{"id":2078,"date":"2023-07-23T19:27:32","date_gmt":"2023-07-23T19:27:32","guid":{"rendered":"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/"},"modified":"2023-07-23T19:27:32","modified_gmt":"2023-07-23T19:27:32","slug":"normalizuj-dane-w-pythonie","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/","title":{"rendered":"Jak normalizowa\u0107 dane w pythonie"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Cz\u0119sto w statystyce i uczeniu maszynowym <strong>normalizujemy<\/strong> zmienne w taki spos\u00f3b, \u017ce zakres warto\u015bci mie\u015bci si\u0119 w przedziale od 0 do 1.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Najcz\u0119stszym powodem normalizacji zmiennych jest sytuacja, gdy przeprowadzamy pewnego rodzaju analiz\u0119 wielowymiarow\u0105 (tj. chcemy zrozumie\u0107 zwi\u0105zek pomi\u0119dzy kilkoma zmiennymi predykcyjnymi a zmienn\u0105 odpowiedzi) i chcemy, aby ka\u017cda zmienna w r\u00f3wnym stopniu przyczynia\u0142a si\u0119 do analizy.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kiedy zmienne s\u0105 mierzone w r\u00f3\u017cnych skalach, cz\u0119sto nie wnosz\u0105 one jednakowego wk\u0142adu do analizy. Przyk\u0142adowo, je\u015bli warto\u015bci jednej zmiennej mieszcz\u0105 si\u0119 w przedziale od 0 do 100 000, a warto\u015bci innej zmiennej mieszcz\u0105 si\u0119 w przedziale od 0 do 100, to zmienna o wi\u0119kszym zakresie b\u0119dzie mia\u0142a w analizie wi\u0119ksz\u0105 wag\u0119.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Standaryzuj\u0105c zmienne, mo\u017cemy mie\u0107 pewno\u015b\u0107, \u017ce ka\u017cda zmienna w r\u00f3wnym stopniu przyczynia si\u0119 do analizy.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Aby znormalizowa\u0107 warto\u015bci od 0 do 1, mo\u017cemy u\u017cy\u0107 nast\u0119puj\u0105cego wzoru:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>x <sub>norma<\/sub> = (x <sub>i<\/sub> \u2013 x <sub>min<\/sub> ) \/ (x <sub>max<\/sub> \u2013 x <sub>min<\/sub> )<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Z\u0142oto:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>x <sub>norma<\/sub> :<\/strong> <sup>i-ta<\/sup> znormalizowana warto\u015b\u0107 w zbiorze danych<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>x <sub>i<\/sub> :<\/strong> <sup>i-ta<\/sup> warto\u015b\u0107 zbioru danych<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>x <sub>max<\/sub><\/strong> : Minimalna warto\u015b\u0107 w zbiorze danych<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>x <sub>min<\/sub> :<\/strong> Maksymalna warto\u015b\u0107 w zestawie danych<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Poni\u017csze przyk\u0142ady pokazuj\u0105, jak normalizowa\u0107 jedn\u0105 lub wi\u0119cej zmiennych w Pythonie.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Przyk\u0142ad 1: normalizacja tablicy NumPy<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak znormalizowa\u0107 wszystkie warto\u015bci w tablicy NumPy:<\/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;\">#create NumPy array\n<\/span>data = np. <span style=\"color: #3366ff;\">array<\/span> ([[13, 16, 19, 22, 23, 38, 47, 56, 58, 63, 65, 70, 71]])\n\n<span style=\"color: #008080;\">#normalize all values in array\n<\/span>data_norm = (data - data. <span style=\"color: #3366ff;\">min<\/span> ())\/ (data. <span style=\"color: #3366ff;\">max<\/span> () - data. <span style=\"color: #3366ff;\">min<\/span> ())\n\n<span style=\"color: #008080;\">#view normalized values\n<\/span>data_norm\n\narray([[0. , 0.05172414, 0.10344828, 0.15517241, 0.17241379,\n        0.43103448, 0.5862069, 0.74137931, 0.77586207, 0.86206897,\n        0.89655172, 0.98275862, 1. ]])<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Ka\u017cda z warto\u015bci w znormalizowanej tablicy ma teraz warto\u015b\u0107 od 0 do 1.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Przyk\u0142ad 2: Normalizuj wszystkie zmienne w Pandas DataFrame<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak normalizowa\u0107 wszystkie zmienne w ramce DataFrame pandy:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n<span style=\"color: #008080;\">#normalize values in every column\n<\/span>df_norm = (df-df. <span style=\"color: #3366ff;\">min<\/span> ())\/ (df. <span style=\"color: #3366ff;\">max<\/span> () - df. <span style=\"color: #3366ff;\">min<\/span> ())\n\n<span style=\"color: #008080;\">#view normalized DataFrame\n<\/span>df_norm\n\n        points assists rebounds\n0 0.764706 0.125 0.857143\n1 0.000000 0.375 0.428571\n2 0.176471 0.375 0.714286\n3 0.117647 0.625 0.142857\n4 0.411765 1.000 0.142857\n5 0.647059 0.625 0.000000\n6 0.764706 0.625 0.571429\n7 1.000000 0.000 1.000000\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Ka\u017cda z warto\u015bci w ka\u017cdej kolumnie wynosi teraz od 0 do 1.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Przyk\u0142ad 3: Normalizuj okre\u015blone zmienne w Pandas DataFrame<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017cszy kod pokazuje, jak znormalizowa\u0107 okre\u015blon\u0105 zmienn\u0105 w ramce DataFrame pandy:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame<\/span>\ndf = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29],\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n                   ' <span style=\"color: #ff0000;\">rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n<span style=\"color: #008080;\">define columns to normalize<\/span>\nx = df. <span style=\"color: #3366ff;\">iloc<\/span> [:,0:2]\n\n<span style=\"color: #008080;\">#normalize values in first two columns only<\/span>\ndf. <span style=\"color: #3366ff;\">iloc<\/span> [:,0:2] = (xx. <span style=\"color: #3366ff;\">min<\/span> ())\/ (x. <span style=\"color: #3366ff;\">max<\/span> () - x. <span style=\"color: #3366ff;\">min<\/span> ())\n\n<span style=\"color: #008080;\">#view normalized DataFrame<\/span>\ndf\n\n\tpoints assists rebounds\n0 0.764706 0.125 11\n1 0.000000 0.375 8\n2 0.176471 0.375 10\n3 0.117647 0.625 6\n4 0.411765 1.000 6\n5 0.647059 0.625 5\n6 0.764706 0.625 9\n7 1.000000 0.000 12\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Nale\u017cy pami\u0119ta\u0107, \u017ce normalizowane s\u0105 tylko warto\u015bci w pierwszych dw\u00f3ch kolumnach.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Dodatkowe zasoby<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poni\u017csze samouczki zawieraj\u0105 dodatkowe informacje na temat normalizacji danych:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/pl\/normalizuj-dane-od-0-do-1\/\" target=\"_blank\" rel=\"noopener\">Jak normalizowa\u0107 dane mi\u0119dzy 0 a 1<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/normalizuj-dane-w-zakresie-od-0-do-100\/\" target=\"_blank\" rel=\"noopener\">Jak normalizowa\u0107 dane od 0 do 100<\/a><br \/> <a href=\"https:\/\/statorials.org\/pl\/standaryzacja-vs-normalizacja\/\" target=\"_blank\" rel=\"noopener\">Standaryzacja czy normalizacja: jaka jest r\u00f3\u017cnica?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cz\u0119sto w statystyce i uczeniu maszynowym normalizujemy zmienne w taki spos\u00f3b, \u017ce zakres warto\u015bci mie\u015bci si\u0119 w przedziale od 0 do 1. Najcz\u0119stszym powodem normalizacji zmiennych jest sytuacja, gdy przeprowadzamy pewnego rodzaju analiz\u0119 wielowymiarow\u0105 (tj. chcemy zrozumie\u0107 zwi\u0105zek pomi\u0119dzy kilkoma zmiennymi predykcyjnymi a zmienn\u0105 odpowiedzi) i chcemy, aby ka\u017cda zmienna w r\u00f3wnym stopniu przyczynia\u0142a si\u0119 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-2078","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Jak normalizowa\u0107 dane w Pythonie - Statologia<\/title>\n<meta name=\"description\" content=\"W tym samouczku wyja\u015bniono, jak normalizowa\u0107 dane w j\u0119zyku Python, podaj\u0105c kilka przyk\u0142ad\u00f3w.\" \/>\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\/pl\/normalizuj-dane-w-pythonie\/\" \/>\n<meta property=\"og:locale\" content=\"pl_PL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Jak normalizowa\u0107 dane w Pythonie - Statologia\" \/>\n<meta property=\"og:description\" content=\"W tym samouczku wyja\u015bniono, jak normalizowa\u0107 dane w j\u0119zyku Python, podaj\u0105c kilka przyk\u0142ad\u00f3w.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-23T19:27:32+00:00\" \/>\n<meta name=\"author\" content=\"Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Napisane przez\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Szacowany czas czytania\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minuty\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/\",\"url\":\"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/\",\"name\":\"Jak normalizowa\u0107 dane w Pythonie - Statologia\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/pl\/#website\"},\"datePublished\":\"2023-07-23T19:27:32+00:00\",\"dateModified\":\"2023-07-23T19:27:32+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\"},\"description\":\"W tym samouczku wyja\u015bniono, jak normalizowa\u0107 dane w j\u0119zyku Python, podaj\u0105c kilka przyk\u0142ad\u00f3w.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/#breadcrumb\"},\"inLanguage\":\"pl-PL\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Dom\",\"item\":\"https:\/\/statorials.org\/pl\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Jak normalizowa\u0107 dane w pythonie\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/pl\/#website\",\"url\":\"https:\/\/statorials.org\/pl\/\",\"name\":\"Statorials\",\"description\":\"Tw\u00f3j przewodnik po kompetencjach statystycznych!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/pl\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pl-PL\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965\",\"name\":\"Benjamin Anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pl-PL\",\"@id\":\"https:\/\/statorials.org\/pl\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin Anderson\"},\"description\":\"Cze\u015b\u0107, jestem Benjamin i jestem emerytowanym profesorem statystyki, kt\u00f3ry zosta\u0142 oddanym nauczycielem Statorials. Dzi\u0119ki bogatemu do\u015bwiadczeniu i wiedzy specjalistycznej w dziedzinie statystyki ch\u0119tnie dziel\u0119 si\u0119 swoj\u0105 wiedz\u0105, aby wzmocni\u0107 pozycj\u0119 uczni\u00f3w za po\u015brednictwem Statorials. Wiedzie\u0107 wi\u0119cej\",\"sameAs\":[\"https:\/\/statorials.org\/pl\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Jak normalizowa\u0107 dane w Pythonie - Statologia","description":"W tym samouczku wyja\u015bniono, jak normalizowa\u0107 dane w j\u0119zyku Python, podaj\u0105c kilka przyk\u0142ad\u00f3w.","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\/pl\/normalizuj-dane-w-pythonie\/","og_locale":"pl_PL","og_type":"article","og_title":"Jak normalizowa\u0107 dane w Pythonie - Statologia","og_description":"W tym samouczku wyja\u015bniono, jak normalizowa\u0107 dane w j\u0119zyku Python, podaj\u0105c kilka przyk\u0142ad\u00f3w.","og_url":"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/","og_site_name":"Statorials","article_published_time":"2023-07-23T19:27:32+00:00","author":"Benjamin Anderson","twitter_card":"summary_large_image","twitter_misc":{"Napisane przez":"Benjamin Anderson","Szacowany czas czytania":"2 minuty"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/","url":"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/","name":"Jak normalizowa\u0107 dane w Pythonie - Statologia","isPartOf":{"@id":"https:\/\/statorials.org\/pl\/#website"},"datePublished":"2023-07-23T19:27:32+00:00","dateModified":"2023-07-23T19:27:32+00:00","author":{"@id":"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965"},"description":"W tym samouczku wyja\u015bniono, jak normalizowa\u0107 dane w j\u0119zyku Python, podaj\u0105c kilka przyk\u0142ad\u00f3w.","breadcrumb":{"@id":"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/#breadcrumb"},"inLanguage":"pl-PL","potentialAction":[{"@type":"ReadAction","target":["https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/statorials.org\/pl\/normalizuj-dane-w-pythonie\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Dom","item":"https:\/\/statorials.org\/pl\/"},{"@type":"ListItem","position":2,"name":"Jak normalizowa\u0107 dane w pythonie"}]},{"@type":"WebSite","@id":"https:\/\/statorials.org\/pl\/#website","url":"https:\/\/statorials.org\/pl\/","name":"Statorials","description":"Tw\u00f3j przewodnik po kompetencjach statystycznych!","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/statorials.org\/pl\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"pl-PL"},{"@type":"Person","@id":"https:\/\/statorials.org\/pl\/#\/schema\/person\/6484727a4612df3e69f016c3129c6965","name":"Benjamin Anderson","image":{"@type":"ImageObject","inLanguage":"pl-PL","@id":"https:\/\/statorials.org\/pl\/#\/schema\/person\/image\/","url":"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg","contentUrl":"https:\/\/statorials.org\/pl\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg","caption":"Benjamin Anderson"},"description":"Cze\u015b\u0107, jestem Benjamin i jestem emerytowanym profesorem statystyki, kt\u00f3ry zosta\u0142 oddanym nauczycielem Statorials. Dzi\u0119ki bogatemu do\u015bwiadczeniu i wiedzy specjalistycznej w dziedzinie statystyki ch\u0119tnie dziel\u0119 si\u0119 swoj\u0105 wiedz\u0105, aby wzmocni\u0107 pozycj\u0119 uczni\u00f3w za po\u015brednictwem Statorials. Wiedzie\u0107 wi\u0119cej","sameAs":["https:\/\/statorials.org\/pl"]}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"_links":{"self":[{"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/posts\/2078","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/comments?post=2078"}],"version-history":[{"count":0,"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/posts\/2078\/revisions"}],"wp:attachment":[{"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/media?parent=2078"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/categories?post=2078"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statorials.org\/pl\/wp-json\/wp\/v2\/tags?post=2078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}