Which of the following code blocks returns a DataFrame where rows in DataFrame storesDF containing missing values in every column have been dropped?

Question

Options

• AstoresDF.na.drop()
• BstoresDF.dropna()
• CstoresDF.na.drop("all", subset = "sqft")
• DstoresDF.na.drop("all")
• EstoresDF.nadrop("all")

• A
  4% (1)
• B
  4% (1)
• C
  4% (1)
• D
  88% (23)

Explanation

storesDF.na.drop('all') drops only the rows where every column contains a null (missing) value. The 'all' argument means a row is dropped only if all its values are null. Choice A (na.drop() with no arguments) uses the default 'any', which drops rows that have at least one null in any column. Choice B (dropna()) is pandas syntax and is not valid for Spark DataFrames. Choice C applies 'all' but restricts the check to the sqft subset. Choice E uses nadrop() which is not a valid method.

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