Pandas Python Groupby 累积和反向

Question

我找到了Pandas groupby cumulative sum，发现它非常有用。但是，我想确定如何计算反向累积和。

该链接建议以下内容。

df.groupby(by=['name','day']).sum().groupby(level=[0]).cumsum()

为了求和，我尝试对数据进行切片，但失败了。

df.groupby(by=['name','day']).ix[::-1, 'no'].sum().groupby(level=[0]).cumsum()


Jack | Monday    | 10 | 90
Jack | Tuesday   | 30 | 80
Jack | Wednesday | 50 | 50
Jill | Monday    | 40 | 80
Jill | Wednesday | 40 | 40 

编辑： 根据反馈，我尝试实现代码并使数据框更大：

import pandas as pd
df = pd.DataFrame(
    {'name': ['Jack', 'Jack', 'Jack', 'Jill', 'Jill'],
     'surname' : ['Jones','Jones','Jones','Smith','Smith'],
     'car' : ['VW','Mazda','VW','Merc','Merc'],
     'country' : ['UK','US','UK','EU','EU'],
     'year' : [1980,1980,1980,1980,1980],
     'day': ['Monday', 'Tuesday','Wednesday','Monday','Wednesday'],
     'date': ['2016-02-31','2016-01-31','2016-01-31','2016-01-31','2016-01-31'],
     'no': [10,30,50,40,40],
     'qty' : [100,500,200,433,222]})

然后，我尝试对多个列进行分组，但无法应用分组。

df = df.groupby(by=['name','surname','car','country','year','day','date']).sum().iloc[::-1].groupby(level=[0]).cumsum().iloc[::-1].reset_index()

为什么会这样？我希望开着马自达车的杰克·琼斯与开着大众汽车的杰克·琼斯是一个单独的累积数量。

Answer 1

你可以使用双iloc：

df = df.groupby(by=['name','day']).sum().iloc[::-1].groupby(level=[0]).cumsum().iloc[::-1]
print (df)
                no
name day          
Jack Monday     90
     Tuesday    80
     Wednesday  50
Jill Monday     80
     Wednesday  40

对于另一列的解决方案是简化：

df = df.groupby(by=['name','day']).sum()
df['new'] = df.iloc[::-1].groupby(level=[0]).cumsum()
print (df)
                no  new
name day               
Jack Monday     10   90
     Tuesday    30   80
     Wednesday  50   50
Jill Monday     40   80
     Wednesday  40   40

编辑：

第二个groupby 有问题需要附加更多级别 - level=[0,1,2] 表示按第一个name、第二个surname 和第三个car 级别分组。

df1 = (df.groupby(by=['name','surname','car','country','year','day','date'])
        .sum())
print (df1)
                                                      no  qty
name surname car   country year day       date               
Jack Jones   Mazda US      1980 Tuesday   2016-01-31  30  500
             VW    UK      1980 Monday    2016-02-31  10  100
                                Wednesday 2016-01-31  50  200
Jill Smith   Merc  EU      1980 Monday    2016-01-31  40  433
                                Wednesday 2016-01-31  40  222

df2 = (df.groupby(by=['name','surname','car','country','year','day','date'])
        .sum()
        .iloc[::-1]
        .groupby(level=[0,1,2])
        .cumsum()
        .iloc[::-1]
        .reset_index())
print (df2)
   name surname    car country  year        day        date  no  qty
0  Jack   Jones  Mazda      US  1980    Tuesday  2016-01-31  30  500
1  Jack   Jones     VW      UK  1980     Monday  2016-02-31  60  300
2  Jack   Jones     VW      UK  1980  Wednesday  2016-01-31  50  200
3  Jill   Smith   Merc      EU  1980     Monday  2016-01-31  80  655
4  Jill   Smith   Merc      EU  1980  Wednesday  2016-01-31  40  222

或者可以按名称选择 - 请参阅 groupby enhancements in 0.20.1+:

df2 = (df.groupby(by=['name','surname','car','country','year','day','date'])
        .sum()
        .iloc[::-1]
        .groupby(['name','surname','car'])
        .cumsum()
        .iloc[::-1]
        .reset_index())
print (df2)

   name surname    car country  year        day        date  no  qty
0  Jack   Jones  Mazda      US  1980    Tuesday  2016-01-31  30  500
1  Jack   Jones     VW      UK  1980     Monday  2016-02-31  60  300
2  Jack   Jones     VW      UK  1980  Wednesday  2016-01-31  50  200
3  Jill   Smith   Merc      EU  1980     Monday  2016-01-31  80  655
4  Jill   Smith   Merc      EU  1980  Wednesday  2016-01-31  40  222