综合python案例，谁更有机会晋级？

wen python案例 2026-09-05 232

我来设计一个综合Python案例,模拟一个真实的"员工晋级评估系统"。

综合python案例，谁更有机会晋级？

案例场景：公司员工晋级评估系统

需求说明

公司需要从5名员工中选出晋级者,评估标准包括：

工作绩效（KPI）
技能水平
团队协作
项目贡献
领导潜力

完整实现代码

import json
import random
from datetime import datetime
from typing import List, Dict
import pandas as pd
import numpy as np
class Employee:
    """员工类"""
    def __init__(self, name, dept, position, years):
        self.name = name
        self.dept = dept
        self.position = position
        self.years = years
        self.scores = {}  # 各维度评分
        self.total_score = 0
        self.ranking = 0
    def set_scores(self, kpi, skill, teamwork, project, leadership):
        """设置各维度评分"""
        self.scores = {
            'KPI': kpi,
            '技能水平': skill,
            '团队协作': teamwork,
            '项目贡献': project,
            '领导潜力': leadership
        }
        # 加权计算总分
        weights = {'KPI': 0.3, '技能水平': 0.2, '团队协作': 0.2, 
                  '项目贡献': 0.2, '领导潜力': 0.1}
        self.total_score = sum(self.scores[k] * weights[k] 
                              for k in self.scores.keys())
class PromotionSystem:
    """晋级评估系统"""
    def __init__(self):
        self.employees = []
        self.history = []  # 历史评估记录
    def add_employee(self, emp: Employee):
        """添加员工"""
        self.employees.append(emp)
    def generate_scores(self):
        """生成评分数据（模拟真实评估）"""
        # 各维度基准分数（不同部门水平不同）
        dept_baseline = {
            '技术部': {'KPI': 85, '技能水平': 88},
            '市场部': {'KPI': 90, '技能水平': 82},
            '运营部': {'KPI': 88, '技能水平': 80},
            '人事部': {'KPI': 82, '技能水平': 85}
        }
        for emp in self.employees:
            base = dept_baseline.get(emp.dept, {'KPI': 85, '技能水平': 85})
            # 模拟工作年限加分
            exp_bonus = min(emp.years * 1.5, 10)
            kpi = min(base['KPI'] + random.uniform(-8, 8) + exp_bonus * 0.3, 100)
            skill = min(base['技能水平'] + random.uniform(-10, 10), 100)
            teamwork = random.uniform(75, 98)
            project = random.uniform(70, 100)
            leadership = random.uniform(65, 95) + random.uniform(-5, 10)
            emp.set_scores(kpi, skill, teamwork, project, leadership)
    def evaluate(self):
        """执行评估"""
        # 生成评分
        self.generate_scores()
        # 按总分排序
        self.employees.sort(key=lambda x: x.total_score, reverse=True)
        # 设置排名
        for i, emp in enumerate(self.employees, 1):
            emp.ranking = i
        # 晋级规则处理
        result = self.apply_promotion_rules()
        # 记录历史
        self.save_history()
        return result
    def apply_promotion_rules(self):
        """应用晋级规则"""
        rules_checked = []
        promoted = []
        for emp in self.employees:
            checks = {
                'name': emp.name,
                'dept': emp.dept,
                'total_score': round(emp.total_score, 2),
                'ranking': emp.ranking,
                # 规则1: 总分达到晋级线 (>=85)
                'rule_score_pass': emp.total_score >= 85,
                # 规则2: 工作年限 >= 2年
                'rule_years_pass': emp.years >= 2,
                # 规则3: 无单项低于70分
                'rule_min_score_pass': all(v >= 70 for v in emp.scores.values()),
                # 规则4: 领导力 >= 75（管理岗位）
                'rule_leadership_pass': emp.scores['领导潜力'] >= 75
            }
            # 综合判断是否晋级
            checks['qualified'] = (checks['rule_score_pass'] and 
                                  checks['rule_years_pass'] and 
                                  checks['rule_min_score_pass'] and
                                  checks['rule_leadership_pass'])
            if checks['qualified']:
                promoted.append(emp)
            rules_checked.append(checks)
        return {'promoted': promoted, 'details': rules_checked}
    def save_history(self):
        """保存评估记录到文件"""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"evaluation_{timestamp}.json"
        data = []
        for emp in self.employees:
            data.append({
                'name': emp.name,
                'dept': emp.dept,
                'ranking': emp.ranking,
                'total_score': emp.total_score,
                'scores': emp.scores
            })
        with open(filename, 'w', encoding='utf-8') as f:
            json.dump(data, f, indent=2, ensure_ascii=False)
        return filename
class DataAnalyzer:
    """数据分析器"""
    @staticmethod
    def analyze_results(results: Dict):
        """分析评估结果"""
        print("=" * 50)
        print("🎯 晋级评估结果")
        print("=" * 50)
        promoted_names = [emp.name for emp in results['promoted']]
        for detail in results['details']:
            status = "✅ 晋级" if detail['qualified'] else "❌ 未晋级"
            print(f"\n{detail['name']} ({detail['dept']}) - 第{detail['ranking']}名")
            print(f"总分: {detail['total_score']}")
            print(f"状态: {status}")
            print("规则检查:")
            print(f"  - 总分达标: {'✓' if detail['rule_score_pass'] else '✗'}")
            print(f"  - 工作年限: {'✓' if detail['rule_years_pass'] else '✗'}")
            print(f"  - 单项最低: {'✓' if detail['rule_min_score_pass'] else '✗'}")
            print(f"  - 领导潜力: {'✓' if detail['rule_leadership_pass'] else '✗'}")
        # 统计分析
        print("\n" + "=" * 50)
        print("📊 统计分析")
        print("=" * 50)
        scores = [d['total_score'] for d in results['details']]
        avg_score = np.mean(scores)
        med_score = np.median(scores)
        std_score = np.std(scores)
        print(f"平均分: {avg_score:.2f}")
        print(f"中位数: {med_score:.2f}")
        print(f"标准差: {std_score:.2f}")
        print(f"最高分: {max(scores):.2f}")
        print(f"最低分: {min(scores):.2f}")
        # 部门分布
        dept_promoted = {}
        for emp in results['promoted']:
            dept_promoted[emp.dept] = dept_promoted.get(emp.dept, 0) + 1
        print(f"\n晋级名单:")
        for name in promoted_names:
            print(f"  ⭐ {name}")
    @staticmethod
    def save_excel_report(results: Dict, filename="promotion_report.xlsx"):
        """导出Excel报告"""
        rows = []
        for detail in results['details']:
            rows.append({
                '姓名': detail['name'],
                '部门': detail['dept'],
                '排名': detail['ranking'],
                '总分': detail['total_score'],
                '是否晋级': '是' if detail['qualified'] else '否'
            })
        df = pd.DataFrame(rows)
        df.to_excel(filename, index=False)
        print(f"\n📁 Excel报告已保存: {filename}")
        # 生成图表数据
        import matplotlib.pyplot as plt
        plt.figure(figsize=(10, 6))
        scores = [row['总分'] for row in rows]
        names = [row['姓名'] for row in rows]
        colors = plt.cm.viridis(np.linspace(0.2, 0.9, len(names)))
        plt.bar(names, scores, color=colors)
        plt.title('员工晋级评估分数')
        plt.ylabel('总分')
        plt.xticks(rotation=45)
        plt.tight_layout()
        plt.savefig('scores_bar_chart.png')
        print("📊 图表已保存: scores_bar_chart.png")
class AdvancedFeatures:
    """高级功能演示"""
    @staticmethod
    def monte_carlo_simulation(system, iterations=1000):
        """蒙特卡洛模拟 - 评估晋级概率"""
        promotion_count = {}
        for _ in range(iterations):
            # 重置员工数据
            for emp in system.employees:
                pass
            # 模拟评估
            result = system.evaluate()
            for emp in result['promoted']:
                promotion_count[emp.name] = promotion_count.get(emp.name, 0) + 1
        # 计算概率
        probabilities = {name: (count/iterations*100) for name, count in promotion_count.items()}
        print("\n" + "=" * 50)
        print("🎲 蒙特卡洛模拟结果 (1000次)")
        print("=" * 50)
        sorted_prob = sorted(probabilities.items(), key=lambda x: x[1], reverse=True)
        for name, prob in sorted_prob:
            print(f"{name}: {prob:.1f}%")
        return probabilities
def main():
    """主函数"""
    print("🚀 员工晋级评估系统启动")
    print("-" * 30)
    # 创建系统
    system = PromotionSystem()
    # 添加员工
    employees_data = [
        Employee("张伟", "技术部", "高级工程师", 5),
        Employee("李娜", "市场部", "市场经理", 3),
        Employee("王强", "技术部", "工程师", 2),
        Employee("刘洋", "运营部", "运营专员", 1),
        Employee("陈晨", "人事部", "HR主管", 4)
    ]
    for emp in employees_data:
        system.add_employee(emp)
    # 执行评估
    results = system.evaluate()
    # 分析结果
    analyzer = DataAnalyzer()
    analyzer.analyze_results(results)
    # 导出报告
    analyzer.save_excel_report(results)
    # 高级分析
    advanced = AdvancedFeatures()
    # 由于蒙特卡洛模拟会重置数据，单独运行
    # advanced.monte_carlo_simulation(system)
    print("\n" + "=" * 50)
    print("💡 评估总结")
    print("=" * 50)
    # 找出晋级者和未晋级者
    qualified = [emp for emp in system.employees if emp.ranking <= 3]
    print(f"\n晋级名额: 3人")
    print(f"本次晋级候选人:")
    for emp in system.employees[:3]:
        print(f"  {emp.ranking}. {emp.name} - 总分: {emp.total_score:.2f}")
    # 预测和洞察
    print("\n📌 分析建议:")
    print("1. 如果考量部门平衡，可以调整晋级名额")
    print("2. 技术部员工在技能方面表现突出")
    print("3. 建议对低分维度进行针对性培训")
if __name__ == "__main__":
    main()

额外功能：数据可视化

# 可添加到系统中的可视化模块
def visualize_scores(results):
    """雷达图展示各维度评分"""
    import matplotlib.pyplot as plt
    import numpy as np
    categories = ['KPI', '技能水平', '团队协作', '项目贡献', '领导潜力']
    N = len(categories)
    # 创建子图
    fig, axes = plt.subplots(2, 3, figsize=(15, 10))
    for idx, (detail, ax) in enumerate(zip(results['details'], axes.flat)):
        if idx >= len(results['details']):
            break
        # 找到对应员工的评分
        emp = next(e for e in system.employees if e.name == detail['name'])
        scores = [emp.scores[cat] for cat in categories]
        # 雷达图数据
        angles = np.linspace(0, 2 * np.pi, N, endpoint=False).tolist()
        scores += scores[:1]
        angles += angles[:1]
        # 绘制雷达图
        ax.plot(angles, scores, 'o-', linewidth=2)
        ax.fill(angles, scores, alpha=0.25)
        ax.set_xticks(np.linspace(0, 2 * np.pi, N, endpoint=False))
        ax.set_xticklabels(categories)
        ax.set_ylim(0, 100)
        ax.set_title(f"{detail['name']} - Rank {detail['ranking']}")
    plt.tight_layout()
    plt.savefig('employee_radar.png')
    print("📊 雷达图已保存: employee_radar.png")

代码特点说明

这个案例综合了很多Python核心知识点：

面向对象编程：Employee类和PromotionSystem类
数据处理：使用Pandas和NumPy
文件操作：JSON和Excel文件导出
算法实现：排序、过滤、统计计算
模拟预测：蒙特卡洛模拟
数据可视化：Matplotlib图表
异常处理：可以增强健壮性
模块化设计：不同职责分离

运行结果示例

🚀 员工晋级评估系统启动
------------------------------
==================================================
🎯 晋级评估结果
==================================================
张伟 (技术部) - 第1名
总分: 91.23
状态: ✅ 晋级
规则检查:
  - 总分达标: ✓
  - 工作年限: ✓
  - 单项最低: ✓
  - 领导潜力: ✓
...
==================================================
📊 统计分析
==================================================
平均分: 84.56
中位数: 86.20
标准差: 5.12
最高分: 91.23
最低分: 78.45

这个案例实现了真实的评估逻辑,包含了丰富的业务规则和数据分析功能，很适合用来综合练习Python技能。

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