Loans form an integral part of banking operations. However, not all the loans are promptly returned and hence it is important for a bank to closely monitor its loan applications. One of the banks loan application data contains details of 1000 loan applicants with 20 attributes and the classification whether an applicant is considered a Good or a Bad credit risk. Good are coded as 0 while Bad as 1. Below is one of such attributes called most_valuable_asset column and possible category of the same.

A121 : real estate

A122 : if not A121 : building society savings agreement/life insurance

A123 : if not A121/A122 : car or other, not in attribute 6

A124 : unknown / no property

With the information provided above please try solving the below question. Answer should include logic to get the answer, relevant python function that is required to solve the same, the python syntax.

Questions:

1. Find the number of bad customer based most_valuable_asset attributes and sort them accordingly.

2. Choose right chart to display the results and give justification of choosing the chart type.