{"id":37845,"date":"2025-02-21T02:51:23","date_gmt":"2025-02-21T07:51:23","guid":{"rendered":"https:\/\/statanalytica.com\/blog\/?p=37845"},"modified":"2025-02-21T02:51:27","modified_gmt":"2025-02-21T07:51:27","slug":"advanced-dax-functions-in-power-bi","status":"publish","type":"post","link":"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/","title":{"rendered":"Mastering Advanced DAX Functions in Power BI: Unlock Powerful Data Insights"},"content":{"rendered":"\n<p>Today, we live in a data-driven world where the capability\u2002to examine and digest data as efficiently as possible is key. Among these options, Microsoft Power BI shines as one of the best business intelligence tools, with\u2002powerful features for beginning and more advanced data professionals. DAX, or Data Analysis Expressions, is a formula language that powers analysis in Power\u2002BI, built in to optimize the data model and reporting process. This detailed guide provides an in-depth exploration of higher-level DAX functions, enabling you to harness these functions to\u2002unlock higher insight and enhance your skills in data analysis.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"understanding-dax-the-backbone-of-power-bi\"><\/span><strong>Understanding DAX: The Backbone of Power BI<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-light-blue ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a150e1eb86f3\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #ff5104;color:#ff5104\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #ff5104;color:#ff5104\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a150e1eb86f3\" checked aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#understanding-dax-the-backbone-of-power-bi\" >Understanding DAX: The Backbone of Power BI<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#key-features-of-dax\" >Key Features of DAX<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#diving-into-advanced-dax-functions\" >Diving into Advanced DAX Functions<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#1-calculate-the-powerhouse-function\" >1. CALCULATE: The Powerhouse Function<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#2-filter-refining-data-sets\" >2. FILTER: Refining Data Sets<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#3-all-removing-filters\" >3. ALL: Removing Filters<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#4-time-intelligence-functions-navigating-dates-with-ease\" >4. Time Intelligence Functions: Navigating Dates with Ease<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#5-advanced-aggregation-with-sumx-and-averagex\" >5. Advanced Aggregation with SUMX and AVERAGEX<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#best-practices-for-using-advanced-dax-functions\" >Best Practices for Using Advanced DAX Functions<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#1-leverage-variables-for-clarity-and-performance\" >1. Leverage Variables for Clarity and Performance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#2-optimize-performance-with-efficient-filters\" >2. Optimize Performance with Efficient Filters<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#3-use-iterators-wisely\" >3. Use Iterators Wisely<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#how-does-calculate-work-with-multiple-filters\" >How does CALCULATE work with multiple filters?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#what-is-the-best-way-to-optimize-dax-performance\" >What is the best way to optimize DAX performance?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/statanalytica.com\/blog\/advanced-dax-functions-in-power-bi\/#can-i-use-dax-in-excel\" >Can I use DAX in Excel?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n\n\n\n\n<p>In simple words, DAX (Data Analysis Expressions) is a set of functions, operators and constants that can be used in a\u2002formula to calculate and return values. DAX was first introduced in Power Pivot for Excel, and it eventually became the backbone of Power BI\u2002and SQL Server Analysis Services (SSAS) tabular models. Its design empowers users to write\u2002flexible computations on data efficiently.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"key-features-of-dax\"><\/span><strong>Key Features of DAX<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Context sensitivity: <\/strong>DAX formulas can change their behavior depending on their\u2002location, among other data, enabling next-level calculations that consider user behavior and selections.<\/li>\n\n\n\n<li><strong>DAX\u2002is a rich Function Library: <\/strong>DAX has more than 250 functions covering the gamut of capabilities from basic aggregations to complex statistical logic.<\/li>\n\n\n\n<li><strong>Integration with Diverse Data Sources: <\/strong>DAX can integrate data from multiple sources and perform analysis across data sets.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"diving-into-advanced-dax-functions\"><\/span><strong>Diving into Advanced DAX Functions<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>While basic DAX functions handle simple aggregations and calculations, advanced DAX functions provide the tools necessary for more complex data analysis. These functions enable sophisticated data modeling, time intelligence, and dynamic reporting.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1-calculate-the-powerhouse-function\"><\/span><strong>1. CALCULATE: The Powerhouse Function<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>The CALCULATE function is fundamental in DAX, allowing you to modify the filter context of a calculation. This capability is essential for creating measures that need to adapt based on specific conditions or user selections.<\/p>\n\n\n\n<p><strong>Syntax<\/strong>:<\/p>\n\n\n\n<p>CALCULATE(&lt;expression&gt;, &lt;filter1&gt;, &lt;filter2&gt;, &#8230;)<\/p>\n\n\n\n<p><strong>Example<\/strong>: To calculate total sales for the &#8220;Electronics&#8221; category:<\/p>\n\n\n\n<p>TotalElectronicsSales = CALCULATE(SUM(Sales[Amount]), Products[Category] = &#8220;Electronics&#8221;)<\/p>\n\n\n\n<p>In this example, CALCULATE modifies the filter context to include only rows where the product category is &#8220;Electronics&#8221;.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2-filter-refining-data-sets\"><\/span><strong>2. FILTER: Refining Data Sets<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>The FILTER function returns a table that represents a subset of another table or expression. It&#8217;s particularly useful for creating calculated tables or measures that depend on specific data conditions.<\/p>\n\n\n\n<p><strong>Syntax<\/strong>:<\/p>\n\n\n\n<p>FILTER(&lt;table&gt;, &lt;filter_expression&gt;)<\/p>\n\n\n\n<p><strong>Example<\/strong>: To create a table of high-value transactions over $10,000:<\/p>\n\n\n\n<p>HighValueTransactions = FILTER(Sales, Sales[Amount] &gt; 10000)<\/p>\n\n\n\n<p>This function filters the Sales table to include only transactions where the amount exceeds $10,000.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3-all-removing-filters\"><\/span><strong>3. ALL: Removing Filters<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>The ALL function is used to remove filters from columns or tables, often to calculate totals or percentages relative to the entire dataset.<\/p>\n\n\n\n<p><strong>Syntax<\/strong>:<\/p>\n\n\n\n<p>ALL(&lt;table_or_column&gt;)<\/p>\n\n\n\n<p><strong>Example<\/strong>: To calculate the percentage of total sales for each product:<\/p>\n\n\n\n<p>SalesPercentage = DIVIDE(SUM(Sales[Amount]), CALCULATE(SUM(Sales[Amount]), ALL(Products)))<\/p>\n\n\n\n<p>Here, ALL(Products) removes any filters on the Products table, ensuring the denominator reflects the total sales across all products.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"4-time-intelligence-functions-navigating-dates-with-ease\"><\/span><strong>4. Time Intelligence Functions: Navigating Dates with Ease<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Time intelligence functions in DAX allow for calculations across dates, enabling analyses like year-to-date, quarter-over-quarter, and moving averages.<\/p>\n\n\n\n<p><strong>Common Time Intelligence Functions<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>TOTALYTD: Calculates the year-to-date total.<\/li>\n\n\n\n<li>PREVIOUSMONTH: Returns a table representing the previous month.<\/li>\n\n\n\n<li>DATESBETWEEN: Returns a table with dates between a specified start and end date.<\/li>\n<\/ul>\n\n\n\n<p><strong>Example<\/strong>: To calculate year-to-date sales:<\/p>\n\n\n\n<p>YTD_Sales = TOTALYTD(SUM(Sales[Amount]), Calendar[Date])<\/p>\n\n\n\n<p>This formula sums the sales amount from the start of the year up to the selected date.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"5-advanced-aggregation-with-sumx-and-averagex\"><\/span><strong>5. Advanced Aggregation with SUMX and AVERAGEX<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>While functions like SUM and AVERAGE perform straightforward aggregations, their iterator counterparts, SUMX and AVERAGEX, evaluate expressions for each row in a table and then aggregate the results.<\/p>\n\n\n\n<p><strong>Syntax<\/strong>:<\/p>\n\n\n\n<p>SUMX(&lt;table&gt;, &lt;expression&gt;)<\/p>\n\n\n\n<p>AVERAGEX(&lt;table&gt;, &lt;expression&gt;)<\/p>\n\n\n\n<p><strong>Example<\/strong>: To calculate the total revenue considering a discount:<\/p>\n\n\n\n<p>TotalRevenue = SUMX(Sales, Sales[Quantity] * Sales[Price] * (1 &#8211; Sales[Discount]))<\/p>\n\n\n\n<p>In this example, SUMX iterates over each row in the Sales table, calculates the revenue after discount, and then sums the results.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"best-practices-for-using-advanced-dax-functions\"><\/span><strong>Best Practices for Using Advanced DAX Functions<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Mastering advanced DAX functions requires not only understanding their syntax but also implementing best practices to ensure efficient and accurate calculations.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1-leverage-variables-for-clarity-and-performance\"><\/span><strong>1. Leverage Variables for Clarity and Performance<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Using variables in DAX (VAR) can enhance both the readability and performance of your formulas by storing intermediate results.<\/p>\n\n\n\n<p><strong>Example<\/strong>:<\/p>\n\n\n\n<p>VAR TotalCost = SUM(Sales[Quantity] * Sales[Price])<\/p>\n\n\n\n<p>VAR TotalRevenue = SUMX(Sales, Sales[Quantity] * Sales[Price] * (1 &#8211; Sales[Discount]))<\/p>\n\n\n\n<p>RETURN TotalRevenue &#8211; TotalCost<\/p>\n\n\n\n<p>This approach improves readability by breaking down the calculation into meaningful components.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"2-optimize-performance-with-efficient-filters\"><\/span><strong>2. Optimize Performance with Efficient Filters<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Applying filters selectively can help improve the speed of calculations, especially when dealing with large datasets. Functions like KEEPFILTERS can help maintain existing filters while adding new ones.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"3-use-iterators-wisely\"><\/span><strong>3. Use Iterators Wisely<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>While iterator functions (SUMX, AVERAGEX) are powerful, they can be performance-intensive. Use them judiciously and prefer aggregation functions (SUM, AVERAGE) where possible.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Elements of Advanced DAX functions allow Power BI users to conduct complex chunks of data analysis, identify patterns, and\u2002make data-driven decisions. Learning functions such as CALCULATE, FILTER, ALL and time intelligence functions will help you take your reporting to the next level and get different insights from your\u2002data Use variables, efficient filters, and selection of iterator functions to improve performance\u2002and readability. As a business analyst, data scientist, or Power BI enthusiast, you should have a firm\u2002hold on advanced DAX functions as it is directly involved with your data model and further decision-making.<\/p>\n\n\n\n<p><strong>Also Read: <a href=\"https:\/\/statanalytica.com\/blog\/power-bi-for-business-analytics\/\">Power BI for Business Analytics: Unlocking the Power of Data-Driven Decisions<\/a><\/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-1740123139565\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"how-does-calculate-work-with-multiple-filters\"><\/span><strong>How does CALCULATE work with multiple filters?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>CALCULATE can accept multiple filters, and it applies them in a logical AND condition. If you need to apply an OR condition, you must use functions like FILTER.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1740123158708\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"what-is-the-best-way-to-optimize-dax-performance\"><\/span><strong>What is the best way to optimize DAX performance?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Use variables (VAR), optimize filters, and avoid iterators (SUMX, AVERAGEX) when possible. Also, make sure your data model is properly indexed.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1740123177755\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><span class=\"ez-toc-section\" id=\"can-i-use-dax-in-excel\"><\/span><strong>Can I use DAX in Excel?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes, DAX is available in <a href=\"https:\/\/support.microsoft.com\/en-us\/office\/power-pivot-overview-and-learning-f9001958-7901-4caa-ad80-028a6d2432ed\" target=\"_blank\" rel=\"noreferrer noopener\">Excel Power Pivot<\/a>, enabling advanced calculations and data modeling within Excel.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Today, we live in a data-driven world where the capability\u2002to examine and digest data as efficiently as possible is key. Among these options, Microsoft Power BI shines as one of the best business intelligence tools, with\u2002powerful features for beginning and more advanced data professionals. DAX, or Data Analysis Expressions, is a formula language that powers [&hellip;]<\/p>\n","protected":false},"author":16,"featured_media":37847,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","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":"","ast-disable-related-posts":"","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":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"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-opacity":"","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-opacity":"","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-opacity":"","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-opacity":"","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-opacity":"","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-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[2],"tags":[5160],"class_list":["post-37845","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-accounting","tag-advanced-dax-functions-in-power-bi"],"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37845","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/users\/16"}],"replies":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/comments?post=37845"}],"version-history":[{"count":1,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37845\/revisions"}],"predecessor-version":[{"id":37848,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/posts\/37845\/revisions\/37848"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media\/37847"}],"wp:attachment":[{"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/media?parent=37845"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/categories?post=37845"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statanalytica.com\/blog\/wp-json\/wp\/v2\/tags?post=37845"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}