{"id":1134326,"date":"2025-01-08T21:14:43","date_gmt":"2025-01-08T13:14:43","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1134326.html"},"modified":"2025-01-08T21:14:47","modified_gmt":"2025-01-08T13:14:47","slug":"python%e5%a6%82%e4%bd%95%e5%81%9a%e4%b8%80%e4%b8%aa%e8%b4%a2%e5%8a%a1%e6%8a%a5%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1134326.html","title":{"rendered":"python\u5982\u4f55\u505a\u4e00\u4e2a\u8d22\u52a1\u62a5\u8868"},"content":{"rendered":"

\"python\u5982\u4f55\u505a\u4e00\u4e2a\u8d22\u52a1\u62a5\u8868\"<\/p>\n

\u8981\u4f7f\u7528Python\u5236\u4f5c\u8d22\u52a1\u62a5\u8868\uff0c\u6838\u5fc3\u6b65\u9aa4\u5305\u62ec\u6570\u636e\u6536\u96c6\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406\u3001\u751f\u6210\u62a5\u8868\u4ee5\u53ca\u53ef\u89c6\u5316\u3002<\/strong>\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u6bcf\u4e00\u4e2a\u6b65\u9aa4\uff0c\u5e76\u63d0\u4f9b\u76f8\u5e94\u7684Python\u4ee3\u7801\u793a\u4f8b\u548c\u5de5\u5177\u63a8\u8350\uff0c\u5e2e\u52a9\u4f60\u5feb\u901f\u4e0a\u624b\u3002<\/p>\n<\/p>\n

\u5236\u4f5c\u8d22\u52a1\u62a5\u8868\u7684\u57fa\u672c\u6d41\u7a0b\u53ef\u4ee5\u6982\u62ec\u4e3a\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

    \n
  1. \u6570\u636e\u6536\u96c6<\/li>\n
  2. \u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406<\/li>\n
  3. \u751f\u6210\u62a5\u8868<\/li>\n
  4. \u6570\u636e\u53ef\u89c6\u5316<\/li>\n<\/ol>\n

    \u4e0b\u9762\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u6bcf\u4e2a\u6b65\u9aa4\uff0c\u5e76\u5206\u4eab\u4e00\u4e9b\u5b9e\u9645\u64cd\u4f5c\u4e2d\u7684\u7ecf\u9a8c\u548c\u6280\u5de7\u3002<\/p>\n<\/p>\n

    \u4e00\u3001\u6570\u636e\u6536\u96c6<\/h2>\n<\/p>\n

    \u6570\u636e\u6536\u96c6\u662f\u5236\u4f5c\u8d22\u52a1\u62a5\u8868\u7684\u7b2c\u4e00\u6b65\u3002\u5728\u5b9e\u9645\u64cd\u4f5c\u4e2d\uff0c\u6570\u636e\u53ef\u4ee5\u6765\u6e90\u4e8e\u591a\u4e2a\u6e20\u9053\uff0c\u5305\u62ec\u6570\u636e\u5e93\u3001Excel\u6587\u4ef6\u3001API\u63a5\u53e3\u7b49\u3002\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u6536\u96c6\uff0c\u53ef\u4ee5\u5229\u7528\u591a\u79cd\u5e93\u548c\u5de5\u5177\uff0c\u5982pandas<\/code>\u3001requests<\/code>\u3001SQLAlchemy<\/code>\u7b49\u3002<\/p>\n<\/p>\n

    1.1 \u4eceExcel\u6587\u4ef6\u8bfb\u53d6\u6570\u636e<\/h3>\n<\/p>\n

    Excel\u6587\u4ef6\u662f\u8d22\u52a1\u6570\u636e\u6700\u5e38\u89c1\u7684\u5b58\u50a8\u683c\u5f0f\u4e4b\u4e00\u3002Python\u7684pandas<\/code>\u5e93\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u63a5\u53e3\u6765\u8bfb\u53d6Excel\u6587\u4ef6\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u8bfb\u53d6Excel\u6587\u4ef6<\/strong><\/h2>\n
    df = pd.read_excel('financial_data.xlsx', sheet_name='Sheet1')<\/p>\n
    \u67e5\u770b\u6570\u636e<\/strong><\/h2>\n
    print(df.head())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.2 \u4ece\u6570\u636e\u5e93\u8bfb\u53d6\u6570\u636e<\/h3>\n<\/p>\n
    \u5982\u679c\u8d22\u52a1\u6570\u636e\u5b58\u50a8\u5728\u6570\u636e\u5e93\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528SQLAlchemy<\/code>\u548cpandas<\/code>\u6765\u8bfb\u53d6\u6570\u636e\u3002<\/p>\n<\/p>\n
    from sqlalchemy import create_engine<\/p>\n
    import pandas as pd<\/p>\n
    \u521b\u5efa\u6570\u636e\u5e93\u8fde\u63a5<\/strong><\/h2>\n
    engine = create_engine('mysql+pymysql:\/\/username:password@host:port\/database')<\/p>\n
    \u6267\u884cSQL\u67e5\u8be2\u5e76\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
    query = "SELECT * FROM financial_data"<\/p>\n
    df = pd.read_sql(query, engine)<\/p>\n
    \u67e5\u770b\u6570\u636e<\/strong><\/h2>\n
    print(df.head())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    1.3 \u4eceAPI\u63a5\u53e3\u83b7\u53d6\u6570\u636e<\/h3>\n<\/p>\n
    \u6709\u65f6\uff0c\u8d22\u52a1\u6570\u636e\u53ef\u80fd\u9700\u8981\u901a\u8fc7API\u63a5\u53e3\u83b7\u53d6\u3002Python\u7684requests<\/code>\u5e93\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5b8c\u6210\u8fd9\u4e00\u4efb\u52a1\u3002<\/p>\n<\/p>\n
    import requests<\/p>\n
    import pandas as pd<\/p>\n
    \u53d1\u9001GET\u8bf7\u6c42<\/strong><\/h2>\n
    response = requests.get('https:\/\/api.example.com\/financial_data')<\/p>\n
    \u5c06JSON\u54cd\u5e94\u8f6c\u6362\u4e3aDataFrame<\/strong><\/h2>\n
    data = response.json()<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u67e5\u770b\u6570\u636e<\/strong><\/h2>\n
    print(df.head())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u5904\u7406<\/h2>\n<\/p>\n
    \u5728\u83b7\u53d6\u5230\u539f\u59cb\u6570\u636e\u540e\uff0c\u901a\u5e38\u9700\u8981\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u51c6\u786e\u6027\u548c\u4e00\u81f4\u6027\u3002\u5e38\u89c1\u7684\u6570\u636e\u6e05\u6d17\u4efb\u52a1\u5305\u62ec\u7f3a\u5931\u503c\u5904\u7406\u3001\u6570\u636e\u683c\u5f0f\u8f6c\u6362\u3001\u6570\u636e\u5408\u5e76\u7b49\u3002<\/p>\n<\/p>\n
    2.1 \u5904\u7406\u7f3a\u5931\u503c<\/h3>\n<\/p>\n
    \u7f3a\u5931\u503c\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e38\u89c1\u95ee\u9898\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u63d0\u4f9b\u7684\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
    # \u67e5\u770b\u7f3a\u5931\u503c\u60c5\u51b5<\/p>\n
    print(df.isnull().sum())<\/p>\n
    \u586b\u5145\u7f3a\u5931\u503c<\/strong><\/h2>\n
    df.fillna(0, inplace=True)<\/p>\n
    \u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c<\/strong><\/h2>\n
    df.dropna(inplace=True)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2.2 \u6570\u636e\u683c\u5f0f\u8f6c\u6362<\/h3>\n<\/p>\n
    \u8d22\u52a1\u6570\u636e\u4e2d\u53ef\u80fd\u5305\u542b\u591a\u79cd\u6570\u636e\u7c7b\u578b\uff0c\u5982\u65e5\u671f\u3001\u5b57\u7b26\u4e32\u3001\u6570\u503c\u7b49\u3002\u6211\u4eec\u9700\u8981\u786e\u4fdd\u6570\u636e\u7c7b\u578b\u7684\u6b63\u786e\u6027\uff0c\u4ee5\u4fbf\u540e\u7eed\u5206\u6790\u3002<\/p>\n<\/p>\n
    # \u5c06\u65e5\u671f\u5217\u8f6c\u6362\u4e3a\u65e5\u671f\u7c7b\u578b<\/p>\n
    df['date'] = pd.to_datetime(df['date'])<\/p>\n
    \u5c06\u6570\u503c\u5217\u8f6c\u6362\u4e3a\u6d6e\u70b9\u6570\u7c7b\u578b<\/strong><\/h2>\n
    df['amount'] = df['amount'].astype(float)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2.3 \u6570\u636e\u5408\u5e76<\/h3>\n<\/p>\n
    \u5982\u679c\u6570\u636e\u5206\u6563\u5728\u591a\u4e2a\u6570\u636e\u6e90\u4e2d\uff0c\u6211\u4eec\u9700\u8981\u5c06\u5176\u5408\u5e76\u6210\u4e00\u4e2a\u7edf\u4e00\u7684\u6570\u636e\u96c6\u3002pandas<\/code>\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u5408\u5e76\u65b9\u6cd5\uff0c\u5982merge<\/code>\u3001concat<\/code>\u7b49\u3002<\/p>\n<\/p>\n
    # \u8bfb\u53d6\u7b2c\u4e8c\u4e2a\u6570\u636e\u96c6<\/p>\n
    df2 = pd.read_excel('additional_data.xlsx', sheet_name='Sheet1')<\/p>\n
    \u5408\u5e76\u6570\u636e\u96c6<\/strong><\/h2>\n
    df = pd.merge(df, df2, on='key_column')<\/p>\n
    \u67e5\u770b\u5408\u5e76\u540e\u7684\u6570\u636e<\/strong><\/h2>\n
    print(df.head())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e09\u3001\u751f\u6210\u62a5\u8868<\/h2>\n<\/p>\n
    \u5728\u5b8c\u6210\u6570\u636e\u6e05\u6d17\u548c\u5904\u7406\u540e\uff0c\u6211\u4eec\u5c31\u53ef\u4ee5\u751f\u6210\u8d22\u52a1\u62a5\u8868\u4e86\u3002\u5e38\u89c1\u7684\u8d22\u52a1\u62a5\u8868\u5305\u62ec\u8d44\u4ea7\u8d1f\u503a\u8868\u3001\u5229\u6da6\u8868\u3001\u73b0\u91d1\u6d41\u91cf\u8868\u7b49\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u7684\u5206\u7ec4\u548c\u805a\u5408\u529f\u80fd\u6765\u751f\u6210\u8fd9\u4e9b\u62a5\u8868\u3002<\/p>\n<\/p>\n
    3.1 \u751f\u6210\u8d44\u4ea7\u8d1f\u503a\u8868<\/h3>\n<\/p>\n
    \u8d44\u4ea7\u8d1f\u503a\u8868\u5c55\u793a\u4e86\u516c\u53f8\u7684\u8d22\u52a1\u72b6\u51b5\uff0c\u5305\u62ec\u8d44\u4ea7\u3001\u8d1f\u503a\u548c\u80a1\u4e1c\u6743\u76ca\u3002<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u603b\u8d44\u4ea7<\/p>\n
    total_assets = df[df['category'] == 'Asset']['amount'].sum()<\/p>\n
    \u8ba1\u7b97\u603b\u8d1f\u503a<\/strong><\/h2>\n
    total_liabilities = df[df['category'] == 'Liability']['amount'].sum()<\/p>\n
    \u8ba1\u7b97\u80a1\u4e1c\u6743\u76ca<\/strong><\/h2>\n
    shareholders_equity = total_assets - total_liabilities<\/p>\n
    \u521b\u5efa\u8d44\u4ea7\u8d1f\u503a\u8868<\/strong><\/h2>\n
    balance_sheet = pd.DataFrame({<\/p>\n
        'Category': ['Total Assets', 'Total Liabilities', 'Shareholders Equity'],<\/p>\n
        'Amount': [total_assets, total_liabilities, shareholders_equity]<\/p>\n
    })<\/p>\n
    \u67e5\u770b\u8d44\u4ea7\u8d1f\u503a\u8868<\/strong><\/h2>\n
    print(balance_sheet)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.2 \u751f\u6210\u5229\u6da6\u8868<\/h3>\n<\/p>\n
    \u5229\u6da6\u8868\u5c55\u793a\u4e86\u516c\u53f8\u7684\u6536\u5165\u3001\u6210\u672c\u548c\u51c0\u5229\u6da6\u3002<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u603b\u6536\u5165<\/p>\n
    total_revenue = df[df['category'] == 'Revenue']['amount'].sum()<\/p>\n
    \u8ba1\u7b97\u603b\u6210\u672c<\/strong><\/h2>\n
    total_expenses = df[df['category'] == 'Expense']['amount'].sum()<\/p>\n
    \u8ba1\u7b97\u51c0\u5229\u6da6<\/strong><\/h2>\n
    net_profit = total_revenue - total_expenses<\/p>\n
    \u521b\u5efa\u5229\u6da6\u8868<\/strong><\/h2>\n
    income_statement = pd.DataFrame({<\/p>\n
        'Category': ['Total Revenue', 'Total Expenses', 'Net Profit'],<\/p>\n
        'Amount': [total_revenue, total_expenses, net_profit]<\/p>\n
    })<\/p>\n
    \u67e5\u770b\u5229\u6da6\u8868<\/strong><\/h2>\n
    print(income_statement)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3.3 \u751f\u6210\u73b0\u91d1\u6d41\u91cf\u8868<\/h3>\n<\/p>\n
    \u73b0\u91d1\u6d41\u91cf\u8868\u5c55\u793a\u4e86\u516c\u53f8\u7684\u73b0\u91d1\u6d41\u5165\u548c\u6d41\u51fa\u60c5\u51b5\u3002<\/p>\n<\/p>\n
    # \u8ba1\u7b97\u7ecf\u8425\u6d3b\u52a8\u73b0\u91d1\u6d41<\/p>\n
    operating_cash_flow = df[df['activity'] == 'Operating']['amount'].sum()<\/p>\n
    \u8ba1\u7b97\u6295\u8d44\u6d3b\u52a8\u73b0\u91d1\u6d41<\/strong><\/h2>\n
    investing_cash_flow = df[df['activity'] == 'Investing']['amount'].sum()<\/p>\n
    \u8ba1\u7b97\u878d\u8d44\u6d3b\u52a8\u73b0\u91d1\u6d41<\/strong><\/h2>\n
    financing_cash_flow = df[df['activity'] == 'Financing']['amount'].sum()<\/p>\n
    \u8ba1\u7b97\u73b0\u91d1\u51c0\u589e\u52a0\u989d<\/strong><\/h2>\n
    net_cash_flow = operating_cash_flow + investing_cash_flow + financing_cash_flow<\/p>\n
    \u521b\u5efa\u73b0\u91d1\u6d41\u91cf\u8868<\/strong><\/h2>\n
    cash_flow_statement = pd.DataFrame({<\/p>\n
        'Activity': ['Operating', 'Investing', 'Financing', 'Net Cash Flow'],<\/p>\n
        'Amount': [operating_cash_flow, investing_cash_flow, financing_cash_flow, net_cash_flow]<\/p>\n
    })<\/p>\n
    \u67e5\u770b\u73b0\u91d1\u6d41\u91cf\u8868<\/strong><\/h2>\n
    print(cash_flow_statement)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u56db\u3001\u6570\u636e\u53ef\u89c6\u5316<\/h2>\n<\/p>\n
    \u8d22\u52a1\u62a5\u8868\u751f\u6210\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u6570\u636e\u53ef\u89c6\u5316\u6765\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u8d22\u52a1\u6570\u636e\u3002Python\u7684matplotlib<\/code>\u548cseaborn<\/code>\u5e93\u662f\u5e38\u7528\u7684\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u3002<\/p>\n<\/p>\n
    4.1 \u53ef\u89c6\u5316\u8d44\u4ea7\u8d1f\u503a\u8868<\/h3>\n<\/p>\n
    import matplotlib.pyplot as plt<\/p>\n
    \u7ed8\u5236\u8d44\u4ea7\u8d1f\u503a\u8868<\/strong><\/h2>\n
    plt.figure(figsize=(10, 6))<\/p>\n
    plt.bar(balance_sheet['Category'], balance_sheet['Amount'], color=['blue', 'red', 'green'])<\/p>\n
    plt.title('Balance Sheet')<\/p>\n
    plt.xlabel('Category')<\/p>\n
    plt.ylabel('Amount')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.2 \u53ef\u89c6\u5316\u5229\u6da6\u8868<\/h3>\n<\/p>\n
    # \u7ed8\u5236\u5229\u6da6\u8868<\/p>\n
    plt.figure(figsize=(10, 6))<\/p>\n
    plt.bar(income_statement['Category'], income_statement['Amount'], color=['blue', 'red', 'green'])<\/p>\n
    plt.title('Income Statement')<\/p>\n
    plt.xlabel('Category')<\/p>\n
    plt.ylabel('Amount')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    4.3 \u53ef\u89c6\u5316\u73b0\u91d1\u6d41\u91cf\u8868<\/h3>\n<\/p>\n
    # \u7ed8\u5236\u73b0\u91d1\u6d41\u91cf\u8868<\/p>\n
    plt.figure(figsize=(10, 6))<\/p>\n
    plt.bar(cash_flow_statement['Activity'], cash_flow_statement['Amount'], color=['blue', 'red', 'green', 'purple'])<\/p>\n
    plt.title('Cash Flow Statement')<\/p>\n
    plt.xlabel('Activity')<\/p>\n
    plt.ylabel('Amount')<\/p>\n
    plt.show()<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u8f7b\u677e\u5236\u4f5c\u8d22\u52a1\u62a5\u8868\uff0c\u5e76\u901a\u8fc7\u6570\u636e\u53ef\u89c6\u5316\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u8d22\u52a1\u6570\u636e\u3002\u8fd9\u4e0d\u4ec5\u63d0\u9ad8\u4e86\u8d22\u52a1\u62a5\u8868\u7684\u5236\u4f5c\u6548\u7387\uff0c\u8fd8\u589e\u5f3a\u4e86\u6570\u636e\u7684\u53ef\u8bfb\u6027\u548c\u5206\u6790\u4ef7\u503c\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u4f60\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u4f60\u5728\u8d22\u52a1\u62a5\u8868\u5236\u4f5c\u4e2d\u53d6\u5f97\u6210\u529f\uff01<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
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