Crosstab and pivot tables
Often, financial data is viewed as a pivot table in spreadsheets like Excel.
With cross tables, you get a high level view of selected columns and even aggregation like a count or average. For most credit risk models, especially for probability of default, columns like person_emp_length and person_home_ownership are common to begin investigating.
You will be able to see how the values are populated throughout the data, and visualize them. For now, you need to check how loan_status is affected by factors like home ownership status, loan grade, and loan percentage of income.
The data set cr_loan has been loaded in the workspace.
This exercise is part of the course
Credit Risk Modeling in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a cross table of the loan intent and loan status
print(pd.____(cr_loan[____], cr_loan[____], margins = True))