Which of the following code blocks returns a new DataFrame with column storeDescription where the pattern "Description: " has been removed from the beginning of column storeDescription in DataFrame…

Question

Options

• AstoresDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description:
• BstoresDF.withColumn("storeDescription", col("storeDescription").regexp_replace("^Description: ",
• CstoresDF.withColumn("storeDescription", regexp_extract(col("storeDescription"), "^Description: ",
• DstoresDF.withColumn("storeDescription", regexp_replace("storeDescription", "^Description: ", ""))
• EstoresDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: ",

• A
  3% (1)
• B
  11% (4)
• C
  5% (2)
• D
  3% (1)
• E
  79% (30)

Explanation

regexp_replace(column, pattern, replacement) is the correct PySpark function to replace a regex pattern with a string. Its first argument must be a Column object (via col()), the second is the regex pattern, and the third is the replacement string (empty string "" to delete the match). Option E - regexp_replace(col("storeDescription"), "^Description: ", "") - is correct. Option C uses regexp_extract, which captures a match rather than removing it. Option D passes a plain string instead of a col() object as the first argument. Option B incorrectly calls .regexp_replace() as a method on a Column object, which is not valid in PySpark's Python API. Option A appears truncated but also lacks the replacement argument.

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