{"version":"1.0","provider_name":"insideBIGDATA","provider_url":"https:\/\/insidebigdata.com","author_name":"Editorial Team","author_url":"https:\/\/insidebigdata.com\/author\/editorial\/","title":"Circular Statistics in Python: An Intuitive Intro - insideBIGDATA","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"dr7reJx6ps\"><a href=\"https:\/\/insidebigdata.com\/2021\/02\/12\/circular-statistics-in-python-an-intuitive-intro\/\">Circular Statistics in Python: An Intuitive Intro<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/insidebigdata.com\/2021\/02\/12\/circular-statistics-in-python-an-intuitive-intro\/embed\/#?secret=dr7reJx6ps\" width=\"600\" height=\"338\" title=\"&#8220;Circular Statistics in Python: An Intuitive Intro&#8221; &#8212; insideBIGDATA\" data-secret=\"dr7reJx6ps\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/*! This file is auto-generated *\/\n!function(c,d){\"use strict\";var e=!1,o=!1;if(d.querySelector)if(c.addEventListener)e=!0;if(c.wp=c.wp||{},c.wp.receiveEmbedMessage);else if(c.wp.receiveEmbedMessage=function(e){var t=e.data;if(!t);else if(!(t.secret||t.message||t.value));else if(\/[^a-zA-Z0-9]\/.test(t.secret));else{for(var r,s,a,i=d.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),n=d.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),o=new RegExp(\"^https?:$\",\"i\"),l=0;l<n.length;l++)n[l].style.display=\"none\";for(l=0;l<i.length;l++)if(r=i[l],e.source!==r.contentWindow);else{if(r.removeAttribute(\"style\"),\"height\"===t.message){if(1e3<(s=parseInt(t.value,10)))s=1e3;else if(~~s<200)s=200;r.height=s}if(\"link\"===t.message)if(s=d.createElement(\"a\"),a=d.createElement(\"a\"),s.href=r.getAttribute(\"src\"),a.href=t.value,!o.test(a.protocol));else if(a.host===s.host)if(d.activeElement===r)c.top.location.href=t.value}}},e)c.addEventListener(\"message\",c.wp.receiveEmbedMessage,!1),d.addEventListener(\"DOMContentLoaded\",t,!1),c.addEventListener(\"load\",t,!1);function t(){if(o);else{o=!0;for(var e,t,r,s=-1!==navigator.appVersion.indexOf(\"MSIE 10\"),a=!!navigator.userAgent.match(\/Trident.*rv:11\\.\/),i=d.querySelectorAll(\"iframe.wp-embedded-content\"),n=0;n<i.length;n++){if(!(r=(t=i[n]).getAttribute(\"data-secret\")))r=Math.random().toString(36).substr(2,10),t.src+=\"#?secret=\"+r,t.setAttribute(\"data-secret\",r);if(s||a)(e=t.cloneNode(!0)).removeAttribute(\"security\"),t.parentNode.replaceChild(e,t);t.contentWindow.postMessage({message:\"ready\",secret:r},\"*\")}}}}(window,document);\n<\/script>\n","thumbnail_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2020\/09\/data_engineering_shutterstock_669226057.jpg","thumbnail_width":300,"thumbnail_height":168,"description":"In this contributed article, Amit Babayoff, a data scientist at Deeyook, discusses the principles of circular statistics, by looking at some its basic principles and tools and why conventional linear methods don\u2019t work well on circular data. She also explores how a simple filtering for handling noise can be constructed from these basic tools."}