{"id":2260,"date":"2023-07-23T00:47:34","date_gmt":"2023-07-23T00:47:34","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%b2%d1%96%d1%81%d1%8c-0-%d0%b2%d1%96%d1%81%d1%8c-1-python-pandas\/"},"modified":"2023-07-23T00:47:34","modified_gmt":"2023-07-23T00:47:34","slug":"%d0%b2%d1%96%d1%81%d1%8c-0-%d0%b2%d1%96%d1%81%d1%8c-1-python-pandas","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%b2%d1%96%d1%81%d1%8c-0-%d0%b2%d1%96%d1%81%d1%8c-1-python-pandas\/","title":{"rendered":"\u0420\u0456\u0437\u043d\u0438\u0446\u044f \u043c\u0456\u0436 axis=0 \u0456 axis=1 \u0443 pandas"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0414\u043b\u044f \u0431\u0430\u0433\u0430\u0442\u044c\u043e\u0445 \u0444\u0443\u043d\u043a\u0446\u0456\u0439 \u0443 <a href=\"https:\/\/pandas.pydata.org\/\" target=\"_blank\" rel=\"noopener\">pandas<\/a> \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0432\u043a\u0430\u0437\u0430\u0442\u0438 \u0432\u0456\u0441\u044c, \u0443\u0437\u0434\u043e\u0432\u0436 \u044f\u043a\u043e\u0457 \u0437\u0430\u0441\u0442\u043e\u0441\u043e\u0432\u0443\u0432\u0430\u0442\u0438\u043c\u0435\u0442\u044c\u0441\u044f \u043f\u0435\u0432\u043d\u0435 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042f\u043a \u043f\u0440\u0430\u0432\u0438\u043b\u043e, \u0437\u0430\u0441\u0442\u043e\u0441\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0442\u0430\u043a\u0435 \u0435\u043c\u043f\u0456\u0440\u0438\u0447\u043d\u0435 \u043f\u0440\u0430\u0432\u0438\u043b\u043e:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>axis=0<\/strong> : \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u0442\u0438 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u00ab\u043d\u0430 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c\u00bb.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>axis=1<\/strong> : \u0417\u0430\u0441\u0442\u043e\u0441\u0443\u0439\u0442\u0435 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u00ab\u043d\u0430 \u043b\u0456\u043d\u0456\u044e\u00bb.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0432\u0435\u0434\u0435\u043d\u0438\u0445 \u043d\u0438\u0436\u0447\u0435 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 <strong>axis<\/strong> \u0443 \u0440\u0456\u0437\u043d\u0438\u0445 \u0441\u0446\u0435\u043d\u0430\u0440\u0456\u044f\u0445 \u0456\u0437 \u0442\u0430\u043a\u0438\u043c\u0438 pandas DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #ff0000;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'],\n                   ' <span style=\"color: #ff0000;\">points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df\n\n\tteam points assists rebounds\n0 to 25 5 11\n1 to 12 7 8\n2 B 15 7 10\n3 B 14 9 6\n4 B 19 12 6\n5 B 23 9 5\n6 C 25 9 9\n7 C 29 4 12<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434 1. \u0417\u043d\u0430\u0445\u043e\u0434\u0436\u0435\u043d\u043d\u044f \u0441\u0435\u0440\u0435\u0434\u043d\u044c\u043e\u0433\u043e \u043f\u043e \u0440\u0456\u0437\u043d\u0438\u0445 \u043e\u0441\u044f\u0445<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 <strong>axis=0<\/strong> , \u0449\u043e\u0431 \u0437\u043d\u0430\u0439\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u0432 DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#find mean of each column\n<\/span>df. <span style=\"color: #3366ff;\">mean<\/span> (axis= <span style=\"color: #008000;\">0<\/span> )\n\npoints 20.250\nassists 7,750\nrebounds 8,375\ndtype:float64\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0447\u0438\u0441\u043b\u043e\u0432\u043e\u0433\u043e \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u0432 DataFrame.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e pandas \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u043d\u043e \u0443\u043d\u0438\u043a\u0430\u0454 \u0443\u0441\u0435\u0440\u0435\u0434\u043d\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u00ab\u043a\u043e\u043c\u0430\u043d\u0434\u0430\u00bb, \u043e\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u0446\u0435 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c \u0441\u0438\u043c\u0432\u043e\u043b\u0456\u0432.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 <strong>axis=1<\/strong> , \u0449\u043e\u0431 \u0437\u043d\u0430\u0439\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u043a\u0430 \u0432 DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#find mean of each row\n<\/span>df. <span style=\"color: #3366ff;\">mean<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n\n0 13.666667\n1 9.000000\n2 10.666667\n3 9.666667\n4 12.333333\n5 12.333333\n6 14.333333\n7 15.000000\ndtype:float64<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u0421\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043f\u0435\u0440\u0448\u043e\u0433\u043e \u0440\u044f\u0434\u043a\u0430 \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c <strong>13,667<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0421\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0443 \u0434\u0440\u0443\u0433\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443 <strong>9000<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0421\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u0442\u0440\u0435\u0442\u044c\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443 <strong>10667<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0406 \u0442\u0430\u043a \u0434\u0430\u043b\u0456.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0417\u043d\u0430\u0445\u043e\u0434\u0436\u0435\u043d\u043d\u044f \u0441\u0443\u043c\u0438 \u043f\u043e \u0440\u0456\u0437\u043d\u0438\u0445 \u043e\u0441\u044f\u0445<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 <strong>axis=0<\/strong> , \u0449\u043e\u0431 \u0437\u043d\u0430\u0439\u0442\u0438 \u0441\u0443\u043c\u0443 \u043f\u0435\u0432\u043d\u0438\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#find sum of 'points' and 'assists' columns<\/span>\ndf[[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">assists<\/span> ']]. <span style=\"color: #3366ff;\">sum<\/span> (axis= <span style=\"color: #008000;\">0<\/span> )\n\npoints 162\nassists 62\ndtype: int64<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 <strong>axis=1<\/strong> , \u0449\u043e\u0431 \u0437\u043d\u0430\u0439\u0442\u0438 \u0441\u0443\u043c\u0443 \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u043a\u0430 \u0432 DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#find sum of each row\n<\/span>df. <span style=\"color: #3366ff;\">sum<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n\n0 41\n1 27\n2 32\n3 29\n4 37\n5 37\n6 43\n7 45\ndtype: int64<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u041f\u0440\u0438\u043a\u043b\u0430\u0434 3: \u041f\u043e\u0448\u0443\u043a \u041c\u0430\u043a\u0441\u0430 \u0432\u0437\u0434\u043e\u0432\u0436 \u0440\u0456\u0437\u043d\u0438\u0445 \u043e\u0441\u0435\u0439<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 <strong>axis=0<\/strong> , \u0449\u043e\u0431 \u0437\u043d\u0430\u0439\u0442\u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043f\u0435\u0432\u043d\u0438\u0445 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#find max of 'points', 'assists', and 'rebounds' columns\n<\/span>df[[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">assists<\/span> ', ' <span style=\"color: #ff0000;\">rebounds<\/span> ']]. <span style=\"color: #3366ff;\">max<\/span> (axis= <span style=\"color: #008000;\">0<\/span> )\n\npoints 29\nassists 12\nrebounds 12\ndtype: int64<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u041c\u0438 \u0442\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 <strong>axis=1<\/strong> , \u0449\u043e\u0431 \u0437\u043d\u0430\u0439\u0442\u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043a\u043e\u0436\u043d\u043e\u0433\u043e \u0440\u044f\u0434\u043a\u0430 \u0432 DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#find max of each row\n<\/span>df. <span style=\"color: #3366ff;\">max<\/span> (axis= <span style=\"color: #008000;\">1<\/span> )\n\n0 25\n1 12\n2 15\n3 14\n4 19\n5 23\n6 25\n7 29\ndtype: int64<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u041c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u043f\u0435\u0440\u0448\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443 \u2013 <strong>25<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u041c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0443 \u0434\u0440\u0443\u0433\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443 \u2013 <strong>12<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u041c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432 \u0442\u0440\u0435\u0442\u044c\u043e\u043c\u0443 \u0440\u044f\u0434\u043a\u0443 \u2013 <strong>15<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0406 \u0442\u0430\u043a \u0434\u0430\u043b\u0456.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0445 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0430\u0445 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0448\u0456 \u0442\u0438\u043f\u043e\u0432\u0456 \u043e\u043f\u0435\u0440\u0430\u0446\u0456\u0457 \u0432 pandas:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/uk\/\u0441\u0435\u0440\u0435\u0434\u043d\u0456\u0438-\u0441\u0442\u043e\u0432\u043f\u0447\u0438\u043a-\u043f\u0430\u043d\u0434\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 Pandas<\/a><br \/> <a href=\"https:\/\/statorials.org\/uk\/\u0446\u0435-\u043a\u043e\u043b\u043e\u043d\u0438-\u043f\u0430\u043d\u0434\u0438\/\" target=\"_blank\" rel=\"noopener\">\u042f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0443\u043c\u0443 \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 Pandas<\/a><br \/> \u042f\u043a \u0437\u043d\u0430\u0439\u0442\u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u0456\u0432 \u0443 Pandas<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0414\u043b\u044f \u0431\u0430\u0433\u0430\u0442\u044c\u043e\u0445 \u0444\u0443\u043d\u043a\u0446\u0456\u0439 \u0443 pandas \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0432\u043a\u0430\u0437\u0430\u0442\u0438 \u0432\u0456\u0441\u044c, \u0443\u0437\u0434\u043e\u0432\u0436 \u044f\u043a\u043e\u0457 \u0437\u0430\u0441\u0442\u043e\u0441\u043e\u0432\u0443\u0432\u0430\u0442\u0438\u043c\u0435\u0442\u044c\u0441\u044f \u043f\u0435\u0432\u043d\u0435 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f. \u042f\u043a \u043f\u0440\u0430\u0432\u0438\u043b\u043e, \u0437\u0430\u0441\u0442\u043e\u0441\u043e\u0432\u0443\u0454\u0442\u044c\u0441\u044f \u0442\u0430\u043a\u0435 \u0435\u043c\u043f\u0456\u0440\u0438\u0447\u043d\u0435 \u043f\u0440\u0430\u0432\u0438\u043b\u043e: axis=0 : \u0437\u0430\u0441\u0442\u043e\u0441\u0443\u0432\u0430\u0442\u0438 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u00ab\u043d\u0430 \u0441\u0442\u043e\u0432\u043f\u0435\u0446\u044c\u00bb. axis=1 : \u0417\u0430\u0441\u0442\u043e\u0441\u0443\u0439\u0442\u0435 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u00ab\u043d\u0430 \u043b\u0456\u043d\u0456\u044e\u00bb. \u0423 \u043d\u0430\u0432\u0435\u0434\u0435\u043d\u0438\u0445 \u043d\u0438\u0436\u0447\u0435 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 axis \u0443 \u0440\u0456\u0437\u043d\u0438\u0445 \u0441\u0446\u0435\u043d\u0430\u0440\u0456\u044f\u0445 \u0456\u0437 \u0442\u0430\u043a\u0438\u043c\u0438 pandas DataFrame: import pandas as pd #createDataFrame df = pd. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u0420\u0456\u0437\u043d\u0438\u0446\u044f \u043c\u0456\u0436 axis=0 \u0456 axis=1 \u0443 Pandas \u2013 Statology<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454 \u0440\u0456\u0437\u043d\u0438\u0446\u044e \u043c\u0456\u0436 axis=0 \u0456 axis=1 \u043f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f \u0440\u0456\u0437\u043d\u0438\u0445 \u0444\u0443\u043d\u043a\u0446\u0456\u0439 pandas.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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