{"id":2495,"date":"2023-07-22T00:36:10","date_gmt":"2023-07-22T00:36:10","guid":{"rendered":"https:\/\/statorials.org\/ko\/numpy-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%80%e1%85%b2%e1%84%92%e1%85%aa-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%85%e1%85%a7%e1%86%af\/"},"modified":"2023-07-22T00:36:10","modified_gmt":"2023-07-22T00:36:10","slug":"numpy-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%80%e1%85%b2%e1%84%92%e1%85%aa-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%85%e1%85%a7%e1%86%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/numpy-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%80%e1%85%b2%e1%84%92%e1%85%aa-%e1%84%92%e1%85%a2%e1%86%bc%e1%84%85%e1%85%a7%e1%86%af\/","title":{"rendered":"Numpy \ud589\ub82c\uc744 \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95: \uc608\uc81c \ud3ec\ud568"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\ud589\ub82c\uc744 <strong>\uc815\uaddc\ud654\ud55c\ub2e4\ub294<\/strong> \uac83\uc740 \ud589 \ub610\ub294 \uc5f4 \uac12\uc758 \ubc94\uc704\uac00 0\uacfc 1 \uc0ac\uc774\uac00 \ub418\ub3c4\ub85d \uac12\uc758 \ud06c\uae30\ub97c \uc870\uc815\ud558\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">NumPy \ud589\ub82c\uc758 \uac12\uc744 \uc815\uaddc\ud654\ud558\ub294 \uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95\uc740 \ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 sklearn \ud328\ud0a4\uc9c0\uc758 <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.normalize.html\" target=\"_blank\" rel=\"noopener\">Normalize()<\/a> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize rows of matrix\n<\/span>normalize(x, axis= <span style=\"color: #008000;\">1<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#normalize columns of matrix\n<span style=\"color: #000000;\">normalize(x, axis= <span style=\"color: #008000;\">0<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')<\/span>\n<\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc774 \uad6c\ubb38\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\uc608 1: NumPy \ud589\ub82c\uc758 \ud589 \uc815\uaddc\ud654<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uacfc \uac19\uc740 NumPy \ud589\ub82c\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create matrix\n<\/span>x = np. <span style=\"color: #3366ff;\">arange<\/span> (0, 36, 4). <span style=\"color: #3366ff;\">reshape<\/span> (3,3)\n\n<span style=\"color: #008080;\">#view matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x)\n\n[[ 0 4 8]\n [12 16 20]\n [24 28 32]]\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 NumPy \ud589\ub82c\uc758 \ud589\uc744 \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize matrix by rows\n<\/span>x_normed = normalize(x, axis= <span style=\"color: #008000;\">1<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#view normalized matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x_normed)\n\n[[0. 0.33333333 0.66666667]\n [0.25 0.33333333 0.41666667]\n [0.28571429 0.33333333 0.38095238]]<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\uc774\uc81c \uac01 \ud589\uc758 \uac12\uc744 \ub354\ud558\uba74 1\uc774 \ub429\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uccab \ubc88\uc9f8 \uc904\uc758 \ud569: 0 + 0.33 + 0.67 = <strong>1<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\ub450 \ubc88\uc9f8 \uc904\uc758 \ud569: 0.25 + 0.33 + 0.417 = <strong>1<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc138 \ubc88\uc9f8 \ud589\uc758 \ud569: 0.2857 + 0.3333 + 0.3809 = <strong>1<\/strong><\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>\uc608\uc81c 2: NumPy \ud589\ub82c\uc758 \uc5f4 \uc815\uaddc\ud654<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uacfc \uac19\uc740 NumPy \ud589\ub82c\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#create matrix\n<\/span>x = np. <span style=\"color: #3366ff;\">arange<\/span> (0, 36, 4). <span style=\"color: #3366ff;\">reshape<\/span> (3,3)\n\n<span style=\"color: #008080;\">#view matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x)\n\n[[ 0 4 8]\n [12 16 20]\n [24 28 32]]\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub294 NumPy \ud589\ub82c\uc758 \ud589\uc744 \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">preprocessing<\/span> <span style=\"color: #008000;\">import<\/span> normalize\n\n<span style=\"color: #008080;\">#normalize matrix by columns\n<\/span>x_normed = normalize(x, axis= <span style=\"color: #008000;\">0<\/span> , norm=' <span style=\"color: #ff0000;\">l1<\/span> ')\n\n<span style=\"color: #008080;\">#view normalized matrix\n<\/span><span style=\"color: #008000;\">print<\/span> (x_normed)\n\n[[0. 0.08333333 0.13333333]\n [0.33333333 0.33333333 0.33333333]\n [0.66666667 0.58333333 0.53333333]]<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\uc774\uc81c \uac01 \uc5f4\uc758 \uac12\uc744 \ub354\ud558\uba74 1\uc774 \ub429\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uccab \ubc88\uc9f8 \uc5f4\uc758 \ud569: 0 + 0.33 + 0.67 = <strong>1<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\ub450 \ubc88\uc9f8 \uc5f4\uc758 \ud569: 0.083 + 0.333 + 0.583 = <strong>1<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc138 \ubc88\uc9f8 \uc5f4\uc758 \ud569: 0.133 + 0.333 + 0.5333 = <strong>1<\/strong><\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\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<p> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\u110b\u1166\u1109\u1165-\u1103\u1166\u110b\u1175\u1110\u1165-\u110c\u1165\u11bc\u1100\u1172\u1112\u116a\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ubc30\uc5f4\uc744 \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1162\u11ab\u1103\u1165-\u1103\u1166\u110b\u1175\u1110\u1165-\u1111\u1173\u1105\u1166\u110b\u1175\u11b7-\u110b\u1167\u11af-\u110c\u1165\u11bc\u1100\u1172\u1112\u116a\/\" target=\"_blank\" rel=\"noopener\">Pandas DataFrame\uc758 \uc5f4\uc744 \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ud589\ub82c\uc744 \uc815\uaddc\ud654\ud55c\ub2e4\ub294 \uac83\uc740 \ud589 \ub610\ub294 \uc5f4 \uac12\uc758 \ubc94\uc704\uac00 0\uacfc 1 \uc0ac\uc774\uac00 \ub418\ub3c4\ub85d \uac12\uc758 \ud06c\uae30\ub97c \uc870\uc815\ud558\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. NumPy \ud589\ub82c\uc758 \uac12\uc744 \uc815\uaddc\ud654\ud558\ub294 \uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95\uc740 \ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 sklearn \ud328\ud0a4\uc9c0\uc758 Normalize() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. from sklearn. preprocessing import normalize #normalize rows of matrix normalize(x, axis= 1 , norm=&#8217; l1 &#8216;) #normalize columns of matrix normalize(x, [&hellip;]<\/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-2495","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>NumPy \ud589\ub82c\uc744 \uc815\uaddc\ud654\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568) - 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