{"id":993410,"date":"2024-12-27T08:45:47","date_gmt":"2024-12-27T00:45:47","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/993410.html"},"modified":"2024-12-27T08:45:50","modified_gmt":"2024-12-27T00:45:50","slug":"%e5%a6%82%e4%bd%95%e5%88%a9%e7%94%a8python%e5%a4%84%e7%90%86%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/993410.html","title":{"rendered":"\u5982\u4f55\u5229\u7528python\u5904\u7406\u6570\u636e"},"content":{"rendered":"

\"\u5982\u4f55\u5229\u7528python\u5904\u7406\u6570\u636e\"<\/p>\n

\u5982\u4f55\u5229\u7528Python\u5904\u7406\u6570\u636e <\/p>\n<\/p>\n

\u5229\u7528Python\u5904\u7406\u6570\u636e\u7684\u6838\u5fc3\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\u548c\u7075\u6d3b\u7684\u7f16\u7a0b\u80fd\u529b\u3002\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u3001NumPy\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u3001Matplotlib\u548cSeaborn\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u3001Scikit-learn\u8fdb\u884c\u673a\u5668\u5b66\u4e60<\/a>\u5904\u7406<\/strong>\u662fPython\u6570\u636e\u5904\u7406\u7684\u5173\u952e\u73af\u8282\u3002Pandas\u662f\u6570\u636e\u5904\u7406\u7684\u6838\u5fc3\u5e93\uff0c\u5b83\u7684DataFrame\u7ed3\u6784\u975e\u5e38\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\u3002NumPy\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u8ba1\u7b97\u529f\u80fd\uff0c\u53ef\u4ee5\u5927\u5e45\u5ea6\u63d0\u9ad8\u6570\u636e\u5904\u7406\u901f\u5ea6\u3002Matplotlib\u548cSeaborn\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u53ef\u89c6\u5316\u624b\u6bb5\uff0c\u4f7f\u6570\u636e\u5206\u6790\u7ed3\u679c\u66f4\u52a0\u76f4\u89c2\u3002Scikit-learn\u5219\u63d0\u4f9b\u4e86\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u5b9e\u73b0\uff0c\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u7684\u6df1\u5165\u5206\u6790\u3002\u5728\u5229\u7528Pandas\u8fdb\u884c\u6570\u636e\u64cd\u4f5c\u65f6\uff0c\u638c\u63e1DataFrame\u7684\u7d22\u5f15\u3001\u5207\u7247\u548c\u805a\u5408\u64cd\u4f5c\u662f\u975e\u5e38\u91cd\u8981\u7684\uff0c\u901a\u8fc7\u8fd9\u4e9b\u57fa\u672c\u64cd\u4f5c\uff0c\u53ef\u4ee5\u5b9e\u73b0\u6570\u636e\u7684\u6e05\u6d17\u3001\u8f6c\u6362\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001PANDAS\u6570\u636e\u64cd\u4f5c<\/p>\n<\/p>\n

Pandas\u662fPython\u6570\u636e\u5904\u7406\u7684\u6838\u5fc3\u5e93\u4e4b\u4e00\uff0c\u4e3b\u8981\u7528\u4e8e\u6570\u636e\u7684\u6e05\u6d17\u3001\u8f6c\u6362\u548c\u5206\u6790\u3002Pandas\u7684\u6838\u5fc3\u6570\u636e\u7ed3\u6784\u662fDataFrame\u548cSeries\uff0cDataFrame\u662f\u4e00\u4e2a\u4e8c\u7ef4\u7684\u8868\u683c\u578b\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8eExcel\u4e2d\u7684\u8868\u683c\uff0c\u800cSeries\u662f\u4e00\u7ef4\u7684\u6570\u636e\u7ed3\u6784\uff0c\u7c7b\u4f3c\u4e8e\u4e00\u5217\u6570\u636e\u3002<\/p>\n<\/p>\n

    \n
  1. \u6570\u636e\u5bfc\u5165\u4e0e\u5bfc\u51fa<\/strong><\/li>\n<\/ol>\n

    Pandas\u652f\u6301\u591a\u79cd\u6570\u636e\u683c\u5f0f\u7684\u5bfc\u5165\u4e0e\u5bfc\u51fa\uff0c\u5982CSV\u3001Excel\u3001SQL\u6570\u636e\u5e93\u3001JSON\u7b49\u3002\u901a\u8fc7read_csv<\/code>\u3001read_excel<\/code>\u3001to_csv<\/code>\u3001to_excel<\/code>\u7b49\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u6570\u636e\u7684\u5bfc\u5165\u4e0e\u5bfc\u51fa\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u5bfc\u5165CSV\u6587\u4ef6<\/strong><\/h2>\n
    df = pd.read_csv('data.csv')<\/p>\n
    \u5bfc\u51fa\u4e3aExcel\u6587\u4ef6<\/strong><\/h2>\n
    df.to_excel('data.xlsx', index=False)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u6570\u636e\u9009\u62e9\u4e0e\u8fc7\u6ee4<\/strong><\/li>\n<\/ol>\n
      Pandas\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u9009\u62e9\u4e0e\u8fc7\u6ee4\u529f\u80fd\uff0c\u53ef\u4ee5\u901a\u8fc7\u6807\u7b7e\u3001\u4f4d\u7f6e\u3001\u5e03\u5c14\u6761\u4ef6\u7b49\u65b9\u5f0f\u6765\u9009\u62e9\u6570\u636e\u3002\u5e38\u7528\u7684\u9009\u62e9\u65b9\u6cd5\u6709loc<\/code>\u3001iloc<\/code>\u3001\u5e03\u5c14\u7d22\u5f15\u7b49\u3002<\/p>\n<\/p>\n
      # \u901a\u8fc7\u6807\u7b7e\u9009\u62e9\u6570\u636e<\/p>\n
      selected_data = df.loc[df['column_name'] > 10]<\/p>\n
      \u901a\u8fc7\u4f4d\u7f6e\u9009\u62e9\u6570\u636e<\/strong><\/h2>\n
      subset = df.iloc[0:5, 1:3]<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u6570\u636e\u6e05\u6d17\u4e0e\u8f6c\u6362<\/strong><\/li>\n<\/ol>\n
        \u6570\u636e\u6e05\u6d17\u662f\u6570\u636e\u5206\u6790\u7684\u57fa\u7840\u6b65\u9aa4\uff0cPandas\u63d0\u4f9b\u4e86\u591a\u79cd\u6570\u636e\u6e05\u6d17\u4e0e\u8f6c\u6362\u7684\u65b9\u6cd5\uff0c\u5982\u5904\u7406\u7f3a\u5931\u503c\u3001\u6570\u636e\u7c7b\u578b\u8f6c\u6362\u3001\u91cd\u590d\u503c\u5904\u7406\u7b49\u3002<\/p>\n<\/p>\n
        # \u5904\u7406\u7f3a\u5931\u503c<\/p>\n
        df.fillna(value=0, inplace=True)<\/p>\n
        \u6570\u636e\u7c7b\u578b\u8f6c\u6362<\/strong><\/h2>\n
        df['column_name'] = df['column_name'].astype('int')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u4e8c\u3001NUMPY\u6570\u503c\u8ba1\u7b97<\/p>\n<\/p>\n
        NumPy\u662fPython\u7684\u4e00\u4e2a\u6838\u5fc3\u5e93\uff0c\u7528\u4e8e\u8fdb\u884c\u6570\u503c\u8ba1\u7b97\u3002\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u591a\u7ef4\u6570\u7ec4\u5bf9\u8c61\uff0c\u4ee5\u53ca\u5bf9\u6570\u7ec4\u8fdb\u884c\u64cd\u4f5c\u7684\u591a\u79cd\u529f\u80fd\u3002<\/p>\n<\/p>\n
          \n
        1. \u6570\u7ec4\u521b\u5efa\u4e0e\u64cd\u4f5c<\/strong><\/li>\n<\/ol>\n
          NumPy\u7684\u6838\u5fc3\u662f\u5176\u6570\u7ec4\u5bf9\u8c61ndarray<\/code>\u3002\u901a\u8fc7array<\/code>\u3001zeros<\/code>\u3001ones<\/code>\u3001arange<\/code>\u7b49\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u521b\u5efa\u6570\u7ec4\u3002<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          \u521b\u5efa\u6570\u7ec4<\/strong><\/h2>\n
          a = np.array([1, 2, 3, 4])<\/p>\n
          \u521b\u5efa\u96f6\u6570\u7ec4<\/strong><\/h2>\n
          b = np.zeros((2, 3))<\/p>\n
          \u521b\u5efa\u7b49\u5dee\u6570\u7ec4<\/strong><\/h2>\n
          c = np.arange(0, 10, 2)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u6570\u7ec4\u8fd0\u7b97\u4e0e\u51fd\u6570\u5e94\u7528<\/strong><\/li>\n<\/ol>\n
            NumPy\u652f\u6301\u6570\u7ec4\u7684\u591a\u79cd\u8fd0\u7b97\uff0c\u5305\u62ec\u5143\u7d20\u7ea7\u8fd0\u7b97\u3001\u77e9\u9635\u8fd0\u7b97\u7b49\u3002NumPy\u8fd8\u63d0\u4f9b\u4e86\u8bb8\u591a\u6570\u5b66\u51fd\u6570\uff0c\u5982sin<\/code>\u3001cos<\/code>\u3001log<\/code>\u7b49\uff0c\u53ef\u4ee5\u76f4\u63a5\u5e94\u7528\u4e8e\u6570\u7ec4\u3002<\/p>\n<\/p>\n
            # \u6570\u7ec4\u8fd0\u7b97<\/p>\n
            d = a + 10<\/p>\n
            \u6570\u5b66\u51fd\u6570\u5e94\u7528<\/strong><\/h2>\n
            e = np.sin(a)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e09\u3001MATPLOTLIB\u4e0eSEABORN\u6570\u636e\u53ef\u89c6\u5316<\/p>\n<\/p>\n
            \u6570\u636e\u53ef\u89c6\u5316\u662f\u6570\u636e\u5206\u6790\u7684\u91cd\u8981\u73af\u8282\u3002Matplotlib\u548cSeaborn\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u4e24\u4e2a\u53ef\u89c6\u5316\u5e93\uff0cMatplotlib\u63d0\u4f9b\u4e86\u5e95\u5c42\u7684\u7ed8\u56fe\u63a5\u53e3\uff0c\u800cSeaborn\u5728\u5176\u57fa\u7840\u4e0a\u8fdb\u884c\u4e86\u9ad8\u7ea7\u5c01\u88c5\uff0c\u63d0\u4f9b\u4e86\u66f4\u4e3a\u7b80\u6d01\u7684\u63a5\u53e3\u3002<\/p>\n<\/p>\n
              \n
            1. \u57fa\u672c\u7ed8\u56fe<\/strong><\/li>\n<\/ol>\n
              Matplotlib\u7684pyplot<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u7c7b\u4f3c\u4e8eMatlab\u7684\u7ed8\u56fe\u63a5\u53e3\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u7ed8\u5236\u6298\u7ebf\u56fe\u3001\u67f1\u72b6\u56fe\u3001\u6563\u70b9\u56fe\u7b49\u3002<\/p>\n<\/p>\n
              import matplotlib.pyplot as plt<\/p>\n
              \u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
              plt.plot([1, 2, 3, 4], [1, 4, 9, 16])<\/p>\n
              plt.xlabel('X-axis')<\/p>\n
              plt.ylabel('Y-axis')<\/p>\n
              plt.title('Line Plot')<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u9ad8\u7ea7\u7ed8\u56fe<\/strong><\/li>\n<\/ol>\n
                Seaborn\u63d0\u4f9b\u4e86\u66f4\u52a0\u7b80\u6d01\u548c\u9ad8\u7ea7\u7684\u7ed8\u56fe\u63a5\u53e3\uff0c\u7279\u522b\u9002\u7528\u4e8e\u7edf\u8ba1\u56fe\u5f62\u7684\u7ed8\u5236\uff0c\u5982\u7bb1\u7ebf\u56fe\u3001\u5206\u5e03\u56fe\u3001\u70ed\u529b\u56fe\u7b49\u3002<\/p>\n<\/p>\n
                import seaborn as sns<\/p>\n
                \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
                sns.boxplot(x='category', y='value', data=df)<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u56db\u3001SCIKIT-LEARN\u673a\u5668\u5b66\u4e60\u5904\u7406<\/p>\n<\/p>\n
                Scikit-learn\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u63d0\u4f9b\u4e86\u591a\u79cd\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u7684\u5b9e\u73b0\uff0c\u53ef\u4ee5\u7528\u4e8e\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u7b49\u4efb\u52a1\u3002<\/p>\n<\/p>\n
                  \n
                1. \u6570\u636e\u9884\u5904\u7406<\/strong><\/li>\n<\/ol>\n
                  \u5728\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u4e4b\u524d\uff0c\u901a\u5e38\u9700\u8981\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\uff0c\u5982\u7279\u5f81\u7f29\u653e\u3001\u7f16\u7801\u5206\u7c7b\u53d8\u91cf\u3001\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\u7b49\u3002Scikit-learn\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u6570\u636e\u9884\u5904\u7406\u5de5\u5177\u3002<\/p>\n<\/p>\n
                  from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
                  from sklearn.preprocessing import StandardScaler<\/p>\n
                  \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/strong><\/h2>\n
                  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
                  \u7279\u5f81\u7f29\u653e<\/strong><\/h2>\n
                  scaler = StandardScaler()<\/p>\n
                  X_train_scaled = scaler.fit_transform(X_train)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                    \n
                  1. \u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30<\/strong><\/li>\n<\/ol>\n
                    Scikit-learn\u63d0\u4f9b\u4e86\u591a\u79cd\u673a\u5668\u5b66\u4e60\u6a21\u578b\u7684\u5b9e\u73b0\uff0c\u5982\u7ebf\u6027\u56de\u5f52\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u51b3\u7b56\u6811\u7b49\u3002\u53ef\u4ee5\u901a\u8fc7fit<\/code>\u65b9\u6cd5\u8bad\u7ec3\u6a21\u578b\uff0c\u5e76\u901a\u8fc7predict<\/code>\u65b9\u6cd5\u8fdb\u884c\u9884\u6d4b\u3002<\/p>\n<\/p>\n
                    from sklearn.linear_model import LinearRegression<\/p>\n
                    from sklearn.metrics import mean_squared_error<\/p>\n
                    \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
                    model = LinearRegression()<\/p>\n
                    model.fit(X_train_scaled, y_train)<\/p>\n
                    \u9884\u6d4b\u4e0e\u8bc4\u4f30<\/strong><\/h2>\n
                    y_pred = model.predict(X_test)<\/p>\n
                    mse = mean_squared_error(y_test, y_pred)<\/p>\n
                    print(f'Mean Squared Error: {mse}')<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u4e94\u3001\u7efc\u5408\u5e94\u7528\u4e0e\u6848\u4f8b\u5206\u6790<\/p>\n<\/p>\n
                    \u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u6570\u636e\u5904\u7406\u901a\u5e38\u9700\u8981\u7ed3\u5408\u591a\u4e2a\u5de5\u5177\u548c\u6280\u672f\uff0c\u4e0b\u9762\u4ee5\u4e00\u4e2a\u7b80\u5355\u7684\u6848\u4f8b\u6765\u5c55\u793a\u5982\u4f55\u5229\u7528Python\u8fdb\u884c\u6570\u636e\u5904\u7406\u7684\u7efc\u5408\u5e94\u7528\u3002<\/p>\n<\/p>\n
                      \n
                    1. \u6848\u4f8b\u80cc\u666f<\/strong><\/li>\n<\/ol>\n
                      \u5047\u8bbe\u6211\u4eec\u6709\u4e00\u4efd\u5305\u542b\u623f\u5c4b\u9500\u552e\u6570\u636e\u7684CSV\u6587\u4ef6\uff0c\u5176\u4e2d\u5305\u62ec\u623f\u5c4b\u7684\u9762\u79ef\u3001\u5367\u5ba4\u6570\u91cf\u3001\u552e\u4ef7\u7b49\u4fe1\u606f\u3002\u6211\u4eec\u7684\u76ee\u6807\u662f\u5229\u7528\u8fd9\u4e9b\u6570\u636e\u9884\u6d4b\u623f\u5c4b\u7684\u552e\u4ef7\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u6570\u636e\u5bfc\u5165\u4e0e\u6e05\u6d17<\/strong><\/li>\n<\/ol>\n
                        \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5bfc\u5165\u6570\u636e\u5e76\u8fdb\u884c\u6e05\u6d17\uff0c\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u5f02\u5e38\u503c\u7b49\u3002<\/p>\n<\/p>\n
                        # \u5bfc\u5165\u6570\u636e<\/p>\n
                        df = pd.read_csv('house_prices.csv')<\/p>\n
                        \u5904\u7406\u7f3a\u5931\u503c<\/strong><\/h2>\n
                        df.fillna(df.mean(), inplace=True)<\/p>\n
                        \u53bb\u9664\u5f02\u5e38\u503c<\/strong><\/h2>\n
                        df = df[(df['price'] > 10000) & (df['price'] < 1000000)]<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u7279\u5f81\u5de5\u7a0b<\/strong><\/li>\n<\/ol>\n
                          \u5728\u6570\u636e\u6e05\u6d17\u4e4b\u540e\uff0c\u6211\u4eec\u9700\u8981\u8fdb\u884c\u7279\u5f81\u5de5\u7a0b\uff0c\u5982\u521b\u5efa\u65b0\u7279\u5f81\u3001\u7279\u5f81\u7f29\u653e\u7b49\u3002<\/p>\n<\/p>\n
                          # \u521b\u5efa\u65b0\u7279\u5f81<\/p>\n
                          df['price_per_sqft'] = df['price'] \/ df['sqft']<\/p>\n
                          \u7279\u5f81\u7f29\u653e<\/strong><\/h2>\n
                          scaler = StandardScaler()<\/p>\n
                          df[['sqft', 'bedrooms', 'price_per_sqft']] = scaler.fit_transform(df[['sqft', 'bedrooms', 'price_per_sqft']])<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                            \n
                          1. \u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30<\/strong><\/li>\n<\/ol>\n
                            \u6700\u540e\uff0c\u6211\u4eec\u9009\u62e9\u5408\u9002\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\uff0c\u5e76\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n<\/p>\n
                            # \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/p>\n
                            X = df[['sqft', 'bedrooms', 'price_per_sqft']]<\/p>\n
                            y = df['price']<\/p>\n
                            X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
                            \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
                            model = LinearRegression()<\/p>\n
                            model.fit(X_train, y_train)<\/p>\n
                            \u9884\u6d4b\u4e0e\u8bc4\u4f30<\/strong><\/h2>\n
                            y_pred = model.predict(X_test)<\/p>\n
                            mse = mean_squared_error(y_test, y_pred)<\/p>\n
                            print(f'Mean Squared Error: {mse}')<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                            \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5229\u7528Python\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u80fd\u529b\uff0c\u4ece\u6570\u636e\u5bfc\u5165\u3001\u6e05\u6d17\u3001\u7279\u5f81\u5de5\u7a0b\u5230\u6a21\u578b\u8bad\u7ec3\u4e0e\u8bc4\u4f30\uff0c\u5168\u9762\u5b8c\u6210\u6570\u636e\u5206\u6790\u4e0e\u5904\u7406\u4efb\u52a1\u3002<\/p>\n<\/p>\n
                            \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                             \u5982\u4f55\u5f00\u59cb\u4f7f\u7528Python\u8fdb\u884c\u6570\u636e\u5904\u7406\uff1f<\/strong>
                            Python\u4ee5\u5176\u7b80\u5355\u6613\u5b66\u7684\u7279\u6027\u548c\u5f3a\u5927\u7684\u5e93\u652f\u6301\uff0c\u6210\u4e3a\u6570\u636e\u5904\u7406\u7684\u70ed\u95e8\u9009\u62e9\u3002\u4e3a\u4e86\u5f00\u59cb\u6570\u636e\u5904\u7406\uff0c\u60a8\u53ef\u4ee5\u5b89\u88c5Anaconda\uff0c\u8fd9\u4e2a\u96c6\u6210\u4e86Python\u548c\u8bb8\u591a\u6570\u636e\u79d1\u5b66\u5e93\u7684\u73af\u5883\u3002\u63a5\u4e0b\u6765\uff0c\u5b66\u4e60\u4f7f\u7528Pandas\u548cNumPy\u5e93\uff0c\u5b83\u4eec\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002\u60a8\u53ef\u4ee5\u901a\u8fc7\u5728\u7ebf\u6559\u7a0b\u3001\u4e66\u7c4d\u6216\u8005\u53c2\u52a0\u76f8\u5173\u8bfe\u7a0b\u6765\u638c\u63e1\u8fd9\u4e9b\u5de5\u5177\u3002<\/p>\n
                            Python\u5904\u7406\u6570\u636e\u65f6\u6709\u54ea\u4e9b\u5e38\u7528\u5e93\uff1f<\/strong>
                            \u5728Python\u4e2d\uff0c\u6709\u51e0\u4e2a\u5e38\u7528\u7684\u5e93\u53ef\u4ee5\u5e2e\u52a9\u60a8\u9ad8\u6548\u5730\u5904\u7406\u6570\u636e\u3002Pandas\u662f\u6700\u6d41\u884c\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u9002\u5408\u5904\u7406\u8868\u683c\u6570\u636e\uff1bNumPy\u7528\u4e8e\u9ad8\u6548\u7684\u6570\u503c\u8ba1\u7b97\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u5927\u89c4\u6a21\u6570\u7ec4\u65f6\uff1bMatplotlib\u548cSeaborn\u53ef\u4ee5\u5e2e\u52a9\u60a8\u53ef\u89c6\u5316\u6570\u636e\uff0c\u4fbf\u4e8e\u5206\u6790\u7ed3\u679c\u7684\u7406\u89e3\u3002\u6b64\u5916\uff0cScikit-learn\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u53ef\u4ee5\u7528\u4e8e\u6570\u636e\u5efa\u6a21\u4e0e\u9884\u6d4b\u3002<\/p>\n
                            \u6570\u636e\u9884\u5904\u7406\u5728Python\u4e2d\u91cd\u8981\u5417\uff1f\u5982\u4f55\u8fdb\u884c\uff1f<\/strong>
                            \u6570\u636e\u9884\u5904\u7406\u5728\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u4e2d\u81f3\u5173\u91cd\u8981\uff0c\u56e0\u4e3a\u5e72\u51c0\u7684\u6570\u636e\u80fd\u591f\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u3002\u5728Python\u4e2d\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u8fdb\u884c\u6570\u636e\u6e05\u6d17\uff0c\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u53bb\u9664\u91cd\u590d\u6570\u636e\u4ee5\u53ca\u683c\u5f0f\u5316\u65e5\u671f\u7b49\u3002\u6570\u636e\u89c4\u8303\u5316\u548c\u6807\u51c6\u5316\u4e5f\u662f\u91cd\u8981\u7684\u6b65\u9aa4\uff0c\u53ef\u4ee5\u5e2e\u52a9\u6539\u5584\u6a21\u578b\u8bad\u7ec3\u6548\u679c\u3002\u901a\u8fc7\u5bf9\u6570\u636e\u7684\u63a2\u7d22\u6027\u5206\u6790\uff0c\u60a8\u53ef\u4ee5\u8bc6\u522b\u5e76\u89e3\u51b3\u6f5c\u5728\u7684\u95ee\u9898\uff0c\u4ece\u800c\u4e3a\u540e\u7eed\u5206\u6790\u5960\u5b9a\u57fa\u7840\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5982\u4f55\u5229\u7528Python\u5904\u7406\u6570\u636e \u5229\u7528Python\u5904\u7406\u6570\u636e\u7684\u6838\u5fc3\u5728\u4e8e\u5176\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5e93\u548c\u7075\u6d3b\u7684\u7f16\u7a0b\u80fd\u529b\u3002\u4f7f\u7528Pan […]","protected":false},"author":3,"featured_media":993417,"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\/993410"}],"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=993410"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/993410\/revisions"}],"predecessor-version":[{"id":993418,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/993410\/revisions\/993418"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/993417"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=993410"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=993410"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=993410"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}