PySpark and AWS: Master Big Data with PySpark and AWS - Solution (Distinct, Duplicate)

The video tutorial explains how to solve a quiz by reading data from a CSV file into a data frame and extracting unique rows using two methods: distinct and drop duplicates. The distinct method is applied to selected columns, while drop duplicates is used to remove duplicate rows based on specific column combinations. The tutorial provides step-by-step instructions and examples, ensuring learners understand how to implement these methods effectively.

What is the first step in solving the quiz as mentioned in the video?

• Export data to a new format

• Create a new CSV file

• Read data from a CSV file and create a data frame

• Apply filters to the data

Which two methods are discussed for obtaining unique rows?

• Filter and Sort

• Join and Merge

• Group By and Aggregate

• Distinct and Drop Duplicates

What does the 'distinct' method do when applied to a data frame?

• It sorts the data

• It removes duplicate rows based on all columns

• It merges two data frames

• It filters rows based on a condition

How many unique rows are obtained after applying 'distinct' in the example?

• 50

• 1000

• 500

• 24

What is the purpose of using 'drop duplicates' in data processing?

• To split data into multiple frames

• To remove duplicate rows based on specified columns

• To change data types

• To add new rows

In the 'drop duplicates' method, what happens if a row has the same values for specified columns?

• The row is highlighted

• The row is duplicated

• The row is dropped

• The row is moved to a new data frame

How many unique rows are expected after using 'drop duplicates' in the example?

• 1000

• 24

• 10

• 500