I'd like to add subtotal rows for index #1 (ie. Fruits and Animal) and subtotal columns for columns (ie. 2015 and 2016).

For the subtotal columns, I could do something like this, but it seems inefficient to run this type of code for each year (2015 & 2016). Is there a better way? I don't think 'margins' will work because there are multiple subtotals.

df[('2015','2015_Total')] = df[('2015','1st')]+df[('2015','2nd')]

For the subtotal rows (e.g., fruits total and animal total), I'm not sure where to begin.

It is very complicated, because you need create Multiindex in columns and index.

Create subtotals is easy - use groupby with sum. Then create Multiindex and last concat new columns to original DataFrame. Last you have to sort_index (I add Total_ before value for correct sorting):

print df2015_____     2016_______    1st 2nd         1st 2nd
Fruits Apple         10   9          11  10Banana        20  22          21  20
Animal Lion           5   3           2   1Tiger          2   3           5   0df1 = df.groupby(level=0, axis=1).sum()
print df12015_____  2016_______
Fruits Apple          19           21Banana         42           41
Animal Lion            8            3Tiger           5            5print df.columns.get_level_values(0).to_series().drop_duplicates().tolist()
['2015_____', '2016_______']#change index to multiindex
new_columns = zip(df.columns.get_level_values(0).to_series().drop_duplicates().tolist(),"Total_" + df1.columns.str[:4])
print new_columns
[('2015_____', 'Total_2015'), ('2016_______', 'Total_2016')]df1.columns = pd.MultiIndex.from_tuples(new_columns)
print df12015_____ 2016_______Total_2015  Total_2016
Fruits Apple          19          21Banana         42          41
Animal Lion            8           3Tiger           5           5df = pd.concat([df,df1], axis=1)

df2 = df.groupby(level=0, sort=False).sum()
print df22015_____     2016_______      2015_____ 2016_______1st 2nd         1st 2nd Total_2015  Total_2016
Animal         7   6           7   1         13           8
Fruits        30  31          32  30         61          62print df.index.levels[0][df.columns.labels[0]].to_series().drop_duplicates().tolist()
['Animal', 'Fruits']#change index to multiindex
new_idx=zip(df.index.levels[0][df.columns.labels[0]].to_series().drop_duplicates().tolist(),"Total_" + df2.index )
print new_idx
[('Animal', 'Total_Animal'), ('Fruits', 'Total_Fruits')]df2.index = pd.MultiIndex.from_tuples(new_idx)
print df22015_____     2016_______      2015_____ 2016_______1st 2nd         1st 2nd Total_2015  Total_2016
Animal Total_Animal         7   6           7   1         13           8
Fruits Total_Fruits        30  31          32  30         61          62df = pd.concat([df,df2])
df = df.sort_index(axis=1).sort_index()
print df2015_____                2016_______               1st 2nd Total_2015         1st 2nd Total_2016
Animal Lion                 5   3          8           2   1          3Tiger                2   3          5           5   0          5Total_Animal         7   6         13           7   1          8
Fruits Apple               10   9         19          11  10         21Banana              20  22         42          21  20         41Total_Fruits        30  31         61          32  30         62