{"id":3604,"date":"2023-07-16T14:11:02","date_gmt":"2023-07-16T14:11:02","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d0%bc%d0%bd%d0%be%d0%b3%d0%be%d0%bc%d0%b5%d1%80%d0%bd%d0%be%d0%b5-%d0%bc%d0%b0%d1%81%d1%88%d1%82%d0%b0%d0%b1%d0%b8%d1%80%d0%be%d0%b2%d0%b0%d0%bd%d0%b8%d0%b5-%d0%b2-python\/"},"modified":"2023-07-16T14:11:02","modified_gmt":"2023-07-16T14:11:02","slug":"%d0%bc%d0%bd%d0%be%d0%b3%d0%be%d0%bc%d0%b5%d1%80%d0%bd%d0%be%d0%b5-%d0%bc%d0%b0%d1%81%d1%88%d1%82%d0%b0%d0%b1%d0%b8%d1%80%d0%be%d0%b2%d0%b0%d0%bd%d0%b8%d0%b5-%d0%b2-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d0%bc%d0%bd%d0%be%d0%b3%d0%be%d0%bc%d0%b5%d1%80%d0%bd%d0%be%d0%b5-%d0%bc%d0%b0%d1%81%d1%88%d1%82%d0%b0%d0%b1%d0%b8%d1%80%d0%be%d0%b2%d0%b0%d0%bd%d0%b8%d0%b5-%d0%b2-python\/","title":{"rendered":"\u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0432 python"},"content":{"rendered":"

<\/p>\n

\n

\u0412 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0435 \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435<\/strong> \u2014 \u044d\u0442\u043e \u0441\u043f\u043e\u0441\u043e\u0431 \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441\u0445\u043e\u0434\u0441\u0442\u0432\u043e \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u0439 \u0432 \u043d\u0430\u0431\u043e\u0440\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0442\u043d\u043e\u043c \u0434\u0435\u043a\u0430\u0440\u0442\u043e\u0432\u043e\u043c \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435 (\u043e\u0431\u044b\u0447\u043d\u043e \u0432 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u043e\u043c \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435).<\/span><\/p>\n

\u0421\u0430\u043c\u044b\u0439 \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u0441\u043f\u043e\u0441\u043e\u0431 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0432 Python \u2014 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e MDS()<\/strong> \u043f\u043e\u0434\u043c\u043e\u0434\u0443\u043b\u044f sklearn.manifold<\/strong> .<\/span><\/p>\n

\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \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

\u041f\u0440\u0438\u043c\u0435\u0440: \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0432 Python<\/strong><\/span><\/h2>\n

\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\u0439 DataFrame pandas, \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0449\u0438\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0431\u0430\u0441\u043a\u0435\u0442\u0431\u043e\u043b\u0438\u0441\u0442\u0430\u0445:<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#create DataFrane\n<\/span>df = pd. DataFrame<\/span> ({' player<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K '],\n                   ' points<\/span> ': [4, 4, 6, 7, 8, 14, 16, 19, 25, 25, 28],\n                   ' assists<\/span> ': [3, 2, 2, 5, 4, 8, 7, 6, 8, 10, 11],\n                   ' blocks<\/span> ': [7, 3, 6, 7, 5, 8, 8, 4, 2, 2, 1],\n                   ' rebounds<\/span> ': [4, 5, 5, 6, 5, 8, 10, 4, 3, 2, 2]})\n\n#set player column as index column\n<\/span>df = df. set_index<\/span> (' player<\/span> ')\n\n#view Dataframe\n<\/span>print<\/span> (df)\n\n        points assists blocks rebounds\nplayer                                   \nA 4 3 7 4\nB 4 2 3 5\nC 6 2 6 5\nD 7 5 7 6\nE 8 4 5 5\nF 14 8 8 8\nG 16 7 8 10\nH 19 6 4 4\nI 25 8 2 3\nD 25 10 2 2\nK 28 11 1 2\n<\/strong><\/pre>\n
 \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0433\u043e \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 MDS()<\/strong> \u043c\u043e\u0434\u0443\u043b\u044f sklearn.manifold<\/strong> :<\/span><\/p>\n
 from<\/span> sklearn. manifold<\/span> import<\/span> MDS\n\n#perform multi-dimensional scaling\n<\/span>mds = MDS(random_state= 0<\/span> )\nscaled_df = mds. fit_transform<\/span> (df)\n\n#view results of multi-dimensional scaling\n<\/span>print<\/span> (scaled_df)\n\n[[ 7.43654469 8.10247222]\n [4.13193821 10.27360901]\n [5.20534681 7.46919526]\n [6.22323046 4.45148627]\n [3.74110999 5.25591459]\n [3.69073384 -2.88017811]\n [3.89092087 -5.19100988]\n [ -3.68593169 -3.0821144 ]\n [ -9.13631889 -6.81016012]\n [ -8.97898385 -8.50414387]\n [-12.51859044 -9.08507097]]<\/span><\/span><\/strong><\/pre>\n
 \u041a\u0430\u0436\u0434\u0430\u044f \u0441\u0442\u0440\u043e\u043a\u0430 \u0438\u0441\u0445\u043e\u0434\u043d\u043e\u0433\u043e DataFrame \u0431\u044b\u043b\u0430 \u0443\u043c\u0435\u043d\u044c\u0448\u0435\u043d\u0430 \u0434\u043e \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442\u044b (x, y).<\/span><\/p>\n
 \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u044d\u0442\u0438\u0445 \u043a\u043e\u043e\u0440\u0434\u0438\u043d\u0430\u0442 \u0432 2D-\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435:<\/span> <\/p>\n
 import<\/span> matplotlib.pyplot as<\/span> plt\n\n#create scatterplot\n<\/span>plt. scatter<\/span> (scaled_df[:,0], scaled_df[:,1])\n\n#add axis labels\n<\/span>plt. xlabel<\/span> (' Coordinate 1<\/span> ')\nplt. ylabel<\/span> (' Coordinate 2<\/span> ')\n\n#add lables to each point\n<\/span>for<\/span> i, txt in<\/span> enumerate( df.index<\/span> ):\n    plt. annotate<\/span> (txt, (scaled_df[:,0][i]+.3, scaled_df[:,1][i]))\n\n#display scatterplot\n<\/span>plt. show<\/span> ()\n<\/strong><\/pre>\n
\"\u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0435<\/p>\n
 \u0418\u0433\u0440\u043e\u043a\u0438 \u0432 \u0438\u0441\u0445\u043e\u0434\u043d\u043e\u043c DataFrame, \u0443 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0432 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0445 \u0447\u0435\u0442\u044b\u0440\u0435\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u0445 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f (\u043e\u0447\u043a\u0438, \u043f\u0435\u0440\u0435\u0434\u0430\u0447\u0438, \u0431\u043b\u043e\u043a\u0438 \u0438 \u043f\u043e\u0434\u0431\u043e\u0440\u044b), \u043d\u0430\u0445\u043e\u0434\u044f\u0442\u0441\u044f \u043d\u0430 \u0433\u0440\u0430\u0444\u0438\u043a\u0435 \u0431\u043b\u0438\u0437\u043a\u043e \u0434\u0440\u0443\u0433 \u043a \u0434\u0440\u0443\u0433\u0443.<\/span><\/p>\n
 \u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0438\u0433\u0440\u043e\u043a\u0438 F<\/strong> \u0438 G<\/strong> \u0437\u0430\u043a\u0440\u044b\u0442\u044b \u0434\u0440\u0443\u0433 \u0434\u043b\u044f \u0434\u0440\u0443\u0433\u0430. \u0412\u043e\u0442 \u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0438\u0437 \u0438\u0441\u0445\u043e\u0434\u043d\u043e\u0433\u043e DataFrame:<\/span><\/p>\n
 #select rows with index labels 'F' and 'G'\ndf. loc<\/span> [[' F<\/span> ',' G<\/span> ']]\n\n        points assists blocks rebounds\nplayer\t\t\t\t\nF 14 8 8 8\nG 16 7 8 10\n<\/span><\/span><\/strong><\/pre>\n
 \u0418\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043e\u0447\u043a\u043e\u0432, \u043f\u0435\u0440\u0435\u0434\u0430\u0447, \u0431\u043b\u043e\u043a\u043e\u0432 \u0438 \u043f\u043e\u0434\u0431\u043e\u0440\u043e\u0432 \u043e\u0447\u0435\u043d\u044c \u043f\u043e\u0445\u043e\u0436\u0438, \u0447\u0442\u043e \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442, \u043f\u043e\u0447\u0435\u043c\u0443 \u043e\u043d\u0438 \u0442\u0430\u043a \u0431\u043b\u0438\u0437\u043a\u0438 \u0434\u0440\u0443\u0433 \u043a \u0434\u0440\u0443\u0433\u0443 \u043d\u0430 2D-\u0433\u0440\u0430\u0444\u0438\u043a\u0435.<\/span><\/p>\n
 \u041d\u0430\u043f\u0440\u043e\u0442\u0438\u0432, \u0440\u0430\u0441\u0441\u043c\u043e\u0442\u0440\u0438\u043c \u0438\u0433\u0440\u043e\u043a\u043e\u0432 B<\/strong> \u0438 K<\/strong> , \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043d\u0430\u0445\u043e\u0434\u044f\u0442\u0441\u044f \u0434\u0430\u043b\u0435\u043a\u043e \u0434\u0440\u0443\u0433 \u043e\u0442 \u0434\u0440\u0443\u0433\u0430 \u0432 \u0441\u044e\u0436\u0435\u0442\u0435.<\/span><\/p>\n
 \u0415\u0441\u043b\u0438 \u043c\u044b \u043e\u0431\u0440\u0430\u0442\u0438\u043c\u0441\u044f \u043a \u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c \u0432 \u0438\u0441\u0445\u043e\u0434\u043d\u043e\u043c DataFrame, \u0442\u043e \u0443\u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u043e\u043d\u0438 \u0441\u043e\u0432\u0435\u0440\u0448\u0435\u043d\u043d\u043e \u0440\u0430\u0437\u043d\u044b\u0435:<\/span><\/p>\n
 #select rows with index labels 'B' and 'K'\ndf. loc<\/span> [[' B<\/span> ',' K<\/span> ']]<\/span><\/span>\n\n        points assists blocks rebounds\nplayer\t\t\t\t\nB 4 2 3 5\nK 28 11 1 2<\/strong><\/pre>\n
 \u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c, \u0434\u0432\u0443\u0445\u043c\u0435\u0440\u043d\u044b\u0439 \u0433\u0440\u0430\u0444\u0438\u043a \u2014 \u0445\u043e\u0440\u043e\u0448\u0438\u0439 \u0441\u043f\u043e\u0441\u043e\u0431 \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043f\u043e\u0445\u043e\u0436\u0438 \u043a\u0430\u0436\u0434\u044b\u0439 \u0438\u0433\u0440\u043e\u043a \u043f\u043e \u0432\u0441\u0435\u043c \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u043c \u0432 DataFframe.<\/span><\/p>\n
 \u0418\u0433\u0440\u043e\u043a\u0438 \u0441\u043e \u0441\u0445\u043e\u0436\u0438\u043c\u0438 \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0430\u043c\u0438 \u0433\u0440\u0443\u043f\u043f\u0438\u0440\u0443\u044e\u0442\u0441\u044f \u0431\u043b\u0438\u0437\u043a\u043e \u0434\u0440\u0443\u0433 \u043a \u0434\u0440\u0443\u0433\u0443, \u0430 \u0438\u0433\u0440\u043e\u043a\u0438 \u0441 \u043e\u0447\u0435\u043d\u044c \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0438\u0441\u0442\u0438\u043a\u0430\u043c\u0438 \u043d\u0430\u0445\u043e\u0434\u044f\u0442\u0441\u044f \u0434\u0430\u043b\u044c\u0448\u0435 \u0434\u0440\u0443\u0433 \u043e\u0442 \u0434\u0440\u0443\u0433\u0430 \u043f\u043e \u0441\u044e\u0436\u0435\u0442\u0443.<\/span><\/p>\n
 \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h2>\n
 \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430\u0445 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u0434\u0440\u0443\u0433\u0438\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u0437\u0430\u0434\u0430\u0447\u0438 \u043d\u0430 Python:<\/span><\/p>\n
 \u041a\u0430\u043a \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u0432 Python<\/a>
 \u041a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0432\u044b\u0431\u0440\u043e\u0441\u044b \u0432 Python<\/a>
 \u041a\u0430\u043a \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0432 Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0435 \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u2014 \u044d\u0442\u043e \u0441\u043f\u043e\u0441\u043e\u0431 \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0441\u0445\u043e\u0434\u0441\u0442\u0432\u043e \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u0439 \u0432 \u043d\u0430\u0431\u043e\u0440\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0430\u0431\u0441\u0442\u0440\u0430\u043a\u0442\u043d\u043e\u043c \u0434\u0435\u043a\u0430\u0440\u0442\u043e\u0432\u043e\u043c \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435 (\u043e\u0431\u044b\u0447\u043d\u043e \u0432 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u043e\u043c \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435). \u0421\u0430\u043c\u044b\u0439 \u043f\u0440\u043e\u0441\u0442\u043e\u0439 \u0441\u043f\u043e\u0441\u043e\u0431 \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0432 Python \u2014 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e MDS() \u043f\u043e\u0434\u043c\u043e\u0434\u0443\u043b\u044f sklearn.manifold . \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \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: \u043c\u043d\u043e\u0433\u043e\u043c\u0435\u0440\u043d\u043e\u0435 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