How to summarize on different groupby combinations?

Question

I am compiling a table of top-3 crops by county. Some counties have the same crop varieties in the same order. Other counties have the same crop varieties in a different ord

Answer 1

np.bincount

i, u = pd.factorize([*map(frozenset, zip(df1.Crop1, df1.Crop2, df1.Crop3))])
s = np.bincount(i, df1.Total_pop)

pd.Series(s, u)

(melons, grain, apples)      8000.0
(carrots, raddish, pears)    9200.0
dtype: float64

Or, if you want separate columns

pd.Series(dict(zip(map(tuple, u), s)))

melons   grain    apples    8000.0
carrots  raddish  pears     9200.0
dtype: float64

And fully pretty

pd.Series(dict(zip(map(tuple, u), s))) \
  .rename_axis(['Crop1', 'Crop2', 'Crop3']).reset_index(name='Total_pop')

     Crop1    Crop2   Crop3  Total_pop
0   melons    grain  apples     8000.0
1  carrots  raddish   pears     9200.0