[英]Pandas Group By and Count
A pandas dataframe df has 3 columns: 一个pandas数据帧df有3列:
user_id, session, revenue user_id,session,revenue
What I want to do now is group df by unique user_id and derive 2 new columns - one called number_sessions (counts the number of sessions associated with a particular user_id) and another called number_transactions (counts the number of rows under the revenue column that has a value > 0 for each user_id). 我现在要做的是通过唯一的user_id分组df并派生2个新列 - 一个名为number_sessions(计算与特定user_id关联的会话数),另一个名为number_transactions(计算收入列下具有的列数)每个user_id的值> 0)。 How do I go about doing this? 我该怎么做呢?
I tried doing something like this: 我尝试过这样的事情:
df.groupby('user_id')['session', 'revenue'].agg({'number sessions': lambda x: len(x.session),
'number_transactions': lambda x: len(x[x.revenue>0])})
I think you can use: 我想你可以用:
df = pd.DataFrame({'user_id':['a','a','s','s','s'],
'session':[4,5,4,5,5],
'revenue':[-1,0,1,2,1]})
print (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
a = df.groupby('user_id') \
.agg({'session': len, 'revenue': lambda x: len(x[x>0])}) \
.rename(columns={'session':'number sessions','revenue':'number_transactions'})
print (a)
number sessions number_transactions
user_id
a 2 0
s 3 3
a = df.groupby('user_id') \
.agg({'session':{'number sessions': len},
'revenue':{'number_transactions': lambda x: len(x[x>0])}})
a.columns = a.columns.droplevel()
print (a)
number sessions number_transactions
user_id
a 2 0
s 3 3
I'd use nunique
for session
to not double count the same session for a particular user 我会使用nunique
进行session
,而不是为特定用户重复计算同一会话
funcs = dict(session={'number sesssions': 'nunique'},
revenue={'number transactions': lambda x: x.gt(0).sum()})
df.groupby('user_id').agg(funcs)
setup 建立
df = pd.DataFrame({'user_id':['a','a','s','s','s'],
'session':[4,5,4,5,5],
'revenue':[-1,0,1,2,1]})
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