{"id":3574,"date":"2023-07-16T18:08:36","date_gmt":"2023-07-16T18:08:36","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%89%e1%85%a5%e1%86%af%e1%84%86%e1%85%a7%e1%86%bc\/"},"modified":"2023-07-16T18:08:36","modified_gmt":"2023-07-16T18:08:36","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%89%e1%85%a5%e1%86%af%e1%84%86%e1%85%a7%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%89%e1%85%a5%e1%86%af%e1%84%86%e1%85%a7%e1%86%bc\/","title":{"rendered":"Pandas: \uadf8\ub8f9\ubcc4\ub85c explain()\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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explain()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \ubcc0\uc218\uc5d0 \ub300\ud55c \uc124\uba85 \ud1b5\uacc4\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas\uc758 groupby<\/strong> () \ud568\uc218\uc640 \ud568\uaed8 explain<\/strong> () \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 df. groupby<\/span> (' group_var<\/span> ')[' values_var<\/span> ']. describe<\/span> ()\n<\/strong><\/span><\/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\uc5d0\uc11c \uadf8\ub8f9\ubcc4\ub85c explain() \uc0ac\uc6a9<\/strong><\/span><\/h2>\n
 \uc11c\ub85c \ub2e4\ub978 \ub450 \ud300\uc758 \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\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' points<\/span> ': [8, 12, 14, 14, 15, 22, 27, 24],\n                   ' assists<\/span> ':[2, 2, 3, 5, 7, 6, 8, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists\n0 to 8 2\n1 to 12 2\n2 to 14 3\n3 to 14 5\n4 B 15 7\n5 B 22 6\n6 B 27 8\n7 B 24 12<\/strong><\/span><\/pre>\n
 \uc124\uba85()<\/strong> \ud568\uc218\ub97c groupby()<\/strong> \ud568\uc218\uc640 \ud568\uaed8 \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud300<\/strong> \uc758 \ud3ec\uc778\ud2b8<\/strong> \uc5f4 \uac12\uc744 \uc694\uc57d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #summarize points by team\ndf. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. describe<\/span> ()\n<\/span>\n<\/span>count mean std min 25% 50% 75% max\nteam\t\t\t\t\t\t\t\t\nA 4.0 12.0 2.828427 8.0 11.00 13.0 14.00 14.0\nB 4.0 22.0 5.099020 15.0 20.25 23.0 24.75 27.0<\/strong><\/pre>\n
 \uacb0\uacfc\uc5d0\uc11c \uac01 \ud300\uc758 \ud3ec\uc778\ud2b8<\/strong> \ubcc0\uc218\uc5d0 \ub300\ud574 \ub2e4\uc74c \uac12\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n