{"id":792,"date":"2020-08-21T08:17:25","date_gmt":"2020-08-21T08:17:25","guid":{"rendered":"https:\/\/ricovictor.com\/?p=792"},"modified":"2026-01-29T22:07:36","modified_gmt":"2026-01-29T22:07:36","slug":"pre-procesamiento-de-datos-en-python-deteccion-de-outliers","status":"publish","type":"post","link":"https:\/\/ricovictor.com\/index.php\/2020\/08\/21\/pre-procesamiento-de-datos-en-python-deteccion-de-outliers\/","title":{"rendered":"Pre-Procesamiento de Datos en Python: Detecci\u00f3n de Outliers"},"content":{"rendered":"\t\t
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Anteriormente ya explicamos como trabajar cuando ten\u00edamos valores perdidos<\/a><\/strong> en nuestros datos.Si no viste ese art\u00edculo puedes recordarlo aqu\u00ed<\/a><\/strong>.<\/p>\n

Despu\u00e9s de eso vamos ahora a trabajar en la detecci\u00f3n de outliers<\/strong>.<\/p>\n

Un outlier<\/strong> es un valor que num\u00e9ricamente es muy diferente<\/strong> al resto de los datos, lo que puede afectar al momento de su an\u00e1lisis pudiendo ser enga\u00f1osos los resultados obtenidos, por lo que es aconsejable detectarlos<\/strong> y retirarlos antes de seguir adelante con el mismo.<\/p>\n

En el siguiente v\u00eddeo hablo sobre este tema.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t

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Si quieres aprender m\u00e1s acerca de este tipo de t\u00e9cnicas, suscr\u00edbete a mi canal o visita mi p\u00e1gina rociochavezml.com<\/strong>, en donde estar\u00e9 subiendo videos de Machine Learning, Estad\u00edstica y de Matem\u00e1ticas en general aplicadas a los negocios.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\tAprende Arbitraje Estad\u00edstico<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\tCurso GRATIS Multicointegraci\u00f3n en FOREX<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Anteriormente ya explicamos como trabajar cuando ten\u00edamos valores perdidos en nuestros datos.Si no viste ese art\u00edculo puedes recordarlo aqu\u00ed. Despu\u00e9s de eso vamos ahora a trabajar en la detecci\u00f3n de outliers. Un outlier es un valor que num\u00e9ricamente es muy diferente al resto de los datos, lo que puede afectar al momento de su an\u00e1lisis …<\/p>\n

Pre-Procesamiento de Datos en Python: Detecci\u00f3n de Outliers<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":2,"featured_media":795,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[15],"tags":[],"yoast_head":"\nPre-Procesamiento de Datos en Python: Detecci\u00f3n de Outliers - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ricovictor.com\/index.php\/2020\/08\/21\/pre-procesamiento-de-datos-en-python-deteccion-de-outliers\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pre-Procesamiento de Datos en Python: Detecci\u00f3n de Outliers - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas\" \/>\n<meta property=\"og:description\" content=\"Anteriormente ya explicamos como trabajar cuando ten\u00edamos valores perdidos en nuestros datos.Si no viste ese art\u00edculo puedes recordarlo aqu\u00ed. Despu\u00e9s de eso vamos ahora a trabajar en la detecci\u00f3n de outliers. 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