Resolved: How to mark values while keep count each time we see a value?

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Question:

Does anyone know a way to count values while annotating them? For example.
We have a reparative state with 3 different values. A, B, C. We want to count how many times we saw state A but values A are different length of A’s in a column. We have Column A and want to get column B. So we have time series A, B, C and we want to count time wise that A is 1 then B is 1 and C is 1 but when we again see A is 2 and B is 2 and C is 2 and the A again is 3 and so on… (The order is always the same first A then B then C and again A…)
Any ideas?

Column A	Column B
A	1
A	1
A	1
B	1
B	1
C	1
A	2
B	2
C	2
A	3
A	3
A	3
A	3
A	3
A	3
B	3
B	3
B	3
C	3
C	3

Tried to get a loop but don’t know how to count the state.

Best Answer:

Here is a one way to do it :

N = len(df["Column A"].unique())

# is the current ts equal to the previous ? if so, cumsum
cs = df["Column A"].ne(df["Column A"].shift().bfill()).cumsum() 

df["Column B"] = (cs // N + 1).astype(int)

Output :

print(df)

   Column A  Column B
0         A         1
1         A         1
2         A         1
3         B         1
4         B         1
5         C         1
6         A         2
7         B         2
8         C         2
9         A         3
10        A         3
11        A         3
12        A         3
13        A         3
14        A         3
15        B         3
16        B         3
17        B         3
18        C         3
19        C         3

If you have better answer, please add a comment about this, thank you!

Source: Stackoverflow.com

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