相当于SQL窗口函数的Pandas

Question

在Pandas中是否有一个与SQL的窗口函数类似的习惯用法？例如，在熊猫身上写类似的东西最简洁的方法是什么？以下内容：

SELECT state_name,  
       state_population,
       SUM(state_population)
        OVER() AS national_population
FROM population   
ORDER BY state_name 

还是这个？以下内容：

SELECT state_name,  
       state_population,
       region,
       SUM(state_population)
        OVER(PARTITION BY region) AS regional_population
FROM population    
ORDER BY state_name

Answer 1

对于第一个SQL：

SELECT state_name,  
       state_population,
       SUM(state_population)
        OVER() AS national_population
FROM population   
ORDER BY state_name 

熊猫：

df.assign(national_population=df.state_population.sum()).sort_values('state_name')

对于第二个SQL：

SELECT state_name,  
       state_population,
       region,
       SUM(state_population)
        OVER(PARTITION BY region) AS regional_population
FROM population    
ORDER BY state_name

熊猫：

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

全国人口：

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

地区人口：

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