给定另一列的值时，如何增加一列的计数？

Question

我正在尝试找到一种在非常大的数据集中生成值caseid的方法。我希望caseid变量做两件事：（1）当1时增加y = 1。重要的是，在观察到caseid之后的行中，y = 1的值应增加，并且当1的变化为（2）时，{2}的值应增加case。值，即从A到B。

示例数据如下：

case = pd.Series(['A', 'A', 'A', 'A', 
                  'B', 'B', 'B', 'B', 
                  'C', 'C', 'C', 'C'])
y = pd.Series([0, 1, 0, 0, 
               0, 1, 0, 0, 
               0, 0, 1, 0])
year = [2016, 2017, 2018, 2019, 
        2016, 2017, 2018, 2019,
        2016, 2017, 2018, 2019]
caseid = pd.Series([1, 1, 2, 2,
                    3, 3, 4, 4,
                    5, 5, 5, 6])
dict = {'case': case, 'y': y, 'year': year, 'caseid' : caseid}  
df = pd.DataFrame(dict) 

   case  y  year  caseid
0     A  0  2016       1
1     A  1  2017       1
2     A  0  2018       2
3     A  0  2019       2
4     B  0  2016       3
5     B  1  2017       3
6     B  0  2018       4
7     B  0  2019       4
8     C  0  2016       5
9     C  0  2017       5
10    C  1  2018       5
11    C  0  2019       6

非常感谢您的慷慨帮助！

Answer 1

将=(COUNTIFS($C$3:$C$14,C3,$D$3:$D$14,"<"&D3,$E$3:$E$14,"Wash")+1)+IF(LEN(C3)>6,COUNTIFS($C$3:$C$14,$C$3,$E$3:$E$14,"Wash"),0) 与DataFrame.cumsum一起使用：

boolean mask

Answer 2

这有效：

select currentStockDate as startDate, LEAD(currentStockDate,1) as EndDate, currentStock from (select * from (select LAG(transaction_date,1) over(order by transaction_date) as prevStockDate, transaction_date as CurrentstockDate, LAG(stock,1) over(order by transaction_date) as prevStock, stock as currentStock from sample_table) as t where (prevStock <> currentStock) or (prevStock is null) ) as t2

积分：@Quang Hoang（仅缺少括号）