Duplicates on specific columns in Pandas - Cheat Sheet & Quick Revision

Recall & Review

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What does the pandas function duplicated() do when used on a DataFrame?

It returns a Boolean Series indicating whether each row is a duplicate of a previous row. By default, it checks all columns.

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How can you check for duplicates only on specific columns in a pandas DataFrame?

Use df.duplicated(subset=[column_names]) where column_names is a list of columns to check for duplicates.

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What does the parameter keep='first' do in duplicated()?

It marks all duplicates as True except for the first occurrence, which is marked as False.

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How do you remove duplicate rows based on specific columns in pandas?

Use df.drop_duplicates(subset=[column_names]) to keep only the first occurrence of duplicates in those columns.

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Why might you want to check duplicates on specific columns instead of the whole DataFrame?

Because sometimes only certain columns define the uniqueness of a row, and other columns may have different data but still represent the same entity.

Which pandas method helps identify duplicate rows based on specific columns?

A fillna()

B dropna()

C groupby()

D duplicated(subset=[...])

The duplicated() method with the subset parameter checks duplicates on specific columns.

What does df.duplicated(subset=['col1', 'col2'], keep='last') do?

A Marks all duplicates except the last occurrence as True

B Marks all duplicates except the first occurrence as True

C Removes duplicates from the DataFrame

D Checks duplicates on all columns

The keep='last' parameter marks duplicates as True except for the last occurrence.

How do you remove duplicate rows based on columns 'A' and 'B' in pandas?

A df.duplicated(['A', 'B'])

B df.dropna(['A', 'B'])

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

D df.fillna(['A', 'B'])

drop_duplicates with subset=['A', 'B'] removes duplicates based on those columns.

If you want to find duplicates considering all columns, what should you do?

A Call duplicated() without subset parameter

B Use duplicated(subset=[])

C Use drop_duplicates(subset=[])

D Use duplicated(subset=None)

By default, duplicated() checks duplicates on all columns if subset is not provided.

Why is it useful to specify columns in the subset parameter when checking duplicates?

A To sort the DataFrame

B To focus on columns that define uniqueness

C To change data types

D To speed up the DataFrame

Specifying subset focuses duplicate detection on columns that matter for uniqueness.

Explain how to find and remove duplicate rows based on specific columns in a pandas DataFrame.

Think about how to tell pandas which columns to check for duplicates.

Why might checking duplicates on the entire DataFrame be less useful than checking on specific columns?

Consider real-life examples where only some details matter for identifying duplicates.