Find the min/max of rows with overlapping column values, create new column to represent the full range of both - pandas

Question

I'm using Pandas DataFrames. I'm looking to identify all rows where both columns A and B == True, then represent in Column C the all points on other side of that intersection where only A or B is still true but not the other. For example:
A B C
0 False False False
1 True False True
2 True True True
3 True True True
4 False True True
5 False False False
6 True False False
7 True False False
I can find the direct overlaps quite easily:
df.loc[(df['A'] == True) & (df['B'] == True), 'C'] = True
... however this does not take into account the overlap need.
I considered creating column 'C' in this way, then grouping each column:
grp_a = df.loc[(df['A'] == True), 'A'].groupby(df['A'].astype('int').diff.ne(0).cumsum())
grp_b = df.loc[(df['A'] == True), 'A'].groupby(df['A'].astype('int').diff.ne(0).cumsum())
grp_c = df.loc[(df['A'] == True), 'A'].groupby(df['A'].astype('int').diff.ne(0).cumsum())
From there I thought to iterate over the indexes in grp_c.indices and test the indices in grp_a and grp_b against those, find the min/max index of A and B and update column C. This feels like an inefficient way of getting to the result I want though.
Ideas?

Answer 1

Try this:
#Input df just columns 'A' and 'B'
df = df[['A','B']]
df['C'] = df.assign(C=df.min(1)).groupby((df[['A','B']].max(1) == 0).cumsum())['C']\
.transform('max').mask(df.max(1)==0, False)
print(df)
Output:
A B C
0 False False False
1 True False True
2 True True True
3 True True True
4 False True True
5 False False False
6 True False False
7 True False False
Explanation:
First, create column 'C' with the assignment of minimum value, what this does is to ass True to C where both A and B are True. Next, using
df[['A','B']].max(1) == 0
0 True
1 False
2 False
3 False
4 False
5 True
6 False
7 False
dtype: bool
We can find all of the records were A and B are both False. Then we use cumsum to create a count of those False False records. Allowing us to create grouping of records with the False False recording having a count up until the next False False record which gets incremented.
(df[['A','B']].max(1) == 0).cumsum()
0 1
1 1
2 1
3 1
4 1
5 2
6 2
7 2
dtype: int32
Let's group the dataframe with the newly assigned column C by this grouping created with cumsum. Then take the maximum value of column C from that group. So, if the group has a True True record, assign True to all the records in that group. Lastly, use mask to turn the first False False record back to False.
df.assign(C=df.min(1)).groupby((df[['A','B']].max(1) == 0).cumsum())['C']\
.transform('max').mask(df.max(1)==0, False)
0 False
1 True
2 True
3 True
4 True
5 False
6 False
7 False
Name: C, dtype: bool
And, assign that series to df['C'] overwriting the temporarily assigned C in the statement.
df['C'] = df.assign(C=df.min(1)).groupby((df[['A','B']].max(1) == 0).cumsum())['C']\
.transform('max').mask(df.max(1)==0, False)