I have a dataframe:
date type
2021-08-12 fail
2021-08-12 fail
2021-08-12 win
2021-08-12 great_win
2021-08-13 fail
2021-08-13 win
2021-08-13 win
2021-08-13 win
I want to calculate percentage of each 'type' within date group and then average values among all dates. So desired results must be:
date type type_perc
2021-08-12 fail 0.5
2021-08-12 win 0.25
2021-08-12 great_win 0.25
2021-08-13 fail 0.25
2021-08-13 win 0.75
2021-08-13 great_win 0.0
and then average among all dates. this is the desired final result:
type type_perc
fail 0.375
win 0.5
great_win 0.175
How to do that?
CodePudding user response:
You can try this:
tmp = df.groupby(['date', 'type']).size()/df.groupby('date')['type'].size()
print(tmp)
date type
2021-08-12 fail 0.50
great_win 0.25
win 0.25
2021-08-13 fail 0.25
win 0.75
dtype: float64
result = tmp.groupby(level=1).sum()/tmp.sum()
print(result)
type
fail 0.375
great_win 0.125
win 0.500
dtype: float64
or this:
result = tmp.groupby(level=1).mean()
print(result)
type
fail 0.375
great_win 0.250
win 0.500
dtype: float64
It's not quite clear by your question