{"id":2518,"date":"2023-07-21T22:11:18","date_gmt":"2023-07-21T22:11:18","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%e1%84%87%e1%85%a7%e1%86%af-%e1%84%8c%e1%85%a5%e1%86%a8%e1%84%8b%e1%85%ad%e1%86%bc\/"},"modified":"2023-07-21T22:11:18","modified_gmt":"2023-07-21T22:11:18","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%e1%84%87%e1%85%a7%e1%86%af-%e1%84%8c%e1%85%a5%e1%86%a8%e1%84%8b%e1%85%ad%e1%86%bc","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%e1%84%87%e1%85%a7%e1%86%af-%e1%84%8c%e1%85%a5%e1%86%a8%e1%84%8b%e1%85%ad%e1%86%bc\/","title":{"rendered":"Pandas groupby\uc5d0 \uae30\ub2a5\uc744 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc5d0\uc11c groupby()<\/strong> \ubc0f apply()<\/strong> \ud568\uc218\ub97c \ud568\uaed8 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 df. groupby<\/span> (' var1<\/span> '). apply<\/span> ( lambda<\/span> x: some function)\n<\/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\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' points_for<\/span> ': [18, 22, 19, 14, 11, 20, 28],\n                   ' points_against<\/span> ': [14, 21, 19, 14, 12, 20, 21]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points_for points_against\n0 to 18 14\n1 To 22 21\n2 A 19 19\n3 B 14 14\n4 B 11 12\n5 B 20 20\n6 B 28 21<\/b><\/pre>\n
 \uc608\uc81c 1: groupby() \ubc0f apply()\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0c1\ub300 \ube48\ub3c4 \ucc3e\uae30<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 groupby(<\/strong> ) \ubc0f apply()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc5d0\uc11c \uac01 \ud300 \uc774\ub984\uc758 \uc0c1\ub300 \ube48\ub3c4\ub97c \ucc3e\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #find relative frequency of each team name in DataFrame\ndf. groupby<\/span> (' team<\/span> '). apply<\/span> ( lambda<\/span> x:x[' team<\/span> ']. count<\/span> ()\/ df.shape<\/span> [0])\n\nteam\nA 0.428571\nB 0.571429\ndtype:float64\n<\/span><\/span><\/strong><\/pre>\n
 \uacb0\uacfc\ub97c \ubcf4\uba74 A\ud300\uc774 \uc804\uccb4 \ud589\uc758 42.85%\uc5d0 \ub098\ud0c0\ub098\uace0 B\ud300\uc774 \uc804\uccb4 \ud589\uc758 57.14%\uc5d0 \ub098\ud0c0\ub098\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc81c 2: groupby() \ubc0f apply()\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd5c\ub300\uac12 \ucc3e\uae30<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 groupby(<\/strong> ) \ubc0f apply()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud300\uc758 \ucd5c\ub300 \u201cpoints_for\u201d \uac12\uc744 \ucc3e\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #find max \"points_for\" values for each team\ndf. groupby<\/span> (' team<\/span> '). apply<\/span> ( lambda<\/span> x:x[' points_for<\/span> ']. max<\/span> ())\n\nteam\nAt 22\nB28\ndtype: int64<\/span><\/span><\/strong><\/pre>\n
 \uacb0\uacfc\ub97c \ubcf4\uba74 A\ud300\uc758 \ucd5c\ub300\ub4dd\uc810\uc740 22\uc810, B\ud300\uc758 \ucd5c\ub300\ub4dd\uc810\uc740 28\uc810\uc784\uc744 \uc54c \uc218 \uc788\ub2e4.<\/span><\/p>\n
 \uc608\uc81c 3: groupby() \ubc0f apply()\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc6a9\uc790 \uc815\uc758 \uacc4\uc0b0 \uc218\ud589<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 groupby(<\/strong> ) \ubc0f apply()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud300\uc758 “points_for”\uc640 “points_against” \uac04\uc758 \ud3c9\uade0 \ucc28\uc774\ub97c \ucc3e\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #find max \"points_for\" values for each team\ndf. groupby<\/span> (' team<\/span> '). apply<\/span> ( lambda<\/span> x: (x[' points_for<\/span> '] - x[' points_against<\/span> ']). mean<\/span> ())\n\nteam\nA 1.666667\nB 1.500000\ndtype:float64\n<\/span><\/span><\/strong><\/pre>\n
 \uacb0\uacfc\ub97c \ubcf4\uba74 ‘\ub4dd\uc810’\uacfc ‘\ubc18\ub300’\uc758 \ud3c9\uade0 \ucc28\uc774\uac00 A\ud300\uc5d0\uc11c\ub294 1.67<\/strong> , B\ud300\uc5d0\uc11c\ub294 1.50<\/strong> \uc784\uc744 \uc54c \uc218 \uc788\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\uc5d0\uc11c GroupBy \ud569\uacc4\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c Groupby \ubc0f Plot\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c GroupBy\ub97c \uc0ac\uc6a9\ud558\uc5ec \uace0\uc720\ud55c \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc5d0\uc11c groupby() \ubc0f apply() \ud568\uc218\ub97c \ud568\uaed8 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. df. groupby (‘ var1 ‘). apply ( lambda x: some function) \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’, ‘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-2518","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas Groupby\uc5d0 \uae30\ub2a5\uc744 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc608\uc81c\ub97c \ud3ec\ud568\ud558\uc5ec pandas\uc5d0\uc11c groupby() \ubc0f apply() \ud568\uc218\ub97c \ud568\uaed8 \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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