Databricks-Spark-Assoc Pandas API on Spark Practice Question
A developer wants to use the pandas API on Spark in a Databricks notebook. They have an existing PySpark DataFrame `sdf`. Which code snippet correctly creates a pandas-on-Spark DataFrame from `sdf` while preserving the distributed execution plan?
⚠ Common exam trap
It's easy for candidates to confuse the conversion direction: `from_pandas` converts a local pandas DataFrame, while `DataFrame(sdf)` wraps a PySpark DataFrame for distributed operations.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
import pyspark.pandas as ps; psdf = ps.DataFrame(sdf)
To convert a PySpark DataFrame to a pandas-on-Spark DataFrame while preserving the distributed plan, use `pyspark.pandas.DataFrame(sdf)`. This wraps the Spark DataFrame and allows pandas-like operations that execute on Spark. Other approaches either collect data to the driver or use incorrect methods that do not exist or are meant for different conversions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
import pandas as pd; psdf = pd.DataFrame(sdf)
Why it's wrong here
`pd.DataFrame(sdf)` attempts to create a standard pandas DataFrame from a Spark DataFrame, which typically fails because Spark DataFrames are not directly convertible without collecting to the driver. Even if it worked, it would pull all data into memory and lose distributed execution. This is not the pandas API on Spark and does not preserve the Spark plan.
- ✗
import pyspark.pandas as ps; psdf = sdf.to_pandas_on_spark()
Why it's wrong here
There is no method `to_pandas_on_spark()` on a PySpark DataFrame. The correct conversion is via the `pyspark.pandas` module, typically using `ps.DataFrame(sdf)` or `sdf.pandas_api()`. The latter exists in some versions but is not the canonical approach. The method `to_pandas_on_spark` is not part of the API, so this snippet would raise an AttributeError.
- ✗
import pyspark.pandas as ps; psdf = ps.from_pandas(sdf)
Why it's wrong here
`ps.from_pandas()` is used to convert a standard pandas DataFrame to a pandas-on-Spark DataFrame, not a PySpark DataFrame. Passing a PySpark DataFrame would cause an error or unexpected behavior. To convert a PySpark DataFrame, you should use `ps.DataFrame(sdf)`. The function `from_pandas` is specifically for local pandas objects, often creating a single-partition DataFrame.
- ✓
import pyspark.pandas as ps; psdf = ps.DataFrame(sdf)
Why this is correct
`ps.DataFrame(sdf)` creates a pandas-on-Spark DataFrame that wraps the existing Spark DataFrame. The underlying Spark plan is preserved, and operations on psdf will execute distributedly. This is the standard way to convert a PySpark DataFrame to a pandas-on-Spark DataFrame without collecting data to the driver. The import `pyspark.pandas as ps` is the correct module for the pandas API on Spark.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-Spark-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-Spark-Assoc exam.