在熊猫

Question

计算YTD汇总我有一个数据帧，看起来像这样：

FinancialYearStart MonthOfFinancialYear SalesTotal 
0    2015      1   10 
1    2015      2   10 
2    2015      5   10 
3    2015      6   50 
4    2016      1   10 
5    2016      3   20 
6    2016      2   30 
7    2017      6   70 
8    2017      7   80 

而且我想计算销售累计总额为每个月，产生一个表，如下所示：

FinancialYearStart MonthOfFinancialYear SalesTotal YTDTotal 
0    2015      1   10  10 
1    2015      2   10  20 
2    2015      5   10  30 
3    2015      6   50  50 
4    2016      1   10  60 
5    2016      3   20  80 
6    2016      2   30  110 
7    2017      6   70  70 
8    2017      7   80  150 

我该怎么做到这一点？

更具体地说，我实际上需要在一组一组的基础上进行计算。

例如：

Year Month Customer TotalMonthlySales 
2015 1 Dog 10 
2015 2 Dog 10 
2015 3 Cat 20 
2015 4 Dog 30 
2015 5 Cat 10 
2015 7 Cat 20 
2015 7 Dog 10 
2016 1 Dog 40 
2016 2 Dog 20 
2016 3 Cat 70 
2016 4 Dog 30 
2016 5 Cat 10 
2016 6 Cat 20 
2016 7 Dog 10 

还会送：

Year Month Customer TotalMonthlySales YTDSales 
2015 1 Dog 10 10 
2015 2 Dog 10 20 
2015 3 Cat 20 20 
2015 4 Dog 30 50 
2015 5 Cat 10 30 
2015 7 Cat 20 40 
2015 7 Dog 10 60 
2016 1 Dog 40 40 
2016 2 Dog 20 60 
2016 3 Cat 70 70 
2016 4 Dog 30 90 
2016 5 Cat 10 80 
2016 6 Cat 20 100 
2016 7 Dog 10 100 

Answer 1

使用groupby + cumsum：

df['YTDSales'] = df.groupby(['Year','Customer'])['TotalMonthlySales'].cumsum() 
print (df) 
    Year Month Customer TotalMonthlySales YTDSales 
0 2015  1  Dog     10  10 
1 2015  2  Dog     10  20 
2 2015  3  Cat     20  20 
3 2015  4  Dog     30  50 
4 2015  5  Cat     10  30 
5 2015  7  Cat     20  50 
6 2015  7  Dog     10  60 
7 2016  1  Dog     40  40 
8 2016  2  Dog     20  60 
9 2016  3  Cat     70  70 
10 2016  4  Dog     30  90 
11 2016  5  Cat     10  80 
12 2016  6  Cat     20  100 
13 2016  7  Dog     10  100 

对于第一：

df['YTDTotal'] = df.groupby('FinancialYearStart')['SalesTotal'].cumsum() 
print (df) 
    FinancialYearStart MonthOfFinancialYear SalesTotal YTDTotal 
0    2015      1   10  10 
1    2015      2   10  20 
2    2015      5   10  30 
3    2015      6   50  80 
4    2016      1   10  10 
5    2016      3   20  30 
6    2016      2   30  60 
7    2017      6   70  70 
8    2017      7   80  150