{"id":1824,"date":"2023-07-24T20:30:21","date_gmt":"2023-07-24T20:30:21","guid":{"rendered":"https:\/\/statorials.org\/id\/standarisasi-data-python\/"},"modified":"2023-07-24T20:30:21","modified_gmt":"2023-07-24T20:30:21","slug":"standarisasi-data-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/standarisasi-data-python\/","title":{"rendered":"Cara menstandarkan data dengan python: dengan contoh"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Menstandarkan<\/strong> suatu kumpulan data berarti menskalakan semua nilai dalam kumpulan data sedemikian rupa sehingga nilai rata-ratanya adalah 0 dan simpangan bakunya adalah 1.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kami menggunakan rumus berikut untuk menormalkan nilai dalam kumpulan data:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>x <sub>baru<\/sub> = ( <sub>xi<\/sub> \u2013 <span style=\"text-decoration: overline;\">x<\/span> ) \/ s<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>x <sub>i<\/sub><\/strong> : nilai <sup>ke-i<\/sup> dari kumpulan data<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong><span style=\"text-decoration: overline;\">x<\/span><\/strong> : Maksud sampel<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>s<\/strong> : simpangan baku sampel<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Kita dapat menggunakan sintaks berikut untuk dengan cepat menormalkan semua kolom di pandas DataFrame dengan Python:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>(df- <span style=\"color: #3366ff;\">df.mean<\/span> ())\/df. <span style=\"color: #3366ff;\">std<\/span> ()\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan sintaksis ini dalam praktiknya.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 1: standarisasi semua kolom DataFrame<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menstandardisasi semua kolom di pandas DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create data frame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">y<\/span> ': [8, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">x1<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' <span style=\"color: #ff0000;\">x3<\/span> ': [2, 2, 3, 2, 5, 5, 7, 9]})\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n\ty x1 x2 x3\n0 8 5 11 2\n1 12 7 8 2\n2 15 7 10 3\n3 14 9 6 2\n4 19 12 6 5\n5 23 9 5 5\n6 25 9 9 7\n7 29 4 12 9\n\n<span style=\"color: #008080;\">#standardize the values in each column\n<\/span>df_new = (df- <span style=\"color: #3366ff;\">df.mean<\/span> ())\/df. <span style=\"color: #3366ff;\">std<\/span> ()\n\n<span style=\"color: #008080;\">#view new data frame\n<\/span>df_new\n\n\t        y x1 x2 x3\n0 -1.418032 -1.078639 1.025393 -0.908151\n1 -0.857822 -0.294174 -0.146485 -0.908151\n2 -0.437664 -0.294174 0.634767 -0.525772\n3 -0.577717 0.490290 -0.927736 -0.908151\n4 0.122546 1.666987 -0.927736 0.238987\n5 0.682756 0.490290 -1.318362 0.238987\n6 0.962861 0.490290 0.244141 1.003746\n7 1.523071 -1.470871 1.416019 1.768505<\/b><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat memverifikasi bahwa mean dan deviasi standar setiap kolom masing-masing sama dengan 0 dan 1:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008080;\">#view mean of each column\n<\/span>df_new. <span style=\"color: #3366ff;\">mean<\/span> ()\n\ny 0.000000e+00\nx1 2.775558e-17\nx2 -4.163336e-17\nx3 5.551115e-17\ndtype:float64\n\n<span style=\"color: #008080;\">#view standard deviation of each column\n<\/span>df_new. <span style=\"color: #3366ff;\">std<\/span> ()\n\ny 1.0\nx1 1.0\nx2 1.0\nx3 1.0\ndtype:float64\n<\/b><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Contoh 2: Normalisasikan kolom DataFrame tertentu<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Terkadang Anda mungkin hanya ingin menormalkan kolom tertentu di DataFrame.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Misalnya, untuk banyak algoritme pembelajaran mesin, Anda mungkin hanya ingin menstandarkan variabel prediktor sebelum menyesuaikan model tertentu ke data.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menstandardisasi kolom tertentu di pandas DataFrame:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#create data frame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">y<\/span> ': [8, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">x1<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n                   ' <span style=\"color: #ff0000;\">x3<\/span> ': [2, 2, 3, 2, 5, 5, 7, 9]})\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n\ty x1 x2 x3\n0 8 5 11 2\n1 12 7 8 2\n2 15 7 10 3\n3 14 9 6 2\n4 19 12 6 5\n5 23 9 5 5\n6 25 9 9 7\n7 29 4 12 9\n\n<span style=\"color: #008080;\">#define predictor variable columns<\/span>\ndf_x = df[[' <span style=\"color: #ff0000;\">x1<\/span> ', ' <span style=\"color: #ff0000;\">x2<\/span> ', ' <span style=\"color: #ff0000;\">x3<\/span> ']]\n\n<span style=\"color: #008080;\">#standardize the values for each predictor variable\n<\/span>df[[' <span style=\"color: #ff0000;\">x1<\/span> ',' <span style=\"color: #ff0000;\">x2<\/span> ',' <span style=\"color: #ff0000;\">x3<\/span> ']] = (df_x- <span style=\"color: #3366ff;\">df_x.mean<\/span> ())\/df_x. <span style=\"color: #3366ff;\">std<\/span> ()\n\n<span style=\"color: #008080;\">#view new data frame\n<\/span>df\n\n         y x1 x2 x3\n0 8 -1.078639 1.025393 -0.908151\n1 12 -0.294174 -0.146485 -0.908151\n2 15 -0.294174 0.634767 -0.525772\n3 14 0.490290 -0.927736 -0.908151\n4 19 1.666987 -0.927736 0.238987\n5 23 0.490290 -1.318362 0.238987\n6 25 0.490290 0.244141 1.003746\n7 29 -1.470871 1.416019 1.768505<\/b><\/pre>\n<p> <span style=\"color: #000000;\">Perhatikan bahwa kolom &#8220;y&#8221; tetap tidak berubah, tetapi kolom &#8220;x1&#8221;, &#8220;x2&#8221; dan &#8220;x3&#8221; semuanya terstandarisasi.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kita dapat memverifikasi bahwa mean dan deviasi standar setiap kolom variabel prediktor masing-masing sama dengan 0 dan 1:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <b><span style=\"color: #008080;\">#view mean of each predictor variable column\n<\/span>df[[' <span style=\"color: #ff0000;\">x1<\/span> ', ' <span style=\"color: #ff0000;\">x2<\/span> ', ' <span style=\"color: #ff0000;\">x3<\/span> ']]. <span style=\"color: #3366ff;\">mean<\/span> ()\n\nx1 2.775558e-17\nx2 -4.163336e-17\nx3 5.551115e-17\ndtype:float64\n\n<span style=\"color: #008080;\">#view standard deviation of each predictor variable column\n<\/span>df[[' <span style=\"color: #ff0000;\">x1<\/span> ', ' <span style=\"color: #ff0000;\">x2<\/span> ', ' <span style=\"color: #ff0000;\">x3<\/span> ']]. <span style=\"color: #3366ff;\">std<\/span> ()\n\nx1 1.0\nx2 1.0\nx3 1.0\ndtype:float64<\/b><\/pre>\n<h3> <strong><span style=\"color: #000000;\">Sumber daya tambahan<\/span><\/strong><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/id\/menormalkan-kolom-kerangka-data-panda\/\" target=\"_blank\" rel=\"noopener\">Cara menormalkan kolom di Pandas DataFrame<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/menghapus-pencilan-python\/\" target=\"_blank\" rel=\"noopener\">Cara Menghapus Pencilan dengan Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/standardisasi-vs-normalisasi\/\" target=\"_blank\" rel=\"noopener\">Standardisasi atau normalisasi: apa bedanya?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Menstandarkan suatu kumpulan data berarti menskalakan semua nilai dalam kumpulan data sedemikian rupa sehingga nilai rata-ratanya adalah 0 dan simpangan bakunya adalah 1. Kami menggunakan rumus berikut untuk menormalkan nilai dalam kumpulan data: x baru = ( xi \u2013 x ) \/ s Emas: x i : nilai ke-i dari kumpulan data x : Maksud [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Cara Menstandarkan Data dengan Python: Dengan Contoh<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara standarisasi data dengan Python, dengan beberapa contoh.\" \/>\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\/id\/standarisasi-data-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Menstandarkan Data dengan Python: Dengan Contoh\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara standarisasi data dengan Python, dengan beberapa contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/standarisasi-data-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-24T20:30:21+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Ditulis oleh\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimasi waktu membaca\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/standarisasi-data-python\/\",\"url\":\"https:\/\/statorials.org\/id\/standarisasi-data-python\/\",\"name\":\"Cara Menstandarkan Data dengan Python: Dengan Contoh\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-24T20:30:21+00:00\",\"dateModified\":\"2023-07-24T20:30:21+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara standarisasi data dengan Python, dengan beberapa contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/standarisasi-data-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/standarisasi-data-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/standarisasi-data-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menstandarkan data dengan python: dengan contoh\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/id\/#website\",\"url\":\"https:\/\/statorials.org\/id\/\",\"name\":\"Statorials\",\"description\":\"Panduan anda untuk kompetensi statistik!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/id\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"id\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"id\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. Dengan pengalaman dan keahlian yang luas di bidang statistika, saya ingin berbagi ilmu untuk memberdayakan mahasiswa melalui Statorials. Baca selengkapnya\",\"sameAs\":[\"http:\/\/statorials.org\/id\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Cara Menstandarkan Data dengan Python: Dengan Contoh","description":"Tutorial ini menjelaskan cara standarisasi data dengan Python, dengan beberapa contoh.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/statorials.org\/id\/standarisasi-data-python\/","og_locale":"id_ID","og_type":"article","og_title":"Cara Menstandarkan Data dengan Python: Dengan Contoh","og_description":"Tutorial ini menjelaskan cara standarisasi data dengan Python, dengan beberapa contoh.","og_url":"https:\/\/statorials.org\/id\/standarisasi-data-python\/","og_site_name":"Statorials","article_published_time":"2023-07-24T20:30:21+00:00","author":"Benjamin anderson","twitter_card":"summary_large_image","twitter_misc":{"Ditulis oleh":"Benjamin anderson","Estimasi waktu membaca":"2 menit"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/id\/standarisasi-data-python\/","url":"https:\/\/statorials.org\/id\/standarisasi-data-python\/","name":"Cara Menstandarkan Data dengan Python: Dengan Contoh","isPartOf":{"@id":"https:\/\/statorials.org\/id\/#website"},"datePublished":"2023-07-24T20:30:21+00:00","dateModified":"2023-07-24T20:30:21+00:00","author":{"@id":"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81"},"description":"Tutorial ini menjelaskan cara standarisasi data dengan Python, dengan beberapa contoh.","breadcrumb":{"@id":"https:\/\/statorials.org\/id\/standarisasi-data-python\/#breadcrumb"},"inLanguage":"id","potentialAction":[{"@type":"ReadAction","target":["https:\/\/statorials.org\/id\/standarisasi-data-python\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/statorials.org\/id\/standarisasi-data-python\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/statorials.org\/id\/"},{"@type":"ListItem","position":2,"name":"Cara menstandarkan data dengan python: dengan contoh"}]},{"@type":"WebSite","@id":"https:\/\/statorials.org\/id\/#website","url":"https:\/\/statorials.org\/id\/","name":"Statorials","description":"Panduan anda untuk kompetensi statistik!","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/statorials.org\/id\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"id"},{"@type":"Person","@id":"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81","name":"Benjamin anderson","image":{"@type":"ImageObject","inLanguage":"id","@id":"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/","url":"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","contentUrl":"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg","caption":"Benjamin anderson"},"description":"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. Dengan pengalaman dan keahlian yang luas di bidang statistika, saya ingin berbagi ilmu untuk memberdayakan mahasiswa melalui Statorials. Baca selengkapnya","sameAs":["http:\/\/statorials.org\/id"]}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"_links":{"self":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts\/1824"}],"collection":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/comments?post=1824"}],"version-history":[{"count":0,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts\/1824\/revisions"}],"wp:attachment":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/media?parent=1824"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/categories?post=1824"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/tags?post=1824"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}