{"id":4152,"date":"2023-07-13T05:53:17","date_gmt":"2023-07-13T05:53:17","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%92%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%a9%e1%84%80%e1%85%b3%e1%84%85%e1%85%a2%e1%86%b7-%e1%84%87%e1%85%b5%e1%84%8b%e1%85%b2%e1%86%af\/"},"modified":"2023-07-13T05:53:17","modified_gmt":"2023-07-13T05:53:17","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%92%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%a9%e1%84%80%e1%85%b3%e1%84%85%e1%85%a2%e1%86%b7-%e1%84%87%e1%85%b5%e1%84%8b%e1%85%b2%e1%86%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%92%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%a9%e1%84%80%e1%85%b3%e1%84%85%e1%85%a2%e1%86%b7-%e1%84%87%e1%85%b5%e1%84%8b%e1%85%b2%e1%86%af\/","title":{"rendered":"Pandas \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 y\ucd95\uc5d0 \ubc31\ubd84\uc728\uc744 \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

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\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 y\ucd95\uc5d0 \ubc31\ubd84\uc728\uc744 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nfrom<\/span> matplotlib. ticker<\/span> import<\/span> PercentFormatter\n\n#create histogram, using percentages instead of counts<\/span>\nplt. hist<\/span> (df[' my_column<\/span> '], weights= np.ones<\/span> ( len<\/span> (df)) \/ len<\/span> (df))\n\n#apply percentage format to y-axis\n<\/span>plt. gca<\/span> (). yaxis<\/span> . set_major_formatter<\/span> (PercentFormatter(1))\nplt. show<\/span> ()\n<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: Pandas \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 Y\ucd95\uc5d0 \ubc31\ubd84\uc728 \ud45c\uc2dc<\/strong><\/span><\/h2>\n
 \ub2e4\uc591\ud55c \ub18d\uad6c \uc120\uc218\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (1)\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': np. random<\/span> . normal<\/span> (loc=20, scale=2, size=300),\n                   ' assists<\/span> ': np. random<\/span> . normal<\/span> (loc=14, scale=3, size=300),\n                   ' rebounds<\/span> ': np. random<\/span> . normal<\/span> (loc=12, scale=1, size=300)})\n\n#view head of DataFrame\n<\/span>print<\/span> ( df.head<\/span> ())\n\n      points assists rebounds\n0 23.248691 20.197350 10.927036\n1 18.776487 9.586529 12.495159\n2 18.943656 11.509484 11.047938\n3 17.854063 11.358267 11.481854\n4 21.730815 13.162707 10.538596\n<\/span><\/span><\/strong><\/pre>\n
 \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc758 \uac12 \ubd84\ud3ec\ub97c \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud574 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \uc0dd\uc131\ud558\uba74 \uae30\ubcf8\uc801\uc73c\ub85c y\ucd95\uc5d0 \uac1c\uc218\uac00 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> matplotlib. pyplot<\/span> as<\/span> plt \n\n#create histogram for points columb\n<\/span>plt. hist<\/span> (df[' points<\/span> '], edgecolor=' black<\/span> ')\n<\/span><\/span><\/strong><\/pre>\n
\"\"<\/p>\n
 \ub300\uc2e0 y\ucd95\uc5d0 \ubc31\ubd84\uc728\uc744 \ud45c\uc2dc\ud558\ub824\uba74 PercentFormatter<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nfrom<\/span> matplotlib. ticker<\/span> import<\/span> PercentFormatter\n\n#create histogram, using percentages instead of counts<\/span>\nplt. hist<\/span> (df[' points<\/span> '], weights=np. ones<\/span> ( len<\/span> (df)) \/ len<\/span> (df), edgecolor=' black<\/span> ')\n\n#apply percentage format to y-axis\n<\/span>plt. gca<\/span> (). yaxis<\/span> . set_major_formatter<\/span> (PercentFormatter(1))\nplt. show<\/span> ()<\/strong> <\/pre>\n
\"y<\/p>\n
 \uc774\uc81c Y\ucd95\uc5d0 \ubc31\ubd84\uc728\uc774 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc31\ubd84\uc728\uc5d0\uc11c \uc18c\uc218 \uc790\ub9bf\uc218\ub97c \uc81c\uac70\ud558\ub824\uba74 PercentFormatter()<\/strong> \ud568\uc218\uc5d0\uc11c \uc18c\uc218 = 0<\/strong> \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 \ub429\ub2c8\ub2e4.<\/span> <\/p>\n
 import<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nfrom<\/span> matplotlib. ticker<\/span> import<\/span> PercentFormatter\n\n#create histogram, using percentages instead of counts<\/span>\nplt. hist<\/span> (df[' points<\/span> '], weights=np. ones<\/span> ( len<\/span> (df)) \/ len<\/span> (df), edgecolor=' black<\/span> ')\n\n#apply percentage format to y-axis\n<\/span>plt. gca<\/span> (). yaxis<\/span> . set_major_formatter<\/span> (PercentFormatter(1, decimals= 0<\/span> ))\nplt. show<\/span> ()<\/strong> <\/pre>\n
\"Pandas<\/p>\n
 \uc774\uc81c Y\ucd95\uc5d0 \uc18c\uc218 \uc790\ub9bf\uc218 \uc5c6\uc774 \ubc31\ubd84\uc728\uc774 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Pandas\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Pandas \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc5d0 \uc0ac\uc6a9\ub418\ub294 \uc800\uc7a5\uc18c \uc218\ub97c \ubcc0\uacbd\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc5d0\uc11c X\ucd95 \ubc94\uc704\ub97c \ubcc0\uacbd\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \uadf8\ub8f9\ubcc4\ub85c \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 y\ucd95\uc5d0 \ubc31\ubd84\uc728\uc744 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. import pandas as pd import numpy as np import matplotlib. pyplot as plt from matplotlib. ticker import PercentFormatter #create histogram, using percentages instead of counts plt. hist (df[‘ my_column ‘], weights= np.ones ( len (df)) \/ len (df)) #apply percentage format to y-axis plt. […]<\/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-4152","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\n\ud32c\ub354 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 Y\ucd95\uc5d0 \ubc31\ubd84\uc728\uc744 \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ud32c\ub354 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 y\ucd95\uc5d0 \ubc31\ubd84\uc728\uc744 \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \uc608\uc640 \ud568\uaed8 \uc124\uba85\ud569\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\u1162\u11ab\u1103\u1165-\u1112\u1175\u1109\u1173\u1110\u1169\u1100\u1173\u1105\u1162\u11b7-\u1107\u1175\u110b\u1172\u11af\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ud32c\ub354 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc758 Y\ucd95\uc5d0 \ubc31\ubd84\uc728\uc744 \ud45c\uc2dc\ud558\ub294 \ubc29\ubc95 - 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