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  
#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

Answer 2

>>> 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) PS in modern pandas versions there is no option take_last , use keep instead - see the doc .

    A   B
4   1   PhD
8   2   PhD

Answer 3

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)

Answer 4

Remove duplicates retain original:

Sort your columns to put the one you want to keep on the top, then drop_duplicates does the right thing.

import pandas as pd 
df = pd.DataFrame([(1, '2022-01-25'), 
                   (1, '2022-05-25'), 
                   (2, '2021-12-20'), 
                   (2, '2021-11-20'), 
                   (3, '2020-03-03'), 
                   (3, '2020-03-04'), 
                   (4, '2019-07-06'), 
                   (4, '2019-07-07'), 
                   (4, '2019-07-05')], columns=['A', 'B']) 
print("Original data") 
print(df.to_string(index=False) ) 
 
#Sort your dataframe so that the one you want is on the top:
df.sort_values(['A', 'B'], inplace=True, ascending=True) 
print("custom sort") 
print(df.to_string(index=False) ) 

# dropping duplicates this way keeps first 
df_unique = df.drop_duplicates('A') 
print("Keep first") 
print(df_unique.to_string(index=False) ) 

Prints:

Original data
 A           B
 1  2022-01-25
 1  2022-05-25
 2  2021-12-20
 2  2021-11-20
 3  2020-03-03
 3  2020-03-04
 4  2019-07-06
 4  2019-07-07
 4  2019-07-05

custom sort
 A           B
 1  2022-01-25
 1  2022-05-25
 2  2021-11-20
 2  2021-12-20
 3  2020-03-03
 3  2020-03-04
 4  2019-07-05
 4  2019-07-06
 4  2019-07-07

Keep first
 A           B
 1  2022-01-25
 2  2021-11-20
 3  2020-03-03
 4  2019-07-05