Databricks Machine Learning Associate Question 347
Single answerYou are working with a large dataset in a Databricks notebook. The dataset is initially loaded as a PySpark DataFrame, and you need to convert it to a Pandas on Spark DataFrame to leverage some pandas-like operations while maintaining the scalability of Spark. Which of the following methods can be used to achieve this conversion?
- A
Use the
to_pandas_on_spark()method on the PySpark DataFrame. - B
Use the
to_spark()method on the PySpark DataFrame. - C
Use the
pandas_api()method on the PySpark DataFrame. - D
Use the
toPandas()method on the PySpark DataFrame.
Show answer and explanation
Correct answer: A
Explanation
To convert a PySpark DataFrame to a Pandas on Spark DataFrame, the to_pandas_on_spark() method should be used. This ensures that the data remains distributed and scalable, while enabling pandas-like operations using the Pandas on Spark API. Other methods, like toPandas(), create a pandas DataFrame, which does not scale well with large datasets.
- A. Correct.
Correct. The
to_pandas_on_spark()method is specifically designed to convert a PySpark DataFrame to a Pandas on Spark DataFrame, ensuring scalability and compatibility with Spark. - B. Incorrect.
Incorrect. The
to_spark()method does not exist for PySpark DataFrames. This is not a valid approach. - C. Incorrect.
Incorrect. While
pandas_api()is related to Pandas on Spark, it is not used for converting a PySpark DataFrame directly to a Pandas on Spark DataFrame. - D. Incorrect.
Incorrect. The
toPandas()method converts a PySpark DataFrame to a standard pandas DataFrame, which does not provide the scalability of Spark. This is not the desired conversion for this scenario.