{"id":477756,"date":"2023-08-09T09:19:52","date_gmt":"2023-08-09T09:19:52","guid":{"rendered":""},"modified":"2023-09-05T11:15:22","modified_gmt":"2023-09-05T11:15:22","slug":"jupyter","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/jupyter\/","title":{"rendered":"\u6731\u76ae\u7279"},"content":{"rendered":"

Jupyter\uff08\u4ee5\u524d\u79f0\u4e3a IPython\uff09\u662f\u4e00\u4e2a\u5f00\u6e90\u9879\u76ee\uff0c\u5f7b\u5e95\u6539\u53d8\u4e86\u4ea4\u4e92\u5f0f\u8ba1\u7b97\u548c\u6570\u636e\u79d1\u5b66\u3002\u5b83\u63d0\u4f9b\u4e86\u4e00\u4e2a\u57fa\u4e8e\u7f51\u7edc\u7684\u5e73\u53f0\uff0c\u5141\u8bb8\u7528\u6237\u521b\u5efa\u548c\u5171\u4eab\u5305\u542b\u5b9e\u65f6\u4ee3\u7801\u3001\u65b9\u7a0b\u3001\u53ef\u89c6\u5316\u548c\u53d9\u8ff0\u6587\u672c\u7684\u6587\u6863\u3002 \u201cJupyter\u201d\u8fd9\u4e2a\u540d\u5b57\u662f\u4e09\u79cd\u6838\u5fc3\u7f16\u7a0b\u8bed\u8a00\u7684\u7ec4\u5408\uff1aJulia\u3001Python \u548c R\uff0c\u53cd\u6620\u4e86\u5b83\u7684\u591a\u8bed\u8a00\u652f\u6301\u3002\u7531\u4e8e\u5176\u6613\u7528\u6027\u548c\u5f3a\u5927\u7684\u529f\u80fd\uff0c\u8fd9\u79cd\u591a\u529f\u80fd\u5de5\u5177\u5728\u6570\u636e\u79d1\u5b66\u5bb6\u3001\u7814\u7a76\u4eba\u5458\u3001\u6559\u80b2\u5de5\u4f5c\u8005\u548c\u5f00\u53d1\u4eba\u5458\u4e2d\u5e7f\u6cdb\u6d41\u884c\u3002<\/p>\n

Jupyter \u7684\u8d77\u6e90\u5386\u53f2\u548c\u9996\u6b21\u63d0\u53ca<\/h2>\n

Jupyter \u7684\u8d77\u6e90\u53ef\u4ee5\u8ffd\u6eaf\u5230 2001 \u5e74\uff0c\u5f53\u65f6\u7269\u7406\u5b66\u5bb6 Fernando P\u00e9rez \u5f00\u53d1\u4e86 IPython \u4f5c\u4e3a\u4e00\u4e2a\u4e1a\u4f59\u9879\u76ee\uff0c\u4ee5\u7b80\u5316\u4ed6\u5728\u8fdb\u884c\u590d\u6742\u79d1\u5b66\u8ba1\u7b97\u65f6\u7684\u5de5\u4f5c\u6d41\u7a0b\u3002 IPython \u6700\u521d\u662f\u4e00\u4e2a\u547d\u4ee4\u884c\u5de5\u5177\uff0c\u65e8\u5728\u589e\u5f3a Python \u4ea4\u4e92\u4f1a\u8bdd\u3002\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\uff0c\u5b83\u5728\u79d1\u5b66\u754c\u83b7\u5f97\u4e86\u5173\u6ce8\uff0c2014 \u5e74\uff0cIPython \u8fdb\u884c\u4e86\u91cd\u5927\u54c1\u724c\u91cd\u5851\u5e76\u6f14\u53d8\u6210 Jupyter\u3002<\/p>\n

\u9996\u6b21\u63d0\u53ca\u4eca\u5929\u6240\u77e5\u7684 Jupyter \u662f\u5728 2014 \u5e74\uff0c\u5f53\u65f6 P\u00e9rez \u548c Brian Granger \u5c06\u5176\u4f5c\u4e3a IPython \u9879\u76ee\u7684\u4e00\u90e8\u5206\u5f15\u5165\u3002\u4e24\u4eba\u7684\u613f\u666f\u662f\u521b\u5efa\u4e00\u4e2a\u652f\u6301\u591a\u79cd\u7f16\u7a0b\u8bed\u8a00\u7684\u4ea4\u4e92\u5f0f\u8ba1\u7b97\u5e73\u53f0\uff0c\u4f7f\u79d1\u5b66\u5bb6\u548c\u7814\u7a76\u4eba\u5458\u66f4\u5bb9\u6613\u6709\u6548\u5730\u534f\u4f5c\u548c\u5206\u4eab\u4ed6\u4eec\u7684\u53d1\u73b0\u3002<\/p>\n

\u6709\u5173 Jupyter \u7684\u8be6\u7ec6\u4fe1\u606f\uff1a\u6269\u5c55\u4e3b\u9898 Jupyter<\/h2>\n

Jupyter \u57fa\u4e8e\u7b14\u8bb0\u672c\u7684\u6982\u5ff5\u8fdb\u884c\u64cd\u4f5c\uff0c\u7b14\u8bb0\u672c\u662f\u5305\u542b\u5b9e\u65f6\u4ee3\u7801\u3001\u6587\u672c\u89e3\u91ca\u3001\u65b9\u7a0b\u548c\u53ef\u89c6\u5316\u7684\u4ea4\u4e92\u5f0f\u6587\u6863\u3002\u8fd9\u4e9b\u7b14\u8bb0\u672c\u4f7f\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u7814\u7a76\u4eba\u5458\u80fd\u591f\u6267\u884c\u6570\u636e\u5206\u6790\u3001\u6a21\u62df\u5b9e\u9a8c\u5e76\u4e0e\u4ed6\u4eba\u65e0\u7f1d\u5171\u4eab\u4ed6\u4eec\u7684\u5de5\u4f5c\u3002 Jupyter \u7684\u5173\u952e\u7ec4\u4ef6\u5305\u62ec\uff1a<\/p>\n

    \n
  1. \n

    \u6838\u5fc3<\/strong>\uff1a\u8ba1\u7b97\u5f15\u64ce\uff0c\u5728\u7b14\u8bb0\u672c\u4e2d\u6267\u884c\u4ee3\u7801\u5e76\u5c06\u7ed3\u679c\u4f20\u56de\u7528\u6237\u754c\u9762\u3002<\/p>\n<\/li>\n

  2. \n

    \u7b14\u8bb0\u672c\u63a5\u53e3<\/strong>\uff1a\u4e00\u4e2a Web \u5e94\u7528\u7a0b\u5e8f\uff0c\u63d0\u4f9b\u4ea4\u4e92\u5f0f\u73af\u5883\uff0c\u7528\u6237\u53ef\u4ee5\u5728\u5176\u4e2d\u521b\u5efa\u3001\u7f16\u8f91\u548c\u8fd0\u884c\u7b14\u8bb0\u672c\u3002<\/p>\n<\/li>\n

  3. \n

    \u7ec6\u80de<\/strong>\uff1aJupyter \u7b14\u8bb0\u672c\u7684\u57fa\u672c\u5355\u5143\uff0c\u5305\u542b\u4ee3\u7801\u6216 Markdown \u6587\u672c\u3002\u7528\u6237\u53ef\u4ee5\u5355\u72ec\u6267\u884c\u4ee3\u7801\u5355\u5143\uff0c\u4ece\u800c\u53ef\u4ee5\u8f7b\u677e\u5730\u8bd5\u9a8c\u5206\u6790\u7684\u4e0d\u540c\u90e8\u5206\u3002<\/p>\n<\/li>\n

  4. \n

    \u964d\u4ef7<\/strong>\uff1a\u4e00\u79cd\u8f7b\u91cf\u7ea7\u6807\u8bb0\u8bed\u8a00\uff0c\u5141\u8bb8\u7528\u6237\u683c\u5f0f\u5316\u6587\u672c\u3001\u521b\u5efa\u6807\u9898\u3001\u5217\u8868\u3001\u8868\u683c\u4ee5\u53ca\u5728\u7b14\u8bb0\u672c\u4e2d\u5408\u5e76\u591a\u5a92\u4f53\u5143\u7d20\u3002<\/p>\n<\/li>\n

  5. \n

    \u4ee3\u7801\u6267\u884c<\/strong>\uff1aJupyter \u7b14\u8bb0\u672c\u5141\u8bb8\u5b9e\u65f6\u6267\u884c\u4ee3\u7801\uff0c\u63d0\u4f9b\u7ed3\u679c\u7684\u5373\u65f6\u53cd\u9988\u5e76\u4fc3\u8fdb\u8fed\u4ee3\u5de5\u4f5c\u6d41\u7a0b\u3002<\/p>\n<\/li>\n

  6. \n

    \u53ef\u89c6\u5316<\/strong>\uff1aJupyter Notebook \u652f\u6301\u5404\u79cd\u53ef\u89c6\u5316\u5e93\uff0c\u4f8b\u5982 Matplotlib \u548c Seaborn\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u76f4\u63a5\u5728 Notebook \u4e2d\u521b\u5efa\u4ea4\u4e92\u5f0f\u56fe\u8868\u548c\u56fe\u5f62\u3002<\/p>\n<\/li>\n<\/ol>\n

    Jupyter\u7684\u5185\u90e8\u7ed3\u6784\uff1aJupyter\u5982\u4f55\u5de5\u4f5c<\/h2>\n

    \u4e3a\u4e86\u4e86\u89e3 Jupyter \u7684\u5185\u90e8\u5de5\u4f5c\u539f\u7406\uff0c\u8ba9\u6211\u4eec\u6df1\u5165\u4e86\u89e3\u5b83\u7684\u67b6\u6784\u3002\u5f53\u7528\u6237\u6253\u5f00 Jupyter \u7b14\u8bb0\u672c\u65f6\uff0c\u4f1a\u53d1\u751f\u4ee5\u4e0b\u6b65\u9aa4\uff1a<\/p>\n

      \n
    1. \n

      Jupyter \u670d\u52a1\u5668\u542f\u52a8\u5e76\u4fa6\u542c\u6765\u81ea\u7528\u6237 Web \u6d4f\u89c8\u5668\u7684\u4f20\u5165\u8fde\u63a5\u3002<\/p>\n<\/li>\n

    2. \n

      \u7b14\u8bb0\u672c\u754c\u9762\u5448\u73b0\u5728\u7528\u6237\u7684\u6d4f\u89c8\u5668\u4e2d\uff0c\u5141\u8bb8\u4ed6\u4eec\u521b\u5efa\u3001\u4fee\u6539\u548c\u8fd0\u884c\u5355\u5143\u3002<\/p>\n<\/li>\n

    3. \n

      \u5f53\u7528\u6237\u8fd0\u884c\u4ee3\u7801\u5355\u5143\u65f6\uff0c\u4ee3\u7801\u5c06\u88ab\u53d1\u9001\u5230 Jupyter \u670d\u52a1\u5668\uff0c\u540e\u8005\u5c06\u5176\u8f6c\u53d1\u5230\u9002\u5f53\u7684\u5185\u6838\u3002<\/p>\n<\/li>\n

    4. \n

      \u5185\u6838\u6267\u884c\u4ee3\u7801\u5e76\u5c06\u8f93\u51fa\u8fd4\u56de\u5230 Jupyter \u670d\u52a1\u5668\u3002<\/p>\n<\/li>\n

    5. \n

      Jupyter \u670d\u52a1\u5668\u5c06\u8f93\u51fa\u53d1\u9001\u56de\u7528\u6237\u7684\u6d4f\u89c8\u5668\uff0c\u5e76\u663e\u793a\u5728\u4ee3\u7801\u5355\u5143\u4e0b\u65b9\u3002<\/p>\n<\/li>\n

    6. \n

      Markdown \u5355\u5143\u683c\u76f4\u63a5\u5728\u7b14\u8bb0\u672c\u754c\u9762\u4e2d\u5448\u73b0\u4e3a\u683c\u5f0f\u5316\u6587\u672c\u3002<\/p>\n<\/li>\n<\/ol>\n

      \u8fd9\u79cd\u67b6\u6784\u5141\u8bb8\u5c06\u7528\u6237\u754c\u9762\uff08\u7b14\u8bb0\u672c\u754c\u9762\uff09\u4e0e\u8ba1\u7b97\u5f15\u64ce\uff08\u5185\u6838\uff09\u5206\u79bb\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u5728\u4e0d\u540c\u7684\u7f16\u7a0b\u8bed\u8a00\u4e4b\u95f4\u5207\u6362\u800c\u65e0\u9700\u66f4\u6539\u754c\u9762\u3002<\/p>\n

      Jupyter\u5173\u952e\u7279\u6027\u5206\u6790<\/h2>\n

      Jupyter \u7684\u4e3b\u8981\u529f\u80fd\u4f7f\u5176\u6210\u4e3a\u6570\u636e\u79d1\u5b66\u5bb6\u3001\u7814\u7a76\u4eba\u5458\u548c\u6559\u80b2\u5de5\u4f5c\u8005\u7684\u5fc5\u5907\u5de5\u5177\u3002\u5b83\u7684\u4e00\u4e9b\u663e\u7740\u7279\u70b9\u5305\u62ec\uff1a<\/p>\n

        \n
      1. \n

        \u4e92\u52a8\u6027<\/strong>\uff1aJupyter \u63d0\u4f9b\u4e86\u4e00\u4e2a\u4ea4\u4e92\u5f0f\u73af\u5883\uff0c\u5141\u8bb8\u7528\u6237\u4fee\u6539\u548c\u6267\u884c\u4ee3\u7801\u5355\u5143\uff0c\u4f7f\u5176\u6210\u4e3a\u6570\u636e\u63a2\u7d22\u548c\u5b9e\u9a8c\u7684\u7406\u60f3\u9009\u62e9\u3002<\/p>\n<\/li>\n

      2. \n

        \u6570\u636e\u53ef\u89c6\u5316<\/strong>\uff1aJupyter \u652f\u6301\u5404\u79cd\u53ef\u89c6\u5316\u5e93\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u76f4\u63a5\u5728\u7b14\u8bb0\u672c\u4e2d\u521b\u5efa\u4ee4\u4eba\u60ca\u53f9\u7684\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u3002<\/p>\n<\/li>\n

      3. \n

        \u5408\u4f5c<\/strong>\uff1aJupyter \u7b14\u8bb0\u672c\u53ef\u4ee5\u4e0e\u5176\u4ed6\u4eba\u5171\u4eab\uff0c\u4ece\u800c\u4fc3\u8fdb\u56e2\u961f\u6210\u5458\u6216\u7814\u7a76\u4eba\u5458\u4e4b\u95f4\u7684\u534f\u4f5c\u3002<\/p>\n<\/li>\n

      4. \n

        \u6587\u6863<\/strong>\uff1aJupyter \u7b14\u8bb0\u672c\u4e2d\u4ee3\u7801\u548c Markdown \u6587\u672c\u7684\u7ec4\u5408\u4f7f\u5176\u6210\u4e3a\u521b\u5efa\u4ea4\u4e92\u5f0f\u548c\u4fe1\u606f\u4e30\u5bcc\u7684\u6570\u636e\u5206\u6790\u62a5\u544a\u7684\u7edd\u4f73\u5e73\u53f0\u3002<\/p>\n<\/li>\n

      5. \n

        \u5e76\u884c\u8ba1\u7b97<\/strong>\uff1aJupyter \u652f\u6301\u5e76\u884c\u8ba1\u7b97\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u5229\u7528\u591a\u4e2a\u6838\u5fc3\u6216\u96c6\u7fa4\u6765\u6267\u884c\u8ba1\u7b97\u5bc6\u96c6\u578b\u4efb\u52a1\u3002<\/p>\n<\/li>\n

      6. \n

        \u6559\u80b2<\/strong>\uff1aJupyter \u5728\u6559\u80b2\u73af\u5883\u4e2d\u5177\u6709\u91cd\u8981\u7528\u9014\uff0c\u53ef\u4fc3\u8fdb\u4ea4\u4e92\u5f0f\u5b66\u4e60\u4f53\u9a8c\u548c\u5b9e\u8df5\u7f16\u7a0b\u7ec3\u4e60\u3002<\/p>\n<\/li>\n<\/ol>\n

        Jupyter\u7684\u7c7b\u578b\uff1a\u4f7f\u7528\u8868\u683c\u548c\u5217\u8868\u6765\u7f16\u5199<\/h2>\n

        Jupyter \u901a\u8fc7\u5176\u591a\u6837\u5316\u7684\u5185\u6838\u751f\u6001\u7cfb\u7edf\u652f\u6301\u5404\u79cd\u7f16\u7a0b\u8bed\u8a00\u3002\u4e0b\u8868\u5c55\u793a\u4e86\u4e00\u4e9b\u53ef\u7528\u7684\u6d41\u884c\u5185\u6838\uff1a<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
        \u6838\u5fc3<\/th>\n\u652f\u6301\u7684\u8bed\u8a00<\/th>\n<\/tr>\n<\/thead>\n
        Python<\/td>\nPython\u3001Julia\u3001R \u7b49<\/td>\n<\/tr>\n
        IR\u5185\u6838<\/td>\n\u53f3<\/td>\n<\/tr>\n
        \u5c24\u8389\u5a05<\/td>\n\u6731\u8389\u5a05<\/td>\n<\/tr>\n
        \u54c8\u65af\u514b\u5c14<\/td>\n\u54c8\u65af\u514b\u5c14<\/td>\n<\/tr>\n
        MATLAB<\/td>\nMATLAB<\/td>\n<\/tr>\n
        \u7ea2\u5b9d\u77f3<\/td>\n\u7ea2\u5b9d\u77f3<\/td>\n<\/tr>\n
        \u65af\u5361\u62c9<\/td>\n\u65af\u5361\u62c9<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u9664\u4e86\u8fd9\u4e9b\u6807\u51c6\u5185\u6838\u4e4b\u5916\uff0c\u7528\u6237\u8fd8\u53ef\u4ee5\u627e\u5230 Lua\u3001C++\u3001Go \u7b49\u8bed\u8a00\u7684\u793e\u533a\u9a71\u52a8\u5185\u6838\uff0c\u4ece\u800c\u6269\u5c55 Jupyter \u7684\u591a\u529f\u80fd\u6027\u4ee5\u6ee1\u8db3\u5404\u79cd\u7f16\u7a0b\u9700\u6c42\u3002<\/p>\n

        Jupyter\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u4f7f\u7528\u8fc7\u7a0b\u4e2d\u9047\u5230\u7684\u95ee\u9898\u4ee5\u53ca\u89e3\u51b3\u65b9\u6cd5<\/h2>\n

        Jupyter \u5728\u5e7f\u6cdb\u7684\u7528\u4f8b\u4e2d\u627e\u5230\u4e86\u5e94\u7528\u7a0b\u5e8f\uff0c\u5305\u62ec\uff1a<\/p>\n

          \n
        1. \n

          \u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316<\/strong>\uff1a\u6570\u636e\u79d1\u5b66\u5bb6\u5229\u7528 Jupyter \u63a2\u7d22\u6570\u636e\u96c6\u3001\u521b\u5efa\u53ef\u89c6\u5316\u5e76\u6267\u884c\u7edf\u8ba1\u5206\u6790\u3002<\/p>\n<\/li>\n

        2. \n

          \u673a\u5668\u5b66\u4e60<\/strong>\uff1aJupyter Notebook \u6709\u52a9\u4e8e\u673a\u5668\u5b66\u4e60\u9879\u76ee\u4e2d\u7684\u6a21\u578b\u5f00\u53d1\u3001\u57f9\u8bad\u548c\u8bc4\u4f30\u3002<\/p>\n<\/li>\n

        3. \n

          \u79d1\u5b66\u8ba1\u7b97<\/strong>\uff1a\u7814\u7a76\u4eba\u5458\u548c\u79d1\u5b66\u5bb6\u4f7f\u7528 Jupyter \u8fdb\u884c\u6a21\u62df\u3001\u8ba1\u7b97\u5efa\u6a21\u548c\u5206\u6790\u5b9e\u9a8c\u6570\u636e\u3002<\/p>\n<\/li>\n

        4. \n

          \u6559\u5b66\u4e0e\u5b66\u4e60<\/strong>\uff1aJupyter \u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6559\u80b2\u5de5\u5177\uff0c\u7528\u4e8e\u6559\u6388\u7f16\u7a0b\u3001\u6570\u636e\u79d1\u5b66\u548c\u5176\u4ed6\u79d1\u5b66\u5b66\u79d1\u3002<\/p>\n<\/li>\n<\/ol>\n

          \u7136\u800c\uff0c\u4e0e\u4efb\u4f55\u6280\u672f\u4e00\u6837\uff0c\u7528\u6237\u5728\u4f7f\u7528 Jupyter \u65f6\u53ef\u80fd\u4f1a\u9047\u5230\u4e00\u4e9b\u6311\u6218\u3002\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898\u53ca\u5176\u89e3\u51b3\u65b9\u6848\u5305\u62ec\uff1a<\/p>\n

            \n
          1. \n

            \u5185\u5b58\u4f7f\u7528\u60c5\u51b5<\/strong>\uff1a\u5927\u578b\u6570\u636e\u96c6\u6216\u5185\u5b58\u5bc6\u96c6\u578b\u64cd\u4f5c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u5185\u5b58\u6d88\u8017\u8fc7\u591a\u3002\u7528\u6237\u5e94\u8003\u8651\u4f18\u5316\u4ee3\u7801\u6216\u4f7f\u7528\u4e91\u8d44\u6e90\u6765\u83b7\u5f97\u66f4\u591a\u5185\u5b58\u3002<\/p>\n<\/li>\n

          2. \n

            \u5185\u6838\u5d29\u6e83<\/strong>\uff1a\u6709\u65f6\uff0c\u5185\u6838\u53ef\u80fd\u4f1a\u7531\u4e8e\u4ee3\u7801\u95ee\u9898\u800c\u5d29\u6e83\u3002\u5b9a\u671f\u4fdd\u5b58\u7b14\u8bb0\u672c\u6709\u52a9\u4e8e\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\u6062\u590d\u5de5\u4f5c\u3002<\/p>\n<\/li>\n

          3. \n

            \u7248\u672c\u51b2\u7a81<\/strong>\uff1a\u5e93\u4e4b\u95f4\u7684\u4f9d\u8d56\u95ee\u9898\u53ef\u80fd\u4f1a\u5bfc\u81f4\u51b2\u7a81\u3002\u5229\u7528\u865a\u62df\u73af\u5883\u6216\u5bb9\u5668\u5316\u53ef\u4ee5\u7f13\u89e3\u8fd9\u4e9b\u95ee\u9898\u3002<\/p>\n<\/li>\n

          4. \n

            \u5b89\u5168\u95ee\u9898<\/strong>\uff1a\u5171\u4eab\u7b14\u8bb0\u672c\u7535\u8111\u672a\u7ecf\u9002\u5f53\u6d88\u6bd2\u53ef\u80fd\u4f1a\u5bfc\u81f4\u6f5c\u5728\u7684\u5b89\u5168\u98ce\u9669\u3002\u907f\u514d\u66b4\u9732\u654f\u611f\u6570\u636e\u6216\u4f7f\u7528\u4e0d\u53d7\u4fe1\u4efb\u7684\u4ee3\u7801\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<\/li>\n<\/ol>\n

            \u4e3b\u8981\u7279\u5f81\u4ee5\u53ca\u4e0e\u7c7b\u4f3c\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83\u4ee5\u8868\u683c\u548c\u5217\u8868\u7684\u5f62\u5f0f<\/h2>\n

            \u8ba9\u6211\u4eec\u5c06 Jupyter \u4e0e\u7c7b\u4f3c\u7684\u4ea4\u4e92\u5f0f\u8ba1\u7b97\u5e73\u53f0\u8fdb\u884c\u6bd4\u8f83\uff0c\u4ee5\u7a81\u51fa\u5176\u4e3b\u8981\u7279\u70b9\uff1a<\/p>\n\n\n\n\n\n\n\n\n\n
            \u7279\u5f81<\/th>\n\u6731\u76ae\u7279<\/th>\nRStudio<\/th>\n\u8c37\u6b4c\u5408\u4f5c\u5b9e\u9a8c\u5ba4<\/th>\n<\/tr>\n<\/thead>\n
            \u591a\u8bed\u8a00\u652f\u6301<\/td>\n\u662f\uff08\u901a\u8fc7\u5185\u6838\uff09<\/td>\n\u6709\u9650\uff08\u4e3b\u8981\u662f R\uff09<\/td>\nPython<\/td>\n<\/tr>\n
            \u57fa\u4e8e\u4e91\u7684\u6267\u884c<\/td>\n\u53ef\u80fd\u7684<\/td>\n\u4e0d<\/td>\n\u662f\u7684<\/td>\n<\/tr>\n
            \u5408\u4f5c<\/td>\n\u662f\u7684<\/td>\n\u6709\u9650\u7684<\/td>\n\u662f\u7684<\/td>\n<\/tr>\n
            \u53ef\u89c6\u5316\u5e93<\/td>\n\u5e7f\u6cdb\u7684\u652f\u6301<\/td>\n\u6709\u9650\u7684<\/td>\n\u662f\u7684<\/td>\n<\/tr>\n
            \u5b66\u4e60\u66f2\u7ebf<\/td>\n\u7f13\u548c<\/td>\n\u4f4e\u7684<\/td>\n\u4f4e\u7684<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            Jupyter \u56e0\u5176\u591a\u8bed\u8a00\u652f\u6301\u3001\u57fa\u4e8e\u4e91\u7684\u6267\u884c\u548c\u5e7f\u6cdb\u7684\u53ef\u89c6\u5316\u5e93\u800c\u8131\u9896\u800c\u51fa\u3002\u53e6\u4e00\u65b9\u9762\uff0cRStudio \u4f5c\u4e3a R \u7f16\u7a0b\u7684\u4e13\u7528\u5e73\u53f0\u8868\u73b0\u51fa\u8272\uff0c\u800c Google Colab \u56e0\u5176\u6613\u7528\u6027\u4ee5\u53ca\u4e0e Google Drive \u7684\u76f4\u63a5\u96c6\u6210\u800c\u5e7f\u53d7\u6b22\u8fce\u3002<\/p>\n

            \u4e0e Jupyter \u76f8\u5173\u7684\u672a\u6765\u524d\u666f\u548c\u6280\u672f<\/h2>\n

            Jupyter \u7684\u672a\u6765\u770b\u8d77\u6765\u5145\u6ee1\u5e0c\u671b\uff0c\u6709\u51e0\u9879\u8fdb\u5c55\u5373\u5c06\u5230\u6765\uff1a<\/p>\n

              \n
            1. \n

              \u4eba\u5de5\u667a\u80fd\u548c\u673a\u5668\u5b66\u4e60\u7684\u96c6\u6210<\/strong>\uff1aJupyter \u53ef\u80fd\u4f1a\u4e0e\u4eba\u5de5\u667a\u80fd\u548c\u673a\u5668\u5b66\u4e60\u6280\u672f\u8fdb\u4e00\u6b65\u96c6\u6210\uff0c\u4ece\u800c\u7b80\u5316\u9ad8\u7ea7\u6a21\u578b\u7684\u5f00\u53d1\u548c\u90e8\u7f72\u3002<\/p>\n<\/li>\n

            2. \n

              \u52a0\u5f3a\u534f\u4f5c<\/strong>\uff1a\u589e\u5f3a\u534f\u4f5c\u529f\u80fd\u7684\u52aa\u529b\u5c06\u5141\u8bb8\u5728\u7b14\u8bb0\u672c\u4e0a\u8fdb\u884c\u5b9e\u65f6\u534f\u4f5c\uff0c\u4f7f\u8fdc\u7a0b\u56e2\u961f\u5408\u4f5c\u66f4\u52a0\u9ad8\u6548\u3002<\/p>\n<\/li>\n

            3. \n

              \u57fa\u4e8e\u4e91\u7684\u8fdb\u6b65<\/strong>\uff1a\u57fa\u4e8e\u4e91\u7684 Jupyter \u5e73\u53f0\u53ef\u80fd\u4f1a\u5728\u6027\u80fd\u3001\u53ef\u6269\u5c55\u6027\u548c\u53ef\u8bbf\u95ee\u6027\u65b9\u9762\u5f97\u5230\u6539\u8fdb\uff0c\u4ece\u800c\u4f7f\u5b83\u4eec\u5bf9\u6570\u636e\u5bc6\u96c6\u578b\u4efb\u52a1\u66f4\u5177\u5438\u5f15\u529b\u3002<\/p>\n<\/li>\n

            4. \n

              \u4ea4\u4e92\u5f0f\u6570\u636e\u5e94\u7528<\/strong>\uff1aJupyter \u7684\u53d1\u5c55\u53ef\u80fd\u4f1a\u5bfc\u81f4\u4ea4\u4e92\u5f0f\u6570\u636e\u5e94\u7528\u7a0b\u5e8f\u7684\u521b\u5efa\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u6784\u5efa\u548c\u5171\u4eab\u4ea4\u4e92\u5f0f\u6570\u636e\u9a71\u52a8\u7684 Web \u5e94\u7528\u7a0b\u5e8f\u3002<\/p>\n<\/li>\n<\/ol>\n

              \u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e Jupyter \u5173\u8054<\/h2>\n

              \u4ee3\u7406\u670d\u52a1\u5668\uff08\u4f8b\u5982 OneProxy \u63d0\u4f9b\u7684\u4ee3\u7406\u670d\u52a1\u5668\uff09\u53ef\u4ee5\u5728\u589e\u5f3a Jupyter \u4f53\u9a8c\u65b9\u9762\u53d1\u6325\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u4e0e Jupyter \u5173\u8054\u7684\u4e00\u4e9b\u65b9\u6cd5\uff1a<\/p>\n

                \n
              1. \n

                \u589e\u5f3a\u5b89\u5168\u6027<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5145\u5f53\u7528\u6237\u548c Jupyter \u670d\u52a1\u5668\u4e4b\u95f4\u7684\u4e2d\u4ecb\uff0c\u901a\u8fc7\u9690\u85cf\u7528\u6237\u7684 IP \u5730\u5740\u5e76\u51cf\u8f7b\u6f5c\u5728\u7684\u7f51\u7edc\u5a01\u80c1\u6765\u589e\u52a0\u989d\u5916\u7684\u5b89\u5168\u5c42\u3002<\/p>\n<\/li>\n

              2. \n

                \u7ed5\u8fc7\u9650\u5236<\/strong>\uff1a\u5728\u67d0\u4e9b\u533a\u57df\u6216\u7f51\u7edc\u4e2d\uff0c\u5bf9 Jupyter \u6216\u7279\u5b9a\u5185\u6838\u7684\u8bbf\u95ee\u53ef\u80fd\u4f1a\u53d7\u5230\u9650\u5236\u3002\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u7ed5\u8fc7\u8fd9\u4e9b\u9650\u5236\u5e76\u65e0\u7f1d\u8bbf\u95ee Jupyter\u3002<\/p>\n<\/li>\n

              3. \n

                \u533f\u540d\u548c\u9690\u79c1<\/strong>\uff1a\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u4e3a\u7528\u6237\u63d0\u4f9b\u589e\u5f3a\u7684\u533f\u540d\u6027\u548c\u9690\u79c1\u6027\uff0c\u5141\u8bb8\u4ed6\u4eec\u5728\u4e0d\u6cc4\u9732\u771f\u5b9e\u8eab\u4efd\u7684\u60c5\u51b5\u4e0b\u4f7f\u7528 Jupyter\u3002<\/p>\n<\/li>\n

              4. \n

                \u8d1f\u8f7d\u5747\u8861<\/strong>\uff1a\u5728\u90e8\u7f72\u591a\u4e2aJupyter\u670d\u52a1\u5668\u7684\u573a\u666f\u4e2d\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u6709\u6548\u5730\u5206\u53d1\u4f20\u5165\u6d41\u91cf\uff0c\u4f18\u5316\u6027\u80fd\u548c\u8d44\u6e90\u5229\u7528\u7387\u3002<\/p>\n<\/li>\n<\/ol>\n

                \u901a\u8fc7\u5229\u7528\u4ee3\u7406\u670d\u52a1\u5668\uff0c\u7528\u6237\u53ef\u4ee5\u589e\u5f3a Jupyter \u4f53\u9a8c\u5e76\u514b\u670d\u5730\u7406\u9650\u5236\u6216\u5b89\u5168\u95ee\u9898\u5e26\u6765\u7684\u6f5c\u5728\u9650\u5236\u3002<\/p>\n

                \u76f8\u5173\u94fe\u63a5<\/h2>\n

                \u6709\u5173 Jupyter \u7684\u66f4\u591a\u4fe1\u606f\uff0c\u8bf7\u53c2\u9605\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n

                  \n
                1. Jupyter\u5b98\u65b9\u7f51\u7ad9<\/a><\/li>\n
                2. Jupyter \u6587\u6863<\/a><\/li>\n
                3. Jupyter GitHub \u5b58\u50a8\u5e93<\/a><\/li>\n
                4. Jupyter \u7b14\u8bb0\u672c\u793a\u4f8b<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468719,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477756","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Jupyter: Empowering Data Science and Interactive Computing<\/mark>","faq_items":[{"question":"What is Jupyter?","answer":"

                  Jupyter is an open-source project that provides a web-based platform for interactive computing and data science. It allows users to create documents containing live code, visualizations, equations, and text explanations.<\/p>"},{"question":"How did Jupyter originate, and when was it first mentioned?","answer":"

                  Jupyter originated as IPython in 2001 when physicist Fernando P\u00e9rez developed it to streamline his scientific computations. In 2014, IPython was rebranded as Jupyter, with its first mention as part of the IPython project.<\/p>"},{"question":"What is the internal structure of Jupyter, and how does it work?","answer":"

                  Jupyter consists of a kernel, notebook interface, code cells, Markdown cells, and visualization capabilities. When a user runs a code cell, the code is executed by the kernel, and the output is sent back to the notebook interface.<\/p>"},{"question":"What are the key features of Jupyter?","answer":"

                  Jupyter's key features include interactivity, data visualization support, collaboration options, extensive documentation capabilities, and the ability to perform parallel computing tasks.<\/p>"},{"question":"What types of Jupyter exist?","answer":"

                  Jupyter supports various programming languages through its kernels. Some popular kernels include IPython (Python, Julia, R, and more), IRkernel (R), IJulia (Julia), IHaskell (Haskell), IMATLAB (MATLAB), IRuby (Ruby), and IScala (Scala).<\/p>"},{"question":"How can Jupyter be used, and what are the common problems and solutions related to its use?","answer":"

                  Jupyter finds applications in data analysis, machine learning, scientific computing, and education. Common problems include memory usage, kernel crashes, version conflicts, and security concerns, which can be addressed through optimization, regular saving, virtual environments, and careful sharing.<\/p>"},{"question":"How does Jupyter compare to similar platforms like RStudio and Google Colab?","answer":"

                  Jupyter stands out for its multi-language support, cloud-based execution, and extensive visualization libraries. RStudio excels as a dedicated platform for R programming, while Google Colab is known for its simplicity and direct integration with Google Drive.<\/p>"},{"question":"What are the future perspectives and technologies related to Jupyter?","answer":"

                  The future of Jupyter holds possibilities for integration with AI and machine learning, improved collaboration features, advancements in cloud-based execution, and the development of interactive data applications.<\/p>"},{"question":"How can proxy servers be associated with Jupyter?","answer":"

                  Proxy servers, like those provided by OneProxy, can enhance Jupyter's security, bypass restrictions, provide anonymity, and enable load balancing for optimal performance.<\/p>"},{"question":"Where can I find more information about Jupyter?","answer":"

                  For more information about Jupyter, visit the official website, explore the documentation, check out the GitHub repository, and find useful Jupyter notebook examples.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477756","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/477756\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468719"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=477756"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}