Forward filling missing dates into Python Panel Pandas Dataframe

Question

Suppose I have the following pandas dataframe:

df = pd.DataFrame({'Date':['2015-01-31','2015-01-31', '2015-02-28', '2015-03-31', '2015-04-30', '2015-04-30'], 'ID':[1,2,2,2,1,2], 'value':[1,2,3,4,5,6]})
print(df)


Date       ID  value
2015-01-31  1   1
2015-01-31  2   2
2015-02-28  2   3
2015-03-31  2   4
2015-04-30  1   5
2015-04-30  2   6

I want to forward fill the data such that I have the values for each end of month till 2015-05-31 (ie for each date - ID combination). That is, I would like the dataframe to look as follows:

Date       ID  value
2015-01-31  1   1
2015-01-31  2   2
2015-02-28  2   3
2015-02-28  1   1
2015-03-31  2   4
2015-03-31  1   1
2015-04-30  1   5
2015-04-30  2   6
2015-05-31  1   5
2015-05-31  2   6

Is something like this possible? I saw several similar questions on Stackoverflow on forward filling dates, however this was without an index column (where the same date can occur many times).

Answer 1

You can pivot then fill value with reindex + ffill

out = df.pivot(*df.columns).reindex(pd.date_range('2015-01-31',periods = 5,freq='M')).ffill().stack().reset_index()
out.columns = df.columns
out
Out[1077]: 
        Date  ID  value
0 2015-01-31   1    1.0
1 2015-01-31   2    2.0
2 2015-02-28   1    1.0
3 2015-02-28   2    3.0
4 2015-03-31   1    1.0
5 2015-03-31   2    4.0
6 2015-04-30   1    5.0
7 2015-04-30   2    6.0
8 2015-05-31   1    5.0
9 2015-05-31   2    6.0

Answer 2

Another solution:

idx = pd.MultiIndex.from_product(
    [
        pd.date_range(df["Date"].min(), "2015-05-31", freq="M"),
        df["ID"].unique(),
    ],
    names=["Date", "ID"],
)

df = df.set_index(["Date", "ID"]).reindex(idx).groupby(level=1).ffill()
print(df.reset_index())

Prints:

        Date  ID  value
0 2015-01-31   1    1.0
1 2015-01-31   2    2.0
2 2015-02-28   1    1.0
3 2015-02-28   2    3.0
4 2015-03-31   1    1.0
5 2015-03-31   2    4.0
6 2015-04-30   1    5.0
7 2015-04-30   2    6.0
8 2015-05-31   1    5.0
9 2015-05-31   2    6.0