Which of the following code blocks returns a new DataFrame where column sqft from DataFrame storesDF has had its missing values replaced with the value 30,000? A sample of DataFrame storesDF is below:

Question

Options

• AstoresDF.na.fill(30000, Seq("sqft"))
• BstoresDF.nafill(30000, col("sqft"))
• CstoresDF.na.fill(30000, col("sqft"))
• DstoresDF.fillna(30000, col("sqft"))
• EstoresDF.na.fill(30000, "sqft")

• B
  4% (1)
• C
  4% (1)
• E
  92% (23)

Explanation

In PySpark, DataFrame.na.fill(value, subset) fills null/NaN values. The subset parameter accepts a string column name or a list of strings. Option E - storesDF.na.fill(30000, 'sqft') - correctly passes the fill value and the column name as a string. Option A uses Seq('sqft'), which is Scala syntax (not valid in Python). Option B uses a non-existent method nafill. Option C passes a Column object col('sqft') as the subset, which is invalid - it must be a string or list of strings. Option D uses fillna (a valid alias) but also incorrectly passes a Column object rather than a string.

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