Select, filter, and where operations in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the 'name' column from the DataFrame.

Apache Spark

df.select([1]).show()

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A['name']

Bname

C"name"

Ddf.name

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column name without quotes causes an error.

Using a list instead of a string for a single column.

In Spark, to select a column by name, you pass the column name as a string to select().

2fill in blank

medium

Complete the code to filter rows where the 'age' column is greater than 30.

Apache Spark

df.filter(df.age [1] 30).show()

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A<

B==

C<=

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' changes the filter logic.

Using '==' filters only rows with age exactly 30.

The filter condition should check if 'age' is greater than 30, so use the '>' operator.

3fill in blank

hard

Fix the error in the code to filter rows where 'salary' is less than 50000.

Apache Spark

df.where(df.salary [1] 50000).show()

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A>=

B<

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '>' filters salaries greater than 50000, which is incorrect.

Using '==' filters only salaries exactly 50000.

The condition should be 'salary' less than 50000, so use the '<' operator.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths for words longer than 3 characters.

Apache Spark

lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B<=

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using '<=' instead of '>' in the filter condition.

The dictionary maps each word to its length, so the value is len(word). The filter keeps words longer than 3 characters, so use >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their values for values greater than 0.

Apache Spark

result = { [1]: [2] for k, v in data.items() if v [3] 0 }

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys instead of uppercase keys.

Using '<' or '==' instead of '>' in the filter condition.

The keys are uppercase versions of k, so use k.upper(). The values are v. The filter keeps items where v is greater than 0, so use >.