Drop a row when a column value is duplicated if another column meets a criteria

Question

I have a DataFrame where I want to drop a row (or rows) where more than one consecutive row has the same value for a column based on the value of a different column. In this instance, I want to keep the row with the highest value in B if high or the lowest if low. In essence, I'm trying to only have highs followed by lows and lows by highs.

df = pd.DataFrame({'A': ['low', 'high', 'high', 'low', 'low','low'],                   
                   'B': [10, 70, 90, 40, 50,60]}) 

Output:

     A     B
0   low   10
1   high  70
2   high  90
3   low   40
4   low   50
5   low   60

Desired:

     A     B
0   low   10
2   high  90
3   low   40

Trying to get my head around how to implement the logic and have run into a brick wall.

Answer 1

Here is another way:

d = {'low':-1}

(df.assign(B = df['B'].mul(df['A'].map(d),fill_value=1))
 .groupby(['A',pd.Series(pd.factorize(df['A'])[0]).diff().ne(0).cumsum()]).max()
 .abs()
 .sort_index(level=1)
 .droplevel(1)
 .reset_index())

or

df.loc[df['A'].map({'low':-1}).mul(df['B'],fill_value=1).groupby(df['A'].ne(df['A'].shift()).cumsum()).idxmax()]

Output:

      A     B
0   low  10.0
1  high  90.0
2   low  40.0