Databricks Machine Learning Associate Question 344
Single answerYou are working on a Databricks notebook and have a PySpark DataFrame named spark_df containing a large dataset. You want to perform some operations using Pandas APIs while ensuring the operations can scale to large datasets. How can you efficiently convert the PySpark DataFrame to a Pandas on Spark DataFrame?
- A
Use the
.toPandas()method onspark_df. - B
Use the
pandas_api()method onspark_df. - C
Use the
ps.DataFrame(spark_df)function from thepyspark.pandasmodule. - D
Use the
.toPandasOnSpark()method onspark_df.
Show answer and explanation
Correct answer: B
Explanation
To convert a PySpark DataFrame to a Pandas on Spark DataFrame, you use the pandas_api() method. This maintains the distributed nature of the data and allows scalable operations using Pandas-like APIs. Other methods like .toPandas() are not suitable for large datasets as they bring all data into the driver node.
- A. Incorrect.
The
.toPandas()method converts the PySpark DataFrame to a Pandas DataFrame, which is not suitable for large datasets as it loads all data into the driver’s memory. - B. Correct.
The
pandas_api()method on a PySpark DataFrame converts it to a Pandas on Spark DataFrame efficiently, allowing the use of Pandas-like APIs while maintaining scalability for large datasets. - C. Incorrect.
The
ps.DataFrame(spark_df)function is incorrect because Pandas on Spark, now called pandas API on Spark, does not provide this function for converting PySpark DataFrames. - D. Incorrect.
The
.toPandasOnSpark()method does not exist in PySpark or Pandas on Spark APIs.