Multi-column joins in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to join two DataFrames on multiple columns.

Apache Spark

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

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

A'date'

B['id', 'date']

C'id'

Ddf2.id == df1.id

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single column name as a string when multiple columns are needed.

Using a condition expression instead of a list for the 'on' parameter.

To join on multiple columns, pass a list of column names to the on parameter.

2fill in blank

medium

Complete the code to perform an inner join on columns 'user_id' and 'order_id'.

Apache Spark

result = df_orders.join(df_users, on=[1], how='inner')

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

A'order_id'

Bdf_orders.user_id == df_users.user_id

C['user_id', 'order_id']

D'user_id'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a single column name instead of a list for multiple columns.

Not specifying the join type when needed.

For joining on multiple columns, provide a list of column names to the on parameter.

3fill in blank

hard

Fix the error in the join condition to correctly join on 'city' and 'state'.

Apache Spark

joined = df_a.join(df_b, (df_a.city == df_b.city) & (df_a.[1] == df_b.state))

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

Astate

Bcity

Czip

Dcountry

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name on one side of the condition.

Mixing column names that don't exist in the DataFrame.

The join condition must compare the 'state' columns from both DataFrames.

4fill in blank

hard

Fill both blanks to create a dictionary for joining on 'country' and 'city' with different column names.

Apache Spark

join_cols = { [1]: [2] }

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

A'country'

B'city'

C'country_name'

D'city_name'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list instead of a dictionary for columns with different names.

Swapping keys and values in the dictionary.

When joining on columns with different names, use a dictionary mapping left to right column names.

5fill in blank

hard

Fill all three blanks to create a join condition using multiple columns with different names.

Apache Spark

join_condition = (df1.[1] == df2.[2]) & (df1.[3] == df2.city)

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

Acountry

Bcountry_name

Cstate

Dstate_name

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up column names between DataFrames.

Using | instead of & for combining conditions.

Match columns with different names by comparing df1's column to df2's corresponding column.