{"id":1158070,"date":"2025-01-13T18:36:14","date_gmt":"2025-01-13T10:36:14","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1158070.html"},"modified":"2025-01-13T18:36:17","modified_gmt":"2025-01-13T10:36:17","slug":"python%e5%a6%82%e4%bd%95%e6%9f%a5%e7%9c%8b%e6%95%b0%e6%8d%ae%e5%88%86%e5%b8%83","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1158070.html","title":{"rendered":"python\u5982\u4f55\u67e5\u770b\u6570\u636e\u5206\u5e03"},"content":{"rendered":"

\"python\u5982\u4f55\u67e5\u770b\u6570\u636e\u5206\u5e03\"<\/p>\n

\u5f00\u5934\u6bb5\u843d:<\/p>\n

\u8981\u67e5\u770b\u6570\u636e\u5206\u5e03\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3001Seaborn\u5e93\u3001Matplotlib\u5e93\u3001Scipy\u5e93<\/strong>\u3002\u5176\u4e2d\uff0cPandas\u5e93\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u53ef\u4ee5\u5feb\u901f\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\uff1bSeaborn\u5e93\u548cMatplotlib\u5e93\u5219\u53ef\u4ee5\u901a\u8fc7\u7ed8\u5236\u56fe\u8868\u6765\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u5206\u5e03\uff1bScipy\u5e93\u5219\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u5de5\u5177\uff0c\u53ef\u4ee5\u8fdb\u884c\u66f4\u6df1\u5165\u7684\u6570\u636e\u5206\u6790\u3002\u4f7f\u7528Seaborn\u5e93\u53ef\u4ee5\u7ed8\u5236\u76f4\u65b9\u56fe\uff08Histogram\uff09\u6765\u8be6\u7ec6\u5c55\u793a\u6570\u636e\u5206\u5e03<\/strong>\u3002\u76f4\u65b9\u56fe\u901a\u8fc7\u5c06\u6570\u636e\u5206\u6210\u82e5\u5e72\u4e2a\u533a\u95f4\uff0c\u5e76\u7edf\u8ba1\u6bcf\u4e2a\u533a\u95f4\u7684\u6570\u636e\u6570\u91cf\uff0c\u4ece\u800c\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u5e93\u67e5\u770b\u6570\u636e\u5206\u5e03<\/p>\n<\/p>\n

Pandas\u662fPython\u6570\u636e\u5206\u6790\u4e2d\u6700\u5e38\u7528\u7684\u5e93\u4e4b\u4e00\uff0c\u5b83\u4e0d\u4ec5\u80fd\u5904\u7406\u6570\u636e\uff0c\u8fd8\u80fd\u5feb\u901f\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\u3002\u4f7f\u7528Pandas\u5e93\u67e5\u770b\u6570\u636e\u5206\u5e03\u7684\u65b9\u6cd5\u6709\uff1a<\/p>\n<\/p>\n

    \n
  1. describe()<\/code> \u65b9\u6cd5<\/li>\n
  2. value_counts()<\/code> \u65b9\u6cd5<\/li>\n
  3. groupby()<\/code> \u548c agg()<\/code> \u65b9\u6cd5<\/li>\n<\/ol>\n

    1. describe()<\/code> \u65b9\u6cd5<\/h3>\n<\/p>\n

    Pandas\u7684 describe()<\/code> \u65b9\u6cd5\u53ef\u4ee5\u5feb\u901f\u67e5\u770b\u6570\u636e\u7684\u7edf\u8ba1\u4fe1\u606f\uff0c\u5305\u62ec\u8ba1\u6570\u3001\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u6700\u5c0f\u503c\u3001\u56db\u5206\u4f4d\u6570\u548c\u6700\u5927\u503c\u7b49\u3002\u8fd9\u4e9b\u7edf\u8ba1\u4fe1\u606f\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5feb\u901f\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
    data = pd.DataFrame({<\/p>\n
        'age': [23, 45, 12, 35, 40, 30, 25, 19, 28, 33]<\/p>\n
    })<\/p>\n
    \u67e5\u770b\u6570\u636e\u7684\u7edf\u8ba1\u4fe1\u606f<\/strong><\/h2>\n
    print(data.describe())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    2. value_counts()<\/code> \u65b9\u6cd5<\/h3>\n<\/p>\n
    value_counts()<\/code> \u65b9\u6cd5\u53ef\u4ee5\u7edf\u8ba1\u6bcf\u4e2a\u503c\u51fa\u73b0\u7684\u9891\u6b21\uff0c\u5bf9\u4e8e\u5206\u7c7b\u6570\u636e\u7279\u522b\u6709\u7528\u3002\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6570\u636e\u4e2d\u6bcf\u4e2a\u7c7b\u522b\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
    # \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n
    data = pd.Series(['apple', 'banana', 'orange', 'apple', 'banana', 'apple'])<\/p>\n
    \u67e5\u770b\u6bcf\u4e2a\u503c\u7684\u9891\u6b21<\/strong><\/h2>\n
    print(data.value_counts())<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    3. groupby()<\/code> \u548c agg()<\/code> \u65b9\u6cd5<\/h3>\n<\/p>\n
    groupby()<\/code> \u548c agg()<\/code> \u65b9\u6cd5\u53ef\u4ee5\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\uff0c\u5e76\u5e94\u7528\u805a\u5408\u51fd\u6570\u3002\u8fd9\u79cd\u65b9\u6cd5\u5728\u5904\u7406\u590d\u6742\u6570\u636e\u96c6\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
    # \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n
    data = pd.DataFrame({<\/p>\n
        'category': ['A', 'B', 'A', 'B', 'A', 'B'],<\/p>\n
        'value': [10, 15, 10, 20, 10, 25]<\/p>\n
    })<\/p>\n
    \u6309\u7c7b\u522b\u5206\u7ec4\u5e76\u8ba1\u7b97\u5747\u503c<\/strong><\/h2>\n
    grouped_data = data.groupby('category').agg({'value': 'mean'})<\/p>\n
    print(grouped_data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u4e8c\u3001SEABORN\u5e93\u67e5\u770b\u6570\u636e\u5206\u5e03<\/p>\n<\/p>\n
    Seaborn\u662f\u4e00\u4e2a\u57fa\u4e8eMatplotlib\u7684\u7edf\u8ba1\u6570\u636e\u53ef\u89c6\u5316\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u7528\u4e8e\u7ed8\u5236\u7edf\u8ba1\u56fe\u8868\u7684\u51fd\u6570\u3002\u4f7f\u7528Seaborn\u5e93\u67e5\u770b\u6570\u636e\u5206\u5e03\u7684\u65b9\u6cd5\u6709\uff1a<\/p>\n<\/p>\n
      \n
    1. \u7ed8\u5236\u76f4\u65b9\u56fe<\/li>\n
    2. \u7ed8\u5236\u5bc6\u5ea6\u56fe<\/li>\n
    3. \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/li>\n<\/ol>\n
      1. \u7ed8\u5236\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
      \u76f4\u65b9\u56fe\u662f\u5c55\u793a\u6570\u636e\u5206\u5e03\u6700\u5e38\u7528\u7684\u56fe\u8868\u4e4b\u4e00\u3002\u5b83\u901a\u8fc7\u5c06\u6570\u636e\u5206\u6210\u82e5\u5e72\u4e2a\u533a\u95f4\uff0c\u5e76\u7edf\u8ba1\u6bcf\u4e2a\u533a\u95f4\u7684\u6570\u636e\u6570\u91cf\uff0c\u4ece\u800c\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
      import seaborn as sns<\/p>\n
      import matplotlib.pyplot as plt<\/p>\n
      \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
      data = [23, 45, 12, 35, 40, 30, 25, 19, 28, 33]<\/p>\n
      \u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
      sns.histplot(data, kde=True)<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      2. \u7ed8\u5236\u5bc6\u5ea6\u56fe<\/h3>\n<\/p>\n
      \u5bc6\u5ea6\u56fe\u662f\u76f4\u65b9\u56fe\u7684\u5e73\u6ed1\u7248\u672c\uff0c\u901a\u8fc7\u4f30\u8ba1\u6570\u636e\u7684\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\u6765\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
      # \u7ed8\u5236\u5bc6\u5ea6\u56fe<\/p>\n
      sns.kdeplot(data)<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      3. \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/h3>\n<\/p>\n
      \u7bb1\u7ebf\u56fe\u662f\u4e00\u79cd\u5e38\u7528\u7684\u7edf\u8ba1\u56fe\u8868\uff0c\u53ef\u4ee5\u5c55\u793a\u6570\u636e\u7684\u4e2d\u4f4d\u6570\u3001\u56db\u5206\u4f4d\u6570\u548c\u5f02\u5e38\u503c\u7b49\u4fe1\u606f\u3002<\/p>\n<\/p>\n
      # \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n
      data = pd.DataFrame({<\/p>\n
          'age': [23, 45, 12, 35, 40, 30, 25, 19, 28, 33]<\/p>\n
      })<\/p>\n
      \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
      sns.boxplot(data['age'])<\/p>\n
      plt.show()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e09\u3001MATPLOTLIB\u5e93\u67e5\u770b\u6570\u636e\u5206\u5e03<\/p>\n<\/p>\n
      Matplotlib\u662fPython\u4e2d\u6700\u57fa\u7840\u7684\u7ed8\u56fe\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7ed8\u56fe\u529f\u80fd\u3002\u4f7f\u7528Matplotlib\u5e93\u67e5\u770b\u6570\u636e\u5206\u5e03\u7684\u65b9\u6cd5\u6709\uff1a<\/p>\n<\/p>\n
        \n
      1. \u7ed8\u5236\u76f4\u65b9\u56fe<\/li>\n
      2. \u7ed8\u5236\u6563\u70b9\u56fe<\/li>\n
      3. \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/li>\n<\/ol>\n
        1. \u7ed8\u5236\u76f4\u65b9\u56fe<\/h3>\n<\/p>\n
        \u76f4\u65b9\u56fe\u662f\u5c55\u793a\u6570\u636e\u5206\u5e03\u6700\u5e38\u7528\u7684\u56fe\u8868\u4e4b\u4e00\u3002\u5b83\u901a\u8fc7\u5c06\u6570\u636e\u5206\u6210\u82e5\u5e72\u4e2a\u533a\u95f4\uff0c\u5e76\u7edf\u8ba1\u6bcf\u4e2a\u533a\u95f4\u7684\u6570\u636e\u6570\u91cf\uff0c\u4ece\u800c\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
        import matplotlib.pyplot as plt<\/p>\n
        \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
        data = [23, 45, 12, 35, 40, 30, 25, 19, 28, 33]<\/p>\n
        \u7ed8\u5236\u76f4\u65b9\u56fe<\/strong><\/h2>\n
        plt.hist(data, bins=10, edgecolor='black')<\/p>\n
        plt.xlabel('Age')<\/p>\n
        plt.ylabel('Frequency')<\/p>\n
        plt.title('Age Distribution')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        2. \u7ed8\u5236\u6563\u70b9\u56fe<\/h3>\n<\/p>\n
        \u6563\u70b9\u56fe\u53ef\u4ee5\u5c55\u793a\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u901a\u8fc7\u89c2\u5bdf\u6563\u70b9\u56fe\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u53ef\u4ee5\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
        # \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n
        data = {<\/p>\n
            'age': [23, 45, 12, 35, 40, 30, 25, 19, 28, 33],<\/p>\n
            'score': [85, 90, 78, 92, 88, 76, 80, 83, 87, 91]<\/p>\n
        }<\/p>\n
        \u7ed8\u5236\u6563\u70b9\u56fe<\/strong><\/h2>\n
        plt.scatter(data['age'], data['score'])<\/p>\n
        plt.xlabel('Age')<\/p>\n
        plt.ylabel('Score')<\/p>\n
        plt.title('Age vs Score')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        3. \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/h3>\n<\/p>\n
        \u7bb1\u7ebf\u56fe\u662f\u4e00\u79cd\u5e38\u7528\u7684\u7edf\u8ba1\u56fe\u8868\uff0c\u53ef\u4ee5\u5c55\u793a\u6570\u636e\u7684\u4e2d\u4f4d\u6570\u3001\u56db\u5206\u4f4d\u6570\u548c\u5f02\u5e38\u503c\u7b49\u4fe1\u606f\u3002<\/p>\n<\/p>\n
        # \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n
        data = [23, 45, 12, 35, 40, 30, 25, 19, 28, 33]<\/p>\n
        \u7ed8\u5236\u7bb1\u7ebf\u56fe<\/strong><\/h2>\n
        plt.boxplot(data)<\/p>\n
        plt.xlabel('Age')<\/p>\n
        plt.title('Age Distribution')<\/p>\n
        plt.show()<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u56db\u3001SCIPY\u5e93\u67e5\u770b\u6570\u636e\u5206\u5e03<\/p>\n<\/p>\n
        Scipy\u662f\u4e00\u4e2a\u57fa\u4e8eNumpy\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u5de5\u5177\u3002\u4f7f\u7528Scipy\u5e93\u67e5\u770b\u6570\u636e\u5206\u5e03\u7684\u65b9\u6cd5\u6709\uff1a<\/p>\n<\/p>\n
          \n
        1. \u8ba1\u7b97\u57fa\u672c\u7edf\u8ba1\u91cf<\/li>\n
        2. \u7ed8\u5236\u6982\u7387\u5bc6\u5ea6\u51fd\u6570<\/li>\n
        3. \u8fdb\u884c\u6b63\u6001\u6027\u68c0\u9a8c<\/li>\n<\/ol>\n
          1. \u8ba1\u7b97\u57fa\u672c\u7edf\u8ba1\u91cf<\/h3>\n<\/p>\n
          Scipy\u5e93\u53ef\u4ee5\u8ba1\u7b97\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u91cf\uff0c\u5305\u62ec\u5747\u503c\u3001\u6807\u51c6\u5dee\u3001\u504f\u5ea6\u548c\u5cf0\u5ea6\u7b49\u3002<\/p>\n<\/p>\n
          from scipy import stats<\/p>\n
          \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
          data = [23, 45, 12, 35, 40, 30, 25, 19, 28, 33]<\/p>\n
          \u8ba1\u7b97\u5747\u503c\u548c\u6807\u51c6\u5dee<\/strong><\/h2>\n
          mean = stats.tmean(data)<\/p>\n
          std_dev = stats.tstd(data)<\/p>\n
          print(f'Mean: {mean}, Standard Deviation: {std_dev}')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          2. \u7ed8\u5236\u6982\u7387\u5bc6\u5ea6\u51fd\u6570<\/h3>\n<\/p>\n
          Scipy\u5e93\u53ef\u4ee5\u7ed8\u5236\u6570\u636e\u7684\u6982\u7387\u5bc6\u5ea6\u51fd\u6570\uff0c\u4ece\u800c\u5c55\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002<\/p>\n<\/p>\n
          import numpy as np<\/p>\n
          import matplotlib.pyplot as plt<\/p>\n
          from scipy.stats import norm<\/p>\n
          \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/strong><\/h2>\n
          data = [23, 45, 12, 35, 40, 30, 25, 19, 28, 33]<\/p>\n
          \u7ed8\u5236\u6982\u7387\u5bc6\u5ea6\u51fd\u6570<\/strong><\/h2>\n
          density = stats.gaussian_kde(data)<\/p>\n
          x = np.linspace(min(data), max(data), 100)<\/p>\n
          plt.plot(x, density(x))<\/p>\n
          plt.xlabel('Age')<\/p>\n
          plt.ylabel('Density')<\/p>\n
          plt.title('Age Distribution')<\/p>\n
          plt.show()<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          3. \u8fdb\u884c\u6b63\u6001\u6027\u68c0\u9a8c<\/h3>\n<\/p>\n
          Scipy\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u6b63\u6001\u6027\u68c0\u9a8c\u65b9\u6cd5\uff0c\u53ef\u4ee5\u68c0\u9a8c\u6570\u636e\u662f\u5426\u7b26\u5408\u6b63\u6001\u5206\u5e03\u3002<\/p>\n<\/p>\n
          # \u521b\u5efa\u4e00\u4e2a\u793a\u4f8b\u6570\u636e\u96c6<\/p>\n
          data = [23, 45, 12, 35, 40, 30, 25, 19, 28, 33]<\/p>\n
          \u8fdb\u884cShapiro-Wilk\u6b63\u6001\u6027\u68c0\u9a8c<\/strong><\/h2>\n
          stat, p_value = stats.shapiro(data)<\/p>\n
          print(f'Statistic: {stat}, P-value: {p_value}')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e94\u3001\u603b\u7ed3<\/p>\n<\/p>\n
          \u67e5\u770b\u6570\u636e\u5206\u5e03\u662f\u6570\u636e\u5206\u6790\u4e2d\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u5b83\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u4e86\u89e3\u6570\u636e\u7684\u57fa\u672c\u60c5\u51b5\uff0c\u4ece\u800c\u4e3a\u540e\u7eed\u7684\u5206\u6790\u548c\u5efa\u6a21\u63d0\u4f9b\u4f9d\u636e\u3002Pandas\u5e93\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u6570\u636e\u64cd\u4f5c\u529f\u80fd\uff0c\u53ef\u4ee5\u5feb\u901f\u67e5\u770b\u6570\u636e\u7684\u57fa\u672c\u7edf\u8ba1\u4fe1\u606f\uff1bSeaborn\u5e93\u548cMatplotlib\u5e93\u5219\u53ef\u4ee5\u901a\u8fc7\u7ed8\u5236\u56fe\u8868\u6765\u76f4\u89c2\u5730\u5c55\u793a\u6570\u636e\u5206\u5e03\uff1bScipy\u5e93\u5219\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u7edf\u8ba1\u5de5\u5177\uff0c\u53ef\u4ee5\u8fdb\u884c\u66f4\u6df1\u5165\u7684\u6570\u636e\u5206\u6790\u3002<\/p>\n<\/p>\n
          \u65e0\u8bba\u662f\u4f7f\u7528\u54ea\u79cd\u65b9\u6cd5\uff0c\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u90fd\u662f\u6570\u636e\u5206\u6790\u7684\u57fa\u7840\u3002\u901a\u8fc7\u5408\u7406\u5730\u9009\u62e9\u548c\u5e94\u7528\u8fd9\u4e9b\u5de5\u5177\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u5206\u6790\u6570\u636e\uff0c\u4ece\u800c\u505a\u51fa\u66f4\u51c6\u786e\u7684\u51b3\u7b56\u3002<\/p>\n<\/p>\n
          \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
           \u5982\u4f55\u4f7f\u7528Python\u67e5\u770b\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\uff1f<\/strong>
          \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u5e93\u6765\u67e5\u770b\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\u3002\u6700\u5e38\u7528\u7684\u5e93\u5305\u62ecPandas\u3001Matplotlib\u548cSeaborn\u3002\u901a\u8fc7\u8fd9\u4e9b\u5de5\u5177\uff0c\u4f60\u53ef\u4ee5\u7ed8\u5236\u76f4\u65b9\u56fe\u3001\u5bc6\u5ea6\u56fe\u6216\u7bb1\u7ebf\u56fe\uff0c\u4ee5\u4fbf\u76f4\u89c2\u5c55\u793a\u6570\u636e\u5206\u5e03\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Seaborn\u7684distplot()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u751f\u6210\u6570\u636e\u7684\u5bc6\u5ea6\u56fe\u548c\u76f4\u65b9\u56fe\uff0c\u5e2e\u52a9\u4f60\u7406\u89e3\u6570\u636e\u7684\u5206\u5e03\u7279\u5f81\u3002<\/p>\n
          \u6211\u5e94\u8be5\u9009\u62e9\u54ea\u4e9b\u56fe\u8868\u6765\u8868\u793a\u6570\u636e\u7684\u5206\u5e03\uff1f<\/strong>
          \u9009\u62e9\u5408\u9002\u7684\u56fe\u8868\u53d6\u51b3\u4e8e\u6570\u636e\u7684\u7c7b\u578b\u548c\u5206\u6790\u76ee\u7684\u3002\u76f4\u65b9\u56fe\u9002\u5408\u4e8e\u67e5\u770b\u8fde\u7eed\u6570\u636e\u7684\u5206\u5e03\uff0c\u800c\u7bb1\u7ebf\u56fe\u5219\u6709\u52a9\u4e8e\u8bc6\u522b\u6570\u636e\u7684\u4e2d\u5fc3\u8d8b\u52bf\u53ca\u5f02\u5e38\u503c\u3002\u5bc6\u5ea6\u56fe\u63d0\u4f9b\u4e86\u6570\u636e\u5206\u5e03\u7684\u5e73\u6ed1\u4f30\u8ba1\uff0c\u9002\u5408\u4e8e\u6bd4\u8f83\u4e0d\u540c\u6570\u636e\u96c6\u7684\u5206\u5e03\u3002\u53ef\u4ee5\u6839\u636e\u9700\u8981\u4f7f\u7528Matplotlib\u6216Seaborn\u6765\u521b\u5efa\u8fd9\u4e9b\u56fe\u8868\u3002<\/p>\n
          \u5982\u4f55\u8bc4\u4f30\u6570\u636e\u5206\u5e03\u7684\u504f\u6001\u548c\u5cf0\u6001\uff1f<\/strong>
          \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u504f\u5ea6\u548c\u5cf0\u5ea6\u6765\u8bc4\u4f30\u6570\u636e\u7684\u5206\u5e03\u7279\u6027\u3002\u504f\u5ea6\u8868\u793a\u5206\u5e03\u7684\u5bf9\u79f0\u6027\uff0c\u800c\u5cf0\u5ea6\u5219\u8861\u91cf\u5206\u5e03\u7684\u5c16\u5ced\u7a0b\u5ea6\u3002\u4f7f\u7528Pandas\u5e93\u7684skew()<\/code>\u548ckurtosis()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u83b7\u5f97\u8fd9\u4e9b\u7edf\u8ba1\u91cf\u3002\u4e86\u89e3\u8fd9\u4e9b\u6307\u6807\u6709\u52a9\u4e8e\u5224\u65ad\u6570\u636e\u662f\u5426\u7b26\u5408\u6b63\u6001\u5206\u5e03\uff0c\u4ece\u800c\u51b3\u5b9a\u4f7f\u7528\u4f55\u79cd\u7edf\u8ba1\u5206\u6790\u65b9\u6cd5\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5f00\u5934\u6bb5\u843d: \u8981\u67e5\u770b\u6570\u636e\u5206\u5e03\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u3001Seaborn\u5e93\u3001Matplotlib\u5e93\u3001Scipy\u5e93\u3002\u5176 […]","protected":false},"author":3,"featured_media":1158074,"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\/1158070"}],"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=1158070"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1158070\/revisions"}],"predecessor-version":[{"id":1158075,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1158070\/revisions\/1158075"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1158074"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1158070"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1158070"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1158070"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}