综合评估：Altman Z-Score计算

"""
金融风险管理 - q083
综合财务评估：Z-Score计算（Altman模型）
"""

metadata = {
    "id": "q083",
    "title": "综合评估：Altman Z-Score计算",
    "module": "综合财务风险评估",
    "difficulty": "beginner",
    "data_files": ["zscore.csv"],
    "skills": ["Z-Score", "Altman模型", "破产预测"],
    "estimated_minutes": 8
}

"""
【题目】
读取zscore.csv，筛选2023年数据，按Z-Score区间分类：
- Z > 2.99：安全区
- 1.81 <= Z <= 2.99：灰色区间（不确定）
- Z < 1.81：危险区

计算各区间公司数量和占比。

输出DataFrame，包含：区间名称、Z-Score范围、公司数量、占比（保留4位小数）
并输出一行汇总：ZScore中位数（保留4位小数）
"""

def solve():
    import pandas as pd
    from pyodide.http import open_url
    from io import StringIO

    BASE_URL = "https://data.zuihe.com/finance/"
    zs = pd.read_csv(StringIO(open_url(BASE_URL + "zscore.csv").read()))

    df = zs[zs['Accper'] == '2023-12-31'].dropna(subset=['ZScore'])
    total = len(df)

    zones = [
        ('安全区', 'Z > 2.99', (df['ZScore'] > 2.99).sum()),
        ('灰色区间', '1.81 <= Z <= 2.99', ((df['ZScore'] >= 1.81) & (df['ZScore'] <= 2.99)).sum()),
        ('危险区', 'Z < 1.81', (df['ZScore'] < 1.81).sum()),
    ]

    rows = [{'区间名称': name, 'Z-Score范围': rng,
             '公司数量': n, '占比': round(n / total, 4)} for name, rng, n in zones]
    result = pd.DataFrame(rows).to_string()
    median = round(df['ZScore'].median(), 4)
    return result + f"

ZScore中位数: {median}"

if __name__ == "__main__":
    print(solve())