Which of the following operations can perform an outer join on two DataFrames?

The correct answer is D. DataFrame.join(). DataFrame.join() is the correct answer because it accepts a how parameter that supports outer join variants: "outer", "full", "full_outer", "left", "left_outer", "right", and "right_outer". DataFrame.crossJoin() (A) performs a Cartesian product, not an outer join. There is no…

Performing Data Transformations with Spark DataFrames

Question

Options

ADataFrame.crossJoin()
BStandalone join() function
CDataFrame.outerJoin()
DDataFrame.join()
EDataFrame.merge()

How the community answered

(58 responses)

B
2% (1)
C
3% (2)
D
93% (54)
E
2% (1)

Explanation

DataFrame.join() is the correct answer because it accepts a how parameter that supports outer join variants: "outer", "full", "full_outer", "left", "left_outer", "right", and "right_outer". DataFrame.crossJoin() (A) performs a Cartesian product, not an outer join. There is no standalone join() function (B) or DataFrame.outerJoin() method (C) in PySpark. DataFrame.merge() (E) is a pandas method and does not exist on PySpark DataFrames.

Topics

#Spark DataFrames#Join Operations#Outer Join#DataFrame API

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