import pandas as pd
import numpy as np
print(pd.__version__)

rng = np.random.default_rng(seed=22)

teams = {
    'Comets': ['Rylan', 'Tho', 'Bea', 'Omar', 'Nia'],
    'Falcons': ['Dara', 'Mo', 'Ines', 'Kwame', 'Suri']
}
games_per_player = 20
print(teams)

rows = []
for team, players in teams.items():
    for player in players:
        for game_num in range(1, games_per_player + 1):
            minutes = round(float(rng.uniform(15, 38)), 1)
            points = int(rng.poisson(14))
            rebounds = int(rng.poisson(5))
            assists = int(rng.poisson(4))
            rows.append([team, player, game_num, minutes, points, rebounds, assists])

games = pd.DataFrame(rows, columns=['team', 'player', 'game_num', 'minutes', 'points', 'rebounds', 'assists'])
print(games.shape)

games.to_csv('season_box_scores.csv', index=False)
games = pd.read_csv('season_box_scores.csv')
games.head()

print(games.shape)
games.info()

games[['minutes', 'points', 'rebounds', 'assists']].describe()

games['team'].value_counts()

season_totals = games.groupby('player').agg(
    team=('team', 'first'),
    games_played=('game_num', 'count'),
    total_points=('points', 'sum'),
    total_rebounds=('rebounds', 'sum'),
    total_assists=('assists', 'sum')
).sort_values('total_points', ascending=False)
season_totals

season_totals['points_rank'] = season_totals['total_points'].rank(ascending=False).astype(int)
season_totals[['team', 'total_points', 'points_rank']]

per_game_avg = games.groupby('player')[['points', 'rebounds', 'assists']].mean().round(1)
per_game_avg = per_game_avg.sort_values('points', ascending=False)
per_game_avg

games['efficiency'] = (games['points'] + games['rebounds'] + games['assists']) / games['minutes']
games[['player', 'game_num', 'minutes', 'efficiency']].head()

avg_efficiency = games.groupby('player')['efficiency'].mean().sort_values(ascending=False)
avg_efficiency.round(3)

games = games.sort_values(['player', 'game_num'])
games['cumulative_points'] = games.groupby('player')['points'].cumsum()
games[games['player'] == 'Rylan'][['game_num', 'points', 'cumulative_points']].tail()

games['rolling_5g_avg'] = games.groupby('player')['points'].transform(lambda s: s.rolling(window=5).mean())
games[games['player'] == 'Dara'][['game_num', 'points', 'rolling_5g_avg']].tail(8)

team_comparison = games.groupby('team').agg(
    avg_points=('points', 'mean'),
    avg_rebounds=('rebounds', 'mean'),
    avg_assists=('assists', 'mean'),
    avg_efficiency=('efficiency', 'mean')
).round(2)
team_comparison

games['season_third'] = pd.cut(games['game_num'], bins=[0, 7, 14, 20], labels=['Games 1-7', 'Games 8-14', 'Games 15-20'])
scoring_trend = pd.pivot_table(games, values='points', index='player', columns='season_third', aggfunc='mean', observed=True).round(1)
scoring_trend

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(8, 5))
per_game_avg['points'].plot(kind='bar', ax=ax, color='darkorange')
ax.set_title('Points Per Game by Player')
ax.set_ylabel('Points Per Game')
plt.tight_layout()
plt.savefig('points_per_game.png', dpi=150)
plt.close(fig)
print('Saved points_per_game.png')

rylan_games = games[games['player'] == 'Rylan']
fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(rylan_games['game_num'], rylan_games['points'], color='gray', alpha=0.5, marker='o', label='Points per game')
ax.plot(rylan_games['game_num'], rylan_games['rolling_5g_avg'], color='crimson', linewidth=2, label='5-game rolling average')
ax.set_title("Rylan's Scoring Trend This Season")
ax.set_xlabel('Game Number')
ax.set_ylabel('Points')
ax.legend()
plt.tight_layout()
plt.savefig('player_scoring_trend.png', dpi=150)
plt.close(fig)
print('Saved player_scoring_trend.png')