{"id":478332,"date":"2023-08-09T09:31:12","date_gmt":"2023-08-09T09:31:12","guid":{"rendered":""},"modified":"2023-09-05T11:16:31","modified_gmt":"2023-09-05T11:16:31","slug":"pandas-profiling","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/pandas-profiling\/","title":{"rendered":"Pandas \u5206\u6790"},"content":{"rendered":"<p>Pandas \u5206\u6790\u662f\u4e00\u6b3e\u529f\u80fd\u5f3a\u5927\u7684\u6570\u636e\u5206\u6790\u548c\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u65e8\u5728\u7b80\u5316 Python \u4e2d\u7684\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790\u8fc7\u7a0b\u3002\u5b83\u662f\u4e00\u4e2a\u57fa\u4e8e\u6d41\u884c\u7684\u6570\u636e\u5904\u7406\u5e93 Pandas \u6784\u5efa\u7684\u5f00\u6e90\u5e93\uff0c\u5e7f\u6cdb\u7528\u4e8e\u6570\u636e\u79d1\u5b66\u3001\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u5206\u6790\u9879\u76ee\u3002\u901a\u8fc7\u81ea\u52a8\u751f\u6210\u5bcc\u6709\u6d1e\u5bdf\u529b\u7684\u62a5\u544a\u548c\u53ef\u89c6\u5316\u6548\u679c\uff0cPandas \u5206\u6790\u53ef\u4ee5\u63d0\u4f9b\u6709\u5173\u6570\u636e\u7ed3\u6784\u548c\u5185\u5bb9\u7684\u5b9d\u8d35\u89c1\u89e3\uff0c\u4ece\u800c\u4e3a\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u5206\u6790\u5e08\u8282\u7701\u65f6\u95f4\u3002<\/p>\n<h2>Pandas \u5206\u6790\u7684\u8d77\u6e90\u5386\u53f2\u4ee5\u53ca\u9996\u6b21\u63d0\u53ca\u5b83\u3002<\/h2>\n<p>Pandas \u5206\u6790\u529f\u80fd\u6700\u521d\u7531\u4e00\u7fa4\u624d\u534e\u6a2a\u6ea2\u7684\u6570\u636e\u7231\u597d\u8005\u4e8e 2016 \u5e74\u63a8\u51fa\uff0c\u5f53\u65f6\u4ed6\u4eec\u7531 Stefanie Molin \u9886\u5bfc\u3002\u6700\u521d\uff0c\u5b83\u4f5c\u4e3a\u4e00\u4e2a\u9644\u5e26\u9879\u76ee\u53d1\u5e03\uff0c\u7531\u4e8e\u5176\u7b80\u5355\u6027\u548c\u6709\u6548\u6027\u800c\u8fc5\u901f\u6d41\u884c\u8d77\u6765\u3002Pandas \u5206\u6790\u529f\u80fd\u9996\u6b21\u51fa\u73b0\u5728 GitHub \u4e0a\uff0c\u6e90\u4ee3\u7801\u5728\u90a3\u91cc\u516c\u5f00\uff0c\u4f9b\u793e\u533a\u8d21\u732e\u548c\u589e\u5f3a\u529f\u80fd\u3002\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\uff0c\u5b83\u9010\u6e10\u53d1\u5c55\u6210\u4e3a\u4e00\u79cd\u53ef\u9760\u4e14\u5e7f\u6cdb\u4f7f\u7528\u7684\u5de5\u5177\uff0c\u5438\u5f15\u4e86\u4e00\u4e2a\u5145\u6ee1\u6d3b\u529b\u7684\u6570\u636e\u4e13\u4e1a\u4eba\u5458\u793e\u533a\uff0c\u4ed6\u4eec\u4e0d\u65ad\u6539\u8fdb\u548c\u6269\u5c55\u5176\u529f\u80fd\u3002<\/p>\n<h2>\u6709\u5173 Pandas \u5206\u6790\u7684\u8be6\u7ec6\u4fe1\u606f\u3002\u6269\u5c55 Pandas \u5206\u6790\u4e3b\u9898\u3002<\/h2>\n<p>Pandas \u5206\u6790\u5229\u7528 Pandas \u7684\u529f\u80fd\u63d0\u4f9b\u5168\u9762\u7684\u6570\u636e\u5206\u6790\u62a5\u544a\u3002\u8be5\u5e93\u53ef\u751f\u6210\u8be6\u7ec6\u7684\u7edf\u8ba1\u6570\u636e\u3001\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u4ee5\u53ca\u5bf9\u6570\u636e\u96c6\u5404\u4e2a\u65b9\u9762\u7684\u5b9d\u8d35\u89c1\u89e3\uff0c\u4f8b\u5982\uff1a<\/p>\n<ul>\n<li>\u57fa\u7840\u7edf\u8ba1\uff1a\u6570\u636e\u5206\u5e03\u6982\u8ff0\uff0c\u5305\u62ec\u5e73\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u4f17\u6570\u3001\u6700\u5c0f\u503c\u3001\u6700\u5927\u503c\u548c\u56db\u5206\u4f4d\u6570\u3002<\/li>\n<li>\u6570\u636e\u7c7b\u578b\uff1a\u6807\u8bc6\u6bcf\u5217\u7684\u6570\u636e\u7c7b\u578b\uff0c\u5e2e\u52a9\u8bc6\u522b\u6f5c\u5728\u7684\u6570\u636e\u4e0d\u4e00\u81f4\u3002<\/li>\n<li>\u7f3a\u5931\u503c\uff1a\u8bc6\u522b\u7f3a\u5931\u7684\u6570\u636e\u70b9\u53ca\u5176\u5728\u6bcf\u5217\u4e2d\u7684\u767e\u5206\u6bd4\u3002<\/li>\n<li>\u76f8\u5173\u6027\uff1a\u5206\u6790\u53d8\u91cf\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6709\u52a9\u4e8e\u7406\u89e3\u5173\u7cfb\u548c\u4f9d\u8d56\u5173\u7cfb\u3002<\/li>\n<li>\u5e38\u89c1\u503c\uff1a\u8bc6\u522b\u5206\u7c7b\u5217\u4e2d\u6700\u5e38\u89c1\u548c\u6700\u4e0d\u5e38\u89c1\u7684\u503c\u3002<\/li>\n<li>\u76f4\u65b9\u56fe\uff1a\u6570\u503c\u5217\u7684\u6570\u636e\u5206\u5e03\u53ef\u89c6\u5316\uff0c\u6709\u52a9\u4e8e\u8bc6\u522b\u6570\u636e\u504f\u659c\u548c\u5f02\u5e38\u503c\u3002<\/li>\n<\/ul>\n<p>\u751f\u6210\u7684\u62a5\u544a\u4ee5 HTML \u683c\u5f0f\u5448\u73b0\uff0c\u4fbf\u4e8e\u56e2\u961f\u548c\u5229\u76ca\u76f8\u5173\u8005\u4e4b\u95f4\u5171\u4eab\u3002<\/p>\n<h2>Pandas \u5206\u6790\u7684\u5185\u90e8\u7ed3\u6784\u3002Pandas \u5206\u6790\u7684\u5de5\u4f5c\u539f\u7406\u3002<\/h2>\n<p>Pandas \u5206\u6790\u5229\u7528\u7edf\u8ba1\u7b97\u6cd5\u3001Pandas \u51fd\u6570\u548c\u6570\u636e\u53ef\u89c6\u5316\u6280\u672f\u7684\u7ec4\u5408\u6765\u5206\u6790\u548c\u6c47\u603b\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u5176\u5185\u90e8\u7ed3\u6784\u7684\u6982\u8ff0\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u91c7\u96c6\uff1a<\/strong> Pandas \u5206\u6790\u9996\u5148\u6536\u96c6\u6709\u5173\u6570\u636e\u96c6\u7684\u57fa\u672c\u4fe1\u606f\uff0c\u4f8b\u5982\u5217\u540d\u3001\u6570\u636e\u7c7b\u578b\u548c\u7f3a\u5931\u503c\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u63cf\u8ff0\u6027\u7edf\u8ba1\uff1a<\/strong> \u8be5\u5e93\u8ba1\u7b97\u6570\u503c\u5217\u7684\u5404\u79cd\u63cf\u8ff0\u7edf\u8ba1\u6570\u636e\uff0c\u5305\u62ec\u5e73\u5747\u503c\u3001\u4e2d\u4f4d\u6570\u3001\u6807\u51c6\u5dee\u548c\u5206\u4f4d\u6570\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u53ef\u89c6\u5316\uff1a<\/strong> Pandas \u5206\u6790\u751f\u6210\u5404\u79cd\u53ef\u89c6\u5316\u6548\u679c\uff0c\u4f8b\u5982\u76f4\u65b9\u56fe\u3001\u6761\u5f62\u56fe\u548c\u6563\u70b9\u56fe\uff0c\u4ee5\u5e2e\u52a9\u7406\u89e3\u6570\u636e\u6a21\u5f0f\u548c\u5206\u5e03\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u76f8\u5173\u6027\u5206\u6790\uff1a<\/strong> \u8be5\u5de5\u5177\u8ba1\u7b97\u6570\u5b57\u5217\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u751f\u6210\u76f8\u5173\u77e9\u9635\u548c\u70ed\u56fe\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5206\u7c7b\u5206\u6790\uff1a<\/strong> \u5bf9\u4e8e\u5206\u7c7b\u5217\uff0c\u5b83\u8bc6\u522b\u5e38\u89c1\u503c\uff0c\u751f\u6210\u6761\u5f62\u56fe\u548c\u9891\u7387\u8868\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7f3a\u5931\u503c\u5206\u6790\uff1a<\/strong> Pandas \u5206\u6790\u68c0\u67e5\u7f3a\u5931\u503c\u5e76\u4ee5\u6613\u4e8e\u7406\u89e3\u7684\u683c\u5f0f\u5448\u73b0\u5b83\u4eec\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8b66\u544a\u548c\u5efa\u8bae\uff1a<\/strong> \u8be5\u5e93\u6807\u8bb0\u4e86\u6f5c\u5728\u95ee\u9898\uff0c\u4f8b\u5982\u9ad8\u57fa\u6570\u6216\u5e38\u91cf\u5217\uff0c\u5e76\u63d0\u51fa\u4e86\u6539\u8fdb\u5efa\u8bae\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u5206\u6790Pandas\u6982\u51b5\u7684\u5173\u952e\u7279\u5f81\u3002<\/h2>\n<p>Pandas \u5206\u6790\u63d0\u4f9b\u4e86\u5927\u91cf\u529f\u80fd\uff0c\u4f7f\u5176\u6210\u4e3a\u6570\u636e\u5206\u6790\u4e0d\u53ef\u6216\u7f3a\u7684\u5de5\u5177\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u81ea\u52a8\u62a5\u544a\u751f\u6210\uff1a<\/strong> Pandas profiling \u81ea\u52a8\u751f\u6210\u8be6\u7ec6\u7684\u6570\u636e\u5206\u6790\u62a5\u544a\uff0c\u8282\u7701\u5206\u6790\u5e08\u7684\u65f6\u95f4\u548c\u7cbe\u529b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\uff1a<\/strong> HTML \u62a5\u544a\u5305\u62ec\u4ea4\u4e92\u5f0f\u53ef\u89c6\u5316\u529f\u80fd\uff0c\u5141\u8bb8\u7528\u6237\u4ee5\u5f15\u4eba\u5165\u80dc\u4e14\u7528\u6237\u53cb\u597d\u7684\u65b9\u5f0f\u63a2\u7d22\u6570\u636e\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u53ef\u5b9a\u5236\u7684\u5206\u6790\uff1a<\/strong> \u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u6240\u9700\u7684\u7ec6\u8282\u7ea7\u522b\u3001\u7701\u7565\u7279\u5b9a\u90e8\u5206\u6216\u8bbe\u7f6e\u76f8\u5173\u9608\u503c\u6765\u5b9a\u5236\u5206\u6790\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7b14\u8bb0\u672c\u96c6\u6210\uff1a<\/strong> Pandas \u5206\u6790\u4e0e Jupyter Notebooks \u65e0\u7f1d\u96c6\u6210\uff0c\u589e\u5f3a\u4e86\u7b14\u8bb0\u672c\u73af\u5883\u4e2d\u7684\u6570\u636e\u63a2\u7d22\u4f53\u9a8c\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6982\u51b5\u6bd4\u8f83\uff1a<\/strong> \u5b83\u652f\u6301\u591a\u4e2a\u6570\u636e\u914d\u7f6e\u6587\u4ef6\u7684\u6bd4\u8f83\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u4e86\u89e3\u6570\u636e\u96c6\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5bfc\u51fa\u9009\u9879\uff1a<\/strong> \u751f\u6210\u7684\u62a5\u544a\u53ef\u4ee5\u8f7b\u677e\u5bfc\u51fa\u4e3a\u4e0d\u540c\u7684\u683c\u5f0f\uff0c\u4f8b\u5982 HTML\u3001JSON \u6216 YAML\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>Pandas \u5206\u6790\u7684\u7c7b\u578b<\/h2>\n<p>Pandas \u5206\u6790\u63d0\u4f9b\u4e24\u79cd\u4e3b\u8981\u7c7b\u578b\u7684\u5206\u6790\uff1a\u6982\u89c8\u62a5\u544a\u548c\u5b8c\u6574\u62a5\u544a\u3002<\/p>\n<h3>\u6982\u89c8\u62a5\u544a<\/h3>\n<p>\u6982\u89c8\u62a5\u544a\u662f\u5bf9\u6570\u636e\u96c6\u7684\u7b80\u660e\u6458\u8981\uff0c\u5305\u62ec\u5fc5\u8981\u7684\u7edf\u8ba1\u6570\u636e\u548c\u53ef\u89c6\u5316\u6548\u679c\u3002\u5b83\u53ef\u4f5c\u4e3a\u6570\u636e\u5206\u6790\u5e08\u7684\u5feb\u901f\u53c2\u8003\uff0c\u5e2e\u52a9\u4ed6\u4eec\u5bf9\u6570\u636e\u96c6\u6709\u4e00\u4e2a\u5927\u81f4\u7684\u4e86\u89e3\uff0c\u800c\u65e0\u9700\u6df1\u5165\u7814\u7a76\u5404\u4e2a\u7279\u5f81\u3002<\/p>\n<h3>\u5b8c\u6574\u62a5\u544a<\/h3>\n<p>\u5b8c\u6574\u62a5\u544a\u662f\u5bf9\u6570\u636e\u96c6\u7684\u5168\u9762\u5206\u6790\uff0c\u63d0\u4f9b\u5bf9\u6bcf\u4e2a\u7279\u5f81\u7684\u6df1\u5165\u89c1\u89e3\u3001\u9ad8\u7ea7\u53ef\u89c6\u5316\u548c\u8be6\u7ec6\u7edf\u8ba1\u6570\u636e\u3002\u6b64\u62a5\u544a\u975e\u5e38\u9002\u5408\u5f7b\u5e95\u7684\u6570\u636e\u63a2\u7d22\uff0c\u66f4\u9002\u5408\u9700\u8981\u66f4\u6df1\u5165\u5730\u4e86\u89e3\u6570\u636e\u7684\u60c5\u51b5\u3002<\/p>\n<h2>Pandas \u6027\u80fd\u5206\u6790\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u4f7f\u7528\u4e2d\u9047\u5230\u7684\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6cd5\u3002<\/h2>\n<p>Pandas \u5206\u6790\u662f\u4e00\u79cd\u591a\u529f\u80fd\u5de5\u5177\uff0c\u5177\u6709\u591a\u79cd\u7528\u9014\uff0c\u4f8b\u5982\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u6e05\u7406\uff1a<\/strong> \u68c0\u6d4b\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u548c\u5f02\u5e38\u6709\u52a9\u4e8e\u6570\u636e\u6e05\u7406\u548c\u51c6\u5907\u8fdb\u4e00\u6b65\u5206\u6790\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u9884\u5904\u7406\uff1a<\/strong> \u4e86\u89e3\u6570\u636e\u5206\u5e03\u548c\u76f8\u5173\u6027\u6709\u52a9\u4e8e\u9009\u62e9\u5408\u9002\u7684\u9884\u5904\u7406\u6280\u672f\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u7279\u5f81\u5de5\u7a0b\uff1a<\/strong> \u8bc6\u522b\u7279\u5f81\u4e4b\u95f4\u7684\u5173\u7cfb\u6709\u52a9\u4e8e\u751f\u6210\u65b0\u7279\u5f81\u6216\u9009\u62e9\u76f8\u5173\u7279\u5f81\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u53ef\u89c6\u5316\uff1a<\/strong> Pandas \u5206\u6790\u7684\u53ef\u89c6\u5316\u529f\u80fd\u5bf9\u4e8e\u6f14\u793a\u548c\u5411\u5229\u76ca\u76f8\u5173\u8005\u4f20\u8fbe\u6570\u636e\u89c1\u89e3\u5f88\u6709\u7528\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u5c3d\u7ba1 Pandas \u5206\u6790\u5177\u6709\u8bf8\u591a\u4f18\u70b9\uff0c\u4f46\u5b83\u4ecd\u53ef\u80fd\u9762\u4e34\u4e00\u4e9b\u6311\u6218\uff0c\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u5927\u578b\u6570\u636e\u96c6\uff1a<\/strong> \u5bf9\u4e8e\u5f02\u5e38\u5927\u7684\u6570\u636e\u96c6\uff0c\u5206\u6790\u8fc7\u7a0b\u53ef\u80fd\u4f1a\u53d8\u5f97\u8017\u65f6\u4e14\u8017\u8d39\u8d44\u6e90\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5185\u5b58\u4f7f\u7528\u60c5\u51b5\uff1a<\/strong> \u751f\u6210\u5b8c\u6574\u7684\u62a5\u544a\u53ef\u80fd\u9700\u8981\u5927\u91cf\u5185\u5b58\uff0c\u53ef\u80fd\u4f1a\u5bfc\u81f4\u5185\u5b58\u4e0d\u8db3\u9519\u8bef\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\uff0c\u7528\u6237\u53ef\u4ee5\uff1a<\/p>\n<ul>\n<li><strong>\u5b50\u96c6\u6570\u636e\uff1a<\/strong> \u5206\u6790\u6570\u636e\u96c6\u7684\u4ee3\u8868\u6027\u6837\u672c\u800c\u4e0d\u662f\u6574\u4e2a\u6570\u636e\u96c6\uff0c\u4ee5\u52a0\u5feb\u5206\u6790\u8fc7\u7a0b\u3002<\/li>\n<li><strong>\u4f18\u5316\u4ee3\u7801\uff1a<\/strong> \u4f18\u5316\u6570\u636e\u5904\u7406\u4ee3\u7801\u5e76\u6709\u6548\u5229\u7528\u5185\u5b58\u6765\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u3002<\/li>\n<\/ul>\n<h2>\u4ee5\u8868\u683c\u548c\u5217\u8868\u7684\u5f62\u5f0f\u5217\u51fa\u4e3b\u8981\u7279\u5f81\u4ee5\u53ca\u4e0e\u7c7b\u4f3c\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83\u3002<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>Pandas \u5206\u6790<\/th>\n<th>\u81ea\u52a8\u53ef\u89c6\u5316<\/th>\n<th>SweetViz<\/th>\n<th>D-\u6545\u4e8b<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u6267\u7167<\/td>\n<td>\u9ebb\u7701\u7406\u5de5\u5b66\u9662<\/td>\n<td>\u9ebb\u7701\u7406\u5de5\u5b66\u9662<\/td>\n<td>\u9ebb\u7701\u7406\u5de5\u5b66\u9662<\/td>\n<td>\u9ebb\u7701\u7406\u5de5\u5b66\u9662<\/td>\n<\/tr>\n<tr>\n<td>Python\u7248\u672c<\/td>\n<td>3.6+<\/td>\n<td>2.7+<\/td>\n<td>3.5+<\/td>\n<td>3.6+<\/td>\n<\/tr>\n<tr>\n<td>\u7b14\u8bb0\u672c\u652f\u6301<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u62a5\u544a\u8f93\u51fa<\/td>\n<td>\u8d85\u6587\u672c\u6807\u8bb0\u8bed\u8a00<\/td>\n<td>\u4e0d\u9002\u7528<\/td>\n<td>\u8d85\u6587\u672c\u6807\u8bb0\u8bed\u8a00<\/td>\n<td>Web \u7528\u6237\u754c\u9762<\/td>\n<\/tr>\n<tr>\n<td>\u4ea4\u4e92\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<\/tr>\n<tr>\n<td>\u53ef\u5b9a\u5236<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<td>\u6709\u9650\u7684<\/td>\n<td>\u662f\u7684<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>\u718a\u732b\u5256\u6790\uff1a<\/strong> \u57fa\u4e8ePandas\u7684\u5168\u9762\u3001\u4ea4\u4e92\u5f0f\u7684\u6570\u636e\u5206\u6790\u5de5\u5177\u3002<\/p>\n<p><strong>AutoViz\uff1a<\/strong> \u81ea\u52a8\u53ef\u89c6\u5316\u4efb\u4f55\u6570\u636e\u96c6\uff0c\u65e0\u9700\u5b9a\u5236\u5373\u53ef\u63d0\u4f9b\u5feb\u901f\u6d1e\u5bdf\u3002<\/p>\n<p><strong>SweetViz\uff1a<\/strong> \u751f\u6210\u6f02\u4eae\u7684\u53ef\u89c6\u5316\u6548\u679c\u548c\u9ad8\u5bc6\u5ea6\u7684\u6570\u636e\u5206\u6790\u62a5\u544a\u3002<\/p>\n<p><strong>D-\u6545\u4e8b\uff1a<\/strong> \u7528\u4e8e\u6570\u636e\u63a2\u7d22\u548c\u5904\u7406\u7684\u57fa\u4e8e\u7f51\u7edc\u7684\u4ea4\u4e92\u5f0f\u5de5\u5177\u3002<\/p>\n<h2>\u4e0e\u718a\u732b\u5206\u6790\u76f8\u5173\u7684\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f\u3002<\/h2>\n<p>Pandas \u5206\u6790\u7684\u672a\u6765\u4e00\u7247\u5149\u660e\uff0c\u56e0\u4e3a\u6570\u636e\u5206\u6790\u4ecd\u7136\u662f\u5404\u884c\u5404\u4e1a\u7684\u5173\u952e\u7ec4\u6210\u90e8\u5206\u3002\u4e00\u4e9b\u6f5c\u5728\u7684\u53d1\u5c55\u548c\u8d8b\u52bf\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6027\u80fd\u6539\u8fdb\uff1a<\/strong> \u672a\u6765\u7684\u66f4\u65b0\u53ef\u80fd\u96c6\u4e2d\u5728\u4f18\u5316\u5185\u5b58\u4f7f\u7528\u548c\u52a0\u5feb\u5927\u578b\u6570\u636e\u96c6\u7684\u5206\u6790\u8fc7\u7a0b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u4e0e\u5927\u6570\u636e\u6280\u672f\u7684\u6574\u5408\uff1a<\/strong> \u4e0e Dask \u6216 Apache Spark \u7b49\u5206\u5e03\u5f0f\u8ba1\u7b97\u6846\u67b6\u7684\u96c6\u6210\u53ef\u4ee5\u5b9e\u73b0\u5bf9\u5927\u6570\u636e\u96c6\u7684\u5206\u6790\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u9ad8\u7ea7\u53ef\u89c6\u5316\uff1a<\/strong> \u53ef\u89c6\u5316\u529f\u80fd\u7684\u8fdb\u4e00\u6b65\u589e\u5f3a\u53ef\u4ee5\u5e26\u6765\u66f4\u5177\u4ea4\u4e92\u6027\u548c\u6d1e\u5bdf\u529b\u7684\u6570\u636e\u5448\u73b0\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u673a\u5668\u5b66\u4e60\u96c6\u6210\uff1a<\/strong> \u4e0e\u673a\u5668\u5b66\u4e60\u5e93\u7684\u96c6\u6210\u53ef\u4ee5\u5b9e\u73b0\u57fa\u4e8e\u5206\u6790\u89c1\u89e3\u7684\u81ea\u52a8\u5316\u7279\u5f81\u5de5\u7a0b\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u57fa\u4e8e\u4e91\u7684\u89e3\u51b3\u65b9\u6848\uff1a<\/strong> \u57fa\u4e8e\u4e91\u7684\u5b9e\u65bd\u53ef\u80fd\u4f1a\u63d0\u4f9b\u66f4\u5177\u53ef\u6269\u5c55\u6027\u548c\u8d44\u6e90\u6548\u7387\u7684\u5206\u6790\u9009\u9879\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e Pandas \u5206\u6790\u5173\u8054\u3002<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\uff08\u4f8b\u5982 OneProxy \u63d0\u4f9b\u7684\u4ee3\u7406\u670d\u52a1\u5668\uff09\u5728 Pandas \u5206\u6790\u4e2d\u53d1\u6325\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff0c\u5177\u4f53\u5982\u4e0b\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u9690\u79c1\uff1a<\/strong> \u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u654f\u611f\u6570\u636e\u96c6\u53ef\u80fd\u9700\u8981\u989d\u5916\u7684\u5b89\u5168\u63aa\u65bd\u3002\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5145\u5f53\u6570\u636e\u6e90\u548c\u5206\u6790\u5de5\u5177\u4e4b\u95f4\u7684\u4e2d\u4ecb\uff0c\u786e\u4fdd\u6570\u636e\u7684\u9690\u79c1\u548c\u4fdd\u62a4\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u89c4\u907f\u9650\u5236\uff1a<\/strong> \u5728\u5bf9\u5177\u6709\u8bbf\u95ee\u9650\u5236\u7684\u57fa\u4e8e Web \u7684\u6570\u636e\u96c6\u8fdb\u884c\u6570\u636e\u5206\u6790\u65f6\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5e2e\u52a9\u7ed5\u8fc7\u8fd9\u4e9b\u9650\u5236\u5e76\u5b9e\u73b0\u6570\u636e\u68c0\u7d22\u4ee5\u8fdb\u884c\u5206\u6790\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8d1f\u8f7d\u5747\u8861\uff1a<\/strong> \u5bf9\u4e8e\u7f51\u7edc\u6293\u53d6\u548c\u6570\u636e\u63d0\u53d6\u4efb\u52a1\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5c06\u8bf7\u6c42\u5206\u914d\u5230\u591a\u4e2a IP \u5730\u5740\uff0c\u9632\u6b62\u7531\u4e8e\u5355\u4e00\u6765\u6e90\u7684\u6d41\u91cf\u8fc7\u5927\u800c\u5bfc\u81f4 IP \u88ab\u963b\u6b62\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u5730\u7406\u4f4d\u7f6e\u591a\u6837\u5316\uff1a<\/strong> \u4ee3\u7406\u670d\u52a1\u5668\u5141\u8bb8\u7528\u6237\u6a21\u62df\u4ece\u4e0d\u540c\u5730\u7406\u4f4d\u7f6e\u7684\u8bbf\u95ee\uff0c\u8fd9\u5728\u5206\u6790\u7279\u5b9a\u533a\u57df\u7684\u6570\u636e\u65f6\u7279\u522b\u6709\u7528\u3002<\/p>\n<\/li>\n<\/ol>\n<p>\u901a\u8fc7\u4f7f\u7528\u50cf OneProxy \u8fd9\u6837\u7684\u53ef\u9760\u4ee3\u7406\u670d\u52a1\u5668\u63d0\u4f9b\u5546\uff0c\u6570\u636e\u4e13\u4e1a\u4eba\u5458\u53ef\u4ee5\u589e\u5f3a\u4ed6\u4eec\u7684\u6570\u636e\u5206\u6790\u80fd\u529b\uff0c\u5e76\u786e\u4fdd\u65e0\u7f1d\u8bbf\u95ee\u5916\u90e8\u6570\u636e\u6e90\uff0c\u800c\u4e0d\u53d7\u4efb\u4f55\u9650\u5236\u6216\u9690\u79c1\u95ee\u9898\u3002<\/p>\n<h2>\u76f8\u5173\u94fe\u63a5<\/h2>\n<p>\u6709\u5173 Pandas \u5206\u6790\u7684\u66f4\u591a\u4fe1\u606f\uff0c\u60a8\u53ef\u4ee5\u63a2\u7d22\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n<ul>\n<li><a href=\"https:\/\/pandas-profiling.github.io\/pandas-profiling\/docs\/\" target=\"_new\" rel=\"noopener nofollow\">Pandas \u5206\u6790\u6587\u6863<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/pandas-profiling\/pandas-profiling\" target=\"_new\" rel=\"noopener nofollow\">GitHub \u5b58\u50a8\u5e93<\/a><\/li>\n<li><a href=\"https:\/\/www.datacamp.com\/community\/tutorials\/pandas-profiling-python\" target=\"_new\" rel=\"noopener nofollow\">DataCamp \u6559\u7a0b<\/a><\/li>\n<\/ul>","protected":false},"featured_media":469109,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478332","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Pandas Profiling: Unveiling the Power of Data Analysis and Visualization<\/mark>","faq_items":[{"question":"What is Pandas profiling?","answer":"<p>Pandas profiling is a powerful data analysis and visualization tool in Python. It simplifies exploratory data analysis by automatically generating insightful reports and visualizations, providing valuable insights into the structure and content of data.<\/p>"},{"question":"Who developed Pandas profiling, and when was it first introduced?","answer":"<p>Pandas profiling was developed by Stefanie Molin and a group of data enthusiasts in 2016. It was initially released as a side project and gained rapid popularity among data professionals.<\/p>"},{"question":"What does the Pandas profiling report include?","answer":"<p>The Pandas profiling report includes detailed statistics such as mean, median, minimum, maximum, and quartiles for numerical columns. It also identifies data types, missing values, correlations between variables, common values in categorical columns, and provides histograms for data distribution.<\/p>"},{"question":"How does Pandas profiling work internally?","answer":"<p>Pandas profiling collects basic information about the dataset, computes descriptive statistics, generates visualizations, performs correlation analysis, and identifies categorical values and missing data points.<\/p>"},{"question":"What are the types of Pandas profiling reports available?","answer":"<p>Pandas profiling provides two types of reports: the overview report, which offers a concise summary of the dataset, and the full report, which provides a comprehensive analysis of each feature.<\/p>"},{"question":"In which Python environment does Pandas profiling integrate seamlessly?","answer":"<p>Pandas profiling seamlessly integrates with Jupyter Notebooks, enhancing the data exploration experience within the notebook environment.<\/p>"},{"question":"What are the challenges faced while using Pandas profiling?","answer":"<p>For exceptionally large datasets, the profiling process may become time-consuming and resource-intensive, potentially leading to memory issues. However, users can address these challenges by analyzing a representative sample of the dataset or optimizing code for memory usage.<\/p>"},{"question":"How can proxy servers be associated with Pandas profiling?","answer":"<p>Proxy servers, like those provided by OneProxy, can ensure data privacy and security by acting as intermediaries between the data source and the profiling tool. They can also help bypass access restrictions and distribute requests across multiple IP addresses for improved load balancing and geolocation diversification.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/478332","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/478332\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/469109"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=478332"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}