pandas - Apply different groupby functions to a dataframe -

i coming pandas sql background, have used partition function aggregate 2 columns different level.

here dataframe

 teamname   playerid    playerlevel           1           beginner           2           beginner           3           intermediate           4           intermediate           5           intermediate           6           advanced  b          7           beginner  b          8           beginner  b          9           advanced  b          10          intermediate  b          11          beginner  b          12          advanced 

i want count players fall each player level, can using

             .groupby(['teamname', 'playerlevel'], as_index=false) \         .agg({'playerid': 'count'}) 

this gets me this

 teamname   playerlevel     playerid           beginner        2           intermediate    3           advanced        1  b          beginner        3  b          intermediate    1  b          advanced        2 

but want "denominator", total number of players each team. example data frame (with columns renamed) (the denominator happens 6 both teams in example).

 teamname   playerlevel     numerator  denominator           beginner        2          6           intermediate    3          6           advanced        1          6  b          beginner        3          6  b          intermediate    1          6  b          advanced        2          6 

but can't figure out how multiple grouby aggregations play nicely together.

per @root's suggestion , motivated @jeff's comments
happens equivalent @maxu's answer if looks different.

df1 = df.groupby(['teamname', 'playerlevel']).size().to_frame('numerator') df1['denominator'] = df1.groupby(level='teamname').transform(sum)  df1 

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old answer

numerator = df.groupby(['teamname', 'playerlevel']).size().rename('numerator') numerator  teamname  playerlevel          advanced        1           beginner        2           intermediate    3 b         advanced        2           beginner        3           intermediate    1 name: numerator, dtype: int64 

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denominator = df.groupby(['teamname']).size().rename('denominator') denominator  teamname    6 b    6 name: denominator, dtype: int64 

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numerator.to_frame().merge(denominator.to_frame(),                            right_index=true, left_index=true) 

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df.groupby(['teamname', 'playerlevel']).size().unstack() \     .div(df.groupby(['teamname']).size(), axis=0) 

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