from collections import namedtuple

import csv

import datetime

import itertools as it

import statistics

class Event(namedtuple('Event', ['stroke', 'name', 'time'])):

__slots__ = ()

def __lt__(self, other):

return self.time < other.time

def sort_and_group(iterable, key=None):

return it.groupby(sorted(iterable, key=key), key=key)

def grouper(iterable, n, fillvalue=None):

iters = [iter(iterable)] * n

return it.zip_longest(*iters, fillvalue=fillvalue)

def read_events(csvfile, _strptime=datetime.datetime.strptime):

def _median(times):

return statistics.median((_strptime(time, '%M:%S:%f').time()

for time in row['Times']))

fieldnames = ['Event', 'Name', 'Stroke']

with open(csvfile) as infile:

reader = csv.DictReader(infile, fieldnames=fieldnames, restkey='Times')

next(reader) # Skip header.

for row in reader:

yield Event(row['Stroke'], row['Name'], _median(row['Times']))

events = tuple(read_events('swimmers.csv'))

for stroke, evts in sort_and_group(events, key=lambda evt: evt.stroke):

events_by_name = sort_and_group(evts, key=lambda evt: evt.name)

best_times = (min(evt) for _, evt in events_by_name)

sorted_by_time = sorted(best_times, key=lambda evt: evt.time)

teams = zip(('A', 'B'), it.islice(grouper(sorted_by_time, 4), 2))

for team, swimmers in teams:

print('{stroke} {team}: {names}'.format(

stroke=stroke.capitalize(),

team=team,

names=', '.join(swimmer.name for swimmer in swimmers)

))