{"id":886,"date":"2025-01-11T15:51:49","date_gmt":"2025-01-11T07:51:49","guid":{"rendered":"https:\/\/www.laixuexila.com\/?p=886"},"modified":"2025-01-11T15:51:49","modified_gmt":"2025-01-11T07:51:49","slug":"%e5%a6%82%e4%bd%95%e7%94%a8python%e8%bf%9b%e8%a1%8c%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%ef%bc%9a%e5%9f%ba%e7%a1%80%e5%88%b0%e8%bf%9b%e9%98%b6%e6%95%99%e7%a8%8b","status":"publish","type":"post","link":"https:\/\/www.laixuexila.com\/index.php\/2025\/01\/11\/%e5%a6%82%e4%bd%95%e7%94%a8python%e8%bf%9b%e8%a1%8c%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%ef%bc%9a%e5%9f%ba%e7%a1%80%e5%88%b0%e8%bf%9b%e9%98%b6%e6%95%99%e7%a8%8b\/","title":{"rendered":"\u5982\u4f55\u7528Python\u8fdb\u884c\u673a\u5668\u5b66\u4e60\uff1a\u57fa\u7840\u5230\u8fdb\u9636\u6559\u7a0b"},"content":{"rendered":"\n
\n

Python \u662f\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u9996\u9009\u8bed\u8a00\u4e4b\u4e00\uff0c\u62e5\u6709\u5f3a\u5927\u7684\u5e93\u548c\u6846\u67b6\uff0c\u5e2e\u52a9\u5f00\u53d1\u8005\u9ad8\u6548\u5730\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002\u4ece\u57fa\u7840\u5230\u8fdb\u9636\uff0c\u672c\u6559\u7a0b\u5c06\u9010\u6b65\u4ecb\u7ecd\u5982\u4f55\u7528 Python \u8fdb\u884c\u673a\u5668\u5b66\u4e60\uff0c\u5e76\u63a8\u8350\u4e00\u4e9b\u5e38\u7528\u7684\u5de5\u5177\u548c\u6700\u4f73\u5b9e\u8df5\u3002<\/p>\n<\/blockquote>\n\n\n\n

1. \u5b89\u88c5\u548c\u914d\u7f6e\u73af\u5883<\/h3>\n\n\n\n

\u5728\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u914d\u7f6e\u5408\u9002\u7684\u5f00\u53d1\u73af\u5883\u3002\u6211\u4eec\u63a8\u8350\u4f7f\u7528 Anaconda<\/strong>\uff0c\u5b83\u662f\u4e00\u4e2a\u5f00\u6e90\u7684 Python \u6570\u636e\u79d1\u5b66\u5e73\u53f0\uff0c\u96c6\u6210\u4e86\u4f17\u591a\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u5904\u7406\u7684\u5e93\u3002<\/p>\n\n\n\n

\u5b89\u88c5 Anaconda<\/h4>\n\n\n\n
    \n
  1. \u8bbf\u95ee Anaconda \u5b98\u7f51<\/a>\uff0c\u4e0b\u8f7d\u9002\u5408\u4f60\u64cd\u4f5c\u7cfb\u7edf\u7684\u7248\u672c\u3002<\/li>\n\n\n\n
  2. \u5b89\u88c5\u540e\uff0c\u6253\u5f00 Anaconda Prompt<\/strong>\uff0c\u521b\u5efa\u4e00\u4e2a\u865a\u62df\u73af\u5883\u5e76\u6fc0\u6d3b\u5b83\uff1a<\/li>\n<\/ol>\n\n\n\n
    conda create -n ml_env python=3.9\nconda activate ml_env<\/code><\/pre>\n\n\n\n
    2. \u673a\u5668\u5b66\u4e60\u5e38\u7528\u5e93<\/h3>\n\n\n\n
    Python \u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u5e93\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e9b\u6700\u5e38\u7528\u7684\u5e93\u548c\u5de5\u5177\u3002<\/p>\n\n\n\n
    2.1 NumPy<\/strong> \u548c Pandas<\/strong> \u2014 \u6570\u636e\u5904\u7406<\/h4>\n\n\n\n
      \n
    • NumPy<\/strong>\uff1a\u7528\u4e8e\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\u548c\u6570\u7ec4\u64cd\u4f5c\u3002<\/li>\n\n\n\n
    • Pandas<\/strong>\uff1a\u7528\u4e8e\u6570\u636e\u6e05\u6d17\u3001\u5904\u7406\u548c\u5206\u6790\uff0c\u63d0\u4f9b\u4e86 DataFrame<\/code> \u6570\u636e\u7ed3\u6784\u3002<\/li>\n<\/ul>\n\n\n\n
      \u5b89\u88c5\uff1a<\/p>\n\n\n\n
      pip install numpy pandas<\/code><\/pre>\n\n\n\n
      2.2 Scikit-learn<\/strong> \u2014 \u673a\u5668\u5b66\u4e60\u5e93<\/h4>\n\n\n\n
      Scikit-learn<\/strong> \u662f\u6700\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u5e38\u89c1\u7684\u7b97\u6cd5\u548c\u5de5\u5177\uff0c\u5305\u62ec\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u3001\u964d\u7ef4\u3001\u7279\u5f81\u9009\u62e9\u7b49\u3002<\/p>\n\n\n\n
      \u5b89\u88c5\uff1a<\/p>\n\n\n\n
      pip install scikit-learn<\/code><\/pre>\n\n\n\n
      2.3 Matplotlib<\/strong> \u548c Seaborn<\/strong> \u2014 \u6570\u636e\u53ef\u89c6\u5316<\/h4>\n\n\n\n
      \u673a\u5668\u5b66\u4e60\u4e0d\u4ec5\u4ec5\u662f\u5efa\u6a21\uff0c\u6570\u636e\u7684\u53ef\u89c6\u5316\u4e5f\u975e\u5e38\u91cd\u8981\u3002Matplotlib<\/strong> \u548c Seaborn<\/strong> \u63d0\u4f9b\u4e86\u8bb8\u591a\u5e38\u7528\u7684\u7ed8\u56fe\u529f\u80fd\uff0c\u5e2e\u52a9\u6211\u4eec\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u3002<\/p>\n\n\n\n
      \u5b89\u88c5\uff1a<\/p>\n\n\n\n
      pip install matplotlib seaborn<\/code><\/pre>\n\n\n\n
      2.4 TensorFlow<\/strong> \u548c Keras<\/strong> \u2014 \u6df1\u5ea6\u5b66\u4e60\u6846\u67b6<\/h4>\n\n\n\n
      TensorFlow<\/strong> \u548c Keras<\/strong> \u662f\u76ee\u524d\u6700\u6d41\u884c\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\uff0c\u7528\u4e8e\u6784\u5efa\u548c\u8bad\u7ec3\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\u3002<\/p>\n\n\n\n
      \u5b89\u88c5\uff1a<\/p>\n\n\n\n
      pip install tensorflow<\/code><\/pre>\n\n\n\n
      3. \u673a\u5668\u5b66\u4e60\u57fa\u7840<\/h3>\n\n\n\n
      \u673a\u5668\u5b66\u4e60\u4efb\u52a1\u901a\u5e38\u5206\u4e3a\u4ee5\u4e0b\u51e0\u7c7b\uff1a<\/p>\n\n\n\n
        \n
      1. \u76d1\u7763\u5b66\u4e60<\/strong>\uff1a\u4f7f\u7528\u6807\u8bb0\u6570\u636e\uff08\u8f93\u5165\u548c\u5bf9\u5e94\u7684\u8f93\u51fa\uff09\u6765\u8bad\u7ec3\u6a21\u578b\u3002<\/li>\n\n\n\n
      2. \u65e0\u76d1\u7763\u5b66\u4e60<\/strong>\uff1a\u6570\u636e\u6ca1\u6709\u6807\u7b7e\uff0c\u6a21\u578b\u81ea\u5df1\u5bfb\u627e\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u3002<\/li>\n\n\n\n
      3. \u5f3a\u5316\u5b66\u4e60<\/strong>\uff1a\u6a21\u578b\u901a\u8fc7\u4e0e\u73af\u5883\u4ea4\u4e92\u6765\u5b66\u4e60\u5982\u4f55\u5728\u7279\u5b9a\u4efb\u52a1\u4e2d\u505a\u51fa\u51b3\u7b56\u3002<\/li>\n<\/ol>\n\n\n\n
        3.1 \u6570\u636e\u9884\u5904\u7406<\/h4>\n\n\n\n
        \u5728\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u6e05\u6d17\u548c\u9884\u5904\u7406\u3002\u5e38\u89c1\u7684\u6570\u636e\u9884\u5904\u7406\u6b65\u9aa4\u5305\u62ec\uff1a<\/p>\n\n\n\n
          \n
        • \u7f3a\u5931\u503c\u5904\u7406<\/strong>\uff1a\u4f7f\u7528\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u6216\u4f17\u6570\u586b\u5145\u7f3a\u5931\u503c\uff0c\u6216\u5220\u9664\u542b\u7f3a\u5931\u503c\u7684\u884c\/\u5217\u3002<\/li>\n\n\n\n
        • \u6570\u636e\u6807\u51c6\u5316<\/strong>\uff1a\u5c06\u7279\u5f81\u7f29\u653e\u5230\u76f8\u540c\u7684\u5c3a\u5ea6\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u8bad\u7ec3\u6a21\u578b\u3002<\/li>\n\n\n\n
        • \u7c7b\u522b\u6570\u636e\u7f16\u7801<\/strong>\uff1a\u5c06\u7c7b\u522b\u6570\u636e\u8f6c\u5316\u4e3a\u6570\u503c\u578b\u6570\u636e\u3002<\/li>\n<\/ul>\n\n\n\n
          import pandas as pd\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\n\n# \u8bfb\u53d6\u6570\u636e\ndf = pd.read_csv('data.csv')\n\n# \u7f3a\u5931\u503c\u586b\u5145\ndf.fillna(df.mean(), inplace=True)\n\n# \u7279\u5f81\u6807\u51c6\u5316\nscaler = StandardScaler()\ndf[['feature1', 'feature2']] = scaler.fit_transform(df[['feature1', 'feature2']])\n\n# \u7c7b\u522b\u6570\u636e\u7f16\u7801\nencoder = LabelEncoder()\ndf['category'] = encoder.fit_transform(df['category'])<\/code><\/pre>\n\n\n\n
          3.2 \u6570\u636e\u5212\u5206<\/h4>\n\n\n\n
          \u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u5c06\u6570\u636e\u5212\u5206\u4e3a \u8bad\u7ec3\u96c6<\/strong> \u548c \u6d4b\u8bd5\u96c6<\/strong>\u3002\u8bad\u7ec3\u96c6\u7528\u4e8e\u8bad\u7ec3\u6a21\u578b\uff0c\u6d4b\u8bd5\u96c6\u7528\u4e8e\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n\n\n\n
          from sklearn.model_selection import train_test_split\n\n# \u6570\u636e\u5212\u5206\nX = df[['feature1', 'feature2']]\ny = df['target']\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/code><\/pre>\n\n\n\n
          4. \u8bad\u7ec3\u4e00\u4e2a\u7b80\u5355\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/h3>\n\n\n\n
          \u6211\u4eec\u4ee5 \u7ebf\u6027\u56de\u5f52<\/strong> \u4e3a\u4f8b\uff0c\u6765\u6f14\u793a\u5982\u4f55\u7528 Scikit-learn<\/strong> \u8fdb\u884c\u673a\u5668\u5b66\u4e60\u5efa\u6a21\u3002<\/p>\n\n\n\n
          4.1 \u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/h4>\n\n\n\n
          \u7ebf\u6027\u56de\u5f52\u662f\u76d1\u7763\u5b66\u4e60\u4e2d\u7684\u4e00\u79cd\u57fa\u672c\u7b97\u6cd5\uff0c\u5e38\u7528\u4e8e\u9884\u6d4b\u95ee\u9898\uff0c\u5047\u8bbe\u7279\u5f81\u4e0e\u76ee\u6807\u4e4b\u95f4\u5b58\u5728\u7ebf\u6027\u5173\u7cfb\u3002<\/p>\n\n\n\n
          from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# \u521b\u5efa\u6a21\u578b\nmodel = LinearRegression()\n\n# \u8bad\u7ec3\u6a21\u578b\nmodel.fit(X_train, y_train)\n\n# \u9884\u6d4b\ny_pred = model.predict(X_test)\n\n# \u8bc4\u4f30\u6a21\u578b\nmse = mean_squared_error(y_test, y_pred)\nr2 = r2_score(y_test, y_pred)\n\nprint(f'Mean Squared Error: {mse}')\nprint(f'R2 Score: {r2}')<\/code><\/pre>\n\n\n\n
          4.2 \u5206\u7c7b\u6a21\u578b\uff08\u4f8b\u5982\uff0c\u903b\u8f91\u56de\u5f52\uff09<\/h4>\n\n\n\n
          \u903b\u8f91\u56de\u5f52\u662f\u7528\u4e8e\u5206\u7c7b\u95ee\u9898\u7684\u7ecf\u5178\u6a21\u578b\uff0c\u5e38\u7528\u4e8e\u4e8c\u5206\u7c7b\u4efb\u52a1\u3002<\/p>\n\n\n\n
          from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\n\n# \u521b\u5efa\u6a21\u578b\nclf = LogisticRegression()\n\n# \u8bad\u7ec3\u6a21\u578b\nclf.fit(X_train, y_train)\n\n# \u9884\u6d4b\ny_pred = clf.predict(X_test)\n\n# \u8bc4\u4f30\u6a21\u578b\naccuracy = accuracy_score(y_test, y_pred)\n\nprint(f'Accuracy: {accuracy}')<\/code><\/pre>\n\n\n\n
          5. \u8fdb\u9636\uff1a\u6df1\u5ea6\u5b66\u4e60\u4e0e\u795e\u7ecf\u7f51\u7edc<\/h3>\n\n\n\n
          \u5982\u679c\u4f60\u60f3\u5c1d\u8bd5\u66f4\u52a0\u590d\u6742\u7684\u4efb\u52a1\uff0c\u5982\u56fe\u50cf\u5206\u7c7b\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7b49\uff0c\u53ef\u4ee5\u8f6c\u5411 \u6df1\u5ea6\u5b66\u4e60<\/strong>\u3002\u4f7f\u7528 TensorFlow<\/strong> \u548c Keras<\/strong> \u6784\u5efa\u795e\u7ecf\u7f51\u7edc\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u9009\u62e9\u3002<\/p>\n\n\n\n
          5.1 \u57fa\u672c\u7684\u795e\u7ecf\u7f51\u7edc<\/h4>\n\n\n\n
          \u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684 Keras<\/strong> \u795e\u7ecf\u7f51\u7edc\u793a\u4f8b\uff0c\u7528\u4e8e\u5206\u7c7b\u4efb\u52a1\u3002<\/p>\n\n\n\n
          import tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\n# \u6570\u636e\u9884\u5904\u7406\nX = df[['feature1', 'feature2']]\ny = df['target']\nX = StandardScaler().fit_transform(X)\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n# \u521b\u5efa\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\nmodel = Sequential([\n    Dense(64, activation='relu', input_dim=X_train.shape[1]),\n    Dense(32, activation='relu'),\n    Dense(1, activation='sigmoid')\n])\n\n# \u7f16\u8bd1\u6a21\u578b\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n\n# \u8bad\u7ec3\u6a21\u578b\nmodel.fit(X_train, y_train, epochs=10, batch_size=32)\n\n# \u8bc4\u4f30\u6a21\u578b\nloss, accuracy = model.evaluate(X_test, y_test)\nprint(f'Loss: {loss}')\nprint(f'Accuracy: {accuracy}')<\/code><\/pre>\n\n\n\n
          5.2 \u6df1\u5ea6\u5b66\u4e60\u4e2d\u7684\u5e38\u89c1\u95ee\u9898<\/h4>\n\n\n\n
            \n
          • \u8fc7\u62df\u5408<\/strong>\uff1a\u6a21\u578b\u5728\u8bad\u7ec3\u96c6\u4e0a\u8868\u73b0\u5f88\u597d\uff0c\u4f46\u5728\u6d4b\u8bd5\u96c6\u4e0a\u6548\u679c\u5dee\u3002\u89e3\u51b3\u65b9\u6cd5\uff1a\u4f7f\u7528\u6b63\u5219\u5316\u6280\u672f\u3001\u589e\u52a0\u8bad\u7ec3\u6570\u636e\u3001\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u7b49\u3002<\/li>\n\n\n\n
          • \u68af\u5ea6\u6d88\u5931\u548c\u68af\u5ea6\u7206\u70b8<\/strong>\uff1a\u5728\u8bad\u7ec3\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u65f6\uff0c\u68af\u5ea6\u53ef\u80fd\u4f1a\u53d8\u5f97\u975e\u5e38\u5c0f\u6216\u975e\u5e38\u5927\u3002\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5408\u9002\u7684\u6fc0\u6d3b\u51fd\u6570\uff08\u5982 ReLU\uff09\u548c\u6743\u91cd\u521d\u59cb\u5316\u65b9\u6cd5\u6765\u89e3\u51b3\u3002<\/li>\n<\/ul>\n\n\n\n
            6. \u6a21\u578b\u8bc4\u4f30\u4e0e\u8c03\u4f18<\/h3>\n\n\n\n
            6.1 \u6a21\u578b\u8bc4\u4f30<\/h4>\n\n\n\n
            \u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u5bf9\u4e8e\u673a\u5668\u5b66\u4e60\u9879\u76ee\u81f3\u5173\u91cd\u8981\uff0c\u5e38\u89c1\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\uff1a<\/p>\n\n\n\n
              \n
            • \u5206\u7c7b\u95ee\u9898<\/strong>\uff1a\u51c6\u786e\u7387\uff08Accuracy\uff09\u3001\u7cbe\u786e\u7387\uff08Precision\uff09\u3001\u53ec\u56de\u7387\uff08Recall\uff09\u3001F1-score<\/li>\n\n\n\n
            • \u56de\u5f52\u95ee\u9898<\/strong>\uff1a\u5747\u65b9\u8bef\u5dee\uff08MSE\uff09\u3001\u5747\u65b9\u6839\u8bef\u5dee\uff08RMSE\uff09\u3001R\u00b2\u5f97\u5206<\/li>\n<\/ul>\n\n\n\n
              from sklearn.metrics import confusion_matrix, classification_report\n\n# \u5206\u7c7b\u95ee\u9898\u7684\u8bc4\u4f30\nprint(confusion_matrix(y_test, y_pred))\nprint(classification_report(y_test, y_pred))<\/code><\/pre>\n\n\n\n
              6.2 \u8d85\u53c2\u6570\u8c03\u4f18<\/h4>\n\n\n\n
              \u8d85\u53c2\u6570\u8c03\u4f18\u662f\u673a\u5668\u5b66\u4e60\u4e2d\u975e\u5e38\u91cd\u8981\u7684\u4e00\u90e8\u5206\uff0c\u901a\u5e38\u4f7f\u7528 GridSearchCV<\/strong> \u6216 RandomizedSearchCV<\/strong> \u6765\u8fdb\u884c\u8d85\u53c2\u6570\u7684\u9009\u62e9\u548c\u4f18\u5316\u3002<\/p>\n\n\n\n
              from sklearn.model_selection import GridSearchCV\n\n# \u8bbe\u5b9a\u8d85\u53c2\u6570\u7684\u8303\u56f4\nparam_grid = {\n    'C': [0.1, 1, 10],\n    'kernel': ['linear', 'rbf']\n}\n\n# \u521b\u5efa\u652f\u6301\u5411\u91cf\u673a\u6a21\u578b\nsvm = SVC()\n\n# \u4f7f\u7528\u7f51\u683c\u641c\u7d22\u8fdb\u884c\u8d85\u53c2\u6570\u8c03\u4f18\ngrid_search = GridSearchCV(svm, param_grid, cv=5)\ngrid_search.fit(X_train, y_train)\n\n# \u8f93\u51fa\u6700\u4f73\u53c2\u6570\nprint(f'Best Parameters: {grid_search.best_params_}')<\/code><\/pre>\n\n\n\n
              7. \u603b\u7ed3<\/h3>\n\n\n\n
                \n
              • \u673a\u5668\u5b66\u4e60\u7684\u6838\u5fc3\u6d41\u7a0b\u5305\u62ec\u6570\u636e\u9884\u5904\u7406\u3001\u6a21\u578b\u8bad\u7ec3\u3001\u8bc4\u4f30\u4e0e\u8c03\u4f18\u3002<\/li>\n\n\n\n
              • Scikit-learn<\/strong> \u63d0\u4f9b\u4e86\u5e7f\u6cdb\u7684\u7b97\u6cd5\u548c\u5de5\u5177\uff0c\u9002\u5408\u5927\u591a\u6570\u5e38\u89c1\u7684\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002<\/li>\n\n\n\n
              • \u5bf9\u4e8e\u66f4\u590d\u6742\u7684\u4efb\u52a1\uff0c\u5c24\u5176\u662f\u56fe<\/li>\n<\/ul>\n\n\n\n
                \u50cf\u548c\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff0c\u53ef\u4ee5\u4f7f\u7528 TensorFlow<\/strong> \u6216 PyTorch<\/strong> \u8fdb\u884c\u6df1\u5ea6\u5b66\u4e60\u5efa\u6a21\u3002<\/p>\n\n\n\n
                  \n
                • \u5b66\u4e60\u548c\u5b9e\u8df5\u662f\u6210\u4e3a\u673a\u5668\u5b66\u4e60\u9ad8\u624b\u7684\u5173\u952e\uff0c\u5c3d\u91cf\u591a\u505a\u9879\u76ee\u5e76\u89e3\u51b3\u5b9e\u9645\u95ee\u9898\u3002<\/li>\n<\/ul>\n\n\n\n
                  \u5e0c\u671b\u8fd9\u4e2a\u6559\u7a0b\u80fd\u5e2e\u52a9\u4f60\u5165\u95e8\u673a\u5668\u5b66\u4e60\u3002\u5982\u679c\u4f60\u5bf9\u67d0\u4e9b\u6b65\u9aa4\u6216\u6982\u5ff5\u6709\u7591\u95ee\uff0c\u6b22\u8fce\u968f\u65f6\u63d0\u95ee\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"
                  Python \u662f\u673a\u5668\u5b66\u4e60\u9886\u57df\u7684\u9996\u9009\u8bed\u8a00\u4e4b\u4e00\uff0c\u62e5\u6709\u5f3a\u5927\u7684\u5e93\u548c\u6846\u67b6\uff0c\u5e2e\u52a9\u5f00\u53d1\u8005\u9ad8\u6548\u5730\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002\u4ece\u57fa\u7840\u5230\u8fdb\u9636 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[54],"tags":[],"class_list":["post-886","post","type-post","status-publish","format-standard","hentry","category-python"],"_links":{"self":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/886","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/comments?post=886"}],"version-history":[{"count":1,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/886\/revisions"}],"predecessor-version":[{"id":887,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/posts\/886\/revisions\/887"}],"wp:attachment":[{"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/media?parent=886"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/categories?post=886"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.laixuexila.com\/index.php\/wp-json\/wp\/v2\/tags?post=886"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}