Pandas Dataframe Filtering based on rows

[mvdlm]

New to python and this forum

I have a dataset like this:

df1 = pd.DataFrame({'opA': [1,1,1,1,0], 
           'opB': [1,1,1,0,1],
            'opC': [1,1,1,1,2], 
           'opD': [0,1,1,0,3],
           'Active': ['opC','opD', 'opD', 'opC', 0]})
df1 = df1.rename(index={df1.last_valid_index() : 'Priority'})
df1.loc['Priority','Active'] = ''
print(df1)

The Active column consists of the OPcolumn name that has the max value in each row, while taking into factor the 'Priority' of each OPcolumn. For this, I have this code working:

df = df.sort_index(axis=1,key=lambda x:df.loc['Priority',x],ascending=False)
df['Active'] = df.idxmax(axis=1)

Now I need to do this:

df1 = pd.DataFrame({'opA': [1,1,1,1,0,0], 
           'opB': [1,1,1,0,1,0],
            'opC': [1,1,1,1,2,0], 
           'opD': [0,1,1,0,3,3],
           'Active': ['opC','opC', 'opC', 'opC', 0,0]})
df1 = df1.rename(index={df1.last_valid_index() - 1 : 'Priority'})
df1 = df1.rename(index={df1.last_valid_index() : 'minOccurrence'})
df1.loc['Priority','Active'] = ''
df1.loc['minOccurrence','Active'] = ''
print(df1)

Since opD doesn't have 3 straight "Actives" it isn't active at index 1 or 2 where previously it was Active based on 'Priority' column only.

vs. if opD had a 1 at index 0.

df1 = pd.DataFrame({'opA': [1,1,1,1,0,0], 
           'opB': [1,1,1,0,1,0],
            'opC': [1,1,1,1,2,0], 
           'opD': [1,1,1,0,3,3],
           'Active': ['opD','opD', 'opD', 'opC', 0,0]})
df1 = df1.rename(index={df1.last_valid_index() - 1 : 'Priority'})
df1 = df1.rename(index={df1.last_valid_index() : 'minOccurrence'})
df1.loc['Priority','Active'] = ''
df1.loc['minOccurrence','Active'] = ''
print(df1)

How do I do this? The minOccurrence row can have any values not just 0,0,0,3. (e.g. 0,1,3,2)