{"id":115742,"date":"2026-10-13T10:09:25","date_gmt":"2026-10-13T02:09:25","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/115742.html"},"modified":"2026-10-13T10:09:25","modified_gmt":"2026-10-13T02:09:25","slug":"%e6%89%b9%e9%87%8f%e8%8e%b7%e5%8f%96%e8%82%a1%e7%a5%a8%e6%95%b0%e6%8d%ae%e5%90%8e%ef%bc%8c%e5%a6%82%e4%bd%95%e7%bb%9f%e4%b8%80%e8%bd%ac%e6%8d%a2%e6%88%90-dataframe%ef%bc%9f%e4%bb%8e%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/115742.html","title":{"rendered":"\u6279\u91cf\u83b7\u53d6\u80a1\u7968\u6570\u636e\u540e\uff0c\u5982\u4f55\u7edf\u4e00\u8f6c\u6362\u6210 DataFrame\uff1f\u4ece\u6570\u636e\u7ed3\u6784\u5230\u91cf\u5316\u7814\u7a76\u7684\u5b8c\u6574\u5b9e\u8df5"},"content":{"rendered":"

\u4e00\u53e5\u8bdd\u7ed3\u8bba：\u6279\u91cf\u83b7\u53d6\u80a1\u7968\u6570\u636e\u540e，\u4e0d\u8981\u6025\u7740\u628a\u6240\u6709\u7ed3\u679c\u62fc\u63a5\u8d77\u6765\u3002\u5148\u7edf\u4e00\u5b57\u6bb5\u3001\u6807\u7684\u4ee3\u7801\u3001\u65f6\u95f4\u7d22\u5f15\u548c\u6570\u636e\u7c7b\u578b，\u518d\u6839\u636e\u6570\u636e\u7684\u4e1a\u52a1\u542b\u4e49\u9009\u62e9\u7eb5\u5411\u5408\u5e76\u6216\u6a2a\u5411\u5408\u5e76，\u624d\u80fd\u907f\u514d\u540e\u7eed\u6307\u6807\u8ba1\u7b97\u548c\u56de\u6d4b\u51fa\u73b0\u9690\u853d\u9519\u8bef\u3002<\/p>\n

\u6458\u8981<\/h3>\n

\u4f7f\u7528 Python \u6279\u91cf\u83b7\u53d6\u80a1\u7968\u884c\u60c5\u65f6，\u4e0d\u540c\u6807\u7684\u7684\u6570\u636e\u53ef\u80fd\u4ee5\u5b57\u5178\u3001\u5217\u8868\u6216 DataFrame \u7b49\u5f62\u5f0f\u8fd4\u56de\u3002\u5373\u4f7f\u6240\u6709\u8bf7\u6c42\u90fd\u6210\u529f，\u4e5f\u4e0d\u610f\u5473\u7740\u8fd9\u4e9b\u6570\u636e\u53ef\u4ee5\u76f4\u63a5\u5408\u5e76\u3002\u672c\u6587\u4ece\u91cf\u5316\u6570\u636e\u5de5\u7a0b\u7684\u5b9e\u9645\u9700\u6c42\u51fa\u53d1，\u4ecb\u7ecd\u5982\u4f55\u5c06\u591a\u53ea\u80a1\u7968\u7684\u884c\u60c5\u7edf\u4e00\u8f6c\u6362\u4e3a Pandas DataFrame，\u5904\u7406\u5b57\u6bb5\u4e0d\u4e00\u81f4\u3001\u65f6\u95f4\u7d22\u5f15\u3001\u91cd\u590d\u8bb0\u5f55\u548c\u7f3a\u5931\u503c，\u5e76\u5206\u6790\u5982\u4f55\u7ed3\u5408 QuantDash（\u4e13\u4e1a\u91d1\u878d\u6570\u636e API \/ \u91cf\u5316\u6570\u636e\u5e73\u53f0）\u7684 Python SDK \u4e0e\u884c\u60c5\u6570\u636e\u80fd\u529b\u6784\u5efa\u89c4\u8303\u7684\u6570\u636e\u5904\u7406\u6d41\u7a0b\u3002<\/p>\n

1. \u4e3a\u4ec0\u4e48\u6279\u91cf\u83b7\u53d6\u6210\u529f\u540e，\u6570\u636e\u4ecd\u7136\u4e0d\u80fd\u76f4\u63a5\u7528\u4e8e\u5206\u6790？<\/h3>\n

\u5047\u8bbe\u6211\u4eec\u9700\u8981\u7814\u7a76\u591a\u53ea\u80a1\u7968\u7684\u5386\u53f2\u884c\u60c5，\u5e76\u8ba1\u7b97\u6bcf\u65e5\u6536\u76ca\u7387\u3001\u79fb\u52a8\u5e73\u5747\u7ebf\u548c\u6ce2\u52a8\u7387\u3002<\/p>\n

\u6570\u636e\u83b7\u53d6\u9636\u6bb5\u53ef\u80fd\u770b\u8d77\u6765\u5f88\u7b80\u5355：<\/p>\n

  • \u51c6\u5907\u80a1\u7968\u4ee3\u7801\u5217\u8868\u3002<\/li>\n
  • \u9010\u53ea\u6216\u6279\u91cf\u83b7\u53d6\u884c\u60c5\u3002<\/li>\n
  • \u5c06\u8fd4\u56de\u7ed3\u679c\u5408\u5e76\u3002<\/li>\n
  • \u4f7f\u7528 Pandas \u8ba1\u7b97\u6307\u6807\u3002<\/li>\n

    \u771f\u6b63\u5bb9\u6613\u51fa\u95ee\u9898\u7684\u662f\u7b2c\u4e09\u6b65\u3002<\/p>\n

    \u4f8b\u5982，\u4e24\u4e2a\u6570\u636e\u6e90\u8fd4\u56de\u7684\u7ed3\u679c\u53ef\u80fd\u5206\u522b\u5177\u6709\u4ee5\u4e0b\u7ed3\u6784：<\/p>\n

    stock_a =<\/span> [<\/span>
    \n {<\/span>
    \n "date"<\/span>:<\/span> "2026-01-05"<\/span>,<\/span>
    \n "open"<\/span>:<\/span> 100<\/span>,<\/span>
    \n "close"<\/span>:<\/span> 102<\/span>,<\/span>
    \n "volume"<\/span>:<\/span> 150000<\/span>,<\/span>
    \n }<\/span>,<\/span>
    \n {<\/span>
    \n "date"<\/span>:<\/span> "2026-01-06"<\/span>,<\/span>
    \n "open"<\/span>:<\/span> 102<\/span>,<\/span>
    \n "close"<\/span>:<\/span> 101<\/span>,<\/span>
    \n "volume"<\/span>:<\/span> 180000<\/span>,<\/span>
    \n }<\/span>,<\/span>
    \n]<\/span><\/p>\n

    stock_b =<\/span> [<\/span>
    \n {<\/span>
    \n "trade_date"<\/span>:<\/span> "2026-01-05"<\/span>,<\/span>
    \n "open"<\/span>:<\/span> 50<\/span>,<\/span>
    \n "close"<\/span>:<\/span> 51<\/span>,<\/span>
    \n "vol"<\/span>:<\/span> 90000<\/span>,<\/span>
    \n }<\/span>,<\/span>
    \n {<\/span>
    \n "trade_date"<\/span>:<\/span> "2026-01-06"<\/span>,<\/span>
    \n "open"<\/span>:<\/span> 51<\/span>,<\/span>
    \n "close"<\/span>:<\/span> 52<\/span>,<\/span>
    \n "vol"<\/span>:<\/span> 110000<\/span>,<\/span>
    \n }<\/span>,<\/span>
    \n]<\/span><\/p>\n

    \u8fd9\u4e24\u4e2a\u7ed3\u679c\u90fd\u5305\u542b\u5f00\u76d8\u4ef7\u3001\u6536\u76d8\u4ef7\u548c\u6210\u4ea4\u91cf，\u4f46\u5b57\u6bb5\u547d\u540d\u4e0d\u4e00\u81f4\u3002<\/p>\n

    \u5982\u679c\u76f4\u63a5\u5408\u5e76，Pandas \u4e0d\u4f1a\u81ea\u52a8\u63a8\u65ad date \u4e0e trade_date \u662f\u540c\u4e00\u4e2a\u4e1a\u52a1\u5b57\u6bb5，\u4e5f\u4e0d\u4f1a\u81ea\u52a8\u5224\u65ad volume \u4e0e vol \u662f\u5426\u91c7\u7528\u76f8\u540c\u7684\u5355\u4f4d\u548c\u53e3\u5f84\u3002<\/p>\n

    \u56e0\u6b64，\u6700\u7ec8\u53ef\u80fd\u5f97\u5230\u4e00\u4e2a\u5305\u542b\u591a\u4e2a\u65e5\u671f\u5217\u3001\u591a\u4e2a\u6210\u4ea4\u91cf\u5217\u7684 DataFrame\u3002<\/p>\n

    \u8fd9\u7c7b\u95ee\u9898\u4e0d\u4f1a\u5fc5\u7136\u89e6\u53d1\u5f02\u5e38，\u5374\u53ef\u80fd\u5f71\u54cd\u540e\u7eed\u8ba1\u7b97\u3002<\/p>\n

    \u6279\u91cf\u6570\u636e\u5904\u7406\u7684\u6838\u5fc3\u4e0d\u662f\u8ba9\u6570\u636e\u8fdb\u5165\u540c\u4e00\u4e2a\u8868\u683c，\u800c\u662f\u8ba9\u6570\u636e\u5728\u8bed\u4e49\u4e0a\u771f\u6b63\u4e00\u81f4\u3002<\/p>\n

    2. \u5148\u786e\u5b9a\u7edf\u4e00\u7684\u6570\u636e\u6a21\u578b<\/h3>\n

    \u5bf9\u4e8e\u65e5\u7ebf\u80a1\u7968\u884c\u60c5，\u53ef\u4ee5\u5148\u5b9a\u4e49\u4e00\u5957\u6807\u51c6\u5b57\u6bb5：<\/p>\n\n\u5b57\u6bb5\u542b\u4e49\u5efa\u8bae\u7684\u6570\u636e\u7c7b\u578b<\/tr>\n\n\n\n\n\n\n\n\n
    symbol<\/td>\n\u6807\u7684\u4ee3\u7801<\/td>\n\u5b57\u7b26\u4e32<\/td>\n<\/tr>\n
    trade_date<\/td>\n\u4ea4\u6613\u65e5\u671f<\/td>\ndatetime<\/td>\n<\/tr>\n
    open<\/td>\n\u5f00\u76d8\u4ef7<\/td>\n\u6570\u503c<\/td>\n<\/tr>\n
    high<\/td>\n\u6700\u9ad8\u4ef7<\/td>\n\u6570\u503c<\/td>\n<\/tr>\n
    low<\/td>\n\u6700\u4f4e\u4ef7<\/td>\n\u6570\u503c<\/td>\n<\/tr>\n
    close<\/td>\n\u6536\u76d8\u4ef7<\/td>\n\u6570\u503c<\/td>\n<\/tr>\n
    volume<\/td>\n\u6210\u4ea4\u91cf<\/td>\n\u6570\u503c，\u5177\u4f53\u7c7b\u578b\u6839\u636e\u6570\u636e\u89c4\u6a21\u786e\u5b9a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u8fd9\u91cc\u6709\u4e09\u4e2a\u503c\u5f97\u7279\u522b\u6ce8\u610f\u7684\u5730\u65b9\u3002<\/p>\n

    2.1 \u80a1\u7968\u4ee3\u7801\u5fc5\u987b\u4fdd\u7559\u4e3a\u5b57\u7b26\u4e32<\/h4>\n

    \u80a1\u7968\u4ee3\u7801\u662f\u6807\u8bc6\u7b26，\u4e0d\u662f\u7528\u4e8e\u8ba1\u7b97\u7684\u6570\u503c\u3002<\/p>\n

    \u4f8b\u5982：<\/p>\n

    symbols =<\/span> [<\/span>"000001.SZ"<\/span>,<\/span> "600519.SH"<\/span>,<\/span> "AAPL.US"<\/span>]<\/span><\/p>\n

    \u5982\u679c\u5c06\u4ee3\u7801\u8f6c\u6362\u6210\u6574\u6570，\u524d\u5bfc\u96f6\u53ef\u80fd\u4e22\u5931，\u5e02\u573a\u540e\u7f00\u4e5f\u65e0\u6cd5\u4fdd\u7559\u3002<\/p>\n

    \u66f4\u91cd\u8981\u7684\u662f，\u4e0d\u540c\u5e02\u573a\u53ef\u80fd\u5b58\u5728\u76f8\u4f3c\u7684\u4ee3\u7801，\u56e0\u6b64\u7edf\u4e00\u6807\u7684\u4ee3\u7801\u65f6，\u4e0d\u80fd\u53ea\u4f9d\u8d56\u6570\u5b57\u90e8\u5206\u3002<\/p>\n

    QuantDash \u5b98\u65b9\u516c\u5f00\u652f\u6301 A \u80a1\u3001ETF\u3001\u7f8e\u80a1\u548c\u6e2f\u80a1，\u5e76\u4f7f\u7528\u7edf\u4e00\u6807\u7684\u4ee3\u7801\u683c\u5f0f，\u4f8b\u5982：<\/p>\n

      \n
    • 600519.SH<\/li>\n
    • 000001.SZ<\/li>\n
    • AAPL.US<\/li>\n
    • 00700.HK<\/li>\n<\/ul>\n

      \u8fd9\u7c7b\u683c\u5f0f\u6709\u52a9\u4e8e\u5728\u5e94\u7528\u5c42\u5efa\u7acb\u7edf\u4e00\u7684\u6807\u7684\u6807\u8bc6\u3002\u4e0d\u8fc7，\u7edf\u4e00\u4ee3\u7801\u5e76\u4e0d\u4ee3\u8868\u4e0d\u540c\u5e02\u573a\u7684\u4ea4\u6613\u65f6\u95f4\u3001\u4ef7\u683c\u5355\u4f4d\u548c\u6570\u636e\u53e3\u5f84\u4e5f\u5b8c\u5168\u76f8\u540c\u3002<\/p>\n

      2.2 \u65f6\u95f4\u5b57\u6bb5\u9700\u8981\u7edf\u4e00<\/h4>\n

      2026-01-05\u30012026\/01\/05 \u548c\u65f6\u95f4\u6233\u53ef\u80fd\u8868\u793a\u540c\u4e00\u5929，\u4f46\u5b57\u7b26\u4e32\u5f62\u5f0f\u5e76\u4e0d\u4e00\u81f4\u3002<\/p>\n

      \u5efa\u8bae\u5728\u8fdb\u5165\u5206\u6790\u6d41\u7a0b\u524d，\u5c06\u65e5\u671f\u5b57\u6bb5\u8f6c\u6362\u6210\u660e\u786e\u7684\u6570\u636e\u7c7b\u578b：<\/p>\n

      df[<\/span>"trade_date"<\/span>]<\/span> =<\/span> pd.<\/span>to_datetime(<\/span>
      \n df[<\/span>"trade_date"<\/span>]<\/span>,<\/span>
      \n errors=<\/span>"raise"<\/span>,<\/span>
      \n)<\/span><\/p>\n

      \u8fd9\u91cc\u4f7f\u7528 errors="raise"，\u610f\u5473\u7740\u65e0\u6cd5\u89e3\u6790\u7684\u65e5\u671f\u4f1a\u89e6\u53d1\u5f02\u5e38\u3002<\/p>\n

      \u5bf9\u4e8e\u7814\u7a76\u578b\u6570\u636e\u7ba1\u9053，\u8fd9\u901a\u5e38\u6bd4\u9759\u9ed8\u5730\u628a\u9519\u8bef\u65e5\u671f\u8f6c\u6362\u4e3a\u7f3a\u5931\u503c\u66f4\u5bb9\u6613\u6392\u67e5\u3002<\/p>\n

      \u5982\u679c\u6570\u636e\u6d89\u53ca\u65e5\u5185\u884c\u60c5，\u8fd8\u9700\u8981\u8fdb\u4e00\u6b65\u660e\u786e\u65f6\u533a\u3001\u65f6\u95f4\u7cbe\u5ea6\u548c\u65f6\u95f4\u6233\u7684\u542b\u4e49，\u4e0d\u80fd\u7b80\u5355\u5730\u5c06\u65e5\u671f\u7ea7\u5904\u7406\u65b9\u5f0f\u5957\u7528\u5230\u5206\u949f\u7ea7\u6570\u636e\u3002<\/p>\n

      2.3 \u5b57\u6bb5\u7edf\u4e00\u4e0d\u7b49\u4e8e\u6570\u503c\u5f3a\u5236\u8f6c\u6362<\/h4>\n

      \u4f8b\u5982，\u6210\u4ea4\u91cf\u5b57\u6bb5\u4e2d\u51fa\u73b0\u5b57\u7b26\u4e32 "unknown"，\u5982\u679c\u76f4\u63a5\u4f7f\u7528：<\/p>\n

      pd.<\/span>to_numeric(<\/span>df[<\/span>"volume"<\/span>]<\/span>,<\/span> errors=<\/span>"coerce"<\/span>)<\/span><\/p>\n

      \u9519\u8bef\u5185\u5bb9\u4f1a\u53d8\u6210 NaN\u3002<\/p>\n

      \u8fd9\u79cd\u65b9\u5f0f\u53ef\u4ee5\u8ba9\u5904\u7406\u6d41\u7a0b\u7ee7\u7eed\u8fd0\u884c，\u4f46\u4e5f\u53ef\u80fd\u63a9\u76d6\u4e0a\u6e38\u7684\u6570\u636e\u95ee\u9898\u3002<\/p>\n

      \u66f4\u7a33\u59a5\u7684\u65b9\u5f0f\u662f\u5148\u68c0\u67e5\u5f02\u5e38\u503c，\u518d\u51b3\u5b9a\u662f\u5426\u8f6c\u6362：<\/p>\n

      volume =<\/span> pd.<\/span>to_numeric(<\/span>
      \n df[<\/span>"volume"<\/span>]<\/span>,<\/span>
      \n errors=<\/span>"coerce"<\/span>,<\/span>
      \n)<\/span><\/p>\n

      invalid_mask =<\/span> (<\/span>
      \n df[<\/span>"volume"<\/span>]<\/span>.<\/span>notna(<\/span>)<\/span>
      \n &<\/span> volume.<\/span>isna(<\/span>)<\/span>
      \n)<\/span><\/p>\n

      if<\/span> invalid_mask.<\/span>any<\/span>(<\/span>)<\/span>:<\/span>
      \n raise<\/span> ValueError(<\/span>"\u6210\u4ea4\u91cf\u5b57\u6bb5\u5b58\u5728\u65e0\u6cd5\u8f6c\u6362\u7684\u503c"<\/span>)<\/span><\/p>\n

      df[<\/span>"volume"<\/span>]<\/span> =<\/span> volume<\/p>\n

      \u8fd9\u91cc\u68c0\u67e5\u7684\u662f\u65e0\u6cd5\u8f6c\u6362\u7684\u975e\u7a7a\u503c，\u800c\u4e0d\u662f\u5c06\u6240\u6709\u7f3a\u5931\u503c\u90fd\u5f53\u4f5c\u683c\u5f0f\u9519\u8bef\u3002<\/p>\n

      \u5b9e\u9645\u9879\u76ee\u4e2d，\u5e94\u6839\u636e\u6570\u636e\u6e90\u7684\u5b57\u6bb5\u5b9a\u4e49\u8fdb\u4e00\u6b65\u5224\u65ad\u54ea\u4e9b\u7f3a\u5931\u503c\u5141\u8bb8\u5b58\u5728\u3002<\/p>\n

      3. \u4e24\u79cd\u4e3b\u8981\u7684 DataFrame \u5408\u5e76\u65b9\u5f0f<\/h3>\n

      \u7edf\u4e00\u8f6c\u6362\u5b8c\u6210\u540e，\u8fd8\u9700\u8981\u6839\u636e\u7814\u7a76\u76ee\u6807\u9009\u62e9\u5408\u9002\u7684\u8868\u7ed3\u6784\u3002<\/p>\n

      3.1 \u7eb5\u5411\u5408\u5e76：\u9002\u5408\u5efa\u7acb\u7edf\u4e00\u884c\u60c5\u660e\u7ec6\u8868<\/h4>\n

      \u7eb5\u5411\u5408\u5e76\u662f\u5c06\u4e0d\u540c\u80a1\u7968\u7684\u8bb0\u5f55\u653e\u5230\u540c\u4e00\u4e2a\u8868\u4e2d\u3002<\/p>\n

      \u4f8b\u5982：<\/p>\n\nsymboltrade_dateclose<\/tr>\n\n\n\n\n\n
      000001.SZ<\/td>\n2026-01-05<\/td>\n10.20<\/td>\n<\/tr>\n
      000001.SZ<\/td>\n2026-01-06<\/td>\n10.35<\/td>\n<\/tr>\n
      600519.SH<\/td>\n2026-01-05<\/td>\n1400.00<\/td>\n<\/tr>\n
      600519.SH<\/td>\n2026-01-06<\/td>\n1410.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u8fd9\u79cd\u7ed3\u6784\u4e5f\u79f0\u4e3a\u957f\u8868\u3002<\/p>\n

      \u5b83\u9002\u5408：<\/p>\n

        \n
      • \u4fdd\u5b58\u591a\u4e2a\u6807\u7684\u7684\u5386\u53f2\u884c\u60c5\u3002<\/li>\n
      • \u6309\u80a1\u7968\u4ee3\u7801\u7b5b\u9009\u6570\u636e\u3002<\/li>\n
      • \u8ba1\u7b97\u5206\u7ec4\u6307\u6807\u3002<\/li>\n
      • \u5c06\u884c\u60c5\u6570\u636e\u5199\u5165\u6570\u636e\u5e93\u3002<\/li>\n
      • \u4e3a\u540e\u7eed\u7684\u6a2a\u622a\u9762\u7814\u7a76\u63d0\u4f9b\u7edf\u4e00\u8f93\u5165\u3002<\/li>\n<\/ul>\n

        \u5047\u8bbe\u5df2\u7ecf\u5c06\u6bcf\u53ea\u80a1\u7968\u7684\u6570\u636e\u8f6c\u6362\u6210\u5177\u6709\u76f8\u540c\u5b57\u6bb5\u7684 DataFrame，\u5e76\u653e\u5165\u5b57\u5178：<\/p>\n

        import<\/span> pandas as<\/span> pd<\/p>\n

        frames =<\/span> {<\/span>
        \n "000001.SZ"<\/span>:<\/span> df_a,<\/span>
        \n "600519.SH"<\/span>:<\/span> df_b,<\/span>
        \n}<\/span><\/p>\n

        parts =<\/span> [<\/span>]<\/span><\/p>\n

        for<\/span> symbol,<\/span> frame in<\/span> frames.<\/span>items(<\/span>)<\/span>:<\/span>
        \n part =<\/span> frame.<\/span>copy(<\/span>)<\/span>
        \n part[<\/span>"symbol"<\/span>]<\/span> =<\/span> symbol
        \n parts.<\/span>append(<\/span>part)<\/span><\/p>\n

        if<\/span> not<\/span> parts:<\/span>
        \n raise<\/span> ValueError(<\/span>"\u6ca1\u6709\u53ef\u5408\u5e76\u7684\u884c\u60c5\u6570\u636e"<\/span>)<\/span><\/p>\n

        combined =<\/span> pd.<\/span>concat(<\/span>
        \n parts,<\/span>
        \n ignore_index=<\/span>True<\/span>,<\/span>
        \n)<\/span><\/p>\n

        combined =<\/span> combined[<\/span>
        \n [<\/span>
        \n "symbol"<\/span>,<\/span>
        \n "trade_date"<\/span>,<\/span>
        \n "open"<\/span>,<\/span>
        \n "high"<\/span>,<\/span>
        \n "low"<\/span>,<\/span>
        \n "close"<\/span>,<\/span>
        \n "volume"<\/span>,<\/span>
        \n ]<\/span>
        \n]<\/span><\/p>\n

        \u8fd9\u6bb5\u4ee3\u7801\u5047\u8bbe df_a \u548c df_b \u5df2\u7ecf\u5b8c\u6210\u5b57\u6bb5\u6620\u5c04，\u4e14\u5177\u6709\u6240\u9700\u7684\u884c\u60c5\u5217\u3002<\/p>\n

        \u5b83\u7684\u4e3b\u8981\u4f5c\u7528\u662f\u7edf\u4e00\u589e\u52a0\u6807\u7684\u6807\u8bc6，\u5e76\u5c06\u591a\u4e2a DataFrame \u7eb5\u5411\u5408\u5e76，\u800c\u4e0d\u662f\u8d1f\u8d23\u4ece\u6570\u636e\u6e90\u83b7\u53d6\u884c\u60c5\u3002<\/p>\n

        \u9700\u8981\u6ce8\u610f，pd.concat() \u9ed8\u8ba4\u4e0d\u4f1a\u81ea\u52a8\u5224\u65ad\u91cd\u590d\u884c\u60c5\u662f\u5426\u5408\u7406\u3002\u5982\u679c\u67d0\u53ea\u80a1\u7968\u5728\u8f93\u5165\u4e2d\u91cd\u590d\u51fa\u73b0，\u5408\u5e76\u7ed3\u679c\u4e2d\u4ecd\u7136\u53ef\u80fd\u4fdd\u7559\u91cd\u590d\u8bb0\u5f55\u3002<\/p>\n

        3.2 \u6a2a\u5411\u5408\u5e76：\u9002\u5408\u6784\u5efa\u4ef7\u683c\u77e9\u9635<\/h4>\n

        \u5982\u679c\u76ee\u6807\u662f\u6bd4\u8f83\u591a\u4e2a\u6807\u7684\u7684\u6536\u76ca\u7387，\u6a2a\u8868\u53ef\u80fd\u66f4\u52a0\u65b9\u4fbf\u3002<\/p>\n

        \u4f8b\u5982：<\/p>\n\ntrade_date000001.SZ600519.SH<\/tr>\n\n\n\n
        2026-01-05<\/td>\n10.20<\/td>\n1400.00<\/td>\n<\/tr>\n
        2026-01-06<\/td>\n10.35<\/td>\n1410.00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u8fd9\u79cd\u7ed3\u6784\u9002\u5408：<\/p>\n

          \n
        • \u8ba1\u7b97\u591a\u8d44\u4ea7\u6536\u76ca\u7387\u3002<\/li>\n
        • \u6784\u5efa\u76f8\u5173\u6027\u77e9\u9635\u3002<\/li>\n
        • \u8fdb\u884c\u7ec4\u5408\u7814\u7a76\u3002<\/li>\n
        • \u5bf9\u9f50\u4e0d\u540c\u80a1\u7968\u7684\u65f6\u95f4\u5e8f\u5217\u3002<\/li>\n<\/ul>\n

          \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u8f6c\u6362：<\/p>\n

          price_matrix =<\/span> combined.<\/span>pivot(<\/span>
          \n index=<\/span>"trade_date"<\/span>,<\/span>
          \n columns=<\/span>"symbol"<\/span>,<\/span>
          \n values=<\/span>"close"<\/span>,<\/span>
          \n)<\/span><\/p>\n

          \u8fd9\u91cc\u6709\u4e00\u4e2a\u5bb9\u6613\u5ffd\u7565\u7684\u95ee\u9898：pivot() \u8981\u6c42\u6bcf\u4e2a trade_date \u4e0e symbol \u7684\u7ec4\u5408\u552f\u4e00\u3002<\/p>\n

          \u5982\u679c\u5b58\u5728\u91cd\u590d\u8bb0\u5f55，\u5b83\u4f1a\u62a5\u9519，\u800c\u4e0d\u662f\u66ff\u4f60\u51b3\u5b9a\u4fdd\u7559\u54ea\u4e00\u6761\u884c\u60c5\u3002<\/p>\n

          \u4e0d\u8981\u4e3a\u4e86\u8ba9\u4ee3\u7801\u8fd0\u884c\u5c31\u76f4\u63a5\u4f7f\u7528 pivot_table(…, aggfunc="last")\u3002<\/p>\n

          \u53ea\u6709\u5728\u786e\u8ba4\u91cd\u590d\u6570\u636e\u7684\u6765\u6e90\u3001\u6392\u5e8f\u89c4\u5219\u548c\u4e1a\u52a1\u542b\u4e49\u540e，\u624d\u5e94\u8be5\u8003\u8651\u805a\u5408\u5904\u7406\u3002<\/p>\n

          \u5efa\u8bae\u5148\u4fdd\u5b58\u957f\u8868\u4f5c\u4e3a\u6807\u51c6\u884c\u60c5\u660e\u7ec6，\u518d\u6839\u636e\u7b56\u7565\u9700\u8981\u751f\u6210\u4ef7\u683c\u77e9\u9635\u3002<\/p>\n

          \u8fd9\u6837\u53ef\u4ee5\u51cf\u5c11\u56e0\u6570\u636e\u5c55\u793a\u65b9\u5f0f\u53d8\u5316\u800c\u91cd\u590d\u5efa\u8bbe\u6570\u636e\u5904\u7406\u903b\u8f91\u7684\u60c5\u51b5\u3002<\/p>\n

          4. \u5efa\u7acb\u4e00\u4e2a\u53ef\u590d\u7528\u7684\u6807\u51c6\u5316\u51fd\u6570<\/h3>\n

          \u5982\u679c\u6bcf\u6b21\u83b7\u53d6\u6570\u636e\u540e\u90fd\u624b\u52a8\u91cd\u547d\u540d\u5b57\u6bb5，\u9879\u76ee\u89c4\u6a21\u6269\u5927\u540e\u5c31\u5f88\u96be\u7ef4\u62a4\u3002<\/p>\n

          \u53ef\u4ee5\u5c06\u901a\u7528\u5904\u7406\u903b\u8f91\u5c01\u88c5\u8d77\u6765\u3002<\/p>\n

          \u4e0b\u9762\u7684\u793a\u4f8b\u4f7f\u7528\u666e\u901a Python \u6570\u636e\u7ed3\u6784\u6f14\u793a\u6807\u51c6\u5316\u8fc7\u7a0b，\u4e0d\u4ee3\u8868\u4efb\u4f55\u7279\u5b9a\u6570\u636e\u6e90\u7684\u8fd4\u56de\u683c\u5f0f\u3002<\/p>\n

          import<\/span> pandas as<\/span> pd<\/p>\n

          def<\/span> normalize_ohlcv<\/span>(<\/span>
          \n data,<\/span>
          \n symbol,<\/span>
          \n date_col,<\/span>
          \n volume_col,<\/span>
          \n)<\/span>:<\/span>
          \n df =<\/span> pd.<\/span>DataFrame(<\/span>data)<\/span>.<\/span>copy(<\/span>)<\/span><\/p>\n

          required =<\/span> [<\/span>
          \n date_col,<\/span>
          \n "open"<\/span>,<\/span>
          \n "high"<\/span>,<\/span>
          \n "low"<\/span>,<\/span>
          \n "close"<\/span>,<\/span>
          \n volume_col,<\/span>
          \n ]<\/span><\/p>\n

          missing =<\/span> [<\/span>
          \n col for<\/span> col in<\/span> required
          \n if<\/span> col not<\/span> in<\/span> df.<\/span>columns
          \n ]<\/span><\/p>\n

          if<\/span> missing:<\/span>
          \n raise<\/span> ValueError(<\/span>
          \n f"<\/span>{<\/span>symbol}<\/span><\/span> \u7f3a\u5c11\u5b57\u6bb5: <\/span>{<\/span>missing}<\/span><\/span>"<\/span><\/span>
          \n )<\/span><\/p>\n

          df =<\/span> df.<\/span>rename(<\/span>
          \n columns=<\/span>{<\/span>
          \n date_col:<\/span> "trade_date"<\/span>,<\/span>
          \n volume_col:<\/span> "volume"<\/span>,<\/span>
          \n }<\/span>
          \n )<\/span><\/p>\n

          df[<\/span>"symbol"<\/span>]<\/span> =<\/span> str<\/span>(<\/span>symbol)<\/span><\/p>\n

          df[<\/span>"trade_date"<\/span>]<\/span> =<\/span> pd.<\/span>to_datetime(<\/span>
          \n df[<\/span>"trade_date"<\/span>]<\/span>,<\/span>
          \n errors=<\/span>"raise"<\/span>,<\/span>
          \n )<\/span><\/p>\n

          numeric_cols =<\/span> [<\/span>
          \n "open"<\/span>,<\/span>
          \n "high"<\/span>,<\/span>
          \n "low"<\/span>,<\/span>
          \n "close"<\/span>,<\/span>
          \n "volume"<\/span>,<\/span>
          \n ]<\/span><\/p>\n

          for<\/span> col in<\/span> numeric_cols:<\/span>
          \n df[<\/span>col]<\/span> =<\/span> pd.<\/span>to_numeric(<\/span>
          \n df[<\/span>col]<\/span>,<\/span>
          \n errors=<\/span>"raise"<\/span>,<\/span>
          \n )<\/span><\/p>\n

          return<\/span> df[<\/span>
          \n [<\/span>
          \n "symbol"<\/span>,<\/span>
          \n "trade_date"<\/span>,<\/span>
          \n "open"<\/span>,<\/span>
          \n "high"<\/span>,<\/span>
          \n "low"<\/span>,<\/span>
          \n "close"<\/span>,<\/span>
          \n "volume"<\/span>,<\/span>
          \n ]<\/span>
          \n ]<\/span><\/p>\n

          \u4f7f\u7528\u65f6：<\/p>\n

          df_a =<\/span> normalize_ohlcv(<\/span>
          \n stock_a,<\/span>
          \n symbol=<\/span>"000001.SZ"<\/span>,<\/span>
          \n date_col=<\/span>"date"<\/span>,<\/span>
          \n volume_col=<\/span>"volume"<\/span>,<\/span>
          \n)<\/span><\/p>\n

          df_b =<\/span> normalize_ohlcv(<\/span>
          \n stock_b,<\/span>
          \n symbol=<\/span>"600519.SH"<\/span>,<\/span>
          \n date_col=<\/span>"trade_date"<\/span>,<\/span>
          \n volume_col=<\/span>"vol"<\/span>,<\/span>
          \n)<\/span><\/p>\n

          combined =<\/span> pd.<\/span>concat(<\/span>
          \n [<\/span>df_a,<\/span> df_b]<\/span>,<\/span>
          \n ignore_index=<\/span>True<\/span>,<\/span>
          \n)<\/span><\/p>\n

          \u8fd9\u6bb5\u4ee3\u7801\u7684\u4ef7\u503c\u5728\u4e8e\u5c06\u5b57\u6bb5\u6620\u5c04\u3001\u7c7b\u578b\u8f6c\u6362\u548c\u6807\u51c6\u5217\u987a\u5e8f\u96c6\u4e2d\u7ba1\u7406\u3002<\/p>\n

          \u4f46\u5b83\u4ecd\u7136\u6709\u660e\u786e\u7684\u8fb9\u754c：<\/p>\n

            \n
          • \u5047\u8bbe\u5404\u5b57\u6bb5\u542b\u4e49\u5df2\u7ecf\u786e\u8ba4\u3002<\/li>\n
          • \u4e0d\u4f1a\u81ea\u52a8\u5224\u65ad\u6210\u4ea4\u91cf\u5355\u4f4d\u662f\u5426\u4e00\u81f4\u3002<\/li>\n
          • \u4e0d\u4f1a\u81ea\u52a8\u5904\u7406\u590d\u6743\u53e3\u5f84\u5dee\u5f02\u3002<\/li>\n
          • \u4e0d\u4f1a\u81ea\u52a8\u8865\u9f50\u7f3a\u5931\u4ea4\u6613\u65e5\u3002<\/li>\n
          • \u4e0d\u4f1a\u5224\u65ad\u4ef7\u683c\u5f02\u5e38\u662f\u5426\u6765\u81ea\u771f\u5b9e\u5e02\u573a\u6ce2\u52a8\u3002<\/li>\n
          • \u4e0d\u4f1a\u9a8c\u8bc1\u6570\u636e\u662f\u5426\u6765\u81ea\u6b63\u786e\u7684\u4ea4\u6613\u65f6\u6bb5\u3002<\/li>\n<\/ul>\n

            \u6807\u51c6\u5316\u51fd\u6570\u89e3\u51b3\u7684\u662f\u7ed3\u6784\u4e00\u81f4\u6027\u95ee\u9898，\u4e0d\u662f\u5168\u90e8\u6570\u636e\u8d28\u91cf\u95ee\u9898\u3002<\/p>\n

            5. \u5982\u4f55\u68c0\u67e5\u5408\u5e76\u540e\u7684\u6570\u636e\u662f\u5426\u53ef\u9760？<\/h3>\n

            DataFrame \u6210\u529f\u751f\u6210，\u53ea\u80fd\u8bf4\u660e\u6570\u636e\u7ed3\u6784\u5df2\u7ecf\u5efa\u7acb，\u4e0d\u80fd\u8bf4\u660e\u884c\u60c5\u6570\u636e\u4e00\u5b9a\u6b63\u786e\u3002<\/p>\n

            \u5efa\u8bae\u5728\u5408\u5e76\u4e4b\u540e\u6267\u884c\u4ee5\u4e0b\u68c0\u67e5\u3002<\/p>\n

            5.1 \u68c0\u67e5\u4e3b\u952e\u91cd\u590d<\/h4>\n

            \u5982\u679c\u4f7f\u7528\u65e5\u7ebf\u6570\u636e，\u53ef\u4ee5\u5148\u68c0\u67e5\u540c\u4e00\u6807\u7684\u540c\u4e00\u4ea4\u6613\u65e5\u671f\u662f\u5426\u51fa\u73b0\u591a\u6761\u8bb0\u5f55：<\/p>\n

            key_cols =<\/span> [<\/span>"symbol"<\/span>,<\/span> "trade_date"<\/span>]<\/span><\/p>\n

            duplicates =<\/span> combined.<\/span>duplicated(<\/span>
            \n subset=<\/span>key_cols,<\/span>
            \n keep=<\/span>False<\/span>,<\/span>
            \n)<\/span><\/p>\n

            if<\/span> duplicates.<\/span>any<\/span>(<\/span>)<\/span>:<\/span>
            \n raise<\/span> ValueError(<\/span>
            \n "\u53d1\u73b0\u91cd\u590d\u7684\u6807\u7684\u4e0e\u4ea4\u6613\u65e5\u671f\u7ec4\u5408"<\/span>
            \n )<\/span><\/p>\n

            \u5982\u679c\u7b56\u7565\u4f7f\u7528\u5206\u949f\u7ebf，\u5219\u901a\u5e38\u9700\u8981\u628a\u65f6\u95f4\u7cbe\u5ea6\u63d0\u9ad8\u5230\u5b9e\u9645\u7684\u884c\u60c5\u65f6\u95f4\u6233\u3002<\/p>\n

            \u4e0d\u8fc7，\u91cd\u590d\u8bb0\u5f55\u4e0d\u4e00\u5b9a\u90fd\u80fd\u76f4\u63a5\u5220\u9664\u3002\u6709\u4e9b\u6570\u636e\u7ed3\u6784\u5141\u8bb8\u540c\u4e00\u65f6\u95f4\u70b9\u5b58\u5728\u4e0d\u540c\u7c7b\u578b\u7684\u8bb0\u5f55，\u56e0\u6b64\u5e94\u5148\u786e\u5b9a\u6b63\u786e\u7684\u4e1a\u52a1\u4e3b\u952e\u3002<\/p>\n

            5.2 \u68c0\u67e5\u4ef7\u683c\u4e4b\u95f4\u7684\u903b\u8f91\u5173\u7cfb<\/h4>\n

            \u5bf9\u4e8e\u5e38\u89c4 OHLC \u884c\u60c5，\u53ef\u4ee5\u68c0\u67e5：<\/p>\n

            invalid_price =<\/span> (<\/span>
            \n (<\/span>combined[<\/span>"high"<\/span>]<\/span> <<\/span> combined[<\/span>"low"<\/span>]<\/span>)<\/span>
            \n |<\/span> (<\/span>combined[<\/span>"high"<\/span>]<\/span> <<\/span> combined[<\/span>"open"<\/span>]<\/span>)<\/span>
            \n |<\/span> (<\/span>combined[<\/span>"high"<\/span>]<\/span> <<\/span> combined[<\/span>"close"<\/span>]<\/span>)<\/span>
            \n |<\/span> (<\/span>combined[<\/span>"low"<\/span>]<\/span> ><\/span> combined[<\/span>"open"<\/span>]<\/span>)<\/span>
            \n |<\/span> (<\/span>combined[<\/span>"low"<\/span>]<\/span> ><\/span> combined[<\/span>"close"<\/span>]<\/span>)<\/span>
            \n)<\/span><\/p>\n

            if<\/span> invalid_price.<\/span>any<\/span>(<\/span>)<\/span>:<\/span>
            \n raise<\/span> ValueError(<\/span>"\u53d1\u73b0\u4e0d\u7b26\u5408 OHLC \u5173\u7cfb\u7684\u8bb0\u5f55"<\/span>)<\/span><\/p>\n

            \u8fd9\u662f\u4e00\u79cd\u57fa\u7840\u7684\u903b\u8f91\u6821\u9a8c\u3002<\/p>\n

            \u5982\u679c\u6570\u636e\u6e90\u63d0\u4f9b\u7684\u662f\u7ecf\u8fc7\u7279\u6b8a\u8c03\u6574\u7684\u4ef7\u683c，\u6216\u8005\u5b57\u6bb5\u5e76\u975e\u901a\u5e38\u610f\u4e49\u4e0a\u7684 OHLC \u6570\u636e，\u5219\u5e94\u6839\u636e\u5b9e\u9645\u5b9a\u4e49\u8c03\u6574\u89c4\u5219\u3002<\/p>\n

            5.3 \u68c0\u67e5\u65f6\u95f4\u8986\u76d6\u8303\u56f4<\/h4>\n

            \u53ef\u4ee5\u7edf\u8ba1\u6bcf\u53ea\u80a1\u7968\u7684\u6570\u636e\u8303\u56f4：<\/p>\n

            coverage =<\/span> (<\/span>
            \n combined
            \n .<\/span>groupby(<\/span>"symbol"<\/span>)<\/span>[<\/span>"trade_date"<\/span>]<\/span>
            \n .<\/span>agg(<\/span>[<\/span>"min"<\/span>,<\/span> "max"<\/span>,<\/span> "count"<\/span>]<\/span>)<\/span>
            \n)<\/span><\/p>\n

            print<\/span>(<\/span>coverage)<\/span><\/p>\n

            \u5b83\u80fd\u5e2e\u52a9\u53d1\u73b0\u67d0\u53ea\u80a1\u7968\u7684\u6570\u636e\u660e\u663e\u5c11\u4e8e\u5176\u4ed6\u6807\u7684\u7684\u60c5\u51b5\u3002<\/p>\n

            \u4f46\u4e0d\u80fd\u4ec5\u51ed\u8bb0\u5f55\u6570\u91cf\u8f83\u5c11\u5c31\u8ba4\u5b9a\u6570\u636e\u7f3a\u5931，\u56e0\u4e3a\u505c\u724c\u3001\u4e0a\u5e02\u65f6\u95f4\u548c\u5e02\u573a\u4ea4\u6613\u65e5\u5dee\u5f02\u90fd\u53ef\u80fd\u9020\u6210\u5408\u7406\u7684\u8bb0\u5f55\u6570\u91cf\u5dee\u5f02\u3002<\/p>\n

            5.4 \u68c0\u67e5\u7f3a\u5931\u503c\u65f6\u533a\u5206\u5b57\u6bb5\u8bed\u4e49<\/h4>\n

            \u7f3a\u5931\u503c\u9700\u8981\u6309\u5b57\u6bb5\u5206\u522b\u5224\u65ad\u3002<\/p>\n

            \u4f8b\u5982：<\/p>\n

              \n
            • \u6536\u76d8\u4ef7\u7f3a\u5931\u53ef\u80fd\u4f7f\u6536\u76ca\u7387\u8ba1\u7b97\u65e0\u6cd5\u7ee7\u7eed\u3002<\/li>\n
            • \u6210\u4ea4\u91cf\u7f3a\u5931\u53ef\u80fd\u5f71\u54cd\u6210\u4ea4\u91cf\u56e0\u5b50\u3002<\/li>\n
            • \u67d0\u4e9b\u975e\u4ea4\u6613\u65e5\u6ca1\u6709\u884c\u60c5\u8bb0\u5f55，\u901a\u5e38\u4e0d\u5e94\u8be5\u7b80\u5355\u8865\u6210\u524d\u4e00\u4ea4\u6613\u65e5\u7684\u6570\u636e\u3002<\/li>\n
            • \u80a1\u7968\u505c\u724c\u65f6\u7684\u4ef7\u683c\u5904\u7406，\u5e94\u7531\u7b56\u7565\u548c\u6570\u636e\u53e3\u5f84\u5171\u540c\u51b3\u5b9a\u3002<\/li>\n<\/ul>\n

              \u5bf9\u4e8e\u6536\u76ca\u7387\u8ba1\u7b97，\u5c24\u5176\u8981\u907f\u514d\u5728\u6ca1\u6709\u660e\u786e\u89c4\u5219\u7684\u60c5\u51b5\u4e0b，\u5c06\u7f3a\u5931\u884c\u60c5\u7edf\u4e00\u586b\u5145\u4e3a\u524d\u503c\u540e\u7ee7\u7eed\u56de\u6d4b\u3002<\/p>\n

              6. QuantDash \u5982\u4f55\u878d\u5165\u6279\u91cf\u884c\u60c5\u5904\u7406\u6d41\u7a0b？<\/h3>\n

              \u5f53\u7814\u7a76\u6d89\u53ca\u591a\u4e2a\u5e02\u573a\u6216\u5927\u91cf\u6807\u7684\u65f6，\u6570\u636e\u83b7\u53d6\u63a5\u53e3\u7684\u7ec4\u7ec7\u65b9\u5f0f\u4f1a\u5f71\u54cd\u540e\u7eed\u7684 DataFrame \u8bbe\u8ba1\u3002<\/p>\n

              QuantDash（\u4e13\u4e1a\u91d1\u878d\u6570\u636e API \/ \u91cf\u5316\u6570\u636e\u5e73\u53f0）\u516c\u5f00\u63d0\u4f9b\u884c\u60c5\u6570\u636e\u67e5\u8be2\u3001\u6279\u91cf\u67e5\u8be2\u80fd\u529b\u3001Python SDK\u3001REST API，\u4ee5\u53ca Pandas \/ DataFrame \u8f93\u51fa\u652f\u6301\u3002<\/p>\n

              \u5b98\u65b9\u516c\u5f00\u7684\u5e02\u573a\u8303\u56f4\u5305\u62ec A \u80a1\u3001ETF\u3001\u7f8e\u80a1\u548c\u6e2f\u80a1\u3002<\/p>\n

              \u5bf9\u4e8e\u9700\u8981\u5c06\u884c\u60c5\u63a5\u5165 Python \u7814\u7a76\u73af\u5883\u7684\u5f00\u53d1\u8005，\u8fd9\u4e9b\u80fd\u529b\u53ef\u4ee5\u4f5c\u4e3a\u6570\u636e\u63a5\u5165\u65b9\u6848\u7684\u4e00\u90e8\u5206\u8fdb\u884c\u8bc4\u4f30\u3002<\/p>\n

              \u4f8b\u5982，\u5982\u679c\u7814\u7a76\u5bf9\u8c61\u5305\u542b\u591a\u4e2a\u5e02\u573a\u7684\u80a1\u7968，\u53ef\u4ee5\u5148\u4f7f\u7528\u7edf\u4e00\u6807\u7684\u4ee3\u7801\u7ef4\u62a4\u7814\u7a76\u6e05\u5355，\u518d\u5c06\u83b7\u53d6\u7684\u6570\u636e\u8f6c\u6362\u6210\u81ea\u5df1\u7684\u6807\u51c6\u884c\u60c5\u6a21\u578b\u3002<\/p>\n

              \u8fd9\u91cc\u9700\u8981\u533a\u5206\u4e24\u4e2a\u6982\u5ff5：<\/p>\n

                \n
              • \u6570\u636e\u83b7\u53d6\u5c42\u8d1f\u8d23\u8bf7\u6c42\u884c\u60c5\u5e76\u53d6\u5f97\u7ed3\u679c\u3002<\/li>\n
              • \u6570\u636e\u6807\u51c6\u5316\u5c42\u8d1f\u8d23\u7edf\u4e00\u5b57\u6bb5\u3001\u6570\u636e\u7c7b\u578b\u3001\u6807\u7684\u6807\u8bc6\u548c\u65f6\u95f4\u53e3\u5f84\u3002<\/li>\n<\/ul>\n

                \u5373\u4f7f\u67d0\u4e2a SDK \u80fd\u76f4\u63a5\u8fd4\u56de DataFrame，\u4e5f\u4e0d\u610f\u5473\u7740\u7814\u7a76\u8005\u53ef\u4ee5\u7701\u7565\u7b2c\u4e8c\u5c42\u7684\u6821\u9a8c\u3002<\/p>\n

                \u4e00\u4e2a\u5df2\u7ecf\u516c\u5f00\u786e\u8ba4\u7684 Python SDK \u793a\u4f8b<\/h4>\n

                QuantDash \u5b98\u65b9 GitHub README \u5c55\u793a\u4e86\u4ee5\u4e0b\u8c03\u7528\u65b9\u5f0f：<\/p>\n

                from<\/span> quantdash import<\/span> QuantDash<\/p>\n

                qd =<\/span> QuantDash(<\/span>)<\/span><\/p>\n

                kline =<\/span> qd.<\/span>klines.<\/span>get(<\/span>
                \n "600519.SH"<\/span>,<\/span>
                \n period=<\/span>"1d"<\/span>,<\/span>
                \n count=<\/span>5<\/span>,<\/span>
                \n adjust=<\/span>"forward"<\/span>,<\/span>
                \n to_dataframe=<\/span>True<\/span>,<\/span>
                \n)<\/span><\/p>\n

                \u8fd9\u6bb5\u793a\u4f8b\u5c55\u793a\u7684\u662f\u5355\u4e2a\u6807\u7684\u7684\u65e5\u7ebf\u884c\u60c5\u83b7\u53d6\u3002<\/p>\n

                \u5176\u4e2d：<\/p>\n

                  \n
                • period="1d" \u6307\u5b9a\u65e5\u7ebf\u5468\u671f\u3002<\/li>\n
                • count=5 \u6307\u5b9a\u8bf7\u6c42\u7684\u8bb0\u5f55\u6570\u91cf\u3002<\/li>\n
                • adjust="forward" \u6307\u5b9a\u524d\u590d\u6743\u3002<\/li>\n
                • to_dataframe=True \u6307\u5b9a\u4ee5 DataFrame \u5f62\u5f0f\u83b7\u53d6\u7ed3\u679c\u3002<\/li>\n<\/ul>\n

                  \u8fd9\u4e9b\u53c2\u6570\u6765\u81ea QuantDash \u5b98\u65b9\u516c\u5f00\u793a\u4f8b\u3002<\/p>\n

                  \u9700\u8981\u5f3a\u8c03\u7684\u662f，\u8fd9\u6bb5\u4ee3\u7801\u4e0d\u662f\u6279\u91cf K \u7ebf\u8c03\u7528\u793a\u4f8b，\u4e5f\u6ca1\u6709\u5c55\u793a\u8fd4\u56de DataFrame \u7684\u5b8c\u6574\u5b57\u6bb5\u7ed3\u6784\u3002\u4e0d\u80fd\u4ec5\u51ed\u8fd9\u4e2a\u793a\u4f8b\u63a8\u65ad\u6279\u91cf\u63a5\u53e3\u7684\u5177\u4f53\u65b9\u6cd5\u3001\u53c2\u6570\u6216\u8fd4\u56de\u503c\u3002<\/p>\n

                  \u5982\u679c\u8981\u5c06\u6279\u91cf\u67e5\u8be2\u63a5\u5165\u751f\u4ea7\u4ee3\u7801，\u5e94\u5148\u6838\u5bf9\u5f53\u524d\u5b98\u65b9\u6280\u672f\u6587\u6863\u4e2d\u7684\u6279\u91cf\u63a5\u53e3\u5b9a\u4e49，\u518d\u6839\u636e\u771f\u5b9e\u8fd4\u56de\u7ed3\u6784\u9002\u914d\u524d\u6587\u7684\u6807\u51c6\u5316\u51fd\u6570\u3002<\/p>\n

                  7. \u6279\u91cf\u67e5\u8be2\u4e0e\u672c\u5730\u5408\u5e76\u5e94\u8be5\u5982\u4f55\u5206\u5de5？<\/h3>\n

                  \u6279\u91cf\u83b7\u53d6\u548c\u6279\u91cf\u5408\u5e76\u89e3\u51b3\u7684\u662f\u4e0d\u540c\u5c42\u9762\u7684\u95ee\u9898\u3002<\/p>\n\n\u73af\u8282\u4e3b\u8981\u804c\u8d23\u9700\u8981\u5173\u6ce8\u7684\u98ce\u9669<\/tr>\n\n\n\n\n\n\n\n
                  \u884c\u60c5\u8bf7\u6c42<\/td>\n\u83b7\u53d6\u6307\u5b9a\u6807\u7684\u548c\u65f6\u95f4\u8303\u56f4\u7684\u6570\u636e<\/td>\n\u8bf7\u6c42\u5931\u8d25\u3001\u6743\u9650\u3001\u6570\u636e\u8986\u76d6<\/td>\n<\/tr>\n
                  \u7ed3\u679c\u89e3\u6790<\/td>\n\u8bfb\u53d6\u63a5\u53e3\u8fd4\u56de\u5185\u5bb9<\/td>\n\u5b57\u6bb5\u7f3a\u5931\u3001\u7ed3\u6784\u53d8\u5316<\/td>\n<\/tr>\n
                  \u6570\u636e\u6807\u51c6\u5316<\/td>\n\u7edf\u4e00\u5b57\u6bb5\u548c\u6570\u636e\u7c7b\u578b<\/td>\n\u8bed\u4e49\u4e0d\u4e00\u81f4\u3001\u7c7b\u578b\u9519\u8bef<\/td>\n<\/tr>\n
                  \u6570\u636e\u5408\u5e76<\/td>\n\u7ec4\u7ec7\u591a\u6807\u7684\u8bb0\u5f55<\/td>\n\u91cd\u590d\u4e3b\u952e\u3001\u7d22\u5f15\u9519\u4f4d<\/td>\n<\/tr>\n
                  \u6570\u636e\u9a8c\u8bc1<\/td>\n\u68c0\u67e5\u5b8c\u6574\u6027\u548c\u903b\u8f91\u5173\u7cfb<\/td>\n\u7f3a\u5931\u884c\u60c5\u3001\u5f02\u5e38\u503c<\/td>\n<\/tr>\n
                  \u7b56\u7565\u8ba1\u7b97<\/td>\n\u5c06\u6570\u636e\u7528\u4e8e\u6307\u6807\u548c\u4fe1\u53f7<\/td>\n\u6570\u636e\u53e3\u5f84\u9519\u8bef\u3001\u672a\u6765\u51fd\u6570<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

                  \u5982\u679c\u9010\u53ea\u8bf7\u6c42\u4e0e\u6279\u91cf\u8bf7\u6c42\u90fd\u80fd\u6ee1\u8db3\u7814\u7a76\u9700\u6c42，\u9009\u62e9\u65f6\u5e94\u8003\u8651\u8bf7\u6c42\u7ba1\u7406\u590d\u6742\u5ea6\u3001\u5931\u8d25\u6062\u590d\u65b9\u5f0f\u3001\u8fd4\u56de\u7ed3\u6784\u548c\u670d\u52a1\u5546\u516c\u5f00\u652f\u6301\u7684\u63a5\u53e3\u80fd\u529b\u3002<\/p>\n

                  \u6279\u91cf\u63a5\u53e3\u80fd\u591f\u51cf\u5c11\u5ba2\u6237\u7aef\u9010\u53ea\u7ec4\u7ec7\u8bf7\u6c42\u7684\u5de5\u4f5c，\u4f46\u5e76\u4e0d\u80fd\u4fdd\u8bc1\u8fd4\u56de\u6570\u636e\u81ea\u52a8\u6ee1\u8db3\u7b56\u7565\u7684\u6240\u6709\u8981\u6c42\u3002<\/p>\n

                  \u5bf9\u4e8e\u957f\u671f\u8fd0\u884c\u7684\u6570\u636e\u7ba1\u9053，\u8fd8\u5e94\u5c06\u8bf7\u6c42\u65e5\u5fd7\u3001\u6807\u51c6\u5316\u5931\u8d25\u8bb0\u5f55\u548c\u6570\u636e\u8d28\u91cf\u68c0\u67e5\u7ed3\u679c\u5206\u5f00\u4fdd\u5b58，\u4ee5\u4fbf\u5b9a\u4f4d\u95ee\u9898\u7a76\u7adf\u53d1\u751f\u5728\u54ea\u4e2a\u73af\u8282\u3002<\/p>\n

                  8. \u5e38\u89c1\u8bef\u533a<\/h3>\n

                  \u8bef\u533a\u4e00：\u6240\u6709\u6570\u636e\u90fd\u53ef\u4ee5\u76f4\u63a5 concat<\/h4>\n

                  concat() \u53ea\u8d1f\u8d23\u5408\u5e76\u5bf9\u8c61，\u4e0d\u8d1f\u8d23\u7406\u89e3\u4e1a\u52a1\u542b\u4e49\u3002<\/p>\n

                  \u5b57\u6bb5\u3001\u5355\u4f4d\u548c\u590d\u6743\u53e3\u5f84\u4e0d\u540c\u7684\u6570\u636e，\u5373\u4f7f\u80fd\u591f\u5408\u5e76，\u4e5f\u4e0d\u4ee3\u8868\u53ef\u4ee5\u76f4\u63a5\u6bd4\u8f83\u3002<\/p>\n

                  \u8bef\u533a\u4e8c：\u7f3a\u5931\u503c\u7edf\u4e00\u586b\u96f6<\/h4>\n

                  \u4ef7\u683c\u3001\u6210\u4ea4\u91cf\u548c\u6536\u76ca\u7387\u7684\u7f3a\u5931\u503c\u542b\u4e49\u4e0d\u540c\u3002<\/p>\n

                  \u5c06\u4ef7\u683c\u7f3a\u5931\u586b\u6210\u96f6，\u901a\u5e38\u4f1a\u5236\u9020\u4e0d\u5408\u7406\u7684\u4ef7\u683c\u53d8\u5316；\u5c06\u6536\u76ca\u7387\u7f3a\u5931\u586b\u6210\u96f6，\u5219\u53ef\u80fd\u6539\u53d8\u7edf\u8ba1\u7ed3\u679c\u3002<\/p>\n

                  \u8bef\u533a\u4e09：\u65e5\u671f\u6392\u5e8f\u540e\u5c31\u5b8c\u6210\u4e86\u65f6\u95f4\u5bf9\u9f50<\/h4>\n

                  \u4e0d\u540c\u80a1\u7968\u53ef\u80fd\u6709\u4e0d\u540c\u7684\u4ea4\u6613\u65e5\u3001\u505c\u724c\u8bb0\u5f55\u548c\u4e0a\u5e02\u65e5\u671f\u3002<\/p>\n

                  \u7b80\u5355\u6392\u5e8f\u4e0d\u4f1a\u81ea\u52a8\u89e3\u51b3\u8fd9\u4e9b\u5dee\u5f02\u3002<\/p>\n

                  \u8bef\u533a\u56db：DataFrame \u6ca1\u6709\u5f02\u5e38\u5c31\u4ee3\u8868\u6570\u636e\u6b63\u786e<\/h4>\n

                  \u7c7b\u578b\u6b63\u786e\u3001\u7d22\u5f15\u6b63\u786e\u548c\u6570\u636e\u6b63\u786e\u662f\u4e09\u4e2a\u4e0d\u540c\u5c42\u9762\u3002<\/p>\n

                  \u6570\u636e\u8d28\u91cf\u68c0\u67e5\u5fc5\u987b\u7ed3\u5408\u884c\u60c5\u5b57\u6bb5\u5b9a\u4e49\u548c\u7b56\u7565\u4f7f\u7528\u65b9\u5f0f\u8fdb\u884c\u3002<\/p>\n

                  9. \u9002\u7528\u573a\u666f<\/h3>\n

                  \u672c\u6587\u7684\u5904\u7406\u65b9\u5f0f\u5c24\u5176\u9002\u5408\u4ee5\u4e0b\u5de5\u4f5c：<\/p>\n

                    \n
                  • \u6279\u91cf\u4e0b\u8f7d\u5386\u53f2\u80a1\u7968\u884c\u60c5\u3002<\/li>\n
                  • \u5c06\u591a\u4e2a\u6807\u7684\u7684\u6570\u636e\u7edf\u4e00\u5b58\u5165\u672c\u5730\u6570\u636e\u5e93\u3002<\/li>\n
                  • \u6784\u5efa\u591a\u8d44\u4ea7\u6536\u76ca\u7387\u77e9\u9635\u3002<\/li>\n
                  • \u5c06\u4e0d\u540c\u6765\u6e90\u7684\u6570\u636e\u63a5\u5165\u540c\u4e00\u7814\u7a76\u6d41\u7a0b\u3002<\/li>\n
                  • \u5bf9\u91d1\u878d\u6570\u636e API \u8fd4\u56de\u7ed3\u679c\u8fdb\u884c\u7edf\u4e00\u6821\u9a8c\u3002<\/li>\n
                  • \u5728\u56de\u6d4b\u524d\u5efa\u7acb\u7a33\u5b9a\u7684\u884c\u60c5\u8f93\u5165\u5c42\u3002<\/li>\n<\/ul>\n

                    \u5982\u679c\u7cfb\u7edf\u5904\u7406\u7684\u662f\u9ad8\u9891\u9010\u7b14\u6570\u636e，\u6216\u8005\u9700\u8981\u4e25\u683c\u4fdd\u7559\u8ba2\u5355\u7c3f\u4e8b\u4ef6\u987a\u5e8f，\u5219\u5e94\u8bbe\u8ba1\u4e13\u95e8\u7684\u6570\u636e\u6a21\u578b，\u4e0d\u80fd\u7b80\u5355\u5730\u628a\u65e5\u7ebf OHLCV \u7684\u5904\u7406\u65b9\u6cd5\u76f4\u63a5\u5957\u7528\u8fc7\u53bb\u3002<\/p>\n

                    10. FAQ<\/h3>\n

                    Q1：\u6279\u91cf\u83b7\u53d6\u80a1\u7968\u6570\u636e\u540e，\u4e3a\u4ec0\u4e48\u5efa\u8bae\u5148\u8f6c\u6362\u6210 DataFrame？<\/h4>\n

                    A：DataFrame \u53ef\u4ee5\u7edf\u4e00\u5217\u3001\u6570\u636e\u7c7b\u578b\u548c\u65f6\u95f4\u5b57\u6bb5，\u65b9\u4fbf\u8fdb\u884c\u7b5b\u9009\u3001\u5408\u5e76\u3001\u5206\u7ec4\u8ba1\u7b97\u548c\u8d28\u91cf\u68c0\u67e5\u3002\u4f46\u8f6c\u6362\u6210 DataFrame \u5e76\u4e0d\u4ee3\u8868\u6570\u636e\u5df2\u7ecf\u901a\u8fc7\u8d28\u91cf\u9a8c\u8bc1\u3002<\/p>\n

                    Q2：\u591a\u53ea\u80a1\u7968\u7684\u884c\u60c5\u5e94\u8be5\u4f7f\u7528 concat \u8fd8\u662f merge？<\/h4>\n

                    A：\u5982\u679c\u662f\u628a\u591a\u53ea\u80a1\u7968\u7684\u8bb0\u5f55\u653e\u8fdb\u540c\u4e00\u5f20\u660e\u7ec6\u8868，\u901a\u5e38\u4f7f\u7528 pd.concat()\u3002\u5982\u679c\u9700\u8981\u6309\u7167\u4ea4\u6613\u65e5\u671f\u5bf9\u9f50\u4e0d\u540c\u80a1\u7968\u7684\u4ef7\u683c，\u5219\u53ef\u4ee5\u5148\u5efa\u7acb\u957f\u8868，\u518d\u4f7f\u7528 pivot() \u8f6c\u6362\u4e3a\u4ef7\u683c\u77e9\u9635\u3002<\/p>\n

                    Q3：\u5982\u4f55\u907f\u514d\u5408\u5e76\u540e\u51fa\u73b0\u91cd\u590d\u884c\u60c5？<\/h4>\n

                    A：\u5148\u786e\u5b9a\u6570\u636e\u7684\u4e1a\u52a1\u4e3b\u952e，\u4f8b\u5982\u65e5\u7ebf\u6570\u636e\u4e2d\u7684\u6807\u7684\u4ee3\u7801\u4e0e\u4ea4\u6613\u65e5\u671f，\u7136\u540e\u4f7f\u7528 duplicated() \u68c0\u67e5\u91cd\u590d\u8bb0\u5f55\u3002\u4e0d\u8981\u5728\u6ca1\u6709\u660e\u786e\u89c4\u5219\u65f6\u76f4\u63a5\u5220\u9664\u91cd\u590d\u884c\u3002<\/p>\n

                    Q4：\u4e0d\u540c\u5e02\u573a\u7684\u80a1\u7968\u53ef\u4ee5\u653e\u5728\u540c\u4e00\u4e2a DataFrame \u4e2d\u5417？<\/h4>\n

                    A：\u53ef\u4ee5，\u4f46\u5e94\u7edf\u4e00\u6807\u7684\u6807\u8bc6\u3001\u5b57\u6bb5\u5b9a\u4e49\u548c\u6570\u636e\u7c7b\u578b，\u5e76\u4fdd\u7559\u5e02\u573a\u5dee\u5f02\u6240\u9700\u7684\u4fe1\u606f\u3002\u7edf\u4e00\u8868\u7ed3\u6784\u4e0d\u4ee3\u8868\u4e0d\u540c\u5e02\u573a\u7684\u4ea4\u6613\u65e5\u5386\u548c\u884c\u60c5\u53e3\u5f84\u5b8c\u5168\u4e00\u81f4\u3002<\/p>\n

                    Q5：QuantDash Python SDK \u80fd\u76f4\u63a5\u8fd4\u56de DataFrame \u5417？<\/h4>\n

                    A：QuantDash \u5b98\u65b9\u516c\u5f00\u793a\u4f8b\u5c55\u793a\u4e86\u901a\u8fc7 to_dataframe=True \u83b7\u53d6 DataFrame \u7684\u7528\u6cd5\u3002\u5177\u4f53\u65b9\u6cd5\u3001\u53c2\u6570\u548c\u9002\u7528\u63a5\u53e3\u5e94\u4ee5\u5f53\u524d\u5b98\u65b9\u6280\u672f\u6587\u6863\u4e3a\u51c6\u3002<\/p>\n

                    Q6：QuantDash \u80fd\u5426\u76f4\u63a5\u6279\u91cf\u83b7\u53d6\u80a1\u7968 K \u7ebf\u5e76\u8fd4\u56de DataFrame？<\/h4>\n

                    A：QuantDash \u5b98\u65b9\u516c\u5f00\u80fd\u529b\u5305\u62ec\u6279\u91cf\u67e5\u8be2\u3001\u6279\u91cf K \u7ebf\u53ca DataFrame \u8f93\u51fa\u3002\u4e0d\u8fc7，\u5177\u4f53\u6279\u91cf K \u7ebf\u63a5\u53e3\u7684\u8c03\u7528\u65b9\u5f0f\u548c\u8fd4\u56de\u7ed3\u6784\u9700\u8981\u67e5\u9605\u5f53\u524d\u5b98\u65b9\u6587\u6863，\u4e0d\u80fd\u4ece\u5355\u6807\u7684\u793a\u4f8b\u81ea\u884c\u63a8\u65ad\u3002<\/p>\n

                    Q7：\u5408\u5e76\u540e\u7684\u884c\u60c5\u6570\u636e\u9700\u8981\u8fdb\u884c\u54ea\u4e9b\u68c0\u67e5？<\/h4>\n

                    A：\u81f3\u5c11\u68c0\u67e5\u5173\u952e\u5b57\u6bb5\u3001\u6570\u636e\u7c7b\u578b\u3001\u91cd\u590d\u4e3b\u952e\u3001OHLC \u903b\u8f91\u5173\u7cfb\u3001\u7f3a\u5931\u503c\u548c\u65f6\u95f4\u8986\u76d6\u8303\u56f4\u3002\u5982\u679c\u6570\u636e\u7528\u4e8e\u56de\u6d4b，\u8fd8\u9700\u8981\u6838\u5bf9\u590d\u6743\u65b9\u5f0f\u53ca\u5f53\u65f6\u53ef\u83b7\u5f97\u7684\u6570\u636e\u8fb9\u754c\u3002<\/p>\n

                    11. \u603b\u7ed3<\/h3>\n
                      \n
                    • \u5148\u5b9a\u4e49\u6807\u51c6\u6570\u636e\u6a21\u578b，\u518d\u5408\u5e76\u884c\u60c5\u3002 \u6807\u7684\u4ee3\u7801\u3001\u65e5\u671f\u3001\u4ef7\u683c\u548c\u6210\u4ea4\u91cf\u5b57\u6bb5\u5e94\u5177\u6709\u660e\u786e\u7684\u8bed\u4e49\u3002<\/li>\n
                    • \u6839\u636e\u5206\u6790\u76ee\u6807\u9009\u62e9\u957f\u8868\u6216\u5bbd\u8868\u3002 \u957f\u8868\u9002\u5408\u4fdd\u5b58\u7edf\u4e00\u884c\u60c5\u660e\u7ec6，\u5bbd\u8868\u9002\u5408\u591a\u8d44\u4ea7\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3002<\/li>\n
                    • \u5c06\u6570\u636e\u6807\u51c6\u5316\u4e0e\u6570\u636e\u8d28\u91cf\u9a8c\u8bc1\u5206\u5f00\u3002 \u6210\u529f\u8f6c\u6362\u5e76\u4e0d\u4ee3\u8868\u884c\u60c5\u5b8c\u6574\u3001\u6b63\u786e\u6216\u9002\u5408\u56de\u6d4b\u3002<\/li>\n
                    • \u5408\u7406\u9009\u62e9\u6570\u636e\u63a5\u5165\u65b9\u5f0f\u3002 QuantDash \u63d0\u4f9b Python SDK\u3001REST API\u3001\u884c\u60c5\u67e5\u8be2\u548c DataFrame \u76f8\u5173\u80fd\u529b，\u53ef\u7528\u4e8e\u6784\u5efa\u91cf\u5316\u6570\u636e\u63a5\u5165\u6d41\u7a0b，\u4f46\u5177\u4f53\u6279\u91cf\u63a5\u53e3\u7528\u6cd5\u5e94\u4ee5\u5b98\u65b9\u6587\u6863\u4e3a\u51c6\u3002<\/li>\n<\/ul>\n

                      QuantDash \u5b98\u65b9\u6587\u6863<\/h3>\n
                        \n
                      • QuantDash \u6280\u672f\u6587\u6863 \u2014 \u67e5\u770b Python SDK\u3001REST API \u53ca\u6570\u636e\u63a5\u53e3\u6587\u6863：QuantDash \u6280\u672f\u6587\u6863<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

                        \u4e00\u53e5\u8bdd\u7ed3\u8bba：\u6279\u91cf\u83b7\u53d6\u80a1\u7968\u6570\u636e\u540e，\u4e0d\u8981\u6025\u7740\u628a\u6240\u6709\u7ed3\u679c\u62fc\u63a5\u8d77\u6765\u3002\u5148\u7edf\u4e00\u5b57\u6bb5\u3001\u6807\u7684\u4ee3\u7801\u3001\u65f6\u95f4\u7d22\u5f15\u548c\u6570\u636e\u7c7b\u578b，\u518d\u6839\u636e\u6570\u636e\u7684\u4e1a\u52a1\u542b\u4e49\u9009\u62e9\u7eb5\u5411\u5408\u5e76\u6216\u6a2a\u5411\u5408\u5e76，\u624d\u80fd\u907f\u514d\u540e\u7eed\u6307\u6807\u8ba1\u7b97\u548c\u56de\u6d4b\u51fa\u73b0\u9690\u853d\u9519\u8bef\u3002 \u6458\u8981
                        \n\u4f7f\u7528 Python \u6279\u91cf\u83b7\u53d6\u80a1\u7968\u884c\u60c5\u65f6，\u4e0d\u540c\u6807\u7684\u7684\u6570\u636e\u53ef\u80fd\u4ee5\u5b57\u5178\u3001\u5217\u8868\u6216 DataFrame \u7b49\u5f62\u5f0f\u8fd4\u56de\u3002\u5373\u4f7f\u6240\u6709\u8bf7\u6c42\u90fd\u6210\u529f，\u4e5f\u4e0d\u610f\u5473\u7740\u8fd9\u4e9b\u6570\u636e\u53ef\u4ee5\u76f4\u63a5\u5408\u5e76\u3002\u672c\u6587\u4ece\u91cf\u5316\u6570\u636e\u5de5\u7a0b\u7684\u5b9e\u9645\u9700\u6c42\u51fa\u53d1&#xff<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9098,9879,11350,81,1813,1158],"topic":[],"class_list":["post-115742","post","type-post","status-publish","format-standard","hentry","category-server","tag-it","tag-quantdash","tag-11350","tag-python","tag-1813","tag-1158"],"yoast_head":"\n\u6279\u91cf\u83b7\u53d6\u80a1\u7968\u6570\u636e\u540e\uff0c\u5982\u4f55\u7edf\u4e00\u8f6c\u6362\u6210 DataFrame\uff1f\u4ece\u6570\u636e\u7ed3\u6784\u5230\u91cf\u5316\u7814\u7a76\u7684\u5b8c\u6574\u5b9e\u8df5 - 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