问题
I have dataframe:
ID,used_at,active_seconds,subdomain,visiting,category
123,2016-02-05 19:39:21,2,yandex.ru,2,Computers
123,2016-02-05 19:43:01,1,mail.yandex.ru,2,Computers
123,2016-02-05 19:43:13,6,mail.yandex.ru,2,Computers
234,2016-02-05 19:46:09,16,avito.ru,2,Automobiles
234,2016-02-05 19:48:36,21,avito.ru,2,Automobiles
345,2016-02-05 19:48:59,58,avito.ru,2,Automobiles
345,2016-02-05 19:51:21,4,avito.ru,2,Automobiles
345,2016-02-05 19:58:55,4,disk.yandex.ru,2,Computers
345,2016-02-05 19:59:21,2,mail.ru,2,Computers
456,2016-02-05 19:59:27,2,mail.ru,2,Computers
456,2016-02-05 20:02:15,18,avito.ru,2,Automobiles
456,2016-02-05 20:04:55,8,avito.ru,2,Automobiles
456,2016-02-05 20:07:21,24,avito.ru,2,Automobiles
567,2016-02-05 20:09:03,58,avito.ru,2,Automobiles
567,2016-02-05 20:10:01,26,avito.ru,2,Automobiles
567,2016-02-05 20:11:51,30,disk.yandex.ru,2,Computers
I need to do
group = df.groupby(['category']).agg({'active_seconds': sum}).rename(columns={'active_seconds': 'count_sec_target'}).reset_index()
but I want to add there condition connected with
df.groupby(['category'])['ID'].count()
and if count for category less than 5, I want to drop this category.
I don't know, how can I write this condition there.
回答1:
As EdChum commented, you can use filter:
Also you can simplify aggregation by sum:
df = df.groupby(['category']).filter(lambda x: len(x) >= 5)
group = df.groupby(['category'], as_index=False)['active_seconds']
.sum()
.rename(columns={'active_seconds': 'count_sec_target'})
print (group)
category count_sec_target
0 Automobiles 233
1 Computers 47
Another solution with reset_index:
df = df.groupby(['category']).filter(lambda x: len(x) >= 5)
group = df.groupby(['category'])['active_seconds'].sum().reset_index(name='count_sec_target')
print (group)
category count_sec_target
0 Automobiles 233
1 Computers 47
来源：https://stackoverflow.com/questions/39634175/pandas-groupby-with-condition