Right join behavior in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a right join of df1 and df2 on the 'key' column.

Pandas

result = df1.merge(df2, how='[1]', on='key')

Drag options to blanks, or click blank then click option'

Aright

Binner

Cleft

Douter

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' instead of 'right' will keep all rows from the left DataFrame.

Using 'inner' will only keep rows with matching keys in both DataFrames.

The right join keeps all rows from the right DataFrame (df2) and matches rows from the left (df1).

2fill in blank

medium

Complete the code to merge df1 and df2 with a right join and suffixes '_left' and '_right' for overlapping columns.

Pandas

result = df1.merge(df2, how='right', on='key', suffixes=[1])

Drag options to blanks, or click blank then click option'

A('_x', '_y')

B('_left', '_right')

C('_l', '_r')

D('_a', '_b')

Attempts:

3 left

💡 Hint

Common Mistakes

Using suffixes that are not tuples causes errors.

Using default suffixes may confuse which column came from which DataFrame.

Using suffixes ('_left', '_right') helps identify columns from each DataFrame after the join.

3fill in blank

hard

Fix the error in the code to perform a right join on 'id' column.

Pandas

result = df1.merge(df2, how='[1]', on='id')

Drag options to blanks, or click blank then click option'

Aright join

BRight

Cright

DRIGHT

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'right join' instead of 'right' causes a ValueError.

Using uppercase 'RIGHT' is not recognized.

The how parameter must be a lowercase string like 'right'. Variations like 'right join' or uppercase cause errors.

4fill in blank

hard

Fill both blanks to create a right join of dfA and dfB on 'user_id' with indicator column.

Pandas

result = dfA.merge(dfB, how='[1]', on=[2], indicator=True)

Drag options to blanks, or click blank then click option'

Aright

Bleft

C'user_id'

D'id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' instead of 'right' changes which DataFrame's rows are kept.

Using the wrong column name causes a KeyError.

Use how='right' to keep all rows from dfB and on='user_id' to join on the correct column.

5fill in blank

hard

Fill all three blanks to perform a right join of dfX and dfY on 'order_id' with suffixes and indicator.

Pandas

result = dfX.merge(dfY, how='[1]', on=[2], suffixes=[3], indicator=True)

Drag options to blanks, or click blank then click option'

Aright

B'order_id'

C('_left', '_right')

Dleft

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' instead of 'right' changes the join behavior.

Not passing suffixes as a tuple causes errors.

Using wrong column name causes join failure.

Use how='right' for right join, on='order_id' for the join key, and suffixes=('_left', '_right') to distinguish overlapping columns.