{"id":1453,"date":"2026-01-09T23:10:36","date_gmt":"2026-01-10T04:10:36","guid":{"rendered":"https:\/\/freerdps.com\/blog\/?p=1453"},"modified":"2026-01-09T23:10:37","modified_gmt":"2026-01-10T04:10:37","slug":"cdf-vs-pd","status":"publish","type":"post","link":"https:\/\/freerdps.com\/blog\/cdf-vs-pd\/","title":{"rendered":"CDF vs PDF: What&#8217;s the Difference? A Super Simple Guide to Probability Functions"},"content":{"rendered":"\n<p>You\u2019re diving into statistics or data science, and suddenly you\u2019re hit with terms like <em>CDF<\/em> and <em>PDF<\/em>. Your brain freezes. Are these secret codes? Math jargon designed to make you feel lost? Nope!<\/p>\n\n\n\n<p>They\u2019re just tools to help you understand probabilities in a super clear way. Whether you\u2019re a student, a data analyst, or just curious about how probability works, understanding the difference between a <strong>Cumulative Distribution Function (CDF)<\/strong> and a <strong>Probability Density Function (PDF)<\/strong> is like unlocking a superpower for interpreting data.<\/p>\n\n\n\n<p>In this fun, beginner-friendly guide, we\u2019ll break down <strong>CDF vs PDF<\/strong> in a way that feels like chatting with a friend. No boring textbook vibes here! We\u2019ll use simple examples, visuals, and even a YouTube video to make things crystal clear.<\/p>\n\n\n\n<p>By the end, you\u2019ll not only know the difference but also feel confident using these concepts in real-world scenarios like data analysis, machine learning, or even gambling odds. <\/p>\n\n\n\n<p>Ready? Let\u2019s dive in!<\/p>\n\n\n\n<div id=\"affiliate-style-e751ce44-0792-42bd-be57-36614a1b56c9\" class=\"wp-block-affiliate-booster-ab-tableof-content affiliate-toc-align-left affiliate-toc-columns-1 affiliate-toc-collapse affiliate-block-e751ce44\" data-scroll=\"true\" data-offset=\"30\" data-delay=\"800\"><div class=\"affiliate-toc-inner affiliate-toc-islist affiliate-toc-align-\"><div class=\"affiliate-toc-wrap\"><div class=\"affiliate-toc-title-wrap\"><div class=\"affiliate-toc-title\">Table Of Contents<\/div><div class=\"affiliate-toc-collapsible-wrap affiliate-table-of-contents-toggle affiliate-toc-collapsed\"><a href=\"javascript:;\" class=\"affiliate-collapsible-icon affiliate-toc-close-icon fas fa-angle-up\"><\/a><a href=\"javascript:;\" class=\"affiliate-collapsible-icon affiliate-toc-open-icon fas fa-angle-down\"><\/a><\/div><\/div><div class=\"affiliate-toc-list-wrap\"><ul class=\"affiliate-toc-list desktop1 tablet1 mobile1\"><li><a href=\"#1--what-are-cdf-and-pdf-the-basics-\">What Are CDF and PDF? The Basics<\/a><ul class=\"affiliate-toc-list\"><li><a href=\"#2--probability-density-function-pdf-the-snapshot-of-probability-\">Probability Density Function (PDF): The Snapshot of Probability<\/a><\/li><li><a href=\"#3--cumulative-distribution-function-cdf-the-running-total-\">Cumulative Distribution Function (CDF): The Running Total<\/a><\/li><\/ul><\/li><li><a href=\"#4--cdf-vs-pdf-the-key-differences-\">CDF vs PDF: The Key Differences<\/a><\/li><li><a href=\"#5--when-to-use-cdf-vs-pdf-\">When to Use CDF vs PDF<\/a><ul class=\"affiliate-toc-list\"><li><a href=\"#6--when-to-use-a-pdf-\">When to Use a PDF<\/a><\/li><li><a href=\"#7--when-to-use-a-cdf-\">When to Use a CDF<\/a><\/li><\/ul><\/li><li><a href=\"#8--visualizing-cdf-vs-pdf-a-picture%E2%80%99s-worth-a-thousand-words-\">Visualizing CDF vs PDF: A Picture\u2019s Worth a Thousand Words<\/a><\/li><li><a href=\"#9--practical-applications-of-cdf-and-pdf-\">Practical Applications of CDF and PDF<\/a><\/li><li><a href=\"#10--tips-for-mastering-cdf-and-pdf-\">Tips for Mastering CDF and PDF<\/a><\/li><li><a href=\"#11--common-questions-about-cdf-vs-pdf-\">Common Questions About CDF vs PDF<\/a><\/li><li><a href=\"#12--conclusion-you%E2%80%99re-now-a-cdf-vs-pdf-pro-\">Conclusion: You\u2019re Now a CDF vs PDF Pro!<\/a><\/li><\/ul><\/div><\/div><\/div><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"1--what-are-cdf-and-pdf-the-basics-\"><strong>What Are CDF and PDF? The Basics<\/strong><\/h2>\n\n\n\n<p>Before we compare <strong>CDF vs PDF<\/strong>, let\u2019s get the basics down. Both are ways to describe probabilities, but they do it differently. Think of them as two sides of the same coin\u2014each tells you something about how likely things are to happen.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"2--probability-density-function-pdf-the-snapshot-of-probability-\"><strong>Probability Density Function (PDF): The Snapshot of Probability<\/strong><\/h3>\n\n\n\n<p>A <strong>PDF<\/strong>, or Probability Density Function, is like a snapshot of how probabilities are spread out for a <strong>continuous random variable<\/strong>. It tells you the likelihood of a variable taking on a specific value. For example, if you\u2019re measuring the height of people, the <a href=\"https:\/\/freerdps.com\/blog\/cutout-pro-review\/\">PDF shows<\/a> how likely it is for someone to be exactly 5\u20196\u201d or 6\u20192\u201d.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function-1024x576.jpg\" alt=\"Probability Density Function\" class=\"wp-image-1479\" srcset=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function-1024x576.jpg 1024w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function-300x169.jpg 300w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function-768x432.jpg 768w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function-400x225.jpg 400w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function-800x450.jpg 800w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function-832x468.jpg 832w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function-1248x702.jpg 1248w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Probability-Density-Function.jpg 1280w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Here\u2019s the catch: for continuous variables, the probability of hitting an <em>exact<\/em> value (like <em>exactly<\/em> 5.6 feet) is technically zero because there are infinite possible values. Instead, the PDF gives you a <em>density<\/em>\u2014a way to measure probability over a tiny range of values.<\/p>\n\n\n\n<p class=\"has-border-background-color has-background\"><strong>Example:<\/strong> Imagine you\u2019re rolling a super-fair die with infinite sides (weird, right?). The PDF would show the probability density for each possible outcome. For a normal distribution (that classic bell curve), the PDF looks like this:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"3--cumulative-distribution-function-cdf-the-running-total-\"><strong>Cumulative Distribution Function (CDF): The Running Total<\/strong><\/h3>\n\n\n\n<p>The <strong>CDF<\/strong>, or Cumulative Distribution Function, is like the PDF\u2019s big-picture sibling. It tells you the probability that a random variable is <em>less than or equal to<\/em> a certain value. It\u2019s a running total of probabilities, adding up as you move along the range of possible values.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"640\" height=\"480\" src=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Cumulative-Distribution-Function.jpg\" alt=\"\" class=\"wp-image-1480\" srcset=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Cumulative-Distribution-Function.jpg 640w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Cumulative-Distribution-Function-300x225.jpg 300w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/Cumulative-Distribution-Function-400x300.jpg 400w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><\/figure>\n\n\n\n<p><strong>Example:<\/strong> Going back to heights, the CDF would tell you the probability that someone\u2019s height is 5\u20196\u201d <em>or less<\/em>. As you move up the scale (5\u20197\u201d, 5\u20198\u201d, etc.), the CDF keeps climbing until it hits 1 (100% probability) when you include all possible heights.<\/p>\n\n\n\n<p>Here\u2019s what a CDF looks like for the same normal distribution:<\/p>\n\n\n\n<p>CDF Example<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"1025\" height=\"494\" src=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/S-shaped-curve-showing-the-CDF-for-a-normal-distribution.png\" alt=\"\" class=\"wp-image-1481\" srcset=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/S-shaped-curve-showing-the-CDF-for-a-normal-distribution.png 1025w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/S-shaped-curve-showing-the-CDF-for-a-normal-distribution-300x145.png 300w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/S-shaped-curve-showing-the-CDF-for-a-normal-distribution-768x370.png 768w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/S-shaped-curve-showing-the-CDF-for-a-normal-distribution-400x193.png 400w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/S-shaped-curve-showing-the-CDF-for-a-normal-distribution-800x386.png 800w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/S-shaped-curve-showing-the-CDF-for-a-normal-distribution-832x401.png 832w\" sizes=\"auto, (max-width: 1025px) 100vw, 1025px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"4--cdf-vs-pdf-the-key-differences-\"><strong>CDF vs PDF: The Key Differences<\/strong><\/h2>\n\n\n\n<p>Now that we\u2019ve got the basics, let\u2019s compare <strong>CDF vs PDF<\/strong> head-to-head. Here\u2019s a simple table to break it down:<\/p>\n\n\n\n<p><strong>The Math Behind It (Don\u2019t Worry, It\u2019s Simple!)<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"309\" src=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/image-1024x309.png\" alt=\"\" class=\"wp-image-1454\" srcset=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/image-1024x309.png 1024w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/image-300x91.png 300w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/image-768x232.png 768w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/image-400x121.png 400w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/image-800x241.png 800w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/image-832x251.png 832w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/image.png 1180w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>In plain English:<\/strong> the CDF is the \u201caccumulated\u201d area under the PDF up to a point.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-border-background-color has-background\"><strong>Real-World Example:<\/strong> Imagine you\u2019re predicting rainfall. The PDF tells you how likely it is to get exactly 2 inches of rain. The CDF tells you the chance of getting 2 inches <em>or less<\/em>. If you\u2019re planning a picnic, the CDF is probably more useful\u2014you want to know the odds of staying dry!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"5--when-to-use-cdf-vs-pdf-\"><strong>When to Use CDF vs PDF<\/strong><\/h2>\n\n\n\n<p>So, when do you actually use these? Let\u2019s break it down with some practical scenarios:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"6--when-to-use-a-pdf-\"><strong>When to Use a PDF<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Understanding Distribution Shapes<\/strong>: PDFs are great for visualizing how data is spread out. For example, in data science, you might use a PDF to see if your data follows a normal distribution or something spikier.<\/li>\n\n\n\n<li><strong>Machine Learning<\/strong>: PDFs are used in algorithms like kernel density estimation to model data distributions.<\/li>\n\n\n\n<li><strong>Example<\/strong>: A company analyzing customer purchase amounts might use a PDF to see the most common spending range.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"7--when-to-use-a-cdf-\"><strong>When to Use a CDF<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Cumulative Probabilities<\/strong>: CDFs are perfect when you need to know the probability of a value falling below a threshold. For instance, what\u2019s the chance a project finishes in 10 days or less?<\/li>\n\n\n\n<li><strong>Percentiles<\/strong>: Want to know the 90th percentile of test scores? The CDF has your back.<\/li>\n\n\n\n<li><strong>Example<\/strong>: In finance, a CDF can help calculate the probability that a stock price stays below a certain value.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-border-background-color has-background\"><strong>Pro Tip<\/strong>: If you\u2019re ever stuck choosing between CDF and PDF, ask yourself: \u201cDo I need the probability for a specific value (PDF) or a range up to a value (CDF)?\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"8--visualizing-cdf-vs-pdf-a-picture%E2%80%99s-worth-a-thousand-words-\"><strong>Visualizing CDF vs PDF: A Picture\u2019s Worth a Thousand Words<\/strong><\/h2>\n\n\n\n<p>To really get the difference, let\u2019s look at both functions for a normal distribution side by side:<\/p>\n\n\n\n<p><strong>PDF vs CDF Comparison<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"871\" height=\"633\" src=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/PDF-vs-CDF-Comparison.webp\" alt=\"PDF vs CDF Comparison\" class=\"wp-image-1482\" srcset=\"https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/PDF-vs-CDF-Comparison.webp 871w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/PDF-vs-CDF-Comparison-300x218.webp 300w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/PDF-vs-CDF-Comparison-768x558.webp 768w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/PDF-vs-CDF-Comparison-400x291.webp 400w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/PDF-vs-CDF-Comparison-800x581.webp 800w, https:\/\/freerdps.com\/blog\/wp-content\/uploads\/2025\/05\/PDF-vs-CDF-Comparison-832x605.webp 832w\" sizes=\"auto, (max-width: 871px) 100vw, 871px\" \/><\/figure>\n\n\n\n<p>Notice how the PDF gives you the \u201cshape\u201d of the data, while the CDF shows a cumulative buildup? The CDF always starts at 0 (no probability for values below the minimum) and ends at 1 (all possible values included).<\/p>\n\n\n\n<p>For a deeper dive, check out this awesome YouTube video that explains CDF and PDF with animations:<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Normal Distribution (PDF, CDF, PPF) in 3 Minutes\" width=\"1200\" height=\"675\" src=\"https:\/\/www.youtube.com\/embed\/3VYupIsbLlY?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"9--practical-applications-of-cdf-and-pdf-\"><strong>Practical Applications of CDF and PDF<\/strong><\/h2>\n\n\n\n<p>Here\u2019s where things get exciting\u2014how do CDF and PDF show up in real life?<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data Science &amp; Machine Learning<\/strong>: PDFs help model data distributions, while CDFs are used for tasks like calculating percentiles or evaluating model performance.<\/li>\n\n\n\n<li><strong>Finance<\/strong>: CDFs are crucial for risk analysis, like determining the probability of a stock price dropping below a threshold.<\/li>\n\n\n\n<li><strong>Engineering<\/strong>: PDFs model things like signal noise, while CDFs help calculate failure probabilities in reliability testing.<\/li>\n\n\n\n<li><strong>Weather Forecasting<\/strong>: Meteorologists use CDFs to predict the chance of rainfall below a certain amount.<\/li>\n<\/ul>\n\n\n\n<p class=\"has-border-background-color has-background\"><strong>Example<\/strong>: Suppose you\u2019re a data scientist analyzing customer wait times at a coffee shop. The PDF might show that most customers wait around 5 minutes, with fewer waiting 10+ minutes. The CDF could tell you that 90% of customers wait 7 minutes or less\u2014super useful for optimizing staff schedules!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"10--tips-for-mastering-cdf-and-pdf-\"><strong>Tips for Mastering CDF and PDF<\/strong><\/h2>\n\n\n\n<p>Want to nail these concepts? Here are some quick tips:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Practice with Visuals<\/strong>: Graph PDFs and CDFs for simple distributions (like normal or exponential) to see how they relate.<\/li>\n\n\n\n<li><strong>Use Software<\/strong>: Tools like Python (scipy.stats), R, or Excel can plot PDFs and CDFs for you. Try coding a normal distribution to see it in action!<\/li>\n\n\n\n<li><strong>Think Intuitively<\/strong>: PDFs are about density, CDFs are about accumulation. Keep that mental image handy.<\/li>\n\n\n\n<li><strong>Check Out Resources<\/strong>: Websites like Khan Academy, Coursera, or StatQuest have awesome tutorials on probability functions.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"11--common-questions-about-cdf-vs-pdf-\"><strong>Common Questions About CDF vs PDF<\/strong><\/h2>\n\n\n\n<p><strong>Let\u2019s tackle some FAQs to clear up any lingering confusion:<\/strong><\/p>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1747970317259\" class=\"rank-math-list-item\">\n<h6 class=\"rank-math-question \"><strong>1. Can a PDF Be Greater Than 1?<\/strong><\/h6>\n<div class=\"rank-math-answer \">\n\n<p>Yes! Since a PDF measures <em>density<\/em>, not probability, its value can exceed 1 over a small range. But the total area under the PDF curve always equals 1.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1747970326362\" class=\"rank-math-list-item\">\n<h6 class=\"rank-math-question \"><strong>2. Is a CDF Always Increasing?<\/strong><\/h6>\n<div class=\"rank-math-answer \">\n\n<p>Yep! A CDF is <em>non-decreasing<\/em> because probabilities accumulate as you move up the scale. It never goes down.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1747970338444\" class=\"rank-math-list-item\">\n<h6 class=\"rank-math-question \"><strong>3. Can I Convert a PDF to a CDF?<\/strong><\/h6>\n<div class=\"rank-math-answer \">\n\n<p>Absolutely! The CDF is the integral of the PDF. Conversely, the PDF is the derivative of the CDF (if it\u2019s differentiable).<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1747970345578\" class=\"rank-math-list-item\">\n<h6 class=\"rank-math-question \"><strong>4. Do CDF and PDF Apply to Discrete Variables?<\/strong><\/h6>\n<div class=\"rank-math-answer \">\n\n<p>Yes, but with a twist. For discrete variables (like rolling a die), you\u2019d use a <strong>Probability Mass Function (PMF)<\/strong> instead of a PDF, but the CDF works similarly for both continuous and discrete cases.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n<h2 class=\"wp-block-heading\" id=\"12--conclusion-you%E2%80%99re-now-a-cdf-vs-pdf-pro-\"><strong>Conclusion: You\u2019re Now a CDF vs PDF Pro!<\/strong><\/h2>\n\n\n\n<p><strong>Congrats\u2014<\/strong>you\u2019ve just unlocked the mystery of <strong>CDF vs PDF<\/strong>! We\u2019ve covered the basics, compared the two, and explored how they\u2019re used in the real world. Whether you\u2019re crunching data, predicting outcomes, or just curious about probability, you now know that:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>PDFs<\/strong> give you a snapshot of probability density for specific values.<\/li>\n\n\n\n<li><strong>CDFs<\/strong> show the cumulative probability up to a point.<\/li>\n<\/ul>\n\n\n\n<p>With these tools in your toolbox, you\u2019re ready to tackle stats problems like a pro. Want to dive deeper? Try plotting your own PDFs and CDFs in Python or check out more probability tutorials online. And if you found this guide helpful, share it with a friend or drop a comment below!<\/p>\n","protected":false},"excerpt":{"rendered":"You\u2019re diving into statistics or data science, and suddenly you\u2019re hit with terms like CDF and PDF. Your&hellip;","protected":false},"author":1,"featured_media":1484,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_singular_sidebar":"","csco_page_header_type":"split","csco_page_load_nextpost":"","footnotes":""},"categories":[5],"tags":[233],"class_list":{"0":"post-1453","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-guides","8":"tag-cdf-vs-pd","9":"cs-entry"},"_links":{"self":[{"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/posts\/1453","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/comments?post=1453"}],"version-history":[{"count":4,"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/posts\/1453\/revisions"}],"predecessor-version":[{"id":3104,"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/posts\/1453\/revisions\/3104"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/media\/1484"}],"wp:attachment":[{"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/media?parent=1453"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/categories?post=1453"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/freerdps.com\/blog\/wp-json\/wp\/v2\/tags?post=1453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}