In this problem, we're giving you a file containing some real data from #the Marta (Atlanta's subway system) database. Each line of the file is #a record of a single ride at a specific Marta station. Riders enter and #exit the subway system by tapping a Breeze Card against a gate at a #specific station. # #You can see a preview of what the file will look like in #marta_sample. csv in the dropdown in the top left. Note, however, that #the real dataset is massive: over 200,000 individual rides are recorded. #So, you're going to be dealing with some pretty big data! # #Each line of the file contains six items, separated by commas: # # - the transit day, in MM/DD/ format. # - the transit time, in HH:MM:SS format. # - the device ID, an identifer of the gate at which the rider entered. # - the station ID, a numeric identifier the station. # - the use type, whether the rider was entering, exiting, etc. # - a serial number, the unique identifier of the rider's Breeze Card. # #Your goal is to use this file to answer three questions: # # - What is the average number of Breeze Card taps per station? # - What is the station ID of the station whose traffic is the closest # to that average? # - What station has the least overall amount of traffic? # #Note that you will answer these questions in the fill-in-the-blank #problems below, _not_ in this coding exercise. So, you don't have to #programmatically find the station ID closest to the average: you could #just print all the stations and their averages, then visually check #which is closest to the average. # #To get you started, we've gone ahead and opened the file: marta_file = open('../resource/lib/public/marta_ 01-18-2016.csv', 'r') #You may add whatever code you want from here on to answer those three #questions. # #HINT: although there are six items on each line of the file, you only #need one of them: station ID. If you use split(",") to split up each #line, station ID will be at index 3 on the list. # #