{"id":4036,"date":"2023-07-14T00:36:09","date_gmt":"2023-07-14T00:36:09","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%86%e1%85%ae%e1%86%ab%e1%84%8c%e1%85%a1%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%86%e1%85%ae\/"},"modified":"2023-07-14T00:36:09","modified_gmt":"2023-07-14T00:36:09","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%86%e1%85%ae%e1%86%ab%e1%84%8c%e1%85%a1%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%86%e1%85%ae","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%86%e1%85%ae%e1%86%ab%e1%84%8c%e1%85%a1%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%86%e1%85%ae\/","title":{"rendered":"Pandas: \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790\ub97c \uc81c\uac70\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame \uc5f4\uc758 \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790\ub97c \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790 \uc81c\uac70<\/strong><\/span><\/p>\n

 df[' my_column<\/span> '] = df[' my_column<\/span> ']. str<\/span> . replace<\/span> (' this_string<\/span> ', '')\n<\/strong><\/pre>\n
 \ubc29\ubc95 2: \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \ubb38\uc790 \uc81c\uac70<\/strong><\/span><\/p>\n
 df[' my_column<\/span> '] = df[' my_column<\/span> ']. str<\/span> . replace (' \\D<\/span> ', '', regex= True<\/span> )<\/span><\/span><\/strong><\/pre>\n
 \ubc29\ubc95 3: \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \uc22b\uc790 \uc81c\uac70<\/strong><\/span><\/p>\n
 df[' my_column<\/span> '] = df[' my_column<\/span> ']. str<\/span> . replace (' \\d+<\/span> ', '', regex= True<\/span> )<\/span><\/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\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['Mavs2', 'Nets44', 'Kings33', 'Cavs90', 'Heat576'],\n                   ' points<\/span> ': [12, 15, 22, 29, 24]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n      team points\n0 Mavs2 12\n1 Nets44 15\n2 Kings33 22\n3 Cavs90 29\n4 Heat576 24\n<\/strong><\/pre>\n
 \uc608\uc2dc 1: \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790 \uc81c\uac70<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \uc5f4\uc758 \uac01 \ubb38\uc790\uc5f4\uc5d0\uc11c “avs”\ub97c \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #remove 'avs' from strings in team column\n<\/span>df[' team<\/span> '] = df[' team<\/span> ']. str<\/span> . replace<\/span> (' avs<\/span> ', '')\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n      team points\n0 M2 12\n1 Nets44 15\n2 Kings33 22\n3 C90 29\n4 Heat576 24\n<\/strong><\/pre>\n
 \ud300<\/strong> \uc5f4\uc5d0 “Mavs” \ubc0f “Cavs”\uac00 \ud3ec\ud568\ub41c \ud589\uc5d0\uc11c “avs”\uac00 \uc81c\uac70\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 2: \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \ubb38\uc790 \uc81c\uac70<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \uc5f4\uc758 \uac01 \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \ubb38\uc790\ub97c \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #remove letters from strings in team column\n<\/span>df[' team<\/span> '] = df[' team<\/span> ']. str<\/span> . replace<\/span> (' \\D<\/span> ', '', regex= True<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n  team points\n0 2 12\n1 44 15\n2 33 22\n3 90 29\n4,576 24\n<\/strong><\/pre>\n
 \ud300<\/strong> \uc5f4\uc758 \uac01 \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \ubb38\uc790\uac00 \uc81c\uac70\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc218\uce58\ub9cc \ub0a8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\uc2dc 3: \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \uc22b\uc790 \uc81c\uac70<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud300<\/strong> \uc5f4\uc758 \uac01 \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \uc22b\uc790\ub97c \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #remove numbers from strings in team column\n<\/span>df[' team<\/span> '] = df[' team<\/span> ']. str<\/span> . replace<\/span> (' \\d+<\/span> ', '', regex= True<\/span> )\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n    team points\n0 Mavs 12\n1 Nets 15\n2 Kings 22\n3 Cavs 29\n4 Heat 24\n<\/strong><\/pre>\n
 \ud300<\/strong> \uc5f4\uc758 \uac01 \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \uc22b\uc790\uac00 \uc81c\uac70\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uae00\uc790\ub9cc \ub0a8\uc558\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\uc5d0\uc11c NaN \uac12\uc744 0\uc73c\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95<\/a>
 Pandas\uc5d0\uc11c \ube48 \ubb38\uc790\uc5f4\uc744 NaN\uc73c\ub85c \ubc14\uafb8\ub294 \ubc29\ubc95<\/a>
 Pandas\uc758 \uc870\uac74\uc5d0 \ub530\ub77c \uc5f4\uc758 \uac12\uc744 \ubc14\uafb8\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec Pandas DataFrame \uc5f4\uc758 \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790\ub97c \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790 \uc81c\uac70 df[‘ my_column ‘] = df[‘ my_column ‘]. str . replace (‘ this_string ‘, ”) \ubc29\ubc95 2: \ubb38\uc790\uc5f4\uc5d0\uc11c \ubaa8\ub4e0 \ubb38\uc790 \uc81c\uac70 df[‘ my_column ‘] = df[‘ my_column ‘]. str . replace (‘ \\D ‘, ”, regex= True ) […]<\/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-4036","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPandas: \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790\ub97c \uc81c\uac70\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc608\uc81c\ub97c \ud3ec\ud568\ud558\uc5ec Pandas DataFrame \uc5f4\uc758 \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790\ub97c \uc81c\uac70\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\" href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165\u1102\u1173\u11ab-\u1106\u116e\u11ab\u110c\u1161\u110b\u1167\u11af\u110b\u1166\u1109\u1165-\u1106\u116e\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pandas: \ubb38\uc790\uc5f4\uc5d0\uc11c \ud2b9\uc815 \ubb38\uc790\ub97c \uc81c\uac70\ud558\ub294 \ubc29\ubc95 - 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