df.drop_duplicates(subset=[‘name’, ‘age’], keep=’first’, inplace=True) vs df[‘name’] = df[‘name’].drop_duplicates(keep=’first’)

1. df['name'] = df['name'].drop_duplicates(keep='first')

• This operation is applied only to the name column.
• It will remove duplicate values in the name column and assign the result back to df['name'], leaving all other columns unchanged.
• After removing duplicates in name, it will fill those positions with NaN or cause misalignment with other columns if assigned back to df['name'].

Example:

df['name'] = df['name'].drop_duplicates(keep='first')

If your original DataFrame was:

The resulting DataFrame would be:

Here, Alice is duplicated, so only the first instance is kept, and the second Alice is dropped, causing a NaN in that row’s name column.

2. df.drop_duplicates(subset=['name', 'age'], keep='first', inplace=True)

This removes duplicate rows based on the combination of the name and age columns.It will keep only the first occurrence of each unique combination of name and age while removing the entire row if a duplicate is found.

df.drop_duplicates(subset=['name', 'age'], keep='first', inplace=True)

If the original DataFrame was:

The resulting DataFrame would be:

Here, only rows with the same name and age combination (in this case, the first two rows with Alice and 25) will be considered duplicates, and only the first occurrence is kept.

Key Differences:

• First operation only removes duplicates in the name column, possibly leaving misaligned rows (resulting in NaNs).
• Second operation removes entire rows based on duplicate combinations of values in name and age, maintaining row alignment.

In most cases, you would prefer using df.drop_duplicates() for handling duplicates in a DataFrame to ensure consistency across rows.