{"id":977,"date":"2023-07-28T02:55:19","date_gmt":"2023-07-28T02:55:19","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%8c%e1%85%ae%e1%86%af-%e1%84%87%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a9%e1%84%85%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a5\/"},"modified":"2023-07-28T02:55:19","modified_gmt":"2023-07-28T02:55:19","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%8c%e1%85%ae%e1%86%af-%e1%84%87%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a9%e1%84%85%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a5","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%8c%e1%85%ae%e1%86%af-%e1%84%87%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a9%e1%84%85%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%a5\/","title":{"rendered":"Pandas dataframe\uc5d0\uc11c \ud589 \ubc88\ud638\ub97c \uc5bb\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\uc885\uc885 \ud2b9\uc815 \uac12\uc744 \ud3ec\ud568\ud558\ub294 pandas DataFrame\uc5d0\uc11c \ud589 \ubc88\ud638\ub97c \uac00\uc838\uc624\uace0 \uc2f6\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\ud589\ud788\ub3c4 .index<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 \uc774 \uc791\uc5c5\uc744 \uc27d\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc758 \uc2e4\uc81c \uc0ac\uc6a9\uc5d0 \ub300\ud55c \uba87 \uac00\uc9c0 \uc608\ub97c \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

\uc608\uc2dc 1: \ud2b9\uc815 \uac12\uc5d0 \ud574\ub2f9\ud558\ub294 \ud589 \ubc88\ud638 \uac00\uc838\uc624\uae30<\/strong><\/span><\/h2>\n

\ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 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({'points': [25, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 9, 12],\n                   'team': ['Mavs', 'Mavs', 'Spurs', 'Celtics', 'Warriors']})\n\n#view DataFrame<\/span> \nprint<\/span> (df)\n\n        team assists points\n0 25 5 Mavs\n1 12 7 Mavs\n2 15 7 Spurs\n3 14 9 Celtics\n4 19 12 Warriors\n<\/strong><\/pre>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec “team”\uc774 Mavs\uc640 \ub3d9\uc77c\ud55c \uc904 \ubc88\ud638\ub97c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/span><\/p>\n
 #get row numbers where 'team' is equal to Mavs<\/span>\ndf[df[' team<\/span> '] == ' Mavs<\/span> ']. index<\/span>\n\nInt64Index([0, 1], dtype='int64')\n<\/strong><\/pre>\n
 \ub77c\uc778 \uc778\ub371\uc2a4 0<\/strong> \uacfc 1<\/strong> \uc5d0\uc11c \ud300 \uc774\ub984\uc774 ‘Mavs’\uc640 \uac19\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ud2b9\uc815 \ud300 \uc774\ub984 \ubaa9\ub85d\uc5d0\uc11c \ud300 \uc774\ub984\uc774 \uc788\ub294 \uc904 \ubc88\ud638\ub97c \uc5bb\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #get row numbers where 'team' is equal to Mavs or Spurs<\/span>\nfilter_list = [' Mavs<\/span> ', ' Spurs<\/span> ']\n\n#return only rows where team is in the list of team names\n<\/span>df[df. team<\/span> . isin<\/span> (filter_list)]. index\n<\/span><\/span><\/span>\nInt64Index([0, 1, 2], dtype='int64')\n<\/strong><\/pre>\n
 \ud589 \uc778\ub371\uc2a4 0<\/strong> , 1<\/strong> , 2<\/strong> \uc5d0\uc11c \ud300 \uc774\ub984\uc774 ‘Mavs’ \ub610\ub294 ‘Spurs’\uc640 \uac19\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 2: \uace0\uc720\ud55c \ud589 \ubc88\ud638 \uac00\uc838\uc624\uae30<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 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({'points': [25, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 9, 12],\n                   'team': ['Mavs', 'Mavs', 'Spurs', 'Celtics', 'Warriors']})<\/strong><\/pre>\n
 \ub2e8\uc77c \ud589\uc774 \ud2b9\uc815 \uac12\uacfc \uc77c\uce58\ud55c\ub2e4\ub294 \uac83\uc744 \uc54c\uace0 \uc788\ub294 \uacbd\uc6b0 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud574\ub2f9 \uace0\uc720 \ud589 \ubc88\ud638\ub97c \uac80\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #get the row number where team is equal to Celtics<\/span>\ndf[df[' team<\/span> '] == ' Celtics<\/span> ']. index<\/span> [ 0<\/span> ]\n\n3<\/strong><\/pre>\n
 \ud589 \uc778\ub371\uc2a4 \ubc88\ud638 3<\/strong> \uc5d0\uc11c \ud300\uc774 “Celtics”\uc640 \ub3d9\uc77c\ud558\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 3: \uc904 \ubc88\ud638 \ud569\uacc4 \uac00\uc838\uc624\uae30<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 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({'points': [25, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 9, 12],\n                   'team': ['Mavs', 'Mavs', 'Spurs', 'Celtics', 'Warriors']})<\/strong><\/pre>\n
 \uc5f4\uc774 \ud2b9\uc815 \uac12\uacfc \uac19\uc740 \ucd1d \ud589 \uc218\ub97c \uc54c\uace0 \uc2f6\ub2e4\uba74 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #find total number of rows where team is equal to Mavs<\/span>\nlen<\/span> (df[df[' team<\/span> '] == ' Celtics<\/span> ']. index )<\/span><\/span>\n\n2<\/strong><\/pre>\n
 \uc774 \ud300\uc740 \ucd1d 2<\/strong> \uc904\uc5d0\uc11c “Mavs”\uc640 \ub3d9\uc77c\ud558\ub2e4\ub294 \uac83\uc744 \uc54c \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\uc758 \uc5ec\ub7ec \uc5f4\uc5d0\uc11c \uace0\uc720\ud55c \uac12\uc744 \ucc3e\ub294 \ubc29\ubc95<\/a>
 \uc5ec\ub7ec \uc870\uac74\uc5d0\uc11c Pandas DataFrame\uc744 \ud544\ud130\ub9c1\ud558\ub294 \ubc29\ubc95<\/a>
 Pandas DataFrame\uc5d0\uc11c \ub204\ub77d\ub41c \uac12\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc885\uc885 \ud2b9\uc815 \uac12\uc744 \ud3ec\ud568\ud558\ub294 pandas DataFrame\uc5d0\uc11c \ud589 \ubc88\ud638\ub97c \uac00\uc838\uc624\uace0 \uc2f6\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\ud589\ud788\ub3c4 .index \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 \uc774 \uc791\uc5c5\uc744 \uc27d\uac8c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc758 \uc2e4\uc81c \uc0ac\uc6a9\uc5d0 \ub300\ud55c \uba87 \uac00\uc9c0 \uc608\ub97c \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc608\uc2dc 1: \ud2b9\uc815 \uac12\uc5d0 \ud574\ub2f9\ud558\ub294 \ud589 \ubc88\ud638 \uac00\uc838\uc624\uae30 \ub2e4\uc74c\uacfc \uac19\uc740 \ud32c\ub354 DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. import pandas as pd #createDataFrame df = pd.DataFrame({‘points’: […]<\/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-977","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nDataFrame Pandas\uc5d0\uc11c \ud589 \ubc88\ud638\ub97c \uc5bb\ub294 \ubc29\ubc95 \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"\uc5ec\ub7ec \uc608\ub97c \ud3ec\ud568\ud558\uc5ec \ud2b9\uc815 \uac12\uacfc \uc77c\uce58\ud558\ub294 Pandas DataFrame\uc5d0\uc11c \ud589 \ubc88\ud638\ub97c \uc5bb\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc124\uba85\uc785\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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