Reading CSV File in Python and Assigning To Different Lists

The biggest concerns I'd have with that code is how specific it is. For example, the names of the teams are encoded into specific variables, which are individually handled. The parceling of players to teams assumes specific number of teams and available players. Etc. Every bit of the processing works only with the specific expected data.

The second concern would be how everything is done in a single function. Reading player data, parceling out players to teams, writing out the team rosters--it's all crammed into one omnibus function.

So, how to improve? First, let's get a little more modular and generic:

def read_players(filepath):
    """
    Read a list of players (each one represented by a 
    dictionary), and return the list.
    """
    with open(filepath) as csvfile:
        soccerplayers = csv.DictReader(csvfile)
        return list(soccerplayers)

This function focuses on just one thing: reading in the players and returning a list. Once read, we can then assign players to teams:

def assign_teams(players, teams):
    """
    Given a list of players and a list of teams, randomly assign
    players to teams--but in a fair way that balances experienced 
    players as evenly as possible across teams. Returns a dictionary 
    mapping team name -> list of players on the team.
    """
    # shuffle order of teams and players so there's no hint of 
    # favoritism
    random.shuffle(teams)
    random.shuffle(players)

    # partition player list into experienced and inexperienced players
    experienced   = [ p for p in players if p['Soccer Experience'] == 'YES']
    inexperienced = [ p for p in players if p['Soccer Experience'] == 'NO']

    # create a roster
    n_teams = len(teams)
    roster = {team_name: [] for team_name in teams}

    # pick the experienced players (round-robin)
    for i, player in enumerate(experienced):
        team = teams[i % n_teams]
        roster[team].append(player)

    # add inexperienced players (round-robin)
    for i, player in enumerate(inexperienced):
        team = teams[i % n_teams]
        roster[team].append(player)

    return roster

Note that specific teams are never mentioned. This makes the routine scalable to an arbitrary number of teams, or players. It uses a common index-modulo-list-size trick to rotate through the team names, assigning each in turn.

Once teams are assigned on the roster, it's time to write them out to a results file:

def write_roster(roster, filepath):
    """
    Write a roster dictionary to the given filepath.
    """
    fields = ["Name", "Soccer Experience", "Guardian Name(s)"]
    with open(filepath, 'w') as f:
        for team in roster.keys():
            f.write(team + '\n')
            for player in roster[team]:
                # select just the desired fields for output
                row = [player[f] for f in fields]
                # construct and write out a formatted record
                formatted_row = ', '.join(row) + '\n'
                f.write(formatted_row)
            # separate teams with a little more white space
            f.write('\n')

Again note that the code is not specific to the team names. It doesn't care if they're the Dragons or the Fighting Unicorns. The values are handled generically.

Finally, you'll need some command and control code to stitch these functions together:

if __name__ == "__main__":
    players = read_players('soccer_players.csv')
    teams = ['Raptors', 'Dragons', 'Sharks']
    roster = assign_teams(players, teams)
    write_roster(roster, 'teams.txt')
    print('done!')

While the assignments are randomized (by design), one run emits a file teams.txt that look as follows. Notice I didn't worry about having a full 18 players. It will work for 18, but it works equally well for 6, 9, 12, ... and will in fact also work for 5, 19, and 67. (Though, if you were going to use this in real life, thus likely to see many odd-sized lists of players, you'd probably want to further improve the rather simplistic fairness algorithm used here.)

Dragons
Todd Jacobs, YES, Robert and Virginia Jacobs
Doug Jones, NO, Mary and Perry Jones
David Nork, NO, Dan and Stacy Nork

Raptors
Joe Smith, YES, Jim and Jan Smith
Will Smith, NO, Bill and Nancy Smith
Mark Jackson, NO, Jack and Frank Jackson

Sharks
Bill Smith, YES, Jim and Jan Smith
Andy Able, NO, Noah Able
Paul Pork, NO, Paul and Paulette Pork