Removing duplicates from Pandas dataFrame with condition for retaining original

>>> df
    A   B
0   1   Ms
1   1   Ms
2   1   Ms
3   1   Ms
4   1   PhD
5   2   Ms
6   2   Ms
7   2   Bs
8   2   PhD

Sorting a dataframe with a custom function:

def sort_df(df, column_idx, key):
    '''Takes a dataframe, a column index and a custom function for sorting, 
    returns a dataframe sorted by that column using that function'''
    
    col = df.ix[:,column_idx]
    df = df.ix[[i[1] for i in sorted(zip(col,range(len(col))), key=key)]]
    return df

Our function for sorting:

cmp = lambda x:2 if 'PhD' in x else 1 if 'Bs' in x else 0

In action:

sort_df(df,'B',cmp).drop_duplicates('A', take_last=True) P.S. in modern pandas versions there is no option take_last, use keep instead - see the doc.

    A   B
4   1   PhD
8   2   PhD

Consider using Categoricals. They're a nice was to group / order text non-alphabetically (among other things.)

import pandas as pd  
#create a pandas dataframe for testing with two columns A integer and B string 
df = pd.DataFrame([(1, 'Ms'),  (1, 'PhD'),  
                   (2, 'Ms'),  (2, 'Bs'),  
                   (3, 'PhD'), (3, 'Bs'),  
                   (4, 'Ms'),  (4, 'PhD'),   (4, 'Bs')], 
                   columns=['A', 'B']) 
print("Original data") 
print(df) 
 
# force the column's string column B to type 'category'  
df['B'] = df['B'].astype('category') 
# define the valid categories: 
df['B'] = df['B'].cat.set_categories(['PhD', 'Bs', 'Ms'], ordered=True) 
#pandas dataframe sort_values to inflicts order on your categories 
df.sort_values(['A', 'B'], inplace=True, ascending=True) 
print("Now sorted by custom categories (PhD > Bs > Ms)") 
print(df) 
# dropping duplicates keeps first
df_unique = df.drop_duplicates('A') 
print("Keep the highest value category given duplicate integer group") 
print(df_unique)

Prints:

Original data
   A    B
0  1   Ms
1  1  PhD
2  2   Ms
3  2   Bs
4  3  PhD
5  3   Bs
6  4   Ms
7  4  PhD
8  4   Bs
Now sorted by custom categories (PhD > Bs > Ms)
   A    B
1  1  PhD
0  1   Ms
3  2   Bs
2  2   Ms
4  3  PhD
5  3   Bs
7  4  PhD
8  4   Bs
6  4   Ms
Keep the highest value category given duplicate integer group
   A    B
1  1  PhD
3  2   Bs
4  3  PhD
7  4  PhD

Assuming uniqueness of B value given A value, and that each A value has a row with Bs in the B column:

df2 = df[df['B']=="PhD"]

will give you a dataframe with the PhD rows you want.

Then remove all the PhD and Ms from df:

df = df[df['B']=="Bs"]

Then concatenate df and df2:

df3 = concat([df2, df])

Then you can use drop_duplicates like you wanted:

df3.drop_duplicates('A', inplace=True)