{"id":4072,"date":"2023-07-13T18:54:14","date_gmt":"2023-07-13T18:54:14","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%80%e1%85%a1%e1%86%a8-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab-%e1%84%91%e1%85%a7%e1%86%ab\/"},"modified":"2023-07-13T18:54:14","modified_gmt":"2023-07-13T18:54:14","slug":"%e1%84%80%e1%85%a1%e1%86%a8-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab-%e1%84%91%e1%85%a7%e1%86%ab","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%80%e1%85%a1%e1%86%a8-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab-%e1%84%91%e1%85%a7%e1%86%ab\/","title":{"rendered":"Pandas: \uac01 \ud589\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame\uc758 \uac01 \ud589\uc5d0 \ub300\ud55c \uac12\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 df. std<\/span> (axis= 1<\/span> , numeric_only= True<\/span> )\n<\/strong><\/pre>\n
 axis=1<\/strong> \uc778\uc218\ub294 \ud32c\ub354\uc5d0\uac8c \uac01 \uc5f4 \ub300\uc2e0 \uac01 \ud589\uc5d0 \ub300\ud574 \uacc4\uc0b0\uc744 \uc218\ud589\ud558\ub3c4\ub85d \uc9c0\uc2dc\ud558\uace0, numeric_only=True<\/strong> \ub294 \uacc4\uc0b0\uc744 \uc218\ud589\ud560 \ub54c \uc22b\uc790 \uc5f4\ub9cc \uace0\ub824\ud558\ub3c4\ub85d \ud32c\ub354\uc5d0\uac8c \uc9c0\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\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\uc758 \uac01 \ud589\uc5d0 \ub300\ud55c \ud45c\uc900 \ud3b8\ucc28 \uacc4\uc0b0<\/strong><\/span><\/h2>\n
 \ub124 \uac00\uc9c0 \uac8c\uc784 \ub3d9\uc548 \ub2e4\uc591\ud55c \ub18d\uad6c \uc120\uc218\uac00 \ub4dd\uc810\ud55c \uc810\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> ({' player<\/span> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' game1<\/span> ': [18, 22, 19, 14, 14, 11, 20, 28],\n                   ' game2<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' game3<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' game4<\/span> ': [9, 8, 8, 9, 14, 15, 10, 11]})\n                   \n#view DataFrame\n<\/span>print<\/span> (df)\n\n  player game1 game2 game3 game4\n0 A 18 5 11 9\n1 B 22 7 8 8\n2 C 19 7 10 8\n3 D 14 9 6 9\n4 E 14 12 6 14\n5 F 11 9 5 15\n6 G 20 9 9 10\n7:28 4 12 11<\/strong><\/span><\/pre>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac01 \ud50c\ub808\uc774\uc5b4\uac00 \ub4dd\uc810\ud55c \uc810\uc218\uc758 \ud45c\uc900 \ud3b8\ucc28\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #calculate standard deviation for each row\n<\/span>df. std<\/span> (axis= 1<\/span> , numeric_only= True<\/span> )\n\n0 5.439056\n1 7.182154\n2 5.477226\n3 3.316625\n4 3.785939\n5 4.163332\n6 5.354126\n7 10.144785\ndtype:float64<\/span><\/span><\/strong><\/pre>\n
 \uacb0\uacfc\ub97c \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n