{"id":4200,"date":"2023-07-12T21:36:56","date_gmt":"2023-07-12T21:36:56","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d1%88%d0%b8%d1%80%d0%b8%d0%bd%d0%b0-%d0%bf%d0%be%d0%bb%d0%be%d1%81%d1%8b-%d0%b2-matplotlib\/"},"modified":"2023-07-12T21:36:56","modified_gmt":"2023-07-12T21:36:56","slug":"%d1%88%d0%b8%d1%80%d0%b8%d0%bd%d0%b0-%d0%bf%d0%be%d0%bb%d0%be%d1%81%d1%8b-%d0%b2-matplotlib","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d1%88%d0%b8%d1%80%d0%b8%d0%bd%d0%b0-%d0%bf%d0%be%d0%bb%d0%be%d1%81%d1%8b-%d0%b2-matplotlib\/","title":{"rendered":"\u041a\u0430\u043a \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0448\u0438\u0440\u0438\u043d\u0443 \u043f\u043e\u043b\u043e\u0441\u044b \u0432 matplotlib"},"content":{"rendered":"

<\/p>\n

\n

\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 \u0448\u0438\u0440\u0438\u043d\u044b<\/strong> , \u0447\u0442\u043e\u0431\u044b \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0448\u0438\u0440\u0438\u043d\u0443 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u043d\u0430 \u0433\u0438\u0441\u0442\u043e\u0433\u0440\u0430\u043c\u043c\u0435, \u0441\u043e\u0437\u0434\u0430\u043d\u043d\u043e\u0439 Matplotlib:<\/span><\/p>\n

 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\nplt. bar<\/span> (x= df.category<\/span> , height= df.amount<\/span> , width= 0.8<\/span> )\n<\/span><\/span><\/strong><\/pre>\n
 \u0417\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0448\u0438\u0440\u0438\u043d\u044b<\/strong> \u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e \u2014 0,8<\/strong> , \u043d\u043e \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0443\u0432\u0435\u043b\u0438\u0447\u0438\u0442\u044c \u044d\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u0447\u0442\u043e\u0431\u044b \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043f\u043e\u043b\u043e\u0441\u044b \u0448\u0438\u0440\u0435, \u0438\u043b\u0438 \u0443\u043c\u0435\u043d\u044c\u0448\u0438\u0442\u044c \u044d\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u0447\u0442\u043e\u0431\u044b \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043f\u043e\u043b\u043e\u0441\u044b \u0443\u0436\u0435.<\/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\u043e\u0442 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0440: \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0448\u0438\u0440\u0438\u043d\u0443 \u043f\u043e\u043b\u043e\u0441\u044b \u0432 Matplotlib<\/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, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e\u0431 \u043e\u0431\u0449\u0435\u043c \u043e\u0431\u044a\u0435\u043c\u0435 \u043f\u0440\u043e\u0434\u0430\u0436 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432 \u0432 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432\u043e\u043c \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u0435:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd<\/span>\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' item<\/span> ': ['Apples', 'Oranges', 'Kiwis', 'Bananas', 'Limes'],\n                   ' sales<\/span> ': [18, 22, 19, 14, 24]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n      item sales\n0 Apples 18\n1 Oranges 22\n2 Kiwis 19\n3 Bananas 14\n4 Files 24\n<\/span><\/span><\/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 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u0433\u0438\u0441\u0442\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0434\u043b\u044f \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0438 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0430 \u043f\u0440\u043e\u0434\u0430\u0436 \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0442\u043e\u0432\u0430\u0440\u0430:<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\n\n#create bar chart\n<\/span>plt. bar<\/span> (x=df. item<\/span> , height=df. sales )<\/span><\/span>\n<\/span><\/span><\/strong><\/pre>\n
\"\"<\/p>\n
 \u041f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e Matplotlib \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0448\u0438\u0440\u0438\u043d\u0443 0,8<\/strong> .<\/span><\/p>\n
 \u041e\u0434\u043d\u0430\u043a\u043e \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 \u0448\u0438\u0440\u0438\u043d\u044b<\/strong> , \u0447\u0442\u043e\u0431\u044b \u0443\u043a\u0430\u0437\u0430\u0442\u044c \u0434\u0440\u0443\u0433\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435:<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\n\n#create bar chart with narrow bars\n<\/span>plt. bar<\/span> (x=df. item<\/span> , height=df. sales , width= 0.4<\/span> )<\/span><\/span><\/span><\/span><\/strong> <\/pre>\n
\"matplotlib<\/p>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043f\u043e\u043b\u043e\u0441\u044b \u043d\u0430\u043c\u043d\u043e\u0433\u043e \u0443\u0436\u0435.<\/span><\/p>\n
 \u0422\u0430\u043a\u0436\u0435 \u043e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u0435\u0441\u043b\u0438 \u0432\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 1<\/strong> \u0434\u043b\u044f \u0448\u0438\u0440\u0438\u043d\u044b, \u043f\u043e\u043b\u043e\u0441\u044b \u0431\u0443\u0434\u0443\u0442 \u0441\u043e\u043f\u0440\u0438\u043a\u0430\u0441\u0430\u0442\u044c\u0441\u044f:<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\n\n#create bar chart with width of 1\n<\/span>plt. bar<\/span> (x=df. item<\/span> , height=df. sales , width= 1<\/span> , edgecolor=' black<\/span> ')<\/span><\/span><\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
 \u041d\u0435 \u0441\u0442\u0435\u0441\u043d\u044f\u0439\u0442\u0435\u0441\u044c \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0430 \u0448\u0438\u0440\u0438\u043d\u044b<\/strong> , \u0447\u0442\u043e\u0431\u044b \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043f\u043e\u043b\u043e\u0441\u044b \u0433\u0440\u0430\u0444\u0438\u043a\u0430 \u043d\u0430\u0441\u0442\u043e\u043b\u044c\u043a\u043e \u0448\u0438\u0440\u043e\u043a\u0438\u043c\u0438 \u0438\u043b\u0438 \u0443\u0437\u043a\u0438\u043c\u0438, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435.<\/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 \u0432 Matplotlib:<\/span><\/p>\n
 \u041a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0433\u0438\u0441\u0442\u043e\u0433\u0440\u0430\u043c\u043c\u044b \u0441 \u043d\u0430\u043a\u043e\u043f\u043b\u0435\u043d\u0438\u0435\u043c \u0432 Matplotlib<\/a>
 \u041a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0433\u0438\u0441\u0442\u043e\u0433\u0440\u0430\u043c\u043c\u0443 \u043e\u0442\u043d\u043e\u0441\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0439 \u0447\u0430\u0441\u0442\u043e\u0442\u044b \u0432 Matplotlib<\/a>
 \u041a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u0430\u043b\u044c\u043d\u0443\u044e \u0433\u0438\u0441\u0442\u043e\u0433\u0440\u0430\u043c\u043c\u0443 \u0432 Seaborn<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 \u0448\u0438\u0440\u0438\u043d\u044b , \u0447\u0442\u043e\u0431\u044b \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0448\u0438\u0440\u0438\u043d\u0443 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u043d\u0430 \u0433\u0438\u0441\u0442\u043e\u0433\u0440\u0430\u043c\u043c\u0435, \u0441\u043e\u0437\u0434\u0430\u043d\u043d\u043e\u0439 Matplotlib: import matplotlib. pyplot as plt plt. bar (x= df.category , height= df.amount , width= 0.8 ) \u0417\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0448\u0438\u0440\u0438\u043d\u044b \u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e \u2014 0,8 , \u043d\u043e \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0443\u0432\u0435\u043b\u0438\u0447\u0438\u0442\u044c \u044d\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u0447\u0442\u043e\u0431\u044b \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043f\u043e\u043b\u043e\u0441\u044b \u0448\u0438\u0440\u0435, \u0438\u043b\u0438 \u0443\u043c\u0435\u043d\u044c\u0448\u0438\u0442\u044c \u044d\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435, \u0447\u0442\u043e\u0431\u044b \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043f\u043e\u043b\u043e\u0441\u044b \u0443\u0436\u0435. […]<\/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-4200","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\n\u041a\u0430\u043a \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0448\u0438\u0440\u0438\u043d\u0443 \u043f\u043e\u043b\u043e\u0441\u044b \u0432 Matplotlib \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\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 \u043d\u0430\u0441\u0442\u0440\u043e\u0438\u0442\u044c \u0448\u0438\u0440\u0438\u043d\u0443 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u043d\u0430 \u0433\u0438\u0441\u0442\u043e\u0433\u0440\u0430\u043c\u043c\u0435 \u0432 Matplotlib, \u043d\u0430 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