Are you aware of why DAX in Power BI is critical for data analytics? Your decision-making is influenced by the establishment of bespoke metrics. Well, you may have already imported some data into Power BI desktop files and even created reports that provide useful information. DAX functions in Power BI and how to create measurements using the Power BI desktop will be covered in this article.
Do you know why the DAX in Power BI plays a vital role in data analytics? How does the creation of custom measures impact your decision-making?
Well, You might’ve imported some data into Power BI desktop files and even create reports that show valuable insights. But what if, you need to analyze the growth (%) of product lists over different date ranges or, need to analyze the annual growth of your company compared to rivals? This is where Power BI DAX comes into the picture.
In this blog, we’re going to discuss the most useful DAX functions in Power BI and the creation of measures by using the Power BI desktop and more.
In this Power BI DAX Basics Article, we will discuss the following topics |
DAX in Power BI: How does it work? DAX in Power BI: Calculated Columns & Measures Calculated Columns vs Measures |
Power BI DAX Tutorial | DAX Basics in Power BI | DAX Functions in Power BI
So, let us begin with the DAX definition first, which I am going to talk about in the section below.
Data Analysis Expressions (DAX) is a syntax language that comprises formulae and expressions that are used in data manipulation. Functions, constants, and operators are used in DAX to create expressions. In simpler terms, DAX is the advanced version of MS Excel with high-end data manipulation and management capabilities. It is developed by Microsoft to interact with its business intelligence and data modeling tools like PowerPivot, Power BI. Power BI is a robust analytics tool by Microsoft that offers several features and functions using DAX as a language.
Some of the major advantages of Power BI are:
All these functions are essentially performed by pre-set tools and commands in the system. But there are several occasions when you want to perform a particular type of function says a certain type of data analysis but you are unable to do so because the command does not pre-exist in the system. What do you do in such a case? This is where DAX comes into the picture.
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DAX is quite an important syntax as working in DAX improves the user experience by deploying functionalities like data visualization, data importing, and manipulating. For normal report creation, the basic knowledge of the dashboard is enough but to be able to build up sophisticated and insightful reports, DAX is required. With several commands and measures that can be used under the DAX syntax, the reports generated are highly intuitive and discerning.
DAX works on a combination of three fundamental concepts:
These inputs are fundamental to the creation of a specific command that fetches desired results.
Syntax refers to the components of the formula you are making. It is the language used in the formula like the command, sign, operators, destination column or row or table, etc. For example, name, parenthesis, summation, name of the table, etc.
Context refers to the target row that has been included in the formula for data retrieval or calculation. Context is present in two types: Row Context and Filter Context.
Functions refer to the predefined or already existing commands in the system. For example, Sum, Add, True, False, etc.
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In this article, we are going to be focusing on the Power BI DAX formulae used in calculations, Measures, and Calculated Columns.
Similar to other regular columns, calculated columns are the ones that come out as a result of computations within two columns of different data sets. They are ideal for row-wise calculations;
Calculated columns are ideal for dynamic calculations as they do not acquire any physical space. These are used when data in rows are grouped together for computations.
Calculated columns and calculated measures both use DAX expressions for data manipulation. The difference lies in the context of the underlying formula. In the calculated column type, the calculation takes place at the row level within a given table. In the calculated measure type, the calculation is done at the cell level in the entire report or the query.
MIN
Fetches the minimum value in a given column.
Syntax
MIN(<column>)
Example
=MIN( [ SellerMargin] )
MINA
Fetches the minimum value along with Aggregate Functions Logical values and text representation of numbers if any
Syntax
MINA( <column> )
Example
=MINA( InternetSales[Freight] )
MINX
Fetches the minimum value after evaluation of each row expression in a given table.
Syntax
MINX ( < table >, <expression> )
Example
=MINX( FILTER( InternetSales ) , [ SalesTerittoryKey ] = 2 , [Freight] )
Other functions
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DISTINCTCOUNT
Fetches the count of distinct numbers avoiding any replication.
Syntax
DISTINCTCOUNT( <column> )
Example
=DISTINCTCOUNT( ProductsList[ProductID] )
COUNT
Fetches the total count of items even if repetitions are present.
Syntax
COUNT( <column> )
Example
=COUNT ( [ShipDate] )
COUNTA
Fetches the count of items in a non-empty column.
Syntax
COUNTA( <column> )
Example
= COUNTA( ‘ProductSeller’[Phone] )
COUNTROWS
Fetches the number of rows in a given table.
Syntax
COUNTROWS( < table > )
Example
=COUNTROWS( ‘Enquiries’ )
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DATE
Fetches the desired date in Date-time format.
Syntax
DATE ( <year>, <month>, <day> )
Example
=DATE ( 2020,02,27 )
HOUR
Fetches hours in the AM PM format.
Syntax
HOUR ( <datetime> )
Example
=HOUR( ‘Orders’ [TransactionTime] )
TODAY
Fetches the current date.
Syntax
TODAY()
Example
= YEAR ( TODAY())-2012
Other functions
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ABS
Fetches the absolute value.
Syntax
ABS( <number> )
Example
=ABS( [LabelPrice] - [SellingPrice] )
EXP
Fetches the exponent's value.
Syntax
EXP( <number> )
Example
=EXP( [Power] )
FACT
Fetches the factorial of a given number.
Syntax
FACT( <number> )
Example
=FACT( [Values] )
Other functions
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AND
Performs the logical conjunction on two specified expressions.
Syntax
AND( <logical1> , <logical2 > )
Example
=IF( AND(1<2 , 2<3) , “All true” , “One or false” )
OR
Performs the logical disjunction on two specified expressions.
Syntax
OR( <logical1 > , <logical2 > )
Example
=( IF(OR(1<2 , 2<3) , “All true” , “One or more false” )
NOT
Performs logical negation on the given expression.
Syntax
NOT( <logical > )
Example
=NOT( [ProductPrices] )
Other functions
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ISBLANK
Declares if the value is blank or not as true or false.
Syntax
ISBLANK( <value> )
Example
=IF( ISBLANK(‘CalculatedMeasures’[PreviousYearTotalSales]) ,BLANK() , (CalculatedMeasures’[PreviousYearTotalSales]/ ‘CalculatedMeasures’[PreviousyearTotalSales])
ISNUMBER
Declares if the value is a number or not.
Syntax
ISNUMBER(<value>)
Example
=IF( ISNUMBER(2), “Is number”, “Is Not number” )
ISNONTEXT
Declare if the value is non-text.
Syntax
ISNONTEXT( <value> )
Example
=IF( ISNONTEXT(“ ”), “Is Non-Text”, “Is Text” )
Other functions
CONCATENATE
Performs joining of two strings.
Syntax
CONCATENATE( <text1> , <text2> )
Example
= CONCATENATE( “Hello” , “Learner” )
FIXED
Performs rounding off of number to a given decimal.
Syntax
FIXED( <number> , <decimals> , <no_commas> )
Example
=FIXED( [LabelPrice], 3,1 )
REPLACE
Replaces part of a string with specified characters.
Syntax
REPLACE( <old_text> , <start_num>, <num_chars> , <new_text> )
Example
=REPLACE( ‘New Services’[Service ID],2,3,”AB” )
Other functions:
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The following steps make your task simple to create a measure using Power BI DAX.
1. First, go to the Power BI Desktop app.
2. Right-click the Sales table or hover over More options (...) of Field pane.
3. Once the menu is presented, select New Measure. This activity stores the new Measure in the Sales table. You can also create a new Measure from the Power BI desktop ribbon in the following way.
Go to Home tab > Calculations group > New Measure.
4. Next, the Formula bar exists on top of the reporting canvas, where you can edit your name and write a Data Analysis Xpression formula.
Let’s discuss the concept before creating the formula. This makes it simple to proceed further
Variables Selection and Formula creation:
5. The Measure is named by default in the formula bar, but we need Measure to be more noticeable, emphasize Measure in the formula bar and alter it to Net Amount.
6. While creating your formula, write Sum after the equal sign. Now, the drop-down suggestion list appears, once you type the starting of the letter. Select from the list, if needed, and then press Enter.
7. Press enter after typing the SUM. Now, you can see an open parenthesis along with a drop-down suggestion list of existing columns. It can be passed to the SUM function.
8. The expression is always between Open parenthesis and Closing parenthesis. For this instance, your expression comprises an argument to push to the SUM function: the SalesQuantity Column. Start Typing SalesQuantity until Sales[SalesQuantity] is the only value that appears in the list.
The Table name is preceded by the column name is called the fully qualified name of the column. It makes your formula easier to comprehend.
9. Select Sales[Sales quantity] from the list then enter closing parenthesis.
Multiply the Value with the First Expression and Subtract the other Column from the Final Value:
Sales[SalesQuantity]* 15000.
10. To finish and validate the formula, we need to enter either Enter or Select CheckMark icon(Commit).
The validated Net Amount measure is now ready to use in the sales table in the Fields pane.
DAX is one of the most commonly used expression syntax these days with a wide range of applicability. Learning it opens up several opportunities in the market, especially in SQL-enabled companies. With DAX in Power BI, you are empowered to build up any kind of logical expression or formula as per requirement.
Reach out to us in the comments if you want to learn and excel in DAX.
Got any questions? Leave a comment below and we will get back to you
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .