{"id":2234,"date":"2023-07-23T03:24:57","date_gmt":"2023-07-23T03:24:57","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%92%e1%85%a1%e1%86%ba-%e1%84%8b%e1%85%b5%e1%86%ab%e1%84%8f%e1%85%a9%e1%84%83%e1%85%b5%e1%86%bc\/"},"modified":"2023-07-23T03:24:57","modified_gmt":"2023-07-23T03:24:57","slug":"%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%92%e1%85%a1%e1%86%ba-%e1%84%8b%e1%85%b5%e1%86%ab%e1%84%8f%e1%85%a9%e1%84%83%e1%85%b5%e1%86%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab%e1%84%8b%e1%85%b4-%e1%84%92%e1%85%a1%e1%86%ba-%e1%84%8b%e1%85%b5%e1%86%ab%e1%84%8f%e1%85%a9%e1%84%83%e1%85%b5%e1%86%bc\/","title":{"rendered":"Python\uc5d0\uc11c \uc6d0-\ud56b \uc778\ucf54\ub529\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

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\uc6d0-\ud56b \uc778\ucf54\ub529\uc740<\/strong> \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uae30\uacc4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998<\/a> \uc5d0\uc11c \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/span><\/p>\n

\uc6d0-\ud56b \ucf54\ub529\uc758 \uae30\ubcf8 \uc544\uc774\ub514\uc5b4\ub294 \uc6d0\ub798 \ubc94\uc8fc\ud615 \uac12\uc744 \ub098\ud0c0\ub0b4\uae30 \uc704\ud574 0\uacfc 1\uc758 \uac12\uc744 \ucde8\ud558\ub294 \uc0c8\ub85c\uc6b4 \ubcc0\uc218\ub97c \ub9cc\ub4dc\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc74c \uc774\ubbf8\uc9c0\ub294 \ud300 \uc774\ub984\uc774 \ud3ec\ud568\ub41c \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c 0\uacfc 1 \uac12\ub9cc \ud3ec\ud568\ud558\ub294 \uc0c8 \ubcc0\uc218\ub85c \ubcc0\ud658\ud558\uae30 \uc704\ud574 \uc6d0-\ud56b \uc778\ucf54\ub529\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span> <\/p>\n

\"\"<\/p>\n

\ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc81c\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \uc774 \uc815\ud655\ud55c \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub300\ud574 \uc6d0-\ud56b \uc778\ucf54\ub529\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/strong><\/span><\/h3>\n

\uba3c\uc800 \ub2e4\uc74c pandas DataFrame\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'],\n                   ' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points\n0 to 25\n1 to 12\n2 B 15\n3 B 14\n4 B 19\n5 B 23\n6 C 25\n7 C 29<\/strong><\/pre>\n
 2\ub2e8\uacc4: \uc6d0-\ud56b \uc778\ucf54\ub529 \uc218\ud589<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c sklearn<\/strong> \ub77c\uc774\ube0c\ub7ec\ub9ac\uc5d0\uc11c OneHotEncoder()<\/strong> \ud568\uc218\ub97c \uac00\uc838\uc640\uc11c \uc774\ub97c \uc0ac\uc6a9\ud558\uc5ec pandas DataFrame\uc758 ‘team’ \ubcc0\uc218\uc5d0 \ub300\ud574 \ud56b \uc778\ucf54\ub529\uc744 \uc218\ud589\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 from<\/span> sklearn. preprocessing<\/span> import<\/span> OneHotEncoder\n\n#creating instance of one-hot-encoder\n<\/span>encoder = OneHotEncoder(handle_unknown=' ignore<\/span> ')\n\n#perform one-hot encoding on 'team' column \n<\/span>encoder_df = pd. DataFrame<\/span> ( encoder.fit_transform<\/span> (df[[' team<\/span> ']]). toarray<\/span> ())\n\n#merge one-hot encoded columns back with original DataFrame\n<\/span>final_df = df. join<\/span> (encoder_df)\n\n#view final df\n<\/span>print<\/span> (final_df)\n\n  team points 0 1 2\n0 to 25 1.0 0.0 0.0\n1 to 12 1.0 0.0 0.0\n2 B 15 0.0 1.0 0.0\n3 B 14 0.0 1.0 0.0\n4 B 19 0.0 1.0 0.0\n5 B 23 0.0 1.0 0.0\n6 C 25 0.0 0.0 1.0\n7 C 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 \uc6d0\ub798 “\ud300” \uc5f4\uc5d0 \uc138 \uac1c\uc758 \uace0\uc720 \uac12\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc73c\ubbc0\ub85c \uc138 \uac1c\uc758 \uc0c8 \uc5f4\uc774 DataFrame\uc5d0 \ucd94\uac00\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 3\ub2e8\uacc4: \uc6d0\ub798 \ubc94\uc8fc\ud615 \ubcc0\uc218 \uc81c\uac70<\/strong><\/span><\/h3>\n
 \ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc6d0\ub798 ‘team’ \ubcc0\uc218\ub294 \ub354 \uc774\uc0c1 \ud544\uc694\ud558\uc9c0 \uc54a\uc73c\ubbc0\ub85c DataFrame\uc5d0\uc11c \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #drop 'team' column\n<\/span>final_df. drop<\/span> (' team<\/span> ', axis= 1<\/span> , inplace= True<\/span> )\n\n#view final df\n<\/span>print<\/span> (final_df)\n\n   points 0 1 2\n0 25 1.0 0.0 0.0\n1 12 1.0 0.0 0.0\n2 15 0.0 1.0 0.0\n3 14 0.0 1.0 0.0\n4 19 0.0 1.0 0.0\n5 23 0.0 1.0 0.0\n6 25 0.0 0.0 1.0\n7 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 \ub354 \uc27d\uac8c \uc77d\uc744 \uc218 \uc788\ub3c4\ub85d \ucd5c\uc885 DataFrame\uc758 \uc5f4 \uc774\ub984\uc744 \ubc14\uafc0 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #rename columns\n<\/span>final_df. columns<\/span> = ['points', 'teamA', 'teamB', 'teamC']\n\n#view final df<\/span>\nprint<\/span> (final_df)\n\n   points teamA teamB teamC\n0 25 1.0 0.0 0.0\n1 12 1.0 0.0 0.0\n2 15 0.0 1.0 0.0\n3 14 0.0 1.0 0.0\n4 19 0.0 1.0 0.0\n5 23 0.0 1.0 0.0\n6 25 0.0 0.0 1.0\n7 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 \uc6d0-\ud56b \uc778\ucf54\ub529\uc774 \uc644\ub8cc\ub418\uc5c8\uc73c\uba70 \uc774\uc81c \uc774 \ud32c\ub354 DataFrame\uc744 \uc6d0\ud558\ub294 \uae30\uacc4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc5d0 \uc0bd\uc785\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 Python\uc5d0\uc11c \uc808\uc0ac \ud3c9\uade0\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc6d0-\ud56b \uc778\ucf54\ub529\uc740 \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uae30\uacc4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998 \uc5d0\uc11c \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc6d0-\ud56b \ucf54\ub529\uc758 \uae30\ubcf8 \uc544\uc774\ub514\uc5b4\ub294 \uc6d0\ub798 \ubc94\uc8fc\ud615 \uac12\uc744 \ub098\ud0c0\ub0b4\uae30 \uc704\ud574 0\uacfc 1\uc758 \uac12\uc744 \ucde8\ud558\ub294 \uc0c8\ub85c\uc6b4 \ubcc0\uc218\ub97c \ub9cc\ub4dc\ub294 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub2e4\uc74c \uc774\ubbf8\uc9c0\ub294 \ud300 \uc774\ub984\uc774 \ud3ec\ud568\ub41c \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c 0\uacfc 1 \uac12\ub9cc \ud3ec\ud568\ud558\ub294 \uc0c8 \ubcc0\uc218\ub85c \ubcc0\ud658\ud558\uae30 \uc704\ud574 \uc6d0-\ud56b \uc778\ucf54\ub529\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ub2e4\uc74c […]<\/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-2234","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc5d0\uc11c \uc6d0-\ud56b \uc778\ucf54\ub529\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95 - 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 \uad00\ub828 \ud56d\ubaa9:<\/strong><\/span> Pandas\uc5d0\uc11c \uc5f4\uc744 \uc0ad\uc81c\ud558\ub294 \ubc29\ubc95(4\uac00\uc9c0 \ubc29\ubc95)<\/a><\/p>\n
 \ucc38\uace0<\/strong> : \uc5ec\uae30\uc5d0\uc11c<\/a> OneHotEncoder()<\/strong> \ud568\uc218\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub97c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n