{"id":2576,"date":"2023-07-21T15:54:54","date_gmt":"2023-07-21T15:54:54","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%8b%e1%85%ad%e1%84%89%e1%85%a9\/"},"modified":"2023-07-21T15:54:54","modified_gmt":"2023-07-21T15:54:54","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%8b%e1%85%ad%e1%84%89%e1%85%a9","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%8b%e1%85%ad%e1%84%89%e1%85%a9\/","title":{"rendered":"Pandas: factorize()\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc22b\uc790\ub85c \uc778\ucf54\ub529\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

pandas Factorize()<\/a> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc22b\uc790 \uac12\uc73c\ub85c \uc778\ucf54\ub529\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc758 \uc5f4\uc5d0 Factorize()<\/strong> \ud568\uc218\ub97c \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \uc5f4 \uc778\uc218\ubd84\ud574<\/strong><\/span><\/p>\n

 df[' col1<\/span> '] = pd. factorize<\/span> (df[' col<\/span> '])[0]\n<\/strong><\/pre>\n
 \ubc29\ubc95 2: \uc694\uc778\ubcc4 \uc5f4<\/strong><\/span><\/p>\n
 df[[' col1<\/span> ', ' col3<\/span> ']] = df[[' col1<\/span> ', ' col3<\/span> ']]. apply<\/span> ( lambda<\/span> x: pd.factorize<\/span> (x)[ 0<\/span> ])\n<\/strong><\/pre>\n
 \ubc29\ubc95 3: \ubaa8\ub4e0 \uc5f4 \uc778\uc218\ubd84\ud574<\/strong><\/span><\/p>\n
 df = df. apply<\/span> ( lambda<\/span> x: pd.factorize<\/span> (x)[ 0<\/span> ])<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c pandas DataFrame\uc5d0\uc11c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' conf<\/span> ': ['West', 'West', 'East', 'East'],\n                   ' team<\/span> ': ['A', 'B', 'C', 'D'],\n                   ' position<\/span> ': ['Guard', 'Forward', 'Guard', 'Center'] })\n\n#view DataFrame\n<\/span>df\n\n   conf team position\n0 West A Guard\n1 West B Forward\n2 East C Guard\n3 East D Center\n<\/strong><\/pre>\n
 \uc608 1: \uc5f4 \uc778\uc218\ubd84\ud574<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 DataFrame\uc5d0\uc11c \uc5f4\uc744 \uc778\uc218\ubd84\ud574\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #factorize the conf column only\n<\/span>df[' conf<\/span> '] = pd. factorize<\/span> (df[' conf<\/span> '])[ 0<\/span> ]\n\n#view updated DataFrame\n<\/span>df\n\n\tconf team position\n0 0 A Guard\n1 0 B Forward\n2 1 C Guard\n3 1 D Center<\/strong><\/pre>\n
 ‘conf’ \uc5f4\ub9cc \uc778\uc218\ubd84\ud574\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 “West”\uc600\ub358 \ubaa8\ub4e0 \uac12\uc740 \uc774\uc81c 0\uc774\uace0 “East”\uc600\ub358 \ubaa8\ub4e0 \uac12\uc740 \uc774\uc81c 1\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2: \uc694\uc778\ubcc4 \uc5f4<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 DataFrame\uc5d0\uc11c \ud2b9\uc815 \uc5f4\uc744 \uc778\uc218\ubd84\ud574\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #factorize conf and team columns only\n<\/span>df[[' conf<\/span> ', ' team<\/span> ']] = df[[' conf<\/span> ', ' team<\/span> ']]. apply<\/span> ( lambda<\/span> x: pd.factorize<\/span> (x)[ 0<\/span> ])\n\n#view updated DataFrame\n<\/span>df\n\n        conf team position\n0 0 0 Guard\n1 0 1 Forward\n2 1 2 Guard\n3 1 3 Center\n<\/strong><\/pre>\n
 “conf” \ubc0f “team” \uc5f4\uc774 \ubaa8\ub450 \uace0\ub824\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 3: \ubaa8\ub4e0 \uc5f4\uc744 \uc778\uc218\ubd84\ud574<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 DataFrame\uc758 \ubaa8\ub4e0 \uc5f4\uc744 \uc778\uc218\ubd84\ud574\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #factorize all columns\n<\/span>df = df. apply<\/span> ( lambda<\/span> x: pd.factorize<\/span> (x)[ 0<\/span> ])\n\n#view updated DataFrame\n<\/span>df\n\n     conf team position\n0 0 0 0\n1 0 1 1\n2 1 2 0\n3 1 3 2\n<\/strong><\/pre>\n
 \ubaa8\ub4e0 \uc5f4\uc774 \uc778\uc218\ubd84\ud574\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\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 DataFrame \uc5f4\uc744 \ubb38\uc790\uc5f4\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uc22b\uc790\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas DataFrame \uc5f4\uc744 \uc815\uc218\ub85c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
pandas Factorize() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc22b\uc790 \uac12\uc73c\ub85c \uc778\ucf54\ub529\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc758 \uc5f4\uc5d0 Factorize() \ud568\uc218\ub97c \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \uc5f4 \uc778\uc218\ubd84\ud574 df[‘ col1 ‘] = pd. factorize (df[‘ col ‘])[0] \ubc29\ubc95 2: \uc694\uc778\ubcc4 \uc5f4 df[[‘ col1 ‘, ‘ col3 ‘]] = df[[‘ col1 ‘, ‘ col3 ‘]]. apply ( lambda x: […]<\/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-2576","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: Factorize()\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc22b\uc790\ub85c \uc778\ucf54\ub529\ud558\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uac00\uc9c0 \uc608\ub97c \ud1b5\ud574 pandas Factorize() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc22b\uc790\ub85c \uc778\ucf54\ub529\ud558\ub294 \ubc29\ubc95\uc744 \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-\u110b\u116d\u1109\u1169\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: Factorize()\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubb38\uc790\uc5f4\uc744 \uc22b\uc790\ub85c \uc778\ucf54\ub529\ud558\ub294 \ubc29\ubc95 \u2013 Statorials\" \/>\n<meta property=\"og:description\" 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