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财务舞弊识别：预付款项大幅增加分析

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中级参考 代码结构已给出，请填写 ____ 处 
"____""____""____"

metadata = {
    "____": "____",
    "____": "____",
    "____": "____",
    "____": "____",
    "____": ["____", "____"],
    "____": ["____", "____", "____"],
    "____": ____
}

"____""____""____"

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

    BASE_URL = "____"
    df_a = pd.read_csv(StringIO(open_url(____).read()))
    df_p = pd.read_csv(StringIO(open_url(____).read()))

    df = df_a.merge(____)
    df['____'] = (____).round(____)
    df['____'] = (____).round(____)

    result = df[['____', '____', '____', '____',
                 '____', '____']].to_string()

    v13 = df[df['____'] == ____]['____'].values[____]
    v15 = df[df['____'] == ____]['____'].values[____]
    growth = round((____) / v13 * ____, ____)
    return result + f"____"

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

示例

输入
solve()
期望输出
year   revenue  prepayments  monetary_funds  预付款占收入比  预付款占货币资金比 | 0  2010  23388.43      1126.07        27205.37   0.0481     0.0414 | 1  2011  22822.61       430.97        19805.45   0.0189     0.0218 | 2  2012  20512.75       379.38        18603.91   0.0185     0.0204 | 3  2013  16612.78       385.24        16210.33   0.0232     0.0238 | 4  2014  15096.79       617.03        13933.54   0.0409     0.0443 | 5  2015  10523.02      1778.73        13726.11   0.1690     0.1296 |  | 2015vs2013预付款增长率: 361.72%
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