Pandas replacing values depending on prior row - python-2.7

Question

I'm pretty new to pandas and would like your input on how to tackle my problem. I've got the following data frame:
df = pd.DataFrame({'A' : ["me","you","you","me","me","me","me"],
'B' : ["Y","X","X","X","X","X","Z"],
'C' : ["1","2","3","4","5","6","7"]
})
I need to transform it based on the row values in column A and B. The logic should be that as soon as values in column A and B are the same on consecutive rows, the first row in this sequence should be maintained but following rows should have an 'A' set in column B.
For example: Values in column A and B are the same in row 1 and 2. Value in column B row 2 should be replaced with A. This is my expected output:
df2= pd.DataFrame({'A' : ["me","you","you","me","me","me","me"],
'B' : ["Y","X","A","X","A","A","Z"],
'C' : ["1","2","3","4","5","6","7"]})

Answer 1

You can first sum columns A and B:
a = df.A + df.B
Then compare with shifted version:
print (a != a.shift())
0 True
1 True
2 False
3 True
4 False
5 False
6 True
dtype: bool
Create unique groups by cumsum:
print ((a != a.shift()).cumsum())
0 1
1 2
2 2
3 3
4 3
5 3
6 4
dtype: int32
Get boolean mask where values are duplicated:
print ((a != a.shift()).cumsum().duplicated())
0 False
1 False
2 True
3 False
4 True
5 True
6 False
dtype: bool
Solutions for replace True values to A:
df.loc[(a != a.shift()).cumsum().duplicated(), 'B'] = 'A'
print (df)
A B C
0 me Y 1
1 you X 2
2 you A 3
3 me X 4
4 me A 5
5 me A 6
6 me Z 7
df.B = df.B.mask((a != a.shift()).cumsum().duplicated(), 'A')
print (df)
A B C
0 me Y 1
1 you X 2
2 you A 3
3 me X 4
4 me A 5
5 me A 6
6 me Z 7
print (df2.equals(df))
True