[英]Groupby df column using pandas
我有數據
1 member_id application_name active_seconds
2 192180 Opera 6
3 192180 Opera 7
4 192180 Chrome 243
5 5433112 Chrome 52
6 5433112 Opera 34
7 5433112 Chrome 465
我需要使用application_name
數量和active_seconds
數量對它進行分組
我使用print df.groupby(['member_id', 'application_name']).count()
但我得到的結果是active_second
,並且
print df.groupby(['member_id', 'application_name'])['active_seconds'].count()
工作不正確。 我做錯了什么?
我認為你需要aggregate
:
df1 = df.groupby(['member_id', 'application_name'])
.agg({'application_name':len, 'active_seconds':sum})
print (df1)
active_seconds application_name
member_id application_name
192180 Chrome 243 1
Opera 13 2
5433112 Chrome 517 2
Opera 34 1
如果需要reset_index
,首先rename
列(因為ValueError: cannot insert application_name, already exists
):
df1 = df.groupby(['member_id', 'application_name'])
.agg({'application_name':len, 'active_seconds':sum})
.rename(columns={'active_seconds':'count_sec','application_name':'sum_app'})
.reset_index()
print (df1)
member_id application_name count_sec sum_app
0 192180 Chrome 243 1
1 192180 Opera 13 2
2 5433112 Chrome 517 2
3 5433112 Opera 34 1
時間 :
In [208]: %timeit df.groupby(['member_id', 'application_name']).agg({'application_name':len, 'active_seconds':sum}).rename(columns={'active_seconds':'count_sec','application_name':'sum_app'}).reset_index()
10 loops, best of 3: 93.6 ms per loop
In [209]: %timeit (f1(df))
10 loops, best of 3: 127 ms per loop
測試代碼 :
import pandas as pd
df = pd.DataFrame({'member_id': {0: 192180, 1: 192180, 2: 192180, 3: 5433112, 4: 5433112, 5: 5433112},
'active_seconds': {0: 6, 1: 7, 2: 243, 3: 52, 4: 34, 5: 465},
'application_name': {0: 'Opera', 1: 'Opera', 2: 'Chrome', 3: 'Chrome', 4: 'Opera', 5: 'Chrome'}})
print (df)
# active_seconds application_name member_id
#0 6 Opera 192180
#1 7 Opera 192180
#2 243 Chrome 192180
#3 52 Chrome 5433112
#4 34 Opera 5433112
#5 465 Chrome 5433112
df = pd.concat([df]*1000).reset_index(drop=True)
print (len(df))
#6000
df1 = df.groupby(['member_id', 'application_name']).agg({'application_name':len, 'active_seconds':sum}).rename(columns={'active_seconds':'count_sec','application_name':'sum_app'}).reset_index()
print (df1)
def f1(df):
a = (df.groupby(['member_id', 'application_name'])['active_seconds'].sum() )
b = (df.groupby(['member_id', 'application_name']).size())
return (pd.concat([a,b], axis=1, keys=['count_sec','sum_app']).reset_index())
print (f1(df))
# member_id application_name count_sec sum_app
#0 192180 Chrome 243000 1000
#1 192180 Opera 13000 2000
#2 5433112 Chrome 517000 2000
#3 5433112 Opera 34000 1000
# member_id application_name count_sec sum_app
#0 192180 Chrome 243000 1000
#1 192180 Opera 13000 2000
#2 5433112 Chrome 517000 2000
#3 5433112 Opera 34000 1000
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