Does PySpark df.explain() Calculate Summary Statistics?

DP-600 By islever Editorial Last updated

Query and analyze data

Short answer

B You must use df.summary() or df.describe() to calculate statistics, as df.explain() only shows the execution plan.

Question

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().show() Does this meet the goal?
  1. Yes
  2. No Correct Answer

Key Takeaways

The question tests knowledge of PySpark DataFrame methods, specifically the trap of confusing explain() (which shows the execution plan) with describe() or summary() (which compute statistics).

This page evaluates whether the PySpark expression df.explain().show() meets the goal of calculating summary statistics for string and numeric columns. It establishes that explain() only displays the query execution plan, making the proposed solution incorrect.

Common mistake: Choosing Yes because they assume explain() provides a detailed breakdown of the data values, rather than just the Spark execution plan.

Expert Analysis

Why the Answer Is Correct

The correct answer is B (No) because the df.explain method in PySpark is used to display the logical and physical execution plans of the DataFrame query, not to calculate summary statistics. It does not compute or return the min, max, mean, or standard deviation of the columns. Therefore, the proposed solution does not meet the stated goal.

Why the Other Options Are Wrong

Option A (Yes) is incorrect because it assumes explain generates statistical summaries. To actually calculate the min, max, mean, and standard deviation for string and numeric columns, you must use df.summary or df.describe. Using explain will only output how Spark intends to execute the query.

Community Comment Notes

Community members correctly identified that explain only provides the execution plan, as stilferx noted by linking to the official Spark documentation. Others, like bigdave987, pointed out that "describe and summary provide the summary statistics," and testtaker45 agreed that "you would need something like df.summary". These comments reinforce that explain is the wrong function for statistical evaluation.

Exam Strategy

Know the specific purpose of common PySpark DataFrame methods. If a question asks for statistical metrics like min, max, or mean, look for describe() or summary(); explain() is exclusively for viewing the query execution plan.

Community Votes

B
100%

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

Discussion 4 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 4

  1. bigdave987 👍 6 Answered B
    No. describe() and summary() provide the summary statistics.
  2. stilferx 👍 3 Answered B
    IMHO, No, Because: df.explain gives execution plan. https://spark.apache.org/docs/3.1.2/api/python/reference/api/pyspark.sql.DataFrame.explain.html
  3. hello2tomoki 👍 2 Answered B
    https://www.datasciencemadesimple.com/descriptive-statistics-or-summary-statistics-of-dataframe-in-pyspark/
  4. testtaker45 👍 1 Answered B
    Pretty sure you would need something like df.summary() to get the info they are asking for.

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