# Soccer - league table, old v new point system

# Here is a sample line of data ...
# 13 Queens Park Rangers 15 1 4 2 3 0 5 -11 16

# define any functions / methods we will need later
# JUST a definition - they're not run at this time!

def bypoints(this,that):
        return teamstats[that] - teamstats[this]

# Set up an empty "dictionary" called teamstats. As a dictionary, the indexes can be anything
# and we will use team names. We're going to store the number of points under the old scheme
# as the values

teamstats = {}

# Open the file of soccer data statistics

fh = open("soccer.data","r")

# Read each line of the data in turn into the variable called team

for team in fh:

        # take the trailing newline character off with strip.
        # then break the line into individual components with split (tab separated data)
        # store each value you get back into a separate variable - names as listed

        if team[0] == "#": continue

        position,name,played,homewins,homedraws,homelosses,awaywins, \
                awaydraws,awaylosses,goaldifference,points = \
                        team.strip().split("\t")

        # Calculate the points under the old 2 points for a win, 1 for a draw regime
        # store it into the teamstats dictionary we created earlier

        alternative = (int(homewins) + int(awaywins)) * 2 + \
                        int(homedraws) + int(awaydraws)
        teamstats[name] = alternative

        # print out the name, current points, and old style points

        print("{:25s} {:>2s} {:>8d}".format(name,points,alternative))

print("--------------------------------------------")

# get a list of the team names
teamnames = list(teamstats.keys())

# Sort the team names, based on the points that each would have gained under the old system
try:
        teamnames.sort(bypoints)
except:
        teamnames.sort(key=lambda x:-teamstats[x])

# Report on the teams (again), this time in the league order as it would have been under the old system
for team in teamnames:
        print("{:25s} {:>6d}".format(team,teamstats[team]))

""" Sample output

WomanWithCat:y107 grahamellis$ python3 soccer_py
Manchester City 38 26
Manchester United 36 25
Chelsea 31 21
Tottenham Hotspur 31 21
Arsenal 29 20
Liverpool 26 19
Newcastle United 26 19
Stoke City 21 15
Aston Villa 19 15
Norwich City 19 14
Swansea City 17 13
Everton 16 11
Queens Park Rangers 16 12
Fulham 15 12
West Bromwich Albion 15 11
Sunderland 14 11
Wolverhampton Wanderers 14 10
Wigan Athletic 12 9
Blackburn Rovers 10 8
Bolton Wanderers 9 6
--------------------------------------------
Manchester City 26
Manchester United 25
Tottenham Hotspur 21
Chelsea 21
Arsenal 20
Liverpool 19
Newcastle United 19
Stoke City 15
Aston Villa 15
Norwich City 14
Swansea City 13
Fulham 12
Queens Park Rangers 12
West Bromwich Albion 11
Sunderland 11
Everton 11
Wolverhampton Wanderers 10
Wigan Athletic 9
Blackburn Rovers 8
Bolton Wanderers 6
WomanWithCat:y107 grahamellis$

"""