Apache Spark 3 for Data Engineering and Analytics with Python - Adding, Renaming, and Dropping Columns

This video tutorial covers how to manipulate dataframes in PySpark by adding, renaming, and dropping columns. It begins with setting up the environment and importing necessary functions. The tutorial demonstrates adding a 'salary increase' column, verifying its addition, and performing advanced operations like renaming and dropping columns. The session concludes with finalizing changes and addressing any errors encountered.

What are the main objectives of this lesson?

• Learning about machine learning algorithms

• Learning to add, rename, and drop columns in a DataFrame

• Understanding basic Python syntax

• Exploring data visualization techniques

Which PySpark SQL function is used to add a new column to a DataFrame?

• insertColumn

• withColumn

• appendColumn

• addColumn

What is the purpose of the 'salary increase' column?

• To store employee names

• To calculate a 10% increase on the salary

• To track employee attendance

• To list employee departments

How can you confirm the addition of a new column in a DataFrame?

• By exporting the DataFrame to a CSV

• By counting the rows

• By printing the list of columns

• By checking the DataFrame size

What operation is performed after renaming 'Favorite Movies' to 'Movies'?

• Adding a new column for birth year

• Dropping the 'salary increase' column

• Rounding off salary values

• Merging two DataFrames

What is the final step in the DataFrame manipulation process described?

• Sorting the DataFrame

• Adding a new column

• Renaming a column

• Dropping an unnecessary column

What is the purpose of rounding off the salary values?

• To simplify calculations

• To improve data accuracy

• To reduce data size

• To format the data for better readability