overflow and it took me surprising amount of time to figure it out. That being said, feel free to suggest other options.

The Question

I have a pandas DataFrame with three columns, one keeps a track of date, one keeps track of related observations (i.e. my grouping column) and third variable stores some numeric values. For each group in my dataframe, i'd like to calculate a rolling sum over the date column. The caveat: some dates are missing in the dataframe, i'd like to treat those as observations with value 0. I do not want to use cross join to add all the dates.

Reproducible example

Let's have a dataframe like this:

df = pd.DataFrame({'id_col' : np.array([1,1,1,1,1,1,2,2,2,2,2,3,3,3]),
                  'value_col' : np.random.randint(0,5,size=14),
                  'dates' : pd.to_datetime(np.array([
                  '2018-01-01',
                  '2018-02-01',
                  '2018-03-01',
                  '2018-05-01',
                  '2018-06-01',
                  '2018-09-01',
                  '2018-01-01',
                  '2018-02-01',
                  '2018-05-01',
                  '2018-06-01',
                  '2018-07-01',
                  '2018-01-01',
                  '2018-02-01',
                  '2018-03-01'])
                  )}
                 )

The data looks something like this:

    id_col  value_col   dates
0   1   0   2018-01-01
1   1   1   2018-02-01
2   1   4   2018-03-01
3   1   0   2018-05-01
4   1   3   2018-06-01
5   1   3   2018-09-01
6   2   4   2018-01-01
7   2   3   2018-02-01
8   2   2   2018-05-01
9   2   0   2018-06-01
10  2   2   2018-07-01
11  3   4   2018-01-01
12  3   2   2018-02-01
13  3   3   2018-03-01

Approaches that i tried and do not work:

Option 1: completely ignores missing dates

df.groupby(['id_col']).rolling(2)['value_col'].sum().reset_index()

Option 2: Based on pandas rolling documentation, replace width of window using the pandas offset parameters (returns ValueError: window must be an integer). This approach would work if the dates column was used as index of a dataframe. Unfortunately, we can't use simple index here as 2 different IDs from id_col can contain the same date. (We could create MultiIndex, but then get the same value error).

df.groupby(['id_col']).rolling('60d')['value_col'].sum().reset_index()

What does work but isn't very straightforward:

Option 1: Cross join to fill in all missing dates (might be tough if you have a lot of data)

Option 2: Build multiindex from cartesian product of iterables as in this answer. This is actually quite similar to the above option.