Choosing a Data File: You are permitted to choose any publicly-available CSV formatted data file. The minimum requirements for this data are: 1. The data should have at least 3 numeric variables (columns) that are being observed. 2. The data should have at least 100 observations (rows). You are permitted to make changes to the file (i.e. clean the data), however you must provide a complete list of the modifications that you made to the file. A complete list means that someone should be able to download the original raw data file, and reproduce your file exactly by following your list of changes. Create a file called README.txt (you can use Spyder to do this). Inside of README.txt include: 1. The name of the data set that you've downloaded. 2. The URL where you downloaded the data. 3. The complete list of modifications you made to the file. Submit your README and .csv data file with your code to the assignment 2 submission folder. For question 2, you may hardcode your filename (nota full path!) directly in your program. The marker will place the .csv file in the same directory as the.py file before running your program. Statistics: For each variable (column) in your data file, you will produce a five-number summary and the average of the data in that column 1. Minimum, the smallest value 2. First Quartile 3. Median, the value in the middle of a sorted list (**see note below). 4. Third Quartile 5. Maximum, the largest value 6. Average Prior to calculating the statistics, you should load your data into a dictionary of lists, where the column headers (observed variable names) are the keys to the dictionary, and the values are lists of values in those columns