Which of the following operations can be used to create a DataFrame with a subset of columns from DataFrame storesDF that are specified by name?

The correct answer is B. storesDF.select(). select() is the standard Spark DataFrame operation for projecting a subset of columns by name or column expression. For example, storesDF.select('col1', 'col2') returns a new DataFrame with only those columns. filter() operates on rows, not columns - it keeps all columns but…

Performing Basic DataFrame Transformations

Question

Options

AstoresDF.subset()
BstoresDF.select()
CstoresDF.selectColumn()
DstoresDF.filter()
EstoresDF.drop()

How the community answered

(20 responses)

B
90% (18)
D
5% (1)
E
5% (1)

Explanation

select() is the standard Spark DataFrame operation for projecting a subset of columns by name or column expression. For example, storesDF.select('col1', 'col2') returns a new DataFrame with only those columns. filter() operates on rows, not columns - it keeps all columns but removes rows that don't match a condition. drop() works on columns but removes the named columns rather than selecting them. subset() and selectColumn() are not valid Spark DataFrame methods.

Topics

#Spark DataFrame#Column Selection#DataFrame Transformations#PySpark

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