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The code block displayed below contains an error. The code block should return a DataFrame in which column predErrorAdded contains the results of Python function add_2_if_geq_3 as applied to numeric and nullable column predError in DataFrame transactionsDf. Find the error. Code block: 1.def add_2_if_geq_3(x): 2. if x is None: 3. return x 4. elif x >= 3: 5. return x+2 6. return x 7. 8.add_2_if_geq_3_udf = udf(add_2_if_geq_3) 9. 10.transactionsDf.withColumnRenamed("predErrorAdded", add_2_if_geq_3_udf(col("predError")))
Correct Answer: A
Explanation Correct code block: def add_2_if_geq_3(x): if x is None: return x elif x >= 3: return x+2 return x add_2_if_geq_3_udf = udf(add_2_if_geq_3) transactionsDf.withColumn("predErrorAdded", add_2_if_geq_3_udf(col("predError"))).show() Instead of withColumnRenamed, you should use the withColumn operator. The udf() method does not declare a return type. It is fine that the udf() method does not declare a return type, this is not a required argument. However, the default return type is StringType. This may not be the ideal return type for numeric, nullable data - but the code will run without specified return type nevertheless. The Python function is unable to handle null values, resulting in the code block crashing on execution. The Python function is able to handle null values, this is what the statement if x is None does. UDFs are only available through the SQL API, but not in the Python API as shown in the code block. No, they are available through the Python API. The code in the code block that concerns UDFs is correct. Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so transactionsDf.predError. You may choose to use the transactionsDf.predError syntax, but the col("predError") syntax is fine.