{"id":481,"date":"2025-04-03T18:00:09","date_gmt":"2025-04-03T10:00:09","guid":{"rendered":"https:\/\/www.wunen.com\/index.php\/2025\/04\/03\/%e3%80%90%e5%a4%a7%e6%95%b0%e6%8d%ae%e5%ae%9e%e8%ae%ad%e3%80%91%e5%9f%ba%e4%ba%8e%e5%bd%93%e5%bd%93%e7%bd%91%e5%9b%be%e4%b9%a6%e4%bf%a1%e6%81%af%e7%9a%84%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e4%b8%8e\/"},"modified":"2025-04-03T18:00:09","modified_gmt":"2025-04-03T10:00:09","slug":"%e3%80%90%e5%a4%a7%e6%95%b0%e6%8d%ae%e5%ae%9e%e8%ae%ad%e3%80%91%e5%9f%ba%e4%ba%8e%e5%bd%93%e5%bd%93%e7%bd%91%e5%9b%be%e4%b9%a6%e4%bf%a1%e6%81%af%e7%9a%84%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e4%b8%8e","status":"publish","type":"post","link":"http:\/\/www.wunen.com\/index.php\/2025\/04\/03\/%e3%80%90%e5%a4%a7%e6%95%b0%e6%8d%ae%e5%ae%9e%e8%ae%ad%e3%80%91%e5%9f%ba%e4%ba%8e%e5%bd%93%e5%bd%93%e7%bd%91%e5%9b%be%e4%b9%a6%e4%bf%a1%e6%81%af%e7%9a%84%e6%95%b0%e6%8d%ae%e5%88%86%e6%9e%90%e4%b8%8e\/","title":{"rendered":"\u3010\u5927\u6570\u636e\u5b9e\u8bad\u3011\u57fa\u4e8e\u5f53\u5f53\u7f51\u56fe\u4e66\u4fe1\u606f\u7684\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316(\u516b)"},"content":{"rendered":"
\n \n \n
\n \n \n <\/path>\n <\/svg><\/p>\n

\n
\n <\/a>
\n \u57fa\u4e8e\u5f53\u5f53\u7f51\u56fe\u4e66\u4fe1\u606f\u7684\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316
\n <\/h3>\n

\n \u4e00\u3001\u5b9e\u9a8c\u73af\u5883\n <\/p>\n

\n \uff081\uff09Linux\uff1a Ubuntu 16.04
\n
\n \uff082\uff09Python: 3.5
\n
\n \uff083\uff09Hadoop\uff1a3.1.3\uff084\uff09Spark: 2.4.0\uff085\uff09Web\u6846\u67b6\uff1aflask 1.0.3
\n
\n \uff086\uff09\u53ef\u89c6\u5316\u5de5\u5177\uff1aEcharts
\n
\n \uff087\uff09\u5f00\u53d1\u5de5\u5177\uff1aVisual Studio Code\n <\/p>\n

\n \u4e8c\u3001\u5c0f\u7ec4\u6210\u5458\u53ca\u5206\u5de5\n <\/p>\n

\n \uff081\uff09\u6210\u5458\uff1a\u6797\u6d77\u6ee2\uff0c\u738b\u60e0\u73b2\uff0c\u9648\u5609\u6021\uff0c\u90ed\u8bd7\u5ff5\n <\/p>\n

\n \uff082\uff09\u5206\u5de5\uff1axxx\u8d1f\u8d23xxxx\u90e8\u5206\uff0cxxx\u8d1f\u8d23xxxx\u90e8\u5206\uff0cxxx\u8d1f\u8d23xxxx\u90e8\u5206\u3002\n <\/p>\n

\n \u4e09\u3001\u6570\u636e\u91c7\u96c6\n <\/p>\n

\n 3.1\u6570\u636e\u96c6\u8bf4\u660e\n <\/p>\n

\n \u722c\u53d6\u7f51\u7ad9\uff1ahttp:\/\/search.dangdang.com\/?key=java\uff0c\u662f\u5f53\u5f53\u7f51\u7684java\u56fe\u4e66\u7684\u4fe1\u606f\u7f51\u7ad9\u3002\u6570\u636e\u6587\u4ef6\uff1ajava_books.xlsx\u3002\u5176\u4e2d\u5305\u542b\u4e861661\u6761\u4e0ejava\u56fe\u4e66\u4fe1\u606f\u6709\u5173\u7684\u6570\u636e\u3002\n <\/p>\n

\n \u6570\u636e\u683c\u5f0f\u4e3a\uff1a\n <\/p>\n<\/p>\n

\n \u56fe 3. 1 \u91c7\u96c6\u6570\u636e\u683c\u5f0f\n <\/p>\n

\n \u6570\u636e\u4e2d\u5305\u542b\u7684\u5185\u5bb9\u5982\u4e0b\uff1a
\n
\n \uff081\uff09book_name\uff1a \u56fe\u4e66\u7684\u6807\u9898\n <\/p>\n

\n \uff082\uff09introduction\uff1a\u56fe\u4e66\u7684\u7b80\u4ecb\n <\/p>\n

\n \uff083\uff09author\uff1a \u56fe\u4e66\u7684\u4f5c\u8005
\n
\n \uff084\uff09price\uff1a \u56fe\u4e66\u7684\u4ef7\u683c\uff08\u5143\/\u672c\uff09\n <\/p>\n

\n \uff085\uff09press\uff1a \u56fe\u4e66\u51fa\u7248\u793e
\n
\n \uff086\uff09comment\uff1a \u56fe\u4e66\u7684\u8bc4\u8bba\n <\/p>\n

\n 3.2.\u722c\u53d6\u6570\u636e\u96c6\u4ee5\u53ca\u5c06\u5176\u4fdd\u5b58\u5230\u672c\u5730D\u76d8\u6587\u4ef6\u4e2d\u7684\u6d41\u7a0b\n <\/p>\n

\n \uff081\uff09\u9009\u53d6\u6240\u9700\u8981\u722c\u53d6\u7684\u9875\u9762\u8fdb\u884c\u904d\u5386\u722c\u53d6\n <\/p>\n

\n \uff082\uff09\u901a\u8fc7\u6b63\u5219\u8868\u8fbe\u5f0f\u6293\u53d6\u6240\u9700\u8981\u7684\u6570\u636e\n <\/p>\n

\n \uff083\uff09\u5c06\u722c\u53d6\u51fa\u7684\u6570\u636e\u8f6c\u5316\u4e3adataframe\u683c\u5f0f\u5e76\u4fdd\u5b58\u4e3axlsx\u6587\u4ef6\u5b58\u653e\u5728D\u76d8\n <\/p>\n

\n \u56db\u3001\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406\n <\/p>\n

\n 4.1\u9884\u5904\u7406\u4e2d\u63d0\u53d6\u7684\u6570\u636e\n <\/p>\n<\/p>\n

\n \u56fe 4.1 \u6570\u636e\u5904\u7406\u524d\u7684\u6570\u636e\u683c\u5f0f\u53ca\u5b58\u5728\u95ee\u9898\u7684\u7279\u5f81\u5217\n <\/p>\n

\n 4.2 \u6e05\u6d17\u9884\u5904\u7406\u540e\u7684\u6570\u636e\u683c\u5f0f\n <\/p>\n<\/p>\n

\n \u56fe 4.2 \u6570\u636e\u5904\u7406\u540e\u7684\u6570\u636e\u683c\u5f0f\u53ca\u7279\u5f81\u5217\n <\/p>\n

\n 4.3 \u6e05\u6d17\u4e0e\u9884\u5904\u7406\u7684\u6d41\u7a0b\n <\/p>\n

\n \uff081\uff09\u9996\u5148\u68c0\u67e5\u6570\u636e\u7684\u7ed3\u6784\u4ee5\u53ca\u662f\u5426\u6709\u6570\u636e\u7f3a\u5931\u3002\n <\/p>\n

\n \uff082\uff09\u53d1\u73b0book_name\u7279\u5f81\u5217\u7684\u6570\u636e\u683c\u5f0f\u4e0d\u5bf9\uff0c\u4e8e\u662f\u5904\u7406\u8f6c\u6362\u4e3a\u4e86\u6574\u6570\u7c7b\u578b\u3002\n <\/p>\n

\n \uff083\uff09\u53d1\u73b0price\u7279\u5f81\u5217\u7684\u6570\u636e\u683c\u5f0f\u4e0d\u5bf9\uff0c\u4e8e\u662f\u5904\u7406\u8f6c\u6362\u4e3a\u4e86\u6d6e\u70b9\u7c7b\u578b\u3002\n <\/p>\n

\n \uff084\uff09\u5185\u5bb9\u7b80\u4ecb\u5217\u6570\u636e\u6e05\u6d17 \u5220\u9664\u5f02\u5e38\u503c\u3002\n <\/p>\n

\n \uff085\uff09\u4fdd\u5b58\u6e05\u6d17\u4e0e\u9884\u5904\u7406\u540e\u7684\u6570\u636e\u96c6\u3002\n <\/p>\n

\n \u4e94\u3001spark\u6570\u636e\u5206\u6790\n <\/p>\n

\n 5.1 \u6570\u636e\u5206\u6790\u76ee\u6807\n <\/p>\n

\n \uff081\uff09\u56fe\u4e66\u7684\u552e\u4ef7\u5206\u5e03\u60c5\u51b5\uff08\u89c2\u5bdf\u56fe\u4e66\u4ef7\u683c\u5927\u4f53\u96c6\u4e2d\u5728\u54ea\u4e2a\u5206\u6bb5\u5f97\u51fa\u56fe\u4e66\u4ef7\u683c\u8d8b\u52bf\uff09\n <\/p>\n<\/p>\n

\n \uff082\uff09\u90e8\u5206\u56fe\u4e66\u51fa\u7248\u793e\u7684\u51fa\u4e66\u6570\u91cf\u7edf\u8ba1\n <\/p>\n<\/p>\n

\n \uff083\uff09\u56fe\u4e66\u7684\u4f5c\u8005\u51fa\u4e66\uff08\u89c2\u5bdf\u54ea\u4e2a\u4f5c\u8005\u51fa\u7684\u4e66\u6700\u591a\uff09\n <\/p>\n

\n \uff084\uff09\u56fe\u4e66\u7684\u8bc4\u8bba\u5206\u5e03\u60c5\u51b5\uff08\u89c2\u5bdf\u56fe\u4e66\u8bc4\u8bba\u5927\u4f53\u96c6\u4e2d\u5728\u54ea\u4e2a\u5206\u6bb5\u5f97\u51fa\u56fe\u4e66\u8bc4\u8bba\u8d8b\u52bf\uff09\n <\/p>\n<\/p>\n

\n \uff085\uff09\u56fe\u4e66\u7684\u90e8\u5206\u4f5c\u8005\u6570\u91cf\u7edf\u8ba1\n <\/p>\n<\/p>\n

\n \uff086\uff09\u5206\u6790\u4ef7\u683c\u7684\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u3001\u5747\u503c\u3001\u65b9\u5dee\u548c\u4e2d\u4f4d\u6570\n <\/p>\n<\/p>\n

\n \u516d\u3001\u6570\u636e\u53ef\u89c6\u5316\n <\/p>\n

\n \u672c\u5b9e\u9a8c\u7684\u53ef\u89c6\u5316\u57fa\u4e8emutplotlib\u5b9e\u73b0\u3002\n <\/p>\n

\n 6.1.\u53ef\u89c6\u5316\u73af\u5883\n <\/p>\n

\n \u5229\u7528\u548canaconda\u91cc\u9762\u7684jubiter\u548cvscode\u8fdb\u884c\u53ef\u89c6\u5316\u64cd\u4f5c\uff0c\u6700\u540e\u7684\u4ee3\u7801\u7ed3\u6784\u5982\u4e0b\u3002\n <\/p>\n

\n 6.2 \u56fe\u8868\u5c55\u793a\u4e0e\u7ed3\u8bba\u5206\u6790\n <\/p>\n

\n \uff081\uff09\u56fe\u4e66\u7684\u552e\u4ef7\u5206\u5e03\u60c5\u51b5\uff08\u89c2\u5bdf\u56fe\u4e66\u4ef7\u683c\u5927\u4f53\u96c6\u4e2d\u5728\u54ea\u4e2a\u5206\u6bb5\u5f97\u51fa\u56fe\u4e66\u4ef7\u683c\u8d8b\u52bf\uff09\n <\/p>\n<\/p>\n

\n \u56fe6.2.1\u56fe\u4e66\u7684\u552e\u4ef7\u5206\u5e03\u60c5\u51b5\n <\/p>\n

\n \u5206\u6790\u7ed3\u8bba\uff1a\u901a\u8fc7\u8fd9\u4e2a\u67f1\u72b6\u56fe\u53ef\u4ee5\u770b\u51fa\u56fe\u4e66\u552e\u5356\u4ef7\u683c\u96c6\u4e2d\u572820
\n
\n 60\u8fd9\u91cc\u3002\u8bf4\u660e\u4e86\u5927\u591a\u6570\u4eba\u8d2d\u4e66\u503e\u5411\u4e8e\u4e2d\u7aef\u4ef7\u683c\u3002\u6bd4\u598220
\n <\/sub>
\n 40\u8fd9\u91cc\uff0c\u552e\u4ef7\u6bd4\u8f83\u4fbf\u5b9c\u56fe\u4e66\u7684\u9500\u91cf\u5c31\u4f1a\u591a\u3002\u800c60
\n
\n 80\u8fd9\u91cc\u7684\u4ef7\u683c\u4e0a\u5347\u4e86\u8d2d\u4e70\u7684\u4eba\u5c31\u76f8\u5bf9\u5c11\u4e86\uff0c\u9500\u91cf\u4e5f\u968f\u4e4b\u51cf\u5c11\u3002\u6211\u4eec\u4e5f\u53ef\u4ee5\u4ece\u4e2d\u5f97\u51fa20
\n <\/sub>
\n 60\u7684\u9500\u91cf\u67091841\uff0c\u800c20\u4e00\u4e0b\u548c60\u4ee5\u4e0a\u7684\u9500\u91cf\u67091159\u3002\u6240\u4ee5\u5927\u80c6\u63a8\u6d4b\u51fa\u6211\u56fd\u4e2d\u5c42\u6536\u5165\u4eba\u6570\u662f\u4f4e\u9ad8\u5c42\u6536\u5165\u4eba\u6570\u76841.6\u500d\u5de6\u53f3\u3002\n <\/p>\n

\n \uff082\uff09\u90e8\u5206\u56fe\u4e66\u51fa\u7248\u793e\u7684\u51fa\u4e66\u6570\u91cf\u7edf\u8ba1\n <\/p>\n<\/p>\n

\n \u56fe6.2.2\u90e8\u5206\u56fe\u4e66\u51fa\u7248\u793e\u7684\u51fa\u4e66\u6570\u91cf\u7edf\u8ba1\n <\/p>\n

\n \uff083\uff09\u56fe\u4e66\u7684\u4f5c\u8005\u51fa\u4e66\uff08\u89c2\u5bdf\u54ea\u4e2a\u4f5c\u8005\u51fa\u7684\u4e66\u6700\u591a\uff09\n <\/p>\n

\n \uff084\uff09\u56fe\u4e66\u7684\u8bc4\u8bba\u5206\u5e03\u60c5\u51b5\uff08\u89c2\u5bdf\u56fe\u4e66\u8bc4\u8bba\u5927\u4f53\u96c6\u4e2d\u5728\u54ea\u4e2a\u5206\u6bb5\u5f97\u51fa\u56fe\u4e66\u8bc4\u8bba\u8d8b\u52bf\uff09\n <\/p>\n<\/p>\n

\n \u56fe6.2.4\u56fe\u4e66\u7684\u8bc4\u8bba\u5206\u5e03\u60c5\u51b5\n <\/p>\n

\n \u5206\u6790\u7ed3\u8bba\uff1a\u901a\u8fc7\u8fd9\u4e2a\u56fe\u6211\u4eec\u53ef\u4ee5\u770b\u51fa92%\u7684\u56fe\u4e66\u8bc4\u8bba\u90fd\u662f\u57280~100\u4e4b\u95f4\u3002\u4e5f\u5c31\u662f\u8bf492%\u7684\u4eba\u4e0d\u7231\u5bf9\u56fe\u4e66\u505a\u51fa\u8bc4\u8bba\uff0c\u5176\u4f59\u5c11\u90e8\u5206\u4eba\u4f1a\u5bf9\u56fe\u4e66\u505a\u51fa\u8bc4\u8bba\u3002\u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u5927\u80c6\u63a8\u6d4b\u73b0\u5728\u5927\u591a\u6570\u4eba\u90fd\u4e0d\u7231\u5bf9\u770b\u8fc7\u7684\u4e66\u53d1\u5e03\u4e4b\u95f4\u7684\u770b\u6cd5\u3002\n <\/p>\n

\n \uff085\uff09\u56fe\u4e66\u7684\u90e8\u5206\u4f5c\u8005\u6570\u91cf\u7edf\u8ba1\u8fdb\u884c\u6570\u636e\u53ef\u89c6\u5316\u56fe\u8868\u5206\u6790\n <\/p>\n<\/p>\n

\n \u56fe6.2.5\u90e8\u5206\u4f5c\u8005\u6570\u91cf\u7edf\u8ba1\n <\/p>\n

\n \u5206\u6790\u7ed3\u8bba\uff1a\u901a\u8fc7\u8fd9\u4e2a\u56fe\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\u6211\u4eec\u627e\u51fa\u6765\u7684\u90e8\u5206\u4f5c\u800554%\u5de6\u53f3\u7684\u4f5c\u8005\u5199\u7684\u4e66\u90fd\u572865\u672c\u4ee5\u4e0b\uff08\u8fd9\u5c31\u4e0e\u7b2c\u4e94\u90e8\u5206\u7684\u6570\u636e\u5206\u6790\u76f8\u5bf9\u5e94\uff09\uff0c\u4e5f\u4fa7\u9762\u8bf4\u660e\u4e86\u8fd9\u4e2a\u6570\u636e\u91cc\u976250%\u5de6\u53f3\u7684\u4f5c\u8005\u5199\u7684java\u7684\u4e66\u4e0d\u591a\u537350%\u4ee5\u4e0b\u7684\u4f5c\u8005\u53ef\u80fd\u4e0d\u662f\u4e13\u95e8\u505ajava\u8fd9\u4e2a\u9886\u57df\u7684\uff0c\u53ef\u80fd\u8fd8\u5305\u62ec\u522b\u7684\u9886\u57df\uff0c\u5927\u591a\u6570\u5173\u4e8ejava\u7684\u56fe\u4e66\u90fd\u662f\u4e13\u653b\u8fd9\u4e2a\u4e13\u4e1a\u7684\u7ec4\u7ec7\u548c\u4f5c\u8005\u5199\u7684\u3002\n <\/p>\n

\n \uff085\uff09\u5206\u6790\u4ef7\u683c\u7684\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u3001\u5747\u503c\u3001\u65b9\u5dee\u548c\u4e2d\u4f4d\u6570\n <\/p>\n<\/p>\n

\n \u56fe6.2.6\u5206\u6790\u4ef7\u683c\u7684\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u3001\u5747\u503c\u3001\u65b9\u5dee\u548c\u4e2d\u4f4d\u6570\n <\/p>\n

\n \u5206\u6790\u7ed3\u8bba\uff1a\u901a\u8fc7\u8fd9\u4e2a\u56fe\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\u56fe\u4e66\u4ef7\u683c\u7684\u4e2d\u4f4d\u6570\u548c\u5747\u503c\u572855\u5757\u94b1\u5de6\u53f3\uff0c\u65b9\u5dee\u572850\u5de6\u53f3\uff0c\u8bc1\u660e\u4e86\u56fe\u4e66\u7684\u4ef7\u683c\u6ce2\u52a8\u4e0d\u662f\u5f88\u5927\uff0c\u4ece\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u4e2d\u53ef\u4ee5\u770b\u51fa\u6700\u4fbf\u5b9c\u7684\u56fe\u4e66\u662f10\u5143\u5de6\u53f3\uff0c\u6700\u8d35\u7684\u56fe\u4e66\u662f120\u5143\u5de6\u53f3\uff0c\u6240\u4ee5\u8868\u660e\u4e86\u4e70\u4e00\u672cjava\u56fe\u4e66\u603b\u4f53\u7684\u5747\u4ef7\u4e3a55\u5143\u5de6\u53f3\u3002\n <\/p>\n

\n
\n <\/a>
\n \u4ee3\u7801\u90e8\u5206\uff1a
\n <\/h3>\n

\n \u9644\u5f55A\uff08\u4ee3\u7801\uff09\uff1a\n <\/p>\n

#\u6e05\u6d17\u4ee3\u7801\uff1a<\/span>\n# coding=utf-8<\/span>\n# \u6570\u636e\u6e05\u6d17<\/span>\nimport<\/span> os\nimport<\/span> numpy as np\nimport<\/span> pandas as pd\nimport<\/span> csv\ndf<\/span> =<\/span> pd.read_csv(<\/span>\"java_books.csv\"<\/span>)<\/span>\n\ndf[<\/span>'book_name'<\/span>]<\/span>=<\/span>df[<\/span>'book_name'<\/span>]<\/span>.str.strip(<\/span>)<\/span>#\u53bb\u6389book_name\u5217\u6240\u6709\u503c\u524d\u540e\u7684\u7a7a\u683c<\/span>\ndf[<\/span>'author'<\/span>]<\/span>=<\/span>df[<\/span>'author'<\/span>]<\/span>.str.strip(<\/span>)<\/span>#\u53bb\u6389price\u5217\u6240\u6709\u503c\u524d\u540e\u7684\u7a7a\u683c<\/span>\ndf[<\/span>'introduction'<\/span>]<\/span>=<\/span>df[<\/span>'introduction'<\/span>]<\/span>.astype(<\/span>str)<\/span>#\u628abook_name\u5217\u6240\u6709\u503c\u8f6c\u5316\u4e3a\u5b57\u7b26\u4e32\u7c7b\u578b<\/span>\ndf.dropna(<\/span>axis=<\/span>0<\/span>,how=<\/span>'any'<\/span>,thresh=<\/span>None,subset=<\/span>None,inplace=<\/span>True)<\/span>\ndf[<\/span>'price'<\/span>]<\/span>.astype(<\/span>float)<\/span>\n# \u5185\u5bb9\u7b80\u4ecb\u5217\u6570\u636e\u6e05\u6d17 \u5220\u9664\u5f02\u5e38\u503c<\/span>\ndf.drop(<\/span>df[<\/span>df[<\/span>'introduction'<\/span>]<\/span>.str.contains(<\/span>'\u5185\u5bb9\u7b80\u4ecb'<\/span>)<\/span>]<\/span>.index,inplace=<\/span>True)<\/span>\n# \u8f93\u51fa\u6e05\u7406\u5b8c\u7684\u6587\u4ef6<\/span>\ndf.to_csv(<\/span>r'D:\/data\/clean_ganji_rent1.csv'<\/span>,encoding=<\/span>'utf-8'<\/span>,index=<\/span>False)<\/span>\n\n\u5904\u7406\u4ee3\u7801+spark\u5206\u6790\u4ee3\u7801:\n\uff081\uff09\u56fe\u4e66\u7684\u552e\u4ef7\u5206\u5e03\u60c5\u51b5\uff08\u89c2\u5bdf\u56fe\u4e66\u4ef7\u683c\u5927\u4f53\u96c6\u4e2d\u5728\u54ea\u4e2a\u5206\u6bb5\u5f97\u51fa\u56fe\u4e66\u4ef7\u683c\u8d8b\u52bf\uff09\nimport<\/span> os\nos.environ[<\/span>\"JAVA_HOME\"<\/span>]<\/span>=<\/span>\"\/usr\/lib\/jvm\/jdk1.8.0_162\"<\/span>\nos.environ[<\/span>\"PYSPARK_PYTHON\"<\/span>]<\/span>=<\/span>\"\/usr\/bin\/python3.5\"<\/span>\n\nfrom pyspark.sql import<\/span> SparkSession\nimport<\/span> pandas as pd\n\nxlsx =<\/span> '\/home\/guosn\/data\/java_books.xlsx'<\/span>\ncsv =<\/span> '\/home\/guosn\/data\/java_books.csv'<\/span>\n\n\ndef save_xlsx_to_text(<\/span>)<\/span>:\n    \"\"<\/span>\"\n    \u4f7f\u7528pandas\u5c06xlsx\u6587\u4ef6\u8f6c\u6362\u4e3acsv\u6587\u4ef6\n    \u4f9bspark\u8bfb\u53d6\n    \"\"<\/span>\"\n    df<\/span> =<\/span> pd.read_excel(<\/span>xlsx)<\/span>\n    df.to_csv(<\/span>csv,index=<\/span>False)<\/span>\n\n\n\ndef price_distribution(<\/span>spark)<\/span>:\n    \"\"<\/span>\"\u8ba1\u7b97\u4ef7\u683c\u533a\u95f4\u5206\u5e03\"\"<\/span>\"\n    df<\/span> =<\/span> spark.read.csv(<\/span>csv,header=<\/span>True)<\/span>\n    df.printSchema(<\/span>)<\/span>\n    df.createOrReplaceTempView(<\/span>\"books\"<\/span>)<\/span>\n\n    # \u4e3a\u6bcf\u6761\u6570\u636e\u6253\u4e0a\u6240\u5c5e\u4ef7\u683c\u533a\u95f4\u6807\u8bc6<\/span>\n    price_sql =<\/span> \"\"<\/span>\"\n    select<\/span> \n        case<\/span>  \n        when (<\/span>price <<\/span> 20.0<\/span>)<\/span> then<\/span> '0-20'<\/span>\n        when (<\/span>price >=<\/span>20.0<\/span>)<\/span> and (<\/span>price <<\/span> 40.0<\/span>)<\/span> then<\/span> '20-40'<\/span>\n        when (<\/span>price >=<\/span>40.0<\/span>)<\/span> and price <<\/span>60.0<\/span> then<\/span> '40-60'<\/span>\n        when price >=<\/span>60.0<\/span> and price <<\/span>80.0<\/span> then<\/span> '60-80'<\/span>\n        when price >=<\/span>80.0<\/span> and price <<\/span>100.0<\/span> then<\/span> '80-100'<\/span>\n        else<\/span> 'greater than 100'<\/span>\n        end as price_range,\n        books.*\n    from books   \n    \"\"<\/span>\"\n\n    price_result =<\/span> spark.sql(<\/span>price_sql)<\/span>\n    price_result.createOrReplaceTempView(<\/span>\"price_results\"<\/span>)<\/span>\n\n    price_result.show(<\/span>)<\/span>\n\n    # \u6309\u7167\u4ef7\u683c\u533a\u95f4\u8fdb\u884c\u5206\u7ec4\uff0c\u8ba1\u7b97\u5404\u7ec4\u5185\u6570\u636e\u603b\u6570<\/span>\n    group_price_sql =<\/span> 'select price_range, count(*) from price_results group by price_range'<\/span>\n    group_price_results =<\/span> spark.sql(<\/span>group_price_sql)<\/span>\n\n    group_price_results.show(<\/span>)<\/span>\n\n\ndef comment_distribution(<\/span>spark)<\/span>:\n    \"\"<\/span>\"\u8ba1\u7b97\u4ef7\u683c\u533a\u95f4\u5206\u5e03\"\"<\/span>\"\n    df<\/span> =<\/span> spark.read.csv(<\/span>csv,header=<\/span>True)<\/span>\n    df.printSchema(<\/span>)<\/span>\n    df.createOrReplaceTempView(<\/span>\"books\"<\/span>)<\/span>\n\n    # \u4e3a\u6bcf\u6761\u6570\u636e\u6253\u4e0a\u6240\u5c5e\u4ef7\u683c\u533a\u95f4\u6807\u8bc6<\/span>\n    comment_sql =<\/span> \"\"<\/span>\"\n    select<\/span> \n        case<\/span>  \n        when (<\/span>comment <<\/span> 100<\/span>)<\/span> then<\/span> '0-100'<\/span>\n        when (<\/span>comment >=<\/span>100<\/span>)<\/span> and (<\/span>comment <<\/span> 200.0<\/span>)<\/span> then<\/span> '100-200'<\/span>\n        when (<\/span>comment >=<\/span>200<\/span>)<\/span> and (<\/span>comment <<\/span>400<\/span>)<\/span> then<\/span> '200-400'<\/span>\n        when (<\/span>comment >=<\/span>400<\/span>)<\/span> and (<\/span>comment <<\/span>600<\/span>)<\/span> then<\/span> '400-600'<\/span>\n        when comment >=<\/span>600<\/span> and comment <<\/span>800<\/span> then<\/span> '600-800'<\/span>\n        when comment >=<\/span>800<\/span> and comment <<\/span>1000<\/span> then<\/span> '800-1000'<\/span>\n        else<\/span> 'greater than 10000'<\/span>\n        end as comment_range,\n        books.*\n    from books   \n    \"\"<\/span>\"\n\n    comment_result =<\/span> spark.sql(<\/span>comment_sql)<\/span>\n    comment_result.createOrReplaceTempView(<\/span>\"comment_results\"<\/span>)<\/span>\n    comment_result.show(<\/span>)<\/span>\n\n    # \u6309\u7167\u4ef7\u683c\u533a\u95f4\u8fdb\u884c\u5206\u7ec4\uff0c\u8ba1\u7b97\u5404\u7ec4\u5185\u6570\u636e\u603b\u6570<\/span>\n    group_comment_sql =<\/span> 'select comment_range, count(*) from comment_results group by comment_range'<\/span>\n    group_comment_results =<\/span> spark.sql(<\/span>group_comment_sql)<\/span>\n    group_comment_results.show(<\/span>)<\/span>\n\nif<\/span> __name__ ==<\/span> '__main__'<\/span>:<\/span>\n    sc =<\/span> SparkSession.builder.appName(<\/span>\"Python Spark SQL basic example\"<\/span>)<\/span>.master(<\/span>'local'<\/span>)<\/span> .getOrCreate(<\/span>)<\/span>\n    # \u5177\u4f53\u6267\u884c\u65b9\u6cd5<\/span>\n    save_xlsx_to_text(<\/span>)<\/span>\n    price_distribution(<\/span>sc)<\/span>\n    comment_distribution(<\/span>sc)<\/span>\n\n    sc.stop(<\/span>)<\/span>\n\uff082\uff09\u90e8\u5206\u56fe\u4e66\u51fa\u7248\u793e\u7684\u51fa\u4e66\u6570\u91cf\u7edf\u8ba1\nimport<\/span> os\nos.environ[<\/span>\"JAVA_HOME\"<\/span>]<\/span>=<\/span>\"\/usr\/lib\/jvm\/jdk1.8.0_162\"<\/span>\nos.environ[<\/span>\"PYSPARK_PYTHON\"<\/span>]<\/span>=<\/span>'\/usr\/bin\/python3.5'<\/span>\n\nfrom pyspark import<\/span> SparkConf,SparkContext\nfrom pyspark.sql.session import<\/span> SparkSession\nfrom pyspark import<\/span> SparkContext\nfrom pyspark.sql.types import<\/span> Row\nimport<\/span> pandas as pd\nimport<\/span> numpy as np\n\n#<\/span>\nxlsx =<\/span> '\/home\/hadoop\/data\/java_books.xlsx'<\/span>\ncsv =<\/span> '\/home\/hadoop\/data\/java_books.csv'<\/span>\n\n\ndef save_xlsx_to_text(<\/span>)<\/span>:\n    \"\"<\/span>\"\n    \u4f7f\u7528pandas\u5c06xlsx\u6587\u4ef6\u8f6c\u6362\u4e3acsv\u6587\u4ef6\n    \u4f9bspark\u8bfb\u53d6\n    \"\"<\/span>\"\n    df<\/span> =<\/span> pd.read_excel(<\/span>xlsx)<\/span>\n    df.to_csv(<\/span>csv,index=<\/span>False)<\/span>\n\n\nif<\/span> __name__<\/span>==<\/span>'__main__'<\/span>:<\/span>\n    conf =<\/span> SparkConf(<\/span>)<\/span>.setAppName(<\/span>\"IpSearch\"<\/span>)<\/span>.setMaster(<\/span>\"local[2]\"<\/span>)<\/span>\n    sc =<\/span> SparkContext.getOrCreate(<\/span>conf)<\/span>\n    spark =<\/span> SparkSession.builder.getOrCreate(<\/span>)<\/span>\n    sc.setLogLevel(<\/span>\"WARN\"<\/span>)<\/span>\n\n    # data = spark.read.csv(csv, header=True)<\/span>\n    data<\/span>=<\/span>sc.textFile(<\/span>\"file:\/\/\/home\/hadoop\/data\/java_books .csv\"<\/span>)<\/span>\n    data<\/span>=<\/span>data.map(<\/span>lambda line: line.split(<\/span>\",\"<\/span>))<\/span>\n    datardd<\/span>=<\/span>data.map(<\/span>lambda local:Row(<\/span>press=<\/span>local[<\/span>4<\/span>]<\/span>))<\/span>\n    print(<\/span>datardd.take(<\/span>3<\/span>))<\/span>\n    zhaopin =<\/span> spark.createDataFrame(<\/span>datardd)<\/span>\n    zhaopin.createOrReplaceTempView(<\/span>\"zhaopin\"<\/span>)<\/span>\n\n    # datardd = sc.textFile(\"file:\/\/\/home\/hadoop\/data\/employee.txt\")<\/span>\n    # employee = spark.createDataFrame(datardd)<\/span>\n    # employee.createOrReplaceTempView(\"employee\")<\/span>\n    # loc = spark.sql(\"select count(*) as count from employee group by location order by count desc\")<\/span>\n\n    siming =<\/span> spark.sql(<\/span>\"select count(*) as simingcount from (select press from zhaopin where press Like '%\u673a\u68b0\u5de5\u4e1a%' )\"<\/span>)<\/span>\n    print(<\/span>siming.rdd.map(<\/span>lambda p: '\u673a\u68b0\u5de5\u4e1a\u51fa\u7248\u793e:'<\/span> + str(<\/span>p.simingcount))<\/span>.collect(<\/span>))<\/span>\n    jimei =<\/span> spark.sql(<\/span>\"select count(*) as jimei from (select press from zhaopin where press like '%\u6e05\u534e\u5927\u5b66%' )\"<\/span>)<\/span>\n    print(<\/span>jimei.rdd.map(<\/span>lambda p: '\u6e05\u534e\u5927\u5b66\u51fa\u7248\u793e:'<\/span> + str(<\/span>p.jimei))<\/span>.collect(<\/span>))<\/span>\n    huli =<\/span> spark.sql(<\/span>\"select count(*) as huli from (select press from zhaopin where press like '%\u7535\u5b50\u5de5\u4e1a%')\"<\/span>)<\/span>\n    print(<\/span>huli.rdd.map(<\/span>lambda p: '\u7535\u5b50\u5de5\u4e1a\u51fa\u7248\u793e:'<\/span> + str(<\/span>p.huli))<\/span>.collect(<\/span>))<\/span>\n    haicang =<\/span> spark.sql(<\/span>\"select count(*) as haicang from (select press from zhaopin where press like '%\u4e2d\u56fd\u94c1\u9053%' )\"<\/span>)<\/span>\n    print(<\/span>haicang.rdd.map(<\/span>lambda p: '\u4e2d\u56fd\u94c1\u9053\u51fa\u7248\u793e:'<\/span> + str(<\/span>p.haicang))<\/span>.collect(<\/span>))<\/span>\n    tongan =<\/span> spark.sql(<\/span>\"select count(*) as tongan from (select press from zhaopin where press like '%\u4eba\u6c11\u90ae\u7535%')\"<\/span>)<\/span>\n    print(<\/span>tongan.rdd.map(<\/span>lambda p: '\u4eba\u6c11\u90ae\u7535\u51fa\u7248\u793e:'<\/span> + str(<\/span>p.tongan))<\/span>.collect(<\/span>))<\/span>\n    xiangan =<\/span> spark.sql(<\/span>\"select count(*) as xiangan from (select press from zhaopin where press like '%\u4e1c\u5357\u5927\u5b66%')\"<\/span>)<\/span>\n    print(<\/span>xiangan.rdd.map(<\/span>lambda p: '\u4e1c\u5357\u5927\u5b66\u51fa\u7248\u793e:'<\/span> + str(<\/span>p.xiangan))<\/span>.collect(<\/span>))<\/span>\n\uff083\uff09\u56fe\u4e66\u7684\u4f5c\u8005\u51fa\u4e66\uff08\u89c2\u5bdf\u54ea\u4e2a\u4f5c\u8005\u51fa\u7684\u4e66\u6700\u591a\uff09\n\n\uff084\uff09\u56fe\u4e66\u7684\u8bc4\u8bba\u5206\u5e03\u60c5\u51b5\uff08\u89c2\u5bdf\u56fe\u4e66\u8bc4\u8bba\u5927\u4f53\u96c6\u4e2d\u5728\u54ea\u4e2a\u5206\u6bb5\u5f97\u51fa\u56fe\u4e66\u8bc4\u8bba\u8d8b\u52bf\uff09\ndef comment_distribution(<\/span>spark)<\/span>:\n    \"\"<\/span>\"\u8ba1\u7b97\u4ef7\u683c\u533a\u95f4\u5206\u5e03\"\"<\/span>\"\n    df<\/span> =<\/span> spark.read.csv(<\/span>csv,header=<\/span>True)<\/span>\n    df.printSchema(<\/span>)<\/span>\n    df.createOrReplaceTempView(<\/span>\"books\"<\/span>)<\/span>\n\n    # \u4e3a\u6bcf\u6761\u6570\u636e\u6253\u4e0a\u6240\u5c5e\u4ef7\u683c\u533a\u95f4\u6807\u8bc6<\/span>\n    comment_sql =<\/span> \"\"<\/span>\"\n    select<\/span> \n        case<\/span>  \n        when (<\/span>comment <<\/span> 100<\/span>)<\/span> then<\/span> '0-100'<\/span>\n        when (<\/span>comment >=<\/span>100<\/span>)<\/span> and (<\/span>comment <<\/span> 200.0<\/span>)<\/span> then<\/span> '100-200'<\/span>\n        when (<\/span>comment >=<\/span>200<\/span>)<\/span> and (<\/span>comment <<\/span>400<\/span>)<\/span> then<\/span> '200-400'<\/span>\n        when (<\/span>comment >=<\/span>400<\/span>)<\/span> and (<\/span>comment <<\/span>600<\/span>)<\/span> then<\/span> '400-600'<\/span>\n        when comment >=<\/span>600<\/span> and comment <<\/span>800<\/span> then<\/span> '600-800'<\/span>\n        when comment >=<\/span>800<\/span> and comment <<\/span>1000<\/span> then<\/span> '800-1000'<\/span>\n        else<\/span> 'greater than 10000'<\/span>\n        end as comment_range,\n        books.*\n    from books   \n    \"\"<\/span>\"\n\n    comment_result =<\/span> spark.sql(<\/span>comment_sql)<\/span>\n    comment_result.createOrReplaceTempView(<\/span>\"comment_results\"<\/span>)<\/span>\n    comment_result.show(<\/span>)<\/span>\n\n    # \u6309\u7167\u4ef7\u683c\u533a\u95f4\u8fdb\u884c\u5206\u7ec4\uff0c\u8ba1\u7b97\u5404\u7ec4\u5185\u6570\u636e\u603b\u6570<\/span>\n    group_comment_sql =<\/span> 'select comment_range, count(*) from comment_results group by comment_range'<\/span>\n    group_comment_results =<\/span> spark.sql(<\/span>group_comment_sql)<\/span>\n    group_comment_results.show(<\/span>)<\/span>\n\nif<\/span> __name__ ==<\/span> '__main__'<\/span>:<\/span>\n    sc =<\/span> SparkSession.builder.appName(<\/span>\"Python Spark SQL basic example\"<\/span>)<\/span>.master(<\/span>'local'<\/span>)<\/span> .getOrCreate(<\/span>)<\/span>\n    # \u5177\u4f53\u6267\u884c\u65b9\u6cd5<\/span>\n    save_xlsx_to_text(<\/span>)<\/span>\n    price_distribution(<\/span>sc)<\/span>\n    comment_distribution(<\/span>sc)<\/span>\n\n    sc.stop(<\/span>)<\/span>\n\n\n\uff085\uff09\u56fe\u4e66\u7684\u90e8\u5206\u4f5c\u8005\u6570\u91cf\u7edf\u8ba1\nimport<\/span> os\nos.environ[<\/span>'JAVA_HOME'<\/span>]<\/span>=<\/span>'\/usr\/lib\/jvm\/jdk1.8.0_162'<\/span>\nos.environ[<\/span>\"PYSPARK_PYTHON\"<\/span>]<\/span>=<\/span>'\/usr\/bin\/python3.5'<\/span>\n\nfrom pyspark import<\/span> SparkConf,SparkContext\nfrom pyspark.sql.session import<\/span> SparkSession\nfrom pyspark import<\/span> SparkContext\nfrom pyspark.sql.types import<\/span> Row\nimport<\/span> pandas as pd\nimport<\/span> numpy as np\n\nxlsx =<\/span> '\/home\/hadoop\/data\/java_books.xlsx'<\/span>\ncsv =<\/span> '\/home\/hadoop\/data\/java_books.csv'<\/span>\ndef save_xlsx_to_text(<\/span>)<\/span>:\n    df<\/span> =<\/span> pd.read_excel(<\/span>xlsx)<\/span>\n    df.to_csv(<\/span>csv,index=<\/span>False)<\/span>\n\n\nif<\/span> __name__<\/span>==<\/span>'__main__'<\/span>:<\/span>\n    conf =<\/span> SparkConf(<\/span>)<\/span>.setAppName(<\/span>\"IpSearch\"<\/span>)<\/span>.setMaster(<\/span>\"local[2]\"<\/span>)<\/span>\n    sc =<\/span> SparkContext.getOrCreate(<\/span>conf)<\/span>\n    spark =<\/span> SparkSession.builder.getOrCreate(<\/span>)<\/span>\n    sc.setLogLevel(<\/span>\"WARN\"<\/span>)<\/span>\n\n    # data = spark.read.csv(csv, header=True)<\/span>\n    data<\/span>=<\/span>sc.textFile(<\/span>\"file:\/\/\/home\/hadoop\/data\/java_books .csv\"<\/span>)<\/span>\n    data<\/span>=<\/span>data.map(<\/span>lambda line: line.split(<\/span>\",\"<\/span>))<\/span>\n    datardd<\/span>=<\/span>data.map(<\/span>lambda local:Row(<\/span>press=<\/span>local[<\/span>2<\/span>]<\/span>))<\/span>\n    print(<\/span>datardd.take(<\/span>3<\/span>))<\/span>\n    zhaopin =<\/span> spark.createDataFrame(<\/span>datardd)<\/span>\n    zhaopin.createOrReplaceTempView(<\/span>\"zhaopin\"<\/span>)<\/span>\n    siming =<\/span> spark.sql(<\/span>\"select count(*) as simingcount from (select press from zhaopin where press Like '%\u660e\u65e5\u79d1\u6280%' )\"<\/span>)<\/span>\n    print(<\/span>siming.rdd.map(<\/span>lambda p: '\u660e\u65e5\u79d1\u6280:'<\/span> + str(<\/span>p.simingcount))<\/span>.collect(<\/span>))<\/span>\n    jimei =<\/span> spark.sql(<\/span>\"select count(*) as jimei from (select press from zhaopin where press like '%\u970d\u65af\u7279\u66fc%' )\"<\/span>)<\/span>\n    print(<\/span>jimei.rdd.map(<\/span>lambda p: '\u970d\u65af\u7279\u66fc:'<\/span> + str(<\/span>p.jimei))<\/span>.collect(<\/span>))<\/span>\n    huli =<\/span> spark.sql(<\/span>\"select count(*) as huli from (select press from zhaopin where press like '%\u4f20\u667a\u64ad\u5ba2\u9ad8\u6559\u4ea7\u54c1\u7814\u53d1\u90e8%')\"<\/span>)<\/span>\n    print(<\/span>huli.rdd.map(<\/span>lambda p: '\u4f20\u667a\u64ad\u5ba2\u9ad8\u6559\u4ea7\u54c1\u7814\u53d1\u90e8:'<\/span> + str(<\/span>p.huli))<\/span>.collect(<\/span>))<\/span>\n    haicang =<\/span> spark.sql(<\/span>\"select count(*) as haicang from (select press from zhaopin where press like '%\u4e01\u632f\u51e1%' )\"<\/span>)<\/span>\n    print(<\/span>haicang.rdd.map(<\/span>lambda p: '\u4e01\u632f\u51e1:'<\/span> + str(<\/span>p.haicang))<\/span>.collect(<\/span>))<\/span>\n    tongan =<\/span> spark.sql(<\/span>\"select count(*) as tongan from (select press from zhaopin where press like '%\u9ad8\u6d2a\u5ca9%')\"<\/span>)<\/span>\n    print(<\/span>tongan.rdd.map(<\/span>lambda p: '\u9ad8\u6d2a\u5ca9:'<\/span> + str(<\/span>p.tongan))<\/span>.collect(<\/span>))<\/span>\n    xiangan =<\/span> spark.sql(<\/span>\"select count(*) as xiangan from (select press from zhaopin where press like '%\u674e\u5174\u534e%')\"<\/span>)<\/span>\n    print(<\/span>xiangan.rdd.map(<\/span>lambda p: '\u674e\u5174\u534e:'<\/span> + str(<\/span>p.xiangan))<\/span>.collect(<\/span>))<\/span>\n    quanzhou =<\/span> spark.sql(<\/span>\"select count(*) as quanzhou from (select press from zhaopin where press like '%\u8f6f\u4ef6\u5f00\u53d1\u6280\u672f\u8054\u76df%')\"<\/span>)<\/span>\n    print(<\/span>quanzhou.rdd.map(<\/span>lambda p: '\u8f6f\u4ef6\u5f00\u53d1\u6280\u672f\u8054\u76df:'<\/span> + str(<\/span>p.quanzhou))<\/span>.collect(<\/span>))<\/span>\n    nanan =<\/span> spark.sql(<\/span>\"select count(*) as nanan from (select press from zhaopin where press like '%\u5176\u4ed6\u4f5c\u8005%')\"<\/span>)<\/span>\n    print(<\/span>nanan.rdd.map(<\/span>lambda p: '\u5176\u4ed6\u4f5c\u8005:'<\/span> + str(<\/span>p.nanan))<\/span>.collect(<\/span>))<\/span>\n\n\uff086\uff09\u5206\u6790\u4ef7\u683c\u7684\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u3001\u5747\u503c\u3001\u65b9\u5dee\u548c\u4e2d\u4f4d\u6570\n\nimport<\/span> os\nos.environ[<\/span>'JAVA_HOME'<\/span>]<\/span>=<\/span>'\/usr\/lib\/jvm\/jdk1.8.0_162'<\/span>\nos.environ[<\/span>\"PYSPARK_PYTHON\"<\/span>]<\/span>=<\/span>'\/usr\/bin\/python3.5'<\/span>\n\nimport<\/span> pandas as pd\nfrom pyspark.sql import<\/span> SparkSession\n\ncsv  =<\/span> '\/home\/hadoop\/data\/java_books .csv'<\/span>\n\n# SparkSession \u914d\u7f6e<\/span>\nspark =<\/span> SparkSession.builder .appName(<\/span>\"My test\"<\/span>)<\/span> .getOrCreate(<\/span>)<\/span>\n# spark.conf.set(\"spark.executor.memory\", \"1g\")<\/span>\nspark.conf.set(<\/span>\"spark.sql.execution.arrow.enabled\"<\/span>, \"true\"<\/span>)<\/span>\nsc =<\/span> spark.sparkContext\nsc.setLogLevel(<\/span>\"WARN\"<\/span>)<\/span>\n\nair =<\/span> spark.read.csv(<\/span>csv,header=<\/span>True)<\/span>\nair.printSchema(<\/span>)<\/span>\nair.describe(<\/span>'price'<\/span>)<\/span>.show(<\/span>)<\/span>\n\u53ef\u89c6\u5316\u4ee3\u7801:\n\uff081\uff09\u56fe\u4e66\u7684\u552e\u4ef7\u5206\u5e03\u60c5\u51b5\uff08\u89c2\u5bdf\u56fe\u4e66\u4ef7\u683c\u5927\u4f53\u96c6\u4e2d\u5728\u54ea\u4e2a\u5206\u6bb5\u5f97\u51fa\u56fe\u4e66\u4ef7\u683c\u8d8b\u52bf\uff09\nfrom pyecharts.charts import<\/span> Bar\nfrom pyecharts import<\/span> options as opts\n#\/\/\u8bbe\u7f6e\u884c\u540d<\/span>\ncolumns =<\/span> [<\/span>\"0-20\"<\/span>, \"20-40\"<\/span>, \"40-60\"<\/span>, \"60-80\"<\/span>, \"80-100\"<\/span>, \"more than 100\"<\/span>]<\/span>\n#\/\/\u8bbe\u7f6e\u6570\u636e<\/span>\ndata1 =<\/span> [<\/span>282<\/span>, 1057<\/span>, 784<\/span>, 407<\/span>, 204<\/span>, 266<\/span>]<\/span>\n\n#\/\/\u6dfb\u52a0\u67f1\u72b6\u56fe\u7684\u914d\u7f6e\u9879<\/span>\nbar<\/span>=<\/span>(<\/span>\n         Bar(<\/span>)<\/span>\n         .add_xaxis(<\/span>xaxis_data=<\/span>columns)<\/span>\n         .add_yaxis(<\/span>\"\u6570\u91cf\"<\/span>, y_axis<\/span>=<\/span>data1)<\/span>\n         .set_global_opts(<\/span>title_opts=<\/span>opts.TitleOpts(<\/span>title=<\/span>\"\u56fe\u4e66\u7684\u552e\u4ef7\u5206\u5e03\u60c5\u51b5\"<\/span>))<\/span>\n     )<\/span>\nbar.render(<\/span>)<\/span>\n\uff082\uff09\u90e8\u5206\u56fe\u4e66\u51fa\u7248\u793e\u7684\u51fa\u4e66\u6570\u91cf\u7edf\u8ba1\nfrom pyecharts.charts import<\/span> Bar\nfrom pyecharts import<\/span> options as opts\n\n#\/\/\u8bbe\u7f6e\u884c\u540d<\/span>\ncolumns =<\/span> [<\/span>\"\u6e05\u534e\u5927\u5b66\u51fa\u7248\u793e\"<\/span>, \"\u673a\u68b0\u5de5\u4e1a\u51fa\u7248\u793e\"<\/span>, \"\u7535\u5b50\u5de5\u4e1a\u51fa\u7248\u793e\"<\/span>, \"\u4eba\u6c11\u90ae\u7535\u51fa\u7248\u793e\"<\/span>, \"\u4e2d\u56fd\u94c1\u9053\u51fa\u7248\u793e\"<\/span>, \"\u4e1c\u5357\u5927\u5b66\u51fa\u7248\u793e\"<\/span>]<\/span>\n#\/\/\u8bbe\u7f6e\u6570\u636e<\/span>\ndata1 =<\/span> [<\/span>565<\/span>, 440<\/span>, 387<\/span>, 282<\/span>, 64<\/span>, 40<\/span>]<\/span>\n\n#\/\/\u6dfb\u52a0\u67f1\u72b6\u56fe\u7684\u914d\u7f6e\u9879<\/span>\nbar<\/span>=<\/span>(<\/span>\n     Bar(<\/span>)<\/span>\n         .add_xaxis(<\/span>xaxis_data=<\/span>columns)<\/span>\n         .add_yaxis(<\/span>\"\u6570\u91cf\"<\/span>, y_axis<\/span>=<\/span>data1)<\/span>\n         .set_global_opts(<\/span>title_opts=<\/span>opts.TitleOpts(<\/span>title=<\/span>\"\u56fe\u4e66\u7684\u51fa\u7248\u793e\u51fa\u4e66\u60c5\u51b5\"<\/span>))<\/span>\n     )<\/span>\nbar.render(<\/span>)<\/span>\n\uff083\uff09\u56fe\u4e66\u7684\u4f5c\u8005\u51fa\u4e66\uff08\u89c2\u5bdf\u54ea\u4e2a\u4f5c\u8005\u51fa\u7684\u4e66\u6700\u591a\uff09\n\n\uff084\uff09\u56fe\u4e66\u7684\u8bc4\u8bba\u5206\u5e03\u60c5\u51b5\uff08\u89c2\u5bdf\u56fe\u4e66\u8bc4\u8bba\u5927\u4f53\u96c6\u4e2d\u5728\u54ea\u4e2a\u5206\u6bb5\u5f97\u51fa\u56fe\u4e66\u8bc4\u8bba\u8d8b\u52bf\uff09\nfrom pyecharts.charts import<\/span> Pie\nfrom pyecharts import<\/span> options as opts\n\ncolumns =<\/span> [<\/span>\"0-100\"<\/span>, \"100-200\"<\/span>, \"200-400\"<\/span>, \"400-600\"<\/span>, \"600-800\"<\/span>, \"800-1000\"<\/span>, \"more than 1000\"<\/span>]<\/span>\n#\/\/\u8bbe\u7f6e\u6570\u636e<\/span>\ndata1 =<\/span> [<\/span>2756<\/span>, 61<\/span>, 46,22<\/span> , 15<\/span>, 14<\/span>, 86<\/span>]<\/span>\n\n\n\npie =<\/span> (<\/span>\n        Pie(<\/span>)<\/span>\n        \t# \u4ee5[(lable,value),(lable,value),(lable,value)......]\u5f62\u5f0f\u4f20\u5165\u6570\u636e\u3002<\/span>\n            .add(<\/span>\"\u997c\u56fe\"<\/span>, list(<\/span>z for<\/span> z<\/span> in<\/span> zip(<\/span>columns, data1))<\/span>)<\/span>\n            .set_series_opts(<\/span>label_opts=<\/span>opts.LabelOpts(<\/span>formatter=<\/span>\"{b}: {c}\"<\/span>))<\/span>\n            .set_global_opts(<\/span>title_opts=<\/span>opts.TitleOpts(<\/span>title=<\/span>\"\u8bc4\u8bba\u5206\u5e03\u60c5\u51b5\"<\/span>))<\/span>\n    )<\/span>\npie.render(<\/span>'\u997c\u56fe.html'<\/span>)<\/span>\n\n\uff085\uff09\u56fe\u4e66\u7684\u90e8\u5206\u4f5c\u8005\u6570\u91cf\u7edf\u8ba1\nimport<\/span> numpy as np\nimport<\/span> matplotlib.pyplot as plt\nimport<\/span> pandas as pd\n\nplt.rcParams[<\/span>'font.sans-serif'<\/span>]<\/span> =<\/span> 'SimHei'<\/span>  # \u8bbe\u7f6e\u4e2d\u6587\u663e\u793a\uff0c\u5426\u5219\u53ef\u80fd\u65e0\u6cd5\u663e\u793a\u4e2d\u6587\u6216\u8005\u662f\u5404\u79cd\u5b57\u7b26\u9519\u4e71<\/span>\nplt.rcParams[<\/span>'axes.unicode_minus'<\/span>]<\/span> =<\/span> False\nsize =<\/span> [<\/span>72,65<\/span>,19,20,23,26,24,4]<\/span>\nlabels =<\/span> [<\/span>'\u660e\u65e5\u79d1\u6280'<\/span>, '\u970d\u65af\u7279\u66fc'<\/span>, '\u4f20\u667a\u64ad\u5ba2\u9ad8\u6559\u4ea7\u54c1\u7814\u53d1\u90e8'<\/span>,'\u4e01\u632f\u51e1'<\/span>,'\u9ad8\u6d2a\u5ca9'<\/span>,'\u674e\u5174\u534e'<\/span>,'\u8f6f\u4ef6\u5f00\u53d1\u6280\u672f\u8054\u76df'<\/span>,'\u5176\u4ed6\u4f5c\u8005'<\/span>]<\/span>\nexplode =<\/span> (<\/span>0.1<\/span>,0,0,0,0,0,0,0)<\/span>\nplt.pie(<\/span>size,explode=<\/span>explode,labels=<\/span>labels,autopct=<\/span>'%1.1f%%'<\/span>,shadow=<\/span>False,startangle=<\/span>150<\/span>)<\/span>\nplt.title(<\/span>\"\u997c\u56fe\u793a\u4f8b--\u90e8\u5206\u4f5c\u8005\u6570\u91cf\u7edf\u8ba1\"<\/span>)<\/span>\nplt.show(<\/span>)<\/span>\n\n\uff086\uff09\u5206\u6790\u4ef7\u683c\u7684\u6700\u5927\u503c\u3001\u6700\u5c0f\u503c\u3001\u5747\u503c\u3001\u65b9\u5dee\u548c\u4e2d\u4f4d\u6570\nimport<\/span> numpy as np\nimport<\/span> pandas as pd\nimport<\/span> matplotlib.pyplot as plt\n\nplt.rcParams[<\/span>'font.sans-serif'<\/span>]<\/span> =<\/span> 'SimHei'<\/span>  # \u8bbe\u7f6e\u4e2d\u6587\u663e\u793a\uff0c\u5426\u5219\u53ef\u80fd\u65e0\u6cd5\u663e\u793a\u4e2d\u6587\u6216\u8005\u662f\u5404\u79cd\u5b57\u7b26\u9519\u4e71<\/span>\nplt.rcParams[<\/span>'axes.unicode_minus'<\/span>]<\/span> =<\/span> False\ndata =<\/span> pd.read_csv(<\/span>\"new\/books.csv\"<\/span>)<\/span>  # \u52a0\u8f7d\u6570\u636e \u5fc5\u987b\u52a0\u4e0a<\/span>\nvalues =<\/span> data[<\/span>'price'<\/span>]<\/span>\nplt.figure(<\/span>figsize=<\/span>(<\/span>6<\/span>, 6<\/span>))<\/span>\nplt.ylim(<\/span>0,180<\/span>)<\/span>\nlabel =<\/span> [<\/span>'\u56fe\u4e66\u4ef7\u683c\u7bb1\u7ebf\u56fe'<\/span>]<\/span>\n# gdp = list(values[:, 3])<\/span>\nplt.boxplot(<\/span>values, notch<\/span>=<\/span>True, labels<\/span>=<\/span>label, meanline<\/span>=<\/span>True)<\/span>  # \u7b2c\u4e00\u4e2a\u53c2\u6570\u662f\u6570\u636e\uff0c\u7b2c\u4e8c\u4e2a\u53c2\u6570\u662f\u662f\u5426\u5e26\u6709\u7f3a\u53e3\uff0c\u7b2c\u4e09\u4e2a\u53c2\u6570\u662f\u6807\u7b7e\uff0c\u7b2c\u56db\u4e2a\u53c2\u6570\u662f\u662f\u5426\u5e26\u6709\u5747\u503c\u7ebf<\/span>\nplt.title(<\/span>'\u56fe\u4e66\u4ef7\u683c\u7bb1\u7ebf\u56fe'<\/span>)<\/span>\nplt.savefig(<\/span>'new\/\u56fe\u4e66\u4ef7\u683c\u7bb1\u7ebf\u56fe\u7bb1\u7ebf\u56fe.png'<\/span>)<\/span>\nplt.show(<\/span>)<\/span>\n<\/code><\/pre>\n<\/p><\/div>\n\n \n<\/div>\n","protected":false},"excerpt":{"rendered":"
\u57fa\u4e8e\u5f53\u5f53\u7f51\u56fe\u4e66\u4fe1\u606f\u7684\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316 \u4e00\u3001\u5b9e\u9a8c\u73af\u5883 \uff081\uff09Linux\uff1a Ubuntu 16.04 \uff082\uff09Pyt […]<\/p>\n","protected":false},"author":1,"featured_media":215,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[17],"tags":[],"class_list":["post-481","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-17"],"yoast_head":"\n\u3010\u5927\u6570\u636e\u5b9e\u8bad\u3011\u57fa\u4e8e\u5f53\u5f53\u7f51\u56fe\u4e66\u4fe1\u606f\u7684\u6570\u636e\u5206\u6790\u4e0e\u53ef\u89c6\u5316(\u516b) - \u7269\u5ae9\u8f6f\u4ef6\u8d44\u8baf\u7f51<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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