Conditional cumcount of values in second column

Question

I want to fill numbers in column flag , based on the value in column KEY .

• Instead of using cumcount() to fill incremental numbers, I want to fill same number for every two rows if the value in column KEY stays same.
• If the value in column KEY changes, the number filled changes also.

Here is the example, df1 is what I want from df0.

df0 = pd.DataFrame({'KEY':['0','0','0','0','1','1','1','2','2','2','2','2','3','3','3','3','3','3','4','5','6']})

df1 = pd.DataFrame({'KEY':['0','0','0','0','1','1','1','2','2','2','2','2','3','3','3','3','3','3','4','5','6'],
                    'flag':['0','0','1','1','2','2','3','4','4','5','5','6','7','7','8','8','9','9','10','11','12']})

Answer 1

You want to get the cumcount and add one. Then use %2 to differentiate between odd or even rows. Then, take the cumulative sum and subtract 1 to start counting from zero.

You can use:

df0['flag'] = ((df0.groupby('KEY').cumcount() + 1) % 2).cumsum() - 1
df0
Out[1]: 
   KEY  flag
0    0      0
1    0      0
2    0      1
3    0      1
4    1      2
5    1      2
6    1      3
7    2      4
8    2      4
9    2      5
10   2      5
11   2      6
12   3      7
13   3      7
14   3      8
15   3      8
16   3      9
17   3      9
18   4     10
19   5     11
20   6     12