Right join behavior in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does a right join do in pandas?

A right join returns all rows from the right DataFrame and the matching rows from the left DataFrame. If there is no match, the result will have NaN for columns from the left DataFrame.

beginner

Which pandas function is used to perform a right join?

The pandas function merge() with the parameter how='right' is used to perform a right join.

beginner

If the left DataFrame has no matching keys for some rows in the right DataFrame during a right join, what happens?

Those rows from the right DataFrame still appear in the result, but columns from the left DataFrame will have NaN values for those rows.

intermediate

How is a right join different from a left join in pandas?

A right join keeps all rows from the right DataFrame, while a left join keeps all rows from the left DataFrame. Both include matching rows from the other DataFrame.

beginner

Write a pandas code snippet to perform a right join on DataFrames df1 and df2 using the column 'id'.

result = df1.merge(df2, on='id', how='right')

What does the parameter how='right' do in pandas merge()?

A Returns all rows from the left DataFrame and matching rows from the right

B Returns all rows from the right DataFrame and matching rows from the left

C Returns only matching rows from both DataFrames

D Returns all rows from both DataFrames

Using how='right' returns all rows from the right DataFrame and matching rows from the left DataFrame.

If a row in the right DataFrame has no match in the left DataFrame during a right join, what will the left DataFrame columns show?

A NaN values

B The matching values from the left DataFrame

C Empty strings

D The row will be excluded

Rows from the right DataFrame without matches in the left will have NaN in the left DataFrame columns.

Which of these is a correct way to perform a right join in pandas?

A df1.merge(df2, how='right')

B df1.merge(df2, how='left')

C df1.join(df2, how='inner')

D df1.concat(df2, how='right')

The correct syntax for a right join is merge() with how='right'.

What happens if you perform a right join on two DataFrames with no common keys?

A Result will be empty

B All rows from the left DataFrame with NaN for right DataFrame columns

C All rows from the right DataFrame with NaN for left DataFrame columns

D An error is raised

Right join returns all rows from the right DataFrame even if no keys match, filling left columns with NaN.

Which join type in pandas is the opposite of a right join?

A Inner join

B Outer join

C Cross join

D Left join

A left join returns all rows from the left DataFrame, opposite to right join which returns all rows from the right DataFrame.

Explain how a right join works in pandas and when you might use it.

Think about which DataFrame's rows you want to keep all of.

Describe the difference between left join and right join in pandas with an example.

Focus on which DataFrame's rows are preserved fully.