{"id":1006834,"date":"2024-12-27T10:48:01","date_gmt":"2024-12-27T02:48:01","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1006834.html"},"modified":"2024-12-27T10:48:03","modified_gmt":"2024-12-27T02:48:03","slug":"python%e5%a6%82%e4%bd%95%e5%81%9a%e6%8a%a5%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1006834.html","title":{"rendered":"python\u5982\u4f55\u505a\u62a5\u8868"},"content":{"rendered":"

\"python\u5982\u4f55\u505a\u62a5\u8868\"<\/p>\n

Python\u5236\u4f5c\u62a5\u8868\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9e\u73b0\uff0c\u5305\u62ec\u4f7f\u7528pandas\u5904\u7406\u6570\u636e\u3001\u5229\u7528matplotlib\u6216seaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3001\u901a\u8fc7openpyxl\u6216xlwings\u64cd\u4f5cExcel\u6587\u4ef6\u3001\u501f\u52a9reportlab\u751f\u6210PDF\u62a5\u8868\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528pandas\u5e93\u6765\u5904\u7406\u548c\u6574\u7406\u6570\u636e\u662f\u6700\u57fa\u7840\u4e14\u6700\u91cd\u8981\u7684\u4e00\u6b65\u3002pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u6570\u636e\u6e05\u6d17\u3001\u53d8\u6362\u548c\u5206\u6790\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u63a2\u8ba8\u5982\u4f55\u4f7f\u7528Python\u5236\u4f5c\u62a5\u8868\uff0c\u6db5\u76d6\u6570\u636e\u5904\u7406\u3001\u53ef\u89c6\u5316\u548c\u751f\u6210\u62a5\u8868\u7684\u5177\u4f53\u6b65\u9aa4\u548c\u5de5\u5177\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u8fdb\u884c\u6570\u636e\u5904\u7406<\/p>\n<\/p>\n

pandas\u662fPython\u4e2d\u6700\u53d7\u6b22\u8fce\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u5de5\u5177\u6765\u7b80\u5316\u6570\u636e\u5206\u6790\u4efb\u52a1\u3002<\/p>\n<\/p>\n

    \n
  1. \u6570\u636e\u8bfb\u53d6\u548c\u6e05\u6d17<\/strong><\/li>\n<\/ol>\n

    pandas\u652f\u6301\u4ece\u591a\u79cd\u6570\u636e\u6e90\u8bfb\u53d6\u6570\u636e\uff0c\u5305\u62ecCSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u7b49\u3002\u8bfb\u53d6\u6570\u636e\u540e\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u6574\u7406\u3002\u8fd9\u53ef\u80fd\u5305\u62ec\u5220\u9664\u7f3a\u5931\u503c\u3001\u5904\u7406\u5f02\u5e38\u503c\u3001\u683c\u5f0f\u5316\u65e5\u671f\u7b49\u3002\u5229\u7528pandas\u7684DataFrame<\/code>\u5bf9\u8c61\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u8fd9\u4e9b\u64cd\u4f5c\u3002\u4f8b\u5982\uff1a<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u8bfb\u53d6CSV\u6587\u4ef6<\/strong><\/h2>\n
    df = pd.read_csv('data.csv')<\/p>\n
    \u5220\u9664\u7f3a\u5931\u503c<\/strong><\/h2>\n
    df.dropna(inplace=True)<\/p>\n
    \u683c\u5f0f\u5316\u65e5\u671f<\/strong><\/h2>\n
    df['date'] = pd.to_datetime(df['date'])<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u6570\u636e\u53d8\u6362\u548c\u5206\u6790<\/strong><\/li>\n<\/ol>\n
      \u5728\u6570\u636e\u6e05\u6d17\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528pandas\u8fdb\u884c\u5404\u79cd\u6570\u636e\u53d8\u6362\u548c\u5206\u6790\u64cd\u4f5c\u3002\u4f8b\u5982\uff0c\u5206\u7ec4\u805a\u5408\u3001\u900f\u89c6\u8868\u3001\u5408\u5e76\u6570\u636e\u96c6\u7b49\u3002pandas\u63d0\u4f9b\u4e86\u7b80\u5355\u4e14\u9ad8\u6548\u7684\u65b9\u6cd5\u6765\u5b8c\u6210\u8fd9\u4e9b\u4efb\u52a1\uff1a<\/p>\n<\/p>\n
      # \u5206\u7ec4\u805a\u5408<\/p>\n
      grouped = df.groupby('category').sum()<\/p>\n
      \u900f\u89c6\u8868<\/strong><\/h2>\n
      pivot_table = df.pivot_table(values='sales', index='month', columns='category', aggfunc='sum')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001MATPLOTLIB\u548cSEABORN\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316<\/p>\n<\/p>\n
      \u6570\u636e\u53ef\u89c6\u5316\u662f\u62a5\u8868\u5236\u4f5c\u4e2d\u7684\u91cd\u8981\u90e8\u5206\uff0cmatplotlib\u548cseaborn\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5e93\u3002<\/p>\n<\/p>\n
        \n
      1. MATPLOTLIB<\/strong><\/li>\n<\/ol>\n
        matplotlib\u662fPython\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u53ef\u4ee5\u521b\u5efa\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u56fe\u5f62\u3002\u5b83\u53ef\u4ee5\u7ed8\u5236\u591a\u79cd\u7c7b\u578b\u7684\u56fe\u8868\uff0c\u5305\u62ec\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u7ed8\u5236\u67f1\u72b6\u56fe<\/strong><\/h2>\n
        plt.figure(figsize=(10, 6))<\/p>\n
        plt.bar(grouped.index, grouped['sales'])<\/p>\n
        plt.title('Sales by Category')<\/p>\n
        plt.xlabel('Category')<\/p>\n
        plt.ylabel('Sales')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. SEABORN<\/strong><\/li>\n<\/ol>\n
          seaborn\u662f\u5728matplotlib\u57fa\u7840\u4e0a\u8fdb\u884c\u5c01\u88c5\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u548c\u7f8e\u89c2\u7684\u56fe\u5f62\u8f93\u51fa\u3002\u5b83\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u8fdb\u884c\u6570\u636e\u63a2\u7d22\u6027\u5206\u6790\u3002<\/p>\n<\/p>\n
          import seaborn as sns<\/p>\n
          \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
          sns.boxplot(x='category', y='sales', data=df)<\/p>\n
          plt.title('Sales Distribution by Category')<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001OPENPYXL\u548cXLWINGS\u64cd\u4f5cEXCEL\u6587\u4ef6<\/p>\n<\/p>\n
          \u5bf9\u4e8e\u9700\u8981\u751f\u6210Excel\u683c\u5f0f\u62a5\u8868\u7684\u60c5\u51b5\uff0c\u53ef\u4ee5\u4f7f\u7528openpyxl\u6216xlwings\u5e93\u3002<\/p>\n<\/p>\n
            \n
          1. OPENPYXL<\/strong><\/li>\n<\/ol>\n
            openpyxl\u662f\u4e00\u4e2a\u7528\u4e8e\u8bfb\u5199Excel 2010 xlsx\/xlsm\/xltx\/xltm\u6587\u4ef6\u7684Python\u5e93\u3002\u53ef\u4ee5\u7528\u6765\u521b\u5efaExcel\u6587\u4ef6\uff0c\u5199\u5165\u6570\u636e\uff0c\u8bbe\u7f6e\u683c\u5f0f\u7b49\u3002<\/p>\n<\/p>\n
            from openpyxl import Workbook<\/p>\n
            \u521b\u5efa\u65b0\u7684Excel\u5de5\u4f5c\u7c3f<\/strong><\/h2>\n
            wb = Workbook()<\/p>\n
            ws = wb.active<\/p>\n
            \u5199\u5165\u6570\u636e<\/strong><\/h2>\n
            ws.append(['Category', 'Sales'])<\/p>\n
            for row in grouped.itertuples():<\/p>\n
                ws.append([row.Index, row.sales])<\/p>\n
            \u4fdd\u5b58\u6587\u4ef6<\/strong><\/h2>\n
            wb.save('report.xlsx')<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. XLWINGS<\/strong><\/li>\n<\/ol>\n
              xlwings\u53ef\u4ee5\u901a\u8fc7Python\u81ea\u52a8\u5316Excel\uff0c\u652f\u6301\u590d\u6742\u7684Excel\u64cd\u4f5c\uff0c\u5982\u516c\u5f0f\u8fd0\u7b97\u3001\u56fe\u8868\u751f\u6210\u7b49\u3002<\/p>\n<\/p>\n
              import xlwings as xw<\/p>\n
              \u521b\u5efa\u65b0\u7684Excel\u5de5\u4f5c\u7c3f<\/strong><\/h2>\n
              wb = xw.Book()<\/p>\n
              sheet = wb.sheets[0]<\/p>\n
              \u5199\u5165\u6570\u636e<\/strong><\/h2>\n
              sheet.range('A1').value = ['Category', 'Sales']<\/p>\n
              sheet.range('A2').value = grouped.reset_index().values<\/p>\n
              \u4fdd\u5b58\u6587\u4ef6<\/strong><\/h2>\n
              wb.save('report.xlsx')<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001REPORTLAB\u751f\u6210PDF\u62a5\u8868<\/p>\n<\/p>\n
              \u5982\u679c\u9700\u8981\u751f\u6210PDF\u683c\u5f0f\u7684\u62a5\u8868\uff0c\u53ef\u4ee5\u4f7f\u7528reportlab\u5e93\u3002\u5b83\u5141\u8bb8\u521b\u5efa\u4e13\u4e1a\u7684PDF\u6587\u6863\uff0c\u652f\u6301\u6587\u672c\u3001\u56fe\u5f62\u3001\u8868\u683c\u7b49\u5143\u7d20\u3002<\/p>\n<\/p>\n
                \n
              1. \u521b\u5efa\u57fa\u672cPDF<\/strong><\/li>\n<\/ol>\n
                reportlab\u63d0\u4f9b\u4e86\u7b80\u5355\u7684\u63a5\u53e3\u6765\u521b\u5efaPDF\u6587\u6863\uff0c\u5305\u62ec\u6dfb\u52a0\u6587\u672c\u3001\u56fe\u5f62\u7b49\u3002<\/p>\n<\/p>\n
                from reportlab.lib.pagesizes import letter<\/p>\n
                from reportlab.pdfgen import canvas<\/p>\n
                \u521b\u5efaPDF\u6587\u4ef6<\/strong><\/h2>\n
                c = canvas.Canvas("report.pdf", pagesize=letter)<\/p>\n
                c.drawString(100, 750, "Sales Report")<\/p>\n
                \u6dfb\u52a0\u8868\u683c\u6216\u56fe\u5f62<\/strong><\/h2>\n
                ...<\/strong><\/h2>\n
                \u4fdd\u5b58PDF<\/strong><\/h2>\n
                c.save()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u6dfb\u52a0\u590d\u6742\u5143\u7d20<\/strong><\/li>\n<\/ol>\n
                  reportlab\u652f\u6301\u590d\u6742\u7684\u5e03\u5c40\uff0c\u53ef\u4ee5\u6dfb\u52a0\u56fe\u8868\u3001\u8868\u683c\u7b49\u3002\u5728\u751f\u6210\u590d\u6742\u7684PDF\u62a5\u8868\u65f6\uff0c\u53ef\u4ee5\u7ed3\u5408\u4f7f\u7528\u5176\u4ed6\u5e93\u751f\u6210\u56fe\u8868\uff0c\u5e76\u5c06\u5176\u5d4c\u5165\u5230PDF\u4e2d\u3002<\/p>\n<\/p>\n
                  from reportlab.platypus import SimpleDocTemplate, Table, TableStyle<\/p>\n
                  from reportlab.lib import colors<\/p>\n
                  \u521b\u5efaPDF\u6587\u6863<\/strong><\/h2>\n
                  doc = SimpleDocTemplate("detAI<\/a>led_report.pdf", pagesize=letter)<\/p>\n
                  \u521b\u5efa\u8868\u683c\u6570\u636e<\/strong><\/h2>\n
                  table_data = [['Category', 'Sales']] + grouped.reset_index().values.tolist()<\/p>\n
                  \u521b\u5efa\u8868\u683c\u5bf9\u8c61<\/strong><\/h2>\n
                  table = Table(table_data)<\/p>\n
                  table.setStyle(TableStyle([('BACKGROUND', (0, 0), (-1, 0), colors.grey),<\/p>\n
                                             ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),<\/p>\n
                                             ('ALIGN', (0, 0), (-1, -1), 'CENTER'),<\/p>\n
                                             ('GRID', (0, 0), (-1, -1), 1, colors.black)]))<\/p>\n
                  \u6dfb\u52a0\u8868\u683c\u5230\u6587\u6863<\/strong><\/h2>\n
                  elements = [table]<\/p>\n
                  doc.build(elements)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
                  Python\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5e93\u548c\u5de5\u5177\u6765\u5236\u4f5c\u5404\u79cd\u683c\u5f0f\u7684\u62a5\u8868\uff0c\u65e0\u8bba\u662f\u6570\u636e\u5904\u7406\u3001\u53ef\u89c6\u5316\u8fd8\u662f\u751f\u6210\u5177\u4f53\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u90fd\u53ef\u4ee5\u5728Python\u4e2d\u627e\u5230\u5408\u9002\u7684\u89e3\u51b3\u65b9\u6848\u3002\u901a\u8fc7\u7ed3\u5408\u4f7f\u7528\u8fd9\u4e9b\u5e93\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u5b8c\u6210\u4ece\u6570\u636e\u5206\u6790\u5230\u62a5\u8868\u751f\u6210\u7684\u6574\u4e2a\u6d41\u7a0b\u3002\u8fd9\u4e0d\u4ec5\u63d0\u9ad8\u4e86\u5de5\u4f5c\u6548\u7387\uff0c\u4e5f\u4e3a\u6570\u636e\u5206\u6790\u7ed3\u679c\u7684\u5448\u73b0\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u652f\u6301\u3002<\/p>\n<\/p>\n
                  \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                   \u5982\u4f55\u4f7f\u7528Python\u751f\u6210\u62a5\u8868\uff1f<\/strong>
                  Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u548c\u5de5\u5177\u6765\u751f\u6210\u62a5\u8868\uff0c\u6bd4\u5982Pandas\u3001Matplotlib\u548cReportLab\u7b49\u3002Pandas\u7528\u4e8e\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u80fd\u591f\u8f7b\u677e\u8bfb\u53d6CSV\u3001Excel\u7b49\u683c\u5f0f\u7684\u6570\u636e\u3002Matplotlib\u5219\u53ef\u4ee5\u7528\u4e8e\u53ef\u89c6\u5316\u6570\u636e\uff0c\u901a\u8fc7\u56fe\u8868\u5c55\u793a\u4fe1\u606f\u3002\u800cReportLab\u5219\u9002\u5408\u751f\u6210PDF\u683c\u5f0f\u7684\u62a5\u8868\u3002\u901a\u8fc7\u7ed3\u5408\u8fd9\u4e9b\u5de5\u5177\uff0c\u7528\u6237\u53ef\u4ee5\u521b\u5efa\u51fa\u4e30\u5bcc\u591a\u6837\u7684\u62a5\u8868\u3002<\/p>\n
                  \u5728Python\u4e2d\u5904\u7406\u6570\u636e\u65f6\uff0c\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u5e93\u63a8\u8350\uff1f<\/strong>
                  \u5bf9\u4e8e\u6570\u636e\u5904\u7406\uff0cPandas\u662f\u6700\u53d7\u6b22\u8fce\u7684\u9009\u62e9\u4e4b\u4e00\uff0c\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u6846\u67b6\u548c\u4e30\u5bcc\u7684\u529f\u80fd\u6765\u6e05\u6d17\u548c\u5206\u6790\u6570\u636e\u3002NumPy\u4e5f\u5e38\u7528\u4e8e\u6570\u503c\u8ba1\u7b97\uff0c\u80fd\u591f\u52a0\u901f\u5904\u7406\u5927\u578b\u6570\u7ec4\u548c\u77e9\u9635\u3002\u6b64\u5916\uff0cSciPy\u5728\u79d1\u5b66\u8ba1\u7b97\u4e2d\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u529f\u80fd\uff0c\u8fd9\u4e9b\u5e93\u80fd\u591f\u5e2e\u52a9\u7528\u6237\u9ad8\u6548\u5730\u5904\u7406\u548c\u5206\u6790\u6570\u636e\uff0c\u8fdb\u800c\u751f\u6210\u7cbe\u51c6\u7684\u62a5\u8868\u3002<\/p>\n
                  \u5982\u4f55\u5c06\u751f\u6210\u7684\u62a5\u8868\u5bfc\u51fa\u4e3aExcel\u6216PDF\u683c\u5f0f\uff1f<\/strong>
                  \u4f7f\u7528Pandas\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u6570\u636e\u6846\u5bfc\u51fa\u4e3aExcel\u6587\u4ef6\uff0c\u4f7f\u7528DataFrame.to_excel()<\/code>\u65b9\u6cd5\u5373\u53ef\u5b9e\u73b0\u3002\u5bf9\u4e8ePDF\u683c\u5f0f\uff0c\u53ef\u4ee5\u4f7f\u7528ReportLab\u5e93\u521b\u5efa\u590d\u6742\u7684PDF\u62a5\u8868\uff0c\u6216\u7ed3\u5408Pandas\u548cMatplotlib\uff0c\u5148\u751f\u6210\u56fe\u8868\u518d\u5d4c\u5165\u5230PDF\u4e2d\u3002\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u7528\u6237\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u9009\u62e9\u5408\u9002\u7684\u683c\u5f0f\u5bfc\u51fa\u62a5\u8868\u3002<\/p>\n
                  \u662f\u5426\u53ef\u4ee5\u81ea\u52a8\u5316\u62a5\u8868\u751f\u6210\u7684\u8fc7\u7a0b\uff1f<\/strong>
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