You have had many wonderful examples of dealing with structured data and time series data - both clean and dirty! You are now going to analyze a real-world dataset with missing values and spurious...

You have had many wonderful examples of dealing with structured data and time series data - both clean and dirty!

You are now going to analyze a real-world dataset with missing values and spurious formatting (which will turnnumericcolumns intoobjectcolumns and ruin all of yournumpyfunctions).

The Paycheck Protection Program (PPP) loans provide small businesses with the resources they need to maintain their payroll, hire back employees who may have been laid off, and cover applicable overhead. This data set includes businesses in Connecticut that received PPP funding, how much funding the employer received & how many jobs the employer claims they saved. The NAICS (National Industry Classification) was provided by the loan recipient.

• 
  https://data.ct.gov/Business/PPP-Loans-to-Connecticut-Businesses/kt63-kssz/data
  


• 
  https://www.federalpay.org/paycheck-protection-program/ct
  

This dataset includes loans under 150,000 USD and loans of 150,000 USD and above made to Connecticut businesses through August 8, 2020.

Here is a link to the dataset, which you will download and copy to your Drive:

• 
  https://drive.google.com/file/d/1yv7bCocITzrNw75FFRG0Rv8qCPV-Fi-g/view?usp=sharing
  

Create five plots and five tables that tell a story about the Paycheck Protection Program in Connecticut. Each plot or table should have business value that a stakeholder could use. Give some sincere thought to each visualization that you create - if you were doing this for a national newspaper, what would readers be most interested in knowing? For example, what are the top 10 tens that got money - and how much money did they get? What industry got the most money? Among businesses that received PPP loans, how many were women owned businesses? How much money was disbursed during different months or days of the week?