{"id":4245,"date":"2023-07-12T13:37:34","date_gmt":"2023-07-12T13:37:34","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%b3%e1%84%85%e1%85%a9%e1%86%b8%e1%84%82%e1%85%a1-%e1%84%8a%e1%85%b3%e1%84%85%e1%85%a6%e1%84%89%e1%85%b1\/"},"modified":"2023-07-12T13:37:34","modified_gmt":"2023-07-12T13:37:34","slug":"%e1%84%91%e1%85%a2%e1%86%ab%e1%84%83%e1%85%a5-%e1%84%83%e1%85%b3%e1%84%85%e1%85%a9%e1%86%b8%e1%84%82%e1%85%a1-%e1%84%8a%e1%85%b3%e1%84%85%e1%85%a6%e1%84%89%e1%85%b1","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%83%e1%85%b3%e1%84%85%e1%85%a9%e1%86%b8%e1%84%82%e1%85%a1-%e1%84%8a%e1%85%b3%e1%84%85%e1%85%a6%e1%84%89%e1%85%b1\/","title":{"rendered":"Pandas: thresh\uc640 \ud568\uaed8 dropna()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

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dropna()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub204\ub77d\ub41c \uac12\uc774 \ud3ec\ud568\ub41c Pandas DataFrame\uc5d0\uc11c \ud589\uc744 \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

thresh<\/b> \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud589\uc774\ub098 \uc5f4\uc774 DataFrame\uc5d0 \uc720\uc9c0\ub418\uc5b4\uc57c \ud558\ub294 NaN\uc774 \uc544\ub2cc \uac12\uc758 \ucd5c\uc18c \uac1c\uc218\ub97c \uc9c0\uc815\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc2e4\uc81c\ub85c \uc784\uacc4\uac12<\/strong> \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: NaN\uc774 \uc544\ub2cc \uac12\uc774 \ucd5c\uc18c \uac1c\uc218\uc778 \ud589\ub9cc \uc720\uc9c0<\/strong><\/span><\/p>\n

 #only keep rows with at least 2 non-NaN values\n<\/span>df. dropna<\/span> (thresh= 2<\/span> )\n<\/strong><\/pre>\n
 \ubc29\ubc95 2: NaN\uc774 \uc544\ub2cc \uac12\uc758 \ube44\uc728\uc774 \ucd5c\uc18c\uc778 \ud589\ub9cc \uc720\uc9c0<\/strong><\/span><\/p>\n
 #only keep rows with at least 70% non-NaN values\n<\/span>df. dropna<\/span> (thresh= 0.7<\/span> * len<\/span> (df. columns<\/span> ))<\/strong><\/pre>\n
 \ubc29\ubc95 3: \ucd5c\uc18c \uac1c\uc218\uc758 NaN\uc774 \uc544\ub2cc \uac12\uc774 \uc788\ub294 \uc5f4\ub9cc \uc720\uc9c0<\/strong><\/span><\/p>\n
 #only keep columns with at least 6 non-NaN values\n<\/span>df. dropna<\/span> (thresh= 6<\/span> ,axis= 1<\/span> )<\/strong><\/pre>\n
 \ubc29\ubc95 4: NaN\uc774 \uc544\ub2cc \uac12\uc758 \ucd5c\uc18c \ube44\uc728\uc744 \ud3ec\ud568\ud558\ub294 \uc5f4\ub9cc \uc720\uc9c0<\/strong><\/span><\/p>\n
 #only keep columns with at least 70% non-NaN values\n<\/span>df. dropna<\/span> (thresh= 0.7<\/span> * len<\/span> (df), axis= 1<\/span> )<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c pandas DataFrame\uc5d0\uc11c \uc2e4\uc81c\ub85c \uac01 \uba54\uc11c\ub4dc\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> ': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],\n                   ' points<\/span> ': [18, np.nan, 19, 14, 14, 11, 20, np.nan],\n                   ' assists<\/span> ': [5, np.nan, np.nan, 9, np.nan, 9, 9, 4],\n                   ' rebounds<\/span> ': [11, np.nan, 10, 6, 6, 5, 9, np.nan]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points assists rebounds\n0 A 18.0 5.0 11.0\n1 B NaN NaN NaN\n2 C 19.0 NaN 10.0\n3D 14.0 9.0 6.0\n4 E 14.0 NaN 6.0\n5 F 11.0 9.0 5.0\n6G 20.0 9.0 9.0\n7H NaN 4.0 NaN<\/strong><\/pre>\n
 \uc608 1: NaN\uc774 \uc544\ub2cc \uac12\uc774 \ucd5c\uc18c \uac1c\uc218\uc778 \ud589\ub9cc \uc720\uc9c0<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec NaN\uc774 \uc544\ub2cc \uac12\uc774 2\uac1c \uc774\uc0c1 \uc788\ub294 DataFrame\uc758 \ud589\ub9cc \uc720\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #only keep rows with at least 2 non-NaN values\n<\/span>df. dropna<\/span> (thresh= 2<\/span> )\n\n\tteam points assists rebounds\n0 A 18.0 5.0 11.0\n2 C 19.0 NaN 10.0\n3D 14.0 9.0 6.0\n4 E 14.0 NaN 6.0\n5 F 11.0 9.0 5.0\n6G 20.0 9.0 9.0\n7H NaN 4.0 NaN\n<\/strong><\/pre>\n
 \uc778\ub371\uc2a4 \uc704\uce58 1<\/strong> \uc758 \ud589\uc740 \uc804\uccb4 \ud589\uc5d0\uc11c NaN\uc774 \uc544\ub2cc \uac12\uc774 \ud558\ub098\ub9cc \uc788\uc5c8\uae30 \ub54c\ubb38\uc5d0 \uc81c\uac70\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2: NaN\uc774 \uc544\ub2cc \uac12\uc758 \ube44\uc728\uc774 \ucd5c\uc18c\uc778 \ud589\ub9cc \uc720\uc9c0<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec NaN\uc774 \uc544\ub2cc \uac12\uc774 70% \uc774\uc0c1\uc778 DataFrame\uc758 \ud589\ub9cc \uc720\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #only keep rows with at least 70% non-NaN values\n<\/span>df. dropna<\/span> (thresh= 0.7<\/span> * len<\/span> (df. columns<\/span> ))\n\n        team points assists rebounds\n0 A 18.0 5.0 11.0\n2 C 19.0 NaN 10.0\n3D 14.0 9.0 6.0\n4 E 14.0 NaN 6.0\n5 F 11.0 9.0 5.0\n6G 20.0 9.0 9.0\n<\/strong><\/pre>\n
 \uc778\ub371\uc2a4 \uc704\uce58 1<\/strong> \uacfc 7<\/strong> \uc758 \ud589\uc740 NaN\uc774 \uc544\ub2cc \uac12\uc73c\ub85c \uac12\uc758 70% \uc774\uc0c1\uc744 \ud3ec\ud568\ud558\uc9c0 \uc54a\uc558\uae30 \ub54c\ubb38\uc5d0 \uc81c\uac70\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 3: \ucd5c\uc18c \uac1c\uc218\uc758 NaN\uc774 \uc544\ub2cc \uac12\uc774 \uc788\ub294 \uc5f4\ub9cc \uc720\uc9c0<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec NaN\uc774 \uc544\ub2cc \uac12\uc774 6\uac1c \uc774\uc0c1 \uc788\ub294 DataFrame\uc758 \uc5f4\ub9cc \uc720\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #only keep columns with at least 6 non-NaN values<\/span>\ndf. dropna<\/span> (thresh= 6<\/span> ,axis= 1<\/span> )\n\n        team points rebounds\n0 A 18.0 11.0\n1 B NaN NaN\n2 C 19.0 10.0\n3D 14.0 6.0\n4E 14.0 6.0\n5 F 11.0 5.0\n6G 20.0 9.0\n7 H NaN NaN<\/strong><\/pre>\n
 “\uc9c0\uc6d0” \uc5f4\uc740 \ud574\ub2f9 \uc5f4\uc5d0 NaN\uc774 \uc544\ub2cc \uac12\uc774 6\uac1c \uc774\uc0c1 \ud3ec\ud568\ub418\uc5b4 \uc788\uc9c0 \uc54a\uc558\uae30 \ub54c\ubb38\uc5d0 \uc81c\uac70\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 4: NaN\uc774 \uc544\ub2cc \uac12\uc758 \ucd5c\uc18c \ube44\uc728\uc774 \ud3ec\ud568\ub41c \uc5f4\ub9cc \uc720\uc9c0<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec NaN\uc774 \uc544\ub2cc \uac12\uc774 70% \uc774\uc0c1\uc778 DataFrame\uc758 \uc5f4\ub9cc \uc720\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #only keep columns with at least 70% non-NaN values\n<\/span>df. dropna<\/span> (thresh= 0.7<\/span> * len<\/span> (df), axis= 1<\/span> )\n\n        team points rebounds\n0 A 18.0 11.0\n1 B NaN NaN\n2 C 19.0 10.0\n3D 14.0 6.0\n4E 14.0 6.0\n5 F 11.0 5.0\n6G 20.0 9.0\n7 H NaN NaN\n<\/strong><\/pre>\n
 “\uc9c0\uc6d0” \uc5f4\uc740 NaN\uc774 \uc544\ub2cc \uac12\uc774 70% \uc774\uc0c1 \ud3ec\ud568\ub418\uc9c0 \uc54a\uc558\uae30 \ub54c\ubb38\uc5d0 \uc81c\uac70\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : pandas dropna()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub294 \uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\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 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 Pandas: dropna()\ub97c \uc0ac\uc6a9\ud55c \ud6c4 \uc778\ub371\uc2a4\ub97c \uc7ac\uc124\uc815\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas: \ud2b9\uc815 \uc5f4\uc5d0 dropna()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas: \uc5ec\ub7ec \uc870\uac74\uc5d0 \ub530\ub77c \ud589\uc744 \uc0ad\uc81c\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
dropna() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub204\ub77d\ub41c \uac12\uc774 \ud3ec\ud568\ub41c Pandas DataFrame\uc5d0\uc11c \ud589\uc744 \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. thresh \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud589\uc774\ub098 \uc5f4\uc774 DataFrame\uc5d0 \uc720\uc9c0\ub418\uc5b4\uc57c \ud558\ub294 NaN\uc774 \uc544\ub2cc \uac12\uc758 \ucd5c\uc18c \uac1c\uc218\ub97c \uc9c0\uc815\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \uc2e4\uc81c\ub85c \uc784\uacc4\uac12 \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: NaN\uc774 \uc544\ub2cc \uac12\uc774 \ucd5c\uc18c \uac1c\uc218\uc778 \ud589\ub9cc \uc720\uc9c0 #only keep rows with at least 2 non-NaN values […]<\/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-4245","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: thresh\uc640 \ud568\uaed8 dropna()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uc608\uc81c\uc640 \ud568\uaed8 thresh \uc778\uc218\uc640 \ud568\uaed8 pandas dropna() \ud568\uc218\ub97c \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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