Go to Solution. View solution in original post. Make sure that your tables has cardinality. Both My tables are related. I can't use time intelligence functions as this is a custom calendar. So, i am trying. Hi Felix, Thank you for the response. So its not possible to calculate on the fly. Hence i have pre poulated in my dimension. I made one sample for your reference. If it doesn't meet your requirement, kindly share your sample data and excepted result to me. Turn on suggestions.
Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type. Showing results for. Search instead for. Did you mean:. All forum topics Previous Topic Next Topic. How to filter in DAX using another column in a table? Labels: Need Help. Everyone's tags 1 : DAX. Message 1 of Accepted Solutions.
Re: How to filer in DAX using another column in a table?
DAX – FILTER() – When, Why, & How to Use It
Hi nirupreddyUnderstand your requirements but believe that your setup. Mark my post as a solution!For example, when you write:. From hereinafter, we will describe the syntax of the filter arguments in these functions, identified by in the general syntax:. For this reason, you can write:. The syntax above is internally transformed in the following one, which you might write in an explicit way obtaining the same behavior from your DAX measure. You have several options available, producing different results and potentially with different performance.
You can write a filter over two columns using a filter over the entire table that contains both columns. Using a table filter, you inherit the filter argument existing for the Product table, so you will not include a product Red or of the Contoso brand if it was not present in the existing filter.
What is more important, you will not override the existing filter on such a column. Thus, if you have a slicer filtering the brand Proseware, you will see the sales amount of only the products Red belonging to Proseware brand, ignoring any product of the Contoso brand.
You can write a filter over two columns by creating a special table having only the columns you need. By using the ALL function, you get a table having all the unique combinations of values existing in the underlying table for the referenced columns. In this case, the cardinality of the filter can be lower than the Cartesian product of the values you have in the referenced columns.
In order to obtain such a list, the engine has to execute a table scan. This could be expensive for low cardinality columns in a large table. The result of this filter will override any existing filter over the specified columns. For example, if you have a slicer filtering the brand Proseware, you will see the sales amount of the products Red regardless of the brand, summed to the sales of the entire Contoso brand, regardless of the color — but products of Red color and Contoso brand will be summed only once, without duplicating their value.
Using CROSSJOINyou obtain all the possible combination of the values you have in the columns referenced, regardless of the fact that the combination exists in the underlying table.
In this case, the cardinality of the filter is identical to the Cartesian product of the values you have in the referenced columns. In order to obtain such a list, the engine does not perform a table scan, but only uses the list of values available in the two columns.
This could be expensive for high cardinality columns that have a high correlation, so that the number of existing combinations in the table is much lower than all the possible combinations. The result of this filter is identical to the ALL columns filter, you might just observe different performance in the two approaches. The best one depends on the cardinality of the table and of the columns involved in the filter.
The SUMMARIZE function generates a list of the existing combinations between two or more columns, and can be used with columns belonging to different tables if they are connected in a many-to-one relationships chain. In order to make practice with the different syntaxes, you can download an Excel workbook with the measures described in this article applied to a pivot table with different filters and slicers, comparing the different results.
Where … is any other table expression is allowed in a filter argument. Table Filter You can write a filter over two columns using a filter over the entire table that contains both columns.
ALL Columns Filter You can write a filter over two columns by creating a special table having only the columns you need. Download Insert your email address and press Download for access to the files used in this article.
I want to show a few examples that — at the beginning — act as mind traps. All of these examples are basic variations of how to filter a table. The numbers we will compute are not meaningful by themselves. The goal is just understanding the theory. Mapping it to real-world examples is left as an interesting exercise for the reader. In fact, if you define the measure as follows:. The result is — again — the same value repeated for all the rows. While this holds true, most of my students do not really understand why this is happening.
Unfortunately, this is not correct. If the latter was true, the following code would return the same value for all the rows, while it does return a different value for each one:. The reason for this behavior is — indeed — a very simple one: the filter has never been placed on Sales[ProductKey], the filter is only on Product[Color].
If you think — like you would do in a relational database — that the filter works by linking the two tables by the key, you are thinking the wrong way. Again, the result is a different value for every row.
Even if you remove the filter from the Product[ProductKey], the filter on Product[Color] remains in place. The point is that the key of the relationship has nothing to share with how filtering works. Filters are defined only by means of using expanded tables learn more about expanded tables in the previous article Context Transition and Expanded Tables. The expanded version of Sales contains Product. The reason is that this code removes the filter on Product[Color] because it removes any filter from any of the columns in the expanded version of Sales.
Remember that — in DAX — when you reference a table in a filter argument, such a table is always expanded. Thus ALL Sales argument not only removes the filter from any of the native columns of Sales which — as we have seen — would not have any effect in this casebut it also removes filters from the derived columns of Sales, which include Product[Color].
If you need to convince yourself, you can use ALL including all the columns of Sales, and you will note that the filter on Product[Color] is still in place. The only way to remove it is either to filter the Product table or to use the expanded Sales table.
When activating the bidirectional filtering available in Analysis Services and Power BI, the scenario becomes a bit more complex. In fact, bidirectional filtering does not work through table expansions. Instead, bidirectional filtering is executed by injecting a direct table filter in a transparent way, when executing the query.
This creates the need to take additional care when working with filters. Consider the following model, where we link Customer to Transactions through the usage of an intermediate dimension Accounts and a bridge table AccountCustomer. In order to make the many-to-many relationship work, we enabled bidirectional filtering on the relationship between the bridge table AccountCustomer and the table Account.
A simple report showing the sum of amount sliced by customer produces the correct result, traversing the many-to-many relationship and generating a result like the following one: It is worth to recap what is happening here. Customer is filtering the bridge table, and the bridge table, in turn, filters Account, through the bidirectional relationship.
As a final step, the Account table filters the Transactions table. Let us be more accurate and speak in terms of expanded tables:.Responsive scrolling sidebar
You can obtain a similar behavior by using the good old method of using the bridge table to move the filter from the many side to the one side, like in the following code:. Why is this relevant, while speaking about filters? Because, in this case, the filter introduced by the bidirectional filter cannot be removed by simply using ALL on the fact table.
In fact, if you author a new measure that removes the filter from Transactions and, thus, from its expanded version which includes Accountsthe result is not what you would expect. Consider this formula:. It produces a result identical to the one obtained without ALL : Here the filter is correctly removed from both the Transactions table and the Account one Account is included in the expanded Transactions.
However, the filter on the bridge table is operating on Accounts, through a mechanism that works outside of table expansion.Time to correct that.Thermal conductivity of copper anti seize
In the first formula, I was trying to use a measure on the left side of the comparison, and in the second, I was trying to use a measure on the right side of the comparison. Both are illegal. So my first formula violates the rule that a column name is required on the left. And my second formula violates the rule where a fixed value not an expression or a measure is required on the right.
So this is a good rule really — it forces you to stop and think before accidentally doing something bad.Zenith stereo console for sale
The error message, of course, could and should be a lot better. For a bit more explanation on this, see this brief post. If you look at those two illegal formulas above, they both reflect a perfectly valid intent.La gestione di un evento estremo come processo dinamico di
See that? In my second example, where a measure was used on the right side of the comparison, the formula gets rewritten as:.
So there you go. More details follow, starting with the simplest information and moving to the most subtle of characteristics. Pretty simple. For simple purposes, if you understand the gist of the above, and then points 1 and 2 below, you are good to go. If you want to understand more of the details over time, I recommend revisiting points SincePowerPivotPro has rapidly grown to become the leading firm in the industry, pioneering an agile, results-first methodology never before seen in the Business Intelligence space.
A sought-after public speaker and author of the 1-selling Power BI book, Rob and his team would like to help you revolutionize your business and your career. Wow, thanks so much for this post. It all looks obvious now! Dax takes time to get used to coming from an Excel background, but definitely worth the effort. This is a bit like being able to write dynamic sql queries directly in Excel cells. Thanks for your very clear explanations and infectious enthusiasm!
I am using 5 separate slicers to specify unrelated parameters i. My code seems very inefficient though, as I have a collection of measures which all require the same filter function that I have cut and pasted into each definition. Is there a way to resolve the filter just once and use the result across multiple measures? Your Gender slicer seems like it might just be doable as a connected table. But the x In excelyes. Inyou need PowerPivot for SharePoint.
Actually the SharePoint thing is pretty good for too. I want to create a Rank according to sales amount within each place. For example ALL Periods is not in the above example as mentioned. I am not trying to be ticky tacky, but this seems to be one of the intelligent posts on how filter actually works.
Could you look at the post and see if I am talking crazy or am missing something. If not would you mind updating so we can reference the same material. This post is a bit older but I think may be worth fixing if it indeed does have a problem.
DAX Filter - ALLSELECTED function
Sounds like I cannot do a formula that incorporates a specific date. I have a similar issue as Nathan above.Posted by Andy Brown on 12 January You need a minimum screen resolution of about pixels width to see our blogs. This is because they contain diagrams and tables which would not be viewable easily on a mobile phone or small laptop. Please use a larger tablet, notebook or desktop computer, or change your screen resolution settings.
We'll create 3 measures, showing cheap, expensive and total sales. So for example the Cheap measure will sum the quantity, but only where the price for a transaction is less than or equal to 10 pounds.
To see how the formula above works, consider this shaded cell, giving the total quantity of birds sold for cheap items in the West quadrant for Factory Outlet stores:.
If you double-click on the total sales for Bird items for the given centre type and quadrant The sum of the quantities is 6, which explains where our figure in the pivot table comes from the equivalent total quantity for expensive items is 4.
This is IMHO much easier to understand!Excel call web service json
Here's an example:. This will divide the total quantity of sales for the current pivot table cell by the total quantity of sales for all transactions.Petechiae grid pattern
For tuple aficionados:. The ALL function removes all constraints from the denominator in our ratio.
Filtering Tables in DAX
We want to show total sales for each cell as a percentage of total sales for mammals. So the denominator of the ratio calculates the total quantity for all transactions jettisoning any query context constraintsbut with an additional requirement that the species should be Mammal.
Outstanding course feedback Average score 9. Thanks for looking at our blogs! This provides another way to change the query context for any aggregation. Using filtered data as a replacement for a full table Many DAX expressions ask for a table name: A typical DAX function, asking for a table as one of its arguments. The number 6 in the shaded cell is calculated as below.
If you double-click on the selected cell containing the number We'll calculate the percentage of total sales for each query context.
This blog has 0 threads Add post.As you will see in the following sections, the IN operator can be used also with dynamic expressions, and it can compare more than one or column. The syntax that follows the IN operator in the previous example is a table constructor, and each row can have a row constructor when its content has more than one column. For example, the previous measure RedOrBlack Sales can be written as:.
Thus, the simple syntax for one column is:. In this syntax, the lookupTable is a table with a single column only. It could be a dynamic table expression, such as in the following example, which returns the value of the sales of all the other colors other than the ones selected:. Please note that the syntax above is just an example of a dynamic expression applied to IN. The parenthesis after NOT is not mandatory, but I prefer to make it explicit to improve readability.
The IN operator allows you to compare multiple columns in a single operation.
For example, you can write:. However, it still simplifies the syntax. For example, consider the following measure that sums the two consecutive months of December and January in two different years. However, the syntax of the last three measures uses an iterator over the Calendar table, removing any previous filter context. Applying a table filter instead of a column filter is often a bad ideaso you should consider a FILTER that only includes the columns involved in the IN operator, as in the following example:.
As you see in the following screenshot, NY does not remove the filter over the week day, whereas NY ALL ignores filters applied on any column of the Calendar table. In the previous examples, you have seen a new DAX syntax to create anonymous tables. A table constructor creates a table including all the rows defined through the syntax of row constructors. A table constructor defines a table with one or more rows using a list of row constructors.
For example, the following syntax defines a table constructor made by two rows, each one with two columns: The first is an integer value and the second is a string. If you have a list of values for a single column, you can omit the row constructor syntax. For example, consider the following expression:. A row constructor defines a list of values for a row between two parentheses. It is usually specified to the left of an IN operator or in a table constructor whenever there are two or more columns involved.You can use FILTER to reduce the number of rows in the table that you are working with, and use only specific data in calculations.
FILTER is not used independently, but as a function that is embedded in other functions that require a table as an argument. The following example creates a report of Internet sales outside the United States by using a measure that filters out sales in the United States, and then slicing by calendar year and product categories. The following table demonstrates the proof of concept for the measure, NON USA Internet Sales, the formula for which is provided in the code section below.
The table compares all Internet sales with non- USA Internet sales, to show that the filter expression works, by excluding United States sales from the computation. Skip to main content.DAX Fridays! #41: LOOKUPVALUE with multiple columns
Exit focus mode. The table can also be an expression that results in a table. Remarks You can use FILTER to reduce the number of rows in the table that you are working with, and use only specific data in calculations.
Example The following example creates a report of Internet sales outside the United States by using a measure that filters out sales in the United States, and then slicing by calendar year and product categories. Table 1. Comparing total sales for U. Table 2. Comparing non- U. Yes No. Any additional feedback? Skip Submit. Is this page helpful?
A Boolean expression that is to be evaluated for each row of the table.
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