Pandas Group by before outer Join - pandas

Question

I have two tables with the following formats:
Table1: key = Date, Index
Date Index Value1
0 2015-01-01 A -1.292040
1 2015-04-01 A 0.535893
2 2015-02-01 B -1.779029
3 2015-06-01 B 1.129317
Table2: Key = Date
Date Value2
0 2015-01-01 2.637761
1 2015-02-01 -0.496927
2 2015-03-01 0.226914
3 2015-04-01 -2.010917
4 2015-05-01 -1.095533
5 2015-06-01 0.651244
6 2015-07-01 0.036592
7 2015-08-01 0.509352
8 2015-09-01 -0.682297
9 2015-10-01 1.231889
10 2015-11-01 -1.557481
11 2015-12-01 0.332942
Table2 has more rows and I want to join Table1 into Table2 on Date so I can do stuff with the Values. However, I also want to bring in Index and and fill in for each index, all the Dates they don't have like this:
Result:
Date Index Value1 Value2
0 2015-01-01 A -1.292040 2.637761
1 2015-02-01 A NaN -0.496927
2 2015-03-01 A NaN 0.226914
3 2015-04-01 A 0.535893 -2.010917
4 2015-05-01 A NaN -1.095533
5 2015-06-01 A NaN 0.651244
6 2015-07-01 A NaN 0.036592
7 2015-08-01 A NaN 0.509352
8 2015-09-01 A NaN -0.682297
9 2015-10-01 A NaN 1.231889
10 2015-11-01 A NaN -1.557481
11 2015-12-01 A NaN 0.332942
.... and so on with Index B
I suppose I could manually filter out each Index value from Table1 into Table2, but that would be really tedious and troublesome if I didn't actually know all the indexes. I essentially want to do a "Table1 group by Index and right join to Table2 on Date" at the same time, but I'm stuck on how to express this.
Running the latest versions of Pandas and Jupyter.
EDIT: I have a program to fill in the NaNs, so they're not a problem right now.

Answer 1

It seems you want to merge 'Value1' of df1 with df2 on 'Date', while assigning the Index to every date. You can use pd.concat with a list comprehension
import pandas as pd
pd.concat([df2.assign(Index=i).merge(gp, how='left') for i, gp in df1.groupby('Index')],
ignore_index=True)
Output:
Date Value2 Index Value1
0 2015-01-01 2.637761 A -1.292040
1 2015-02-01 -0.496927 A NaN
2 2015-03-01 0.226914 A NaN
3 2015-04-01 -2.010917 A 0.535893
4 2015-05-01 -1.095533 A NaN
5 2015-06-01 0.651244 A NaN
6 2015-07-01 0.036592 A NaN
7 2015-08-01 0.509352 A NaN
8 2015-09-01 -0.682297 A NaN
9 2015-10-01 1.231889 A NaN
10 2015-11-01 -1.557481 A NaN
11 2015-12-01 0.332942 A NaN
12 2015-01-01 2.637761 B NaN
13 2015-02-01 -0.496927 B -1.779029
14 2015-03-01 0.226914 B NaN
15 2015-04-01 -2.010917 B NaN
16 2015-05-01 -1.095533 B NaN
17 2015-06-01 0.651244 B 1.129317
18 2015-07-01 0.036592 B NaN
19 2015-08-01 0.509352 B NaN
20 2015-09-01 -0.682297 B NaN
21 2015-10-01 1.231889 B NaN
22 2015-11-01 -1.557481 B NaN
23 2015-12-01 0.332942 B NaN
By not specifying the merge keys, it's automatically using the intersection of columns, which is ['Date', 'Index'] for each group.