Pandas: Change subset of rows that contain duplicate values for a particular column based on values across all duplicates

I'm new to Pandas and trying to understand how to modify a subset of rows that have duplicate values for a particular column, with the decision of which rows to change being made based on a conditional check across those duplicates.

Say I have a (contrived) dataframe like so:

    Class      Length     Head Teacher   Premium Course                       
0   Maths      Medium     Mr. Bloggs     Yes
1   English    Short      Mr. Plum       Yes
2   English    Long       Mrs. Green     Yes
3   English    Medium     Mr. Top        Yes 
4   Science    Long       Mrs. Blue      Yes    
5   Science    Long       Mr. Red        Yes
6   ...

Wherever there are duplicate classes I want to replace the Teacher across all the duplicates with the Head Teacher from the longest class, and remove the Premium Course value for all the duplicates that are not the longest class. If the duplicate classes are all the same length, then simply take the teacher from the first duplicate, and the opposite for the Premium Course ie.

    Class      Length     Head Teacher   Premium Course                       
0   Maths      Medium     Mr. Bloggs     Yes
1   English    Short      Mrs. Green     
2   English    Long       Mrs. Green     Yes
3   English    Medium     Mrs. Green      
4   Science    Long       Mrs. Blue      Yes    
5   Science    Long       Mrs. Blue
6   ... 

In Python I would typically use loops, conditional statements etc and build a new list in memory. But I'm trying to determine the best approach in pandas.

I've been looking at the duplicated and groupby functions but have been unable to land on a solution. Any advice or help would be helpful. Trying to make the shift into thinking in a "Vectorized" way.

Example Code

import pandas as pd
data1 = {'Class': ['Maths', 'English', 'English', 'English', 'Science', 'Science'], 
         'Length': ['Medium', 'Short', 'Long', 'Medium', 'Long', 'Long'], 
         'Head Teacher': ['Mr. Bloggs', 'Mr. Plum', 'Mrs. Green', 'Mr. Top', 'Mrs. Blue', 'Mr. Red'], 
         'Premium Course': ['Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes']}
df = pd.DataFrame(data1)

Step1

make condition

m = {'Short':0, 'Medium':1, 'Long':2}
cond = df.groupby('Class')['Length'].transform(lambda x: x.index == x.map(m).idxmax())

cond

0     True
1    False
2     True
3    False
4     True
5    False
Name: Length, dtype: bool

Step2

edit columns

df['Head Teacher'] = df['Head Teacher'].where(cond).groupby(df['Class']).ffill().bfill()
df['Premium Course'] = df['Premium Course'].where(cond)

df

    Class	Length	Head Teacher	Premium Course
0	Maths	Medium	Mr. Bloggs	    Yes
1	English	Short	Mrs. Green	    NaN
2	English	Long	Mrs. Green	    Yes
3	English	Medium	Mrs. Green	    NaN
4	Science	Long	Mrs. Blue	    Yes
5	Science	Long	Mrs. Blue	    NaN

Use ordered Categorical for Length column, so possible create mask by DataFrame.sort_values and DataFrame.duplicated, DataFrame.sort_index is for original order of rows and set NaNs for not matched values in Series.mask with GroupBy.transform for get first non NaN value:

df['Length'] = pd.Categorical(df['Length'], 
                              categories=['Long','Medium','Short'],
                              ordered=True)

mask = df.sort_values('Length').duplicated(['Class']).sort_index()
df['Head Teacher'] = df['Head Teacher'].mask(mask).groupby(df['Class']).transform('first')
df['Premium Course'] = df['Premium Course'].mask(mask)

print (df)
     Class  Length Head Teacher Premium Course
0    Maths  Medium   Mr. Bloggs            Yes
1  English   Short   Mrs. Green            NaN
2  English    Long   Mrs. Green            Yes
3  English  Medium   Mrs. Green            NaN
4  Science    Long    Mrs. Blue            Yes
5  Science    Long    Mrs. Blue            NaN