Apache Spark 3 for Data Engineering and Analytics with Python - Challenge Part 2 - Remove Null Row and Bad Records

The video tutorial guides viewers through the process of cleaning a sales data frame by removing null values, identifying and eliminating bad records, and ensuring data integrity. It begins with an overview of the tasks, followed by setting up headings for data preparation. The tutorial then demonstrates how to remove null values and use the describe function to identify anomalies. Finally, it covers removing duplicate records and performing final checks to confirm the data is clean.

What is the first step mentioned in the video to prepare the data frame for cleansing?

• Delete all existing data.

• Export the data to a CSV file.

• Create a new data frame from scratch.

• Restart the kernel and run the existing code.

Which heading is created to organize the data preparation process?

• Data Export

• Data Preparation and Cleansing

• Data Analysis

• Data Visualization

What function is used to check for null values in the order ID column?

• isMissing()

• isBlank()

• isEmpty()

• isNull()

What is the purpose of using the describe function in the data frame?

• To visualize the data

• To import new data

• To scan each column for anomalies

• To export the data

How are duplicate records removed from the data frame?

• By manually deleting them

• By using a third-party tool

• By using the distinct() function

• By exporting to Excel and removing duplicates

What is the final step to ensure problematic records are removed?

• Export the data to a new file

• Manually inspect each record

• Create a filter to exclude the records

• Re-run the entire script

How is the confirmation of data cleansing achieved?

• By exporting the data

• By running a spot check

• By re-importing the data

• By visualizing the data