Practicum 2: Transit Trends
In Practicum 1, you wrote a program ctarides.py
that tabulated the total number of rides on the CTA for a given year.
In this practicum, we build upon that program by building some data structures
and doing a few more interesting calculations.
(a) Reading the data into a data structure
Start by writing a function read_rides(filename) that reads the ride data
into a list of dictionaries where each dictionary looks something like this:
{
'station_id' : 40010, # Integer
'station_name' : 'Austin-Forest Park', # String
'date' : '01/01/2001', # String
'daytype' : 'U', # String
'rides' : 290 # Integer
}
Your function should work like this:
>>> rides = read_rides('Data/ctarail.csv')
>>> len(rides)
621974
>>> # Examine the first 4 entries
>>> rides[0:4]
[{'date': '01/01/2001', 'daytype': 'U', 'station_name': 'Austin-Forest Park', 'rides': 290, 'station_id': 40010}, {'date': '01/01/2001', 'daytype': 'U', 'station_name': 'Harlem-Lake', 'rides': 633, 'station_id': 40020}, {'date': '01/01/2001', 'daytype': 'U', 'station_name': 'Pulaski-Lake', 'rides': 483, 'station_id': 40030}, {'date': '01/01/2001', 'daytype': 'U', 'station_name': 'Quincy/Wells', 'rides': 374, 'station_id': 40040}]
>>>
Once you have the complete data set loaded, you can start to do more interesting things with it.
(b) Finding the busiest L stations in Chicago
Try these steps to get started:
>>> # Collect all of the unique station_ids into a set
>>> stations = { r['station_id'] for r in rides }
>>>
>>> # Make a dictionary for tabulating counts
>>> station_counts = dict.fromkeys(stations, 0)
>>>
>>> # Count rides
>>> for ride in rides:
station_counts[ride['station_id']] += ride['rides']
>>> # Show number of rides at different stations
>>> station_counts[40010]
6401782
>>> station_counts[40020]
13093093
>>>
Using the data in station_counts, write a program to output a table
showing the total rides at each station ranked from most to least
busy. For example:
Id |Name |Count
-----|-------------------------|--------
40380|Clark/Lake |56772423
41450|Chicago/State |54746133
41660|Lake/State |51524924
...
41510|Morgan-Lake |288944
41680|Oakton-Skokie |173746To do this, you’ll need to figure out how to sort the data in station_counts. Also,
you’ll need a way to associate the station name with the station id. To make the output
look nice, you’ll also need to fiddle with string formatting a bit.
(c) Yearly totals
Write a function yearly_totals(rides, year) that computes the station totals
by year and returns the result in a dictionary:
>>> y2012 = yearly_totals(rides, '2012')
{40960: 1573557, 40450: 3982594, 41220: 4419350, 41430: 1589531, 40710: 2032116, 41480: 1316509, 40970: 457900, 40460: 1916772, ... }
>>> # Find 2012 entries at station 40010
>>> y2012[40010]
643398
>>> # Find change in ridership 2001-2012
>>> y2001 = yearly_totals(rides, '2001')
>>> y2012[40010] - y2001[40010]
177137
>>>
Modify your program so that it outputs an extra column showing the change in ridership from 2001 to 2012. For example:
Id |Name |Count |Change
-----|-------------------------|--------|--------
40380|Clark/Lake |56772423| 1378717
41450|Chicago/State |54746133| 1097009
41660|Lake/State |51524924| 1680578
...
41510|Morgan-Lake | 288944| 288944
41680|Oakton-Skokie | 173746| 173746(d) Re-ordering the data
Modify your program so that it ranks the data according to the total change in ridership. For example:
Id |Name |Count |Change
-----|-------------------------|--------|--------
41400|Roosevelt |33249846| 1817656
41660|Lake/State |51524924| 1680578
40380|Clark/Lake |56772423| 1378717
41220|Fullerton |43086899| 1284827
...
41170|Garfield-Dan Ryan |15741450| -169546
40990|69th |23355328| -310984
40450|95th/Dan Ryan |50304330| -355227
40500|Washington/State |14321124|-2179181To do this last part, you’ll need to collect and sort the data
according to the change value. You might need to investigate
the key argument to the sort() method.