{"id":836,"date":"2023-07-28T14:20:58","date_gmt":"2023-07-28T14:20:58","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%92%e1%85%b5%e1%84%90%e1%85%b3%e1%84%86%e1%85%a2%e1%86%b8\/"},"modified":"2023-07-28T14:20:58","modified_gmt":"2023-07-28T14:20:58","slug":"%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%92%e1%85%b5%e1%84%90%e1%85%b3%e1%84%86%e1%85%a2%e1%86%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%92%e1%85%b5%e1%84%90%e1%85%b3%e1%84%86%e1%85%a2%e1%86%b8\/","title":{"rendered":"Python\uc5d0\uc11c \ud788\ud2b8\ub9f5\uc744 \uc27d\uac8c \ub9cc\ub4dc\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

5\uc8fc \ub3d9\uc548 \ub9e4\uc77c \ud2b9\uc815 \ub9e4\uc7a5\uc758 \ud310\ub9e4 \uac74\uc218\ub97c \ud45c\uc2dc\ud558\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 Python \ub370\uc774\ud130\uc138\ud2b8\uac00 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport pandas<\/span> as pd<\/span> \nimport<\/span> seaborn as<\/span> sns\n\n#create a dataset<\/span>\nnp.random.seed(0)\ndata = {'day': np.tile(['Mon', 'Tue', 'Wed', 'Thur', 'Fri'], 5),\n        'week': np.repeat([1, 2, 3, 4, 5], 5),\n        'sales': np.random.randint(0, 50, size=25)\n        }\n\ndf = pd.DataFrame(data,columns=['day','week','sales'])\ndf = df.pivot('day', 'week', 'sales')\n\nview first ten rows of dataset\n<\/span>df[:10]\n\nweek 1 2 3 4 5\nday\t\t\t\t\t\nFri 3 36 12 46 13\nMy 44 39 23 1 24\nThursday 3 21 24 23 25\nKills 47 9 6 38 17\nWed 0 19 24 39 37\n<\/strong><\/pre>\n
 \uae30\ubcf8 \ud788\ud2b8\ub9f5 \uc0dd\uc131<\/strong><\/span><\/h3>\n
 ns.heatmap()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uae30\ubcf8 \ud788\ud2b8\ub9f5\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 sns.heatmap(df)<\/strong><\/span> <\/pre>\n
\"Python\uc758<\/p>\n
 \uc624\ub978\ucabd\uc758 \uc0c9\uc0c1 \ub9c9\ub300\uc5d0\ub294 \ub2e4\uc591\ud55c \uc0c9\uc0c1\uc774 \ub098\ud0c0\ub0b4\ub294 \uac12\uc744 \ub098\ud0c0\ub0b4\ub294 \ubc94\ub840\uac00 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \ud788\ud2b8\ub9f5\uc5d0 \uc120 \ucd94\uac00<\/strong><\/span><\/h3>\n
 linewidths<\/strong> \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud788\ud2b8\ub9f5\uc758 \uc0ac\uac01\ud615 \uc0ac\uc774\uc5d0 \uc120\uc744 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 sns.heatmap(df, linewidths=.5)<\/strong><\/span> <\/pre>\n
\"\ud574\uc591<\/p>\n
 \ud788\ud2b8\ub9f5\uc5d0 \uc8fc\uc11d \ucd94\uac00<\/strong><\/span><\/h3>\n
 annot=True<\/strong> \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud788\ud2b8\ub9f5\uc5d0 \uc8fc\uc11d\uc744 \ucd94\uac00\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 sns.heatmap(df, linewidths=.5, annot=True)<\/strong><\/span> <\/pre>\n
\"Python\uc73c\ub85c<\/p>\n
 \ud788\ud2b8\ub9f5 \uc0c9\uc0c1 \ub9c9\ub300 \uc228\uae30\uae30<\/strong><\/span><\/h3>\n
 cbar=False<\/strong> \uc635\uc158\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c9\uc0c1 \ub9c9\ub300\ub97c \uc644\uc804\ud788 \uc228\uae38 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 sns.heatmap(df, linewidths=.5, annot=True, cbar=False)<\/strong><\/span> <\/pre>\n
\"Python\uc758<\/p>\n
 \ud788\ud2b8\ub9f5 \uc0c9\uc0c1 \ud14c\ub9c8 \ubcc0\uacbd<\/strong><\/span><\/h3>\n
 cmap<\/strong> \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0c9\uc0c1 \ud14c\ub9c8\ub97c \ubcc0\uacbd\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \ub178\ub780\uc0c9\uc5d0\uc11c \ub179\uc0c9, \ud30c\ub780\uc0c9\uae4c\uc9c0 \uc0c9\uc0c1 \ubc94\uc704\ub97c \uc124\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 sns.heatmap(df, cmap='YlGnBu')\n<\/strong><\/pre>\n
\"Python<\/p>\n
 \ub610\ub294 \ube68\uac04\uc0c9\uc5d0\uc11c \ud30c\ub780\uc0c9\uc73c\ub85c \uc0c9\uc0c1\uc744 \ubcc0\uacbd\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 sns.heatmap(df, cmap='RdBu')<\/strong> <\/pre>\n
\"Python\uc5d0\uc11c<\/p>\n
 \uc0c9\uc0c1 \ud314\ub808\ud2b8\uc758 \uc804\uccb4 \ubaa9\ub85d\uc740 matplotlib \uc124\uba85\uc11c\ub97c<\/a> \ucc38\uc870\ud558\uc138\uc694.<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
5\uc8fc \ub3d9\uc548 \ub9e4\uc77c \ud2b9\uc815 \ub9e4\uc7a5\uc758 \ud310\ub9e4 \uac74\uc218\ub97c \ud45c\uc2dc\ud558\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 Python \ub370\uc774\ud130\uc138\ud2b8\uac00 \uc788\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. import numpy as np import pandas as pd import seaborn as sns #create a dataset np.random.seed(0) data = {‘day’: np.tile([‘Mon’, ‘Tue’, ‘Wed’, ‘Thur’, ‘Fri’], 5), ‘week’: np.repeat([1, 2, 3, 4, 5], 5), ‘sales’: np.random.randint(0, 50, size=25) } df = pd.DataFrame(data,columns=[‘day’,’week’,’sales’]) […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-836","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc5d0\uc11c \uc5f4 \uc9c0\ub3c4\ub97c \uc27d\uac8c \ub9cc\ub4dc\ub294 \ubc29\ubc95 - \ud1b5\uacc4\ud559<\/title>\n<meta name=\"description\" content=\"\uba87 \uac00\uc9c0 \uc608\ub97c \ud3ec\ud568\ud558\uc5ec Python\uc5d0\uc11c \ud788\ud2b8\ub9f5\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc124\uba85\uc785\ub2c8\ub2e4.\" \/>\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\/ko\/\u1111\u1161\u110b\u1175\u110a\u1165\u11ab-\u1112\u1175\u1110\u1173\u1106\u1162\u11b8\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python\uc5d0\uc11c \uc5f4 \uc9c0\ub3c4\ub97c \uc27d\uac8c \ub9cc\ub4dc\ub294 \ubc29\ubc95 - \ud1b5\uacc4\ud559\" \/>\n<meta property=\"og:description\" content=\"\uba87 \uac00\uc9c0 \uc608\ub97c \ud3ec\ud568\ud558\uc5ec Python\uc5d0\uc11c \ud788\ud2b8\ub9f5\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc124\uba85\uc785\ub2c8\ub2e4.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/ko\/\u1111\u1161\u110b\u1175\u110a\u1165\u11ab-\u1112\u1175\u1110\u1173\u1106\u1162\u11b8\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T14:20:58+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/heatmap_python1.png\" \/>\n<meta name=\"author\" content=\"\ubca4\uc790\ubbfc \uc564\ub354\uc2a8\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"\ubca4\uc790\ubbfc \uc564\ub354\uc2a8\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%92%e1%85%b5%e1%84%90%e1%85%b3%e1%84%86%e1%85%a2%e1%86%b8\/\",\"url\":\"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%92%e1%85%b5%e1%84%90%e1%85%b3%e1%84%86%e1%85%a2%e1%86%b8\/\",\"name\":\"Python\uc5d0\uc11c \uc5f4 \uc9c0\ub3c4\ub97c \uc27d\uac8c \ub9cc\ub4dc\ub294 \ubc29\ubc95 - 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