问题
I have dataframe
ID,url,used_at,active_seconds,domain
61a77f9e5fd52a50c10cd2d4d886ec68,mazdaspb.ru,2015-01,6,mazdaspb.ru
61a77f9e5fd52a50c10cd2d4d886ec68,mazdaspb.ru/cars/mazda-cx-5/crossover/overview,2015-01,12,mazdaspb.ru
61a77f9e5fd52a50c10cd2d4d886ec68,mazdaspb.ru/cars/mazda-cx-5/crossover/overview,2015-01,19,mazdaspb.ru
61a77f9e5fd52a50c10cd2d4d886ec68,vw-stat.ru,2015-01,40,vw-stat.ru
61a77f9e5fd52a50c10cd2d4d886ec68,vw-stat.ru/models/new_tiguan,2015-01,12,vw-stat.ru
61a77f9e5fd52a50c10cd2d4d886ec68,vw-stat.ru/models/new_tiguan/comps,2015-01,48,vw-stat.ru
61a77f9e5fd52a50c10cd2d4d886ec68,vw-stat.ru/models/new_tiguan/comps/new_tiguan_track_field,2015-01,4,vw-stat.ru
61a77f9e5fd52a50c10cd2d4d886ec68,vw-stat.ru/models/new_tiguan/comps/new_tiguan_track_field?engine_type=DIESEL&DIESEL=engines_4e53a3c8e986d,2015-01,78,vw-stat.ru
41c2fd7a372729dfe336e44730169f28,avito.ru/saratov/avtomobili_s_probegom/volkswagen/golf?f=188_886b887,2015-01,8,avito.ru
41c2fd7a372729dfe336e44730169f28,avito.ru/saratov/avtomobili_s_probegom/volkswagen/golf?f=188_886b887,2015-01,1,avito.ru
41c2fd7a372729dfe336e44730169f28,avito.ru/saratov/avtomobili_s_probegom/volkswagen/golf?f=188_886b887,2015-01,2,avito.ru
I need to get to make pivot table, and there are should be values of percentage of all unique ID. I can get
group = pd.pivot_table(df, index='used_at', columns='domain', values='ID', aggfunc=(lambda x: x.count()))
but it return quantity of unique ID to every domain to every month. How can I convert that to percentage?
回答1:
IIUC you can use parameter margins for sum values in pivot_table and then divide all values last row All by div:
group = pd.pivot_table(df,
index='used_at',
columns='domain',
values='ID',
aggfunc=len,
margins=True)
print (group)
domain avito.ru mazdaspb.ru vw-stat.ru All
used_at
2015-01 3.0 3.0 5.0 11.0
All 3.0 3.0 5.0 11.0
print (group.iloc[:-1])
domain avito.ru mazdaspb.ru vw-stat.ru All
used_at
2015-01 3.0 3.0 5.0 11.0
print (group.iloc[-1])
domain
avito.ru 3.0
mazdaspb.ru 3.0
vw-stat.ru 5.0
All 11.0
Name: All, dtype: float64
print (group.iloc[:-1].div(group.iloc[-1], axis=1) * 100)
domain avito.ru mazdaspb.ru vw-stat.ru All
used_at
2015-01 100.0 100.0 100.0 100.0
Solution with divide by individual count with div and mul:
group = pd.pivot_table(df,
index='used_at',
columns='domain',
values='ID',
aggfunc=len)
.div(len(df.index))
.mul(100)
print (group)
domain avito.ru mazdaspb.ru vw-stat.ru
used_at
2015-01 27.272727 27.272727 45.454545
回答2:
Divide the individual count values obtained with the total number of rows of the DF to get it's percentage distribution as shown:
func = lambda x: 100*x.count()/df.shape[0]
pd.pivot_table(df, index='used_at', columns='domain', values='ID', aggfunc=func)
来源：https://stackoverflow.com/questions/40301973/pandas-make-pivot-table-with-percentage