{"id":4296,"date":"2023-07-12T04:58:12","date_gmt":"2023-07-12T04:58:12","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%83%e1%85%a9%e1%86%a8%e1%84%90%e1%85%b3%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%82%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b3\/"},"modified":"2023-07-12T04:58:12","modified_gmt":"2023-07-12T04:58:12","slug":"%e1%84%83%e1%85%a9%e1%86%a8%e1%84%90%e1%85%b3%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%82%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b3","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%83%e1%85%a9%e1%86%a8%e1%84%90%e1%85%b3%e1%86%a8%e1%84%92%e1%85%a1%e1%86%ab-%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5%e1%84%82%e1%85%b3%e1%86%ab-%e1%84%82%e1%85%a1%e1%86%ab%e1%84%8b%e1%85%b3\/","title":{"rendered":"Pandas: \uace0\uc720\ud55c \uac12\uc744 \ucc3e\uace0 nan\uc744 \ubb34\uc2dc\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

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\ub2e4\uc74c \uc0ac\uc6a9\uc790 \uc815\uc758 \ud568\uc218\ub97c \uc815\uc758\ud558\uc5ec \ud32c\ub354\uc5d0\uc11c \uace0\uc720\ud55c \uac12\uc744 \uac80\uc0c9\ud558\uace0 NaN \uac12\uc744 \ubb34\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 def<\/span> unique_no_nan(x):\n    return<\/span> x. dropna<\/span> (). single<\/span> ()\n<\/strong><\/pre>\n
 \uc774 \ud568\uc218\ub294 NaN \uac12\uc744 \uc81c\uc678\ud55c \ubaa8\ub4e0 \uace0\uc720 \uac12\uc744 \ud3ec\ud568\ud558\ub294 \ud32c\ub354 \uc2dc\ub9ac\uc988\ub97c \ubc18\ud658\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c Pandas DataFrame\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc2dc\ub098\ub9ac\uc624\uc5d0\uc11c \uc774 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs', 'Mavs', 'Mavs', 'Celtics', 'Celtics', 'Celtics'],\n                   ' points<\/span> ': [95, 95, 100, 113, 100, np.nan]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n      team points\n0 Mavs 95.0\n1 Mavs 95.0\n2 Mavs 100.0\n3 Celtics 113.0\n4 Celtics 100.0\n5 Celtics NaN\n<\/strong><\/pre>\n
 \uc608 1: Pandas \uc5f4\uc5d0\uc11c \uace0\uc720\ud55c \uac12\uc744 \ucc3e\uace0 NaN \uac12\uc744 \ubb34\uc2dc\ud569\ub2c8\ub2e4.<\/strong><\/span><\/h2>\n
 DataFrame\uc758 \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc5d0 \ubaa8\ub4e0 \uace0\uc720 \uac12\uc744 \ud45c\uc2dc\ud558\uae30 \uc704\ud574 pandas Unique()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #display unique values in 'points' column\n<\/span>df[' points<\/span> ']. single<\/span> ()\n\narray([ 95., 100., 113., no])\n<\/strong><\/pre>\n
 Unique()<\/strong> \ud568\uc218\ub294 \uae30\ubcf8\uc801\uc73c\ub85c \uacb0\uacfc\uc5d0 nan\uc744<\/strong> \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uadf8\ub7ec\ub098 \ub300\uc2e0 \uc0ac\uc6a9\uc790 \uc815\uc758 Unique_no_nan()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc5d0 \uace0\uc720\ud55c \uac12\uc744 \ud45c\uc2dc\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #display unique values in 'points' column and ignore NaN\n<\/span>unique_no_nan(df[' points<\/span> '])\n\narray([ 95., 100., 113.])\n<\/strong><\/pre>\n
 \uc6b0\ub9ac \ud568\uc218\ub294 NaN\uc744 \uc81c\uc678\ud558\uace0 \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc758 \ubaa8\ub4e0 \uace0\uc720 \uac12\uc744 \ubc18\ud658\ud569\ub2c8\ub2e4.<\/span><\/span><\/p>\n
 \uc608\uc2dc 2: Pandas Groupby\uc5d0\uc11c \uace0\uc720\ud55c \uac12\uc744 \ucc3e\uace0 NaN \uac12\uc744 \ubb34\uc2dc\ud569\ub2c8\ub2e4.<\/strong><\/span><\/h2>\n
 pandas groupby()<\/strong> \ubc0f agg()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \uc5f4\ubcc4\ub85c \uadf8\ub8f9\ud654\ub41c \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc758 \ubaa8\ub4e0 \uace0\uc720 \uac12\uc744 \ud45c\uc2dc\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #display unique values in 'points' column grouped by team\n<\/span>df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. agg<\/span> ([' single<\/span> '])\n\n\tunique\nteam\t\nCeltics [113.0, 100.0, nah]\nMavs [95.0, 100.0]\n<\/strong><\/pre>\n
 Unique()<\/strong> \ud568\uc218\ub294 \uae30\ubcf8\uc801\uc73c\ub85c \uacb0\uacfc\uc5d0 nan\uc744<\/strong> \ud3ec\ud568\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uadf8\ub7ec\ub098 \ub300\uc2e0 \uc0ac\uc6a9\uc790 \uc815\uc758 Unique_no_nan()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \uc5f4\ubcc4\ub85c \uadf8\ub8f9\ud654\ub41c \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc758 \uace0\uc720 \uac12\uc744 \ud45c\uc2dc\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #display unique values in 'points' column grouped by team and ignore NaN\n<\/span>df. groupby<\/span> (' team<\/span> ')[' points<\/span> ']. apply<\/span> ( lambda<\/span> x: unique_no_nan(x))\n\nteam\nCeltics [113.0, 100.0]\nMavs [95.0, 100.0]\nName: points, dtype: object<\/strong><\/pre>\n
 \uc6b0\ub9ac \ud568\uc218\ub294 NaN \uac12\uc744 \uacc4\uc0b0\ud558\uc9c0 \uc54a\uace0 \uac01 \ud300<\/strong> \uc758 \ud3ec\uc778\ud2b8<\/strong> \uc5f4\uc5d0 \uc788\ub294 \ubaa8\ub4e0 \uace0\uc720 \uac12\uc744 \ubc18\ud658\ud569\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 \uae30\ub2a5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Pandas: DataFrame\uc5d0\uc11c \ub2e8\uc77c \ud589\uc744 \uc120\ud0dd\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas: \uc778\ub371\uc2a4 \uc5f4\uc5d0\uc11c \uace0\uc720\ud55c \uac12\uc744 \uc5bb\ub294 \ubc29\ubc95<\/a>
 Pandas: \ub450 \uc5f4\uc758 \uace0\uc720\ud55c \uc870\ud569\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc74c \uc0ac\uc6a9\uc790 \uc815\uc758 \ud568\uc218\ub97c \uc815\uc758\ud558\uc5ec \ud32c\ub354\uc5d0\uc11c \uace0\uc720\ud55c \uac12\uc744 \uac80\uc0c9\ud558\uace0 NaN \uac12\uc744 \ubb34\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. def unique_no_nan(x): return x. dropna (). single () \uc774 \ud568\uc218\ub294 NaN \uac12\uc744 \uc81c\uc678\ud55c \ubaa8\ub4e0 \uace0\uc720 \uac12\uc744 \ud3ec\ud568\ud558\ub294 \ud32c\ub354 \uc2dc\ub9ac\uc988\ub97c \ubc18\ud658\ud569\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c Pandas DataFrame\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc2dc\ub098\ub9ac\uc624\uc5d0\uc11c \uc774 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. import pandas as pd import numpy as np […]<\/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-4296","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: \uace0\uc720\ud55c \uac12\uc744 \ucc3e\uace0 NaN\uc744 \ubb34\uc2dc\ud558\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ud32c\ub354\uc5d0\uc11c \uace0\uc720\ud55c \uac12\uc744 \ucc3e\uace0 NaN \uac12\uc744 \ubb34\uc2dc\ud558\ub294 \ubc29\ubc95\uc744 \uba87 \uac00\uc9c0 \uc608\uc640 \ud568\uaed8 \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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