Apache Spark 3 for Data Engineering and Analytics with Python - Challenge Part 2 - Get the City and State

The video tutorial demonstrates how to extract city and state information from a purchase address using PySpark's split function. It covers creating a markdown heading, importing necessary functions, and manipulating dataframes to add new columns for city and state. The tutorial emphasizes avoiding user-defined functions by leveraging built-in PySpark capabilities, and it concludes with a demonstration of the final dataframe containing the extracted information.

What is the primary task discussed in the video tutorial?

• Extracting street names from addresses

• Extracting city and state from purchase addresses

• Calculating total sales from data

• Analyzing customer demographics

Which library function is used to split the purchase address?

• PySpark's split function

• Python's split function

• Pandas split function

• NumPy's split function

What is the first step in using the split function on the purchase address?

• Importing the split function

• Creating a new DataFrame

• Deleting unnecessary columns

• Printing the entire DataFrame

How is the city extracted from the split list?

• By accessing the first element

• By accessing the second element

• By accessing the third element

• By accessing the last element

What is the index position of the city in the split list?

• 1

• 0

• 2

• 3

What additional step is needed to extract the state from the split list?

• Use a different function

• Split the state further by spaces

• Access the first character

• Remove spaces from the list

What is the final outcome of the DataFrame after processing?

• It includes only the state

• It includes only the city

• It remains unchanged

• It includes both city and state as new columns