Including missing combinations of values in a pandas groupby aggregation

Question

Problem

Including all possible values or combinations of values in the output of a pandas groupby aggregation.

Example

Answer 1

You can use unstack with stack:

print(example_df.groupby(['User', 'Code']).Subtotal.sum()
                .unstack(fill_value=0)
                .stack()
                .reset_index(name='Subtotal'))
  User  Code  Subtotal
0    a     1         1
1    a     2         1
2    b     1         1
3    b     2         1
4    c     1         2
5    c     2         0

Another solution with reindex by MultiIndex created from_product:

df = example_df.groupby(['User', 'Code']).Subtotal.sum()
mux = pd.MultiIndex.from_product(df.index.levels, names=['User','Code'])
print (mux)
MultiIndex(levels=[['a', 'b', 'c'], [1, 2]],
           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],
           names=['User', 'Code'])

print (df.reindex(mux, fill_value=0).reset_index(name='Subtotal'))
  User  Code  Subtotal
0    a     1         1
1    a     2         1
2    b     1         1
3    b     2         1
4    c     1         2
5    c     2         0