merge() for SQL-like joins in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pandas merge() function do?

It combines two DataFrames based on common columns or indices, similar to SQL joins.

beginner

What are the main types of joins you can perform with merge()?

• Inner join: keeps only matching rows
• Left join: keeps all rows from left DataFrame
• Right join: keeps all rows from right DataFrame
• Outer join: keeps all rows from both DataFrames

beginner

How do you specify the columns to join on in merge()?

Use the on parameter with a column name or list of column names that exist in both DataFrames.

beginner

What happens if you use how='left' in merge()?

All rows from the left DataFrame are kept. Matching rows from the right DataFrame are added. If no match, right side columns have NaN.

intermediate

What is the difference between merge() and concat() in pandas?

merge() joins DataFrames based on keys (like SQL joins). concat() stacks DataFrames vertically or horizontally without matching keys.

Which how parameter in merge() keeps only rows that appear in both DataFrames?

A right

B left

C inner

D outer

The 'inner' join keeps only rows with matching keys in both DataFrames.

If you want to keep all rows from the right DataFrame and add matching rows from the left, which how do you use?

A inner

B right

C left

D outer

The 'right' join keeps all rows from the right DataFrame and matches from the left.

What parameter do you use to specify the column(s) to join on in merge()?

A key

B by

C join

D on

The 'on' parameter specifies the column(s) to join on.

What will be the result of an outer join?

A All rows from both DataFrames, filling missing with NaN

B All rows from the left DataFrame only

C Only rows common to both DataFrames

D Only rows from the right DataFrame

An outer join keeps all rows from both DataFrames, filling missing matches with NaN.

Which pandas function stacks DataFrames without matching keys?

A concat()

B merge()

C join()

D groupby()

concat() stacks DataFrames vertically or horizontally without matching keys.

Explain how to use pandas merge() to combine two DataFrames with a left join.

Think about keeping all rows from the first DataFrame and matching from the second.

Describe the difference between inner join and outer join in pandas merge().

Consider which rows are kept in each join type.