{"id":996785,"date":"2024-12-27T09:17:56","date_gmt":"2024-12-27T01:17:56","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/996785.html"},"modified":"2024-12-27T09:17:59","modified_gmt":"2024-12-27T01:17:59","slug":"python%e5%a6%82%e4%bd%95%e6%b7%bb%e5%8a%a0%e8%99%9a%e6%8b%9f%e5%8f%98%e9%87%8f","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/996785.html","title":{"rendered":"python\u5982\u4f55\u6dfb\u52a0\u865a\u62df\u53d8\u91cf"},"content":{"rendered":"

\"python\u5982\u4f55\u6dfb\u52a0\u865a\u62df\u53d8\u91cf\"<\/p>\n

\u5728Python\u4e2d\u6dfb\u52a0\u865a\u62df\u53d8\u91cf\u7684\u5e38\u89c1\u65b9\u6cd5\u662f\u4f7f\u7528pandas\u5e93\u7684get_dummies<\/code>\u51fd\u6570\u3001\u4f7f\u7528scikit-learn\u5e93\u7684OneHotEncoder<\/code>\uff0c\u6216\u8005\u624b\u52a8\u521b\u5efa\u865a\u62df\u53d8\u91cf\u3002\u4f7f\u7528pandas\u7684get_dummies<\/code>\u51fd\u6570\u3001\u4f7f\u7528scikit-learn\u7684OneHotEncoder<\/code>\u3001\u624b\u52a8\u521b\u5efa\u865a\u62df\u53d8\u91cf<\/strong>\u662f\u5e38\u7528\u7684\u4e09\u79cd\u65b9\u6cd5\u3002\u4ee5\u4e0b\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\uff1a\u4f7f\u7528pandas\u7684get_dummies<\/code>\u51fd\u6570\u3002<\/p>\n<\/p>\n

get_dummies<\/code>\u662fpandas\u5e93\u4e2d\u7684\u4e00\u4e2a\u51fd\u6570\uff0c\u5b83\u53ef\u4ee5\u5c06\u5206\u7c7b\u53d8\u91cf\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf\uff08\u4e5f\u79f0\u4e3a\u54d1\u53d8\u91cf\uff09\u3002\u8fd9\u4e00\u65b9\u6cd5\u975e\u5e38\u7b80\u5355\uff0c\u56e0\u4e3a\u5b83\u81ea\u52a8\u4e3a\u6bcf\u4e2a\u7c7b\u522b\u521b\u5efa\u4e8c\u8fdb\u5236\u5217\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684DataFrame\u5bf9\u8c61\u3002\u4f7f\u7528get_dummies<\/code>\u51fd\u6570\u7684\u65b9\u6cd5\u5982\u4e0b\uff1a<\/p>\n<\/p>\n

\u9996\u5148\uff0c\u9700\u8981\u5bfc\u5165pandas\u5e93\u5e76\u51c6\u5907\u6570\u636e\u3002\u5047\u8bbe\u4f60\u6709\u4e00\u4e2aDataFrame\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u4e2a\u540d\u4e3acolor<\/code>\u7684\u5217\uff0c\u5177\u6709\u4e0d\u540c\u7684\u989c\u8272\u503c\uff1a\u7ea2\u8272\u3001\u7eff\u8272\u548c\u84dd\u8272\u3002\u901a\u8fc7\u8c03\u7528pd.get_dummies(df['color'])<\/code>\uff0cpandas\u4f1a\u4e3a\u6bcf\u79cd\u989c\u8272\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u5217\uff0c\u6bcf\u4e2a\u5217\u5305\u542b0\u62161\u6765\u6307\u793a\u8be5\u884c\u662f\u5426\u5c5e\u4e8e\u8be5\u7279\u5b9a\u989c\u8272\u7c7b\u522b\u3002\u8fd9\u79cd\u65b9\u6cd5\u7b80\u5316\u4e86\u5904\u7406\u5206\u7c7b\u6570\u636e\u7684\u8fc7\u7a0b\uff0c\u4f7f\u5f97\u6570\u636e\u66f4\u9002\u5408\u7528\u4e8e\u673a\u5668\u5b66\u4e60<\/a>\u6a21\u578b\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001PANDAS\u7684get_dummies<\/code>\u51fd\u6570<\/p>\n<\/p>\n

get_dummies<\/code>\u51fd\u6570\u662f\u7528\u4e8e\u5c06\u5206\u7c7b\u53d8\u91cf\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf\u7684\u6700\u7b80\u5355\u65b9\u6cd5\u4e4b\u4e00\u3002\u5b83\u4f1a\u4e3aDataFrame\u4e2d\u7684\u6bcf\u4e2a\u7c7b\u522b\u521b\u5efa\u4e8c\u8fdb\u5236\u5217\u3002<\/p>\n<\/p>\n

    \n
  1. \n

    \u4f7f\u7528\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n

    \u5728\u4f7f\u7528get_dummies<\/code>\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165pandas\u5e93\u3002\u5047\u8bbe\u4f60\u5df2\u7ecf\u6709\u4e00\u4e2aDataFrame\uff0c\u5176\u4e2d\u5305\u542b\u4e00\u5217\u5206\u7c7b\u53d8\u91cf\uff0c\u4f8b\u5982\u989c\u8272\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    data = {'color': ['red', 'blue', 'green', 'blue', 'red']}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u901a\u8fc7\u8c03\u7528get_dummies<\/code>\u51fd\u6570\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u989c\u8272\u5217\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf\u3002<\/p>\n<\/p>\n
    dummies = pd.get_dummies(df['color'])<\/p>\n
    df = pd.concat([df, dummies], axis=1)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8fd9\u6837\uff0c\u539f\u59cb\u7684\u989c\u8272\u5217\u5c31\u88ab\u8f6c\u6362\u4e3a\u591a\u4e2a\u4e8c\u8fdb\u5236\u5217\uff0c\u4ee3\u8868\u6bcf\u79cd\u989c\u8272\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u53c2\u6570\u8bf4\u660e<\/strong><\/p>\n<\/p>\n
    get_dummies<\/code>\u51fd\u6570\u6709\u51e0\u4e2a\u91cd\u8981\u7684\u53c2\u6570\uff1a<\/p>\n<\/p>\n
      \n
    • data<\/code>: \u8981\u521b\u5efa\u865a\u62df\u53d8\u91cf\u7684DataFrame\u6216Series\u3002<\/li>\n
    • prefix<\/code>: \u6dfb\u52a0\u5230\u6bcf\u4e2a\u865a\u62df\u53d8\u91cf\u5217\u540d\u79f0\u524d\u7684\u524d\u7f00\u3002<\/li>\n
    • drop_first<\/code>: \u9ed8\u8ba4\u4e3aFalse\u3002\u5982\u679c\u8bbe\u7f6e\u4e3aTrue\uff0c\u5c06\u5220\u9664\u7b2c\u4e00\u4e2a\u7c7b\u522b\u4ee5\u907f\u514d\u865a\u62df\u53d8\u91cf\u9677\u9631\u3002<\/li>\n<\/ul>\n
      \u793a\u4f8b\uff1a<\/p>\n<\/p>\n
      dummies = pd.get_dummies(df['color'], prefix='color', drop_first=True)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0cdrop_first<\/code>\u53c2\u6570\u88ab\u8bbe\u7f6e\u4e3aTrue\uff0c\u8fd9\u5c06\u5220\u9664\u4e00\u4e2a\u865a\u62df\u53d8\u91cf\u4ee5\u907f\u514d\u5171\u7ebf\u6027\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
      \u4e8c\u3001SCIKIT-LEARN\u7684OneHotEncoder<\/code><\/p>\n<\/p>\n
      Scikit-learn\u63d0\u4f9b\u4e86OneHotEncoder<\/code>\uff0c\u8fd9\u662f\u4e00\u79cd\u5c06\u5206\u7c7b\u7279\u5f81\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf\u7684\u5de5\u5177\u3002\u5b83\u6bd4get_dummies<\/code>\u66f4\u7075\u6d3b\uff0c\u9002\u5408\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7ba1\u9053\u3002<\/p>\n<\/p>\n
        \n
      1. \n
        \u4f7f\u7528\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n
        \u8981\u4f7f\u7528OneHotEncoder<\/code>\uff0c\u9996\u5148\u9700\u8981\u5bfc\u5165\u76f8\u5e94\u7684\u6a21\u5757\u3002<\/p>\n<\/p>\n
        from sklearn.preprocessing import OneHotEncoder<\/p>\n
        import numpy as np<\/p>\n
        data = np.array(['red', 'blue', 'green', 'blue', 'red']).reshape(-1, 1)<\/p>\n
        encoder = OneHotEncoder(sparse=False)<\/p>\n
        onehot = encoder.fit_transform(data)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        OneHotEncoder<\/code>\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u4e00\u4e2a\u7a00\u758f\u77e9\u9635\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6esparse=False<\/code>\u6765\u8f6c\u6362\u4e3a\u6570\u7ec4\u3002<\/p>\n<\/p>\n<\/li>\n
      2. \n
        \u53c2\u6570\u8bf4\u660e<\/strong><\/p>\n<\/p>\n
        OneHotEncoder<\/code>\u6709\u591a\u4e2a\u53c2\u6570\u53ef\u4f9b\u914d\u7f6e\uff1a<\/p>\n<\/p>\n
          \n
        • categories<\/code>: \u6307\u5b9a\u7279\u5f81\u7684\u7c7b\u522b\u3002<\/li>\n
        • drop<\/code>: \u6307\u5b9a\u8981\u5220\u9664\u7684\u7c7b\u522b\u3002<\/li>\n
        • sparse<\/code>: \u9ed8\u8ba4\u4e3aTrue\uff0c\u8fd4\u56de\u7a00\u758f\u77e9\u9635\u3002<\/li>\n
        • handle_unknown<\/code>: \u6307\u5b9a\u672a\u77e5\u7c7b\u522b\u7684\u5904\u7406\u65b9\u5f0f\u3002<\/li>\n<\/ul>\n
          \u793a\u4f8b\uff1a<\/p>\n<\/p>\n
          encoder = OneHotEncoder(drop='first', sparse=False)<\/p>\n
          onehot = encoder.fit_transform(data)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u901a\u8fc7\u8bbe\u7f6edrop='first'<\/code>\uff0c\u6211\u4eec\u53ef\u4ee5\u5220\u9664\u4e00\u4e2a\u865a\u62df\u53d8\u91cf\u4ee5\u907f\u514d\u5171\u7ebf\u6027\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
          \u4e09\u3001\u624b\u52a8\u521b\u5efa\u865a\u62df\u53d8\u91cf<\/p>\n<\/p>\n
          \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u53ef\u80fd\u9700\u8981\u624b\u52a8\u521b\u5efa\u865a\u62df\u53d8\u91cf\u3002\u624b\u52a8\u521b\u5efa\u5141\u8bb8\u5bf9\u6570\u636e\u8fdb\u884c\u66f4\u591a\u7684\u63a7\u5236\u3002<\/p>\n<\/p>\n
            \n
          1. \n
            \u521b\u5efa\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n
            \u53ef\u4ee5\u901a\u8fc7\u904d\u5386DataFrame\u4e2d\u7684\u6bcf\u4e2a\u7c7b\u522b\u6765\u624b\u52a8\u521b\u5efa\u865a\u62df\u53d8\u91cf\u3002<\/p>\n<\/p>\n
            df['red'] = df['color'].apply(lambda x: 1 if x == 'red' else 0)<\/p>\n
            df['blue'] = df['color'].apply(lambda x: 1 if x == 'blue' else 0)<\/p>\n
            df['green'] = df['color'].apply(lambda x: 1 if x == 'green' else 0)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u8fd9\u6837\uff0c\u4f60\u5c31\u4e3a\u6bcf\u79cd\u989c\u8272\u624b\u52a8\u521b\u5efa\u4e86\u4e00\u4e2a\u4e8c\u8fdb\u5236\u5217\u3002<\/p>\n<\/p>\n<\/li>\n
          2. \n
            \u7075\u6d3b\u6027<\/strong><\/p>\n<\/p>\n
            \u624b\u52a8\u521b\u5efa\u865a\u62df\u53d8\u91cf\u63d0\u4f9b\u4e86\u66f4\u5927\u7684\u7075\u6d3b\u6027\u3002\u4f60\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u8981\u5bf9\u6bcf\u4e2a\u7c7b\u522b\u8fdb\u884c\u4e0d\u540c\u7684\u5904\u7406\uff0c\u4f8b\u5982\u5f52\u4e00\u5316\u3002<\/p>\n<\/p>\n
            df['red'] = (df['color'] == 'red').astype(int)<\/p>\n
            df['blue'] = (df['color'] == 'blue').astype(int)<\/p>\n
            df['green'] = (df['color'] == 'green').astype(int)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u8fd9\u79cd\u65b9\u6cd5\u867d\u7136\u4ee3\u7801\u91cf\u8f83\u591a\uff0c\u4f46\u5728\u5904\u7406\u590d\u6742\u6570\u636e\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
            \u56db\u3001\u865a\u62df\u53d8\u91cf\u7684\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
            \u865a\u62df\u53d8\u91cf\u5728\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u5b83\u4eec\u53ef\u4ee5\u7528\u4e8e\u7ebf\u6027\u56de\u5f52\u3001\u903b\u8f91\u56de\u5f52\u3001\u51b3\u7b56\u6811\u7b49\u5404\u79cd\u6a21\u578b\u3002<\/p>\n<\/p>\n
              \n
            1. \n
              \u7ebf\u6027\u56de\u5f52\u4e2d\u7684\u5e94\u7528<\/strong><\/p>\n<\/p>\n
              \u5728\u7ebf\u6027\u56de\u5f52\u4e2d\uff0c\u865a\u62df\u53d8\u91cf\u7528\u4e8e\u8868\u793a\u5206\u7c7b\u7279\u5f81\uff0c\u4ee5\u4fbf\u6a21\u578b\u53ef\u4ee5\u5904\u7406\u8fd9\u4e9b\u7279\u5f81\u3002\u4f8b\u5982\uff0c\u5728\u9884\u6d4b\u623f\u4ef7\u7684\u6a21\u578b\u4e2d\uff0c\u623f\u5c4b\u7c7b\u578b\u53ef\u4ee5\u7528\u865a\u62df\u53d8\u91cf\u8868\u793a\u3002<\/p>\n<\/p>\n<\/li>\n
            2. \n
              \u903b\u8f91\u56de\u5f52\u4e2d\u7684\u5e94\u7528<\/strong><\/p>\n<\/p>\n
              \u5728\u903b\u8f91\u56de\u5f52\u4e2d\uff0c\u865a\u62df\u53d8\u91cf\u540c\u6837\u7528\u4e8e\u5904\u7406\u5206\u7c7b\u7279\u5f81\uff0c\u5e2e\u52a9\u6a21\u578b\u8bc6\u522b\u4e0d\u540c\u7c7b\u522b\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/p>\n<\/p>\n<\/li>\n
            3. \n
              \u51b3\u7b56\u6811\u4e2d\u7684\u5e94\u7528<\/strong><\/p>\n<\/p>\n
              \u5728\u51b3\u7b56\u6811\u6a21\u578b\u4e2d\uff0c\u865a\u62df\u53d8\u91cf\u53ef\u4ee5\u7528\u4e8e\u8868\u793a\u5206\u7c7b\u7279\u5f81\uff0c\u4f7f\u5f97\u6811\u53ef\u4ee5\u6839\u636e\u8fd9\u4e9b\u7279\u5f81\u505a\u51fa\u5206\u88c2\u51b3\u7b56\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
              \u4e94\u3001\u907f\u514d\u865a\u62df\u53d8\u91cf\u9677\u9631<\/p>\n<\/p>\n
              \u5728\u4f7f\u7528\u865a\u62df\u53d8\u91cf\u65f6\uff0c\u4e00\u4e2a\u91cd\u8981\u7684\u95ee\u9898\u662f\u907f\u514d\u865a\u62df\u53d8\u91cf\u9677\u9631\uff0c\u8fd9\u79cd\u60c5\u51b5\u4f1a\u5bfc\u81f4\u5171\u7ebf\u6027\u3002<\/p>\n<\/p>\n
                \n
              1. \n
                \u5171\u7ebf\u6027\u95ee\u9898<\/strong><\/p>\n<\/p>\n
                \u5171\u7ebf\u6027\u662f\u6307\u81ea\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u9ad8\u5ea6\u76f8\u5173\u6027\uff0c\u8fd9\u4f1a\u5bfc\u81f4\u6a21\u578b\u4e0d\u7a33\u5b9a\u3002\u5728\u4f7f\u7528\u865a\u62df\u53d8\u91cf\u65f6\uff0c\u7531\u4e8e\u6bcf\u4e2a\u7c7b\u522b\u90fd\u6709\u4e00\u4e2a\u4e8c\u8fdb\u5236\u5217\uff0c\u56e0\u6b64\u5bb9\u6613\u4ea7\u751f\u5171\u7ebf\u6027\u3002<\/p>\n<\/p>\n<\/li>\n
              2. \n
                \u89e3\u51b3\u65b9\u6cd5<\/strong><\/p>\n<\/p>\n
                \u4e3a\u4e86\u89e3\u51b3\u5171\u7ebf\u6027\u95ee\u9898\uff0c\u53ef\u4ee5\u5220\u9664\u4e00\u4e2a\u865a\u62df\u53d8\u91cf\u5217\u3002\u8fd9\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6eget_dummies<\/code>\u6216OneHotEncoder<\/code>\u7684drop_first<\/code>\u53c2\u6570\u6765\u5b9e\u73b0\u3002<\/p>\n<\/p>\n<\/li>\n
              3. \n
                \u793a\u4f8b<\/strong><\/p>\n<\/p>\n
                dummies = pd.get_dummies(df['color'], drop_first=True)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u6216\u8005\u4f7f\u7528OneHotEncoder<\/code>\uff1a<\/p>\n<\/p>\n
                encoder = OneHotEncoder(drop='first', sparse=False)<\/p>\n
                onehot = encoder.fit_transform(data)<\/p>\n
                <\/code><\/pre>\n<\/p>\n<\/li>\n<\/ol>\n
                \u516d\u3001\u865a\u62df\u53d8\u91cf\u7684\u4f18\u7f3a\u70b9<\/p>\n<\/p>\n
                \u865a\u62df\u53d8\u91cf\u6709\u8bb8\u591a\u4f18\u70b9\uff0c\u4f46\u4e5f\u5b58\u5728\u4e00\u4e9b\u7f3a\u70b9\u3002<\/p>\n<\/p>\n
                  \n
                1. \n
                  \u4f18\u70b9<\/strong><\/p>\n<\/p>\n
                    \n
                  • \u6613\u4e8e\u4f7f\u7528<\/strong>\uff1a\u865a\u62df\u53d8\u91cf\u6613\u4e8e\u751f\u6210\u548c\u89e3\u91ca\u3002<\/li>\n
                  • \u5e7f\u6cdb\u5e94\u7528<\/strong>\uff1a\u9002\u7528\u4e8e\u591a\u79cd\u7edf\u8ba1\u6a21\u578b\u548c\u673a\u5668\u5b66\u4e60\u6a21\u578b\u3002<\/li>\n
                  • \u63d0\u9ad8\u6a21\u578b\u6027\u80fd<\/strong>\uff1a\u5e2e\u52a9\u6a21\u578b\u8bc6\u522b\u5206\u7c7b\u7279\u5f81\u3002<\/li>\n<\/ul>\n<\/li>\n
                  • \n
                    \u7f3a\u70b9<\/strong><\/p>\n<\/p>\n
                      \n
                    • \u589e\u52a0\u7ef4\u5ea6<\/strong>\uff1a\u5bf9\u4e8e\u5177\u6709\u8bb8\u591a\u7c7b\u522b\u7684\u7279\u5f81\uff0c\u865a\u62df\u53d8\u91cf\u4f1a\u663e\u8457\u589e\u52a0\u6570\u636e\u7684\u7ef4\u5ea6\u3002<\/li>\n
                    • \u5f15\u5165\u5171\u7ebf\u6027<\/strong>\uff1a\u5982\u679c\u4e0d\u5c0f\u5fc3\u4f7f\u7528\uff0c\u53ef\u80fd\u4f1a\u5f15\u5165\u5171\u7ebf\u6027\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
                      \u603b\u7ed3\u6765\u8bf4\uff0c\u865a\u62df\u53d8\u91cf\u662f\u5904\u7406\u5206\u7c7b\u6570\u636e\u7684\u4e00\u79cd\u5e38\u7528\u65b9\u6cd5\uff0c\u5b83\u4eec\u53ef\u4ee5\u5e2e\u52a9\u63d0\u9ad8\u673a\u5668\u5b66\u4e60\u6a21\u578b\u7684\u6027\u80fd\uff0c\u4f46\u5728\u4f7f\u7528\u65f6\u9700\u8981\u6ce8\u610f\u907f\u514d\u5171\u7ebf\u6027\u95ee\u9898\u3002\u901a\u8fc7\u7406\u89e3\u548c\u4f7f\u7528pandas\u7684get_dummies<\/code>\u3001scikit-learn\u7684OneHotEncoder<\/code>\uff0c\u4ee5\u53ca\u624b\u52a8\u521b\u5efa\u865a\u62df\u53d8\u91cf\u7684\u65b9\u6cd5\uff0c\u6570\u636e\u79d1\u5b66\u5bb6\u53ef\u4ee5\u66f4\u597d\u5730\u5904\u7406\u5206\u7c7b\u7279\u5f81\uff0c\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u7a33\u5b9a\u6027\u3002<\/p>\n<\/p>\n
                      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                       \u5982\u4f55\u5728Python\u4e2d\u521b\u5efa\u865a\u62df\u53d8\u91cf\uff1f<\/strong>
                      \u5728Python\u4e2d\uff0c\u521b\u5efa\u865a\u62df\u53d8\u91cf\u901a\u5e38\u6d89\u53ca\u4f7f\u7528\u6570\u636e\u5904\u7406\u5e93\uff0c\u5982Pandas\u3002\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528get_dummies()<\/code>\u51fd\u6570\u5c06\u5206\u7c7b\u53d8\u91cf\u8f6c\u6362\u4e3a\u865a\u62df\u53d8\u91cf\uff08\u4e5f\u79f0\u4e3a\u72ec\u70ed\u7f16\u7801\uff09\u3002\u4f8b\u5982\uff0c\u4f7f\u7528pd.get_dummies(df['\u5206\u7c7b\u53d8\u91cf'])<\/code>\u53ef\u4ee5\u5c06\u6307\u5b9a\u7684\u5206\u7c7b\u5217\u8f6c\u6362\u4e3a\u591a\u4e2a\u865a\u62df\u5217\uff0c\u6bcf\u4e2a\u5217\u4ee3\u8868\u4e00\u4e2a\u7c7b\u522b\u3002<\/p>\n
                      \u865a\u62df\u53d8\u91cf\u5728\u6570\u636e\u5206\u6790\u4e2d\u6709\u4ec0\u4e48\u91cd\u8981\u6027\uff1f<\/strong>
                      \u865a\u62df\u53d8\u91cf\u5728\u6570\u636e\u5206\u6790\u4e2d\u81f3\u5173\u91cd\u8981\uff0c\u5c24\u5176\u662f\u5728\u6784\u5efa\u673a\u5668\u5b66\u4e60\u6a21\u578b\u65f6\u3002\u5b83\u4eec\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u5904\u7406\u5206\u7c7b\u6570\u636e\uff0c\u56e0\u4e3a\u5927\u591a\u6570\u7b97\u6cd5\u53ea\u63a5\u53d7\u6570\u503c\u8f93\u5165\u3002\u901a\u8fc7\u5c06\u7c7b\u522b\u6570\u636e\u8f6c\u5316\u4e3a\u865a\u62df\u53d8\u91cf\uff0c\u6a21\u578b\u80fd\u591f\u6355\u6349\u5230\u7c7b\u522b\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u4ece\u800c\u63d0\u9ad8\u9884\u6d4b\u6027\u80fd\u3002<\/p>\n
                      \u5728Python\u4e2d\u5982\u4f55\u907f\u514d\u865a\u62df\u53d8\u91cf\u9677\u9631\uff1f<\/strong>
                      \u865a\u62df\u53d8\u91cf\u9677\u9631\u662f\u6307\u5728\u6a21\u578b\u4e2d\u5f15\u5165\u8fc7\u591a\u7684\u865a\u62df\u53d8\u91cf\uff0c\u5bfc\u81f4\u591a\u91cd\u5171\u7ebf\u6027\u95ee\u9898\u3002\u4e3a\u907f\u514d\u6b64\u95ee\u9898\uff0c\u53ef\u4ee5\u9009\u62e9\u5728\u521b\u5efa\u865a\u62df\u53d8\u91cf\u65f6\u4f7f\u7528drop_first=True<\/code>\u53c2\u6570\uff0c\u8fd9\u6837\u4f1a\u53bb\u6389\u7b2c\u4e00\u4e2a\u7c7b\u522b\u7684\u865a\u62df\u53d8\u91cf\uff0c\u4ece\u800c\u51cf\u5c11\u5197\u4f59\u4fe1\u606f\u3002\u6b64\u5916\uff0c\u5408\u7406\u9009\u62e9\u4f7f\u7528\u7684\u7c7b\u522b\u548c\u63a7\u5236\u6a21\u578b\u590d\u6742\u6027\u4e5f\u662f\u6709\u6548\u7684\u7b56\u7565\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u6dfb\u52a0\u865a\u62df\u53d8\u91cf\u7684\u5e38\u89c1\u65b9\u6cd5\u662f\u4f7f\u7528pandas\u5e93\u7684get_dummies\u51fd\u6570\u3001\u4f7f\u7528scikit-le […]","protected":false},"author":3,"featured_media":996795,"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\/996785"}],"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=996785"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/996785\/revisions"}],"predecessor-version":[{"id":996799,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/996785\/revisions\/996799"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/996795"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=996785"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=996785"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=996785"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}