Spark Programming in Python for Beginners with Apache Spark 3 - Misc Transformations

This video tutorial covers various data transformation techniques using PyCharm, Jupyter, and DataBricks. It demonstrates creating DataFrames, attaching schemas, adding unique identifiers, using CASE expressions, handling data type issues, and casting. The tutorial also explains adding and removing columns, sorting, and dealing with duplicates.

Which tool is NOT mentioned as being used in the video?

• Data Bricks

• PyCharm

• Visual Studio Code

• Jupyter Notebook

What method is used to create a data frame from a list without a schema?

• createDataFrame

• toDF

• parallelize

• collect

What is the purpose of the monotonically increasing ID function?

• To remove duplicates

• To sort data

• To add a unique identifier

• To create a schema

What is the main advantage of using CASE WHEN THEN expressions?

• To simplify if-else statements

• To repartition data

• To create data frames

• To drop columns

What issue can arise from incorrect data types in a data frame?

• Increased processing time

• Unexpected results

• Data loss

• Duplicate records

Which function is used to add a new column to a data frame?

• filter

• drop

• withColumn

• select

How can you remove duplicate records from a data frame?

• Using dropDuplicates

• Using filter

• Using repartition

• Using collect

What is the default sorting order when using the sort function?

• Alphabetical

• Ascending

• Random

• Descending

Which method is used to convert a string to a date in a data frame?

• toDate

• toString

• toInt

• toFloat

What is the recommended approach to avoid unexpected behavior due to data type issues?

• Using default settings

• Ignoring warnings

• Repartitioning data

• Using explicit casting