Databricks Machine Learning Associate Question 351
Single answerYou are working on a Databricks notebook and need to process a large dataset using the Pandas API on Spark. How should you correctly import and initialize Pandas on Spark to ensure compatibility with Spark and large-scale distributed processing?
- A
import pyspark.pandas as ps
- B
import pandas as pd
- C
from pyspark.sql import pandas_on_spark
- D
import pyspark.sql.pandas as ps
Show answer and explanation
Correct answer: A
Explanation
To utilize the Pandas API on Spark in Databricks, you need to import it using import pyspark.pandas as ps. This API provides a distributed implementation of Pandas, allowing you to handle large datasets efficiently across a Spark cluster, while preserving Pandas-like operations and syntax.
- A. Correct.
This is the correct way to import the Pandas API on Spark. The
pyspark.pandasmodule allows you to use Pandas-like syntax while leveraging Spark's distributed computing capabilities. - B. Incorrect.
This imports the standard Pandas library, which is not optimized for distributed processing on Spark. It only works for small-scale, single-node data processing.
- C. Incorrect.
This is not a valid import statement in PySpark. The
pandas_on_sparkmodule does not exist. - D. Incorrect.
This is an invalid import statement. The
pyspark.sqlmodule does not contain apandassubmodule namedpandas.