{"id":949019,"date":"2024-12-27T00:08:55","date_gmt":"2024-12-26T16:08:55","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/949019.html"},"modified":"2024-12-27T00:08:57","modified_gmt":"2024-12-26T16:08:57","slug":"%e5%a6%82%e4%bd%95-python-%e9%87%8d%e5%a4%8d%e6%8a%bd%e6%a0%b7","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/949019.html","title":{"rendered":"\u5982\u4f55 python \u91cd\u590d\u62bd\u6837"},"content":{"rendered":"

\"\u5982\u4f55<\/p>\n

\u5f00\u5934\u6bb5\u843d\uff1a
\u5728Python\u4e2d\uff0c\u91cd\u590d\u62bd\u6837\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5e93\u5982random<\/code>\u3001numpy<\/code>\u548cpandas<\/code>\u6765\u5b9e\u73b0<\/strong>\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u6211\u4eec\u53ef\u4ee5\u8f7b\u677e\u5730\u8fdb\u884c\u5404\u79cd\u7c7b\u578b\u7684\u91cd\u590d\u62bd\u6837\u64cd\u4f5c\uff0c\u4f8b\u5982\u4ece\u5217\u8868\u4e2d\u968f\u673a\u62bd\u53d6\u5143\u7d20\u3001\u4ece\u6570\u7ec4\u4e2d\u8fdb\u884c\u6709\u653e\u56de\u548c\u65e0\u653e\u56de\u62bd\u6837\u3001\u4ee5\u53ca\u4ece\u6570\u636e\u6846\u4e2d\u8fdb\u884c\u5206\u5c42\u62bd\u6837\u7b49\u3002\u4f7f\u7528numpy<\/code>\u5e93\u7684numpy.random.choice<\/code>\u51fd\u6570\u662f\u5b9e\u73b0\u91cd\u590d\u62bd\u6837\u7684\u4e00\u79cd\u9ad8\u6548\u65b9\u6cd5<\/strong>\u3002numpy.random.choice<\/code>\u5141\u8bb8\u6211\u4eec\u4ece\u4e00\u4e2a\u6570\u7ec4\u4e2d\u968f\u673a\u9009\u62e9\u5143\u7d20\uff0c\u5e76\u4e14\u53ef\u4ee5\u6307\u5b9a\u662f\u5426\u653e\u56de\u62bd\u6837\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528numpy.random.choice<\/code>\u8fdb\u884c\u91cd\u590d\u62bd\u6837\u3002<\/p>\n<\/p>\n

\u4e00\u3001PYTHON \u91cd\u590d\u62bd\u6837\u7684\u57fa\u7840\u6982\u5ff5<\/p>\n<\/p>\n

\u91cd\u590d\u62bd\u6837\u662f\u7edf\u8ba1\u5b66\u4e2d\u4e00\u79cd\u91cd\u8981\u7684\u65b9\u6cd5\uff0c\u7528\u4e8e\u4ece\u4e00\u4e2a\u6570\u636e\u96c6\u4e2d\u968f\u673a\u9009\u53d6\u6837\u672c\u3002\u91cd\u590d\u62bd\u6837\u7684\u4e3b\u8981\u76ee\u7684\u662f\u4f30\u8ba1\u603b\u4f53\u7684\u7edf\u8ba1\u7279\u5f81\uff0c\u4f8b\u5982\u5747\u503c\u3001\u65b9\u5dee\u7b49\u3002\u5728\u8fdb\u884c\u91cd\u590d\u62bd\u6837\u65f6\uff0c\u6bcf\u6b21\u62bd\u6837\u540e\uff0c\u5c06\u6837\u672c\u653e\u56de\u5230\u603b\u4f53\u4e2d\uff0c\u8fd9\u6837\u4e0b\u4e00\u6b21\u62bd\u6837\u65f6\uff0c\u6837\u672c\u4ecd\u7136\u53ef\u80fd\u88ab\u9009\u4e2d\u3002<\/p>\n<\/p>\n

\u5728Python\u4e2d\uff0c\u91cd\u590d\u62bd\u6837\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528random<\/code>\u3001numpy<\/code>\u548cpandas<\/code>\u7b49\u5e93\u6765\u5b9e\u73b0\u3002\u8fd9\u4e9b\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u51fd\u6570\u548c\u65b9\u6cd5\u6765\u652f\u6301\u5404\u79cd\u7c7b\u578b\u7684\u62bd\u6837\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

\u4e8c\u3001\u4f7f\u7528 random<\/code> \u6a21\u5757\u8fdb\u884c\u91cd\u590d\u62bd\u6837<\/p>\n<\/p>\n

random<\/code>\u6a21\u5757\u662fPython\u6807\u51c6\u5e93\u7684\u4e00\u90e8\u5206\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e00\u4e9b\u57fa\u672c\u7684\u968f\u673a\u6570\u751f\u6210\u548c\u62bd\u6837\u529f\u80fd\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528random<\/code>\u6a21\u5757\u8fdb\u884c\u91cd\u590d\u62bd\u6837\u7684\u57fa\u672c\u65b9\u6cd5\uff1a<\/p>\n<\/p>\n

    \n
  1. \u4ece\u5217\u8868\u4e2d\u8fdb\u884c\u968f\u673a\u9009\u62e9\uff1a<\/li>\n<\/ol>\n

    import random<\/p>\n
    data = [1, 2, 3, 4, 5]<\/p>\n
    sample = random.choices(data, k=3)<\/p>\n
    print(sample)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    random.choices<\/code>\u51fd\u6570\u5141\u8bb8\u4ece\u7ed9\u5b9a\u7684\u5217\u8868\u4e2d\u968f\u673a\u9009\u62e9k<\/code>\u4e2a\u5143\u7d20\uff0c\u5e76\u4e14\u662f\u6709\u653e\u56de\u7684\u62bd\u6837\u3002\u8fd9\u610f\u5473\u7740\u540c\u4e00\u5143\u7d20\u53ef\u4ee5\u88ab\u591a\u6b21\u9009\u4e2d\u3002<\/p>\n<\/p>\n
      \n
    1. \u4f7f\u7528\u6743\u91cd\u8fdb\u884c\u52a0\u6743\u62bd\u6837\uff1a<\/li>\n<\/ol>\n
      weights = [10, 20, 30, 40, 50]<\/p>\n
      weighted_sample = random.choices(data, weights=weights, k=3)<\/p>\n
      print(weighted_sample)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u901a\u8fc7\u4e3arandom.choices<\/code>\u51fd\u6570\u63d0\u4f9b\u6743\u91cd\u53c2\u6570\uff0c\u53ef\u4ee5\u8fdb\u884c\u52a0\u6743\u62bd\u6837\uff0c\u4f7f\u5f97\u67d0\u4e9b\u5143\u7d20\u88ab\u9009\u4e2d\u7684\u6982\u7387\u66f4\u9ad8\u3002<\/p>\n<\/p>\n
      \u4e09\u3001\u4f7f\u7528 numpy<\/code> \u5e93\u8fdb\u884c\u91cd\u590d\u62bd\u6837<\/p>\n<\/p>\n
      numpy<\/code>\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u6570\u7ec4\u548c\u77e9\u9635\u8fd0\u7b97\u7684\u529f\u80fd\u3002\u5176\u4e2d\uff0cnumpy.random.choice<\/code>\u51fd\u6570\u662f\u5b9e\u73b0\u91cd\u590d\u62bd\u6837\u7684\u5e38\u7528\u5de5\u5177\u3002<\/p>\n<\/p>\n
        \n
      1. \u57fa\u672c\u7528\u6cd5\uff1a<\/li>\n<\/ol>\n
        import numpy as np<\/p>\n
        data = np.array([1, 2, 3, 4, 5])<\/p>\n
        sample = np.random.choice(data, size=3, replace=True)<\/p>\n
        print(sample)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cnumpy.random.choice<\/code>\u4ece\u6570\u7ec4data<\/code>\u4e2d\u968f\u673a\u9009\u62e93\u4e2a\u5143\u7d20\uff0creplace=True<\/code>\u8868\u793a\u6709\u653e\u56de\u62bd\u6837\u3002<\/p>\n<\/p>\n
          \n
        1. \u52a0\u6743\u62bd\u6837\uff1a<\/li>\n<\/ol>\n
          probabilities = [0.1, 0.2, 0.3, 0.2, 0.2]<\/p>\n
          weighted_sample = np.random.choice(data, size=3, replace=True, p=probabilities)<\/p>\n
          print(weighted_sample)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u901a\u8fc7\u6307\u5b9ap<\/code>\u53c2\u6570\uff0c\u53ef\u4ee5\u8fdb\u884c\u52a0\u6743\u62bd\u6837\u3002p<\/code>\u53c2\u6570\u5e94\u4e3a\u4e00\u4e2a\u4e0e\u6570\u636e\u540c\u957f\u5ea6\u7684\u6982\u7387\u6570\u7ec4\u3002<\/p>\n<\/p>\n
          \u56db\u3001\u4f7f\u7528 pandas<\/code> \u5e93\u8fdb\u884c\u91cd\u590d\u62bd\u6837<\/p>\n<\/p>\n
          pandas<\/code>\u5e93\u901a\u5e38\u7528\u4e8e\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u3002\u5b83\u7684DataFrame<\/code>\u5bf9\u8c61\u63d0\u4f9b\u4e86\u8bb8\u591a\u65b9\u4fbf\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\u3002pandas<\/code>\u7684sample<\/code>\u65b9\u6cd5\u53ef\u4ee5\u7528\u4e8e\u4ece\u6570\u636e\u6846\u4e2d\u968f\u673a\u62bd\u6837\u3002<\/p>\n<\/p>\n
            \n
          1. \u4eceDataFrame<\/code>\u4e2d\u62bd\u6837\uff1a<\/li>\n<\/ol>\n
            import pandas as pd<\/p>\n
            df = pd.DataFrame({<\/p>\n
                'A': [1, 2, 3, 4, 5],<\/p>\n
                'B': ['a', 'b', 'c', 'd', 'e']<\/p>\n
            })<\/p>\n
            sample_df = df.sample(n=3, replace=True)<\/p>\n
            print(sample_df)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0csample<\/code>\u65b9\u6cd5\u4eceDataFrame<\/code>\u4e2d\u968f\u673a\u9009\u62e93\u884c\uff0creplace=True<\/code>\u8868\u793a\u6709\u653e\u56de\u62bd\u6837\u3002<\/p>\n<\/p>\n
              \n
            1. \u5206\u5c42\u62bd\u6837\uff1a<\/li>\n<\/ol>\n
              \u5982\u679c\u9700\u8981\u6309\u7167\u67d0\u4e2a\u5206\u7ec4\u53d8\u91cf\u8fdb\u884c\u5206\u5c42\u62bd\u6837\uff0c\u53ef\u4ee5\u5148\u4f7f\u7528groupby<\/code>\u8fdb\u884c\u5206\u7ec4\uff0c\u7136\u540e\u5bf9\u6bcf\u4e2a\u7ec4\u8fdb\u884c\u62bd\u6837\u3002<\/p>\n<\/p>\n
              stratified_sample = df.groupby('B', group_keys=False).apply(lambda x: x.sample(n=1, replace=True))<\/p>\n
              print(stratified_sample)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u4e94\u3001\u91cd\u590d\u62bd\u6837\u5728\u6570\u636e\u5206\u6790\u4e2d\u7684\u5e94\u7528<\/p>\n<\/p>\n
              \u91cd\u590d\u62bd\u6837\u5728\u6570\u636e\u5206\u6790\u4e2d\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u5c24\u5176\u662f\u5728\u4ee5\u4e0b\u51e0\u4e2a\u65b9\u9762\uff1a<\/p>\n<\/p>\n
                \n
              1. \n
                \u4f30\u8ba1\u7edf\u8ba1\u91cf\u7684\u5206\u5e03<\/strong>\uff1a\u901a\u8fc7\u91cd\u590d\u62bd\u6837\uff0c\u53ef\u4ee5\u751f\u6210\u5927\u91cf\u7684\u6837\u672c\uff0c\u4ece\u800c\u4f30\u8ba1\u7edf\u8ba1\u91cf\uff08\u5982\u5747\u503c\u3001\u65b9\u5dee\uff09\u7684\u5206\u5e03\u3002\u8fd9\u5bf9\u4e8e\u6784\u5efa\u7f6e\u4fe1\u533a\u95f4\u548c\u8fdb\u884c\u5047\u8bbe\u68c0\u9a8c\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n<\/li>\n
              2. \n
                \u63d0\u5347\u6a21\u578b\u7684\u7a33\u5065\u6027<\/strong>\uff1a\u5728\u673a\u5668\u5b66\u4e60<\/a>\u4e2d\uff0c\u4f7f\u7528\u91cd\u590d\u62bd\u6837\u53ef\u4ee5\u8fdb\u884c\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u4ece\u800c\u63d0\u9ad8\u6a21\u578b\u7684\u6cdb\u5316\u80fd\u529b\u548c\u7a33\u5065\u6027\u3002<\/p>\n<\/p>\n<\/li>\n
              3. \n
                \u6570\u636e\u589e\u5f3a<\/strong>\uff1a\u5728\u6570\u636e\u9884\u5904\u7406\u4e2d\uff0c\u901a\u8fc7\u91cd\u590d\u62bd\u6837\u53ef\u4ee5\u751f\u6210\u66f4\u591a\u7684\u6570\u636e\u6837\u672c\uff0c\u7279\u522b\u662f\u5728\u6837\u672c\u91cf\u4e0d\u8db3\u7684\u60c5\u51b5\u4e0b\uff0c\u8fd9\u79cd\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u6539\u5584\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                \u516d\u3001PYTHON \u91cd\u590d\u62bd\u6837\u7684\u6ce8\u610f\u4e8b\u9879<\/p>\n<\/p>\n
                  \n
                1. \n
                  \u786e\u5b9a\u5408\u9002\u7684\u6837\u672c\u5927\u5c0f<\/strong>\uff1a\u5728\u8fdb\u884c\u91cd\u590d\u62bd\u6837\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684\u6837\u672c\u5927\u5c0f\u662f\u975e\u5e38\u91cd\u8981\u7684\u3002\u6837\u672c\u592a\u5c0f\u53ef\u80fd\u5bfc\u81f4\u4f30\u8ba1\u4e0d\u51c6\u786e\uff0c\u6837\u672c\u592a\u5927\u5219\u53ef\u80fd\u589e\u52a0\u8ba1\u7b97\u5f00\u9500\u3002<\/p>\n<\/p>\n<\/li>\n
                2. \n
                  \u6743\u91cd\u4e0e\u6982\u7387\u7684\u9009\u62e9<\/strong>\uff1a\u5728\u52a0\u6743\u62bd\u6837\u4e2d\uff0c\u6743\u91cd\u6216\u6982\u7387\u7684\u9009\u62e9\u5bf9\u7ed3\u679c\u6709\u5f88\u5927\u5f71\u54cd\u3002\u5e94\u6839\u636e\u5b9e\u9645\u9700\u6c42\u5408\u7406\u8bbe\u7f6e\u8fd9\u4e9b\u53c2\u6570\u3002<\/p>\n<\/p>\n<\/li>\n
                3. \n
                  \u653e\u56de\u4e0e\u4e0d\u653e\u56de\u62bd\u6837<\/strong>\uff1a\u5e94\u6839\u636e\u5177\u4f53\u5e94\u7528\u573a\u666f\u9009\u62e9\u662f\u5426\u653e\u56de\u62bd\u6837\u3002\u6709\u653e\u56de\u62bd\u6837\u9002\u7528\u4e8e\u65e0\u9650\u603b\u4f53\u6216\u9700\u8981\u6837\u672c\u7684\u72ec\u7acb\u6027\uff0c\u800c\u65e0\u653e\u56de\u62bd\u6837\u5219\u9002\u7528\u4e8e\u6709\u9650\u603b\u4f53\u3002<\/p>\n<\/p>\n<\/li>\n
                4. \n
                  \u4f7f\u7528\u968f\u673a\u79cd\u5b50<\/strong>\uff1a\u4e3a\u4e86\u7ed3\u679c\u7684\u53ef\u91cd\u590d\u6027\uff0c\u5efa\u8bae\u5728\u62bd\u6837\u524d\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u6bcf\u6b21\u8fd0\u884c\u4ee3\u7801\u65f6\u5f97\u5230\u76f8\u540c\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                  np.random.seed(42)  # \u8bbe\u7f6e\u968f\u673a\u79cd\u5b50<\/p>\n
                  sample = np.random.choice(data, size=3, replace=True)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u901a\u8fc7\u4ee5\u4e0a\u5185\u5bb9\u7684\u4ecb\u7ecd\uff0c\u76f8\u4fe1\u4f60\u5bf9\u5982\u4f55\u5728Python\u4e2d\u8fdb\u884c\u91cd\u590d\u62bd\u6837\u5df2\u7ecf\u6709\u4e86\u8f83\u4e3a\u5168\u9762\u7684\u4e86\u89e3\u3002\u65e0\u8bba\u662f\u4f7f\u7528random<\/code>\u6a21\u5757\u7684\u7b80\u5355\u62bd\u6837\uff0c\u8fd8\u662f\u5229\u7528numpy<\/code>\u548cpandas<\/code>\u5e93\u8fdb\u884c\u590d\u6742\u7684\u6570\u636e\u5904\u7406\uff0c\u90fd\u80fd\u5e2e\u52a9\u4f60\u5728\u6570\u636e\u5206\u6790\u548c\u673a\u5668\u5b66\u4e60\u4e2d\u66f4\u597d\u5730\u5e94\u7528\u91cd\u590d\u62bd\u6837\u6280\u672f\u3002\u5e0c\u671b\u8fd9\u4e9b\u65b9\u6cd5\u80fd\u591f\u4e3a\u4f60\u7684\u6570\u636e\u79d1\u5b66\u9879\u76ee\u63d0\u4f9b\u5e2e\u52a9\u3002<\/p>\n<\/p>\n
                  \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                   \u5728Python\u4e2d\uff0c\u5982\u4f55\u5b9e\u73b0\u91cd\u590d\u62bd\u6837\u7684\u529f\u80fd\uff1f<\/strong>
                  \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528numpy<\/code>\u5e93\u7684random.choice()<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u91cd\u590d\u62bd\u6837\u3002\u901a\u8fc7\u8bbe\u7f6ereplace=True<\/code>\uff0c\u53ef\u4ee5\u5141\u8bb8\u9009\u51fa\u7684\u5143\u7d20\u5728\u540e\u7eed\u62bd\u6837\u4e2d\u518d\u6b21\u88ab\u9009\u4e2d\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff1a  <\/p>\n
                  import numpy as np\n\ndata = [1, 2, 3, 4, 5]\nsamples = np.random.choice(data, size=10, replace=True)\nprint(samples)\n<\/code><\/pre>\n
                  \u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u4ecedata<\/code>\u5217\u8868\u4e2d\u91cd\u590d\u62bd\u53d610\u4e2a\u5143\u7d20\u3002<\/p>\n
                  \u91cd\u590d\u62bd\u6837\u5728\u6570\u636e\u5206\u6790\u4e2d\u6709\u4ec0\u4e48\u5e94\u7528\uff1f<\/strong>
                  \u91cd\u590d\u62bd\u6837\u5728\u6570\u636e\u5206\u6790\u4e2d\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5982\u81ea\u52a9\u6cd5\uff08Bootstrap\uff09\u548c\u4ea4\u53c9\u9a8c\u8bc1\u3002\u81ea\u52a9\u6cd5\u53ef\u4ee5\u7528\u6765\u4f30\u8ba1\u6837\u672c\u7edf\u8ba1\u91cf\u7684\u5206\u5e03\uff0c\u800c\u4ea4\u53c9\u9a8c\u8bc1\u5219\u7528\u4e8e\u9a8c\u8bc1\u6a21\u578b\u7684\u8868\u73b0\u3002\u8fd9\u4e9b\u65b9\u6cd5\u5e2e\u52a9\u5206\u6790\u5e08\u4ece\u6709\u9650\u7684\u6570\u636e\u4e2d\u63a8\u65ad\u51fa\u66f4\u5e7f\u6cdb\u7684\u7ed3\u8bba\u3002<\/p>\n
                  \u5982\u4f55\u63a7\u5236\u62bd\u6837\u7684\u6570\u91cf\u548c\u8303\u56f4\uff1f<\/strong>
                  \u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6esize<\/code>\u53c2\u6570\u6765\u63a7\u5236\u62bd\u6837\u7684\u6570\u91cf\u3002\u5982\u679c\u9700\u8981\u9650\u5236\u62bd\u6837\u7684\u8303\u56f4\uff0c\u53ef\u4ee5\u5728\u9009\u62e9\u6570\u636e\u4e4b\u524d\u8fc7\u6ee4\u6570\u636e\u96c6\uff0c\u6216\u8005\u5728random.choice()<\/code>\u4e2d\u4f7f\u7528\u751f\u6210\u7684\u5b50\u96c6\u3002\u4f8b\u5982\uff0c\u5982\u679c\u53ea\u60f3\u4ece\u7279\u5b9a\u7684\u8303\u56f4\u5185\u62bd\u6837\uff0c\u53ef\u4ee5\u5148\u521b\u5efa\u4e00\u4e2a\u65b0\u7684\u5217\u8868\u6216\u6570\u7ec4\uff0c\u7136\u540e\u518d\u8fdb\u884c\u62bd\u6837\u3002\u8fd9\u6837\u53ef\u4ee5\u786e\u4fdd\u62bd\u6837\u7684\u5143\u7d20\u7b26\u5408\u7279\u5b9a\u7684\u6761\u4ef6\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d\uff1a\u5728Python\u4e2d\uff0c\u91cd\u590d\u62bd\u6837\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528\u5e93\u5982random\u3001numpy\u548cpandas\u6765\u5b9e\u73b0\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c […]","protected":false},"author":3,"featured_media":949024,"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\/949019"}],"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=949019"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/949019\/revisions"}],"predecessor-version":[{"id":949025,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/949019\/revisions\/949025"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/949024"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=949019"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=949019"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=949019"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}