{"id":1327,"date":"2023-07-26T20:52:11","date_gmt":"2023-07-26T20:52:11","guid":{"rendered":"https:\/\/statorials.org\/th\/%e0%b8%9e%e0%b8%a5%e0%b9%87%e0%b8%ad%e0%b8%95%e0%b8%ab%e0%b8%a5%e0%b8%b2%e0%b8%a2%e0%b8%9a%e0%b8%a3%e0%b8%a3%e0%b8%97%e0%b8%b1%e0%b8%94-matplotlib\/"},"modified":"2023-07-26T20:52:11","modified_gmt":"2023-07-26T20:52:11","slug":"%e0%b8%9e%e0%b8%a5%e0%b9%87%e0%b8%ad%e0%b8%95%e0%b8%ab%e0%b8%a5%e0%b8%b2%e0%b8%a2%e0%b8%9a%e0%b8%a3%e0%b8%a3%e0%b8%97%e0%b8%b1%e0%b8%94-matplotlib","status":"publish","type":"post","link":"https:\/\/statorials.org\/th\/%e0%b8%9e%e0%b8%a5%e0%b9%87%e0%b8%ad%e0%b8%95%e0%b8%ab%e0%b8%a5%e0%b8%b2%e0%b8%a2%e0%b8%9a%e0%b8%a3%e0%b8%a3%e0%b8%97%e0%b8%b1%e0%b8%94-matplotlib\/","title":{"rendered":"\u0e27\u0e34\u0e18\u0e35\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19 matplotlib"},"content":{"rendered":"

<\/p>\n

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\u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e41\u0e2a\u0e14\u0e07\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19\u0e1e\u0e25\u0e47\u0e2d\u0e15 Matplotlib \u0e40\u0e14\u0e35\u0e22\u0e27\u0e44\u0e14\u0e49\u0e42\u0e14\u0e22\u0e43\u0e0a\u0e49\u0e44\u0e27\u0e22\u0e32\u0e01\u0e23\u0e13\u0e4c\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49:<\/span><\/p>\n

 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\nplt. plot<\/span> (df[' column1<\/span> '])\nplt. plot<\/span> (df[' column2<\/span> '])\nplt. plot<\/span> (df[' column3<\/span> '])\n\n...\nplt. show<\/span> ()\n<\/strong><\/pre>\n
 \u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19\u0e19\u0e35\u0e49\u0e43\u0e2b\u0e49\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19\u0e41\u0e1c\u0e19\u0e20\u0e39\u0e21\u0e34\u0e42\u0e14\u0e22\u0e43\u0e0a\u0e49 Pandas DataFrame \u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49:<\/span><\/p>\n
 import numpy<\/span> as np<\/span> \nimport<\/span> pandas as<\/span> pd\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create dataset\n<\/span>period = np. arange<\/span> (1, 101, 1)\nleads = np. random<\/span> . uniform<\/span> (1, 50, 100)\nprospects = np. random<\/span> . uniform<\/span> (40, 80, 100)\nsales = 60 + 2*period + np. random<\/span> . normal<\/span> (loc=0, scale=.5*period, size=100)\ndf = pd. DataFrame<\/span> ({' period<\/span> ': period, \n                   ' leads<\/span> ': leads,\n                   ' prospects<\/span> ': prospects,\n                   ' sales<\/span> ': sales})\n\n#view first 10 rows\n<\/span>df. head<\/span> (10)\n\n\n        period leads sales prospects\n0 1 27.891862 67.112661 62.563318\n1 2 36.044279 50.800319 62.920068\n2 3 30.535405 69.407761 64.278797\n3 4 27.699276 78.487542 67.124360\n4 5 21.759085 49.950126 68.754919\n5 6 32.648812 63.046293 77.788596\n6 7 22.441773 63.681677 77.322973\n7 8 44.696877 62.890076 76.350205\n8 9 48.219475 48.923265 72.485540\n9 10 19.788634 78.109960 84.221815\n<\/strong><\/pre>\n
 \u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19 Matplotlib<\/strong><\/span><\/h3>\n
 \u0e23\u0e2b\u0e31\u0e2a\u0e15\u0e48\u0e2d\u0e44\u0e1b\u0e19\u0e35\u0e49\u0e41\u0e2a\u0e14\u0e07\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e25\u0e07\u0e08\u0e38\u0e14\u0e2a\u0e32\u0e21\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e41\u0e15\u0e48\u0e25\u0e30\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19\u0e08\u0e38\u0e14\u0e40\u0e14\u0e35\u0e22\u0e27\u0e43\u0e19 matplotlib:<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\n\n#plot individual lines<\/span>\nplt. plot<\/span> (df[' leads<\/span> '])<\/span>\nplt. plot<\/span> (df[' prospects<\/span> '])<\/span>\nplt. plot<\/span> (df[' sales<\/span> '])\n\n<\/span>#displayplot<\/span>\nplt. show<\/span> ()<\/span>\n<\/span><\/strong><\/pre>\n
\"\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19\u0e41\u0e1c\u0e19\u0e20\u0e39\u0e21\u0e34<\/p>\n
 \u0e1b\u0e23\u0e31\u0e1a\u0e41\u0e15\u0e48\u0e07\u0e40\u0e2a\u0e49\u0e19\u0e43\u0e19 Matplotlib<\/strong><\/span><\/h3>\n
 \u0e04\u0e38\u0e13\u0e22\u0e31\u0e07\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e1b\u0e23\u0e31\u0e1a\u0e41\u0e15\u0e48\u0e07\u0e2a\u0e35 \u0e2a\u0e44\u0e15\u0e25\u0e4c \u0e41\u0e25\u0e30\u0e04\u0e27\u0e32\u0e21\u0e01\u0e27\u0e49\u0e32\u0e07\u0e02\u0e2d\u0e07\u0e41\u0e15\u0e48\u0e25\u0e30\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e44\u0e14\u0e49:<\/span> <\/p>\n
 #plot individual lines with custom colors, styles, and widths<\/span>\nplt. plot<\/span> (df[' leads<\/span> '], color=' green<\/span> ')\nplt. plot<\/span> (df[' prospects<\/span> '], color=' steelblue<\/span> ', linewidth= 4<\/span> )\nplt. plot<\/span> (df[' sales<\/span> '], color=' purple<\/span> ', linestyle=' dashed<\/span> ')\n\n<\/span>#displayplot<\/span>\nplt. show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n
\"\u0e1b\u0e23\u0e31\u0e1a\u0e41\u0e15\u0e48\u0e07\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19<\/p>\n
 \u0e40\u0e1e\u0e34\u0e48\u0e21\u0e04\u0e33\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e43\u0e19 Matplotlib<\/strong><\/span><\/h3>\n
 \u0e04\u0e38\u0e13\u0e22\u0e31\u0e07\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e04\u0e33\u0e1a\u0e23\u0e23\u0e22\u0e32\u0e22\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e41\u0e22\u0e01\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e44\u0e14\u0e49:<\/span> <\/p>\n
 #plot individual lines with custom colors, styles, and widths<\/span>\nplt. plot<\/span> (df[' leads<\/span> '], label=' Leads<\/span> ', color=' green<\/span> ')\nplt. plot<\/span> (df[' prospects<\/span> '], label=' Prospects<\/span> ', color=' steelblue<\/span> ', linewidth= 4<\/span> )\nplt. plot<\/span> (df[' sales<\/span> '], label=' Sales<\/span> ', color=' purple<\/span> ', linestyle=' dashed<\/span> ')\n\n#add legend\nplt. legend<\/span> ()<\/span>\n\n<\/span><\/span>#displayplot<\/span>\nplt. show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n
\"\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e04\u0e33\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19<\/p>\n
 \u0e40\u0e1e\u0e34\u0e48\u0e21\u0e1b\u0e49\u0e32\u0e22\u0e01\u0e33\u0e01\u0e31\u0e1a\u0e41\u0e01\u0e19\u0e41\u0e25\u0e30\u0e0a\u0e37\u0e48\u0e2d\u0e43\u0e19 Matplotlib<\/strong><\/span><\/h3>\n
 \u0e2a\u0e38\u0e14\u0e17\u0e49\u0e32\u0e22 \u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e40\u0e1e\u0e34\u0e48\u0e21\u0e1b\u0e49\u0e32\u0e22\u0e01\u0e33\u0e01\u0e31\u0e1a\u0e41\u0e01\u0e19\u0e41\u0e25\u0e30\u0e0a\u0e37\u0e48\u0e2d\u0e40\u0e23\u0e37\u0e48\u0e2d\u0e07\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e17\u0e33\u0e43\u0e2b\u0e49\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e2a\u0e21\u0e1a\u0e39\u0e23\u0e13\u0e4c\u0e44\u0e14\u0e49:<\/span> <\/p>\n
 #plot individual lines with custom colors, styles, and widths<\/span>\nplt. plot<\/span> (df[' leads<\/span> '], label=' Leads<\/span> ', color=' green<\/span> ')\nplt. plot<\/span> (df[' prospects<\/span> '], label=' Prospects<\/span> ', color=' steelblue<\/span> ', linewidth= 4<\/span> )\nplt. plot<\/span> (df[' sales<\/span> '], label=' Sales<\/span> ', color=' purple<\/span> ', linestyle=' dashed<\/span> ')\n\n#add legend\nplt.<\/span> legend<\/span> ()<\/span>\n\n#add axis labels and a title\nplt. ylabel<\/span> (' Sales<\/span> ', fontsize= 14<\/span> )<\/span>\nplt. xlabel<\/span> (' Period<\/span> ', fontsize= 14<\/span> )<\/span>\nplt. title<\/span> (' Company Metrics<\/span> ', fontsize= 16<\/span> )<\/span>\n\n<\/span><\/span>#displayplot<\/span>\nplt. show<\/span> ()<\/span><\/span><\/strong> <\/pre>\n
\"\"<\/p>\n
 \u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e04\u0e49\u0e19\u0e2b\u0e32\u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19 Matplotlib \u0e40\u0e1e\u0e34\u0e48\u0e21\u0e40\u0e15\u0e34\u0e21 \u0e44\u0e14\u0e49\u0e17\u0e35\u0e48\u0e19\u0e35\u0e48<\/a><\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0e04\u0e38\u0e13\u0e2a\u0e32\u0e21\u0e32\u0e23\u0e16\u0e41\u0e2a\u0e14\u0e07\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19\u0e1e\u0e25\u0e47\u0e2d\u0e15 Matplotlib \u0e40\u0e14\u0e35\u0e22\u0e27\u0e44\u0e14\u0e49\u0e42\u0e14\u0e22\u0e43\u0e0a […]<\/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":[],"class_list":["post-1327","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"\n\u0e27\u0e34\u0e18\u0e35\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19 Matplotlib - Statology<\/title>\n<meta name=\"description\" content=\"\u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19\u0e19\u0e35\u0e49\u0e08\u0e30\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e25\u0e07\u0e08\u0e38\u0e14\u0e41\u0e25\u0e30\u0e1b\u0e23\u0e31\u0e1a\u0e41\u0e15\u0e48\u0e07\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19 Matplotlib \u0e1e\u0e23\u0e49\u0e2d\u0e21\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e21\u0e32\u0e01\u0e21\u0e32\u0e22\" \/>\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\/th\/\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14-matplotlib\/\" \/>\n<meta property=\"og:locale\" content=\"th_TH\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u0e27\u0e34\u0e18\u0e35\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19 Matplotlib - Statology\" \/>\n<meta property=\"og:description\" content=\"\u0e1a\u0e17\u0e0a\u0e48\u0e27\u0e22\u0e2a\u0e2d\u0e19\u0e19\u0e35\u0e49\u0e08\u0e30\u0e2d\u0e18\u0e34\u0e1a\u0e32\u0e22\u0e27\u0e34\u0e18\u0e35\u0e01\u0e32\u0e23\u0e25\u0e07\u0e08\u0e38\u0e14\u0e41\u0e25\u0e30\u0e1b\u0e23\u0e31\u0e1a\u0e41\u0e15\u0e48\u0e07\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14\u0e43\u0e19 Matplotlib \u0e1e\u0e23\u0e49\u0e2d\u0e21\u0e15\u0e31\u0e27\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e21\u0e32\u0e01\u0e21\u0e32\u0e22\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/th\/\u0e1e\u0e25\u0e47\u0e2d\u0e15\u0e2b\u0e25\u0e32\u0e22\u0e1a\u0e23\u0e23\u0e17\u0e31\u0e14-matplotlib\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-26T20:52:11+00:00\" 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