{"id":943583,"date":"2024-12-26T22:43:53","date_gmt":"2024-12-26T14:43:53","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/943583.html"},"modified":"2024-12-26T22:43:55","modified_gmt":"2024-12-26T14:43:55","slug":"python-cdf%e5%a6%82%e4%bd%95%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/943583.html","title":{"rendered":"python cdf\u5982\u4f55\u5b9e\u73b0"},"content":{"rendered":"

\"python<\/p>\n

\u5b9e\u73b0Python\u4e2d\u7684\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\uff08CDF\uff09\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6765\u5b8c\u6210\uff0c\u4f7f\u7528SciPy\u5e93\u4e2d\u7684scipy.stats<\/code>\u6a21\u5757\u3001\u901a\u8fc7NumPy\u8fdb\u884c\u81ea\u5b9a\u4e49\u5b9e\u73b0\u3001\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5206\u6790<\/strong>\u3002\u4e0b\u9762\u5c06\u5bf9\u5176\u4e2d\u4e00\u79cd\u65b9\u6cd5\u8fdb\u884c\u8be6\u7ec6\u63cf\u8ff0\uff1a\u4f7f\u7528SciPy\u5e93\u4e2d\u7684scipy.stats<\/code>\u6a21\u5757\u662f\u5b9e\u73b0\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\u7684\u7b80\u4fbf\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4f7f\u7528SciPy\u5e93\u4e2d\u7684scipy.stats\u6a21\u5757<\/strong>\uff1aSciPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u79d1\u5b66\u8ba1\u7b97\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u7edf\u8ba1\u51fd\u6570\uff0c\u5305\u62ec\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\u3002\u901a\u8fc7scipy.stats<\/code>\u6a21\u5757\uff0c\u4f60\u53ef\u4ee5\u8f7b\u677e\u5730\u8ba1\u7b97\u5404\u79cd\u7edf\u8ba1\u5206\u5e03\u7684CDF\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5177\u4f53\u5b9e\u73b0\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n

SciPy\u5e93\u4e2d\u7684scipy.stats<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u591a\u79cd\u7edf\u8ba1\u5206\u5e03\u7684\u5b9e\u73b0\uff0c\u5982\u6b63\u6001\u5206\u5e03\u3001\u6cca\u677e\u5206\u5e03\u3001\u6307\u6570\u5206\u5e03\u7b49\u3002\u6bcf\u79cd\u5206\u5e03\u90fd\u6709\u4e00\u4e2acdf\u65b9\u6cd5\uff0c\u7528\u4e8e\u8ba1\u7b97\u7ed9\u5b9a\u503c\u7684\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u6b63\u6001\u5206\u5e03\uff0c\u53ef\u4ee5\u4f7f\u7528norm.cdf()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u67d0\u4e2a\u503c\u7684CDF\u3002<\/p>\n<\/p>\n

from scipy.stats import norm<\/p>\n
\u5b9a\u4e49\u6b63\u6001\u5206\u5e03\u7684\u5747\u503c\u548c\u6807\u51c6\u5dee<\/strong><\/h2>\n
mu = 0<\/p>\n
sigma = 1<\/p>\n
\u8ba1\u7b97\u7ed9\u5b9a\u503c\u7684CDF<\/strong><\/h2>\n
value = 1.96<\/p>\n
cdf_value = norm.cdf(value, loc=mu, scale=sigma)<\/p>\n
print(f'The CDF of {value} for a normal distribution with mean {mu} and standard deviation {sigma} is {cdf_value}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4e86norm.cdf()<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u5747\u503c\u4e3a0\u3001\u6807\u51c6\u5dee\u4e3a1\u7684\u6807\u51c6\u6b63\u6001\u5206\u5e03\u4e2d\uff0c\u503c\u4e3a1.96\u7684CDF\u3002\u8fd9\u4e2a\u51fd\u6570\u7684\u8fd4\u56de\u503c\u662f0.975\uff0c\u8fd9\u610f\u5473\u7740\u5728\u6b64\u5206\u5e03\u4e2d\uff0c\u7ea6\u670997.5%\u7684\u6570\u636e\u70b9\u5c0f\u4e8e1.96\u3002<\/p>\n<\/p>\n
\u4e0b\u9762\u5c06\u4ece\u591a\u4e2a\u65b9\u9762\u6df1\u5165\u63a2\u8ba8\u5982\u4f55\u5728Python\u4e2d\u5b9e\u73b0CDF\u53ca\u5176\u5e94\u7528\u3002<\/p>\n<\/p>\n
\u4e00\u3001CDF\u7684\u5b9a\u4e49\u4e0e\u91cd\u8981\u6027<\/p>\n<\/p>\n
\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\uff08CDF\uff09\u662f\u7edf\u8ba1\u5b66\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u6982\u5ff5\uff0c\u5b83\u63cf\u8ff0\u4e86\u968f\u673a\u53d8\u91cfX\u5c0f\u4e8e\u6216\u7b49\u4e8e\u67d0\u4e2a\u503cx\u7684\u6982\u7387\u3002\u5bf9\u4e8e\u4e00\u4e2a\u7ed9\u5b9a\u7684\u6982\u7387\u5206\u5e03\uff0cCDF\u662f\u4e00\u4e2a\u4ece\u5b9e\u6570\u5230[0,1]\u533a\u95f4\u7684\u975e\u9012\u51cf\u51fd\u6570\u3002\u8ba1\u7b97CDF\u7684\u76ee\u7684\u662f\u4e3a\u4e86\u7406\u89e3\u548c\u5206\u6790\u6570\u636e\u7684\u5206\u5e03\u7279\u5f81\u3002<\/p>\n<\/p>\n
    \n
  1. \n
    \u7edf\u8ba1\u5b66\u4e2d\u7684CDF<\/strong>\uff1a\u5728\u7edf\u8ba1\u5b66\u4e2d\uff0cCDF\u7528\u4e8e\u63cf\u8ff0\u6570\u636e\u7684\u6574\u4f53\u5206\u5e03\u7279\u6027\u3002\u5b83\u80fd\u591f\u5e2e\u52a9\u6211\u4eec\u8bc6\u522b\u6570\u636e\u7684\u96c6\u4e2d\u8d8b\u52bf\u548c\u79bb\u6563\u7a0b\u5ea6\u3002CDF\u5bf9\u4e8e\u968f\u673a\u53d8\u91cf\u7684\u7814\u7a76\u81f3\u5173\u91cd\u8981\uff0c\u5b83\u53ef\u4ee5\u7528\u4e8e\u8ba1\u7b97\u6982\u7387\u3001\u8fdb\u884c\u5047\u8bbe\u68c0\u9a8c\u548c\u5efa\u7acb\u7f6e\u4fe1\u533a\u95f4\u3002<\/p>\n<\/p>\n<\/li>\n
  2. \n
    \u5de5\u7a0b\u4e0e\u79d1\u5b66\u5e94\u7528\u4e2d\u7684CDF<\/strong>\uff1a\u5728\u5de5\u7a0b\u548c\u79d1\u5b66\u9886\u57df\uff0cCDF\u88ab\u5e7f\u6cdb\u7528\u4e8e\u53ef\u9760\u6027\u5206\u6790\u3001\u98ce\u9669\u8bc4\u4f30\u548c\u4fe1\u53f7\u5904\u7406\u7b49\u9886\u57df\u3002\u4f8b\u5982\uff0c\u5728\u53ef\u9760\u6027\u5206\u6790\u4e2d\uff0cCDF\u7528\u4e8e\u8ba1\u7b97\u7cfb\u7edf\u5728\u7279\u5b9a\u65f6\u95f4\u5185\u5931\u8d25\u7684\u6982\u7387\u3002\u5728\u4fe1\u53f7\u5904\u7406\u4e2d\uff0cCDF\u7528\u4e8e\u5206\u6790\u4fe1\u53f7\u7684\u5e45\u5ea6\u5206\u5e03\u7279\u6027\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
    \u4e8c\u3001\u4f7f\u7528SciPy\u5e93\u8ba1\u7b97\u4e0d\u540c\u5206\u5e03\u7684CDF<\/p>\n<\/p>\n
    SciPy\u5e93\u63d0\u4f9b\u4e86\u591a\u79cd\u7edf\u8ba1\u5206\u5e03\u7684\u5b9e\u73b0\uff0c\u4e0b\u9762\u5c06\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528SciPy\u5e93\u8ba1\u7b97\u4e0d\u540c\u5206\u5e03\u7684CDF\uff0c\u5305\u62ec\u6b63\u6001\u5206\u5e03\u3001\u6cca\u677e\u5206\u5e03\u548c\u6307\u6570\u5206\u5e03\u3002<\/p>\n<\/p>\n
      \n
    1. \u6b63\u6001\u5206\u5e03\u7684CDF<\/strong>\uff1a\u6b63\u6001\u5206\u5e03\u662f\u6700\u5e38\u89c1\u7684\u8fde\u7eed\u6982\u7387\u5206\u5e03\u4e4b\u4e00\u3002\u5b83\u7531\u4e24\u4e2a\u53c2\u6570\uff08\u5747\u503c\u548c\u6807\u51c6\u5dee\uff09\u5b9a\u4e49\u3002\u4f7f\u7528scipy.stats.norm<\/code>\u6a21\u5757\u53ef\u4ee5\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u7684CDF\u3002<\/li>\n<\/ol>\n
      from scipy.stats import norm<\/p>\n
      \u8ba1\u7b97\u6807\u51c6\u6b63\u6001\u5206\u5e03\u4e2d\u503c\u4e3a2\u7684CDF<\/strong><\/h2>\n
      cdf_value = norm.cdf(2)<\/p>\n
      print(f'The CDF of 2 for a standard normal distribution is {cdf_value}')<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u6cca\u677e\u5206\u5e03\u7684CDF<\/strong>\uff1a\u6cca\u677e\u5206\u5e03\u662f\u4e00\u79cd\u5e38\u7528\u7684\u79bb\u6563\u6982\u7387\u5206\u5e03\uff0c\u901a\u5e38\u7528\u4e8e\u63cf\u8ff0\u5728\u56fa\u5b9a\u65f6\u95f4\u95f4\u9694\u5185\u4e8b\u4ef6\u53d1\u751f\u7684\u6b21\u6570\u3002\u4f7f\u7528scipy.stats.poisson<\/code>\u6a21\u5757\u53ef\u4ee5\u8ba1\u7b97\u6cca\u677e\u5206\u5e03\u7684CDF\u3002<\/li>\n<\/ol>\n
        from scipy.stats import poisson<\/p>\n
        \u8ba1\u7b97\u53c2\u6570lambda=3\u7684\u6cca\u677e\u5206\u5e03\u4e2d\u503c\u4e3a2\u7684CDF<\/strong><\/h2>\n
        cdf_value = poisson.cdf(2, mu=3)<\/p>\n
        print(f'The CDF of 2 for a Poisson distribution with lambda=3 is {cdf_value}')<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u6307\u6570\u5206\u5e03\u7684CDF<\/strong>\uff1a\u6307\u6570\u5206\u5e03\u662f\u4e00\u79cd\u8fde\u7eed\u6982\u7387\u5206\u5e03\uff0c\u901a\u5e38\u7528\u4e8e\u63cf\u8ff0\u4e8b\u4ef6\u53d1\u751f\u7684\u65f6\u95f4\u95f4\u9694\u3002\u4f7f\u7528scipy.stats.expon<\/code>\u6a21\u5757\u53ef\u4ee5\u8ba1\u7b97\u6307\u6570\u5206\u5e03\u7684CDF\u3002<\/li>\n<\/ol>\n
          from scipy.stats import expon<\/p>\n
          \u8ba1\u7b97\u53c2\u6570lambda=1\u7684\u6307\u6570\u5206\u5e03\u4e2d\u503c\u4e3a2\u7684CDF<\/strong><\/h2>\n
          cdf_value = expon.cdf(2, scale=1)<\/p>\n
          print(f'The CDF of 2 for an exponential distribution with lambda=1 is {cdf_value}')<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001\u901a\u8fc7NumPy\u5b9e\u73b0\u81ea\u5b9a\u4e49\u7684CDF<\/p>\n<\/p>\n
          \u5982\u679c\u9700\u8981\u81ea\u5b9a\u4e49\u5b9e\u73b0CDF\uff0c\u53ef\u4ee5\u4f7f\u7528NumPy\u5e93\u6765\u8fdb\u884c\u8ba1\u7b97\u3002NumPy\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u6570\u7ec4\u8fd0\u7b97\u548c\u968f\u673a\u6570\u751f\u6210\u529f\u80fd\u3002<\/p>\n<\/p>\n
            \n
          1. \u751f\u6210\u968f\u673a\u6837\u672c<\/strong>\uff1a\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u751f\u6210\u4e00\u7ec4\u968f\u673a\u6837\u672c\uff0c\u8fd9\u53ef\u4ee5\u4f7f\u7528NumPy\u7684random<\/code>\u6a21\u5757\u6765\u5b9e\u73b0\u3002<\/li>\n<\/ol>\n
            import numpy as np<\/p>\n
            \u751f\u62101000\u4e2a\u5747\u503c\u4e3a0\u3001\u6807\u51c6\u5dee\u4e3a1\u7684\u6b63\u6001\u5206\u5e03\u968f\u673a\u6837\u672c<\/strong><\/h2>\n
            samples = np.random.normal(0, 1, 1000)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u8ba1\u7b97CDF<\/strong>\uff1a\u8ba1\u7b97CDF\u7684\u8fc7\u7a0b\u5305\u62ec\u5bf9\u6837\u672c\u8fdb\u884c\u6392\u5e8f\uff0c\u7136\u540e\u8ba1\u7b97\u5c0f\u4e8e\u6216\u7b49\u4e8e\u6bcf\u4e2a\u6837\u672c\u503c\u7684\u6982\u7387\u3002<\/li>\n<\/ol>\n
              # \u5bf9\u6837\u672c\u8fdb\u884c\u6392\u5e8f<\/p>\n
              sorted_samples = np.sort(samples)<\/p>\n
              \u8ba1\u7b97CDF<\/strong><\/h2>\n
              cdf_values = np.arange(1, len(sorted_samples) + 1) \/ len(sorted_samples)<\/p>\n
              \u7ed8\u5236CDF\u66f2\u7ebf<\/strong><\/h2>\n
              import matplotlib.pyplot as plt<\/p>\n
              plt.plot(sorted_samples, cdf_values)<\/p>\n
              plt.xlabel('Sample Value')<\/p>\n
              plt.ylabel('CDF')<\/p>\n
              plt.title('CDF of Normal Distribution Samples')<\/p>\n
              plt.show()<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u751f\u6210\u4e861000\u4e2a\u6b63\u6001\u5206\u5e03\u968f\u673a\u6837\u672c\uff0c\u7136\u540e\u5bf9\u6837\u672c\u8fdb\u884c\u6392\u5e8f\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u6837\u672c\u503c\u7684CDF\u3002\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u4e86CDF\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
              \u56db\u3001\u4f7f\u7528Pandas\u8fdb\u884c\u6570\u636e\u5206\u6790<\/p>\n<\/p>\n
              Pandas\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u5e93\uff0c\u5b83\u63d0\u4f9b\u4e86\u8bb8\u591a\u65b9\u4fbf\u7684\u6570\u636e\u64cd\u4f5c\u51fd\u6570\u3002\u4f7f\u7528Pandas\u53ef\u4ee5\u8f7b\u677e\u5730\u8ba1\u7b97\u6570\u636e\u6846\u4e2d\u6bcf\u4e2a\u53d8\u91cf\u7684CDF\u3002<\/p>\n<\/p>\n
                \n
              1. \u52a0\u8f7d\u6570\u636e<\/strong>\uff1a\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u52a0\u8f7d\u6570\u636e\uff0c\u8fd9\u53ef\u4ee5\u4f7f\u7528Pandas\u7684read_csv<\/code>\u51fd\u6570\u6765\u5b9e\u73b0\u3002<\/li>\n<\/ol>\n
                import pandas as pd<\/p>\n
                \u4eceCSV\u6587\u4ef6\u52a0\u8f7d\u6570\u636e<\/strong><\/h2>\n
                data = pd.read_csv('data.csv')<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u8ba1\u7b97CDF<\/strong>\uff1a\u5bf9\u4e8e\u6570\u636e\u6846\u4e2d\u7684\u6bcf\u4e2a\u53d8\u91cf\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Pandas\u7684rank<\/code>\u548ccount<\/code>\u51fd\u6570\u6765\u8ba1\u7b97\u5176CDF\u3002<\/li>\n<\/ol>\n
                  # \u8ba1\u7b97\u53d8\u91cfx\u7684CDF<\/p>\n
                  data['x_cdf'] = data['x'].rank(method='average') \/ len(data['x'])<\/p>\n
                  \u7ed8\u5236CDF\u66f2\u7ebf<\/strong><\/h2>\n
                  plt.plot(data['x'], data['x_cdf'])<\/p>\n
                  plt.xlabel('Value of x')<\/p>\n
                  plt.ylabel('CDF')<\/p>\n
                  plt.title('CDF of x')<\/p>\n
                  plt.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Pandas\u7684rank<\/code>\u51fd\u6570\u8ba1\u7b97\u4e86\u53d8\u91cfx\u7684\u6392\u540d\uff0c\u7136\u540e\u7528\u6392\u540d\u9664\u4ee5\u6837\u672c\u603b\u6570\u5f97\u5230CDF\u3002\u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528Matplotlib\u5e93\u7ed8\u5236\u4e86CDF\u66f2\u7ebf\u3002<\/p>\n<\/p>\n
                  \u4e94\u3001CDF\u7684\u5e94\u7528<\/p>\n<\/p>\n
                  CDF\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u5177\u6709\u91cd\u8981\u4f5c\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5177\u4f53\u5e94\u7528\u5b9e\u4f8b\uff1a<\/p>\n<\/p>\n
                    \n
                  1. \n
                    \u6982\u7387\u8ba1\u7b97<\/strong>\uff1aCDF\u53ef\u4ee5\u7528\u4e8e\u8ba1\u7b97\u5728\u7279\u5b9a\u6982\u7387\u5206\u5e03\u4e0b\uff0c\u968f\u673a\u53d8\u91cf\u53d6\u503c\u5728\u67d0\u4e2a\u533a\u95f4\u5185\u7684\u6982\u7387\u3002\u4f8b\u5982\uff0c\u5728\u6b63\u6001\u5206\u5e03\u4e2d\uff0cCDF\u53ef\u4ee5\u7528\u4e8e\u8ba1\u7b97\u968f\u673a\u53d8\u91cf\u53d6\u503c\u5c0f\u4e8e\u67d0\u4e2a\u503c\u7684\u6982\u7387\u3002<\/p>\n<\/p>\n<\/li>\n
                  2. \n
                    \u5047\u8bbe\u68c0\u9a8c<\/strong>\uff1aCDF\u53ef\u4ee5\u7528\u4e8e\u7edf\u8ba1\u5b66\u4e2d\u7684\u5047\u8bbe\u68c0\u9a8c\u3002\u901a\u8fc7\u6bd4\u8f83\u6837\u672c\u6570\u636e\u7684CDF\u4e0e\u7406\u8bba\u5206\u5e03\u7684CDF\uff0c\u53ef\u4ee5\u5224\u65ad\u6837\u672c\u6570\u636e\u662f\u5426\u7b26\u5408\u67d0\u4e2a\u7edf\u8ba1\u5047\u8bbe\u3002<\/p>\n<\/p>\n<\/li>\n
                  3. \n
                    \u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316<\/strong>\uff1a\u5728\u6570\u636e\u5206\u6790\u4e2d\uff0cCDF\u53ef\u4ee5\u7528\u4e8e\u4e86\u89e3\u6570\u636e\u7684\u5206\u5e03\u7279\u6027\u3002\u901a\u8fc7\u7ed8\u5236CDF\u66f2\u7ebf\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u89c2\u5bdf\u6570\u636e\u7684\u96c6\u4e2d\u8d8b\u52bf\u548c\u79bb\u6563\u7a0b\u5ea6\u3002<\/p>\n<\/p>\n<\/li>\n
                  4. \n
                    \u98ce\u9669\u8bc4\u4f30<\/strong>\uff1a\u5728\u91d1\u878d\u548c\u5de5\u7a0b\u9886\u57df\uff0cCDF\u7528\u4e8e\u98ce\u9669\u8bc4\u4f30\u548c\u51b3\u7b56\u5206\u6790\u3002\u901a\u8fc7\u8ba1\u7b97\u635f\u5931\u5206\u5e03\u7684CDF\uff0c\u53ef\u4ee5\u8bc4\u4f30\u4e0d\u540c\u98ce\u9669\u60c5\u666f\u4e0b\u7684\u635f\u5931\u6982\u7387\u3002<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
                    \u603b\u7ed3\uff1a\u5b9e\u73b0Python\u4e2d\u7684\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\uff08CDF\uff09\u6709\u591a\u79cd\u65b9\u6cd5\uff0c\u4f7f\u7528SciPy\u5e93\u4e2d\u7684scipy.stats<\/code>\u6a21\u5757\u662f\u6700\u7b80\u4fbf\u7684\u65b9\u6cd5\u4e4b\u4e00<\/strong>\u3002\u901a\u8fc7\u5b66\u4e60\u548c\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5e2e\u52a9\u4f60\u5728\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u5b66\u5e94\u7528\u4e2d\u66f4\u597d\u5730\u7406\u89e3\u548c\u5229\u7528CDF\u3002\u65e0\u8bba\u662f\u4f7f\u7528SciPy\u5e93\u3001NumPy\u5e93\uff0c\u8fd8\u662fPandas\u5e93\uff0c\u6bcf\u79cd\u65b9\u6cd5\u90fd\u6709\u5176\u72ec\u7279\u7684\u4f18\u52bf\u548c\u9002\u7528\u573a\u666f\u3002\u5e0c\u671b\u901a\u8fc7\u672c\u6587\u7684\u4ecb\u7ecd\uff0c\u80fd\u591f\u5e2e\u52a9\u4f60\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u66f4\u597d\u5730\u5b9e\u73b0\u548c\u5e94\u7528CDF\u3002<\/p>\n<\/p>\n
                    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                     \u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\uff08CDF\uff09\uff1f<\/strong>
                    \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528SciPy\u5e93\u6765\u8ba1\u7b97\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\u3002\u901a\u8fc7scipy.stats<\/code>\u6a21\u5757\uff0c\u4f60\u53ef\u4ee5\u9009\u62e9\u7279\u5b9a\u7684\u5206\u5e03\uff08\u5982\u6b63\u6001\u5206\u5e03\u3001\u6cca\u677e\u5206\u5e03\u7b49\uff09\uff0c\u5e76\u4f7f\u7528\u5176CDF\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c\u4f7f\u7528scipy.stats.norm.cdf()<\/code>\u53ef\u4ee5\u8ba1\u7b97\u6b63\u6001\u5206\u5e03\u7684CDF\u503c\u3002\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86SciPy\u5e93\uff0c\u53ef\u4ee5\u901a\u8fc7pip install scipy<\/code>\u6765\u5b89\u88c5\u3002<\/p>\n
                    \u4f7f\u7528Pandas\u5b9e\u73b0CDF\u7684\u6b65\u9aa4\u662f\u4ec0\u4e48\uff1f<\/strong>
                    Pandas\u63d0\u4f9b\u4e86\u65b9\u4fbf\u7684\u6570\u636e\u5904\u7406\u529f\u80fd\uff0c\u53ef\u4ee5\u7528\u6765\u8ba1\u7b97\u6570\u636e\u96c6\u7684CDF\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7\u5bf9\u6570\u636e\u8fdb\u884c\u6392\u5e8f\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u503c\u7684\u6392\u540d\u6765\u5b9e\u73b0\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u53ef\u4ee5\u4f7f\u7528data.rank()<\/code>\u8ba1\u7b97\u6392\u540d\uff0c\u7136\u540e\u5c06\u5176\u9664\u4ee5\u603b\u6570\uff0c\u5f97\u5230\u6bcf\u4e2a\u6570\u636e\u70b9\u7684CDF\u503c\u3002\u8fd9\u6837\uff0cCDF\u5c31\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684Pandas\u64cd\u4f5c\u5b9e\u73b0\u3002<\/p>\n
                    \u5982\u4f55\u53ef\u89c6\u5316Python\u4e2d\u7684CDF\uff1f<\/strong>
                    \u53ef\u89c6\u5316CDF\u901a\u5e38\u4f7f\u7528Matplotlib\u5e93\u3002\u4f60\u53ef\u4ee5\u5148\u8ba1\u7b97CDF\u503c\uff0c\u7136\u540e\u4f7f\u7528plt.plot()<\/code>\u51fd\u6570\u6765\u7ed8\u5236\u66f2\u7ebf\u3002\u5bf9\u4e8e\u79bb\u6563\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528plt.step()<\/code>\u51fd\u6570\u6765\u521b\u5efa\u9636\u68af\u56fe\u3002\u786e\u4fdd\u5728\u56fe\u8868\u4e2d\u6807\u6ce8X\u8f74\u548cY\u8f74\uff0c\u6e05\u6670\u5730\u8868\u793a\u6570\u636e\u7684\u5206\u5e03\u60c5\u51b5\uff0c\u4ece\u800c\u5e2e\u52a9\u89c2\u4f17\u66f4\u597d\u5730\u7406\u89e3CDF\u7684\u542b\u4e49\u3002<\/p>\n
                    \u5728Python\u4e2d\u5982\u4f55\u5904\u7406\u591a\u7ef4\u6570\u636e\u7684CDF\uff1f<\/strong>
                    \u8ba1\u7b97\u591a\u7ef4\u6570\u636e\u7684CDF\u53ef\u4ee5\u4f7f\u7528numpy<\/code>\u548cscipy<\/code>\u7ed3\u5408\u7684\u65b9\u6cd5\u3002\u9996\u5148\uff0c\u4f60\u9700\u8981\u5c06\u591a\u7ef4\u6570\u636e\u8f6c\u6362\u4e3a\u4e00\u7ef4\u6570\u636e\uff0c\u6216\u8005\u4e3a\u6bcf\u4e2a\u7ef4\u5ea6\u5206\u522b\u8ba1\u7b97CDF\u3002\u5bf9\u4e8e\u9ad8\u7ef4\u6570\u636e\uff0c\u53ef\u80fd\u9700\u8981\u4f7f\u7528\u5206\u5e03\u7684\u8054\u5408CDF\u3002\u4f8b\u5982\uff0c\u53ef\u4ee5\u4f7f\u7528scipy.stats.multivariate_normal<\/code>\u6765\u5904\u7406\u591a\u5143\u6b63\u6001\u5206\u5e03\u7684CDF\u8ba1\u7b97\u3002\u5904\u7406\u591a\u7ef4\u6570\u636e\u65f6\uff0c\u8bb0\u5f97\u8003\u8651\u6570\u636e\u7684\u76f8\u5173\u6027\u548c\u5206\u5e03\u7279\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5b9e\u73b0Python\u4e2d\u7684\u7d2f\u79ef\u5206\u5e03\u51fd\u6570\uff08CDF\uff09\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u6765\u5b8c\u6210\uff0c\u4f7f\u7528SciPy\u5e93\u4e2d\u7684scipy.stats\u6a21 […]","protected":false},"author":3,"featured_media":943586,"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\/943583"}],"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=943583"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/943583\/revisions"}],"predecessor-version":[{"id":943587,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/943583\/revisions\/943587"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/943586"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=943583"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=943583"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=943583"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}