drop_duplicates() for removal in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the drop_duplicates() function do in pandas?

It removes duplicate rows from a DataFrame, keeping only unique rows based on all or selected columns.

beginner

How do you keep the first occurrence of duplicates when using drop_duplicates()?

By default, drop_duplicates() keeps the first occurrence and removes later duplicates. You can also set keep='first' explicitly.

intermediate

What parameter do you use to remove duplicates based on specific columns only?

Use the subset parameter with a list of column names to consider only those columns when identifying duplicates.

intermediate

How can you remove duplicates and modify the original DataFrame directly?

Set the parameter inplace=True in drop_duplicates() to remove duplicates without creating a new DataFrame.

advanced

What happens if you set keep=False in drop_duplicates()?

All duplicates are removed, including the first occurrences, so only rows that are unique remain.

What is the default behavior of drop_duplicates() in pandas?

A Remove all duplicates including first occurrences

B Keep the first occurrence of duplicates

C Keep the last occurrence of duplicates

D Do nothing

By default, drop_duplicates() keeps the first occurrence and removes later duplicates.

Which parameter lets you specify columns to check for duplicates?

A columns

B axis

C filter

D subset

The subset parameter takes a list of columns to consider when identifying duplicates.

How do you remove duplicates and update the original DataFrame without creating a new one?

A Set <code>inplace=True</code>

B Set <code>keep=False</code>

C Use <code>drop()</code> instead

D Use <code>reset_index()</code>

Using inplace=True modifies the original DataFrame directly.

What does keep=False do in drop_duplicates()?

A Removes all duplicates including first occurrences

B Keeps the last duplicate

C Keeps all duplicates

D Keeps the first duplicate

Setting keep=False removes all duplicates, leaving only unique rows.

If you want to remove duplicates based on columns 'A' and 'B' only, which code is correct?

A df.drop_duplicates(keep=['A', 'B'])

B df.drop_duplicates(columns=['A', 'B'])

C df.drop_duplicates(subset=['A', 'B'])

D df.drop_duplicates(axis=['A', 'B'])

The subset parameter specifies columns to check for duplicates.

Explain how drop_duplicates() works and how you can control which duplicates to keep or remove.

Think about how to keep or remove duplicates and which columns to consider.

Describe a real-life example where removing duplicates from data is important and how drop_duplicates() helps.

Imagine cleaning a list of names or transactions.