Databricks Data Engineer Associate Question 151
Single answerYou are working with a DataFrame in Databricks that contains a column named event_time with values stored as strings in the format 'yyyy-MM-dd HH:mm:ss'. You need to cast this column to a timestamp datatype for downstream processing. Which of the following code snippets will accomplish this correctly?
- A
df.withColumn('event_time', df['event_time'].cast('timestamp'))
- B
df.withColumn('event_time', df['event_time'].cast('datetime'))
- C
df.withColumn('event_time', df['event_time'].astype('timestamp'))
- D
df.withColumn('event_time', to_timestamp(df['event_time'], 'yyyy-MM-dd HH:mm:ss'))
Show answer and explanation
Correct answer: A
Explanation
To cast a column to a timestamp datatype in PySpark, you use the cast method with the desired datatype as its argument. The correct syntax is df.withColumn('column_name', df['column_name'].cast('timestamp')). This ensures the event_time column is converted to the appropriate timestamp format for further processing.
- A. Correct.
This is the correct syntax for casting a column to a timestamp in PySpark/DataFrame APIs. The
castmethod is used to convert the column datatype to the specified type, in this case, 'timestamp'. - B. Incorrect.
This is incorrect because 'datetime' is not a valid datatype recognized by PySpark's
castmethod. - C. Incorrect.
This is incorrect because
astypeis not a valid method in PySpark for casting a column's datatype. It is used in pandas, not PySpark. - D. Incorrect.
This is incorrect because while
to_timestampis a valid function for converting string columns to timestamps, it is not used in combination withwithColumnlike this. Instead, it is typically used in expressions or SQL transformations.