{"id":550137,"date":"2025-03-18T11:00:12","date_gmt":"2025-03-18T10:00:12","guid":{"rendered":"https:\/\/blog.jetbrains.com\/?post_type=pycharm&p=550137"},"modified":"2025-09-15T17:00:11","modified_gmt":"2025-09-15T16:00:11","slug":"data-cleaning-in-data-science","status":"publish","type":"pycharm","link":"https:\/\/blog.jetbrains.com\/zh-hans\/pycharm\/2025\/03\/data-cleaning-in-data-science\/","title":{"rendered":"\u6570\u636e\u79d1\u5b66\u4e2d\u7684\u6570\u636e\u6e05\u7406"},"content":{"rendered":"

\u5728\u672c\u6570\u636e\u79d1\u5b66\u535a\u6587\u7cfb\u5217\u4e2d\uff0c\u6211\u4eec\u63a2\u8ba8\u4e86\u4ece\u54ea\u91cc\u83b7\u53d6\u6570\u636e<\/a>\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528 pandas \u63a2\u7d22\u6570\u636e<\/a>\uff0c\u4e0d\u8fc7\u867d\u7136\u8fd9\u4e9b\u6570\u636e\u5bf9\u4e8e\u5b66\u4e60\u975e\u5e38\u6709\u7528\uff0c\u4f46\u5b83\u4e0e\u6211\u4eec\u6240\u8bf4\u7684\u771f\u5b9e\u4e16\u754c<\/em>\u6570\u636e\u5e76\u4e0d\u76f8\u4f3c\u3002 \u7528\u4e8e\u5b66\u4e60\u7684\u6570\u636e\u901a\u5e38\u5df2\u7ecf\u8fc7\u6e05\u7406\u548c\u6574\u7406\uff0c\u65b9\u4fbf\u60a8\u5feb\u901f\u5b66\u4e60\u800c\u4e0d\u5fc5\u8003\u8651\u6570\u636e\u6e05\u7406\uff0c\u4f46\u771f\u5b9e\u4e16\u754c\u6570\u636e\u5b58\u5728\u95ee\u9898\u5e76\u4e14\u5f88\u6df7\u4e71\u3002 \u771f\u5b9e\u4e16\u754c\u6570\u636e\u9700\u8981\u6e05\u7406\u624d\u80fd\u63d0\u4f9b\u6709\u7528\u7684\u6d1e\u5bdf\uff0c\u8fd9\u5c31\u662f\u672c\u6587\u7684\u4e3b\u9898\u3002\u00a0<\/p>\n

\u6570\u636e\u95ee\u9898\u53ef\u80fd\u6765\u81ea\u6570\u636e\u672c\u8eab\u7684\u884c\u4e3a\u3001\u6570\u636e\u6536\u96c6\u65b9\u5f0f\uff0c\u751a\u81f3\u662f\u6570\u636e\u8f93\u5165\u65b9\u5f0f\u3002 \u6d41\u7a0b\u7684\u6bcf\u4e2a\u9636\u6bb5\u90fd\u53ef\u80fd\u53d1\u751f\u9519\u8bef\u548c\u758f\u5ffd\u3002\u00a0<\/p>\n

\u6211\u4eec\u5728\u8fd9\u91cc\u4e13\u95e8\u8ba8\u8bba\u6570\u636e\u6e05\u7406\u800c\u4e0d\u662f\u6570\u636e\u8f6c\u6362\u3002 \u6570\u636e\u6e05\u7406\u53ef\u4ee5\u786e\u4fdd\u60a8\u4ece\u6570\u636e\u4e2d\u5f97\u51fa\u7684\u7ed3\u8bba\u53ef\u4ee5\u6cdb\u5316\u5230\u60a8\u5b9a\u4e49\u7684\u603b\u4f53\u3002 \u6570\u636e\u8f6c\u6362\u5219\u6d89\u53ca\u8f6c\u6362\u6570\u636e\u683c\u5f0f\u3001\u5bf9\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\u548c\u805a\u5408\u6570\u636e\u7b49\u4efb\u52a1\u3002\u00a0<\/p>\n

\u6570\u636e\u6e05\u7406\u4e3a\u4ec0\u4e48\u91cd\u8981\uff1f<\/h2>\n

\u5173\u4e8e\u6570\u636e\u96c6\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u4e86\u89e3\u7684\u662f\u5b83\u4eec\u4ee3\u8868\u4ec0\u4e48\u3002 \u5927\u591a\u6570\u6570\u636e\u96c6\u90fd\u662f\u4ee3\u8868\u66f4\u5e7f\u6cdb\u603b\u4f53\u7684\u6837\u672c\uff0c\u901a\u8fc7\u5904\u7406\u6b64\u6837\u672c\uff0c\u60a8\u80fd\u591f\u5c06\u53d1\u73b0\u5916\u63a8\uff08\u6216\u6cdb\u5316<\/em>\uff09\u5230\u8be5\u603b\u4f53\u3002 \u4f8b\u5982\uff0c\u6211\u4eec\u5728\u524d\u4e24\u7bc7\u535a\u6587\u4e2d\u4f7f\u7528\u4e86\u4e00\u4e2a\u6570\u636e\u96c6<\/a>\u3002 \u8fd9\u4e2a\u6570\u636e\u96c6\u5e7f\u6cdb\u6d89\u53ca\u623f\u5c4b\u9500\u552e\uff0c\u4f46\u53ea\u8986\u76d6\u4e00\u5c0f\u5757\u5730\u7406\u533a\u57df\u3001\u4e00\u5c0f\u6bb5\u65f6\u95f4\uff0c\u5e76\u4e14\u53ef\u80fd\u4e0d\u662f\u8be5\u533a\u57df\u548c\u65f6\u95f4\u6bb5\u5185\u7684\u6240\u6709\u623f\u5c4b\uff1b\u5b83\u662f\u4e00\u4e2a\u66f4\u5927\u603b\u4f53\u7684\u6837\u672c\u3002\u00a0<\/p>\n

\u60a8\u7684\u6570\u636e\u9700\u8981\u662f\u66f4\u5e7f\u6cdb\u603b\u4f53\u7684\u4ee3\u8868\u6027\u6837\u672c\uff0c\u4f8b\u5982\u8be5\u5730\u533a\u5728\u4e00\u6bb5\u89c4\u5b9a\u65f6\u95f4\u5185\u7684\u6240\u6709\u623f\u5c4b\u9500\u552e\u60c5\u51b5\u3002 \u4e3a\u4e86\u786e\u4fdd\u6211\u4eec\u7684\u6570\u636e\u662f\u66f4\u5e7f\u6cdb\u603b\u4f53\u7684\u4ee3\u8868\u6027\u6837\u672c\uff0c\u6211\u4eec\u5fc5\u987b\u9996\u5148\u5b9a\u4e49\u603b\u4f53\u7684\u8fb9\u754c\u3002\u00a0<\/p>\n

\u60a8\u53ef\u80fd\u4f1a\u60f3\u5230\uff0c\u9664\u4e86\u4eba\u53e3\u666e\u67e5\u6570\u636e\u4ee5\u5916\uff0c\u4f7f\u7528\u6574\u4e2a\u603b\u4f53\u5f80\u5f80\u4e0d\u5207\u5b9e\u9645\uff0c\u56e0\u6b64\u60a8\u9700\u8981\u786e\u5b9a\u60a8\u7684\u8fb9\u754c\u3002 \u8fd9\u4e9b\u8fb9\u754c\u53ef\u80fd\u662f\u5730\u7406\u7684\u3001\u4eba\u53e3\u7edf\u8ba1\u7684\u3001\u57fa\u4e8e\u65f6\u95f4\u7684\u3001\u57fa\u4e8e\u884c\u52a8\u7684\uff08\u4f8b\u5982\u4e8b\u52a1\u6027\uff09\u6216\u7279\u5b9a\u4e8e\u884c\u4e1a\u7684\u3002 \u60a8\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u5b9a\u4e49\u603b\u4f53\uff0c\u4f46\u8981\u53ef\u9760\u5730\u6cdb\u5316\u6570\u636e\uff0c\u5fc5\u987b\u5728\u6e05\u7406\u6570\u636e\u4e4b\u524d\u8fdb\u884c\u5b9a\u4e49\u3002<\/p>\n

\u603b\u4f53\u800c\u8a00\uff0c\u5982\u679c\u60a8\u8ba1\u5212\u5c06\u6570\u636e\u7528\u4e8e\u4efb\u4f55\u7c7b\u578b\u7684\u5206\u6790\u6216\u673a\u5668\u5b66\u4e60<\/a>\uff0c\u60a8\u90fd\u9700\u8981\u82b1\u65f6\u95f4\u6e05\u7406\u6570\u636e\uff0c\u786e\u4fdd\u60a8\u53ef\u4ee5\u4f9d\u8d56\u60a8\u7684\u6d1e\u5bdf\u5e76\u5c06\u5176\u6cdb\u5316\u5230\u771f\u5b9e\u4e16\u754c<\/em>\u3002 \u6e05\u7406\u6570\u636e\u53ef\u4ee5\u83b7\u5f97\u66f4\u51c6\u786e\u7684\u5206\u6790\uff0c\u5e76\u4e14\u5728\u673a\u5668\u5b66\u4e60\u65b9\u9762\u4e5f\u53ef\u4ee5\u63d0\u9ad8\u6027\u80fd\u3002<\/p>\n

\u5982\u679c\u4e0d\u6e05\u7406\u6570\u636e\uff0c\u60a8\u4f1a\u9762\u4e34\u8bb8\u591a\u98ce\u9669\uff0c\u4f8b\u5982\u65e0\u6cd5\u5c06\u7814\u7a76\u7ed3\u679c\u53ef\u9760\u5730\u6cdb\u5316\u5230\u66f4\u5e7f\u6cdb\u7684\u603b\u4f53\u3001\u4e0d\u51c6\u786e\u7684\u6c47\u603b\u7edf\u8ba1\u4fe1\u606f\u548c\u4e0d\u6b63\u786e\u7684\u53ef\u89c6\u5316\u6548\u679c\u3002 \u5982\u679c\u60a8\u4f7f\u7528\u6570\u636e\u8bad\u7ec3\u673a\u5668\u5b66\u4e60\u6a21\u578b\uff0c\u8fd9\u4e5f\u53ef\u80fd\u5bfc\u81f4\u9519\u8bef\u548c\u4e0d\u51c6\u786e\u7684\u9884\u6d4b\u3002<\/p>\n


\u514d\u8d39\u8bd5\u7528 PyCharm Professional
<\/a><\/p>\n

\u6570\u636e\u6e05\u7406\u793a\u4f8b<\/h2>\n

\u6211\u4eec\u5c06\u4ecb\u7ecd\u53ef\u7528\u4e8e\u6e05\u7406\u6570\u636e\u7684\u4e94\u9879\u4efb\u52a1\u3002 \u8fd9\u4efd\u5217\u8868\u5e76\u4e0d\u5b8c\u6574\uff0c\u4f46\u5bf9\u4e8e\u5904\u7406\u771f\u5b9e\u4e16\u754c\u6570\u636e\u662f\u4e00\u4e2a\u5f88\u597d\u7684\u8d77\u70b9\u3002<\/p>\n

\u5bf9\u6570\u636e\u53bb\u91cd<\/h3>\n

\u91cd\u590d\u95ee\u9898\u4f1a\u626d\u66f2\u60a8\u7684\u6570\u636e\u3002 \u5047\u8bbe\uff0c\u60a8\u6b63\u5728\u7ed8\u5236\u4e00\u4e2a\u4f7f\u7528\u9500\u552e\u4ef7\u683c\u9891\u7387\u7684\u76f4\u65b9\u56fe\u3002 \u5982\u679c\u6709\u76f8\u540c\u503c\u7684\u91cd\u590d\u9879\uff0c\u6700\u7ec8\u4f1a\u5f97\u5230\u4e00\u4e2a\u57fa\u4e8e\u91cd\u590d\u4ef7\u683c\u7684\u4e0d\u51c6\u786e\u6a21\u5f0f\u7684\u76f4\u65b9\u56fe\u3002\u00a0<\/p>\n

\u53e6\u5916\uff0c\u5f53\u6211\u4eec\u8c08\u8bba\u6570\u636e\u96c6\u4e2d\u7684\u91cd\u590d\u95ee\u9898\u65f6\uff0c\u6211\u4eec\u8c08\u8bba\u7684\u662f\u6574\u884c\u7684\u91cd\u590d\uff0c\u6bcf\u4e00\u884c\u90fd\u662f\u4e00\u4e2a\u5355\u72ec\u7684\u89c2\u5bdf\u503c\u3002 \u5217\u4e2d\u5c06\u6709\u91cd\u590d\u503c\uff0c\u5982\u6211\u4eec\u6240\u9884\u6599\u3002 \u6211\u4eec\u53ea\u8c08\u8bba\u91cd\u590d\u89c2\u5bdf\u503c\u3002\u00a0<\/p>\n

\u597d\u5728\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4e00\u79cd pandas \u65b9\u6cd5<\/a>\u5e2e\u52a9\u6211\u4eec\u68c0\u6d4b\u6570\u636e\u4e2d\u662f\u5426\u5b58\u5728\u91cd\u590d\u9879\u3002 \u5982\u679c\u9700\u8981\u63d0\u9192\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 JetBrains AI<\/a> \u804a\u5929\u7f16\u5199\u63d0\u793a\uff0c\u4f8b\u5982\uff1a<\/p>\n

Code to identify duplicate rows<\/em><\/p>\n

\u5f97\u5230\u4ee3\u7801\uff1a<\/p>\n

duplicate_rows = df[df.duplicated()]\nduplicate_rows<\/pre>\n
\u6b64\u4ee3\u7801\u5047\u5b9a\u60a8\u7684 DataFrame \u540d\u4e3a df<\/code>\uff0c<\/em>\u56e0\u6b64\u5982\u679c\u4e0d\u662f\u8fd9\u4e2a\u540d\u5b57\uff0c\u5e94\u8be5\u5c06\u5176\u66f4\u6539\u4e3a DataFrame \u7684\u540d\u79f0\u3002\u00a0<\/p>\n
\u6211\u4eec\u4e00\u76f4\u5728\u4f7f\u7528\u7684 Ames Housing \u6570\u636e\u96c6<\/a>\u4e2d\u6ca1\u6709\u91cd\u590d\u6570\u636e\uff0c\u4f46\u5982\u679c\u60a8\u60f3\u5c1d\u8bd5\uff0c\u53ef\u4ee5\u9009\u62e9 CITES Wildlife Trade Database<\/a> \u6570\u636e\u96c6\uff0c\u770b\u770b\u662f\u5426\u53ef\u4ee5\u4f7f\u7528\u4e0a\u8ff0 pandas \u65b9\u6cd5\u627e\u5230\u91cd\u590d\u9879\u3002<\/p>\n
\u5728\u6570\u636e\u96c6\u4e2d\u53d1\u73b0\u91cd\u590d\u9879\u540e\uff0c\u5fc5\u987b\u5c06\u5176\u79fb\u9664\uff0c\u907f\u514d\u626d\u66f2\u7ed3\u679c\u3002 \u60a8\u540c\u6837\u53ef\u4ee5\u4f7f\u7528 JetBrains AI \u4e3a\u6b64\u83b7\u53d6\u4ee3\u7801\uff0c\u63d0\u793a\u5982\u4e0b\uff1a<\/p>\n
Code to drop duplicates from my dataframe\u00a0<\/em><\/p>\n
\u751f\u6210\u7684\u4ee3\u7801\u4f1a\u5220\u9664\u91cd\u590d\u9879\uff0c\u91cd\u7f6e DataFrame \u7684\u7d22\u5f15\uff0c\u7136\u540e\u5c06\u5176\u663e\u793a\u4e3a\u540d\u4e3a df_cleaned \u7684\u65b0 DataFrame\uff1a<\/p>\n
df_cleaned = df.drop_duplicates()\ndf_cleaned.reset_index(drop=True, inplace=True)\ndf_cleaned<\/pre>\n
\u8fd8\u6709\u5176\u4ed6 pandas \u51fd\u6570\u53ef\u7528\u4e8e\u66f4\u9ad8\u7ea7\u7684\u91cd\u590d\u9879\u7ba1\u7406<\/a>\uff0c\u4f46\u8fd9\u5df2\u7ecf\u8db3\u4ee5\u8ba9\u60a8\u5f00\u59cb\u5bf9\u6570\u636e\u96c6\u8fdb\u884c\u53bb\u91cd\u3002<\/p>\n
\u5904\u7406\u4e0d\u5408\u7406\u503c<\/h3>\n
\u5f53\u6570\u636e\u8f93\u5165\u9519\u8bef\u6216\u6570\u636e\u6536\u96c6\u8fc7\u7a0b\u4e2d\u51fa\u73b0\u95ee\u9898\u65f6\uff0c\u53ef\u80fd\u4f1a\u51fa\u73b0\u4e0d\u5408\u7406\u503c\u3002 \u5bf9\u4e8e\u6211\u4eec\u7684 Ames Housing \u6570\u636e\u96c6<\/a>\uff0c\u4e0d\u5408\u7406\u503c\u53ef\u80fd\u662f\u8d1f\u7684 SalePrice\uff0c\u6216\u8005 Roof Style \u7684\u6570\u5b57\u503c\u3002<\/p>\n
\u8981\u53d1\u73b0\u6570\u636e\u96c6\u4e2d\u7684\u4e0d\u5408\u7406\u503c\uff0c\u9700\u8981\u91c7\u53d6\u5e7f\u6cdb\u7684\u65b9\u5f0f\uff0c\u5305\u62ec\u67e5\u770b\u6c47\u603b\u7edf\u8ba1\u4fe1\u606f<\/a>\u3001\u68c0\u67e5\u6536\u96c6\u5668\u4e3a\u6bcf\u5217\u5b9a\u4e49\u7684\u6570\u636e\u9a8c\u8bc1\u89c4\u5219\u3001\u6ce8\u610f\u4e0d\u5728\u9a8c\u8bc1\u8303\u56f4\u5185\u7684\u4efb\u4f55\u6570\u636e\u70b9\uff0c\u4ee5\u53ca\u4f7f\u7528\u53ef\u89c6\u5316\u6548\u679c\u786e\u5b9a\u6a21\u5f0f\u548c\u770b\u8d77\u6765\u53ef\u80fd\u5f02\u5e38\u7684\u60c5\u51b5\u3002\u00a0<\/p>\n
\u60a8\u9700\u8981\u5904\u7406\u4e0d\u5408\u7406\u503c\uff0c\u56e0\u4e3a\u5b83\u4eec\u4f1a\u589e\u52a0\u566a\u97f3\u5e76\u7ed9\u5206\u6790\u5e26\u6765\u95ee\u9898\u3002 \u4e0d\u8fc7\uff0c\u5982\u4f55\u5904\u7406\u8fd9\u4e9b\u503c\u5219\u9700\u8981\u89c6\u60c5\u51b5\u800c\u5b9a\u3002 \u5982\u679c\u76f8\u5bf9\u4e8e\u6570\u636e\u96c6\u5927\u5c0f\u6ca1\u6709\u592a\u591a\u4e0d\u5408\u7406\u503c\uff0c\u60a8\u53ef\u4ee5\u79fb\u9664\u5305\u542b\u5b83\u4eec\u7684\u8bb0\u5f55\u3002 \u4f8b\u5982\uff0c\u5982\u679c\u5728\u6570\u636e\u96c6\u7684\u7b2c 214 \u884c\u4e2d\u53d1\u73b0\u4e86\u4e00\u4e2a\u4e0d\u5408\u7406\u503c\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528 pandas drop \u51fd\u6570<\/a>\u4ece\u6570\u636e\u96c6\u4e2d\u79fb\u9664\u8be5\u884c\u3002\u00a0<\/p>\n
\u540c\u6837\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u63d0\u793a\u8ba9 JetBrains AI \u751f\u6210\u6211\u4eec\u9700\u8981\u7684\u4ee3\u7801\uff1a\u00a0<\/p>\n
Code that drops index 214 from <\/em>#df_cleaned<\/em><\/p>\n
\u6ce8\u610f\uff0c\u5728 PyCharm \u7684 Jupyter Notebook<\/a> \u4e2d\uff0c\u6211\u53ef\u4ee5\u5728\u5355\u8bcd\u524d\u52a0\u4e0a # \u7b26\u53f7\uff0c\u5411 JetBrains AI Assistant \u8868\u660e\u6211\u6b63\u5728\u63d0\u4f9b\u989d\u5916\u4e0a\u4e0b\u6587\uff0c\u5728\u672c\u4f8b\u4e2d\uff0cDataFrame \u88ab\u79f0\u4e3a df_cleaned<\/code>\u3002<\/p>\n
\u751f\u6210\u7684\u4ee3\u7801\u5c06\u4ece DataFrame \u4e2d\u79fb\u9664\u8be5\u89c2\u5bdf\u503c\uff0c\u91cd\u7f6e\u7d22\u5f15\u5e76\u663e\u793a\uff1a<\/p>\n
df_cleaned = df_cleaned.drop(index=214)\ndf_cleaned.reset_index(drop=True, inplace=True)\ndf_cleaned<\/pre>\n
\u5904\u7406\u4e0d\u5408\u7406\u503c\u7684\u53e6\u4e00\u79cd\u5e38\u7528\u7b56\u7565\u662f\u63d2\u8865\uff0c\u5373\u6839\u636e\u5b9a\u4e49\u7684\u7b56\u7565\u7528\u4e0d\u540c\u7684\u5408\u7406\u503c\u66ff\u6362\u8be5\u503c\u3002 \u6700\u5e38\u89c1\u7684\u7b56\u7565\u4e4b\u4e00\u662f\u4f7f\u7528\u4e2d\u4f4d\u503c\u66ff\u6362\u4e0d\u5408\u7406\u503c\u3002 \u7531\u4e8e\u4e2d\u4f4d\u6570\u4e0d\u53d7\u5f02\u5e38\u503c\u5f71\u54cd\uff0c\u6570\u636e\u79d1\u5b66\u5bb6\u901a\u5e38\u4e3a\u6b64\u76ee\u7684\u9009\u62e9\u5b83\uff0c\u4f46\u540c\u6837\uff0c\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u6570\u636e\u7684\u5e73\u5747\u503c\u6216\u4f17\u6570\u503c\u53ef\u80fd\u66f4\u5408\u9002\u3002\u00a0<\/p>\n
\u6216\u8005\uff0c\u5982\u679c\u60a8\u5177\u6709\u5173\u4e8e\u6570\u636e\u96c6\u4ee5\u53ca\u6570\u636e\u6536\u96c6\u65b9\u5f0f\u7684\u9886\u57df\u77e5\u8bc6\uff0c\u53ef\u4ee5\u7528\u66f4\u6709\u610f\u4e49\u7684\u503c\u66ff\u6362\u4e0d\u5408\u7406\u503c\u3002 \u5982\u679c\u60a8\u53c2\u4e0e\u6216\u4e86\u89e3\u6570\u636e\u6536\u96c6\u6d41\u7a0b\uff0c\u8fd9\u4e2a\u9009\u9879\u53ef\u80fd\u66f4\u5408\u9002\u3002\u00a0<\/p>\n
\u4e0d\u5408\u7406\u503c\u7684\u5904\u7406\u53d6\u51b3\u4e8e\u5b83\u4eec\u5728\u6570\u636e\u96c6\u4e2d\u7684\u666e\u904d\u6027\u3001\u6570\u636e\u7684\u6536\u96c6\u65b9\u5f0f\u548c\u60a8\u6253\u7b97\u5982\u4f55\u5b9a\u4e49\u603b\u4f53\u4ee5\u53ca\u9886\u57df\u77e5\u8bc6\u7b49\u5176\u4ed6\u56e0\u7d20\u3002\u00a0<\/p>\n
\u683c\u5f0f\u5316\u6570\u636e<\/h3>\n
\u60a8\u7ecf\u5e38\u53ef\u4ee5\u5728\u6c47\u603b\u7edf\u8ba1\u4fe1\u606f<\/a>\u6216\u4e3a\u4e86\u89e3\u6570\u636e\u5f62\u72b6\u800c\u6267\u884c\u7684\u65e9\u671f\u53ef\u89c6\u5316<\/a>\u4e2d\u53d1\u73b0\u683c\u5f0f\u8bbe\u7f6e\u95ee\u9898\u3002 \u683c\u5f0f\u4e0d\u4e00\u81f4\u7684\u4e00\u4e9b\u4f8b\u5b50\u5305\u62ec\u6570\u5b57\u503c\u6ca1\u6709\u5168\u90e8\u5b9a\u4e49\u5230\u76f8\u540c\u7684\u5c0f\u6570\u4f4d\uff0c\u6216\u62fc\u5199\u65b9\u9762\u7684\u5dee\u5f02\uff0c\u4f8b\u5982\u201cfirst\u201d\u548c\u201c1st\u201d\u3002 \u4e0d\u6b63\u786e\u7684\u6570\u636e\u683c\u5f0f\u8bbe\u7f6e\u4e5f\u4f1a\u5bf9\u6570\u636e\u7684\u5185\u5b58\u5360\u7528\u4ea7\u751f\u5f71\u54cd\u3002<\/p>\n
\u53d1\u73b0\u6570\u636e\u96c6\u4e2d\u7684\u683c\u5f0f\u8bbe\u7f6e\u95ee\u9898\u540e\uff0c\u5c31\u9700\u8981\u5c06\u503c\u6807\u51c6\u5316\u3002 \u6839\u636e\u9762\u4e34\u7684\u95ee\u9898\uff0c\u8fd9\u901a\u5e38\u6d89\u53ca\u5b9a\u4e49\u60a8\u81ea\u5df1\u7684\u6807\u51c6\u548c\u5e94\u7528\u66f4\u6539\u3002 \u540c\u6837\uff0cpandas \u5e93\u5728\u8fd9\u91cc\u6709\u4e00\u4e9b\u6709\u7528\u7684\u51fd\u6570\uff0c\u4f8b\u5982 round<\/a>\u3002 \u5982\u679c\u8981\u5c06 SalePrice \u5217\u56db\u820d\u4e94\u5165\u5230\u5c0f\u6570\u70b9\u540e 2 \u4f4d\uff0c\u6211\u4eec\u53ef\u4ee5\u8ba9 JetBrains AI \u751f\u6210\u4ee3\u7801\uff1a<\/p>\n
Code to round <\/em>#SalePrice<\/em> to two decimal places\u00a0<\/em><\/p>\n
\u751f\u6210\u7684\u4ee3\u7801\u5c06\u6267\u884c\u820d\u5165\uff0c\u7136\u540e\u6253\u5370\u51fa\u524d 10 \u884c\u4ee5\u4f9b\u68c0\u67e5\uff1a<\/p>\n
df_cleaned['SalePrice'] = df_cleaned['SalePrice].round(2)\ndf_cleaned.head()<\/pre>\n
\u518d\u4e3e\u4e00\u4e2a\u62fc\u5199\u53ef\u80fd\u4e0d\u4e00\u81f4\u7684\u4f8b\u5b50 \u2013 \u4f8b\u5982\uff0cHouseStyle \u5217\u540c\u65f6\u5305\u542b\u201c1Story\u201d\u548c\u201cOneStory\u201d\uff0c\u5e76\u4e14\u60a8\u786e\u4fe1\u5b83\u4eec\u662f\u540c\u4e00\u4e2a\u610f\u601d\u3002 \u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u63d0\u793a\u83b7\u53d6\u4ee3\u7801\uff1a<\/p>\n
Code to change all instances of <\/em>#OneStory<\/em> to <\/em>#1Story<\/em> in <\/em>#HouseStyle<\/em>\u00a0<\/em><\/p>\n
\u751f\u6210\u7684\u4ee3\u7801\u4f1a\u5c06 OneStory \u7684\u6240\u6709\u5b9e\u4f8b\u66ff\u6362\u4e3a 1Story\uff1a<\/p>\n
df_cleaned[HouseStyle'] = df_cleaned['HouseStyle'].replace('OneStory', '1Story')<\/pre>\n
\u89e3\u51b3\u5f02\u5e38\u503c<\/h3>\n
\u5f02\u5e38\u503c\u5728\u6570\u636e\u96c6\u4e2d\u975e\u5e38\u5e38\u89c1\uff0c\u4f46\u5982\u4f55\u5904\u7406\u5b83\u4eec\uff08\u5982\u679c\u6709\uff09\u5219\u975e\u5e38\u4f9d\u8d56\u4e8e\u5177\u4f53\u60c5\u51b5\u3002 \u53d1\u73b0\u5f02\u5e38\u503c\u7684\u6700\u7b80\u5355\u65b9\u5f0f\u4e4b\u4e00\u662f\u4f7f\u7528\u7bb1\u7ebf\u56fe\uff0c\u5b83\u4f7f\u7528 seaborn<\/a> \u548c matplotlib<\/a> \u5e93\u3002 \u5982\u679c\u60a8\u9700\u8981\u5feb\u901f\u56de\u987e\uff0c\u6211\u5148\u524d\u5728\u5173\u4e8e\u4f7f\u7528 pandas \u63a2\u7d22\u6570\u636e<\/a>\u7684\u535a\u6587\u4e2d\u63a2\u8ba8\u8fc7\u7bb1\u7ebf\u56fe\u3002\u00a0<\/p>\n
\u5bf9\u4e8e\u8fd9\u4e2a\u7bb1\u7ebf\u56fe\uff0c\u6211\u4eec\u5c06\u67e5\u770b Ames Housing \u6570\u636e\u96c6<\/a>\u4e2d\u7684 SalePrice\u3002 \u540c\u6837\uff0c\u6211\u5c06\u4f7f\u7528 JetBrains AI \u751f\u6210\u4ee3\u7801\uff0c\u7ed9\u51fa\u5982\u4e0b\u63d0\u793a\uff1a<\/p>\n
Code to create a box plot of <\/em>#SalePrice<\/em>\u00a0<\/em><\/p>\n
\u4ee5\u4e0b\u662f\u6211\u4eec\u9700\u8981\u8fd0\u884c\u7684\u4ee3\u7801\uff1a<\/p>\n
import seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Create a box plot for SalePrice\nplt.figure(figsize=(10, 6))\nsns.boxplot(x=df_cleaned['SalePrice'])\nplt.title('Box Plot of SalePrice')\nplt.xlabel('SalePrice')\nplt.show()<\/pre>\n
\u7bb1\u7ebf\u56fe\u544a\u8bc9\u6211\u4eec\u6709\u4e00\u4e2a\u6b63\u504f\u659c\uff0c\u56e0\u4e3a\u84dd\u8272\u6846\u5185\u7684\u5782\u76f4\u4e2d\u7ebf\u4f4d\u4e8e\u4e2d\u5fc3\u7684\u5de6\u4fa7\u3002 \u6b63\u504f\u659c\u544a\u8bc9\u6211\u4eec\uff0c\u8f83\u4f4e\u7aef\u7684\u623f\u4ef7\u66f4\u591a\uff0c\u8fd9\u5e76\u4e0d\u5947\u602a\u3002 \u7bb1\u7ebf\u56fe\u8fd8\u76f4\u89c2\u5730\u544a\u8bc9\u6211\u4eec\u53f3\u4fa7\u6709\u5f88\u591a\u5f02\u5e38\u503c\u3002 \u8fd9\u4e9b\u623f\u5c4b\u7684\u6570\u91cf\u5f88\u5c11\uff0c\u4f46\u5176\u4ef7\u683c\u5374\u8fdc\u9ad8\u4e8e\u4e2d\u4f4d\u4ef7\u683c\u3002<\/p>\n
\"\"<\/figure>\n
\u60a8\u53ef\u80fd\u4f1a\u63a5\u53d7\u8fd9\u4e9b\u5f02\u5e38\u503c\uff0c\u56e0\u4e3a\u9884\u8ba1\u5c11\u6570\u623f\u5c4b\u7684\u4ef7\u683c\u9ad8\u4e8e\u5927\u591a\u6570\u623f\u5c4b\u7684\u4ef7\u683c\u662f\u5f88\u6b63\u5e38\u7684\u3002 \u4e0d\u8fc7\uff0c\u8fd9\u5b8c\u5168\u53d6\u51b3\u4e8e\u60a8\u60f3\u8981\u6cdb\u5316\u5230\u7684\u603b\u4f53\u4ee5\u53ca\u60a8\u60f3\u8981\u4ece\u6570\u636e\u4e2d\u5f97\u51fa\u7684\u7ed3\u8bba\u3002 \u660e\u786e\u754c\u5b9a\u4ec0\u4e48\u662f\u603b\u4f53\u7684\u4e00\u90e8\u5206\u3001\u4ec0\u4e48\u4e0d\u662f\u603b\u4f53\u7684\u4e00\u90e8\u5206\uff0c\u53ef\u4ee5\u8ba9\u60a8\u505a\u51fa\u660e\u667a\u7684\u51b3\u5b9a\uff0c\u5224\u65ad\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\u662f\u5426\u4f1a\u6210\u4e3a\u95ee\u9898\u3002\u00a0<\/p>\n
\u4f8b\u5982\uff0c\u5982\u679c\u603b\u4f53\u7531\u4e0d\u4f1a\u8d2d\u4e70\u6602\u8d35\u8c6a\u5b85\u7684\u4eba\u7fa4\u7ec4\u6210\uff0c\u90a3\u4e48\u60a8\u53ef\u80fd\u53ef\u4ee5\u5220\u9664\u8fd9\u4e9b\u5f02\u5e38\u503c\u3002 \u5982\u679c\u603b\u4f53\u4eba\u53e3\u7ed3\u6784\u4e2d\u5305\u62ec\u53ef\u80fd\u8d2d\u4e70\u8fd9\u4e9b\u6602\u8d35\u623f\u5c4b\u7684\u4eba\u7fa4\uff0c\u90a3\u4e48\u60a8\u53ef\u80fd\u5e94\u8be5\u5c06\u5b83\u4eec\u4fdd\u7559\uff0c\u56e0\u4e3a\u5b83\u4eec\u4e0e\u60a8\u7684\u603b\u4f53\u76f8\u5173\u3002<\/p>\n
\u6211\u5728\u8fd9\u91cc\u8bb2\u89e3\u4e86\u7bb1\u7ebf\u56fe\u4f5c\u4e3a\u53d1\u73b0\u5f02\u5e38\u503c\u7684\u65b9\u5f0f\uff0c\u4f46\u6563\u70b9\u56fe\u548c\u76f4\u65b9\u56fe\u7b49\u9009\u9879\u4e5f\u53ef\u4ee5\u5feb\u901f\u663e\u793a\u6570\u636e\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u5e38\u503c\uff0c\u8ba9\u60a8\u505a\u51fa\u660e\u667a\u7684\u51b3\u5b9a\uff0c\u5224\u65ad\u662f\u5426\u9700\u8981\u5bf9\u5b83\u4eec\u91c7\u53d6\u4efb\u4f55\u63aa\u65bd\u3002<\/p>\n
\u89e3\u51b3\u5f02\u5e38\u503c\u901a\u5e38\u5206\u4e3a\u4e24\u7c7b \u2013 \u5c06\u5176\u5220\u9664\u6216\u4f7f\u7528\u4e0d\u6613\u51fa\u73b0\u5f02\u5e38\u503c\u7684\u6c47\u603b\u7edf\u8ba1\u4fe1\u606f\u3002 \u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u786e\u5207\u77e5\u9053\u5b83\u4eec\u662f\u54ea\u4e9b\u884c\u3002\u00a0<\/p>\n
\u5230\u76ee\u524d\u4e3a\u6b62\uff0c\u6211\u4eec\u53ea\u8ba8\u8bba\u4e86\u5982\u4f55\u901a\u8fc7\u89c6\u89c9\u8bc6\u522b\u3002 \u8fd8\u6709\u4e0d\u540c\u7684\u65b9\u5f0f\u6765\u786e\u5b9a\u54ea\u4e9b\u89c2\u5bdf\u503c\u662f\u5f02\u5e38\u503c\uff0c\u54ea\u4e9b\u4e0d\u662f\u5f02\u5e38\u503c\u3002 \u4e00\u79cd\u5e38\u89c1\u7684\u65b9\u5f0f\u662f\u4f7f\u7528\u4fee\u6b63 Z \u5206\u6570<\/em>\u3002 \u5728\u6211\u4eec\u4e86\u89e3\u5982\u4f55\u4ee5\u53ca\u4e3a\u4f55\u4fee\u6b63\u4e4b\u524d\uff0cZ \u5206\u6570\u5b9a\u4e49\u4e3a\uff1a<\/p>\n
Z \u5206\u6570 =<\/em> (\u6570\u636e\u70b9\u503c<\/em> \u2013 \u5e73\u5747\u503c<\/em>) \/ \u6807\u51c6\u5dee<\/em><\/p>\n
\u4fee\u6b63 Z \u5206\u6570\u6765\u68c0\u6d4b\u5f02\u5e38\u503c\u7684\u539f\u56e0\u662f\uff0c\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u7531\u4e8e\u8ba1\u7b97\u65b9\u5f0f\u90fd\u5bb9\u6613\u53d7\u5230\u5f02\u5e38\u503c\u7684\u5f71\u54cd\u3002 \u4fee\u6b63 Z \u5206\u6570\u5b9a\u4e49\u4e3a\uff1a<\/p>\n
\u4fee\u6b63 Z \u5206\u6570 =<\/em> (\u6570\u636e\u70b9\u503c<\/em> \u2013 \u4e2d\u4f4d\u6570<\/em>) \/ \u7edd\u5bf9<\/em>\u504f\u5dee\u4e2d\u4f4d\u6570<\/em><\/p>\n
\u6b63\u5982\u6211\u4eec\u5728\u8ba8\u8bba\u6c47\u603b\u7edf\u8ba1\u4fe1\u606f<\/a>\u65f6\u6240\u4e86\u89e3\u5230\u7684\uff0c\u4e2d\u4f4d\u6570\u4e0d\u53d7\u5f02\u5e38\u503c\u5f71\u54cd\u3002 \u7edd\u5bf9\u504f\u5dee\u4e2d\u4f4d\u6570<\/em>\u662f\u6570\u636e\u96c6\u4e0e\u4e2d\u4f4d\u6570<\/em>\u7684\u7edd\u5bf9\u504f\u5dee\u7684\u4e2d\u4f4d<\/em>\u503c\u3002 \u4f8b\u5982\uff0c\u5982\u679c\u6570\u636e\u96c6\u5305\u542b\u4ee5\u4e0b\u503c\uff1a<\/p>\n
1, 2, 2, 2, 3<\/strong>, 3, 3, 5,9<\/p>\n
\u90a3\u4e48\u4e2d\u4f4d\u6570<\/em>\u5c31\u662f 3\uff0c\u5373\u6570\u636e\u96c6\u4e2d\u95f4\u7684\u503c\u3002 \u4e0e\u4e2d\u4f4d\u503c 3 \u7684\u504f\u5dee\u6309\u4e0a\u9762\u5217\u51fa\u7684\u987a\u5e8f\u5982\u4e0b\uff1a<\/p>\n
2, 1, 1, 1, 0, 0, 0, 2, 5<\/p>\n
\u6309\u5347\u5e8f\u6392\u5217\uff1a<\/p>\n
0, 0, 0, 1, 1<\/strong>, 1, 2, 2, 5<\/p>\n
\u8fd9\u610f\u5473\u7740\u6b64\u793a\u4f8b\u4e2d\u7684\u7edd\u5bf9\u504f\u5dee\u4e2d\u4f4d\u6570<\/em>\u4e3a 1\u3002<\/p>\n
\u5982\u4f55\u8ba1\u7b97 SalePrice \u6570\u636e\u96c6\u7684\u4fee\u6b63 Z \u5206\u6570\uff1f \u5411 JetBrains AI \u63d0\u4f9b\u4ee5\u4e0b\u63d0\u793a\uff1a<\/p>\n
Given:<\/em><\/p>\n
Modified Z-score = (data point value \u2013 median) \/ median absolute deviation<\/em><\/p>\n
Calculate the Modified Z-score for <\/em>#SalePrice<\/em>\u00a0<\/em><\/p>\n
\u8fd9\u4f1a\u4ea7\u751f\u4ee5\u4e0b\u4ee3\u7801\uff1a<\/p>\n
import pandas as pd\nfrom scipy.stats import median_abs_deviation\n\n\n# Assuming 'data' is your DataFrame and it contains the 'SalePrice' column\n\n\n# Calculate the median for the SalePrice column\nmedian_sale_price = data['SalePrice'].median()\n\n\n# Calculate the Median Absolute Deviation (MAD) for the SalePrice column\nmad_sale_price = median_abs_deviation(data['SalePrice'], scale='normal')\n\n\n# Calculate the Modified Z-scores for the SalePrice column\ndata['Modified_Z_Score'] = (data['SalePrice'] - median_sale_price) \/ mad_sale_price\n\n\n# Display the first few rows with the Modified Z-scores\nprint(data[['SalePrice', 'Modified_Z_Score']].head())<\/pre>\n
\u5b89\u88c5\u6240\u9700\u8f6f\u4ef6\u5305\u5e76\u8fd0\u884c\u6b64\u4ee3\u7801\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6211\u4eec\u5df2\u7ecf\u5b8c\u6210\u4e86\u4e00\u90e8\u5206\uff0c\u4f46\u73b0\u5728\u6211\u4eec\u9700\u8981\u6839\u636e\u4fee\u6b63 Z \u5206\u6570\u6765\u786e\u5b9a SalePrice \u7684\u5f02\u5e38\u503c\u3002 \u4e00\u822c\u89c2\u70b9\u662f\uff0c\u5f02\u5e38\u503c\u662f\u4efb\u4f55 >=3 \u6216 <=-3 \u7684\u503c\uff0c\u4e0d\u8fc7\uff0c\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u50cf\u5927\u591a\u6570\u7edf\u8ba1\u51b3\u7b56\u4e00\u6837\uff0c\u5b83\u53ef\u4ee5\u5e76\u4e14\u5e94\u5f53\u6839\u636e\u60a8\u7684\u6570\u636e\u96c6\u8fdb\u884c\u8c03\u6574\u3002 <=-3 however, it\u2019s worth noting that like most statistical decisions, it can and should be tailored to your dataset.\u00a0<\/p>\n
\u6211\u4eec\u518d\u7ed9 JetBrains AI \u4f20\u9012\u4e00\u4e2a\u63d0\u793a\uff0c\u8fdb\u4e00\u6b65\u5b9a\u5236\u8f93\u51fa\uff1a<\/p>\n
Just list those that have a <\/em>#Modified_Z_Score<\/em> of 3 or above or -3 or below\u00a0<\/em><\/p>\n
\u6211\u5c06\u9009\u53d6\u8fd9\u6bb5\u4ee3\u7801\uff0c\u628a\u5b83\u66ff\u6362\u4e3a\u4e0a\u9762\u7684\u76f8\u5173\u884c\uff1a<\/p>\n
# Filter the rows where the Modified Z-score is 3 or above, or -3 or below\noutliers = data[(data['Modified_Z_Score'] >= 3) | (data['Modified_Z_Score'] <= -3)]\n\n\n# Print all the filtered rows, showing their index and SalePrice\noutliers = (outliers[['SalePrice', 'Modified_Z_Score']])\noutliers<\/pre>\n
\u6211\u4fee\u6539\u4e86\u8fd9\u6bb5\u4ee3\u7801\uff0c\u5c06\u5f02\u5e38\u503c\u4fdd\u5b58\u5728\u540d\u4e3a outliers \u7684\u65b0 DataFrame \u4e2d\u5e76\u5c06\u5176\u6253\u5370\u51fa\u6765\u4ee5\u4f9b\u67e5\u770b\u3002\u00a0<\/p>\n
\u4e0b\u4e00\u6b65\u662f\u4ece DataFrame \u4e2d\u79fb\u9664\u8fd9\u4e9b\u5f02\u5e38\u503c\u3002 \u6211\u4eec\u518d\u6b21\u4f7f\u7528 JetBrains AI \u751f\u6210\u4ee3\u7801\uff0c\u63d0\u793a\u5982\u4e0b\uff1a<\/p>\n
Create a new dataframe without the outliers\u00a0<\/em><\/p>\n
data_without_outliers = data.drop(index=outliers.index)\n\n\n# Display the new DataFrame without outliers\nprint(data_without_outliers)<\/pre>\n
\u6211\u4eec\u7684\u65b0 DataFrame (data_without_outliers) \u6392\u9664\u4e86\u90a3\u4e9b SalePrice \u53d8\u91cf\u88ab\u89c6\u4e3a\u5f02\u5e38\u503c\u7684\u503c\u3002\u00a0<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u66f4\u65b0\u7bb1\u7ebf\u56fe\u4ee3\u7801\u6765\u67e5\u770b\u65b0\u7684 DataFrame\u3002 \u5982\u6211\u4eec\u7684\u9884\u671f\uff0c\u5b83\u4ecd\u7136\u663e\u793a\u51fa\u6b63\u504f\u659c\uff0c\u4f46\u88ab\u89c6\u4e3a\u5f02\u5e38\u503c\u7684\u503c\u5df2\u88ab\u79fb\u9664\uff1a<\/p>\n
import seaborn as sns\nimport matplotlib.pyplot as plt\n\n\n# Create a box plot for SalePrice\nplt.figure(figsize=(10, 6))\nsns.boxplot(x=data_without_outliers['SalePrice'])\nplt.title('Box Plot of SalePrice')\nplt.xlabel('SalePrice')\nplt.show()<\/pre>\n
\"\"<\/figure>\n
\u5728\u7ed3\u675f\u4e4b\u524d\uff0c\u6211\u4eec\u770b\u4e00\u4e0b\u4ece DataFrame \u4e2d\u79fb\u9664\u7684\u89c2\u5bdf\u503c\u5360\u6bd4\u662f\u591a\u5c11\uff0c\u56e0\u4e3a\u6211\u4eec\u6839\u636e SalePrice \u5c06\u5b83\u4eec\u89c6\u4e3a\u5f02\u5e38\u503c\u3002<\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5982\u4e0b\u63d0\u793a\uff1a<\/p>\n
Calculate the percentage of observations removed between #data and #data_without_outliers<\/em><\/p>\n
# Calculate the number of observations in the original and filtered DataFrames\noriginal_count = len(data)\nfiltered_count = len(data_without_outliers)\n\n# Calculate the number of removed observations\nremoved_count = original_count - filtered_count\n\n# Calculate the percentage of observations removed\npercentage_removed = (removed_count \/ original_count) * 100\n\n# Display the percentage\nprint(f\"Percentage of observations removed: {percentage_removed:.2f}%\")<\/pre>\n
PyCharm \u544a\u8bc9\u6211\u4eec 5.67% \u7684\u89c2\u5bdf\u503c\u5df2\u88ab\u79fb\u9664\u3002<\/p>\n
\u5982\u524d\u6240\u8ff0\uff0c\u5982\u679c\u8981\u4fdd\u7559\u5f02\u5e38\u503c\uff0c\u53ef\u4ee5\u4f7f\u7528\u4e2d\u4f4d\u6570<\/em>\u548c\u56db\u5206\u4f4d\u8ddd<\/em>\u7b49\u4e0d\u6613\u53d7\u5f02\u5e38\u503c\u5f71\u54cd\u7684\u6c47\u603b\u503c\u3002 \u5982\u679c\u60a8\u77e5\u9053\u5904\u7406\u7684\u6570\u636e\u96c6\u5305\u542b\u672a\u79fb\u9664\u7684\u5f02\u5e38\u503c\uff0c\u56e0\u4e3a\u5b83\u4eec\u4e0e\u60a8\u5b9a\u4e49\u7684\u603b\u4f53\u548c\u60f3\u8981\u5f97\u51fa\u7684\u7ed3\u8bba\u76f8\u5173\uff0c\u60a8\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e9b\u6d4b\u91cf\u503c\u6765\u5f97\u51fa\u7ed3\u8bba\u3002<\/p>\n
\u7f3a\u5931\u503c<\/h3>\n
\u53d1\u73b0\u6570\u636e\u96c6\u4e2d\u7f3a\u5931\u503c\u7684\u6700\u5feb\u65b9\u5f0f\u662f\u4f7f\u7528\u6c47\u603b\u7edf\u8ba1\u4fe1\u606f\u3002 \u63d0\u9192\u4e00\u4e0b\uff0c\u5728 DataFrame \u4e2d\uff0c\u70b9\u51fb\u53f3\u4fa7\u7684 Show Column Statistics<\/em>\uff08\u663e\u793a\u5217\u7edf\u8ba1\u4fe1\u606f\uff09\uff0c\u7136\u540e\u9009\u62e9 Compact<\/em>\uff08\u7d27\u51d1\uff09\u3002 \u5217\u4e2d\u7684\u7f3a\u5931\u503c\u4ee5\u7ea2\u8272\u663e\u793a\uff0c\u5982\u6211\u4eec\u7684 Ames Housing \u6570\u636e\u96c6<\/a>\u4e2d\u7684 Lot Frontage \u6240\u793a\uff1a<\/p>\n
<\/p>\n
\u5bf9\u4e8e\u6211\u4eec\u7684\u6570\u636e\uff0c\u6211\u4eec\u9700\u8981\u8003\u8651\u4e09\u79cd\u7f3a\u5931\uff1a<\/p>\n