{"id":4370,"date":"2023-07-11T15:20:37","date_gmt":"2023-07-11T15:20:37","guid":{"rendered":"https:\/\/statorials.org\/ko\/%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%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%a6-%e1%84%8b%e1%85%b5%e1%86%bb%e1%84%89\/"},"modified":"2023-07-11T15:20:37","modified_gmt":"2023-07-11T15:20:37","slug":"%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%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%a6-%e1%84%8b%e1%85%b5%e1%86%bb%e1%84%89","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%82%e1%85%b3%e1%86%ab-%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%a6-%e1%84%8b%e1%85%b5%e1%86%bb%e1%84%89\/","title":{"rendered":"Pandas: \uc5ec\ub7ec \uc5f4\uc5d0 isin\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

pandas isin()<\/strong> \ud568\uc218\uc640 \ud568\uaed8 \ub2e4\uc74c \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc758 \uc5ec\ub7ec \uc5f4\uc744 \uae30\ubc18\uc73c\ub85c \ud544\ud130\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \uc5ec\ub7ec \uc5f4\uc774 \ud2b9\uc815 \uac12\uacfc \ub3d9\uc77c\ud55c \uacbd\uc6b0 \ud544\ud130\ub9c1<\/strong><\/span><\/p>\n

 df = df[df[[' team<\/span> ', ' position<\/span> ']]. isin<\/span> ([' A<\/span> ',' Guard<\/span> ']). all<\/span> (axis= 1<\/span> )]\n<\/strong><\/pre>\n
 \uc774 \ud2b9\uc815 \uc608\uc5d0\uc11c\ub294 \ud300<\/strong> \uc5f4\uc774 “A” \uc774\uace0<\/b> \uc704\uce58<\/strong> \uc5f4\uc774 “Guard”\uc778 \ud589\uc5d0 \ub300\ud574 DataFrame\uc744 \ud544\ud130\ub9c1\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc29\ubc95 2: \ud558\ub098 \uc774\uc0c1\uc758 \uc5f4\uc774 \ud2b9\uc815 \uac12\uacfc \ub3d9\uc77c\ud55c \ud544\ud130<\/strong><\/span><\/p>\n
 df = df[df[[' team<\/span> ', ' position<\/span> ']]. isin<\/span> ([' A<\/span> ',' Guard<\/span> ']). any<\/span> (axis= 1<\/span> )]<\/strong><\/pre>\n
 \uc774 \ud2b9\uc815 \uc608\uc5d0\uc11c\ub294 \ud300<\/strong> \uc5f4\uc774 “A” \uc774\uac70\ub098<\/b> \uc704\uce58<\/strong> \uc5f4\uc774 “Guard”\uc778 \ud589\uc5d0 \ub300\ud574 DataFrame\uc744 \ud544\ud130\ub9c1\ud569\ub2c8\ub2e4.<\/span><\/p>\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\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n                   ' position<\/span> ': ['Guard', 'Guard', 'Forward', 'Forward',\n                                'Guard', 'Guard', 'Forward', 'Forward'],\n                   ' points<\/span> ': [11, 18, 10, 22, 26, 35, 19, 12]})\n                   \n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n2 A Forward 10\n3 A Forward 22\n4 B Guard 26\n5 B Guard 35\n6 B Forward 19\n7 B Forward 12<\/strong><\/pre>\n
 \uc608 1: \uc5ec\ub7ec \uc5f4\uc774 \ud2b9\uc815 \uac12\uacfc \ub3d9\uc77c\ud55c \ud544\ud130<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \uc5f4\uc774 “A” \uc774\uace0<\/b> \uc704\uce58<\/strong> \uc5f4\uc774 “Guard”\uc778 \ud589\ub9cc \ud3ec\ud568\ud558\ub3c4\ub85d DataFrame\uc744 \ud544\ud130\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #filter rows where team column is 'A' and position column is 'Guard'<\/span>\ndf = df[df[[' team<\/span> ', ' position<\/span> ']]. isin<\/span> ([' A<\/span> ',' Guard<\/span> ']). all<\/span> (axis= 1<\/span> )]\n\n#view filtered DataFrame<\/span>\nprint<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n<\/strong><\/pre>\n
 \ud300<\/strong> \uc5f4\uc774 “A” \uc774\uace0<\/b> \uc704\uce58<\/strong> \uc5f4\uc774 “Guard”\uc778 \ud589\ub9cc \ud544\ud130\ub9c1\ub41c DataFrame\uc5d0 \ub0a8\uc544 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608 2: \ud558\ub098 \uc774\uc0c1\uc758 \uc5f4\uc774 \ud2b9\uc815 \uac12\uacfc \ub3d9\uc77c\ud55c \ud544\ud130<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \uc5f4\uc774 “A” \uc774\uac70\ub098<\/b> \uc704\uce58<\/strong> \uc5f4\uc774 “Guard”\uc778 \ud589\ub9cc \ud3ec\ud568\ud558\ub3c4\ub85d DataFrame\uc744 \ud544\ud130\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #filter rows where team column is 'A' or position column is 'Guard'<\/span>\ndf = df[df[[' team<\/span> ', ' position<\/span> ']]. isin<\/span> ([' A<\/span> ',' Guard<\/span> ']). any<\/span> (axis= 1<\/span> )]\n\n#view filtered DataFrame<\/span>\nprint<\/span> (df)\n\n  team position points\n0 A Guard 11\n1 A Guard 18\n2 A Forward 10\n3 A Forward 22\n4 B Guard 26\n5 B Guard 35\n<\/strong><\/pre>\n
 \ud300<\/strong> \uc5f4\uc774 “A” \uc774\uac70\ub098<\/b> \uc704\uce58<\/strong> \uc5f4\uc774 “Guard”\uc778 \ud589\ub9cc \ud544\ud130\ub9c1\ub41c DataFrame\uc5d0 \ub0a8\uc544 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : pandas isin()<\/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: \ud53c\ubc97 \ud14c\uc774\ube14\uc5d0 \ud544\ud130\ub97c \ucd94\uac00\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas: ‘\ud3ec\ud568\ud558\uc9c0 \uc54a\uc74c’\uc744 \ud544\ud130\ub9c1\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas: \ud2b9\uc815 \ubb38\uc790\uc5f4\uc774 \ud3ec\ud568\ub41c \ud589\uc744 \ud544\ud130\ub9c1\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
pandas isin() \ud568\uc218\uc640 \ud568\uaed8 \ub2e4\uc74c \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc758 \uc5ec\ub7ec \uc5f4\uc744 \uae30\ubc18\uc73c\ub85c \ud544\ud130\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \uc5ec\ub7ec \uc5f4\uc774 \ud2b9\uc815 \uac12\uacfc \ub3d9\uc77c\ud55c \uacbd\uc6b0 \ud544\ud130\ub9c1 df = df[df[[‘ team ‘, ‘ position ‘]]. isin ([‘ A ‘,’ Guard ‘]). all (axis= 1 )] \uc774 \ud2b9\uc815 \uc608\uc5d0\uc11c\ub294 \ud300 \uc5f4\uc774 “A” \uc774\uace0 \uc704\uce58 \uc5f4\uc774 “Guard”\uc778 \ud589\uc5d0 \ub300\ud574 DataFrame\uc744 […]<\/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-4370","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: \uc5ec\ub7ec \uc5f4\uc5d0 isin\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95 - 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