Python 熊猫，分组和计数
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Pandas, groupby and count
提问by GoingMyWay
I have a dataframe say like this
我有一个像这样说的数据框
>>> df = pd.DataFrame({'user_id':['a','a','s','s','s'],
'session':[4,5,4,5,5],
'revenue':[-1,0,1,2,1]})
>>> df
revenue session user_id
0 -1 4 a
1 0 5 a
2 1 4 s
3 2 5 s
4 1 5 s
And each value of session and revenue represents a kind of type, and I want to count the number of each kind say the number of revenue=-1and session=4of user_id=ais 1.
和会话和收入的每个值代表了一种类型的，我要统计每个种类的数量表示的数量revenue=-1和session=4的user_id=a为1。
And I found simple call count()function after groupby()can't output the result I want.
count()在groupby()无法输出我想要的结果后，我发现了简单的调用函数。
>>> df.groupby('user_id').count()
revenue session
user_id
a 2 2
s 3 3
How can I do that?
我怎样才能做到这一点？
回答by WNG
You seem to want to group by several columns at once:
您似乎想一次按几列分组：
df.groupby(['revenue','session','user_id'])['user_id'].count()
should give you what you want
应该给你你想要的
回答by Bernard Esterhuyse
I struggled with the same issue, made use of the solution provided above. You can actually designate any of the columns to count:
我遇到了同样的问题，使用了上面提供的解决方案。您实际上可以指定要计算的任何列：
df.groupby(['revenue','session','user_id'])['revenue'].count()
and
和
df.groupby(['revenue','session','user_id'])['session'].count()
would give the same answer.
会给出同样的答案。
