{"id":985127,"date":"2024-12-27T07:34:05","date_gmt":"2024-12-26T23:34:05","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/985127.html"},"modified":"2024-12-27T07:34:08","modified_gmt":"2024-12-26T23:34:08","slug":"%e5%a6%82%e4%bd%95%e8%bf%90%e7%94%a8python%e5%81%9a%e7%a7%91%e7%a0%94","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/985127.html","title":{"rendered":"\u5982\u4f55\u8fd0\u7528python\u505a\u79d1\u7814"},"content":{"rendered":"

\"\u5982\u4f55\u8fd0\u7528python\u505a\u79d1\u7814\"<\/p>\n

\u5728\u79d1\u7814\u4e2d\u8fd0\u7528Python\u7684\u5173\u952e\u5728\u4e8e\uff1a\u6570\u636e\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u3001\u53ef\u89c6\u5316\u3001\u81ea\u52a8\u5316\u53ca\u673a\u5668\u5b66\u4e60<\/a>\u7b49\u65b9\u9762\u3002\u6570\u636e\u5904\u7406\u662f\u79d1\u7814\u4e2d\u6700\u57fa\u7840\u7684\u90e8\u5206\uff0cPython\u63d0\u4f9b\u4e86\u8bf8\u5982Pandas\u548cNumPy\u7b49\u5f3a\u5927\u7684\u5e93\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u5904\u7406\u548c\u6e05\u6d17\u6570\u636e\u3002<\/strong><\/p>\n<\/p>\n

Python\u5728\u79d1\u7814\u4e2d\u7684\u5e94\u7528\u662f\u5e7f\u6cdb\u800c\u6df1\u5165\u7684\uff0c\u6db5\u76d6\u4e86\u4ece\u6570\u636e\u5904\u7406\u5230\u9ad8\u7ea7\u5206\u6790\u7684\u5404\u4e2a\u65b9\u9762\u3002\u9996\u5148\uff0c\u6570\u636e\u5904\u7406\u662f\u79d1\u7814\u7684\u57fa\u7840\u5de5\u4f5c\uff0cPython\u7684Pandas\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784\u548c\u5206\u6790\u5de5\u5177\uff0c\u4f7f\u5f97\u5904\u7406\u548c\u6e05\u6d17\u6570\u636e\u53d8\u5f97\u9ad8\u6548\u800c\u7b80\u5355\u3002\u5176\u6b21\uff0c\u6570\u636e\u5206\u6790\u662f\u79d1\u7814\u7684\u6838\u5fc3\uff0cPython\u7684SciPy\u548cStatsmodels\u5e93\u4e3a\u7edf\u8ba1\u5206\u6790\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u3002\u518d\u8005\uff0c\u6570\u636e\u53ef\u89c6\u5316\u5728\u79d1\u7814\u4e2d\u8d77\u7740\u91cd\u8981\u7684\u4f5c\u7528\uff0cMatplotlib\u548cSeaborn\u7b49\u5e93\u80fd\u591f\u751f\u6210\u7cbe\u7f8e\u7684\u56fe\u8868\uff0c\u5e2e\u52a9\u79d1\u7814\u4eba\u5458\u66f4\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u3002\u6b64\u5916\uff0c\u81ea\u52a8\u5316\u662f\u63d0\u9ad8\u79d1\u7814\u6548\u7387\u7684\u5173\u952e\uff0cPython\u7684\u811a\u672c\u8bed\u8a00\u7279\u6027\u8ba9\u81ea\u52a8\u5316\u91cd\u590d\u6027\u4efb\u52a1\u6210\u4e3a\u53ef\u80fd\u3002\u6700\u540e\uff0c\u673a\u5668\u5b66\u4e60\u7684\u5e94\u7528\u5728\u79d1\u7814\u4e2d\u8d8a\u6765\u8d8a\u666e\u904d\uff0cPython\u7684Scikit-learn\u548cTensorFlow\u7b49\u5e93\u4e3a\u673a\u5668\u5b66\u4e60\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u652f\u6301\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u5904\u7406\u4e0e\u6e05\u6d17<\/p>\n<\/p>\n

Python\u5728\u6570\u636e\u5904\u7406\u65b9\u9762\u7684\u4f18\u52bf\u4e3b\u8981\u4f53\u73b0\u5728Pandas\u548cNumPy\u8fd9\u4e24\u4e2a\u5e93\u4e0a\u3002Pandas\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u7ed3\u6784DataFrame\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5bf9\u6570\u636e\u8fdb\u884c\u64cd\u4f5c\uff0c\u6bd4\u5982\u7b5b\u9009\u3001\u6392\u5e8f\u3001\u5206\u7ec4\u7b49\u3002\u800cNumPy\u5219\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u591a\u7ef4\u6570\u7ec4\u64cd\u4f5c\uff0c\u5bf9\u4e8e\u6570\u503c\u578b\u6570\u636e\u7684\u5904\u7406\u6781\u4e3a\u9ad8\u6548\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    Pandas\u7684\u4f7f\u7528<\/strong><\/p>\n<\/p>\n

    Pandas\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u636e\u64cd\u4f5c\u548c\u5206\u6790\u7684\u5f00\u6e90\u5e93\uff0c\u63d0\u4f9b\u4e86\u6570\u636e\u7ed3\u6784\u548c\u6570\u636e\u5206\u6790\u5de5\u5177\u3002Pandas\u5e93\u7684DataFrame\u7ed3\u6784\u7c7b\u4f3c\u4e8eExcel\u4e2d\u7684\u8868\u683c\uff0c\u975e\u5e38\u9002\u5408\u7528\u4e8e\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e\u3002\u901a\u8fc7Pandas\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u636e\u8bfb\u53d6\u3001\u6570\u636e\u6e05\u6d17\u3001\u6570\u636e\u8f6c\u6362\u7b49\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

    \u4f8b\u5982\uff0c\u8bfb\u53d6CSV\u6587\u4ef6\u53ef\u4ee5\u901a\u8fc7pd.read_csv()<\/code>\u51fd\u6570\u5b9e\u73b0\uff0c\u800c\u6570\u636e\u7684\u6e05\u6d17\u53ef\u4ee5\u901a\u8fc7dropna()<\/code>\u65b9\u6cd5\u53bb\u9664\u7f3a\u5931\u503c\uff0c\u901a\u8fc7fillna()<\/code>\u65b9\u6cd5\u586b\u5145\u7f3a\u5931\u503c\u3002\u5bf9\u4e8e\u6570\u636e\u8f6c\u6362\uff0cPandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u6bd4\u5982apply()<\/code>\u3001map()<\/code>\u7b49\uff0c\u53ef\u4ee5\u5bf9DataFrame\u4e2d\u7684\u6570\u636e\u8fdb\u884c\u7075\u6d3b\u7684\u8f6c\u6362\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n<\/li>\n

  2. \n

    NumPy\u7684\u5e94\u7528<\/strong><\/p>\n<\/p>\n

    NumPy\u662fPython\u4e2d\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u57fa\u7840\u5305\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6027\u80fd\u7684\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\uff0c\u4ee5\u53ca\u5f88\u591a\u7528\u4e8e\u6570\u7ec4\u8fd0\u7b97\u7684\u51fd\u6570\u3002NumPy\u7684\u6570\u7ec4\u5bf9\u8c61ndarray\u662f\u4e00\u4e2a\u5feb\u901f\u800c\u7075\u6d3b\u7684\u5927\u6570\u636e\u5bb9\u5668\uff0c\u80fd\u591f\u66f4\u9ad8\u6548\u5730\u6267\u884c\u6570\u503c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n

    NumPy\u652f\u6301\u7684\u8fd0\u7b97\u5305\u62ec\uff1a\u57fa\u672c\u7684\u7b97\u672f\u8fd0\u7b97\u3001\u7edf\u8ba1\u8fd0\u7b97\u3001\u7ebf\u6027\u4ee3\u6570\u8fd0\u7b97\u7b49\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528np.sum()<\/code>\u8ba1\u7b97\u6570\u7ec4\u5143\u7d20\u7684\u548c\uff0c\u4f7f\u7528np.mean()<\/code>\u8ba1\u7b97\u5e73\u5747\u503c\uff0c\u4f7f\u7528np.linalg.inv()<\/code>\u8ba1\u7b97\u77e9\u9635\u7684\u9006\u7b49\u3002\u8fd9\u4e9b\u529f\u80fd\u4f7f\u5f97NumPy\u6210\u4e3a\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u548c\u6570\u636e\u5904\u7406\u7684\u5f97\u529b\u5de5\u5177\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

    \u4e8c\u3001\u6570\u636e\u5206\u6790<\/p>\n<\/p>\n

    \u6570\u636e\u5206\u6790\u662f\u79d1\u7814\u4e2d\u81f3\u5173\u91cd\u8981\u7684\u4e00\u73af\uff0cPython\u63d0\u4f9b\u4e86\u591a\u4e2a\u5e93\u6765\u652f\u6301\u5404\u79cd\u6570\u636e\u5206\u6790\u9700\u6c42\uff0c\u5982SciPy\u548cStatsmodels\u3002<\/p>\n<\/p>\n

      \n
    1. \n

      SciPy\u7684\u529f\u80fd<\/strong><\/p>\n<\/p>\n

      SciPy\u662f\u4e00\u4e2a\u7528\u4e8e\u6570\u5b66\u3001\u79d1\u5b66\u548c\u5de5\u7a0b\u7684\u5f00\u6e90\u8f6f\u4ef6\u5e93\uff0c\u57fa\u4e8eNumPy\u4e4b\u4e0a\u6784\u5efa\uff0c\u63d0\u4f9b\u4e86\u5f88\u591a\u6709\u7528\u7684\u51fd\u6570\u6765\u8fdb\u884c\u79d1\u5b66\u548c\u5de5\u7a0b\u8ba1\u7b97\u3002SciPy\u5305\u542b\u7684\u6a21\u5757\u6709\uff1a\u7ebf\u6027\u4ee3\u6570\u3001\u79ef\u5206\u3001\u4f18\u5316\u3001\u4fe1\u53f7\u5904\u7406\u3001\u7edf\u8ba1\u7b49\u3002<\/p>\n<\/p>\n

      \u4f8b\u5982\uff0cSciPy\u7684optimize<\/code>\u6a21\u5757\u53ef\u4ee5\u7528\u4e8e\u51fd\u6570\u4f18\u5316\u548c\u6839\u5bfb\u627e\uff0c\u800cstats<\/code>\u6a21\u5757\u5219\u63d0\u4f9b\u4e86\u591a\u79cd\u6982\u7387\u5206\u5e03\u4ee5\u53ca\u7edf\u8ba1\u51fd\u6570\uff0c\u53ef\u4ee5\u8fdb\u884c\u5047\u8bbe\u68c0\u9a8c\u3001\u63cf\u8ff0\u7edf\u8ba1\u7b49\u5206\u6790\u3002\u901a\u8fc7SciPy\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u8fdb\u884c\u590d\u6742\u7684\u6570\u5b66\u8fd0\u7b97\u548c\u7edf\u8ba1\u5206\u6790\uff0c\u4ece\u800c\u83b7\u5f97\u66f4\u6df1\u5165\u7684\u7814\u7a76\u7ed3\u679c\u3002<\/p>\n<\/p>\n<\/li>\n

    2. \n

      Statsmodels\u7684\u5e94\u7528<\/strong><\/p>\n<\/p>\n

      Statsmodels\u662f\u4e00\u4e2a\u7528\u4e8e\u4f30\u8ba1\u7edf\u8ba1\u6a21\u578b\u3001\u8fdb\u884c\u7edf\u8ba1\u6d4b\u8bd5\u548c\u6570\u636e\u63a2\u7d22\u7684\u5e93\u3002\u5b83\u63d0\u4f9b\u4e86\u591a\u79cd\u7edf\u8ba1\u6a21\u578b\u7684\u5b9e\u73b0\uff0c\u5982\u7ebf\u6027\u56de\u5f52\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3001\u5e7f\u4e49\u7ebf\u6027\u6a21\u578b\u7b49\u3002<\/p>\n<\/p>\n

      Statsmodels\u7684\u4f18\u52bf\u5728\u4e8e\u5176\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u68c0\u9a8c\u548c\u5206\u6790\u529f\u80fd\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528OLS<\/code>\u7c7b\u8fdb\u884c\u666e\u901a\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u5206\u6790\uff0c\u4f7f\u7528ARIMA<\/code>\u6a21\u578b\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002\u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u6784\u5efa\u548c\u8bc4\u4f30\u7edf\u8ba1\u6a21\u578b\uff0c\u6df1\u5165\u5206\u6790\u6570\u636e\u80cc\u540e\u7684\u89c4\u5f8b\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

      \u4e09\u3001\u6570\u636e\u53ef\u89c6\u5316<\/p>\n<\/p>\n

      \u6570\u636e\u53ef\u89c6\u5316\u5728\u79d1\u7814\u4e2d\u5177\u6709\u91cd\u8981\u7684\u4f5c\u7528\uff0cPython\u63d0\u4f9b\u4e86\u591a\u4e2a\u5f3a\u5927\u7684\u53ef\u89c6\u5316\u5e93\uff0c\u5982Matplotlib\u548cSeaborn\u3002<\/p>\n<\/p>\n

        \n
      1. \n

        Matplotlib\u7684\u4f7f\u7528<\/strong><\/p>\n<\/p>\n

        Matplotlib\u662fPython\u4e2d\u6700\u8457\u540d\u7684\u7ed8\u56fe\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u53ef\u4ee5\u751f\u6210\u5404\u79cd\u9759\u6001\u3001\u52a8\u6001\u548c\u4ea4\u4e92\u5f0f\u56fe\u8868\u3002Matplotlib\u7684\u6838\u5fc3\u662fpyplot\u6a21\u5757\uff0c\u5b83\u63d0\u4f9b\u4e86\u7c7b\u4f3c\u4e8eMATLAB\u7684\u7ed8\u56feAPI\u3002<\/p>\n<\/p>\n

        \u901a\u8fc7Matplotlib\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u521b\u5efa\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u3001\u997c\u56fe\u7b49\u591a\u79cd\u56fe\u8868\u3002\u4f8b\u5982\uff0c\u4f7f\u7528plt.plot()<\/code>\u53ef\u4ee5\u7ed8\u5236\u6298\u7ebf\u56fe\uff0c\u4f7f\u7528plt.bar()<\/code>\u53ef\u4ee5\u7ed8\u5236\u67f1\u72b6\u56fe\uff0c\u4f7f\u7528plt.scatter()<\/code>\u53ef\u4ee5\u7ed8\u5236\u6563\u70b9\u56fe\u3002Matplotlib\u8fd8\u652f\u6301\u56fe\u5f62\u7684\u81ea\u5b9a\u4e49\uff0c\u5982\u8bbe\u7f6e\u6807\u9898\u3001\u6807\u7b7e\u3001\u56fe\u4f8b\u7b49\uff0c\u4f7f\u5f97\u79d1\u7814\u4eba\u5458\u80fd\u591f\u4ee5\u66f4\u76f4\u89c2\u7684\u65b9\u5f0f\u5c55\u793a\u6570\u636e\u3002<\/p>\n<\/p>\n<\/li>\n

      2. \n

        Seaborn\u7684\u7279\u70b9<\/strong><\/p>\n<\/p>\n

        Seaborn\u662f\u57fa\u4e8eMatplotlib\u6784\u5efa\u7684\u9ad8\u7ea7\u53ef\u89c6\u5316\u5e93\uff0c\u63d0\u4f9b\u4e86\u66f4\u7b80\u6d01\u7684API\u548c\u66f4\u7f8e\u89c2\u7684\u9ed8\u8ba4\u6837\u5f0f\u3002Seaborn\u4e13\u6ce8\u4e8e\u7edf\u8ba1\u6570\u636e\u7684\u53ef\u89c6\u5316\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u7528\u4e8e\u7ed8\u5236\u590d\u6742\u7edf\u8ba1\u56fe\u7684\u63a5\u53e3\uff0c\u5982\u5206\u7c7b\u56fe\u3001\u56de\u5f52\u56fe\u3001\u70ed\u529b\u56fe\u7b49\u3002<\/p>\n<\/p>\n

        Seaborn\u53ef\u4ee5\u8f7b\u677e\u5730\u521b\u5efa\u590d\u6742\u7684\u7edf\u8ba1\u56fe\u8868\u3002\u4f8b\u5982\uff0c\u4f7f\u7528sns.catplot()<\/code>\u53ef\u4ee5\u7ed8\u5236\u5206\u7c7b\u6570\u636e\u56fe\uff0c\u4f7f\u7528sns.heatmap()<\/code>\u53ef\u4ee5\u7ed8\u5236\u70ed\u529b\u56fe\u3002Seaborn\u8fd8\u652f\u6301\u4e0ePandas\u7684\u65e0\u7f1d\u96c6\u6210\uff0c\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528DataFrame\u4e2d\u7684\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\uff0c\u6781\u5927\u5730\u63d0\u9ad8\u4e86\u79d1\u7814\u4eba\u5458\u7684\u5de5\u4f5c\u6548\u7387\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

        \u56db\u3001\u81ea\u52a8\u5316\u4e0e\u811a\u672c\u5316<\/p>\n<\/p>\n

        Python\u662f\u4e00\u79cd\u811a\u672c\u8bed\u8a00\uff0c\u7279\u522b\u9002\u5408\u7528\u4e8e\u81ea\u52a8\u5316\u548c\u811a\u672c\u5316\u4efb\u52a1\uff0c\u8fd9\u5728\u79d1\u7814\u4e2d\u53ef\u4ee5\u6781\u5927\u5730\u63d0\u9ad8\u6548\u7387\u3002<\/p>\n<\/p>\n

          \n
        1. \n

          \u81ea\u52a8\u5316\u811a\u672c<\/strong><\/p>\n<\/p>\n

          \u5728\u79d1\u7814\u4e2d\uff0c\u901a\u5e38\u9700\u8981\u8fdb\u884c\u5927\u91cf\u91cd\u590d\u6027\u7684\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5de5\u4f5c\u3002\u901a\u8fc7\u7f16\u5199Python\u811a\u672c\uff0c\u53ef\u4ee5\u5c06\u8fd9\u4e9b\u91cd\u590d\u6027\u5de5\u4f5c\u81ea\u52a8\u5316\uff0c\u4ece\u800c\u8282\u7701\u5927\u91cf\u7684\u65f6\u95f4\u548c\u7cbe\u529b\u3002<\/p>\n<\/p>\n

          \u4f8b\u5982\uff0c\u53ef\u4ee5\u7f16\u5199\u811a\u672c\u6765\u81ea\u52a8\u4e0b\u8f7d\u548c\u5904\u7406\u6570\u636e\u3001\u751f\u6210\u62a5\u544a\u3001\u6267\u884c\u5206\u6790\u7b49\u3002Python\u7684\u6807\u51c6\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6a21\u5757\uff0c\u5982os\u3001sys\u3001shutil\u7b49\uff0c\u53ef\u4ee5\u7528\u4e8e\u6587\u4ef6\u548c\u76ee\u5f55\u64cd\u4f5c\uff0c\u6b63\u5219\u8868\u8fbe\u5f0f\u6a21\u5757re\u53ef\u4ee5\u7528\u4e8e\u6587\u672c\u5904\u7406\uff0csubprocess\u6a21\u5757\u53ef\u4ee5\u7528\u4e8e\u6267\u884c\u7cfb\u7edf\u547d\u4ee4\u7b49\u3002<\/p>\n<\/p>\n<\/li>\n

        2. \n

          \u4efb\u52a1\u8c03\u5ea6<\/strong><\/p>\n<\/p>\n

          \u9664\u4e86\u7f16\u5199\u81ea\u52a8\u5316\u811a\u672c\u5916\uff0c\u79d1\u7814\u4eba\u5458\u8fd8\u53ef\u4ee5\u5229\u7528Python\u8fdb\u884c\u4efb\u52a1\u8c03\u5ea6\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528Celery\u5e93\u6765\u5b9e\u73b0\u5206\u5e03\u5f0f\u4efb\u52a1\u961f\u5217\uff0c\u901a\u8fc7\u8c03\u5ea6\u5668\u5b9a\u65f6\u6267\u884cPython\u811a\u672c\uff0c\u5b9e\u73b0\u6570\u636e\u7684\u5b9a\u65f6\u66f4\u65b0\u548c\u5904\u7406\u3002<\/p>\n<\/p>\n

          \u4efb\u52a1\u8c03\u5ea6\u7684\u4e00\u4e2a\u5e38\u89c1\u5e94\u7528\u662f\u6570\u636e\u722c\u53d6\u548c\u66f4\u65b0\u3002\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u7f16\u5199\u722c\u866b\u811a\u672c\uff0c\u5b9a\u671f\u4ece\u7f51\u7edc\u4e0a\u722c\u53d6\u6570\u636e\uff0c\u5e76\u5c06\u6570\u636e\u5b58\u50a8\u5230\u6570\u636e\u5e93\u4e2d\u8fdb\u884c\u540e\u7eed\u5206\u6790\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u5b9e\u73b0\u6570\u636e\u7684\u81ea\u52a8\u5316\u6536\u96c6\u548c\u5904\u7406\uff0c\u63d0\u9ad8\u79d1\u7814\u7684\u6548\u7387\u548c\u8d28\u91cf\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

          \u4e94\u3001\u673a\u5668\u5b66\u4e60\u4e0e\u6df1\u5ea6\u5b66\u4e60<\/p>\n<\/p>\n

          \u968f\u7740\u6570\u636e\u79d1\u5b66\u7684\u53d1\u5c55\uff0c\u673a\u5668\u5b66\u4e60\u548c\u6df1\u5ea6\u5b66\u4e60\u5728\u79d1\u7814\u4e2d\u7684\u5e94\u7528\u8d8a\u6765\u8d8a\u5e7f\u6cdb\u3002Python\u63d0\u4f9b\u4e86\u591a\u4e2a\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u5982Scikit-learn\u548cTensorFlow\uff0c\u652f\u6301\u5404\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002<\/p>\n<\/p>\n

            \n
          1. \n

            Scikit-learn\u7684\u5e94\u7528<\/strong><\/p>\n<\/p>\n

            Scikit-learn\u662f\u4e00\u4e2a\u57fa\u4e8eNumPy\u3001SciPy\u548cMatplotlib\u6784\u5efa\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u63d0\u4f9b\u4e86\u7b80\u5355\u800c\u9ad8\u6548\u7684\u5de5\u5177\uff0c\u7528\u4e8e\u6570\u636e\u6316\u6398\u548c\u6570\u636e\u5206\u6790\u3002Scikit-learn\u652f\u6301\u591a\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\uff0c\u5982\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u3001\u964d\u7ef4\u7b49\u3002<\/p>\n<\/p>\n

            \u901a\u8fc7Scikit-learn\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u5feb\u901f\u5730\u5b9e\u73b0\u5404\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002\u4f8b\u5982\uff0c\u4f7f\u7528trAI<\/a>n_test_split()<\/code>\u51fd\u6570\u53ef\u4ee5\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0c\u4f7f\u7528LinearRegression()<\/code>\u7c7b\u53ef\u4ee5\u8fdb\u884c\u7ebf\u6027\u56de\u5f52\u5206\u6790\uff0c\u4f7f\u7528KMeans()<\/code>\u7c7b\u53ef\u4ee5\u8fdb\u884c\u805a\u7c7b\u5206\u6790\u3002Scikit-learn\u8fd8\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6a21\u578b\u8bc4\u4f30\u5de5\u5177\uff0c\u5982\u4ea4\u53c9\u9a8c\u8bc1\u3001\u6df7\u6dc6\u77e9\u9635\u7b49\uff0c\u5e2e\u52a9\u79d1\u7814\u4eba\u5458\u5bf9\u6a21\u578b\u8fdb\u884c\u8bc4\u4f30\u548c\u4f18\u5316\u3002<\/p>\n<\/p>\n<\/li>\n

          2. \n

            TensorFlow\u7684\u7279\u70b9<\/strong><\/p>\n<\/p>\n

            TensorFlow\u662f\u4e00\u4e2a\u5f00\u6e90\u7684\u673a\u5668\u5b66\u4e60\u6846\u67b6\uff0c\u7531Google\u5f00\u53d1\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7814\u7a76\u3002TensorFlow\u63d0\u4f9b\u4e86\u7075\u6d3b\u7684\u8ba1\u7b97\u6a21\u578b\u548c\u5f3a\u5927\u7684\u56fe\u8ba1\u7b97\u529f\u80fd\uff0c\u652f\u6301\u5206\u5e03\u5f0f\u8ba1\u7b97\u548c\u591a\u79cd\u786c\u4ef6\u52a0\u901f\u3002<\/p>\n<\/p>\n

            TensorFlow\u7684\u6838\u5fc3\u662f\u8ba1\u7b97\u56fe\uff08DataFlow Graph\uff09\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u901a\u8fc7\u5b9a\u4e49\u8ba1\u7b97\u56fe\u6765\u6784\u5efa\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002TensorFlow\u652f\u6301\u591a\u79cd\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784\uff0c\u5982\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\u3001\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\uff08RNN\uff09\u7b49\uff0c\u9002\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7b49\u9886\u57df\u3002\u901a\u8fc7TensorFlow\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u5b9e\u73b0\u590d\u6742\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u5e76\u8fdb\u884c\u9ad8\u6548\u7684\u8bad\u7ec3\u548c\u63a8\u7406\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n

            \u603b\u7ed3\u800c\u8a00\uff0cPython\u5728\u79d1\u7814\u4e2d\u7684\u5e94\u7528\u662f\u591a\u65b9\u9762\u7684\uff0c\u5176\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\u3001\u4e30\u5bcc\u7684\u5206\u6790\u5e93\u3001\u7075\u6d3b\u7684\u53ef\u89c6\u5316\u5de5\u5177\u3001\u81ea\u52a8\u5316\u548c\u673a\u5668\u5b66\u4e60\u652f\u6301\uff0c\u4f7f\u5176\u6210\u4e3a\u79d1\u7814\u4eba\u5458\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\u3002\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u6280\u80fd\uff0c\u79d1\u7814\u4eba\u5458\u53ef\u4ee5\u66f4\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u5904\u7406\u548c\u5206\u6790\uff0c\u4ece\u800c\u63a8\u52a8\u79d1\u7814\u5de5\u4f5c\u7684\u8fdb\u5c55\u3002<\/p>\n<\/p>\n

            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n

            \u5982\u4f55\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u79d1\u7814\u9879\u76ee\uff1f<\/strong>
            \u8981\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u79d1\u7814\u9879\u76ee\uff0c\u60a8\u53ef\u4ee5\u4ece\u5b66\u4e60Python\u57fa\u7840\u8bed\u6cd5\u5165\u624b\uff0c\u968f\u540e\u638c\u63e1\u6570\u636e\u5904\u7406\u548c\u5206\u6790\u5e93\uff0c\u4f8b\u5982NumPy\u548cPandas\u3002\u63a5\u7740\uff0c\u5efa\u8bae\u5b66\u4e60\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\u5982Matplotlib\u6216Seaborn\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u5c55\u793a\u7814\u7a76\u7ed3\u679c\u3002\u53ef\u4ee5\u901a\u8fc7\u5728\u7ebf\u8bfe\u7a0b\u3001\u4e66\u7c4d\u6216\u793e\u533a\u8bba\u575b\u83b7\u53d6\u5b66\u4e60\u8d44\u6e90\uff0c\u540c\u65f6\u5c1d\u8bd5\u5728\u5c0f\u578b\u9879\u76ee\u4e2d\u5b9e\u8df5\u6240\u5b66\u77e5\u8bc6\uff0c\u9010\u6b65\u79ef\u7d2f\u7ecf\u9a8c\u3002<\/p>\n

            Python\u5728\u79d1\u7814\u4e2d\u7684\u4e3b\u8981\u5e94\u7528\u9886\u57df\u6709\u54ea\u4e9b\uff1f<\/strong>
            Python\u5728\u79d1\u7814\u4e2d\u5e94\u7528\u5e7f\u6cdb\uff0c\u6db5\u76d6\u591a\u4e2a\u9886\u57df\uff0c\u5305\u62ec\u6570\u636e\u5206\u6790\u3001\u673a\u5668\u5b66\u4e60\u3001\u56fe\u50cf\u5904\u7406\u3001\u751f\u7269\u4fe1\u606f\u5b66\u3001\u5929\u6587\u5b66\u7b49\u3002\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0cPython\u80fd\u591f\u9ad8\u6548\u5904\u7406\u548c\u5206\u6790\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff1b\u5728\u673a\u5668\u5b66\u4e60\u65b9\u9762\uff0c\u4f7f\u7528Scikit-learn\u548cTensorFlow\u7b49\u5e93\u53ef\u4ee5\u6784\u5efa\u548c\u8bad\u7ec3\u590d\u6742\u6a21\u578b\uff1b\u6b64\u5916\uff0cPython\u8fd8\u9002\u7528\u4e8e\u5b9e\u9a8c\u6570\u636e\u7684\u53ef\u89c6\u5316\u548c\u6a21\u62df\u4eff\u771f\u3002<\/p>\n

            \u5982\u4f55\u9009\u62e9\u9002\u5408\u79d1\u7814\u7684Python\u5e93\u548c\u5de5\u5177\uff1f<\/strong>
            \u9009\u62e9\u9002\u5408\u79d1\u7814\u7684Python\u5e93\u548c\u5de5\u5177\u5e94\u6839\u636e\u7814\u7a76\u7684\u5177\u4f53\u9700\u6c42\u6765\u5b9a\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60a8\u9700\u8981\u8fdb\u884c\u7edf\u8ba1\u5206\u6790\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528SciPy\u548cStatsModels\uff1b\u5bf9\u4e8e\u673a\u5668\u5b66\u4e60\uff0cScikit-learn\u548cKeras\u662f\u4e0d\u9519\u7684\u9009\u62e9\u3002\u53ef\u89c6\u5316\u65b9\u9762\uff0cMatplotlib\u548cPlotly\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u7ed8\u56fe\u529f\u80fd\u3002\u9009\u62e9\u65f6\uff0c\u53ef\u4ee5\u53c2\u8003\u793e\u533a\u7684\u63a8\u8350\u548c\u6587\u6863\u7684\u5b8c\u6574\u6027\uff0c\u4ee5\u786e\u4fdd\u5de5\u5177\u7684\u53ef\u9760\u6027\u548c\u6613\u7528\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728\u79d1\u7814\u4e2d\u8fd0\u7528Python\u7684\u5173\u952e\u5728\u4e8e\uff1a\u6570\u636e\u5904\u7406\u3001\u6570\u636e\u5206\u6790\u3001\u53ef\u89c6\u5316\u3001\u81ea\u52a8\u5316\u53ca\u673a\u5668\u5b66\u4e60\u7b49\u65b9\u9762\u3002\u6570\u636e\u5904\u7406\u662f\u79d1\u7814\u4e2d\u6700\u57fa\u7840 […]","protected":false},"author":3,"featured_media":985136,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/985127"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=985127"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/985127\/revisions"}],"predecessor-version":[{"id":985141,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/985127\/revisions\/985141"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/985136"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=985127"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=985127"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=985127"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}