Pandas：如何删除重复但保留某些行值的列值

Question

I have a Pandas dataframe that have duplicate names but with different values, and I want to remove the duplicate names but keep the rows. A snippet of my dataframe looks like this:

And my desired output would look like this:

I've tried using the builtin pandas function.drop_duplicates(), but I end up deleting all duplicates and their respective rows. My current code looks like this:

df = pd.read_csv("merged_db.csv", encoding = "unicode_escape", chunksize=50000)
df = pd.concat(df, ignore_index=True)
df2 = df.drop_duplicates(subset=['auth_given_name', 'auth_surname'])

and this is output I am currently getting:

Basically, I want to return all the values of the coauthor but remove all duplicate data of the original author. My question is what is the best way to achieve the output that I want. I tried using the subset parameter but I don't believe I'm using it correctly.I also found a similar post , but I couldn't really apply it to python. Thank you for your time!

Answer 1

You may consider this code

df = pd.read_csv("merged_db.csv", encoding = "unicode_escape", chunksize=50000)
first_author = df.columns[:24]
df.loc[df.duplicated(first_author), first_author] = np.empty(len(first_author))
print(df)