Inner, left, right, and full outer joins in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform an inner join between two DataFrames df1 and df2 on the column 'id'.

Apache Spark

joined_df = df1.join(df2, on='id', how='[1]')

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

Ainner

Bouter

Cright

Dleft

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' or 'right' instead of 'inner' will include unmatched rows from one side.

Using 'outer' will include all rows from both DataFrames.

The inner join returns only the rows with matching keys in both DataFrames.

2fill in blank

medium

Complete the code to perform a left join between df1 and df2 on the column 'user_id'.

Apache Spark

joined_df = df1.join(df2, on='user_id', how='[1]')

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

Aleft

Bright

Cinner

Dfull

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inner' will exclude unmatched left rows.

Using 'right' will keep all right rows instead.

The left join returns all rows from the left DataFrame (df1) and matched rows from the right DataFrame (df2). Unmatched right rows are null.

3fill in blank

hard

Fix the error in the code to perform a right join between dfA and dfB on 'key'.

Apache Spark

result = dfA.join(dfB, on='key', how='[1]')

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

Ainner

Bleft

Cright

Douter

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' will keep all left rows, not right.

Using 'inner' excludes unmatched rows.

The right join keeps all rows from the right DataFrame (dfB) and matched rows from the left DataFrame (dfA). Unmatched left rows are null.

4fill in blank

hard

Fill both blanks to perform a full outer join between dfX and dfY on 'id' and select only rows where 'id' is not null.

Apache Spark

joined = dfX.join(dfY, on='id', how='[1]')
filtered = joined.filter(joined.id [2] None)

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

Aouter

B==

C!=

Dleft

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' or 'inner' will exclude some rows.

Filtering with '== None' keeps only null ids, which is usually unwanted.

A full outer join keeps all rows from both DataFrames. Filtering with != None keeps rows where 'id' exists (not null).

5fill in blank

hard

Fill all three blanks to create a dictionary of user names and their ages from dfUsers where age is greater than 20.

Apache Spark

user_dict = {row['[1]']: row['[2]'] for row in dfUsers.collect() if row['[3]'] > 20}

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

Aname

Bage

Did

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'id' instead of 'name' for keys.

Filtering on 'name' instead of 'age'.

This dictionary comprehension uses 'name' as keys and 'age' as values, filtering rows where 'age' is greater than 20.