Which of the following lines of code can the data scientist run to accomplish the task?

A data scientist is wanting to explore summary statistics for Spark DataFrame spark_df. The data scientist wants to see the count, mean, standard deviation, minimum, maximum, and interquartile range (IQR) for each numerical feature.

Which of the following lines of code can the data scientist run to accomplish the task?
A . spark_df.summary ()
B . spark_df.stats()
C . spark_df.describe().head()
D . spark_df.printSchema()
E . spark_df.toPandas()

Answer: A

Explanation:

The summary() function in PySpark’s DataFrame API provides descriptive statistics which include count, mean, standard deviation, min, max, and quantiles for numeric columns.

Here are the steps on how it can be used:

Import PySpark: Ensure PySpark is installed and correctly configured in the Databricks environment.

Load Data: Load the data into a Spark DataFrame.

Apply Summary: Use spark_df.summary() to generate summary statistics.

View Results: The output from the summary() function includes the statistics specified in the query (count, mean, standard deviation, min, max, and potentially quartiles which approximate the interquartile range).

Reference

PySpark Documentation:

https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.DataFrame.summary.html