在不重置索引的情况下填充 Pandas 空白 groupby 行

Question

Hi I have a table like this after group by:

t = df.loc[(year-3 <= year) & (year <= year-1), 'Net Sum'].groupby([month, association]).sum()


t
 YearMonth  Type
    1          Other                  27471.73
               base               -14563752.74
               plan                16286620.30
    2          Other                 754691.36
               base                30465722.53
               plan                17906687.29
    3          Other                  20285.92
               base                29339325.21
               plan                15492558.91

How can I fill the blanks with grouped Year Month without resetting the index as I'd like to keep YearMonth as index?

Expected Outcome.

t
 YearMonth  Type
    1          Other                  27471.73
    1          base               -14563752.74
    1          plan                16286620.30
    2          Other                 754691.36
    2          base                30465722.53
    2          plan                17906687.29
    3          Other                  20285.92
    3          base                29339325.21
    3          plan                15492558.91

Answer 1

I think this can only be achieved by altering the display option:

with pd.option_context('display.multi_sparse', False):
    print(t)

If we refer the docs

display.multi_sparse True “Sparsify” MultiIndex display (don't display repeated elements in outer levels within groups)

Hence we can set this to False.

Answer 2

Following should do the work

t.reset_index()

https://pandas.pydata.org/pandasdocs/stable/reference/api/pandas.DataFrame.reset_index.html