{"id":3696,"date":"2023-07-16T01:59:39","date_gmt":"2023-07-16T01:59:39","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8\/"},"modified":"2023-07-16T01:59:39","modified_gmt":"2023-07-16T01:59:39","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8","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%80%e1%85%b3%e1%84%85%e1%85%ae%e1%86%b8\/","title":{"rendered":"Pandas: "\uc790\uc0b0\ubcc4 \uadf8\ub8f9\ud654"\ub97c \uc704\ud55c \uac04\ub2e8\ud55c \uacf5\uc2dd"},"content":{"rendered":"

<\/p>\n

\n

\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec pandas\uc5d0\uc11c “GROUP BY HAVING” SQL \ubb38\uacfc \ub3d9\ub4f1\ud55c \uac83\uc744 \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 df. groupby<\/span> (' some_column<\/span> '). filter<\/span> ( lambda<\/span> x: some condition)\n<\/span><\/span><\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\ub294 \ub2e4\uc74c pandas DataFrame\uc5d0\uc11c \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C'],\n                   ' position<\/span> ': ['G', 'F', 'F', 'G', 'F', 'F', 'G', 'G'],\n                   ' points<\/span> ': [30, 22, 19, 14, 14, 11, 20, 28]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team position points\n0 AG 30\n1 AF 22\n2 FY 19\n3 BG 14\n4 BF 14\n5 BF 11\n6 GC 20\n7 GC 28\n<\/b><\/pre>\n
 \uc608 1: Count\ub97c \uac16\ub294 \ud32c\ub354 \uadf8\ub8f9<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ud300<\/strong> \uc5f4\uc758 \uac12\uc744 \uae30\uc900\uc73c\ub85c \ud589\uc744 \uadf8\ub8f9\ud654\ud55c \ub2e4\uc74c \uac1c\uc218\uac00 2\ubcf4\ub2e4 \ud070 \ud300\ub9cc \ud544\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 #group by team and filter for teams with count > 2<\/span>\ndf. groupby<\/span> (' team<\/span> '). filter<\/span> ( lambda<\/span> x: len<\/span> (x) > 2<\/span> )\n\n\n        team position points\n0 A G 30\n1 A F 22\n2 A F 19\n3 B G 14\n4 B F 14\n5 B F 11<\/span><\/span><\/strong><\/pre>\n
 \ud300 \uac12\uc774 “A” \ub610\ub294 “B”\uc778 \ud589\ub9cc \ubc18\ud658\ub429\ub2c8\ub2e4. \ub450 \ud300 \ubaa8\ub450 \uc22b\uc790\uac00 2\ubcf4\ub2e4 \ud070 \ud300\uc774\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2: \ud3c9\uade0\uc744 \uac00\uc9c4 \ud32c\ub354 \uadf8\ub8f9<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ud300<\/strong> \uc5f4\uc758 \uac12\uc744 \uae30\uc900\uc73c\ub85c \ud589\uc744 \uadf8\ub8f9\ud654\ud55c \ub2e4\uc74c \ud3c9\uade0 \uc810\uc218<\/strong> \uac12\uc774 20\ubcf4\ub2e4 \ud070 \ud300\ub9cc \ud544\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #group by team and filter for teams with mean points > 20<\/span>\ndf. groupby<\/span> (' team<\/span> '). filter<\/span> ( lambda<\/span> x: x[' points<\/span> ']. mean<\/span> () > 20<\/span> )\n\n        team position points\n0 A G 30\n1 A F 22\n2 A F 19\n6 C G 20\n7 C G 28<\/span><\/span><\/strong><\/pre>\n
 \ud3c9\uade0 \uc810\uc218 \uac12\uc774 20\ubcf4\ub2e4 \ud070 \ub450 \ud300\uc774\ubbc0\ub85c \ud300 \uac12\uc774 “A” \ub610\ub294 “C”\uc778 \ud589\ub9cc \ubc18\ud658\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 3: \ud569\uacc4\ub97c \uac16\ub294 \ud32c\ub354 \uadf8\ub8f9<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ud300<\/strong> \uc5f4\uc758 \uac12\uc744 \uae30\uc900\uc73c\ub85c \ud589\uc744 \uadf8\ub8f9\ud654\ud55c \ub2e4\uc74c \ud3ec\uc778\ud2b8<\/strong> \ud569\uacc4\uac00 \uc815\ud655\ud788 48\uc778 \ud300\ub9cc \ud544\ud130\ub9c1\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #group by team and filter for teams with sum of points equal to 48<\/span>\ndf. groupby<\/span> (' team<\/span> '). filter<\/span> ( lambda<\/span> x: x[' points<\/span> ']. sum<\/span> () == 48<\/span> )\n\n        team position points\n6 C G 20\n7 C G 28<\/span><\/span><\/strong><\/pre>\n
 \ud300 \uac12\uc774 “C”\uc778 \ud589\ub9cc \ubc18\ud658\ub429\ub2c8\ub2e4. \uc774\ub294 \ud3ec\uc778\ud2b8 \ud569\uacc4\uac00 48\uc778 \uc720\uc77c\ud55c \ud300\uc774\uae30 \ub54c\ubb38\uc785\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: \uc5f4\uc774 \uac12\uacfc \uc77c\uce58\ud558\ub294 \ud589\uc758 \uc778\ub371\uc2a4 \uac00\uc838\uc624\uae30<\/a>
 Pandas: \ud2b9\uc815 \ubb38\uc790\uc5f4\uc774 \ud3ec\ud568\ub41c \uc5f4\uc744 \uc120\ud0dd\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas:\uc5f4\uc5d0 \ubb38\uc790\uc5f4\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\ub294\uc9c0 \ud655\uc778\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec pandas\uc5d0\uc11c “GROUP BY HAVING” SQL \ubb38\uacfc \ub3d9\ub4f1\ud55c \uac83\uc744 \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df. groupby (‘ some_column ‘). filter ( lambda x: some condition) \ub2e4\uc74c \uc608\ub294 \ub2e4\uc74c pandas DataFrame\uc5d0\uc11c \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. import pandas as pd #createDataFrame df = pd. DataFrame ({‘ team ‘: [‘A’, ‘A’, ‘A’, ‘B’, ‘B’, ‘B’, […]<\/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-3696","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: "\uac00\uc9d0\uc5d0 \ub530\ub978 \uadf8\ub8f9\ud654"\ub97c \uc704\ud55c \uac04\ub2e8\ud55c \uacf5\uc2dd \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 pandas DataFrame\uc5d0\uc11c groupby\uc5d0 \ud574\ub2f9\ud558\ub294 SQL\uc5d0 \ub300\ud55c \uacf5\uc2dd\uc744 \uc0ac\uc6a9\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\" 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