{"id":573514,"date":"2025-06-06T13:55:00","date_gmt":"2025-06-06T12:55:00","guid":{"rendered":"https:\/\/blog.jetbrains.com\/?post_type=pycharm&p=573514"},"modified":"2025-10-16T11:19:08","modified_gmt":"2025-10-16T10:19:08","slug":"data-cleaning-in-data-science","status":"publish","type":"pycharm","link":"https:\/\/blog.jetbrains.com\/ko\/pycharm\/2025\/06\/data-cleaning-in-data-science\/","title":{"rendered":"\ub370\uc774\ud130 \uacfc\ud559\uc5d0\uc11c\uc758 \ub370\uc774\ud130 \uc815\ub9ac"},"content":{"rendered":"

\uc9c0\uae08\uae4c\uc9c0 \uc774 \ub370\uc774\ud130 \uacfc\ud559 \ube14\ub85c\uadf8 \uc2dc\ub9ac\uc988\uc5d0\uc11c\ub294 \ub370\uc774\ud130\ub97c \uc5b4\ub514\uc5d0\uc11c \uc5bb\uace0<\/a>, pandas\ub85c \ub370\uc774\ud130\ub97c \uc5b4\ub5bb\uac8c \ud0d0\uc0c9\ud558\ub294\uc9c0<\/a> \ub17c\uc758\ud588\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc, \uadf8\ub7ec\ud55c \ub370\uc774\ud130\ub294 \ud559\uc2b5\ud558\uae30\uc5d0\ub294 \uc88b\uc9c0\ub9cc \uc2e4\uc81c \ub370\uc774\ud130\uc640\ub294 \ucc28\uc774\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ud559\uc2b5\uc6a9 \ub370\uc774\ud130\ub294 \ube60\ub974\uac8c \ud559\uc2b5\ud560 \uc218 \uc788\ub3c4\ub85d \uc774\ubbf8 \uc815\ub9ac\ub418\uace0 \uc120\ubcc4\ub41c \uacbd\uc6b0\uac00 \ub9ce\uc544 \ub370\uc774\ud130 \uc815\ub9ac\uac00 \ud544\uc694 \uc5c6\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc9c0\ub9cc, \uc2e4\uc81c \ub370\uc774\ud130\uc5d0\ub294 \uc5ec\ub7ec \ubb38\uc81c\uac00 \uc788\uc73c\uba70 \uc815\ub9ac\uac00 \uc798 \ub418\uc5b4 \uc788\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c \ub370\uc774\ud130\uc5d0\uc11c\ub294 \uc720\uc6a9\ud55c \uc778\uc0ac\uc774\ud2b8\ub97c \uc5bb\uae30 \uc804\uc5d0 \uc815\ub9ac\uac00 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774 \ube14\ub85c\uadf8 \uae00\uc5d0\uc11c\ub294 \ubc14\ub85c \uadf8 \uc8fc\uc81c\ub97c \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n

\ub370\uc774\ud130 \ubb38\uc81c\ub294 \ub370\uc774\ud130 \uc790\uccb4\uc758 \ud2b9\uc131, \ub370\uc774\ud130 \uc218\uc9d1 \ubc29\uc2dd, \uc2ec\uc9c0\uc5b4 \ub370\uc774\ud130 \uc785\ub825 \ubc29\uc2dd\uc5d0\uc11c \ube44\ub86f\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc2e4\uc218\uc640 \uac04\uacfc\ub294 \uc5ec\uc815\uc758 \ubaa8\ub4e0 \ub2e8\uacc4\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n

\uc5ec\uae30\uc11c\ub294 \ud2b9\ud788 \ub370\uc774\ud130 \ubcc0\ud658\uc774 \uc544\ub2cc \ub370\uc774\ud130 \uc815\ub9ac\uc5d0 \ub300\ud574 \uc774\uc57c\uae30\ud574 \ubcf4\uace0\uc790 \ud569\ub2c8\ub2e4. \ub370\uc774\ud130 \uc815\ub9ac\ub294 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ub3c4\ucd9c\ud55c \uacb0\ub860\uc774 \uc815\uc758\ub41c \ubaa8\uc9d1\ub2e8\uc73c\ub85c \uc77c\ubc18\ud654\ub420 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ubc18\uba74 \ub370\uc774\ud130 \ubcc0\ud658\uc740 \ub370\uc774\ud130 \ud615\uc2dd \ubcc0\ud658, \ub370\uc774\ud130 \uc815\uaddc\ud654 \ubc0f \ub370\uc774\ud130 \uc9d1\uacc4\uc640 \uac19\uc740 \uc791\uc5c5\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.\u00a0<\/p>\n

\ub370\uc774\ud130 \uc815\ub9ac\uac00 \uc911\uc694\ud55c \uc774\uc720<\/h2>\n

\ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \uac00\uc7a5 \uba3c\uc800 \uc774\ud574\ud574\uc57c \ud560 \uac83\uc740 \uadf8\uac83\uc774 \ub098\ud0c0\ub0b4\ub294 \ubc14\uac00 \ubb34\uc5c7\uc778\uc9c0\uc785\ub2c8\ub2e4. \ub300\ubd80\ubd84\uc758 \ub370\uc774\ud130\uc138\ud2b8\ub294 \ub354 \ub113\uc740 \ubaa8\uc9d1\ub2e8\uc744 \ub300\ud45c\ud558\ub294 \ud45c\ubcf8\uc73c\ub85c, \uc774 \ud45c\ubcf8\uc73c\ub85c \uc791\uc5c5\ud558\uba74\uc11c \ubaa8\uc9d1\ub2e8\uc5d0 \ub300\ud55c \uacb0\uacfc\ub97c \ucd94\ub860(\ub610\ub294 \uc77c\ubc18\ud654<\/em>)\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc774\uc804 \ub450 \uac1c\uc758 \ube14\ub85c\uadf8 \uac8c\uc2dc\ubb3c\uc5d0\uc11c \uc0ac\uc6a9\ud55c \ub370\uc774\ud130\uc138\ud2b8<\/a>\ub294 \uc8fc\ud0dd \ub9e4\ub9e4 \uc804\ubc18\uc5d0 \uad00\ud55c \uac83\uc774\uc9c0\ub9cc, \uc9c0\ub9ac\uc801\uc73c\ub85c \ub9e4\uc6b0 \uc81c\ud55c\ub41c \uc9c0\uc5ed\uacfc \uc9e7\uc740 \uae30\uac04\ub9cc\uc744 \ub2e4\ub8e8\uba70, \ud574\ub2f9 \uc9c0\uc5ed\uacfc \uae30\uac04 \ub0b4\uc758 \ubaa8\ub4e0 \uc8fc\ud0dd(\ub354 \ud070 \ubaa8\uc9d1\ub2e8\uc5d0 \ub300\ud55c \ud45c\ubcf8)\uc774 \ud3ec\ud568\ub418\uc9c0 \uc54a\uc558\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n

\ub370\uc774\ud130\ub294 \ud2b9\uc815 \uae30\uac04 \ub3d9\uc548 \ud574\ub2f9 \uc9c0\uc5ed\uc758 \ubaa8\ub4e0 \uc8fc\ud0dd \ub9e4\ub9e4\uc640 \uac19\uc774 \ub354 \ub113\uc740 \ubaa8\uc9d1\ub2e8\uc744 \ub300\ud45c\ud558\ub294 \ud45c\ubcf8\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uac00 \ub354 \ub113\uc740 \ubaa8\uc9d1\ub2e8\uc744 \ub300\ud45c\ud558\ub294 \ud45c\ubcf8\uc774 \ub418\ub3c4\ub85d \ud558\ub824\uba74 \uba3c\uc800 \ubaa8\uc9d1\ub2e8\uc758 \uacbd\uacc4\ub97c \uc815\uc758\ud574\uc57c \ud569\ub2c8\ub2e4.\u00a0<\/p>\n

\uc778\uad6c\uc870\uc0ac \ub370\uc774\ud130\ub97c \uc81c\uc678\ud558\uba74, \uc804\uccb4 \uc778\uad6c\ub85c \uc791\uc5c5\ud558\ub294 \uac83\uc774 \ube44\ud604\uc2e4\uc801\uc778 \uacbd\uc6b0\uac00 \ub9ce\uc73c\ubbc0\ub85c \uacbd\uacc4\ub97c \uc5b4\ub514\ub85c \uc124\uc815\ud560\uc9c0 \uc815\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uacbd\uacc4\ub294 \uc9c0\ub9ac\uc801, \uc778\uad6c\ud1b5\uacc4\ud559\uc801, \uc2dc\uac04 \uae30\ubc18, \ud589\ub3d9 \uae30\ubc18(\uc608: \uac70\ub798 \uad00\ub828) \ub610\ub294 \uc0b0\uc5c5\ubcc4 \uae30\uc900\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\uc9d1\ub2e8\uc744 \uc815\uc758\ud558\ub294 \ubc29\ubc95\uc740 \uc5ec\ub7ec \uac00\uc9c0\uac00 \uc788\uc9c0\ub9cc, \ub370\uc774\ud130\ub97c \uc548\uc815\uc801\uc73c\ub85c \uc77c\ubc18\ud654\ud558\ub824\uba74 \ub370\uc774\ud130\ub97c \uc815\ub9ac\ud558\uae30 \uc804\uc5d0 \ubc18\ub4dc\uc2dc \uc774 \uc791\uc5c5\uc744 \uc218\ud589\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n

\uc694\uc57d\ud558\uba74, \ubd84\uc11d\uc774\ub098 \uba38\uc2e0\ub7ec\ub2dd<\/a>\uc5d0 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud560 \uacc4\ud68d\uc774\ub77c\uba74 \ub370\uc774\ud130\ub97c \uc815\ub9ac\ud558\ub294 \ub370 \uc2dc\uac04\uc744 \ud22c\uc790\ud574\uc57c \uc778\uc0ac\uc774\ud2b8\ub97c \uc2e0\ub8b0\ud558\uace0 \uc774\ub97c \ud604\uc2e4<\/em>\uc5d0 \ub9de\uac8c \uc77c\ubc18\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \uc815\ub9ac\ud558\uba74 \ub354 \uc815\ud655\ud55c \ubd84\uc11d\uc774 \uac00\ub2a5\ud558\uba70, \uba38\uc2e0\ub7ec\ub2dd\uc758 \uacbd\uc6b0 \uc131\ub2a5\ub3c4 \ud5a5\uc0c1\ub429\ub2c8\ub2e4.<\/p>\n

\ub370\uc774\ud130\ub97c \uc815\ub9ac\ud558\uc9c0 \uc54a\uc73c\uba74 \uc54c\uac8c \ub41c \ubc14\ub97c \ub354 \ub113\uc740 \ubaa8\uc9d1\ub2e8\uc5d0 \uc548\uc815\uc801\uc73c\ub85c \uc77c\ubc18\ud654\ud560 \uc218 \uc5c6\uace0, \uc694\uc57d \ud1b5\uacc4 \ubc0f \uc2dc\uac01\ud654\uac00 \ubd80\uc815\ud655\ud574\uc9c0\ub294 \ubb38\uc81c \ub4f1\uc774 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \ud2b8\ub808\uc774\ub2dd\ud558\ub294 \uacbd\uc6b0, \uc624\ub958\uc640 \ubd80\uc815\ud655\ud55c \uc608\uce21\uc774 \ubc1c\uc0dd\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\n PyCharm Professional \ubb34\ub8cc\ub85c \uc0ac\uc6a9\ud574 \ubcf4\uae30
\n <\/a>\n<\/p>\n

\ub370\uc774\ud130 \uc815\ub9ac\uc758 \uc608<\/h2>\n

\ub370\uc774\ud130 \uc815\ub9ac\uc5d0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 5\uac00\uc9c0 \uc791\uc5c5\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc644\ubcbd\ud55c \ubaa9\ub85d\uc740 \uc544\ub2c8\uc9c0\ub9cc \uc2e4\uc81c \ub370\uc774\ud130\ub97c \ub2e4\ub8f0 \ub54c \uc2dc\uc791\uc810\uc73c\ub85c \uc0bc\uae30\uc5d0 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n

\ub370\uc774\ud130 \uc911\ubcf5 \uc81c\uac70<\/h3>\n

\uc911\ubcf5\uc740 \ub370\uc774\ud130\ub97c \uc65c\uace1\ud560 \uc218 \uc788\uae30 \ub54c\ubb38\uc5d0 \ubb38\uc81c\uac00 \ub429\ub2c8\ub2e4. \ud310\ub9e4 \uac00\uaca9\uc758 \ube48\ub3c4\ub97c \uc0ac\uc6a9\ud558\ub294 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \uadf8\ub9b0\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub3d9\uc77c\ud55c \uac12\uc774 \uc911\ubcf5\ub418\ub294 \uacbd\uc6b0 \uc911\ubcf5\ub41c \uac00\uaca9\uc744 \uae30\uc900\uc73c\ub85c \ubd80\uc815\ud655\ud55c \ud328\ud134\uc744 \uac00\uc9c4 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \ub9cc\ub4e4\uac8c \ub429\ub2c8\ub2e4.\u00a0<\/p>\n

\ubd80\uc5f0 \uc124\uba85\ud558\uc790\uba74, \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \uc911\ubcf5\uc774 \ubb38\uc81c\ub41c\ub2e4\uace0 \ud560 \ub54c, \uc774\ub294 \ub2e8\uc77c \uad00\uce21\uac12\uc73c\ub85c \uad6c\uc131\ub41c \uac01 \ud589\uc774 \ubaa8\ub450 \uc911\ubcf5\ub418\uc5c8\uc74c\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc5f4\uc5d0\ub3c4 \uc911\ubcf5\ub41c \uac12\uc774 \uc788\uc744 \uc218 \uc788\uc9c0\ub9cc, \uc774\ub294 \uc608\uc0c1\ud560 \uc218 \uc788\ub294 \ud604\uc0c1\uc785\ub2c8\ub2e4. \uc9c0\uae08 \ub17c\uc758\ud558\uace0 \uc788\ub294 \uac83\uc740 \uc911\ubcf5\ub41c \uad00\uce21\uac12\uc785\ub2c8\ub2e4.\u00a0<\/p>\n

\ub2e4\ud589\ud788\ub3c4, \ub370\uc774\ud130\uc5d0\uc11c \uc911\ubcf5\ub41c \ud56d\ubaa9\uc774 \uc788\ub294\uc9c0 \ud0d0\uc9c0\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 pandas \uba54\uc11c\ub4dc<\/a>\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \ud504\ub86c\ud504\ud2b8\ub85c \uc54c\ub9bc\uc774 \ud544\uc694\ud55c \uacbd\uc6b0 JetBrains AI<\/a> \ucc44\ud305\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n

\uc911\ubcf5 \ud589\uc744 \uc2dd\ubcc4\ud558\ub294 \ucf54\ub4dc<\/em><\/p>\n

\ub2e4\uc74c\uc740 \uacb0\uacfc \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n

duplicate_rows = df[df.duplicated()]\nduplicate_rows<\/pre>\n
\uc774 \ucf54\ub4dc\uc5d0\uc11c\ub294 DataFrame\uc758 \uc774\ub984\uc774 df<\/code>\ub77c\uace0 \uac00\uc815\ud558\ubbc0\ub85c, DataFrame\uc758 \uc774\ub984\uc774 \ub2e4\ub974\ub2e4\uba74 \ud574\ub2f9 \uc774\ub984\uc73c\ub85c \ubcc0\uacbd\ud574\uc57c \ud569\ub2c8\ub2e4.\u00a0<\/p>\n
\uc5ec\uae30\uc11c \uc0ac\uc6a9\ub418\ub294 Ames Housing \ub370\uc774\ud130\uc138\ud2b8<\/a>\uc5d0\ub294 \uc911\ubcf5\ub41c \ub370\uc774\ud130\uac00 \uc5c6\uc9c0\ub9cc, \uc2dc\ub3c4\ud574 \ubcf4\uace0 \uc2f6\ub2e4\uba74 CITES Wildlife Trade \ub370\uc774\ud130\ubca0\uc774\uc2a4<\/a>\uc758 \ub370\uc774\ud130\uc138\ud2b8\ub97c \uc0b4\ud3b4\ubcf4\uace0 \uc704\uc758 pandas \uba54\uc18c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc911\ubcf5 \ud56d\ubaa9\uc744 \ucc3e\uc744 \uc218 \uc788\ub294\uc9c0 \ud655\uc778\ud574 \ubcf4\uc138\uc694.<\/p>\n
\ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \uc911\ubcf5\ub41c \ud56d\ubaa9\uc744 \ucc3e\uc558\ub2e4\uba74 \uacb0\uacfc\uac00 \uc65c\uace1\ub418\uc9c0 \uc54a\ub3c4\ub85d \uc774\ub97c \uc81c\uac70\ud574\uc57c \ud569\ub2c8\ub2e4. JetBrains AI\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc740 \ud504\ub86c\ud504\ud2b8\ub97c \uc785\ub825\ud558\uc5ec \ud574\ub2f9 \ucf54\ub4dc\ub97c \ub2e4\uc2dc \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub0b4 DataFrame\uc5d0\uc11c \uc911\ubcf5\uc744 \uc0ad\uc81c\ud558\ub294 \ucf54\ub4dc\u00a0<\/em><\/p>\n
\uacb0\uacfc \ucf54\ub4dc\uc5d0\uc11c\ub294 \uc911\ubcf5\uc744 \uc0ad\uc81c\ud558\uace0 DataFrame\uc758 \uc0c9\uc778\uc744 \ucd08\uae30\ud654\ud55c \ub2e4\uc74c df_cleaned\ub77c\ub294 \uc0c8 DataFrame\uc73c\ub85c \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/p>\n
df_cleaned = df.drop_duplicates()\ndf_cleaned.reset_index(drop=True, inplace=True)\ndf_cleaned<\/pre>\n
\ub354 \uc815\ud655\ud55c \uace0\uae09 \uc911\ubcf5 \uad00\ub9ac<\/a>\ub97c \uc704\ud574 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub2e4\ub978 pandas \ud568\uc218\ub3c4 \uc788\uc9c0\ub9cc, \uc774 \uc815\ub3c4\uba74 \ub370\uc774\ud130\uc138\ud2b8 \uc911\ubcf5 \uc81c\uac70\ub97c \uc2dc\uc791\ud558\ub294 \ub370 \ucda9\ubd84\ud569\ub2c8\ub2e4.<\/p>\n
\ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12 \ucc98\ub9ac\ud558\uae30<\/h3>\n
\ub370\uc774\ud130\uac00 \uc798\ubabb \uc785\ub825\ub418\uc5c8\uac70\ub098 \ub370\uc774\ud130 \uc218\uc9d1 \uacfc\uc815\uc5d0\uc11c \ubb38\uc81c\uac00 \ubc1c\uc0dd\ud55c \uacbd\uc6b0 \ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc774 \ub098\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4. Ames Housing \ub370\uc774\ud130\uc138\ud2b8<\/a>\uc758 \uacbd\uc6b0, \ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc740 \uc74c\uc218\uc778 SalePrice \uac12\uc774\ub098 \uc22b\uc790\uc778 Roof Style \uac12\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc744 \ucc3e\uc544\ub0b4\ub824\uba74 \ub2e4\uac01\uc801\uc778 \uc811\uadfc \ubc29\ubc95\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\ub294 \uc694\uc57d \ud1b5\uacc4<\/a>\ub97c \ud655\uc778\ud558\uac70\ub098, \uac01 \uc5f4\uc5d0 \ub300\ud574 \uc218\uc9d1\uae30\uac00 \uc815\uc758\ud55c \uc720\ud6a8\uc131 \uac80\uc0ac \uaddc\uce59\uc744 \ud655\uc778\ud558\uace0 \uc774\ub97c \ubc97\uc5b4\ub098\ub294 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\ub97c \uc2dd\ubcc4\ud558\uac70\ub098, \uc2dc\uac01\ud654\ub97c \ud65c\uc6a9\ud558\uc5ec \ud328\ud134\uc744 \ubd84\uc11d\ud558\uace0 \uc774\uc0c1\uce58\ub85c \ubcf4\uc774\ub294 \ub370\uc774\ud130\ub97c \ud0d0\uc9c0\ud558\ub294 \uac83\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.\u00a0<\/p>\n
\ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc740 \ubd84\uc11d\uc5d0 \ub178\uc774\uc988\ub97c \ucd94\uac00\ud558\uace0 \ubb38\uc81c\ub97c \uc77c\uc73c\ud0ac \uc218 \uc788\uc73c\ubbc0\ub85c \ubc18\ub4dc\uc2dc \ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774\ub7ec\ud55c \uac12\uc744 \uc5b4\ub5bb\uac8c \ucc98\ub9ac\ud560\uc9c0\ub294 \ub2e4\uc18c \uc8fc\uad00\uc801\uc778 \ud310\ub2e8\uc5d0 \ub530\ub77c \ub2ec\ub77c\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc138\ud2b8\uc758 \ud06c\uae30\uc5d0 \ube44\ud574 \ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc774 \ub9ce\uc9c0 \uc54a\ub2e4\uba74, \ud574\ub2f9 \uac12\uc744 \ud3ec\ud568\ud558\ub294 \uae30\ub85d\uc744 \uc81c\uac70\ud558\ub294 \uac83\uc774 \uc88b\uc740 \ubc29\ubc95\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub370\uc774\ud130\uc138\ud2b8\uc758 214\ubc88\uc9f8 \ud589\uc5d0\uc11c \ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc744 \ubc1c\uacac\ud588\ub2e4\uba74, pandas\uc758 drop \ud568\uc218<\/a>\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \ud574\ub2f9 \ud589\uc744 \uc81c\uac70\ud558\uba74 \ub429\ub2c8\ub2e4.\u00a0<\/p>\n
\ub2e4\uc2dc \ud55c \ubc88, \ub2e4\uc74c\uacfc \uac19\uc740 \ud504\ub86c\ud504\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec JetBrains AI\uac00 \ud544\uc694\ud55c \ucf54\ub4dc\ub97c \uc0dd\uc131\ud558\ub3c4\ub85d \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n
#df_cleaned<\/em>\uc5d0\uc11c \uc0c9\uc778 214\ub97c \uc0ad\uc81c\ud558\ub294 \ucf54\ub4dc<\/p>\n
PyCharm\uc758 Jupyter Notebook<\/a>\uc5d0\uc11c\ub294 \ub2e8\uc5b4 \uc55e\uc5d0 # \uae30\ud638\ub97c \ubd99\uc774\uba74 JetBrains AI Assistant\uc5d0 \ucd94\uac00 \ucee8\ud14d\uc2a4\ud2b8\ub97c \uc81c\uacf5\ud55c\ub2e4\ub294 \uc758\ubbf8\uac00 \ub418\uba70, \uc774 \uacbd\uc6b0 DataFrame\uc758 \uc774\ub984\uc740 df_cleaned<\/code>\uc785\ub2c8\ub2e4.<\/p>\n
\uacb0\uacfc \ucf54\ub4dc\ub294 DataFrame\uc5d0\uc11c \ub2e4\uc74c\uacfc \uac19\uc774 \ud574\ub2f9 \uad00\uce21\uac12\uc744 \uc81c\uac70\ud558\uace0 \uc0c9\uc778\uc744 \ucd08\uae30\ud654\ud558\uc5ec \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/p>\n
df_cleaned = df_cleaned.drop(index=214)\ndf_cleaned.reset_index(drop=True, inplace=True)\ndf_cleaned<\/pre>\n
\ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc744 \ucc98\ub9ac\ud558\uae30 \uc704\ud574 \ub9ce\uc774 \uc4f0\ub294 \uc804\ub7b5\uc740 \uc804\uce58\uc785\ub2c8\ub2e4. \uc989, \uc815\uc758\ub41c \uc804\ub7b5\uc5d0 \ub530\ub77c \ud574\ub2f9 \uac12\uc744 \ub2e4\ub978 \uc801\uc808\ud55c \uac12\uc73c\ub85c \ub300\uccb4\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \uc804\ub7b5 \uc911 \ud558\ub098\ub294 \ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12 \ub300\uc2e0 \uc911\uc559\uac12\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc911\uc559\uac12\uc740 \uc774\uc0c1\uce58\uc758 \uc601\ud5a5\uc744 \ubc1b\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 \ub370\uc774\ud130 \uacfc\ud559\uc790\uac00 \uc774\ub7f0 \ubaa9\uc801\uc5d0\uc11c \uc120\ud0dd\ud558\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc9c0\ub9cc, \ub9c8\ucc2c\uac00\uc9c0\ub85c \ub370\uc774\ud130\uc758 \ud3c9\uade0 \ub610\ub294 \ucd5c\ube48\uac12\uc774 \uc0c1\ud669\uc5d0 \ub530\ub77c \ub354 \uc801\uc808\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n
\ub610\ub294 \ub370\uc774\ud130\uc138\ud2b8 \ubc0f \ub370\uc774\ud130 \uc218\uc9d1 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub3c4\uba54\uc778 \uc9c0\uc2dd\uc774 \uc788\ub294 \uacbd\uc6b0, \ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc744 \ub354 \uc758\ubbf8 \uc788\ub294 \uac12\uc73c\ub85c \ub300\uccb4\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc218\uc9d1 \ud504\ub85c\uc138\uc2a4\uc5d0 \uad00\uc5ec\ud588\uac70\ub098 \uc54c\uace0 \uc788\ub294 \uacbd\uc6b0 \uc774 \uc635\uc158\uc774 \uc801\ud569\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n
\ud0c0\ub2f9\ud574 \ubcf4\uc774\uc9c0 \uc54a\ub294 \uac12\uc744 \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc740 \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \ud574\ub2f9 \uac12\uc758 \ube48\ub3c4, \ub370\uc774\ud130 \uc218\uc9d1 \ubc29\ubc95, \ubaa8\uc9d1\ub2e8\uc744 \uc815\uc758\ud558\ub294 \ubc29\ubc95 \ubc0f \ub3c4\uba54\uc778 \uc9c0\uc2dd\uacfc \uac19\uc740 \uae30\ud0c0 \uc694\uc778\uc5d0 \ub530\ub77c \ub2ec\ub77c\uc9d1\ub2c8\ub2e4.\u00a0<\/p>\n
\ub370\uc774\ud130 \uc11c\uc2dd \uc9c0\uc815<\/h3>\n
\ub370\uc774\ud130\uc758 \ud615\ud0dc\ub97c \ud30c\uc545\ud558\uae30 \uc704\ud574 \uc218\ud589\ud558\ub294 \uc694\uc57d \ud1b5\uacc4<\/a> \ub610\ub294 \ucd08\uae30 \uc2dc\uac01\ud654<\/a>\uc5d0\uc11c \uc11c\uc2dd \uc9c0\uc815 \ubb38\uc81c\uac00 \uc885\uc885 \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \uc77c\uad00\ub418\uc9c0 \uc54a\uc740 \uc11c\uc2dd \uc9c0\uc815\uc758 \uba87 \uac00\uc9c0 \uc608\ub85c, \uc22b\uc790 \uac12\uc774 \ubaa8\ub450 \ub3d9\uc77c\ud55c \uc18c\uc218\uc810 \uc790\ub9ac\ub85c \uc815\uc758\ub418\uc5b4 \uc788\uc9c0 \uc54a\uac70\ub098 \ucca0\uc790\uac00 ‘first’\ub098 ‘1st’\ucc98\ub7fc \ub2ec\ub77c\uc9c0\ub294 \uacbd\uc6b0\ub97c \ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc798\ubabb\ub41c \ub370\uc774\ud130 \uc11c\uc2dd \uc9c0\uc815\uc740 \ub370\uc774\ud130\uc758 \uba54\ubaa8\ub9ac \uc0ac\uc6a9\ub7c9\uc5d0\ub3c4 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \uc11c\uc2dd \uc9c0\uc815 \ubb38\uc81c\ub97c \ud655\uc778\ud558\uba74 \uac12\uc744 \ud45c\uc900\ud654\ud574\uc57c \ud569\ub2c8\ub2e4. \uc9c1\uba74\ud55c \ubb38\uc81c\uc5d0 \ub530\ub77c, \uc77c\ubc18\uc801\uc73c\ub85c \uc790\uccb4 \ud45c\uc900\uc744 \uc815\uc758\ud558\uace0 \ubcc0\uacbd \uc0ac\ud56d\uc744 \uc801\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub3c4 pandas \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\ub294 round<\/a>\uc640 \uac19\uc740 \uba87 \uac00\uc9c0 \uc720\uc6a9\ud55c \ud568\uc218\uac00 \uc788\uc2b5\ub2c8\ub2e4. SalePrice \uc5f4\uc744 \uc18c\uc218\uc810 \ub458\uc9f8 \uc790\ub9ac\uae4c\uc9c0 \ubc18\uc62c\ub9bc\ud558\ub824\ub294 \uacbd\uc6b0 JetBrains AI\uc5d0 \ucf54\ub4dc\ub97c \ub2e4\uc74c\uacfc \uac19\uc774 \uc694\uccad\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
SalePrice\ub97c<\/em> \uc18c\uc218\uc810 \ub458\uc9f8 \uc790\ub9ac\uae4c\uc9c0<\/em> \ubc18\uc62c\ub9bc\ud558\ub294 \ucf54\ub4dc<\/em><\/p>\n
\uacb0\uacfc \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \ubc18\uc62c\ub9bc\uc744 \uc218\ud589\ud55c \ud6c4 \ucc98\uc74c 10\uac1c\uc758 \ud589\uc744 \ucd9c\ub825\ud558\ubbc0\ub85c \uc720\ud6a8\uc131 \ud655\uc778\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n
df_cleaned['SalePrice'] = df_cleaned['SalePrice].round(2)\ndf_cleaned.head()<\/pre>\n
\ub610 \ub2e4\ub978 \uc608\ub85c \ucca0\uc790\uac00 \uc77c\uad00\ub418\uc9c0 \uc54a\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 HouseStyle \uc5f4\uc5d0 ‘1Story’\uc640 ‘OneStory’\uac00 \ubaa8\ub450 \uc788\uc9c0\ub9cc \uc774 \ub458\uc758 \uc758\ubbf8\uac00 \uac19\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \ud504\ub86c\ud504\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud574\ub2f9 \ucf54\ub4dc\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
#HouseStyle<\/em>\uc5d0\uc11c \ubaa8\ub4e0 <\/em>#OneStory<\/em> \uc778\uc2a4\ud134\uc2a4\ub97c <\/em>#1Story<\/em>\ub85c \ubcc0\uacbd\ud558\ub294 \ucf54\ub4dc<\/em><\/p>\n
\uacb0\uacfc \ucf54\ub4dc\ub294 \uc815\ud655\ud788 \uc774 \uc791\uc5c5\uc744 \uc218\ud589\ud558\uba70 \ub2e4\uc74c\uacfc \uac19\uc774 OneStory\uc758 \ubaa8\ub4e0 \uc778\uc2a4\ud134\uc2a4\ub97c 1Story\ub85c \ub300\uccb4\ud569\ub2c8\ub2e4.<\/p>\n
df_cleaned[HouseStyle'] = df_cleaned['HouseStyle'].replace('OneStory', '1Story')<\/pre>\n
\uc774\uc0c1\uce58 \ucc98\ub9ac<\/h3>\n
\uc774\uc0c1\uce58\ub294 \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \ub9e4\uc6b0 \ud754\ud558\uc9c0\ub9cc, \uc774\uc0c1\uce58\ub97c \ucc98\ub9ac\ud560\uc9c0 \uc5ec\ubd80\uc640 \ucc98\ub9ac \ubc29\ubc95\uc740 \ub9e5\ub77d\uc5d0 \ud06c\uac8c \uc88c\uc6b0\ub429\ub2c8\ub2e4. \uc774\uc0c1\uce58\ub97c \ubc1c\uacac\ud558\ub294 \uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95 \uc911 \ud558\ub098\ub294 seaborn<\/a> \ubc0f matplotlib<\/a> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc15\uc2a4 \ud50c\ub86f\uc744 \uc801\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\uc804\uc5d0 \uc791\uc131\ud55c pandas\ub97c \ud65c\uc6a9\ud55c \ub370\uc774\ud130 \ud0d0\uc0c9<\/a> \ube14\ub85c\uadf8 \uac8c\uc2dc\ubb3c\uc5d0\uc11c \ubc15\uc2a4 \ud50c\ub86f\uc5d0 \ub300\ud574 \uc124\uba85\ud588\uc73c\ub2c8 \ube60\ub974\uac8c \ubcf5\uc2b5\uc774 \ud544\uc694\ud558\ub2e4\uba74 \ucc38\uace0\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\u00a0<\/p>\n
\uc774 \ubc15\uc2a4 \ud50c\ub86f\uc744 \uc704\ud574 Ames \uc8fc\ud0dd \ub370\uc774\ud130\uc138\ud2b8<\/a>\uc758 SalePrice\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ub2e4\uc2dc \ud55c \ubc88, \ub2e4\uc74c\uacfc \uac19\uc740 \ud504\ub86c\ud504\ud2b8\uc5d0 \ub530\ub77c JetBrains AI\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucf54\ub4dc\ub97c \uc0dd\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
#SalePrice<\/em>\uc758 \ubc15\uc2a4 \ud50c\ub86f\uc744 \uc0dd\uc131\ud558\ub294 \ucf54\ub4dc<\/em><\/p>\n
\ub2e4\uc74c\uc740 \uc2e4\ud589\ud574\uc57c \ud560 \uacb0\uacfc \ucf54\ub4dc\uc785\ub2c8\ub2e4.<\/p>\n
import seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Create a box plot for SalePrice\nplt.figure(figsize=(10, 6))\nsns.boxplot(x=df_cleaned['SalePrice'])\nplt.title('Box Plot of SalePrice')\nplt.xlabel('SalePrice')\nplt.show()<\/pre>\n
\ubc15\uc2a4 \ud50c\ub86f\uc744 \ubcf4\uba74, \ud30c\ub780\uc0c9 \ubc15\uc2a4 \uc548\uc758 \uc138\ub85c \uc911\uc559\uc120\uc774 \uc911\uc2ec\ubcf4\ub2e4 \uc67c\ucabd\uc5d0 \uc704\uce58\ud574 \uc788\uae30 \ub54c\ubb38\uc5d0 \uc591\uc758 \uc65c\ub3c4\ub97c \uac00\uc9c0\uace0 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc591\uc758 \uc65c\ub3c4\ub97c \uac00\uc9c4\ub2e4\ub294 \uac83\uc740 \uc800\ub834\ud55c \uac00\uaca9\ub300\uc5d0 \ub354 \ub9ce\uc740 \uc8fc\ud0dd \uac00\uaca9\uc774 \ubd84\ud3ec\ud558\uace0 \uc788\uc74c\uc744 \uc758\ubbf8\ud558\uba70, \uc774\ub294 \uadf8\ub2e4\uc9c0 \ub180\ub77c\uc6b4 \uc77c\uc740 \uc544\ub2d9\ub2c8\ub2e4. \ubc15\uc2a4 \ud50c\ub86f\uc740 \ub610\ud55c \uc624\ub978\ud3b8\uc5d0 \uc774\uc0c1\uce58\uac00 \ub9ce\ub2e4\ub294 \uac83\uc744 \uc2dc\uac01\uc801\uc73c\ub85c \ubcf4\uc5ec\uc8fc\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \uc911\uac04 \uac00\uaca9\ub300\ubcf4\ub2e4 \ud6e8\uc52c \ub354 \ub192\uc740 \uc8fc\ud0dd \uc218\ub294 \uc801\ub2e4\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/p>\n
\"\"<\/figure>\n
\uc18c\uc218\uc758 \uc8fc\ud0dd\uc774 \ub300\ubd80\ubd84\uc758 \uc8fc\ud0dd\ubcf4\ub2e4 \ub354 \ub192\uc740 \uac00\uaca9\ub300\ub97c \ud615\uc131\ud558\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc774\ubbc0\ub85c \uc774\ub7ec\ud55c \uc774\uc0c1\uce58\ub3c4 \uc218\uae0d\ud560 \ub9cc\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774\ub294 \ubaa8\ub450 \uc77c\ubc18\ud654\ud558\uace0\uc790 \ud558\ub294 \ubaa8\uc9d1\ub2e8\uacfc \ub370\uc774\ud130\uc5d0\uc11c \ub3c4\ucd9c\ud558\uace0\uc790 \ud558\ub294 \uacb0\ub860\uc5d0 \ub530\ub77c \ub2ec\ub77c\uc9d1\ub2c8\ub2e4. \ubaa8\uc9d1\ub2e8\uc5d0 \ud3ec\ud568\ub418\ub294 \uac83\uacfc \ud3ec\ud568\ub418\uc9c0 \uc54a\ub294 \uac83\uc5d0 \ub300\ud574 \uba85\ud655\ud788 \uacbd\uacc4\ub97c \uc124\uc815\ud558\uba74 \ub370\uc774\ud130\uc758 \uc774\uc0c1\uce58\uac00 \ubb38\uc81c\uac00 \ub420\uc9c0 \uc5ec\ubd80\uc5d0 \uc788\uc5b4 \uc815\ubcf4\uc5d0 \uc785\uac01\ud55c \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n
\uc608\ub97c \ub4e4\uc5b4, \ubaa8\uc9d1\ub2e8\uc774 \uace0\uac00\uc758 \uc800\ud0dd\uc744 \uad6c\ub9e4\ud558\uc9c0 \uc54a\ub294 \uc774\ub4e4\ub85c \uad6c\uc131\ub418\uc5b4 \uc788\ub2e4\uba74 \uc774\ub7ec\ud55c \uc774\uc0c1\uce58\ub97c \uc0ad\uc81c\ud574\ub3c4 \ub429\ub2c8\ub2e4. \ubc18\uba74, \ubaa8\uc9d1\ub2e8\uc758 \uc778\uad6c \ud1b5\uacc4\uac00 \uc774\ub7ec\ud55c \uace0\uac00 \uc8fc\ud0dd\uc744 \uad6c\ub9e4\ud560 \uac00\ub2a5\uc131\uc774 \uc788\ub294 \uc0ac\ub78c\ub4e4\uc744 \ud3ec\ud568\ud55c\ub2e4\uba74, \ud574\ub2f9 \ub370\uc774\ud130\ub97c \uc720\uc9c0\ud558\ub294 \uac83\uc774 \uc801\uc808\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud574\ub2f9 \ub370\uc774\ud130\uac00 \ubaa8\uc9d1\ub2e8\uacfc \uad00\ub828\uc131\uc774 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.<\/p>\n
\uc774\uc0c1\uce58\ub97c \ud30c\uc545\ud558\ub294 \ubc29\ubc95\uc73c\ub85c \ubc15\uc2a4 \ud50c\ub86f\uc5d0 \ub300\ud574 \uc774\uc57c\uae30\ud588\uc9c0\ub9cc, \ubd84\uc0b0\ud615 \ucc28\ud2b8 \ubc0f \ud788\uc2a4\ud1a0\uadf8\ub7a8\uacfc \uac19\uc740 \ub2e4\ub978 \uc635\uc158\uc744 \ud1b5\ud574\uc11c\ub3c4 \ub370\uc774\ud130 \uc774\uc0c1\uce58 \uc5ec\ubd80\ub97c \ube60\ub974\uac8c \ud30c\uc545\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc870\uce58\uac00 \ud544\uc694\ud55c\uc9c0 \uc2e0\uc911\ud55c \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774\uc0c1\uce58\ub97c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \ub450 \uac00\uc9c0 \uce74\ud14c\uace0\ub9ac \uc989, \uc774\uc0c1\uce58\ub97c \uc0ad\uc81c\ud558\uac70\ub098 \uc774\uc0c1\uce58\uc5d0 \uc601\ud5a5\uc744 \ub35c \ubc1b\ub294 \uc694\uc57d \ud1b5\uacc4\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc73c\ub85c \ub098\ub269\ub2c8\ub2e4. \uccab \ubc88\uc9f8 \uacbd\uc6b0\uc5d0\ub294 \uc774\uc0c1\uce58\uac00 \uc5b4\ub290 \ud589\uc778\uc9c0 \uc815\ud655\ud788 \uc54c\uc544\uc57c \ud569\ub2c8\ub2e4.\u00a0<\/p>\n
\uc9c0\uae08\uae4c\uc9c0\ub294 \uc774\uc0c1\uce58\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \uc2dd\ubcc4\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c\ub9cc \ub17c\uc758\ud588\uc2b5\ub2c8\ub2e4. \uc5b4\ub5a4 \uad00\uce21\uac12\uc774 \uc774\uc0c1\uce58\uc778\uc9c0 \uc544\ub2cc\uc9c0 \ud310\ub2e8\ud558\ub294 \ubc29\ubc95\uc5d0\ub294 \uc5ec\ub7ec \uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ud55c \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \uc811\uadfc \ubc29\uc2dd\uc740 \uc218\uc815\ub41c Z \uc810\uc218<\/em>\ub77c\ub294 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. Z \uc810\uc218\uac00 \uc218\uc815\ub418\ub294 \ubc29\uc2dd\uacfc \uadf8 \uc774\uc720\ub97c \uc0b4\ud3b4\ubcf4\uae30 \uc55e\uc11c, \ub2e4\uc74c\uc744 \ud1b5\ud574 \uc815\uc758\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
Z \uc810\uc218 =<\/em> (\ub370\uc774\ud130 \ud3ec\uc778\ud2b8 \uac12<\/em> – \ud3c9\uade0<\/em>) \/ \ud45c\uc900 \ud3b8\ucc28<\/em><\/p>\n
\uc774\uc0c1\uce58 \uac10\uc9c0\ub97c \ubaa9\uc801\uc73c\ub85c Z \uc810\uc218\ub97c \uc218\uc815\ud558\ub294 \uc774\uc720\ub294 \ud3c9\uade0\uacfc \ud45c\uc900 \ud3b8\ucc28\uac00 \uacc4\uc0b0 \ubc29\uc2dd\uc758 \ud2b9\uc131\uc0c1 \uc774\uc0c1\uce58\uc758 \uc601\ud5a5\uc744 \ubc1b\uae30\uac00 \uc27d\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \uc218\uc815\ub41c Z \uc810\uc218\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\uc758\ub429\ub2c8\ub2e4.<\/p>\n
\uc218\uc815\ub41c Z \uc810\uc218 =<\/em> (\ub370\uc774\ud130 \ud3ec\uc778\ud2b8 \uac12<\/em> – \uc911\uc559\uac12<\/em>) \/ \uc911\uc559 \uc808\ub300<\/em> \ud3b8\ucc28<\/em><\/p>\n
\uc694\uc57d \ud1b5\uacc4<\/a>\ub97c \ub17c\uc758\ud560 \ub54c \ud655\uc778\ud55c \ubc14\uc640 \uac19\uc774, \uc911\uc559\uac12\uc740 \uc774\uc0c1\uce58\uc758 \uc601\ud5a5\uc744 \ubc1b\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uc911\uc559 \uc808\ub300 \ud3b8\ucc28<\/em>\ub294 \ub370\uc774\ud130\uc138\ud2b8\uc758 \uac01 \uac12\uc774 \uc911\uc559\uac12<\/em>\uacfc \uc5bc\ub9c8\ub098 \ucc28\uc774\uac00 \ub098\ub294\uc9c0\ub97c \uc808\ub300\uac12\uc73c\ub85c \ubcc0\ud658\ud55c \ud6c4, \uc774\ub4e4\uc758 \uc911\uc559\uac12<\/em>\uc744 \uad6c\ud55c \uac12\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub370\uc774\ud130\uc138\ud2b8\uac00 \ub2e4\uc74c\uacfc \uac19\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
1, 2, 2, 2, 3<\/strong>, 3, 3, 5,9<\/p>\n
\uadf8\ub807\ub2e4\uba74 \uc911\uc559\uac12<\/em>\uc740 \ub370\uc774\ud130\uc138\ud2b8\uc758 \uc911\uc559\uc5d0 \uc704\uce58\ud55c \uac12\uc778 3\uc774 \ub429\ub2c8\ub2e4. \uc911\uc559\uac12 3\uc73c\ub85c\ubd80\ud130\uc758 \ud3b8\ucc28\ub294 \uc8fc\uc5b4\uc9c4 \uc21c\uc11c\ub300\ub85c \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
2, 1, 1, 1, 0, 0, 0, 2, 5<\/p>\n
\uc774 \uac12\uc744 \uc624\ub984\ucc28\uc21c\uc73c\ub85c \uc815\ub82c\ud558\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
0, 0, 0, 1, 1<\/strong>, 1, 2, 2, 5<\/p>\n
\uc989, \uc774 \uc608\uc2dc\uc5d0\uc11c\uc758 \uc911\uc559 \uc808\ub300 \ud3b8\ucc28<\/em>\ub294 1\uc774 \ub429\ub2c8\ub2e4.<\/p>\n
\uadf8\ub807\ub2e4\uba74 SalePrice \ub370\uc774\ud130\uc138\ud2b8\uc5d0 \ub300\ud574 \uc218\uc815\ub41c Z \uc810\uc218\ub97c \uc5b4\ub5bb\uac8c \uacc4\uc0b0\ud560 \uc218 \uc788\uc744\uae4c\uc694? JetBrains AI\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc740 \ud504\ub86c\ud504\ud2b8\ub97c \uc81c\uacf5\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc785\ub825 \ub0b4\uc6a9:<\/em><\/p>\n
\uc218\uc815\ub41c Z \uc810\uc218 = (\ub370\uc774\ud130 \ud3ec\uc778\ud2b8 \uac12 – \uc911\uc559\uac12) \/ \uc911\uc559 \uc808\ub300 \ud3b8\ucc28<\/em><\/p>\n
#SalePrice<\/em>\uc758 \uc218\uc815\ub41c Z \uc810\uc218\ub97c \uacc4\uc0b0\ud574 \uc918<\/em><\/p>\n
\uadf8\ub7ec\uba74 \ub2e4\uc74c\uacfc \uac19\uc740 \ucf54\ub4dc\uac00 \uc0dd\uc131\ub429\ub2c8\ub2e4.<\/p>\n
import pandas as pd\nfrom scipy.stats import median_abs_deviation\n\n\n# Assuming 'data' is your DataFrame and it contains the 'SalePrice' column\n\n\n# Calculate the median for the SalePrice column\nmedian_sale_price = data['SalePrice'].median()\n\n\n# Calculate the Median Absolute Deviation (MAD) for the SalePrice column\nmad_sale_price = median_abs_deviation(data['SalePrice'], scale='normal')\n\n\n# Calculate the Modified Z-scores for the SalePrice column\ndata['Modified_Z_Score'] = (data['SalePrice'] - median_sale_price) \/ mad_sale_price\n\n\n# Display the first few rows with the Modified Z-scores\nprint(data[['SalePrice', 'Modified_Z_Score']].head())<\/pre>\n
\ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud558\uace0 \uc774 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud558\uba74 \uc808\ubc18 \uc815\ub3c4 \uc9c4\ud589\ub418\uc5c8\uc74c\uc744 \uc54c \uc218 \uc788\uc9c0\ub9cc, \uc774\uc81c \uc218\uc815\ub41c Z \uc810\uc218\ub97c \uae30\ubc18\uc73c\ub85c SalePrice\uc758 \uc774\uc0c1\uce58\ub97c \uacb0\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\uae30\uc11c \uc77c\ubc18 \uc0c1\uc2dd\uc73c\ub85c\ub294 \uc774\uc0c1\uce58\uac00 3 \uc774\uc0c1\uc774\uac70\ub098 -3 \uc774\ud558\uc9c0\ub9cc, \ub300\ubd80\ubd84\uc758 \ud1b5\uacc4\uc801 \uacb0\uc815\uc5d0\uc11c\uc640 \ub9c8\ucc2c\uac00\uc9c0\ub85c \ub370\uc774\ud130\uc138\ud2b8\uc5d0 \ub9de\ucdb0 \uc870\uc815\ud560 \uc218 \uc788\uc73c\uba70 \uadf8\ub807\uac8c \ud574\uc57c \ud55c\ub2e4\ub294 \uc810\uc5d0 \uc720\ub150\ud558\uc138\uc694. <=-3 however, it\u2019s worth noting that like most statistical decisions, it can and should be tailored to your dataset.\u00a0<\/p>\n
JetBrains AI\uc5d0 \ub610 \ub2e4\ub978 \ud504\ub86c\ud504\ud2b8\ub97c \uc804\ub2ec\ud558\uc5ec \ucd9c\ub825\uc744 \ub354 \uc870\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
#Modified_Z_Score<\/em>\uac00 3 \uc774\uc0c1\uc774\uac70\ub098 -3 \uc774\ud558\uc778 \uac12\ub4e4\ub9cc \ub098\uc5f4\ud574 \uc918<\/em><\/p>\n
\uc774\uc81c \ucf54\ub4dc \uc2a4\ub2c8\ud3ab\uc744 \uac00\uc838\uc640\uc11c \uc704\uc758 \ud574\ub2f9\ud558\ub294 \ud589\uc73c\ub85c \ub300\uccb4\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
# Filter the rows where the Modified Z-score is 3 or above, or -3 or below\noutliers = data[(data['Modified_Z_Score'] >= 3) | (data['Modified_Z_Score'] <= -3)]\n\n\n# Print all the filtered rows, showing their index and SalePrice\noutliers = (outliers[['SalePrice', 'Modified_Z_Score']])\noutliers<\/pre>\n
\uc774 \ucf54\ub4dc\ub97c \uc218\uc815\ud558\uc5ec, \uc774\uc0c1\uce58\ub97c outliers\ub77c\uace0 \ud558\ub294 \uc0c8 DataFrame\uc5d0 \uc800\uc7a5\ud558\uace0 \ucd9c\ub825\ud558\uc5ec \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n
\ub2e4\uc74c \ub2e8\uacc4\ub294 \uc774\uc0c1\uce58\ub97c DataFrame\uc5d0\uc11c \uc81c\uac70\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. JetBrains AI\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc740 \ud504\ub86c\ud504\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucf54\ub4dc\ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc774\uc0c1\uce58\ub97c \uc81c\uc678\ud55c \uc0c8 DataFrame\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.\u00a0<\/em><\/p>\n
data_without_outliers = data.drop(index=outliers.index)\n\n\n# Display the new DataFrame without outliers\nprint(data_without_outliers)<\/pre>\n
\uc0c8 DataFrame\uc778 data_without_outliers\uc5d0\ub294 SalePrice \ubcc0\uc218\uc5d0\uc11c \uc774\uc0c1\uce58\ub85c \uac04\uc8fc\ub41c \uac12\uc774 \uc81c\uc678\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\u00a0<\/p>\n
\uc774\uc81c \ubc15\uc2a4 \ud50c\ub86f \ucf54\ub4dc\ub97c \uc5c5\ub370\uc774\ud2b8\ud558\uba74 \uc0c8 DataFrame\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\uc0c1\ud55c \ubc14\uc640 \uac19\uc774, \uc5ec\uc804\ud788 \uc591\uc758 \uc65c\ub3c4\ub97c \ubcf4\uc5ec\uc8fc\uc9c0\ub9cc \uc774\uc0c1\uce58\ub85c \uac04\uc8fc\ub418\ub294 \uac12\uc740 \uc81c\uac70\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n
import seaborn as sns\nimport matplotlib.pyplot as plt\n\n\n# Create a box plot for SalePrice\nplt.figure(figsize=(10, 6))\nsns.boxplot(x=data_without_outliers['SalePrice'])\nplt.title('Box Plot of SalePrice')\nplt.xlabel('SalePrice')\nplt.show()<\/pre>\n
\"\"<\/figure>\n
\ub9c8\uce58\uae30 \uc55e\uc11c, SalePrice\ub97c \uae30\uc900\uc73c\ub85c \uc774\uc0c1\uce58\ub85c \uac04\uc8fc\ub418\uc5b4 DataFrame\uc5d0\uc11c \uc81c\uac70\ub41c \uad00\uce21\uac12\uc774 \uc804\uccb4\uc5d0\uc11c \ucc28\uc9c0\ud558\ub294 \ube44\uc911\uc744 \ud655\uc778\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ub2e4\uc74c\uacfc \uac19\uc740 \ud504\ub86c\ud504\ud2b8\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
#data\uc640 #data_without_outliers \uac04\uc5d0 \uc81c\uac70\ub41c \uad00\uce21\uac12\uc758 \ube44\uc728\uc744 \uacc4\uc0b0\ud574 \uc918<\/em><\/p>\n
# Calculate the number of observations in the original and filtered DataFrames\noriginal_count = len(data)\nfiltered_count = len(data_without_outliers)\n\n# Calculate the number of removed observations\nremoved_count = original_count - filtered_count\n\n# Calculate the percentage of observations removed\npercentage_removed = (removed_count \/ original_count) * 100\n\n# Display the percentage\nprint(f\"Percentage of observations removed: {percentage_removed:.2f}%\")<\/pre>\n
PyCharm\uc5d0\uc11c\ub294 5.67%\uc758 \uad00\uce21\uac12\uc774 \uc81c\uac70\ub418\uc5c8\ub2e4\uace0 \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/p>\n
\uc55e\uc11c \uc5b8\uae09\ud55c \uac83\ucc98\ub7fc, \uc774\uc0c1\uce58\ub97c \uc720\uc9c0\ud558\ub294 \uacbd\uc6b0, \uc774\uc0c1\uce58\uc5d0 \uc601\ud5a5\uc744 \ub35c \ubc1b\ub294 \uc911\uc559\uac12<\/em>\uacfc \uc0ac\ubd84\uc704 \ubc94\uc704<\/em> \ub4f1\uc758 \uc694\uc57d \ud1b5\uacc4 \uac12\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4. \uc815\uc758\ud55c \ubaa8\uc9d1\ub2e8 \ubc0f \ub3c4\ucd9c\ud558\ub824\ub294 \uacb0\ub860\uacfc \uad00\ub828\uc774 \uc788\uc5b4 \uc81c\uac70\ud558\uc9c0 \uc54a\uc740 \uc774\uc0c1\uce58\uac00 \ud3ec\ud568\ub41c \ub370\uc774\ud130\uc138\ud2b8\ub85c \uc791\uc5c5\ud558\ub294 \uacbd\uc6b0, \uc774\ub7ec\ud55c \uce21\uc815\uac12\uc744 \uc0ac\uc6a9\ud558\uc5ec \uacb0\ub860\uc744 \ub3c4\ucd9c\ud558\ub294 \uac83\uc744 \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\uacb0\uce21\uce58<\/h3>\n
\ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \uacb0\uce21\uce58\ub97c \ube60\ub974\uac8c \ucc3e\ub294 \ubc29\ubc95\uc740 \uc694\uc57d \ud1b5\uacc4\ub97c \ud65c\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ub2e4\uc2dc \ub9d0\ud558\uc790\uba74, DataFrame\uc5d0\uc11c \uc624\ub978\ucabd\uc758 Show Column Statistics(\uc5f4 \ud1b5\uacc4 \ud45c\uc2dc)<\/em>\ub97c \ud074\ub9ad\ud55c \ub2e4\uc74c Compact(\ucf64\ud329\ud2b8)<\/em>\ub97c \uc120\ud0dd\ud569\ub2c8\ub2e4. Ames Housing \ub370\uc774\ud130\uc138\ud2b8<\/a>\uc758 Lot Frontage \uc5f4\uc5d0\uc11c \uacb0\uce21\uce58\uac00 \ube68\uac04\uc0c9\uc73c\ub85c \ud45c\uc2dc\ub41c \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/p>\n
\ud604\uc7ac \ub370\uc774\ud130\uc5d0\uc11c \uace0\ub824\ud574\uc57c \ud560 \uacb0\uce21\uce58 \uc720\ud615\uc5d0\ub294 \uc138 \uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n