Analyze a large public data set in an effort to answer one of the burning questions of our time: Who isn't paying their parking tickets? The work you will do in this assignment is a variation of something done in the amazing and popular blog I Quant NY (iquantny.tumblr.com)
You should read the original article here (http://iquantny.tumblr.com/post/140432262249/parking-immunity-diplomats-owe-nyc-16-million-in)
In a nutshell we are going to look at New York City parking ticket data and determine which country's diplomats owe and how much. The entire data set is available online but it's way too big for our purposes so you will trim it down to only include tickets for street cleaning violations and only those tickets that still have an amount due of over $50.00. You must download the data file for this assignment here (https://data.cityofnewyork.us/City-Government/Open-Parking-and-Camera-Violations/nc67-uf89)
You can use the online filter features or whatever your favorite spreadsheet editor is to remove the undesired tickets and information but the result should be a large CSV file that is in the same format as this (file name: file2) example file.
In class we will learned how to read CSV files using Pandas and you should use that to read the large file above. So your collection of parking tickets can be represented as a single DataFrame.
Problem 1 Use pandas to read Parking.csv and store the entire dataset as a DataFrame.
Problem 2 Write a Python function that takes as input a DataFrame containing all of the ticket information and a plate string and returns a tuple containing the following information: (number of open violations, total dollar amount due of all open violations)
Problem 3 Write a Python function to determine which car owes the most money. Your function should take a DataFrame containing the parking ticket information as input and should return a tuple containing the following information (plate number, total amount due) associated with whichever car owes the most money.