{"id":198589,"date":"2025-10-15T06:49:00","date_gmt":"2025-10-15T05:49:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=198589"},"modified":"2025-10-14T19:49:38","modified_gmt":"2025-10-14T18:49:38","slug":"all-about-managing-missing-data","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-managing-missing-data","title":{"rendered":"Missing Data: How to effectively manage them in data science?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"198589\" class=\"elementor elementor-198589\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d08abe9 e-flex e-con-boxed e-con\" data-id=\"d08abe9\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6792450 elementor-widget elementor-widget-text-editor\" data-id=\"6792450\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><b>In the real world, perfectly complete datasets are rare. Whether it&#8217;s due to manual entry, automated extraction, or merging multiple sources, <\/b><b><i>missing data<\/i><\/b><b> is ubiquitous. If not properly managed, it can skew analyses, reduce model performance, and introduce significant biases.<\/b><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf451cf elementor-widget elementor-widget-text-editor\" data-id=\"cf451cf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><b>Understanding the nature and mechanisms<\/b><span style=\"font-weight: 400;\"> behind these gaps is therefore essential. While it may be tempting to overlook missing data, doing so often means ignoring a significant part of the problem.<\/span><\/p><p><span style=\"font-weight: 400;\">In this article, we will explore in detail how to <\/span><b>identify, categorize, and handle missing data in data science<\/b><span style=\"font-weight: 400;\">. We will also address the criteria for choosing an imputation method and best practices for minimizing their impact.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-77d9fbf e-flex e-con-boxed e-con\" data-id=\"77d9fbf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6795a4a elementor-widget elementor-widget-heading\" data-id=\"6795a4a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-xl\">Understanding the Nature of Missing Data<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-295f1c7 elementor-widget elementor-widget-heading\" data-id=\"295f1c7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Definition and Identification of Missing Data<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d55b3f3 elementor-widget elementor-widget-text-editor\" data-id=\"d55b3f3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">A <\/span><i><span style=\"font-weight: 400;\">missing value<\/span><\/i><span style=\"font-weight: 400;\"> refers to the absence of a value in a cell of a dataset. It can be represented as <span style=\"color: #339966;\">NaN<\/span>, <span style=\"color: #339966;\">None<\/span>, an empty cell, or indicators like &#8220;N\/A&#8221;.<\/span><\/p><p><span style=\"font-weight: 400;\">Several tools exist to identify them:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Visual exploration<\/b><span style=\"font-weight: 400;\">: libraries like <span style=\"color: #339966;\">missingno<\/span> (Python) allow you to visualize patterns of missing values (e.g., heatmaps, matrices).<\/span><span style=\"font-weight: 400;\"><br \/><\/span><b><\/b><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Descriptive functions<\/b><span style=\"font-weight: 400;\">: in Python, <span style=\"color: #339966;\">.isnull().sum()<\/span> on a Pandas DataFrame gives the number of missing values per column.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-32914d9 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"32914d9\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Everything you need to know about missing data<\/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<div class=\"elementor-element elementor-element-d89ec87 elementor-widget elementor-widget-heading\" data-id=\"d89ec87\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Why is Data Missing? Mechanisms of Data Loss?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a66f849 elementor-widget elementor-widget-text-editor\" data-id=\"a66f849\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Understanding why a value is missing is fundamental. Three mechanisms are classically distinguished:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>MCAR (Missing Completely at Random)<\/b><span style=\"font-weight: 400;\">: The probability that a value is missing is independent of all other variables.<\/span><span style=\"font-weight: 400;\"><br \/><\/span><i><span style=\"font-weight: 400;\">Example<\/span><\/i><span style=\"font-weight: 400;\">: a random failure during data collection.<\/span><span style=\"font-weight: 400;\"><br \/><br \/><\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>MAR (Missing at Random)<\/b><span style=\"font-weight: 400;\">: The absence depends on other observed variables, but not on the missing value itself.<\/span><span style=\"font-weight: 400;\"><br \/><\/span><i><span style=\"font-weight: 400;\">Example<\/span><\/i><span style=\"font-weight: 400;\">: men respond less often to a question about depression than women \u2014 the absence depends on gender.<\/span><span style=\"font-weight: 400;\"><br \/><br \/><\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>MNAR (Missing Not at Random)<\/b><span style=\"font-weight: 400;\">: The absence depends on the missing value itself or an unobserved factor.<\/span><span style=\"font-weight: 400;\"><br \/><\/span><i><span style=\"font-weight: 400;\">Example<\/span><\/i><span style=\"font-weight: 400;\">: very high incomes are rarely reported \u2014 it is the value itself that influences the absence.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-be7c938 elementor-widget elementor-widget-heading\" data-id=\"be7c938\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Impact of Different Types of Missingness<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-78b212c elementor-widget elementor-widget-text-editor\" data-id=\"78b212c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The type of mechanism profoundly influences the treatment strategy. While MCAR permits simple treatments, MAR and MNAR require more complex, even domain-specific methods.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-32bc07e elementor-widget elementor-widget-image\" data-id=\"32bc07e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-2.webp\" class=\"attachment-full size-full wp-image-198592\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-2.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-2-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-2-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-2-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7024147 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"7024147\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Train in identifying missing data<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ee97ed5 e-flex e-con-boxed e-con\" data-id=\"ee97ed5\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a4f6e8f elementor-widget elementor-widget-heading\" data-id=\"a4f6e8f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Strategies for Handling Missing Data<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f2ee24 elementor-widget elementor-widget-heading\" data-id=\"6f2ee24\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">1. Deletion of Missing Data<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1cbb4ba elementor-widget elementor-widget-text-editor\" data-id=\"1cbb4ba\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Listwise Deletion<\/b><\/li><\/ul><p><span style=\"font-weight: 400;\">This method involves deleting all rows of a dataset containing at least one missing value. It is commonly used because it is straightforward to implement.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Pairwise Deletion<\/b><\/li><\/ul><p><span style=\"font-weight: 400;\">This approach involves using all available data for each specific analysis, without necessarily excluding an entire row. For instance, a correlation between two variables will only use observations for which these two variables are present.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Variable Deletion<\/b><\/li><\/ul><p><span style=\"font-weight: 400;\">This method involves deleting an entire column if the percentage of missing values is too high (often &gt;50%). It may be relevant when the concerned variable is difficult to recover or not very useful.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e0dc152 elementor-widget elementor-widget-html\" data-id=\"e0dc152\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style type=\"text\/css\">\r\n.tg  {border-collapse:collapse;border-color:#ccc;border-spacing:0;}\r\n.tg td{background-color:#fff;border-color:#ccc;border-style:solid;border-width:1px;color:#333;\r\n  font-family:Rubik, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;word-break:normal;}\r\n.tg th{background-color:#f0f0f0;border-color:#ccc;border-style:solid;border-width:1px;color:#333;\r\n  font-family:Rubik, sans-serif;font-size:14px;font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\r\n.tg .tg-buk4{background-color:#ffffff;font-family:Rubik, Helvetica, sans-serif !important;font-size:18px;text-align:left;\r\n  vertical-align:top}\r\n.tg .tg-yv7t{background-color:#c0c0c0;font-family:Rubik, Helvetica, sans-serif !important;font-size:22px;font-weight:bold;\r\n  text-align:center;vertical-align:top}\r\n.tg .tg-v9r9{background-color:#ffffff;font-family:Rubik, Helvetica, sans-serif !important;font-size:18px;font-weight:bold;\r\n  text-align:left;vertical-align:top}\r\n.tg .tg-08lm{font-family:Rubik, Helvetica, sans-serif !important;font-size:18px;font-weight:bold;text-align:left;vertical-align:top}\r\n.tg .tg-cim1{font-family:Rubik, Helvetica, sans-serif !important;font-size:18px;text-align:left;vertical-align:top}\r\n<\/style>\r\n<table class=\"tg\" style=\"table-layout: fixed; width: 900px\">\r\n<colgroup>\r\n<col style=\"width: 200px\">\r\n<col style=\"width: 350px\">\r\n<col style=\"width: 350px\">\r\n<\/colgroup>\r\n<thead>\r\n  <tr>\r\n    <th class=\"tg-yv7t\">Method<\/th>\r\n    <th class=\"tg-yv7t\">Advantages<\/th>\r\n    <th class=\"tg-yv7t\">Disadvantages<\/th>\r\n  <\/tr>\r\n<\/thead>\r\n<tbody>\r\n  <tr>\r\n    <td class=\"tg-v9r9\">Listwise deletion<\/td>\r\n    <td class=\"tg-buk4\">- Easy to implement<br>- No artificial data added<\/td>\r\n    <td class=\"tg-buk4\">- Significant data loss if not MCAR<br>- Risk of bias<\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td class=\"tg-08lm\">Pairwise deletion<\/td>\r\n    <td class=\"tg-cim1\">- Retains more data<br>- Less destructive<\/td>\r\n    <td class=\"tg-cim1\">- Hard-to-interpret results<br>- Unstable statistical matrices<\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td class=\"tg-v9r9\">Variable deletion<\/td>\r\n    <td class=\"tg-buk4\">- Fast cleaning<br>- Dimensionality reduction<\/td>\r\n    <td class=\"tg-buk4\">- Risk of removing a relevant variable<\/td>\r\n  <\/tr>\r\n<\/tbody>\r\n<\/table>\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-61f1633 elementor-widget elementor-widget-image\" data-id=\"61f1633\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-1.webp\" class=\"attachment-full size-full wp-image-198593\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-1.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-1-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-1-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-1-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cbd9789 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"cbd9789\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Become a Data Scientist<\/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<div class=\"elementor-element elementor-element-baeb0e2 elementor-widget elementor-widget-heading\" data-id=\"baeb0e2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2. Simple Imputation<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bb39e19 elementor-widget elementor-widget-text-editor\" data-id=\"bb39e19\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Imputation by Mean, Median, or Mode<\/b><\/li><\/ul><p><span style=\"font-weight: 400;\">This approach replaces missing values with measures of central tendency. The mean and median are used for numerical variables, while the mode applies to both categorical and numerical variables.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Imputation by a Constant Value or Binary Indicator<\/b><\/li><\/ul><p><span style=\"font-weight: 400;\">An arbitrary value (like -1 or &#8220;Unknown&#8221;) is used to replace missing data. Sometimes, a new binary variable is added to indicate if the original value is missing.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7bbfe0f elementor-widget elementor-widget-html\" data-id=\"7bbfe0f\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style type=\"text\/css\">\r\n.tg  {border-collapse:collapse;border-color:#ccc;border-spacing:0;}\r\n.tg td{background-color:#fff;border-color:#ccc;border-style:solid;border-width:1px;color:#333;\r\n  font-family:Rubik, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;word-break:normal;}\r\n.tg th{background-color:#f0f0f0;border-color:#ccc;border-style:solid;border-width:1px;color:#333;\r\n  font-family:Rubik, sans-serif;font-size:14px;font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\r\n.tg .tg-buk4{background-color:#ffffff;font-family:Rubik, Helvetica, sans-serif !important;font-size:18px;text-align:left;\r\n  vertical-align:top}\r\n.tg .tg-yv7t{background-color:#c0c0c0;font-family:Rubik, Helvetica, sans-serif !important;font-size:22px;font-weight:bold;\r\n  text-align:center;vertical-align:top}\r\n.tg .tg-v9r9{background-color:#ffffff;font-family:Rubik, Helvetica, sans-serif !important;font-size:18px;font-weight:bold;\r\n  text-align:left;vertical-align:top}\r\n<\/style>\r\n<table class=\"tg\" style=\"table-layout: fixed; width: 900px\">\r\n<colgroup>\r\n<col style=\"width: 200px\">\r\n<col style=\"width: 350px\">\r\n<col style=\"width: 350px\">\r\n<\/colgroup>\r\n<thead>\r\n  <tr>\r\n    <th class=\"tg-yv7t\">Method<\/th>\r\n    <th class=\"tg-yv7t\">Advantages<\/th>\r\n    <th class=\"tg-yv7t\">Disadvantages<\/th>\r\n  <\/tr>\r\n<\/thead>\r\n<tbody>\r\n  <tr>\r\n    <td class=\"tg-v9r9\">Mean \/ Median \/ Mode<\/td>\r\n    <td class=\"tg-buk4\">- Easy and quick<br>- Low resource consumption<\/td>\r\n    <td class=\"tg-buk4\">- Reduces variance<br>- Can distort distribution and correlations<\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td class=\"tg-v9r9\">Constant value \/ Indicator<\/td>\r\n    <td class=\"tg-buk4\">- Preserves missingness information<br>- Compatible with certain models<\/td>\r\n    <td class=\"tg-buk4\">- May introduce bias<br>- Sensitive to arbitrarily chosen value<\/td>\r\n  <\/tr>\r\n<\/tbody>\r\n<\/table>\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb8d727 elementor-widget elementor-widget-heading\" data-id=\"fb8d727\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3. Advanced Imputation<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3bcd497 elementor-widget elementor-widget-text-editor\" data-id=\"3bcd497\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Imputation by Regression<\/b><\/li><\/ul><p><span style=\"font-weight: 400;\">This involves predicting the missing value using a regression model that employs other variables in the dataset as predictors.<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Imputation by k-Nearest Neighbors (<\/b><a href=\"https:\/\/datascientest.com\/en\/knn-what-is-the-knn-algorithm\"><b>k-NN<\/b><\/a><b>)<\/b><\/li><\/ul><p><span style=\"font-weight: 400;\">Missing values are imputed by taking the average of the k most similar observations, measured using a distance between observed variables.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6a80715 elementor-widget elementor-widget-html\" data-id=\"6a80715\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style type=\"text\/css\">\r\n.tg  {border-collapse:collapse;border-color:#ccc;border-spacing:0;}\r\n.tg td{background-color:#fff;border-color:#ccc;border-style:solid;border-width:1px;color:#333;\r\n  font-family:Rubik, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px;word-break:normal;}\r\n.tg th{background-color:#f0f0f0;border-color:#ccc;border-style:solid;border-width:1px;color:#333;\r\n  font-family:Rubik, sans-serif;font-size:14px;font-weight:normal;overflow:hidden;padding:10px 5px;word-break:normal;}\r\n.tg .tg-buk4{background-color:#ffffff;font-family:Rubik, Helvetica, sans-serif !important;font-size:18px;text-align:left;\r\n  vertical-align:top}\r\n.tg .tg-yv7t{background-color:#c0c0c0;font-family:Rubik, Helvetica, sans-serif !important;font-size:22px;font-weight:bold;\r\n  text-align:center;vertical-align:top}\r\n.tg .tg-v9r9{background-color:#ffffff;font-family:Rubik, Helvetica, sans-serif !important;font-size:18px;font-weight:bold;\r\n  text-align:left;vertical-align:top}\r\n<\/style>\r\n<table class=\"tg\" style=\"table-layout: fixed; width: 900px\">\r\n<colgroup>\r\n<col style=\"width: 200px\">\r\n<col style=\"width: 350px\">\r\n<col style=\"width: 350px\">\r\n<\/colgroup>\r\n<thead>\r\n  <tr>\r\n    <th class=\"tg-yv7t\">Method<\/th>\r\n    <th class=\"tg-yv7t\">Advantages<\/th>\r\n    <th class=\"tg-yv7t\">Disadvantages<\/th>\r\n  <\/tr>\r\n<\/thead>\r\n<tbody>\r\n  <tr>\r\n    <td class=\"tg-v9r9\">Regression<\/td>\r\n    <td class=\"tg-buk4\">- Leverages inter-variable relationships<\/td>\r\n    <td class=\"tg-buk4\">- Risk of bias if assumptions are violated<br>- May overestimate variable relationships<\/td>\r\n  <\/tr>\r\n  <tr>\r\n    <td class=\"tg-v9r9\">k-Nearest Neighbors (k-NN)<\/td>\r\n    <td class=\"tg-buk4\">- Captures complex relationships<br>- Suitable for numeric and mixed data<\/td>\r\n    <td class=\"tg-buk4\">- High computational cost<br>- Sensitive to choice of k and distance metric<\/td>\r\n  <\/tr>\r\n<\/tbody>\r\n<\/table>\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-15dfc88 elementor-widget elementor-widget-image\" data-id=\"15dfc88\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-3.webp\" class=\"attachment-full size-full wp-image-198594\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-3.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-3-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-3-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-3-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3802508 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"3802508\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Become an expert in Data Science<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d420d24 e-flex e-con-boxed e-con\" data-id=\"d420d24\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-aa82e16 elementor-widget elementor-widget-heading\" data-id=\"aa82e16\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Choosing the Right Strategy and Evaluating its Impact\n<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b49802 elementor-widget elementor-widget-heading\" data-id=\"6b49802\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Key Factors for Selecting a Method<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-78fb5d7 elementor-widget elementor-widget-text-editor\" data-id=\"78fb5d7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The choice of a method for handling missing data depends on several factors. First, <\/span><b>the nature of the variables (numerical, categorical, or mixed)<\/b><span style=\"font-weight: 400;\"> guides the choice of techniques: some methods like mean or regression imputation are applied mainly to numerical variables, while mode or constant values are suitable for categorical variables.<\/span><\/p><p><span style=\"font-weight: 400;\">The rate of missing values is also crucial: below 5%, simple approaches may suffice, but beyond 20%, it becomes risky to delete data or use naive imputations.<\/span><\/p><p><span style=\"font-weight: 400;\">Moreover, it&#8217;s important to determine whether a variable truly impacts the target or ongoing analyses. If a variable has many missing data and does not provide useful information, it is often preferable to delete it to avoid costly and unnecessary processing while simplifying the model or visualizations. This approach is particularly relevant when <\/span><b>exploratory analyses<\/b><span style=\"font-weight: 400;\"> or correlation tests show that the variable is weakly related to others.<\/span><\/p><p><span style=\"font-weight: 400;\">Once the method is applied, it is essential to evaluate its impact: compare variable distributions before\/after imputation, measure model performance via <\/span><a href=\"https:\/\/datascientest.com\/en\/the-importance-of-cross-validation\"><span style=\"font-weight: 400;\">cross-validation<\/span><\/a><span style=\"font-weight: 400;\">, and conduct a sensitivity analysis by testing multiple strategies to ensure the robustness of the results.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-28511eb e-flex e-con-boxed e-con\" data-id=\"28511eb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a8fbb71 elementor-widget elementor-widget-heading\" data-id=\"a8fbb71\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eac3ead elementor-widget elementor-widget-text-editor\" data-id=\"eac3ead\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The <\/span><b>handling of <\/b><b><i>missing data<\/i><\/b><span style=\"font-weight: 400;\"> is an inevitable challenge in data science. Understanding their origins, identifying their nature, choosing the right imputation method, and evaluating its impact are critical steps to <\/span><b>ensure the reliability of analyses<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Rather than seeking a single solution, it is often preferable to test multiple approaches tailored to the specific context. With the evolution of tools and techniques, the management of missing data is becoming increasingly sophisticated and integrated into the processing pipeline.<\/span><\/p><p><span style=\"font-weight: 400;\">Adopting a rigorous, transparent, and informed approach remains key to addressing this central issue.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a0f94bc elementor-widget elementor-widget-image\" data-id=\"a0f94bc\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1536\" height=\"1024\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-4.webp\" class=\"attachment-full size-full wp-image-198595\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-4.webp 1536w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-4-300x200.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-4-1024x683.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/08\/missing-data-datascientest-4-768x512.webp 768w\" sizes=\"(max-width: 1536px) 100vw, 1536px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-75dbd07 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"75dbd07\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Train with DataScientest<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6c811a2 e-flex e-con-boxed e-con\" data-id=\"6c811a2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-86d1b80 elementor-widget elementor-widget-html\" data-id=\"86d1b80\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Understanding the Nature of Missing Data\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"A missing value refers to the absence of a value in a dataset and can be identified using tools like missingno or descriptive functions such as .isnull().sum().\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Why is Data Missing? 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Whether it&#8217;s due to manual entry, automated extraction, or merging multiple sources, missing data is ubiquitous. If not properly managed, it can skew analyses, reduce model performance, and introduce significant biases. Understanding the nature and mechanisms behind these gaps is therefore essential. While it may be [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":198591,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Missing Data: How to effectively manage them in data science?<\/title>\n<meta name=\"description\" content=\"Discover how to identify, categorize, and effectively handle missing data in data science using simple or advanced methods to ensure the reliability of your analyses and models.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/all-about-managing-missing-data\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Missing Data: How to effectively manage them in data science?\" \/>\n<meta property=\"og:description\" content=\"In the real world, perfectly complete datasets are rare. 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