{"id":4316,"date":"2023-07-12T01:20:23","date_gmt":"2023-07-12T01:20:23","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bd%d0%b5-%d0%be%d0%bf%d0%b8%d1%81%d1%83%d1%8e%d1%82%d1%8c-%d0%bd%d0%b0%d1%83%d0%ba%d0%be%d0%b2%d0%b8%d1%85-%d0%bd%d0%be%d1%82%d0%b0%d1%86%d1%96%d0%b8\/"},"modified":"2023-07-12T01:20:23","modified_gmt":"2023-07-12T01:20:23","slug":"%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bd%d0%b5-%d0%be%d0%bf%d0%b8%d1%81%d1%83%d1%8e%d1%82%d1%8c-%d0%bd%d0%b0%d1%83%d0%ba%d0%be%d0%b2%d0%b8%d1%85-%d0%bd%d0%be%d1%82%d0%b0%d1%86%d1%96%d0%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bd%d0%b5-%d0%be%d0%bf%d0%b8%d1%81%d1%83%d1%8e%d1%82%d1%8c-%d0%bd%d0%b0%d1%83%d0%ba%d0%be%d0%b2%d0%b8%d1%85-%d0%bd%d0%be%d1%82%d0%b0%d1%86%d1%96%d0%b8\/","title":{"rendered":"Pandas: \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 describe() \u0456 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e"},"content":{"rendered":"

<\/p>\n

\n

\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e describe()<\/strong> \u0434\u043b\u044f \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043e\u043f\u0438\u0441\u043e\u0432\u043e\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438<\/a> \u0434\u043b\u044f \u0437\u043c\u0456\u043d\u043d\u0438\u0445 \u0443 pandas DataFrame.<\/span><\/p>\n

\u0429\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u0437 \u0432\u0438\u0432\u043e\u0434\u0443 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 describe()<\/strong> , \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0441\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438\u0441\u044f \u0442\u0430\u043a\u0438\u043c\u0438 \u043c\u0435\u0442\u043e\u0434\u0430\u043c\u0438:<\/span><\/p>\n

\u0421\u043f\u043e\u0441\u0456\u0431 1: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u043f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f describe() \u0437\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0435\u043c<\/strong><\/span><\/p>\n

 df[' my_column<\/span> ']. describe<\/span> (). apply<\/span> ( lambda<\/span> x: format<\/span> (x, ' f<\/span> '))<\/strong><\/span><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u043f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f describe() \u0456\u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u044f\u043c\u0438<\/strong><\/span><\/p>\n
 df. describe<\/span> (). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (' {0:.5f}<\/span> '. format<\/span> ))\n<\/strong><\/span><\/pre>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0438 \u043f\u043e\u043a\u0430\u0437\u0443\u044e\u0442\u044c, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043a\u043e\u0436\u0435\u043d \u043c\u0435\u0442\u043e\u0434 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456 \u0437 \u0442\u0430\u043a\u0438\u043c\u0438 pandas DataFrame:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' store<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' sales<\/span> ': [8450550, 406530, 53000, 6000, 2000, 4000, 5400, 6500],\n                   ' returns<\/span> ':[2212200, 145200, 300, 2500, 700, 600, 800, 1200]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  store sales returns\n0 A 8450550 2212200\n1 A 406530 145200\n2 A 53000 300\n3 A 6000 2500\n4 B 2000 700\n5 B 4000 600\n6 B 5400 800\n7 B 6500 1200<\/strong><\/span><\/pre>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 1: \u0432\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u043d\u0430\u0443\u043a\u043e\u0432\u043e\u0457 \u043d\u043e\u0442\u0430\u0446\u0456\u0457 \u043f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f describe() \u0456\u0437 \u0441\u0442\u043e\u0432\u043f\u0446\u0435\u043c<\/strong><\/span><\/h2>\n
 \u042f\u043a\u0449\u043e \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u043c\u043e \u0444\u0443\u043d\u043a\u0446\u0456\u044e describe()<\/strong> \u0434\u043b\u044f \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u043e\u043f\u0438\u0441\u043e\u0432\u043e\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0434\u043b\u044f \u0441\u0442\u043e\u0432\u043f\u0446\u044f \u043f\u0440\u043e\u0434\u0430\u0436\u0456\u0432<\/strong> , \u0432\u0438\u0445\u0456\u0434\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0431\u0443\u0434\u0443\u0442\u044c \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0442\u0438\u0441\u044f \u0432 \u043d\u0430\u0443\u043a\u043e\u0432\u043e\u043c\u0443 \u043d\u043e\u0442\u0430\u0442\u0456\u0457:<\/span><\/p>\n
 #calculate descriptive statistics for sales column\ndf[' sales<\/span> ']. describe<\/span> ()\n<\/span>\n<\/span>count 8.000000e+00\nmean 1.116748e+06\nstd 2.966552e+06\nmin 2.000000e+03\n25% 5.050000e+03\n50% 6.250000e+03\n75% 1.413825e+05\nmax 8.450550e+06\nName: sales, dtype: float64<\/strong><\/pre>\n
 \u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u043a\u043e\u0436\u043d\u0435 \u0437 \u0432\u0438\u0445\u0456\u0434\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454\u0442\u044c\u0441\u044f \u0437 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f\u043c \u043d\u0430\u0443\u043a\u043e\u0432\u043e\u0457 \u043d\u043e\u0442\u0430\u0446\u0456\u0457.<\/span><\/span><\/p>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u0437 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443:<\/span><\/span><\/p>\n
 #calculate descriptive statistics for sales column and suppress scientific notation<\/span>\ndf[' sales<\/span> ']. describe<\/span> (). apply<\/span> ( lambda<\/span> x: format<\/span> (x, ' f<\/span> '))\n\ncount 8.000000\nmean 1116747.500000\nstd 2966551.594104\nmin 2000.000000\n25% 5050.000000\n50% 6250.000000\n75% 141382.500000\nmax 8450550.000000\nName: sales, dtype: object\n<\/strong><\/span><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0443 \u0432\u0438\u0432\u043e\u0434\u0456 \u0442\u0435\u043f\u0435\u0440 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u044e\u0442\u044c\u0441\u044f \u0431\u0435\u0437 \u043d\u0430\u0443\u043a\u043e\u0432\u043e\u0433\u043e \u043f\u043e\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f.<\/span><\/p>\n
 \u041f\u0440\u0438\u043a\u043b\u0430\u0434 2: \u0432\u0438\u0434\u0430\u043b\u0435\u043d\u043d\u044f \u043d\u0430\u0443\u043a\u043e\u0432\u043e\u0457 \u043d\u043e\u0442\u0430\u0446\u0456\u0457 \u043f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f describe() \u0456\u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u0441\u0442\u043e\u0432\u043f\u0446\u044f\u043c\u0438<\/strong><\/span><\/h2>\n
 \u042f\u043a\u0449\u043e \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454\u043c\u043e \u0444\u0443\u043d\u043a\u0446\u0456\u044e describe()<\/strong> \u0434\u043b\u044f \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u043e\u043f\u0438\u0441\u043e\u0432\u043e\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0434\u043b\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\u0438\u0445\u0456\u0434\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0431\u0443\u0434\u0443\u0442\u044c \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u0456 \u0432 \u043d\u0430\u0443\u043a\u043e\u0432\u0456\u0439 \u043d\u043e\u0442\u0430\u0446\u0456\u0457:<\/span><\/p>\n
 #calculate descriptive statistics for each numeric column\ndf. describe<\/span> ()\n<\/span><\/span>\n               sales returns\ncount 8.000000e+00 8.000000e+00\nmean 1.116748e+06 2.954375e+05\nstd 2.966552e+06 7.761309e+05\nmin 2.000000e+03 3.000000e+02\n25% 5.050000e+03 6.750000e+02\n50% 6.250000e+03 1.000000e+03\n75% 1.413825e+05 3.817500e+04\nmax 8.450550e+06 2.212200e+06<\/strong><\/pre>\n
 \u0417\u0432\u0435\u0440\u043d\u0456\u0442\u044c \u0443\u0432\u0430\u0433\u0443, \u0449\u043e \u043a\u043e\u0436\u043d\u0435 \u0437 \u0432\u0438\u0445\u0456\u0434\u043d\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u044c \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u0454\u0442\u044c\u0441\u044f \u0437 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f\u043c \u043d\u0430\u0443\u043a\u043e\u0432\u043e\u0457 \u043d\u043e\u0442\u0430\u0446\u0456\u0457.<\/span><\/p>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438 \u0442\u0430\u043a\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0449\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u0437 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443:<\/span><\/p>\n
 #calculate descriptive statistics for numerical columns and suppress scientific notation<\/span>\ndf. describe<\/span> (). apply<\/span> ( lambda<\/span> x: x.apply<\/span> (' {0:.5f}<\/span> '. format<\/span> ))\n\n                sales returns\ncount 8.00000 8.00000\nmean 1116747.50000 295437.50000\nstd 2966551.59410 776130.93692\nmin 2000.00000 300.00000\n25% 5050.00000 675.00000\n50% 6250.00000 1000.00000\n75% 141382.50000 38175.00000\nmax 8450550.00000 2212200.00000\n<\/strong><\/span><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0443 \u0432\u0438\u0432\u043e\u0434\u0456 \u0442\u0435\u043f\u0435\u0440 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0430\u044e\u0442\u044c\u0441\u044f \u0431\u0435\u0437 \u043d\u0430\u0443\u043a\u043e\u0432\u043e\u0433\u043e \u043f\u043e\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f.<\/span><\/p>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u0432 \u0446\u044c\u043e\u043c\u0443 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0456 \u043c\u0438 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u043b\u0438 0:.5f<\/strong> \u0434\u043b\u044f \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0436\u0435\u043d\u043d\u044f 5<\/strong> \u0437\u043d\u0430\u043a\u0456\u0432 \u043f\u0456\u0441\u043b\u044f \u043a\u043e\u043c\u0438 \u0443 \u0432\u0438\u0445\u0456\u0434\u043d\u0438\u0445 \u0434\u0430\u043d\u0438\u0445.<\/span><\/p>\n
 \u041d\u0435 \u0441\u043e\u0440\u043e\u043c\u0442\u0435\u0441\u044f \u0437\u0430\u043c\u0456\u043d\u0438\u0442\u0438 5<\/strong> \u0456\u043d\u0448\u0438\u043c \u0447\u0438\u0441\u043b\u043e\u043c, \u0449\u043e\u0431 \u0432\u0456\u0434\u043e\u0431\u0440\u0430\u0437\u0438\u0442\u0438 \u0456\u043d\u0448\u0443 \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u0437\u043d\u0430\u043a\u0456\u0432 \u043f\u0456\u0441\u043b\u044f \u043a\u043e\u043c\u0438.<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h2>\n
 \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
 \u041f\u0430\u043d\u0434\u0438: \u044f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0441\u0443\u043a\u0443\u043f\u043d\u0443 \u0441\u0443\u043c\u0443 \u043d\u0430 \u0433\u0440\u0443\u043f\u0443<\/a>
 Pandas: \u044f\u043a \u043f\u0456\u0434\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0443\u043d\u0456\u043a\u0430\u043b\u044c\u043d\u0456 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043f\u043e \u0433\u0440\u0443\u043f\u0430\u0445<\/a>
 Pandas: \u044f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u043a\u043e\u0440\u0435\u043b\u044f\u0446\u0456\u044e \u0437\u0430 \u0433\u0440\u0443\u043f\u043e\u044e<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e describe() \u0434\u043b\u044f \u0441\u0442\u0432\u043e\u0440\u0435\u043d\u043d\u044f \u043e\u043f\u0438\u0441\u043e\u0432\u043e\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0434\u043b\u044f \u0437\u043c\u0456\u043d\u043d\u0438\u0445 \u0443 pandas DataFrame. \u0429\u043e\u0431 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u0437 \u0432\u0438\u0432\u043e\u0434\u0443 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 describe() , \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0441\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u0442\u0438\u0441\u044f \u0442\u0430\u043a\u0438\u043c\u0438 \u043c\u0435\u0442\u043e\u0434\u0430\u043c\u0438: \u0421\u043f\u043e\u0441\u0456\u0431 1: \u0432\u0438\u0434\u0430\u043b\u0456\u0442\u044c \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u043f\u0456\u0434 \u0447\u0430\u0441 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f describe() \u0437\u0456 \u0441\u0442\u043e\u0432\u043f\u0446\u0435\u043c df[‘ my_column ‘]. describe (). apply ( lambda x: format (x, ‘ f ‘)) \u0421\u043f\u043e\u0441\u0456\u0431 2: […]<\/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":"\nPandas: \u042f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 describe() \u0456 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e describe() \u0443 pandas \u0456 \u0432\u0438\u0434\u0430\u043b\u044f\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u0443 \u0432\u0438\u0432\u043e\u0434\u0456 \u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c.\" \/>\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\/uk\/\u043f\u0430\u043d\u0434\u0438-\u043d\u0435-\u043e\u043f\u0438\u0441\u0443\u044e\u0442\u044c-\u043d\u0430\u0443\u043a\u043e\u0432\u0438\u0445-\u043d\u043e\u0442\u0430\u0446\u0456\u0438\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \u042f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 describe() \u0456 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f\" \/>\n<meta property=\"og:description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e describe() \u0443 pandas \u0456 \u0432\u0438\u0434\u0430\u043b\u044f\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u0443 \u0432\u0438\u0432\u043e\u0434\u0456 \u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/uk\/\u043f\u0430\u043d\u0434\u0438-\u043d\u0435-\u043e\u043f\u0438\u0441\u0443\u044e\u0442\u044c-\u043d\u0430\u0443\u043a\u043e\u0432\u0438\u0445-\u043d\u043e\u0442\u0430\u0446\u0456\u0438\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-12T01:20:23+00:00\" \/>\n<meta name=\"author\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u041d\u0430\u043f\u0438\u0441\u0430\u043d\u043e\" \/>\n\t<meta name=\"twitter:data1\" content=\"\u0420\u0435\u0434\u0430\u043a\u0446\u0456\u044f\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u041f\u0440\u0438\u0431\u043b. \u0447\u0430\u0441 \u0447\u0438\u0442\u0430\u043d\u043d\u044f\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 \u0445\u0432\u0438\u043b\u0438\u043d\u0430\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bd%d0%b5-%d0%be%d0%bf%d0%b8%d1%81%d1%83%d1%8e%d1%82%d1%8c-%d0%bd%d0%b0%d1%83%d0%ba%d0%be%d0%b2%d0%b8%d1%85-%d0%bd%d0%be%d1%82%d0%b0%d1%86%d1%96%d0%b8\/\",\"url\":\"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bd%d0%b5-%d0%be%d0%bf%d0%b8%d1%81%d1%83%d1%8e%d1%82%d1%8c-%d0%bd%d0%b0%d1%83%d0%ba%d0%be%d0%b2%d0%b8%d1%85-%d0%bd%d0%be%d1%82%d0%b0%d1%86%d1%96%d0%b8\/\",\"name\":\"Pandas: \u042f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 describe() \u0456 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/uk\/#website\"},\"datePublished\":\"2023-07-12T01:20:23+00:00\",\"dateModified\":\"2023-07-12T01:20:23+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/uk\/#\/schema\/person\/2affa1a5da08a4b61ab4becd078c191a\"},\"description\":\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e describe() \u0443 pandas \u0456 \u0432\u0438\u0434\u0430\u043b\u044f\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u0443 \u0432\u0438\u0432\u043e\u0434\u0456 \u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u043e\u043c.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bd%d0%b5-%d0%be%d0%bf%d0%b8%d1%81%d1%83%d1%8e%d1%82%d1%8c-%d0%bd%d0%b0%d1%83%d0%ba%d0%be%d0%b2%d0%b8%d1%85-%d0%bd%d0%be%d1%82%d0%b0%d1%86%d1%96%d0%b8\/#breadcrumb\"},\"inLanguage\":\"uk\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bd%d0%b5-%d0%be%d0%bf%d0%b8%d1%81%d1%83%d1%8e%d1%82%d1%8c-%d0%bd%d0%b0%d1%83%d0%ba%d0%be%d0%b2%d0%b8%d1%85-%d0%bd%d0%be%d1%82%d0%b0%d1%86%d1%96%d0%b8\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/uk\/%d0%bf%d0%b0%d0%bd%d0%b4%d0%b8-%d0%bd%d0%b5-%d0%be%d0%bf%d0%b8%d1%81%d1%83%d1%8e%d1%82%d1%8c-%d0%bd%d0%b0%d1%83%d0%ba%d0%be%d0%b2%d0%b8%d1%85-%d0%bd%d0%be%d1%82%d0%b0%d1%86%d1%96%d0%b8\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"\u0434\u043e\u0434\u043e\u043c\u0443\",\"item\":\"https:\/\/statorials.org\/uk\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Pandas: \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 describe() \u0456 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/uk\/#website\",\"url\":\"https:\/\/statorials.org\/uk\/\",\"name\":\"Statorials\",\"description\":\"\u0412\u0430\u0448 \u043f\u0443\u0442\u0456\u0432\u043d\u0438\u043a \u0434\u043e \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u043e\u0457 \u043a\u043e\u043c\u043f\u0435\u0442\u0435\u043d\u0442\u043d\u043e\u0441\u0442\u0456!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/uk\/?s={search_term_string}\"},\"query-input\":\"required 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\u0432\u0438\u043a\u043b\u0430\u0434\u0430\u0447\u0435\u043c \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438. \u041c\u0430\u044e\u0447\u0438 \u0432\u0435\u043b\u0438\u043a\u0438\u0439 \u0434\u043e\u0441\u0432\u0456\u0434 \u0456 \u0437\u043d\u0430\u043d\u043d\u044f \u0432 \u0433\u0430\u043b\u0443\u0437\u0456 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438, \u044f \u0433\u043e\u0442\u043e\u0432\u0438\u0439 \u043f\u043e\u0434\u0456\u043b\u0438\u0442\u0438\u0441\u044f \u0441\u0432\u043e\u0457\u043c\u0438 \u0437\u043d\u0430\u043d\u043d\u044f\u043c\u0438, \u0449\u043e\u0431 \u0440\u043e\u0437\u0448\u0438\u0440\u0438\u0442\u0438 \u043c\u043e\u0436\u043b\u0438\u0432\u043e\u0441\u0442\u0456 \u0441\u0442\u0443\u0434\u0435\u043d\u0442\u0456\u0432 \u0447\u0435\u0440\u0435\u0437 Statorials. \u0414\u0456\u0437\u043d\u0430\u0439\u0442\u0435\u0441\u044f \u0431\u0456\u043b\u044c\u0448\u0435\",\"sameAs\":[\"http:\/\/statorials.org\/uk\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Pandas: \u042f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 describe() \u0456 \u0432\u0438\u0434\u0430\u043b\u0438\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e - \u0421\u0442\u0430\u0442\u043e\u043b\u043e\u0433\u0456\u044f","description":"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e describe() \u0443 pandas \u0456 \u0432\u0438\u0434\u0430\u043b\u044f\u0442\u0438 \u043d\u0430\u0443\u043a\u043e\u0432\u0443 \u043d\u043e\u0442\u0430\u0446\u0456\u044e \u0443 \u0432\u0438\u0432\u043e\u0434\u0456 \u0437 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