{"id":68251,"date":"2026-01-29T23:06:10","date_gmt":"2026-01-29T15:06:10","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/68251.html"},"modified":"2026-01-29T23:06:10","modified_gmt":"2026-01-29T15:06:10","slug":"%e3%80%90%e8%ae%a1%e7%ae%97%e6%9c%ba%e6%af%95%e8%ae%be%e9%80%89%e9%a2%98%e3%80%91%e5%9f%ba%e4%ba%8ehadoop%e7%9a%84%e5%a4%a7%e4%bc%97%e7%82%b9%e8%af%84%e7%be%8e%e9%a3%9f%e6%95%b0%e6%8d%ae%e5%88%86","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/68251.html","title":{"rendered":"\u3010\u8ba1\u7b97\u673a\u6bd5\u8bbe\u9009\u9898\u3011\u57fa\u4e8eHadoop\u7684\u5927\u4f17\u70b9\u8bc4\u7f8e\u98df\u6570\u636e\u5206\u6790\u7cfb\u7edf\uff0cPython+Spark\u5927\u6570\u636e\u5904\u7406 \u6bd5\u4e1a\u8bbe\u8ba1 \u9009\u9898\u63a8\u8350 \u6bd5\u8bbe\u9009\u9898 \u6570\u636e\u5206\u6790 \u673a\u5668\u5b66\u4e60 \u6570\u636e\u6316\u6398"},"content":{"rendered":"

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from<\/span> pyspark.<\/span>sql import<\/span> SparkSession
\nfrom<\/span> pyspark.<\/span>sql.<\/span>functions import<\/span> col,<\/span> count,<\/span> split,<\/span> explode,<\/span> when,<\/span> isnan,<\/span> isnull
\n# \u521d\u59cb\u5316SparkSession<\/span>
\nspark =<\/span> SparkSession.<\/span>builder.<\/span>appName(<\/span>"DianpingAnalysis"<\/span>)<\/span>.<\/span>getOrCreate(<\/span>)<\/span><\/p>\n

# \u6838\u5fc3\u529f\u80fd1：\u533a\u57df\u9910\u5385\u5bc6\u5ea6\u5206\u6790<\/span>
\ndef<\/span> get_region_density<\/span>(<\/span>df)<\/span>:<\/span>
\n # \u5047\u8bbedf\u5df2\u52a0\u8f7d，\u5305\u542b'region'\u5b57\u6bb5<\/span>
\n region_count_df =<\/span> df.<\/span>groupBy(<\/span>"region"<\/span>)<\/span>.<\/span>agg(<\/span>count(<\/span>"shop_id"<\/span>)<\/span>.<\/span>alias(<\/span>"restaurant_count"<\/span>)<\/span>)<\/span>
\n # \u8f6c\u6362\u4e3aPython\u5b57\u5178\u4ee5\u4fbf\u8fd4\u56de<\/span>
\n result =<\/span> region_count_df.<\/span>orderBy(<\/span>col(<\/span>"restaurant_count"<\/span>)<\/span>.<\/span>desc(<\/span>)<\/span>)<\/span>.<\/span>collect(<\/span>)<\/span>
\n return<\/span> [<\/span>{<\/span>"region"<\/span>:<\/span> row[<\/span>"region"<\/span>]<\/span>,<\/span> "count"<\/span>:<\/span> row[<\/span>"restaurant_count"<\/span>]<\/span>}<\/span> for<\/span> row in<\/span> result]<\/span><\/p>\n

# \u6838\u5fc3\u529f\u80fd2：\u9ad8\u6027\u4ef7\u6bd4\u9910\u5385\u7279\u5f81\u5206\u6790<\/span>
\ndef<\/span> get_cost_effective_restaurants<\/span>(<\/span>df)<\/span>:<\/span>
\n # \u8ba1\u7b97\u6027\u4ef7\u6bd4\u6307\u6807：\u8bc4\u5206\/\u4eba\u5747\u6d88\u8d39，\u5904\u7406\u7a7a\u503c\u548c0\u503c<\/span>
\n df_with_ratio =<\/span> df.<\/span>withColumn(<\/span>"cost_effective_ratio"<\/span>,<\/span>
\n when(<\/span>(<\/span>col(<\/span>"avg_price"<\/span>)<\/span> ><\/span> 0<\/span>)<\/span> &<\/span> (<\/span>col(<\/span>"detail_rating"<\/span>)<\/span> ><\/span> 0<\/span>)<\/span>,<\/span>
\n col(<\/span>"detail_rating"<\/span>)<\/span> \/<\/span> col(<\/span>"avg_price"<\/span>)<\/span>)<\/span>.<\/span>otherwise(<\/span>None<\/span>)<\/span>)<\/span>
\n # \u8fc7\u6ee4\u6389\u65e0\u6548\u6570\u636e\u5e76\u6392\u5e8f<\/span>
\n effective_df =<\/span> df_with_ratio.<\/span>filter<\/span>(<\/span>col(<\/span>"cost_effective_ratio"<\/span>)<\/span>.<\/span>isNotNull(<\/span>)<\/span>)<\/span>.<\/span>orderBy(<\/span>col(<\/span>"cost_effective_ratio"<\/span>)<\/span>.<\/span>desc(<\/span>)<\/span>)<\/span>
\n # \u83b7\u53d6\u524d20\u4e2a\u9ad8\u6027\u4ef7\u6bd4\u9910\u5385<\/span>
\n result =<\/span> effective_df.<\/span>select(<\/span>"shop_name"<\/span>,<\/span> "main_cuisine"<\/span>,<\/span> "avg_price"<\/span>,<\/span> "detail_rating"<\/span>,<\/span> "cost_effective_ratio"<\/span>)<\/span>.<\/span>limit(<\/span>20<\/span>)<\/span>.<\/span>collect(<\/span>)<\/span>
\n return<\/span> [<\/span>{<\/span>"name"<\/span>:<\/span> row[<\/span>"shop_name"<\/span>]<\/span>,<\/span> "cuisine"<\/span>:<\/span> row[<\/span>"main_cuisine"<\/span>]<\/span>,<\/span> "price"<\/span>:<\/span> row[<\/span>"avg_price"<\/span>]<\/span>,<\/span> "rating"<\/span>:<\/span> row[<\/span>"detail_rating"<\/span>]<\/span>}<\/span> for<\/span> row in<\/span> result]<\/span><\/p>\n

# \u6838\u5fc3\u529f\u80fd3：\u70ed\u95e8\u63a8\u8350\u83dc\u54c1\u5206\u6790<\/span>
\ndef<\/span> get_popular_dishes<\/span>(<\/span>df)<\/span>:<\/span>
\n # \u5047\u8bbedf\u5305\u542b'recommended_dishes'\u5b57\u6bb5，\u83dc\u54c1\u7528\u5206\u53f7\u9694\u5f00<\/span>
\n # \u5c06\u83dc\u54c1\u5b57\u7b26\u4e32\u5206\u5272\u6210\u6570\u7ec4，\u7136\u540e\u5c55\u5f00\u6210\u591a\u884c<\/span>
\n exploded_dishes_df =<\/span> df.<\/span>withColumn(<\/span>"dish"<\/span>,<\/span> explode(<\/span>split(<\/span>col(<\/span>"recommended_dishes"<\/span>)<\/span>,<\/span> ";"<\/span>)<\/span>)<\/span>)<\/span>
\n # \u8fc7\u6ee4\u6389\u7a7a\u7684\u83dc\u54c1\u540d<\/span>
\n cleaned_dishes_df =<\/span> exploded_dishes_df.<\/span>filter<\/span>(<\/span>(<\/span>col(<\/span>"dish"<\/span>)<\/span> !=<\/span> ""<\/span>)<\/span> &<\/span> col(<\/span>"dish"<\/span>)<\/span>.<\/span>isNotNull(<\/span>)<\/span>)<\/span>
\n # \u6309\u83dc\u54c1\u540d\u5206\u7ec4\u8ba1\u6570<\/span>
\n dish_count_df =<\/span> cleaned_dishes_df.<\/span>groupBy(<\/span>"dish"<\/span>)<\/span>.<\/span>agg(<\/span>count(<\/span>"*"<\/span>)<\/span>.<\/span>alias(<\/span>"popularity"<\/span>)<\/span>)<\/span>
\n # \u6309\u70ed\u5ea6\u6392\u5e8f\u5e76\u83b7\u53d6\u524d30<\/span>
\n result =<\/span> dish_count_df.<\/span>orderBy(<\/span>col(<\/span>"popularity"<\/span>)<\/span>.<\/span>desc(<\/span>)<\/span>)<\/span>.<\/span>limit(<\/span>30<\/span>)<\/span>.<\/span>collect(<\/span>)<\/span>
\n return<\/span> [<\/span>{<\/span>"dish"<\/span>:<\/span> row[<\/span>"dish"<\/span>]<\/span>,<\/span> "popularity"<\/span>:<\/span> row[<\/span>"popularity"<\/span>]<\/span>}<\/span> for<\/span> row in<\/span> result]<\/span><\/p>\n

\u5927\u4f17\u70b9\u8bc4\u7f8e\u98df\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316\u7cfb\u7edf-\u7ed3\u8bed<\/h2>\n

\u672c\u9879\u76ee\u6210\u529f\u5b9e\u73b0\u4e86\u57fa\u4e8eHadoop\u7684\u5927\u4f17\u70b9\u8bc4\u7f8e\u98df\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316，\u9a8c\u8bc1\u4e86\u5927\u6570\u636e\u6280\u672f\u5728\u9910\u996e\u9886\u57df\u7684\u5e94\u7528\u4ef7\u503c\u3002\u7cfb\u7edf\u5b8c\u6210\u4e86\u4ece\u6570\u636e\u91c7\u96c6\u3001\u5904\u7406\u5230\u5206\u6790\u5c55\u793a\u7684\u5168\u6d41\u7a0b，\u867d\u7136\u4ecd\u6709\u4f18\u5316\u7a7a\u95f4，\u4f46\u5df2\u80fd\u63d0\u4f9b\u6709\u6548\u7684\u6d1e\u5bdf\u3002\u672a\u6765\u53ef\u5f15\u5165\u66f4\u590d\u6742\u7684\u7b97\u6cd5\u6a21\u578b，\u6216\u878d\u5408\u591a\u6e90\u6570\u636e，\u8fdb\u4e00\u6b65\u63d0\u5347\u5206\u6790\u7684\u6df1\u5ea6\u4e0e\u5e7f\u5ea6，\u4f7f\u5176\u66f4\u5177\u5b9e\u7528\u610f\u4e49\u3002<\/p>\n

\u8fd9\u4e2a\u6bd5\u8bbe\u9879\u76ee\u5bf9\u4f60\u6709\u5e2e\u52a9\u5417？\u5982\u679c\u60f3\u83b7\u53d6\u5b8c\u6574\u4ee3\u7801\u548c\u8bbe\u8ba1\u6587\u6863，\u6b22\u8fce\u6765\u6211\u4e3b\u9875\u770b\u770b\u54e6！\u89c9\u5f97\u5185\u5bb9\u4e0d\u9519\u7684\u8bdd，\u522b\u5fd8\u4e86\u7ed9UP\u4e3b\u4e00\u4e2a\u4e00\u952e\u4e09\u8fde\u652f\u6301\u4e00\u4e0b！\u4e5f\u6b22\u8fce\u5927\u5bb6\u5728\u8bc4\u8bba\u533a\u4ea4\u6d41\u81ea\u5df1\u7684\u6bd5\u8bbe\u5fc3\u5f97，\u6216\u8005\u6709\u4ec0\u4e48\u95ee\u9898\u90fd\u53ef\u4ee5\u63d0\u51fa\u6765，\u6211\u4eec\u4e00\u8d77\u8ba8\u8bba，\u5171\u540c\u8fdb\u6b65！<\/p>\n

\u26a1\u26a1\u83b7\u53d6\u6e90\u7801\u4e3b\u9875\u2013> \u8ba1\u7b97\u673a\u7f16\u7a0b\u6307\u5bfc\u5e08 \u26a1\u26a1\u6709\u6280\u672f\u95ee\u9898\u6216\u8005\u83b7\u53d6\u6e90\u4ee3\u7801！\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u4e00\u8d77\u4ea4\u6d41！ \u26a1\u26a1\u5927\u5bb6\u70b9\u8d5e\u3001\u6536\u85cf\u3001\u5173\u6ce8\u3001\u6709\u95ee\u9898\u90fd\u53ef\u7559\u8a00\u8bc4\u8bba\u4ea4\u6d41！ \u26a1\u26a1\u5982\u679c\u4f60\u9047\u5230\u5177\u4f53\u7684\u6280\u672f\u95ee\u9898\u6216\u8ba1\u7b97\u673a\u6bd5\u8bbe\u65b9\u9762\u9700\u6c42\u53ef\u4ee5\u5728\u4e3b\u9875\u4e0a\u8be6\u7ec6\u8d44\u6599\u91cc\u2191\u2191\u8054\u7cfb\u6211~~<\/p>\n","protected":false},"excerpt":{"rendered":"

\u270d\u270d\u8ba1\u7b97\u673a\u7f16\u7a0b\u6307\u5bfc\u5e08 \u2b50\u2b50\u4e2a\u4eba\u4ecb\u7ecd：\u81ea\u5df1\u975e\u5e38\u559c\u6b22\u7814\u7a76\u6280\u672f\u95ee\u9898！\u4e13\u4e1a\u505aJava\u3001Python\u3001\u5c0f\u7a0b\u5e8f\u3001\u5b89\u5353\u3001\u5927\u6570\u636e\u3001\u722c\u866b\u3001Golang\u3001\u5927\u5c4f\u7b49\u5b9e\u6218\u9879\u76ee\u3002 \u26fd\u26fd\u5b9e\u6218\u9879\u76ee：\u6709\u6e90\u7801\u6216\u8005\u6280\u672f\u4e0a\u7684\u95ee\u9898\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u4e00\u8d77\u8ba8\u8bba\u4ea4\u6d41！ \u26a1\u26a1\u5982\u679c\u4f60\u9047\u5230\u5177\u4f53\u7684\u6280\u672f\u95ee\u9898\u6216\u8ba1\u7b97\u673a\u6bd5\u8bbe\u65b9\u9762\u9700\u6c42\u53ef\u4ee5\u5728\u4e3b\u9875\u4e0a\u8be6\u7ec6\u8d44\u6599\u91cc\u2191\u2191\u8054\u7cfb\u6211~~ Java\u5b9e\u6218 | SpringBoot\/SSM Python\u5b9e\u6218\u9879\u76ee | Django \u5fae\u4fe1\u5c0f\u7a0b\u5e8f\/\u5b89\u5353\u5b9e\u6218\u9879\u76ee \u5927\u6570\u636e\u5b9e<\/p>\n","protected":false},"author":2,"featured_media":68240,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[7209,779,81,365,323,1908,957],"topic":[],"class_list":["post-68251","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-7209","tag-hadoop","tag-python","tag-spark","tag-323","tag-1908","tag-957"],"yoast_head":"\n\u3010\u8ba1\u7b97\u673a\u6bd5\u8bbe\u9009\u9898\u3011\u57fa\u4e8eHadoop\u7684\u5927\u4f17\u70b9\u8bc4\u7f8e\u98df\u6570\u636e\u5206\u6790\u7cfb\u7edf\uff0cPython+Spark\u5927\u6570\u636e\u5904\u7406 \u6bd5\u4e1a\u8bbe\u8ba1 \u9009\u9898\u63a8\u8350 \u6bd5\u8bbe\u9009\u9898 \u6570\u636e\u5206\u6790 \u673a\u5668\u5b66\u4e60 \u6570\u636e\u6316\u6398 - 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