Pandas 相当于 SQL window 函数
Pandas equivalent to SQL window functions
在 Pandas 中是否有与 SQL 的 window 函数等同的惯用语?例如,在 Pandas 中编写等效项的最紧凑方法是什么?:
SELECT state_name,
state_population,
SUM(state_population)
OVER() AS national_population
FROM population
ORDER BY state_name
或者这个?:
SELECT state_name,
state_population,
region,
SUM(state_population)
OVER(PARTITION BY region) AS regional_population
FROM population
ORDER BY state_name
第一个SQL:
SELECT state_name,
state_population,
SUM(state_population)
OVER() AS national_population
FROM population
ORDER BY state_name
Pandas:
df.assign(national_population=df.state_population.sum()).sort_values('state_name')
第二个SQL:
SELECT state_name,
state_population,
region,
SUM(state_population)
OVER(PARTITION BY region) AS regional_population
FROM population
ORDER BY state_name
Pandas:
df.assign(regional_population=df.groupby('region')['state_population'].transform('sum')) \
.sort_values('state_name')
演示:
In [238]: df
Out[238]:
region state_name state_population
0 1 aaa 100
1 1 bbb 110
2 2 ccc 200
3 2 ddd 100
4 2 eee 100
5 3 xxx 55
national_population:
In [246]: df.assign(national_population=df.state_population.sum()).sort_values('state_name')
Out[246]:
region state_name state_population national_population
0 1 aaa 100 665
1 1 bbb 110 665
2 2 ccc 200 665
3 2 ddd 100 665
4 2 eee 100 665
5 3 xxx 55 665
regional_population:
In [239]: df.assign(regional_population=df.groupby('region')['state_population'].transform('sum')) \
...: .sort_values('state_name')
Out[239]:
region state_name state_population regional_population
0 1 aaa 100 210
1 1 bbb 110 210
2 2 ccc 200 400
3 2 ddd 100 400
4 2 eee 100 400
5 3 xxx 55 55
在 Pandas 中是否有与 SQL 的 window 函数等同的惯用语?例如,在 Pandas 中编写等效项的最紧凑方法是什么?:
SELECT state_name,
state_population,
SUM(state_population)
OVER() AS national_population
FROM population
ORDER BY state_name
或者这个?:
SELECT state_name,
state_population,
region,
SUM(state_population)
OVER(PARTITION BY region) AS regional_population
FROM population
ORDER BY state_name
第一个SQL:
SELECT state_name,
state_population,
SUM(state_population)
OVER() AS national_population
FROM population
ORDER BY state_name
Pandas:
df.assign(national_population=df.state_population.sum()).sort_values('state_name')
第二个SQL:
SELECT state_name,
state_population,
region,
SUM(state_population)
OVER(PARTITION BY region) AS regional_population
FROM population
ORDER BY state_name
Pandas:
df.assign(regional_population=df.groupby('region')['state_population'].transform('sum')) \
.sort_values('state_name')
演示:
In [238]: df
Out[238]:
region state_name state_population
0 1 aaa 100
1 1 bbb 110
2 2 ccc 200
3 2 ddd 100
4 2 eee 100
5 3 xxx 55
national_population:
In [246]: df.assign(national_population=df.state_population.sum()).sort_values('state_name')
Out[246]:
region state_name state_population national_population
0 1 aaa 100 665
1 1 bbb 110 665
2 2 ccc 200 665
3 2 ddd 100 665
4 2 eee 100 665
5 3 xxx 55 665
regional_population:
In [239]: df.assign(regional_population=df.groupby('region')['state_population'].transform('sum')) \
...: .sort_values('state_name')
Out[239]:
region state_name state_population regional_population
0 1 aaa 100 210
1 1 bbb 110 210
2 2 ccc 200 400
3 2 ddd 100 400
4 2 eee 100 400
5 3 xxx 55 55