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Someone looking at the code immediately understands what column and table is being referenced in the code. These table names have been prefixed and suffixed with single quotes. While not required for table names without spaces, for consistency, they should always be used.

Spaces inserted after beginning parentheses and before end parentheses, as well as before and after the equals sign, provide visual separation between elements and make things easier to read. Finally, the creation of the column in the SUMMARIZE statement has been created with the name prefixed by two underscore characters, unlike the original formula, where this column is the same name as a column from the original table.

While DAX can generally figure out which column is being referenced, having duplicate names is confusing for the reviewer and can create actual confusion and problems in complex DAX formulas. While DAX functions can be nearly infinitely nested, using variables can avoid doing the same calculation multiple times and also improves overall code readability. Using variables can help you break complex calculations down into smaller, more consumable pieces that can be individually verified step by step.

Variable names must begin with the letters a-z or A-Z. The only other supported characters within variable names are the characters Existing table names and certain keywords are not permitted as variable names.

This variable takes on the value of the table as shown:. Otherwise, the value -1 is returned. Variables can only be referenced within the DAX calculation in which they are created.

There is perhaps no more important subject to understanding DAX than context. Context is essential to DAX and is also something that is relatively unique to the language. Thus, understanding context is crucial to understanding DAX as it is context that provides much of the unbridled power of the DAX language.

Conversely, context also contributes significantly to the learning curve for the DAX language. Most other sources essentially ignore the concept of query context, and the Microsoft documentation is somewhat vague regarding this concept.

The best analysis is that the combination of row and filter creates the final query context for DAX to retrieve the required data from the underlying data model for the requisite calculation.

Users essentially only ever explicitly define row and filter context for DAX, and DAX itself implicitly creates query context from the row and filter context. Thus, we will focus on row and filter context in this recipe. With regard to row context, DAX automatically applies row context to any calculated column.

Therefore, the three columns created, Year , Month , and Weekday , all have row context applied. This is why there is a single value returned despite the fact that we have no aggregation function applied. Thus, within row context, references to columns such as [Value] , when not referenced from within an aggregation function, always return a single value, the value of the referenced column in that row.

This is really as complex as row context gets, with the exception that it is possible to create row context outside of tables and calculated columns. Filter context is somewhat trickier. Filter context is created by the combination of visuals and the fields within those visuals, as well as explicit filters created using the Filters pane in Power BI Desktop or directly within a DAX calculation when using a filters clause. In step 3 , the matrix rows and columns define the context for the CountOfDays measure.

Thus, for each cell, excluding the Total cells, we get the number of days in each month for each year. This is why the cell intersecting February and has 29, and is a leap year.

The Total column removes the filter context for the individual columns but not the individual rows, and so we get the total number of days for all three years, , , and , for each month.

Conversely, the Total row removes the filter context for the individual rows but not for the individual columns, and so we get the total number of days in each year. Finally, the cell on the right in the bottom row removes the filter context for both the individual rows and individual columns, and so we get the total number of day in all three years.

Therefore, the filter context for this cell is effectively no filters or all data referenced by the matrix visualization. Adding the slicer and selecting an individual weekday adds additional filter context to the matrix since the default in Power BI Desktop is to cross-filter visualizations.

Thus, in addition to the filter context of the individual rows and columns in the matrix, the cells also encapsulate the filter context of the slicer, and so we are presented with the number of Saturdays in each month of each year with their corresponding totals in the Totals row and column.

Selecting a different weekday from the slicer, or a combination of weekdays, will present their corresponding counts in the matrix visualization. You may be surprised to see the number in this column for every row of the table.

This is the count of days in all three years of the table. You may have expected to see 1 for each row in this column. This result is driven by the exception mentioned earlier when dealing with column references in row context.

The aggregation function effectively switches the calculation from row context to filter context and, since there is no filter context, the final query context is all rows within the table. Grouping and summarizing information is a powerful feature of Excel pivot tables and Power BI table and matrix visualizations.

However, it is often necessary to group and summarize information in DAX as well. To use the SUMMARIZE function to return the number of weekdays in each month as well as the first day when each weekday occurs in each month, create a new table with the following formula:. Looking at the SUMMARIZE formula, the first parameter is the table that we want to summarize, and the next two columns are the columns according to which we want to group our data.

Note that you can group by one, two, three, or nearly any number of columns. DAX understands where these pairs start when you stop referring to column names in the table and enter a text value for a parameter denoted by double quotes. We first create a column called of Days , and the value to be returned in this column is the count of the rows from our groupings.

In other words, we get the number of each weekday in each month as our value. For the second column called First Date , we return the minimum date from our groupings. In other words, we get the first date of each weekday within each month as our value. The next two columns are the columns according to which we want to group our data.

In other words, you are referring to the current row of the Cartesian product created by your grouping columns. OK, so the most obvious question is likely something related to why there are two extremely similar functions that essentially do precisely the same thing. The answer is that while they are similar and do similar things, the way in which these functions go about what they do is fairly different.

The order of the resulting table rows returned by these functions provides a hint that they are operating somewhat differently within the bowels of DAX.

This can be fairly powerful under the right circumstances. This would work and you would return just the day of the first date of the weekday within each month instead of the full date. Filtering is a critical concept in DAX because filters provide the main context under which DAX calculations evaluate.

In addition, unlike when working with Excel, you cannot specify exact cells or ranges within DAX. Instead, if you want to use particular rows and columns of information within a table, you must filter that table down to the particular rows and columns desired for your calculation.

Conversely, DAX allows you to remove, ignore, and change filter context within calculations. This is powerful and useful in many situations, such as in Power BI, where slicers, page, or report filters may need to be overridden within certain calculations and visualizations. DAX functions that allow the removal or editing of filter behavior include the following:. With all of the slicers set to All , the values for each of these measures is as follows:. This measure counts days in January, ignoring filters for Year , but not other filters.

For January Days? The calculation now becomes the intersection of the two filters instead of a complete override. Thus, since the only value that is in common between the two filters is January, only the count of the days in January is returned! Use the Year slicer to only choose The values for the measures become the following:. This measure is being filtered by the Year slicer and is a leap year. This measure is being filtered by the Year slicer and January has 31 days.

This measure is being filtered by the Year slicer and January has five Wednesdays. The ALL function overrides the filter from the Year slicer, so this is a count of all rows in the table. This measure only counts days in January and is filtered by the Year slicer. Leave the Year slicer set to and now use the Month slicer to only choose February.

This measure is being filtered by the Year slicer and the Month slicer and is a leap year. This measure is being filtered by the Year slicer and the Month slicer, but also has a filter within the calculation of January.

Since February and January have no intersecting days, the ultimate value is blank null. This measure is being filtered by the Year slicer and the Month slicer, but also has a filter within the calculation of January and Wednesday. The ALL function overrides the filter from the Year and Month slicers, so this is a count of all rows in the table. However, the Year slicer refers to a different column, so the filter on the Year column is also enforced.

The Month slicer is effectively ignored. You can test this by switching the Month slicer to November, December, or another value. Leave the Year slicer set to and the Month slicer set to February. Now, change the Weekday slicer to only choose Friday. This measure is being filtered by the Year slicer, Month slicer, and Weekday slicer, and there are only four Fridays in January This measure is being filtered by the Year slicer, Month slicer, and Weekday slicer, but also has a filter within the calculation of January and Wednesday.

This measure would also be blank if you selected January in the Month slicer instead of February since there are no Fridays that are also Wednesdays! The ALL function overrides the filter from the Year , Month , and Weekday slicers, so this is a count of all rows in the table. Since the Month slicer and internal filters all refer to the same column, the default behavior for the CALCULATE function is to override all filters on that column with the innermost filter.

However, the Year and Weekday slicers refer to different columns, so the filters on the Year and Weekday columns are also enforced. Relationships connect tables together within a data model by defining an affiliation between a column in one table and a column in a second table.

Creating a relationship between two columns in a table ties the two tables together such that it is expected that values from a column in the first table will be found in the other column in the second table. These table relationships can be exploited by DAX calculations as DAX intrinsically understands these relationships within the data model.

The second Table visualization also lists the months in alphabetical order and the second column displays 1 for all rows. Essentially, we have explicitly told DAX to use the inactive relationship we created as its filter context between the two tables. Publisher resources Download Example Code. Table of contents Product information.

Contributors About the author About the reviewers Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Sections Getting ready How to do it How it works There's more See also Using variables Getting ready How to do it See also Confronting context Getting ready How to do it See also Grouping and summarizing Getting ready How to do it See also Filtering and unfiltering Getting ready How to do it See also Exploiting relationships Getting ready How to do it See also Implementing iterators Getting ready How to do it See also Using conditional logic Getting ready How to do it See also Creating quarters Getting ready How to do it See also Calculating leap years Getting ready How to do it See also Determining day and working day numbers in a year Getting ready How to do it See also Determining date of the day number of a year Getting ready How to do it See also Finding week start and end dates Getting ready How to do it See also Finding working days for weeks, months, quarters, and years Getting ready How to do it See also Constructing a sequential week number Getting ready How to do it See also Computing rolling weeks Getting ready How to do it See also Working with date intervals Getting ready How to do it See also Computing an hour breakdown Getting ready How to do it See also Adding and subtracting time Getting ready How to do it See also Determining network duration Getting ready How to do it See also Calculating shifts Getting ready How to do it See also Aggregating duration Getting ready How to do it See also Transforming milliseconds into duration Getting ready How to do it See also Tinkering with time zones Getting ready How to do it See also Converting duration into seconds Getting ready How to do it See also Creating a last-refreshed timestamp Getting ready How to do it See also Creating a greeting Getting ready How to do it See also Counting a list of items Getting ready How to do it See also Ranking columns and measures Getting ready How to do it See also Totaling measures Getting ready How to do it See also Converting from Cartesian to polar and back Getting ready How to do it See also Computing percentages Getting ready How to do it See also Calculating mode for single and multiple columns Getting ready How to do it See also Extracting text Getting ready How to do it See also Detecting prime numbers Getting ready How to do it See also Building a revenue growth rate Getting ready How to do it See also Generating an accounts payable turnover ratio Getting ready How to do it See also Fashioning the market share and relative market share Getting ready How to do it See also Determining compound interest Getting ready How to do it See also Comparing budgets and actuals Getting ready How to do it See also Crafting currency exchange rates Getting ready How to do it See also Assessing days sales outstanding Getting ready How to do it See also Finding new and returning customers Getting ready How to do it See also Identifying lost and recovered customers Getting ready How to do it See also Analyzing customer churn rate Getting ready How to do it See also Calculating the customer lifetime value Getting ready How to do it See also Computing the customer acquisition cost Getting ready How to do it See also Computing absenteeism Getting ready How to do it See also Evaluating employee engagement Getting ready How to do it See also Determining human capital value added Getting ready How to do it See also Finding the full-time equivalent Getting ready How to do it See also Projecting planned value Getting ready How to do it See also Estimating earned value Getting ready How to do it

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What you will learn Understand how to create common calculations for dates, time, and duration Create key performance indicators KPIs and other business calculations Develop general DAX calculations that deal with text and numbers Discover new ideas and time-saving techniques for better calculations and models Perform advanced DAX calculations for solving statistical measures and other mathematical formulas Handle errors in DAX and learn how to debug DAX calculations Understand how to optimize your data models Who this book is for Business users, BI developers, data analysts, and SQL users who are looking for solutions to the challenges faced while solving analytical operations using DAX techniques and patterns will find this book useful.

Basic knowledge of the DAX language and Microsoft services is mandatory. Build agile and responsive business intelligence solutions Create a semantic model and analyze data using the tabular model in SQL Server Analysis Services to create corporate-level business intelligence BI solutions.

Led by two BI experts, you will learn how to build, deploy, and query a tabular model by following detailed examples and best practices. Power Query is one component of the Power BI Business Intelligence product from Microsoft, and "M" is the name of the programming language created by it. As more business intelligence pros begin using Power Pivot, they find that they do not have the Excel skills to clean the data in Excel; Power Query solves this problem.

This book shows how to use the Power Query tool to get difficult data sets into both Excel and Power Pivot, and is solely devoted to Power Query dashboarding and reporting. You will learn how to leverage the advanced applications of DAX to solve complex tasks. Often a task seems complex due to a lack of understanding, or a misunderstanding of core principles, and how certain components interact with each other. The authors of this book use solutions and examples to teach you how to solve complex problems.

They explain the intricate workings of important concepts such as Filter Context and Context Transition. You will learn how Power BI, through combining DAX building blocks such as measures, table filtering, and data lineage , can yield extraordinary analytical power. Throughout Pro Dax with Power BI these building blocks are used to create and compose solutions for advanced DAX problems, so you can independently build solutions to your own complex problems, and gain valuable insight from your data.

What You Will Learn Understand the intricate workings of DAX to solve advanced problems Deconstruct problems into manageable parts in order to create your own recipes Apply predefined solutions for addressing problems, and link back step-by-step to the mechanics of DAX, to know the foundation of this powerful query language Get fully on board with DAX, a new and evolving language, by learning best practices Who This Book Is For Anyone who wants to use Power BI to build advanced and complex models.

Skip to content. DAX Patterns. Dax Patterns DAX Cookbook. See also Calculating mode for single and multiple columns Getting ready How to do it See also Extracting text Getting ready How to do it See also Detecting prime numbers Getting ready How to do it See also Building a revenue growth rate Getting ready How to do it See also Generating an accounts payable turnover ratio Getting ready How to do it See also Fashioning the market share and relative market share Getting ready How to do it See also Determining compound interest Getting ready How to do it See also Comparing budgets and actuals Getting ready How to do it See also Crafting currency exchange rates Getting ready How to do it See also Assessing days sales outstanding Getting ready How to do it See also Finding new and returning customers Getting ready How to do it See also Identifying lost and recovered customers Getting ready How to do it See also Analyzing customer churn rate Getting ready How to do it See also Calculating the customer lifetime value Getting ready How to do it See also Computing the customer acquisition cost Getting ready How to do it See also Computing absenteeism Getting ready How to do it See also Evaluating employee engagement Getting ready How to do it See also Determining human capital value added Getting ready How to do it See also Finding the full-time equivalent Getting ready How to do it See also Projecting planned value Getting ready How to do it See also Estimating earned value Getting ready How to do it See also Achieving actual cost Getting Ready How to do it See also Creating project schedule variance Getting ready How to do it See also Computing project cost variance Getting Ready How to do it See also Creating burndown charts Getting Ready How to do it See also Computing the mean time between failures Getting ready How to do it See also Determining overall equipment effectiveness Getting ready How to do it See also Analyzing order cycle time Getting ready How to do it See also Finding in-common and not-in-common things Getting ready How to do it See also Crafting linear interpolation Getting ready How to do it See also Creating an inverse aggregator Getting ready How to do it See also Finding childless nodes Getting ready How to do it See also Calculating transitive closure Getting ready How to do it See also Computing advanced measure totals Getting ready How to do it See also Using measures where you are not allowed to Getting ready How to do it See also Evaluating permutations and combinations Getting ready How to do it See also Creating a dynamic temporal scale Getting ready How to do it See also Emulating loops Getting ready How to do it See also Simulating recursion Getting ready How to do it See also Approximating the area under a curve Getting ready How to do it See also Measuring covariance Getting ready How to do it See also Analyzing kurtosis Getting ready How to do it See also Determining Pearson's coefficient of skewness Getting ready How to do it See also Applying the hypergeometric distribution formula Getting ready How to do it See also Determining the required sample size Getting ready How to do it See also Creating an inverse slicer Getting ready How to do it See also Transposing tables Getting ready How to do it See also Repeating counter with criteria Getting ready How to do it See also Using across then down Getting ready How to do it See also Using matrix multiplication Getting ready How to do it See also Forecasting with a de-seasonalized correlation coefficient Getting ready How to do it See also Making things anonymous Getting ready How to do it See also Debugging with variables Getting ready How to do it See also Debugging with tables Getting ready How to do it See also Debugging context Getting ready How to do it See also Dealing with circular dependencies Getting ready How to do it See also Optimizing the data model Getting ready How to do it