PySpark DataFrame explain vs describe Method

Query and analyze data

Answer Correct answer: B — You must use df.describe() instead of df.explain() to calculate statistical values for a PySpark DataFrame.

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a Fabric tenant that contains a new semantic model in OneLake. You use a Fabric notebook to read the data into a Spark DataFrame. You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns. Solution: You use the following PySpark expression: df.explain() Does this meet the goal?

Yes
No Correct Answer

Community Votes

100%

100% of anonymous learners picked answer B. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

This question tests PySpark DataFrame methods, specifically the trap of confusing df.explain() (execution plan) with df.describe() (statistics).

The df.explain() method in PySpark displays the execution plan, not statistical summaries. This page confirms that df.describe() is the correct method to calculate min, max, mean, and standard deviation.

Choosing 'Yes' because one might assume explain() provides an explanation or breakdown of the data's statistics.

Community Discussion (5 comments)

282b85d 👍 9 Selected: B

The df.explain() method in PySpark is used to print the logical and physical plans of a DataFrame, which helps in understanding how Spark plans to execute the query. It does not compute any statistical values like min, max, mean, or standard deviation.

SamuComqi 👍 5 Selected: B

The correct syntax is df.describe(). Sources: describe --> https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.describe.html explain --> https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.explain.html

stilferx 👍 1 Selected: B

IMHO, NOOO explain() shows the execution plan...

a_51 👍 2 Selected: B

describe is how you get the information.

Momoanwar 👍 4 Selected: B

No explain is for the execut plan

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Expert Analysis

Why the Answer Is Correct

The correct answer is B (No) because the df.explain method in PySpark only prints the logical and physical execution plans of the DataFrame. It does not compute or return any statistical values such as min, max, mean, or standard deviation. To meet the stated goal, one must use df.describe or df.summary.

Why the Other Options Are Wrong

Option A (Yes) is incorrect because it assumes df.explain generates statistical metrics. The method is strictly for debugging query execution and understanding how Spark processes the DataFrame transformations, not for evaluating data distributions.

Community Comment Notes

Commenters unanimously agree that explain shows the execution plan, as SamuComqi noted by providing the official documentation links for both describe and explain. Another user pointed out that "explain is for the execut plan" and that "describe is how you get the information."

Official Reference

Exam Strategy

When evaluating PySpark methods, carefully distinguish between methods that analyze execution plans (like explain()) and those that compute statistics (like describe()). Knowing the specific purpose of common DataFrame methods is essential for these scenario-based questions.

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