Which of the following code blocks returns the number of rows in DataFrame storesDF for each unique value in column division?

Question

Options

• AstoresDF.groupBy("division").agg(count())
• BstoresDF.agg(groupBy("division").count())
• CstoresDF.groupby.count("division")
• DstoresDF.groupBy().count("division")
• EstoresDF.groupBy("division").count()

• A
  4% (1)
• C
  4% (1)
• D
  4% (1)
• E
  89% (25)

Explanation

To count rows per group in PySpark, use groupBy(column).count(). Option E - storesDF.groupBy('division').count() - correctly groups by 'division' and counts the rows in each group. Option A calls count() without any argument inside agg(), which requires count('*') or count(col(...)) to work. Option B reverses the order - agg() and groupBy() cannot be chained that way. Option C uses lowercase groupby with incorrect .count('division') syntax. Option D calls groupBy() with no arguments, meaning no grouping column is specified, and then .count('division') is not valid syntax for the count method.

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