How to use python pandas groupby or .DataFrameGroupBy objects to create unique list of combinations

You don't need to use -- and in fact shouldn't use -- groupby here. You could use drop_duplicates to get unique rows instead:

x.drop_duplicates(['Date','Name'])

Demo:

In [156]: x = pd.DataFrame({'Date':[0,1,2]*2, 'Name':list('ABC')*2})

In [158]: x
Out[158]: 
   Date Name
0     0    A
1     1    B
2     2    C
3     0    A
4     1    B
5     2    C

In [160]: x.drop_duplicates(['Date','Name'])
Out[160]: 
   Date Name
0     0    A
1     1    B
2     2    C

You shouldn't use groupby because

1. x.groupby(['Date','Name']).count() performs a count of the number of elements in each group, but the count is not used -- it's a wasted computation.
2. x.groupby(['Date','Name']).count() raises an AttributeError if x has only Date and Name columns.
3. drop_duplicates is much much faster for this purpose.

Use groupby when you want to perform some operation on each group, such as counting the number of elements in each group, or computing some statistic (e.g. a sum or mean, etc.) per group.