Python for Data Analysis: Step-By-Step with Projects - Pivoting Tables

What is one key advantage of using pivot tables in Pandas compared to Excel?

• They require no coding knowledge.

• They are only for numeric data.

• They can handle larger datasets.

• They are less flexible.

What type of data does the student performance dataset include?

• Only numeric columns

• Only categorical columns

• Both categorical and numeric columns

• Only text columns

Which parameter is used to specify the columns to aggregate in a pivot table?

• aggfunc

• index

• columns

• values

How can you handle missing data in a pivot table?

• By using the index parameter

• By ignoring it

• By using the fill_value parameter

• By deleting the row

What does the 'aggfunc' parameter in a pivot table do?

• Specifies the data source

• Specifies the data type

• Specifies the function to aggregate data

• Specifies the columns to group by

What is the purpose of the 'margins' parameter in a pivot table?

• To sort the data

• To filter the data

• To change the data type

• To add row and column totals

Which parameter allows you to group data by multiple columns in a pivot table?

• index

• columns

• aggfunc

• values

What is a benefit of using a dictionary in the 'aggfunc' parameter?

• It changes the data format.

• It allows for multiple data sources.

• It speeds up computation.

• It specifies different functions for different columns.

What is the result of using a lambda function in the 'aggfunc' parameter?

• It changes the data type.

• It allows for custom aggregation logic.

• It filters the data.

• It sorts the data.

What should you consider before building a pivot table?

• The color scheme

• The questions you want to answer

• The software version

• The file format