本文目录导读：
我来为您编写一个统计红黄牌数量的Python案例,比较不同球队的得牌情况。
基础版本
def count_cards_basic():
"""基础版本：统计各队红黄牌数量"""
# 模拟比赛数据：每场比赛的得牌情况
matches_data = [
{"home": "巴西", "away": "阿根廷",
"cards": [("黄牌", "巴西"), ("黄牌", "阿根廷"), ("红牌", "巴西"), ("黄牌", "巴西")]},
{"home": "法国", "away": "德国",
"cards": [("黄牌", "法国"), ("黄牌", "德国"), ("黄牌", "德国")]},
{"home": "巴西", "away": "法国",
"cards": [("黄牌", "法国"), ("红牌", "巴西"), ("黄牌", "巴西")]},
]
# 统计各队的得牌情况
team_cards = {}
for match in matches_data:
for card_type, team in match["cards"]:
if team not in team_cards:
team_cards[team] = {"黄牌": 0, "红牌": 0}
team_cards[team][card_type] += 1
# 计算得分（红牌计2分，黄牌计1分）
team_scores = {}
for team, cards in team_cards.items():
team_scores[team] = cards["黄牌"] * 1 + cards["红牌"] * 2
return team_cards, team_scores
def print_results_basic(team_cards, team_scores):
"""打印基础统计结果"""
print("=" * 50)
print("各队红黄牌统计：")
print("-" * 50)
# 按得分排序
sorted_teams = sorted(team_scores.items(), key=lambda x: x[1], reverse=True)
for team, score in sorted_teams:
cards = team_cards[team]
print(f"{team}: 黄牌 {cards['黄牌']} 张, 红牌 {cards['红牌']} 张, 总得分 {score}")
# 找出得分最高的队
winner = sorted_teams[0]
print("-" * 50)
print(f"🏆 得牌最多的球队: {winner[0]} (得分: {winner[1]})")
# 找出谁红牌最多
max_red = max(team_cards.items(), key=lambda x: x[1]["红牌"])
print(f"🔴 红牌最多的球队: {max_red[0]} ({max_red[1]['红牌']} 张红牌)")
面向对象版本（更专业）
from collections import defaultdict
class FootballAnalyzer:
"""足球比赛统计分析器"""
def __init__(self):
self.teams = defaultdict(lambda: {"yellow_cards": 0, "red_cards": 0})
def add_match_cards(self, home_team, away_team, cards):
"""
添加比赛的得牌数据
cards: [(card_type, team), ...]
card_type: "yellow" 或 "red"
"""
for card_type, team in cards:
if team in [home_team, away_team]:
if card_type == "yellow":
self.teams[team]["yellow_cards"] += 1
elif card_type == "red":
self.teams[team]["red_cards"] += 1
def get_team_statistics(self):
"""获取所有球队的统计数据"""
statistics = {}
for team, data in self.teams.items():
# 红牌2分，黄牌1分，红牌通常更严重
score = data["yellow_cards"] * 1 + data["red_cards"] * 2
statistics[team] = {
"yellow_cards": data["yellow_cards"],
"red_cards": data["red_cards"],
"total_cards": data["yellow_cards"] + data["red_cards"],
"score": score
}
return statistics
def find_worst_team(self):
"""找出得牌最严重的球队"""
stats = self.get_team_statistics()
if not stats:
return None
# 综合得分最高的球队（需要考虑红牌的双倍价值）
worst_team = max(stats.items(), key=lambda x: x[1]["score"])
return worst_team
def find_most_red_cards_team(self):
"""找出红牌最多的球队（纪律性最差）"""
stats = self.get_team_statistics()
if not stats:
return None
# 红牌最多的球队（如果相同则比黄牌）
worst_team = max(stats.items(),
key=lambda x: (x[1]["red_cards"], x[1]["yellow_cards"]))
return worst_team
def run_oop_version():
"""运行面向对象版本的统计"""
analyzer = FootballAnalyzer()
# 添加多场比赛数据
analyzer.add_match_cards("巴西", "阿根廷", [
("yellow", "巴西"), ("yellow", "阿根廷"),
("red", "巴西"), ("yellow", "巴西")
])
analyzer.add_match_cards("法国", "德国", [
("yellow", "法国"), ("yellow", "德国"), ("yellow", "德国")
])
analyzer.add_match_cards("巴西", "法国", [
("yellow", "法国"), ("red", "巴西"), ("yellow", "巴西")
])
analyzer.add_match_cards("阿根廷", "德国", [
("red", "阿根廷"), ("yellow", "德国")
])
# 输出统计结果
print("=" * 60)
print("🏆 足球比赛纪律性分析")
print("=" * 60)
stats = analyzer.get_team_statistics()
for team, data in sorted(stats.items(), key=lambda x: x[1]["score"], reverse=True):
print(f"\n📊 {team}:")
print(f" 黄牌: {data['yellow_cards']} 张")
print(f" 红牌: {data['red_cards']} 张")
print(f" 总牌数: {data['total_cards']} 张")
print(f" 纪律得分: {data['score']}")
# 找出纪律最差球队
worst_team = analyzer.find_worst_team()
if worst_team:
print(f"\n⚠️ 综合纪律最差: {worst_team[0]}")
# 找出红牌最多球队
most_red = analyzer.find_most_red_cards_team()
if most_red:
print(f"🔴 红牌最多球队: {most_red[0]}")
带可视化的版本
import pandas as pd
import matplotlib.pyplot as plt
def visualize_card_statistics():
"""带数据可视化的统计版本"""
# 测试数据
data = {
'Team': ['巴西', '阿根廷', '法国', '德国', '意大利'],
'Yellow_Cards': [5, 3, 4, 2, 6],
'Red_Cards': [2, 1, 0, 1, 3]
}
df = pd.DataFrame(data)
# 计算综合得分
df['Total_Score'] = df['Yellow_Cards'] * 1 + df['Red_Cards'] * 2
print("=" * 50)
print("🎯 球队纪律性分析数据")
print("=" * 50)
print(df)
# 创建可视化图表
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
# 左图：各种牌的数量
x = range(len(df['Team']))
width = 0.35
axes[0].bar([i - width/2 for i in x], df['Yellow_Cards'],
width, label='黄牌', color='yellow')
axes[0].bar([i + width/2 for i in x], df['Red_Cards'],
width, label='红牌', color='red')
axes[0].set_xlabel('球队')
axes[0].set_ylabel('数量')
axes[0].set_title('各队红黄牌对比')
axes[0].set_xticks(x)
axes[0].set_xticklabels(df['Team'])
axes[0].legend()
# 右图：综合得分
axes[1].bar(df['Team'], df['Total_Score'], color=['red' if score == max(df['Total_Score']) else 'gray'
for score in df['Total_Score']])
axes[1].set_xlabel('球队')
axes[1].set_ylabel('综合得分')
axes[1].set_title('纪律综合得分（红牌2分，黄牌1分）')
# 找出纪律最差的队
worst_team = df.loc[df['Total_Score'].idxmax()]
print(f"\n🏆 纪律最差的球队: {worst_team['Team']}")
print(f" 综合得分: {worst_team['Total_Score']}")
plt.tight_layout()
plt.show()
return df
# 运行示例
if __name__ == "__main__":
print("=== 方案一：基础统计 ===")
team_cards, team_scores = count_cards_basic()
print_results_basic(team_cards, team_scores)
print("\n=== 方案二：面向对象统计 ===")
run_oop_version()
print("\n=== 方案三：可视化展示 ===")
# 运行可视化版本（需要安装 matplotlib）
try:
visualize_card_statistics()
except ImportError:
print("请安装 matplotlib 和 pandas 来运行可视化版本")
运行结果示例
=== 方案一：基础统计 ===
==================================================
各队红黄牌统计：
--------------------------------------------------
巴西: 黄牌 2 张, 红牌 3 张, 总得分 8
阿根廷: 黄牌 1 张, 红牌 1 张, 总得分 3
法国: 黄牌 2 张, 红牌 0 张, 总得分 2
德国: 黄牌 0 张, 红牌 0 张, 总得分 0
--------------------------------------------------
🏆 得牌最多的球队: 巴西 (得分: 8)
🔴 红牌最多的球队: 巴西 (3 张红牌)这道题的关键点是：
- 红牌计分更高：通常红牌违规更严重，应该比黄牌分数高
- 可以多维度比较：黄牌多、红牌多、综合得分三个指标
- 支持多场比赛数据：实际中需要统计整个联赛或赛季的数据
您可以根据实际需求选择适合的版本,或者组合使用这些功能。
