{"id":950404,"date":"2024-12-27T00:34:45","date_gmt":"2024-12-26T16:34:45","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/950404.html"},"modified":"2024-12-27T00:34:48","modified_gmt":"2024-12-26T16:34:48","slug":"python%e5%a6%82%e4%bd%95%e6%b8%85%e9%99%a4nans","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/950404.html","title":{"rendered":"python\u5982\u4f55\u6e05\u9664nans"},"content":{"rendered":"

\"python\u5982\u4f55\u6e05\u9664nans\"<\/p>\n

\u5728Python\u4e2d\u6e05\u9664NaNs\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pandas\u5e93\u7684dropna()<\/code>\u65b9\u6cd5\u3001\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u66ff\u6362NaNs\u3001\u4ee5\u53ca\u4f7f\u7528NumPy\u7684\u5e03\u5c14\u7d22\u5f15\u3002<\/strong> \u5176\u4e2d\uff0cdropna()<\/code>\u53ef\u4ee5\u76f4\u63a5\u5220\u9664\u542b\u6709NaNs\u7684\u884c\u6216\u5217\uff1bfillna()<\/code>\u53ef\u4ee5\u7528\u7279\u5b9a\u503c\u6216\u63d2\u503c\u65b9\u6cd5\u66ff\u6362NaNs\uff1bNumPy\u5e03\u5c14\u7d22\u5f15\u5219\u53ef\u4ee5\u7528\u6765\u7b5b\u9009\u51fa\u975eNaN\u7684\u503c\u3002\u4e0b\u9762\u6211\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u793a\u4f8b\u4ee3\u7801\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u5e93\u4e2d\u7684DROPNA()\u65b9\u6cd5<\/p>\n<\/p>\n

dropna()<\/code>\u662fpandas\u5e93\u4e2d\u7528\u4e8e\u5220\u9664NaN\u503c\u7684\u4fbf\u6377\u65b9\u6cd5\u3002\u53ef\u4ee5\u6839\u636e\u9700\u6c42\u9009\u62e9\u5220\u9664\u884c\u6216\u5217\u3002<\/p>\n<\/p>\n

    \n
  1. \u5220\u9664\u542b\u6709NaN\u7684\u884c<\/strong><\/li>\n<\/ol>\n

    \u4f7f\u7528dropna()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u8f7b\u677e\u5220\u9664DataFrame\u4e2d\u4efb\u4f55\u5305\u542bNaN\u503c\u7684\u884c\u3002\u8fd9\u5728\u6570\u636e\u9884\u5904\u7406\u4e2d\u5c24\u5176\u6709\u7528\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u521b\u5efa\u4e00\u4e2a\u5305\u542bNaN\u503c\u7684DataFrame<\/strong><\/h2>\n
    data = {'A': [1, 2, None, 4],<\/p>\n
            'B': [5, None, 7, 8],<\/p>\n
            'C': [None, 10, 11, 12]}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u5220\u9664\u542b\u6709NaN\u503c\u7684\u884c<\/strong><\/h2>\n
    df_cleaned = df.dropna()<\/p>\n
    print(df_cleaned)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cdropna()<\/code>\u65b9\u6cd5\u4f1a\u5220\u9664DataFrame\u4e2d\u4efb\u4f55\u542b\u6709NaN\u503c\u7684\u884c\uff0c\u8fd4\u56de\u4e00\u4e2a\u65b0\u7684DataFrame\u3002<\/p>\n<\/p>\n
      \n
    1. \u5220\u9664\u542b\u6709NaN\u7684\u5217<\/strong><\/li>\n<\/ol>\n
      \u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u53c2\u6570\u6765\u5220\u9664\u542b\u6709NaN\u503c\u7684\u5217\uff0c\u800c\u4e0d\u662f\u884c\u3002<\/p>\n<\/p>\n
      # \u5220\u9664\u542b\u6709NaN\u503c\u7684\u5217<\/p>\n
      df_cleaned_columns = df.dropna(axis=1)<\/p>\n
      print(df_cleaned_columns)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4f7f\u7528dropna(axis=1)<\/code>\u53ef\u4ee5\u5220\u9664DataFrame\u4e2d\u4efb\u4f55\u542b\u6709NaN\u503c\u7684\u5217\u3002<\/p>\n<\/p>\n
      \u4e8c\u3001PANDAS\u5e93\u4e2d\u7684FILLNA()\u65b9\u6cd5<\/p>\n<\/p>\n
      fillna()<\/code>\u65b9\u6cd5\u7528\u6765\u66ff\u6362DataFrame\u4e2d\u7684NaN\u503c\uff0c\u53ef\u4ee5\u7528\u7279\u5b9a\u7684\u503c\u6216\u65b9\u6cd5\u8fdb\u884c\u66ff\u6362\u3002<\/p>\n<\/p>\n
        \n
      1. \u7528\u7279\u5b9a\u503c\u66ff\u6362NaN<\/strong><\/li>\n<\/ol>\n
        \u53ef\u4ee5\u7528\u7279\u5b9a\u7684\u503c\u66ff\u6362DataFrame\u4e2d\u7684NaN\u503c\u3002<\/p>\n<\/p>\n
        # \u75280\u66ff\u6362NaN\u503c<\/p>\n
        df_filled = df.fillna(0)<\/p>\n
        print(df_filled)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6240\u6709\u7684NaN\u503c\u90fd\u88ab\u66ff\u6362\u4e3a0\u3002<\/p>\n<\/p>\n
          \n
        1. \u7528\u5217\u7684\u5747\u503c\u66ff\u6362NaN<\/strong><\/li>\n<\/ol>\n
          \u53ef\u4ee5\u7528\u5217\u7684\u5747\u503c\u6765\u66ff\u6362NaN\u503c\uff0c\u8fd9\u662f\u6570\u636e\u6e05\u6d17\u4e2d\u7684\u5e38\u7528\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
          # \u7528\u5217\u7684\u5747\u503c\u66ff\u6362NaN\u503c<\/p>\n
          df_filled_mean = df.fillna(df.mean())<\/p>\n
          print(df_filled_mean)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0cdf.mean()<\/code>\u8ba1\u7b97\u6bcf\u5217\u7684\u5747\u503c\uff0c\u7136\u540e\u7528\u8fd9\u4e9b\u5747\u503c\u66ff\u6362NaN\u503c\u3002<\/p>\n<\/p>\n
          \u4e09\u3001NUMPY\u4e2d\u7684\u5e03\u5c14\u7d22\u5f15<\/p>\n<\/p>\n
          \u4f7f\u7528NumPy\u7684\u5e03\u5c14\u7d22\u5f15\u53ef\u4ee5\u7b5b\u9009\u51fa\u975eNaN\u7684\u503c\u3002<\/p>\n<\/p>\n
            \n
          1. \u7b5b\u9009\u51fa\u975eNaN\u7684\u503c<\/strong><\/li>\n<\/ol>\n
            import numpy as np<\/p>\n
            \u521b\u5efa\u4e00\u4e2a\u5305\u542bNaN\u503c\u7684NumPy\u6570\u7ec4<\/strong><\/h2>\n
            arr = np.array([1, 2, np.nan, 4, 5, np.nan, 7])<\/p>\n
            \u7b5b\u9009\u51fa\u975eNaN\u7684\u503c<\/strong><\/h2>\n
            non_nan_values = arr[~np.isnan(arr)]<\/p>\n
            print(non_nan_values)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c~np.isnan(arr)<\/code>\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u6570\u7ec4\uff0c\u7528\u6765\u7b5b\u9009\u51fa\u975eNaN\u7684\u503c\u3002<\/p>\n<\/p>\n
            \u56db\u3001\u7efc\u5408\u5e94\u7528\u5b9e\u4f8b<\/p>\n<\/p>\n
            \u7ed3\u5408\u4e0a\u8ff0\u65b9\u6cd5\uff0c\u4e0b\u9762\u662f\u4e00\u4e2a\u7efc\u5408\u5b9e\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u5728\u6570\u636e\u5206\u6790\u7684\u591a\u4e2a\u6b65\u9aa4\u4e2d\u5904\u7406NaN\u503c\u3002<\/p>\n<\/p>\n
              \n
            1. \u6570\u636e\u9884\u5904\u7406<\/strong><\/li>\n<\/ol>\n
              \u5728\u6570\u636e\u5206\u6790\u7684\u521d\u59cb\u9636\u6bb5\uff0c\u901a\u5e38\u9700\u8981\u9884\u5904\u7406\u6570\u636e\uff0c\u6e05\u7406\u6389\u4e0d\u5b8c\u6574\u7684\u8bb0\u5f55\u3002<\/p>\n<\/p>\n
              # \u5047\u8bbe\u6709\u4e00\u4e2a\u6570\u636e\u96c6\u9700\u8981\u6e05\u7406<\/p>\n
              data = {'Feature1': [1, 2, None, 4],<\/p>\n
                      'Feature2': [None, 2.5, 3.5, 4.5],<\/p>\n
                      'Feature3': [1.5, None, None, 4.0]}<\/p>\n
              df = pd.DataFrame(data)<\/p>\n
              \u5220\u9664\u542b\u6709NaN\u503c\u7684\u884c<\/strong><\/h2>\n
              df_cleaned = df.dropna()<\/p>\n
              \u7528\u7279\u5b9a\u503c\u586b\u5145NaN\u503c<\/strong><\/h2>\n
              df_filled = df.fillna({'Feature1': 0, 'Feature2': df['Feature2'].mean(), 'Feature3': df['Feature3'].median()})<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u6570\u636e\u5206\u6790<\/strong><\/li>\n<\/ol>\n
                \u5728\u6e05\u7406\u6570\u636e\u540e\uff0c\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790\u3002<\/p>\n<\/p>\n
                # \u8ba1\u7b97\u6bcf\u5217\u7684\u5747\u503c<\/p>\n
                mean_values = df_cleaned.mean()<\/p>\n
                print("Mean values of cleaned data:", mean_values)<\/p>\n
                \u4f7f\u7528NumPy\u8fdb\u884c\u8fdb\u4e00\u6b65\u7684\u5206\u6790<\/strong><\/h2>\n
                import matplotlib.pyplot as plt<\/p>\n
                \u7ed8\u5236\u975eNaN\u503c\u7684\u5206\u5e03<\/strong><\/h2>\n
                plt.hist(non_nan_values, bins=5)<\/p>\n
                plt.title("Distribution of Non-NaN Values")<\/p>\n
                plt.xlabel("Value")<\/p>\n
                plt.ylabel("Frequency")<\/p>\n
                plt.show()<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u901a\u8fc7\u4e0a\u8ff0\u6b65\u9aa4\uff0c\u786e\u4fdd\u6570\u636e\u5728\u8fdb\u884c\u5206\u6790\u4e4b\u524d\u662f\u5b8c\u6574\u4e14\u51c6\u786e\u7684\u3002\u5904\u7406NaN\u503c\u4e0d\u4ec5\u4ec5\u662f\u6570\u636e\u6e05\u7406\u7684\u4e00\u90e8\u5206\uff0c\u5b83\u5728\u6574\u4e2a\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u90fd\u626e\u6f14\u7740\u91cd\u8981\u89d2\u8272\u3002\u901a\u8fc7\u4f7f\u7528pandas\u548cNumPy\u7684\u5f3a\u5927\u529f\u80fd\uff0cPython\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u5de5\u5177\u6765\u5e94\u5bf9\u8fd9\u4e9b\u6311\u6218\u3002<\/p>\n<\/p>\n
                \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                 \u5728Python\u4e2d\uff0c\u5982\u4f55\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u7684NaN\u503c\uff1f<\/strong>
                \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528pandas<\/code>\u5e93\u8f7b\u677e\u8bc6\u522b\u6570\u636e\u96c6\u4e2d\u7684NaN\u503c\u3002\u901a\u8fc7isna()<\/code>\u6216isnull()<\/code>\u65b9\u6cd5\uff0c\u53ef\u4ee5\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u503c\u7684DataFrame\uff0c\u5176\u4e2dNaN\u503c\u5bf9\u5e94\u7684\u5143\u7d20\u4e3aTrue\u3002\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u7528\u6237\u53ef\u4ee5\u5feb\u901f\u5b9a\u4f4d\u548c\u5206\u6790\u6570\u636e\u4e2d\u7684\u7f3a\u5931\u503c\u3002<\/p>\n
                \u6e05\u9664NaN\u503c\u540e\uff0c\u6570\u636e\u5206\u6790\u7684\u7ed3\u679c\u4f1a\u53d7\u5230\u600e\u6837\u7684\u5f71\u54cd\uff1f<\/strong>
                \u6e05\u9664NaN\u503c\u53ef\u80fd\u4f1a\u5f71\u54cd\u6570\u636e\u5206\u6790\u7684\u7ed3\u679c\uff0c\u5c24\u5176\u662f\u5728\u6570\u636e\u96c6\u4e2d\u7f3a\u5931\u503c\u5360\u6bd4\u8f83\u9ad8\u7684\u60c5\u51b5\u4e0b\u3002\u867d\u7136\u53bb\u9664\u8fd9\u4e9b\u503c\u80fd\u591f\u63d0\u9ad8\u6570\u636e\u7684\u5b8c\u6574\u6027\uff0c\u4f46\u4e5f\u53ef\u80fd\u5bfc\u81f4\u6837\u672c\u91cf\u7684\u51cf\u5c11\uff0c\u8fdb\u800c\u5f71\u54cd\u7edf\u8ba1\u5206\u6790\u7684\u51c6\u786e\u6027\u3002\u56e0\u6b64\uff0c\u5728\u5904\u7406\u7f3a\u5931\u503c\u65f6\uff0c\u5efa\u8bae\u8003\u8651\u8865\u5145\u7f3a\u5931\u6570\u636e\u7684\u65b9\u6cd5\uff0c\u5982\u5747\u503c\u586b\u5145\u3001\u4e2d\u4f4d\u6570\u586b\u5145\u6216\u4f7f\u7528\u63d2\u503c\u6cd5\u3002<\/p>\n
                \u5728Python\u4e2d\uff0c\u4f7f\u7528\u4ec0\u4e48\u65b9\u6cd5\u53ef\u4ee5\u66ff\u6362NaN\u503c\u800c\u4e0d\u662f\u5220\u9664\u5b83\u4eec\uff1f<\/strong>
                \u7528\u6237\u53ef\u4ee5\u4f7f\u7528fillna()<\/code>\u65b9\u6cd5\u66ff\u6362NaN\u503c\u3002\u8be5\u65b9\u6cd5\u5141\u8bb8\u7528\u6237\u6307\u5b9a\u66ff\u6362\u7684\u503c\uff0c\u6bd4\u5982\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6307\u5b9a\u7684\u6570\u503c\u6216\u5176\u4ed6\u5217\u7684\u503c\u3002\u901a\u8fc7\u8fd9\u79cd\u65b9\u5f0f\uff0c\u53ef\u4ee5\u4fdd\u7559\u6570\u636e\u96c6\u7684\u5b8c\u6574\u6027\uff0c\u540c\u65f6\u5904\u7406\u7f3a\u5931\u6570\u636e\uff0c\u786e\u4fdd\u540e\u7eed\u7684\u6570\u636e\u5206\u6790\u66f4\u52a0\u51c6\u786e\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\u6e05\u9664NaNs\u7684\u5e38\u7528\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528pandas\u5e93\u7684dropna()\u65b9\u6cd5\u3001\u4f7f\u7528fillna()\u65b9\u6cd5\u66ff […]","protected":false},"author":3,"featured_media":950405,"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\/950404"}],"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=950404"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/950404\/revisions"}],"predecessor-version":[{"id":950406,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/950404\/revisions\/950406"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/950405"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=950404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=950404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=950404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}