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?
The df.explain() method does not meet the goal of evaluating data to calculate statistical functions. It is used to display the physical plan that Spark will execute. Reference = The correct usage of the explain() function can be found in the PySpark documentation.
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