iframe-proxy | Sunbelt Computer Software

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# readrides.py
from collections import namedtuple
class Ride(object):
    def __init__(self, route, date, daytype, rides):
        self._route = route
        self._date = date
        self._daytype = daytype
        self._rides = rides
class SlotRide(object):
    __slots__ = ['_route', '_date', '_daytype', '_rides']
    def __init__(self, route, date, daytype, rides):
        self._route = route
        self._date = date
        self._daytype = daytype
        self._rides = rides
TupRide = namedtuple('TupRide', ['route', 'date', 'daytype', 'rides'])
def read_rides_as_strings(filename):
    Read the bus ride data as a list of strings
    records = []
    with open(filename) as f:
        for row in f:
            records.append(row)
    return records
def read_rides_as_tuples(filename):
    Read the bus ride data as a list of tuples
    records = []
    with open(filename) as f:
        rows = csv.reader(f)
        headings = next(rows) # skip headers
        for row in rows:
            route, date, daytype, rides = row
            rides = int(rides)
            records.append((route, date, daytype, rides))
    return records
def read_rides_as_namedtuples(filename):
    Read the bus ride data as a list of named tuples
    records = []
    with open(filename) as f:
        rows = csv.reader(f)
        headings = next(rows) # skip headers
        for row in rows:
            route, date, daytype, rides = row
            rides = int(rides)
            records.append(TupRide(route, date, daytype, rides))
    return records
def read_rides_as_dicts(filename):
    Read the bus ride data as a list of dictionaries
    records = []
    with open(filename) as f:
        rows = csv.reader(f)
        headings = next(rows) # skip headers
        for row in rows:
            route, date, daytype, rides = row
            record = dict(
                        ('route', 'date', 'daytype', 'rides'), 
                        (route, date, daytype, int(rides))))
            records.append(record)
    return records
def read_rides_as_objects(filename):
    Read the bus ride data as a list of objects
    records = []
    with open(filename) as f:
        rows = csv.reader(f)
        headings = next(rows) # skip headers
        for row in rows:
            route, date, daytype, rides = row
            records.append(Ride(route, date, daytype, int(rides)))
    return records
def read_rides_as_slobjects(filename):
    Read the bus ride data as a list of objects with slots
    records = []
    with open(filename) as f:
        rows = csv.reader(f)
        headings = next(rows) # skip headers
        for row in rows:
            route, date, daytype, rides = row
            records.append(SlotRide(route, date, daytype, int(rides)))
    return records
def anal(dicts):
    # how many bus routes exist in Chicago
    routes = {d['route'] for d in dicts}
    print('Number of bus routes: ', len(routes))
    # how many people rode the number 22 bus on February 2, 2011?
    rides = [d['rides'] for d in dicts if d['route']=='22' and d['date']=='02/02/2011']
    print('Riders on a given day: ', sum(rides))
    # what's the total number of rides taken on each bus route?
    from collections import Counter
    totals = Counter()
    for d in dicts:
        totals[d['route']] += d['rides']
    print('Total rides by route: ', totals.most_common(5))
    # what five bus routes had the greatest ten-year increase in ridershiop from 2001 to 2011?
    old = {d['route']: d['rides'] for d in dicts if d['date'].endswith('2001')}
    new = {d['route']: d['rides'] for d in dicts if d['date'].endswith('2011')}
    increases = Counter()
    for route, rides in new.items():
        increases[route] += rides - old.get(route, 0)
    print('Top five increases: ', increases.most_common(5))
if __name__ == "__main__":
    import sys
    import tracemalloc
    if len(sys.argv) != 3:
        raise SystemExit('usage: readrides filename datastructure[strings, tuples, namedtupes, dicts, objects, slots]')
    callmap = {
            "strings": read_rides_as_strings,
            "tuples": read_rides_as_tuples,
            "namedtuples": read_rides_as_namedtuples,
            "dicts": read_rides_as_dicts,
            "obs": read_rides_as_objects,
            "slobs": read_rides_as_slobjects,
    reader, filename = callmap[sys.argv[2]], sys.argv[1]
    tracemalloc.start()
    recs = reader(filename)
    print('Memory Use: Current %d, Peak %d' % tracemalloc.get_traced_memory())
    if sys.argv[2] == 'dicts':
        anal(recs)
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