{"id":972,"date":"2023-07-28T03:15:43","date_gmt":"2023-07-28T03:15:43","guid":{"rendered":"https:\/\/statorials.org\/ru\/pandas-%d0%be%d0%b1%d1%8a%d0%b5%d0%b4%d0%b8%d0%bd%d1%8f%d0%b5%d1%82-%d0%bd%d0%b5%d1%81%d0%ba%d0%be%d0%bb%d1%8c%d0%ba%d0%be-%d1%81%d1%82%d0%be%d0%bb%d0%b1%d1%86%d0%be%d0%b2\/"},"modified":"2023-07-28T03:15:43","modified_gmt":"2023-07-28T03:15:43","slug":"pandas-%d0%be%d0%b1%d1%8a%d0%b5%d0%b4%d0%b8%d0%bd%d1%8f%d0%b5%d1%82-%d0%bd%d0%b5%d1%81%d0%ba%d0%be%d0%bb%d1%8c%d0%ba%d0%be-%d1%81%d1%82%d0%be%d0%bb%d0%b1%d1%86%d0%be%d0%b2","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/pandas-%d0%be%d0%b1%d1%8a%d0%b5%d0%b4%d0%b8%d0%bd%d1%8f%d0%b5%d1%82-%d0%bd%d0%b5%d1%81%d0%ba%d0%be%d0%bb%d1%8c%d0%ba%d0%be-%d1%81%d1%82%d0%be%d0%bb%d0%b1%d1%86%d0%be%d0%b2\/","title":{"rendered":"\u041a\u0430\u043a \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0444\u0440\u0435\u0439\u043c\u044b \u0434\u0430\u043d\u043d\u044b\u0445 pandas \u043f\u043e \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0427\u0430\u0441\u0442\u043e \u0432\u0430\u043c \u043c\u043e\u0436\u0435\u0442 \u043f\u043e\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0434\u0432\u0430 \u043a\u0430\u0434\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 pandas \u043f\u043e \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c. \u041a \u0441\u0447\u0430\u0441\u0442\u044c\u044e, \u044d\u0442\u043e \u043b\u0435\u0433\u043a\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 pandas <a href=\"https:\/\/pandas.pydata.org\/docs\/reference\/api\/pandas.merge.html\" target=\"_blank\" rel=\"noopener noreferrer\">merge()<\/a> , \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>p.d. <span style=\"color: #3366ff;\">merge<\/span> (df1, df2, left_on=['col1','col2'], right_on = ['col1','col2'])<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435.<\/span><\/p>\n<h3> <strong>\u041f\u0440\u0438\u043c\u0435\u0440 1. <span style=\"color: #000000;\">\u041e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435<\/span> \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0438\u043c\u0435\u043d\u0430\u043c\u0438<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0438\u043c, \u0443 \u043d\u0430\u0441 \u0435\u0441\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0434\u0432\u0430 \u043a\u0430\u0434\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 panda:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create and view first DataFrame<\/span>\ndf1 = pd.DataFrame({'a1': [0, 0, 1, 1, 2],\n                   'b': [0, 0, 1, 1, 1],\n                   'c': [11, 8, 10, 6, 6]})\n\n<span style=\"color: #3366ff;\">print<\/span> (df1)\n\n   a1 bc\n0 0 0 11\n1 0 0 8\n2 1 1 10\n3 1 1 6\n4 2 1 6\n\n<span style=\"color: #008080;\">#create and view second DataFrame<\/span> \ndf2 = pd.DataFrame({'a2': [0, 1, 1, 1, 3],\n                   'b': [0, 0, 0, 1, 1],\n                   'd': [22, 24, 25, 33, 37]})\n\n<span style=\"color: #3366ff;\">print<\/span> (df2)\n\n   a2 comic\n0 0 0 22\n1 1 0 24\n2 1 0 25\n3 1 1 33\n4 3 1 37\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043b\u0435\u0432\u043e\u0435 \u0441\u043e\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0438\u0437 \u043e\u0431\u043e\u0438\u0445 DataFrames:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>p.d. <span style=\"color: #3366ff;\">merge<\/span> (df1, df2, how=' <span style=\"color: #008000;\">left<\/span> ', left_on=[' <span style=\"color: #008000;\">a1<\/span> ', ' <span style=\"color: #008000;\">b<\/span> '], right_on = [' <span style=\"color: #008000;\">a2<\/span> ', ' <span style=\"color: #008000;\">b<\/span> '])\n\n\n        a1 b c a2 d\n0 0 0 11 0.0 22.0\n1 0 0 8 0.0 22.0\n2 1 1 10 1.0 33.0\n3 1 1 6 1.0 33.0\n4 2 1 6 NaN NaN\n<\/strong><\/pre>\n<h3> <strong>\u041f\u0440\u0438\u043c\u0435\u0440 2. <span style=\"color: #000000;\">\u041e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435<\/span> \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0441 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u044b\u043c\u0438 \u0438\u043c\u0435\u043d\u0430\u043c\u0438<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0438\u043c, \u0443 \u043d\u0430\u0441 \u0435\u0441\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0434\u0432\u0430 DataFrames pandas \u0441 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u044b\u043c\u0438 \u0438\u043c\u0435\u043d\u0430\u043c\u0438 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrames<\/span>\ndf1 = pd.DataFrame({'a': [0, 0, 1, 1, 2],\n                   'b': [0, 0, 1, 1, 1],\n                   'c': [11, 8, 10, 6, 6]})\n\ndf2 = pd.DataFrame({'a': [0, 1, 1, 1, 3],\n                   'b': [0, 0, 0, 1, 1],\n                   'd': [22, 24, 25, 33, 37]})\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0412 \u044d\u0442\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0443\u043f\u0440\u043e\u0441\u0442\u0438\u0442\u044c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435 <strong>on = [&#8216;a&#8217;, &#8216;b&#8217;],<\/strong> \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u0438\u043c\u0435\u043d\u0430 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u044b \u0432 \u043e\u0431\u043e\u0438\u0445 DataFrames:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>p.d. <span style=\"color: #3366ff;\">merge<\/span> (df1, df2, how=' <span style=\"color: #008000;\">left<\/span> ', on=[' <span style=\"color: #008000;\">a<\/span> ', ' <span style=\"color: #008000;\">b<\/span> '])\n\n\ta b c d\n0 0 0 11 22.0\n1 0 0 8 22.0\n2 1 1 10 33.0\n3 1 1 6 33.0\n4 2 1 6 NaN\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h3>\n<p> \u041a\u0430\u043a \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0434\u0432\u0430 \u0444\u0440\u0435\u0439\u043c\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 Pandas \u0432 \u0438\u043d\u0434\u0435\u043a\u0441\u0435<br \/> <a href=\"https:\/\/statorials.org\/ru\/\u0441\u0442\u0435\u043a-\u0444\u0440\u0435\u0438\u043c\u043e\u0432-\u0434\u0430\u043d\u043d\u044b\u0445-pandas\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u041a\u0430\u043a \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043a\u0430\u0434\u0440\u043e\u0432 \u0434\u0430\u043d\u043d\u044b\u0445 Pandas<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0427\u0430\u0441\u0442\u043e \u0432\u0430\u043c \u043c\u043e\u0436\u0435\u0442 \u043f\u043e\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0434\u0432\u0430 \u043a\u0430\u0434\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 pandas \u043f\u043e \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c. \u041a \u0441\u0447\u0430\u0441\u0442\u044c\u044e, \u044d\u0442\u043e \u043b\u0435\u0433\u043a\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 pandas merge() , \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441: p.d. merge (df1, df2, left_on=[&#8216;col1&#8242;,&#8217;col2&#8217;], right_on = [&#8216;col1&#8242;,&#8217;col2&#8217;]) \u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435. \u041f\u0440\u0438\u043c\u0435\u0440 1. \u041e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0438\u043c\u0435\u043d\u0430\u043c\u0438 \u041f\u0440\u0435\u0434\u043f\u043e\u043b\u043e\u0436\u0438\u043c, \u0443 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-972","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u041a\u0430\u043a \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0444\u0440\u0435\u0439\u043c\u044b \u0434\u0430\u043d\u043d\u044b\u0445 Pandas \u043f\u043e \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c \u2013 \u0421\u0442\u0430\u0442\u043e\u0440\u0438\u0430\u043b\u044b<\/title>\n<meta name=\"description\" content=\"\u041f\u0440\u043e\u0441\u0442\u043e\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u0435\u043d\u0438\u0435 \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0434\u0432\u0430 DataFrames pandas \u0432 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043f\u0440\u0438\u043c\u0435\u0440\u044b.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ru\/pandas-\u043e\u0431\u044a\u0435\u0434\u0438\u043d\u044f\u0435\u0442-\u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e-\u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u041a\u0430\u043a \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0444\u0440\u0435\u0439\u043c\u044b \u0434\u0430\u043d\u043d\u044b\u0445 Pandas \u043f\u043e \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c \u2013 \u0421\u0442\u0430\u0442\u043e\u0440\u0438\u0430\u043b\u044b\" \/>\n<meta property=\"og:description\" content=\"\u041f\u0440\u043e\u0441\u0442\u043e\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u0435\u043d\u0438\u0435 \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0438\u0442\u044c \u0434\u0432\u0430 DataFrames pandas \u0432 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432, \u0432\u043a\u043b\u044e\u0447\u0430\u044f \u043f\u0440\u0438\u043c\u0435\u0440\u044b.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ru\/pandas-\u043e\u0431\u044a\u0435\u0434\u0438\u043d\u044f\u0435\u0442-\u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e-\u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta 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