Removing duplicates from Pandas dataFrame with condition for retaining original

Question

Assuming I have the following DataFrame:

 A | B
 1 | Ms
 1 | PhD
 2 | Ms
 2 | Bs

I want to remove the duplicate rows with respect to column A, and I want to retain the row with value 'PhD' in column B as the original, if I don't find a 'PhD', I want to retain the row with 'Bs' in column B.

I am trying to use

 df.drop_duplicates('A') 

with a condition

Answer 1

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

import pandas as pd
df = pd.DataFrame([(1,'Ms'), (1, 'PhD'), (2, 'Ms'), (2, 'Bs'), (3, 'PhD'), (3, 'Bs'), (4, 'Ms'), (4, 'PhD'), (4, 'Bs')], columns=['A', 'B'])
df['B']=df['B'].astype('category')
# after setting the column's type to 'category', you can set the order
df['B']=df['B'].cat.set_categories(['PhD', 'Bs', 'Ms'], ordered=True)
df.sort(['A', 'B'], inplace=True)
df_unique = df.drop_duplicates('A')