{"id":960317,"date":"2024-12-27T03:48:41","date_gmt":"2024-12-26T19:48:41","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/960317.html"},"modified":"2024-12-27T03:48:43","modified_gmt":"2024-12-26T19:48:43","slug":"python%e5%a6%82%e4%bd%95%e7%ad%9b%e9%80%89%e7%bc%ba%e5%a4%b1%e5%80%bc","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/960317.html","title":{"rendered":"python\u5982\u4f55\u7b5b\u9009\u7f3a\u5931\u503c"},"content":{"rendered":"

\"python\u5982\u4f55\u7b5b\u9009\u7f3a\u5931\u503c\"<\/p>\n

Python\u7b5b\u9009\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\u4e3b\u8981\u5305\u62ec\uff1a\u4f7f\u7528Pandas\u5e93\u7684isnull()<\/code>\u548cnotnull()<\/code>\u51fd\u6570\u3001dropna()<\/code>\u65b9\u6cd5\u3001fillna()<\/code>\u65b9\u6cd5\u3001\u4ee5\u53ca\u7ed3\u5408\u6761\u4ef6\u9009\u62e9\u6570\u636e\u3002<\/strong>\u5176\u4e2d\uff0c\u4f7f\u7528isnull()<\/code>\u548cnotnull()<\/code>\u51fd\u6570\u53ef\u4ee5\u5feb\u901f\u8bc6\u522b\u7f3a\u5931\u503c\uff0c\u901a\u8fc7dropna()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u76f4\u63a5\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\uff0c\u800cfillna()<\/code>\u65b9\u6cd5\u5219\u53ef\u4ee5\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c\u3002\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u7684\u4f7f\u7528\u3002<\/p>\n<\/p>\n

\u4e00\u3001PANDAS\u5e93\u7684ISNULL()\u548cNOTNULL()\u51fd\u6570<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u6765\u5904\u7406\u7f3a\u5931\u503c\u3002isnull()<\/code>\u548cnotnull()<\/code>\u51fd\u6570\u662f\u8bc6\u522b\u7f3a\u5931\u503c\u7684\u57fa\u7840\u5de5\u5177\u3002<\/p>\n<\/p>\n

    \n
  1. \u4f7f\u7528isnull()<\/code>\u51fd\u6570<\/strong><\/li>\n<\/ol>\n

    isnull()<\/code>\u51fd\u6570\u53ef\u4ee5\u68c0\u6d4b\u6570\u636e\u6846\u6216\u7cfb\u5217\u4e2d\u7684\u7f3a\u5931\u503c\uff0c\u5e76\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u503c\u6570\u7ec4\uff0c\u7f3a\u5931\u503c\u5bf9\u5e94True\uff0c\u975e\u7f3a\u5931\u503c\u5bf9\u5e94False\u3002<\/p>\n<\/p>\n

    import pandas as pd<\/p>\n
    \u521b\u5efa\u4e00\u4e2a\u793a\u4f8bDataFrame<\/strong><\/h2>\n
    data = {'A': [1, 2, None, 4],<\/p>\n
            'B': [None, 2, 3, 4],<\/p>\n
            'C': [1, None, None, 4]}<\/p>\n
    df = pd.DataFrame(data)<\/p>\n
    \u4f7f\u7528isnull()\u68c0\u6d4b\u7f3a\u5931\u503c<\/strong><\/h2>\n
    missing_values = df.isnull()<\/p>\n
    print(missing_values)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u4f7f\u7528notnull()<\/code>\u51fd\u6570<\/strong><\/li>\n<\/ol>\n
      \u4e0eisnull()<\/code>\u76f8\u53cd\uff0cnotnull()<\/code>\u51fd\u6570\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u503c\u6570\u7ec4\uff0c\u975e\u7f3a\u5931\u503c\u5bf9\u5e94True\uff0c\u7f3a\u5931\u503c\u5bf9\u5e94False\u3002<\/p>\n<\/p>\n
      # \u4f7f\u7528notnull()\u68c0\u6d4b\u975e\u7f3a\u5931\u503c<\/p>\n
      non_missing_values = df.notnull()<\/p>\n
      print(non_missing_values)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u4e8c\u3001DROPNA()\u65b9\u6cd5<\/p>\n<\/p>\n
      \u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\uff0c\u6709\u65f6\u5019\u9700\u8981\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3002dropna()<\/code>\u65b9\u6cd5\u63d0\u4f9b\u4e86\u8fd9\u79cd\u529f\u80fd\u3002<\/p>\n<\/p>\n
        \n
      1. \u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c<\/strong><\/li>\n<\/ol>\n
        \u901a\u8fc7\u8bbe\u7f6eaxis=0<\/code>\uff08\u9ed8\u8ba4\u503c\uff09\uff0c\u53ef\u4ee5\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u3002<\/p>\n<\/p>\n
        # \u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
        df_dropped_rows = df.dropna()<\/p>\n
        print(df_dropped_rows)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
          \n
        1. \u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u5217<\/strong><\/li>\n<\/ol>\n
          \u901a\u8fc7\u8bbe\u7f6eaxis=1<\/code>\uff0c\u53ef\u4ee5\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u5217\u3002<\/p>\n<\/p>\n
          # \u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u5217<\/p>\n
          df_dropped_columns = df.dropna(axis=1)<\/p>\n
          print(df_dropped_columns)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u4e09\u3001FILLNA()\u65b9\u6cd5<\/p>\n<\/p>\n
          \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u5220\u9664\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u53ef\u80fd\u4f1a\u4e22\u5931\u91cd\u8981\u4fe1\u606f\u3002fillna()<\/code>\u65b9\u6cd5\u53ef\u4ee5\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c\uff0c\u4ece\u800c\u4fdd\u7559\u6570\u636e\u6846\u7684\u7ed3\u6784\u3002<\/p>\n<\/p>\n
            \n
          1. \u7528\u5e38\u6570\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/li>\n<\/ol>\n
            \u53ef\u4ee5\u7528\u4e00\u4e2a\u5e38\u6570\u586b\u5145\u6240\u6709\u7684\u7f3a\u5931\u503c\u3002<\/p>\n<\/p>\n
            # \u75280\u586b\u5145\u7f3a\u5931\u503c<\/p>\n
            df_filled = df.fillna(0)<\/p>\n
            print(df_filled)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
              \n
            1. \u7528\u5e73\u5747\u503c\u586b\u5145\u7f3a\u5931\u503c<\/strong><\/li>\n<\/ol>\n
              \u53ef\u4ee5\u7528\u5217\u7684\u5e73\u5747\u503c\u586b\u5145\u7f3a\u5931\u503c\uff0c\u8fd9\u5728\u6570\u503c\u6570\u636e\u4e2d\u975e\u5e38\u5e38\u89c1\u3002<\/p>\n<\/p>\n
              # \u7528\u5217\u5e73\u5747\u503c\u586b\u5145\u7f3a\u5931\u503c<\/p>\n
              df_filled_mean = df.fillna(df.mean())<\/p>\n
              print(df_filled_mean)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u56db\u3001\u7ed3\u5408\u6761\u4ef6\u9009\u62e9\u6570\u636e<\/p>\n<\/p>\n
              \u6709\u65f6\u5019\uff0c\u6211\u4eec\u9700\u8981\u7ed3\u5408\u6761\u4ef6\u9009\u62e9\u7279\u5b9a\u7684\u542b\u6709\u7f3a\u5931\u503c\u6216\u975e\u7f3a\u5931\u503c\u7684\u6570\u636e\u3002<\/p>\n<\/p>\n
                \n
              1. \u7b5b\u9009\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c<\/strong><\/li>\n<\/ol>\n
                \u53ef\u4ee5\u4f7f\u7528\u5e03\u5c14\u7d22\u5f15\u7ed3\u5408isnull()<\/code>\u51fd\u6570\u7b5b\u9009\u51fa\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c\u3002<\/p>\n<\/p>\n
                # \u7b5b\u9009\u51fa\u542b\u6709\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
                rows_with_missing = df[df.isnull().any(axis=1)]<\/p>\n
                print(rows_with_missing)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u7b5b\u9009\u975e\u7f3a\u5931\u503c\u7684\u884c<\/strong><\/li>\n<\/ol>\n
                  \u540c\u6837\uff0c\u4f7f\u7528notnull()<\/code>\u51fd\u6570\u53ef\u4ee5\u7b5b\u9009\u51fa\u4e0d\u542b\u7f3a\u5931\u503c\u7684\u884c\u3002<\/p>\n<\/p>\n
                  # \u7b5b\u9009\u51fa\u4e0d\u542b\u7f3a\u5931\u503c\u7684\u884c<\/p>\n
                  rows_without_missing = df[df.notnull().all(axis=1)]<\/p>\n
                  print(rows_without_missing)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u603b\u7ed3\uff1a\u5728Python\u4e2d\u5904\u7406\u7f3a\u5931\u503c\uff0cPandas\u5e93\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u5de5\u5177\u548c\u65b9\u6cd5\u3002\u7406\u89e3\u5982\u4f55\u8bc6\u522b\u3001\u5220\u9664\u548c\u586b\u5145\u7f3a\u5931\u503c\u662f\u6570\u636e\u6e05\u6d17\u7684\u91cd\u8981\u73af\u8282\uff0c\u8fd9\u4e9b\u64cd\u4f5c\u53ef\u4ee5\u5e2e\u52a9\u6211\u4eec\u5728\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u4e2d\u66f4\u597d\u5730\u51c6\u5907\u548c\u5904\u7406\u6570\u636e\u3002<\/p>\n<\/p>\n
                  \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                   \u5728Python\u4e2d\uff0c\u5982\u4f55\u68c0\u6d4b\u6570\u636e\u96c6\u4e2d\u7684\u7f3a\u5931\u503c\uff1f<\/strong>
                  \u53ef\u4ee5\u4f7f\u7528Pandas\u5e93\u4e2d\u7684isnull()<\/code>\u6216isna()<\/code>\u51fd\u6570\u6765\u68c0\u6d4b\u7f3a\u5931\u503c\u3002\u8fd9\u4e9b\u51fd\u6570\u4f1a\u8fd4\u56de\u4e00\u4e2a\u5e03\u5c14\u503c\u7684DataFrame\uff0c\u6807\u8bc6\u54ea\u4e9b\u503c\u662f\u7f3a\u5931\u7684\u3002\u7ed3\u5408sum()<\/code>\u51fd\u6570\uff0c\u53ef\u4ee5\u5f97\u5230\u6bcf\u4e00\u5217\u7f3a\u5931\u503c\u7684\u603b\u6570\u3002\u4f8b\u5982\uff1a  <\/p>\n
                  import pandas as pd\n\ndata = pd.read_csv('data.csv')\nmissing_values = data.isnull().sum()\nprint(missing_values)\n<\/code><\/pre>\n
                  \u5982\u4f55\u5728Python\u4e2d\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\uff1f<\/strong>
                  \u5728Pandas\u4e2d\uff0c\u4f7f\u7528dropna()<\/code>\u51fd\u6570\u53ef\u4ee5\u8f7b\u677e\u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\u6216\u5217\u3002\u901a\u8fc7\u8bbe\u7f6eaxis=0<\/code>\u5220\u9664\u884c\uff0c\u8bbe\u7f6eaxis=1<\/code>\u5220\u9664\u5217\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
                  # \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u884c\ncleaned_data = data.dropna(axis=0)\n\n# \u5220\u9664\u5305\u542b\u7f3a\u5931\u503c\u7684\u5217\ncleaned_data = data.dropna(axis=1)\n<\/code><\/pre>\n
                  \u5728Python\u4e2d\uff0c\u5982\u4f55\u7528\u7279\u5b9a\u503c\u586b\u5145\u7f3a\u5931\u503c\uff1f<\/strong>
                  \u53ef\u4ee5\u4f7f\u7528fillna()<\/code>\u51fd\u6570\u6765\u586b\u5145\u7f3a\u5931\u503c\u3002\u6b64\u51fd\u6570\u5141\u8bb8\u60a8\u6307\u5b9a\u4e00\u4e2a\u503c\u6765\u66ff\u6362\u7f3a\u5931\u503c\uff0c\u6216\u4f7f\u7528\u5176\u4ed6\u5217\u7684\u7edf\u8ba1\u503c\uff08\u5982\u5747\u503c\u6216\u4e2d\u4f4d\u6570\uff09\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a  <\/p>\n
                  # \u75280\u586b\u5145\u7f3a\u5931\u503c\nfilled_data = data.fillna(0)\n\n# \u7528\u6bcf\u5217\u7684\u5747\u503c\u586b\u5145\u7f3a\u5931\u503c\nfilled_data = data.fillna(data.mean())\n<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"Python\u7b5b\u9009\u7f3a\u5931\u503c\u7684\u65b9\u6cd5\u4e3b\u8981\u5305\u62ec\uff1a\u4f7f\u7528Pandas\u5e93\u7684isnull()\u548cnotnull()\u51fd\u6570\u3001dropn […]","protected":false},"author":3,"featured_media":960327,"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\/960317"}],"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=960317"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/960317\/revisions"}],"predecessor-version":[{"id":960330,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/960317\/revisions\/960330"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/960327"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=960317"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=960317"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=960317"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}