Evaluate PySpark DataFrame Statistics with df.show()

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Answer Correct answer: B — You must use df.describe() or df.summary() to calculate statistics, as df.show() only displays DataFrame rows.

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.show() 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

The question tests PySpark DataFrame evaluation methods, and the common trap is confusing a data display function (show) with a statistical computation function (describe).

This page clarifies why the PySpark expression df.show() fails to calculate statistical summaries like min, max, mean, and standard deviation for a Spark DataFrame. It establishes that df.describe() or df.summary() are the correct methods for this goal.

Choosing Yes because df.show() successfully interacts with the DataFrame, failing to realize it only displays rows rather than computing statistical metrics.

Community Discussion (6 comments)

282b85d 👍 3 Selected: B

Correct methods: Use df.describe().show() for basic statistics and df.agg() with appropriate functions (min, max, mean, stddev) for detailed statistics.

stilferx 👍 2 Selected: B

IMHO, NOOOOO df.show() - shows the data in the dataframe

a_51 👍 2 Selected: B

Use describe

XiltroX 👍 1 Selected: B

df.summary() is the only right answer.

SamuComqi 👍 2 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 show --> https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.show.html

Momoanwar 👍 1 Selected: B

No show is to display data

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Expert Analysis

Why the Answer Is Correct

The correct answer is B (No) because df.show simply prints the top rows of the DataFrame to the console in a tabular format. It does not perform any statistical computations such as calculating min, max, mean, or standard deviation. Therefore, using df.show completely fails to meet the stated goal of evaluating the data for these specific statistics.

Why the Other Options Are Wrong

Option A (Yes) is incorrect because it assumes df.show calculates statistical metrics. While show is frequently used to preview data during development, it lacks any underlying logic to aggregate and compute descriptive statistics across string and numeric columns.

Community Comment Notes

Multiple commenters pointed out that df.show only displays data, as stilferx noted with "df.show - shows the data in the dataframe". The correct method to compute these statistics is df.describe, which SamuComqi and others highlighted by stating "The correct syntax is df.describe".

Official Reference

Exam Strategy

Know the exact purpose of common PySpark DataFrame methods. If a question asks for statistics (min, max, mean, stddev), immediately look for describe() or summary(), not display methods like show() or print().

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