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Returns all the rows in a table, or all the values in a column, ignoring any filters that might have been applied. This function is useful for clearing filters and creating calculations on all the rows in a table.

## Syntax

`ALL( [<table> | <column>[, <column>[, <column>[,…]]]] ) `

### Parameters

Term | Definition |
---|---|

table | The table that you want to clear filters on. |

column | The column that you want to clear filters on. |

The argument to the ALL function must be either a reference to a base table or a reference to a base column. You cannot use table expressions or column expressions with the ALL function.

## Return value

The table or column with filters removed.

## Remarks

This function is not used by itself, but serves as an intermediate function that can be used to change the set of results over which some other calculation is performed.

(Video) Understanding ALL function in DAXThe normal behavior for DAX expressions containing the ALL() function is that any filters applied will be ignored. However, there are some scenarios where this is not the case because of

*auto-exist*, a DAX technology that optimizes filtering in order to reduce the amount of processing required for certain DAX queries. An example where auto-exist and ALL() provide unexpected results is when filtering on two or more columns of the same table (like when using slicers), and there is a measure on that same table that uses ALL(). In this case, auto-exist will*merge*the multiple filters into one and will only filter on existing combinations of values. Because of this merge, the measure will be calculated on the existing combinations of values and the result will be based on filtered values instead of all values as expected. To learn more about auto-exist and its effect on calculations, see Microsoft MVP Alberto Ferrari's Understanding DAX Auto-Exist article on sql.bi.com.The following table describes how you can use the ALL and ALLEXCEPT functions in different scenarios.

Function and usage Description ALL() Removes all filters everywhere. ALL() can only be used to clear filters but not to return a table. ALL(Table) Removes all filters from the specified table. In effect, ALL(Table) returns all of the values in the table, removing any filters from the context that otherwise might have been applied. This function is useful when you are working with many levels of grouping, and want to create a calculation that creates a ratio of an aggregated value to the total value. The first example demonstrates this scenario. ALL (Column[, Column[, …]]) Removes all filters from the specified columns in the table; all other filters on other columns in the table still apply. All column arguments must come from the same table. The ALL(Column) variant is useful when you want to remove the context filters for one or more specific columns and to keep all other context filters. The second and third examples demonstrate this scenario. ALLEXCEPT(Table, Column1 [,Column2]...) Removes all context filters in the table except filters that are applied to the specified columns. This is a convenient shortcut for situations in which you want to remove the filters on many, but not all, columns in a table. This function is not supported for use in DirectQuery mode when used in calculated columns or row-level security (RLS) rules.

## Example 1

Calculate ratio of Category Sales to Total Sales

Assume that you want to find the amount of sales for the current cell, in your PivotTable, divided by the total sales for all resellers. To ensure that the denominator is the same regardless of how the PivotTable user might be filtering or grouping the data, you define a formula that uses ALL to create the correct grand total.

The following table shows the results when a new measure, **All Reseller Sales Ratio**, is created using the formula shown in the code section. To see how this works, add the field, CalendarYear, to the **Row Labels** area of the PivotTable, and add the field, ProductCategoryName, to the **Column Labels** area. Then, drag the measure, **All Reseller Sales Ratio**, to the **Values** area of the Pivot Table. To view the results as percentages, use the formatting features of Excel to apply a percentage number formatting to the cells that contains the measure.

Row Labels | Accessories | Bikes | Clothing | Components | Grand Total |
---|---|---|---|---|---|

2005 | 0.02% | 9.10% | 0.04% | 0.75% | 9.91% |

2006 | 0.11% | 24.71% | 0.60% | 4.48% | 29.90% |

2007 | 0.36% | 31.71% | 1.07% | 6.79% | 39.93% |

2008 | 0.20% | 16.95% | 0.48% | 2.63% | 20.26% |

Grand Total | 0.70% | 82.47% | 2.18% | 14.65% | 100.00% |

**Formula**

`= SUMX(ResellerSales_USD, ResellerSales_USD[SalesAmount_USD])/SUMX(ALL(ResellerSales_USD), ResellerSales_USD[SalesAmount_USD]) `

The formula is constructed as follows:

The numerator,

`SUMX(ResellerSales_USD, ResellerSales_USD[SalesAmount_USD])`

, is the sum of the values in ResellerSales_USD[SalesAmount_USD] for the current cell in the PivotTable, with context filters applied on CalendarYear and ProductCategoryName.For the denominator, you start by specifying a table, ResellerSales_USD, and use the ALL function to remove all context filters on the table.

You then use the SUMX function to sum the values in the ResellerSales_USD[SalesAmount_USD] column. In other words, you get the sum of ResellerSales_USD[SalesAmount_USD] for all resellers sales.

## Example 2

Calculate Ratio of Product Sales to Total Sales Through Current Year

Assume that you want to create a table showing the percentage of sales compared over the years for each product category (ProductCategoryName). To obtain the percentage for each year over each value of ProductCategoryName, you need to divide the sum of sales for that particular year and product category by the sum of sales for the same product category over all years. In other words, you want to keep the filter on ProductCategoryName but remove the filter on the year when calculating the denominator of the percentage.

The following table shows the results when a new measure, **Reseller Sales Year**, is created using the formula shown in the code section. To see how this works, add the field, CalendarYear, to the **Row Labels** area of a PivotTable, and add the field, ProductCategoryName, to the **Column Labels** area. To view the results as percentages, use Excel's formatting features to apply a percentage number format to the cells containing the measure, **Reseller Sales Year**.

Row labels | Accessories | Bikes | Clothing | Components | Grand Total |
---|---|---|---|---|---|

2005 | 3.48% | 11.03% | 1.91% | 5.12% | 9.91% |

2006 | 16.21% | 29.96% | 27.29% | 30.59% | 29.90% |

2007 | 51.62% | 38.45% | 48.86% | 46.36% | 39.93% |

2008 | 28.69% | 20.56% | 21.95% | 17.92% | 20.26% |

Grand Total | 100.00% | 100.00% | 100.00% | 100.00% | 100.00% |

**Formula**

`= SUMX(ResellerSales_USD, ResellerSales_USD[SalesAmount_USD])/CALCULATE( SUM( ResellerSales_USD[SalesAmount_USD]), ALL(DateTime[CalendarYear])) `

The formula is constructed as follows:

The numerator,

`SUMX(ResellerSales_USD, ResellerSales_USD[SalesAmount_USD])`

, is the sum of the values in ResellerSales_USD[SalesAmount_USD] for the current cell in the pivot table, with context filters applied on the columns CalendarYear and ProductCategoryName.For the denominator, you remove the existing filter on CalendarYear by using the ALL(Column) function. This calculates the sum over the remaining rows on the ResellerSales_USD table, after applying the existing context filters from the column labels. The net effect is that for the denominator the sum is calculated over the selected ProductCategoryName (the implied context filter) and for all values in Year.

(Video) ALL Vs ALLSELECTED Vs ALLEXCEPT DAX Filter Functions | DAX Sundays | DAX Tutorial |BI Consulting Pro

## Example 3

Calculate Contribution of Product Categories to Total Sales Per Year

Assume that you want to create a table that shows the percentage of sales for each product category, on a year-by-year basis. To obtain the percentage for each product category in a particular year, you need to calculate the sum of sales for that particular product category (ProductCategoryName) in year n, and then divide the resulting value by the sum of sales for the year n over all product categories. In other words, you want to keep the filter on year but remove the filter on ProductCategoryName when calculating the denominator of the percentage.

The following table shows the results when a new measure, **Reseller Sales CategoryName**, is created using the formula shown in the code section. To see how this works, add the field, CalendarYear to the **Row Labels** area of the PivotTable, and add the field, ProductCategoryName, to the **Column Labels** area. Then add the new measure to the **Values** area of the PivotTable. To view the results as percentages, use Excel's formatting features to apply a percentage number format to the cells that contain the new measure, **Reseller Sales CategoryName**.

Row Labels | Accessories | Bikes | Clothing | Components | Grand Total |
---|---|---|---|---|---|

2005 | 0.25% | 91.76% | 0.42% | 7.57% | 100.00% |

2006 | 0.38% | 82.64% | 1.99% | 14.99% | 100.00% |

2007 | 0.90% | 79.42% | 2.67% | 17.01% | 100.00% |

2008 | 0.99% | 83.69% | 2.37% | 12.96% | 100.00% |

Grand Total | 0.70% | 82.47% | 2.18% | 14.65% | 100.00% |

**Formula**

`= SUMX(ResellerSales_USD, ResellerSales_USD[SalesAmount_USD])/CALCULATE( SUM( ResellerSales_USD[SalesAmount_USD]), ALL(ProductCategory[ProductCategoryName])) `

The formula is constructed as follows:

The numerator,

`SUMX(ResellerSales_USD, ResellerSales_USD[SalesAmount_USD])`

, is the sum of the values in ResellerSales_USD[SalesAmount_USD] for the current cell in the PivotTable, with context filters applied on the fields, CalendarYear and ProductCategoryName.(Video) All Function in Power BI DAXFor the denominator, you use the function, ALL(Column), to remove the filter on ProductCategoryName and calculate the sum over the remaining rows on the ResellerSales_USD table, after applying the existing context filters from the row labels. The net effect is that, for the denominator, the sum is calculated over the selected Year (the implied context filter) and for all values of ProductCategoryName.

## See also

Filter functions

ALL function

ALLEXCEPT function

FILTER function

## FAQs

### What does the all function do in DAX? ›

**Returns all the rows in a table, or all the values in a column, ignoring any filters that might have been applied**. This function is useful for clearing filters and creating calculations on all the rows in a table.

**Why DAX is so difficult? ›**

Reason #1: **DAX is a functional language**

Functional languages are extremely elegant, from a mathematical point of view. Yet, they are also very counter-intuitive. As humans, when we describe an algorithm, we follow steps. We do not provide a function that computes the result: we provide the steps to reach the result.

**How do I use multiple conditions in DAX? ›**

For multiple criterias in DAX you can **use the AND or OR functions (that only handle up to 2 conditions), or operators like && or ||**. but it becomes too complex and unclear if there are more IFs. With SWITCH we can simply keep the logic condition - result - condition - result etc.

**What is difference between all and Allselected in DAX? ›**

It is different from the ALL function in that **the ALL function ignores all filters regardless of where they come from, while the ALLSELECTED function ignores filters that come from the inner query**.

**What does the function all () do? ›**

Python all() Function

The all() function **returns True if all items in an iterable are true, otherwise it returns False**. If the iterable object is empty, the all() function also returns True.

**How do any () and all () work? ›**

**The any() method returns true if any of the list items are true, and the all() function returns true if all the list items are true**. Often, when you're programming, you may want to check whether any or all of the values in a list evaluate to True.

**How long does it take to master DAX? ›**

How Long Does It Take to Learn DAX? It takes approximately 4-6 weeks to learn DAX. The learning duration can vary depending on the users' previous experience in Microsoft Excel and business intelligence tools. Complete mastery of DAX at the advanced level may take **several months to years**, depending on the learner.

**What is the fastest way to learn DAX? ›**

The best way to learn DAX is to **create some basic formulas, use it with some actual data, and see the results for yourself**. The examples and tasks here use the Contoso Sample DAX Formulas.

**What time is DAX most volatile? ›**

One of the key aspects to the Germany 30 index is active trading times. The index is most active between **5pm-7pm AEST** (depending on daylight savings) and is, therefore, perfect for those with a 9-5 job.

**What does == mean in DAX? ›**

The “strictly equal to” operator == returns TRUE when the two arguments have the same value or are both BLANK. A comparison between BLANK and any other value returns FALSE.

### How do you use if function if there are multiple conditions? ›

How to use IF function with multiple conditions. In essence, there are two types of the IF formula with multiple criteria based on the AND / OR logic. Consequently, in the logical test of your IF formula, you should use one of these functions: **AND function - returns TRUE if all the conditions are met; FALSE otherwise**.

**What does && do in DAX? ›**

&& (double ampersand) **Creates an AND condition between two expressions that each have a Boolean result**. If both expressions return TRUE, the combination of the expressions also returns TRUE; otherwise the combination returns FALSE.

**What is the difference between Removefilters and all functions? ›**

REMOVEFILTERS is like ALL, but it can only be used as a filter argument in CALCULATE. **While REMOVEFILTERS can replace ALL, there is not replacement for ALLEXCEPT and ALLSELECTED used as CALCULATE modifiers**. In this case, ALL is a filter parameter of CALCULATE. As such, it acts as a REMOVEFILTERS, not as an ALL.

**What is the difference between all and Allexcept in DAX? ›**

All function is used when you want to fetch data from the entire table or a few columns without filter action. All except is used similarly to all but would fetch data that is affected after applying filters from the entire table or few columns. syn = ALLEXCEPT/ALL(table name,column name).

**What is all and Allexcept in DAX? ›**

**The ALL(Column) variant is useful when you want to remove the context filters for one or more specific columns and to keep all other context filters**. ALLEXCEPT(Table, Column1 [,Column2]...) Removes all context filters in the table except filters that are applied to the specified columns.

**What happens when you use all () of a list? ›**

The all() function takes an iterable as the argument, **returns True only if all items in the iterable evaluate to True or if the iterable is empty**. In all other cases, the all() function returns False .

**What is true about all functions? ›**

**All functions have an independent variable**. The range of a function includes its domain. A vertical line is an example of a functional relationship. A horizontal line is an example of a functional relationship.

**Which variables are inside of all functions *? ›**

Variables that are declared inside a function or block are called **local variables**.

**What does all work mean? ›**

noun. : **a domestic employee who performs all kinds of work and services about the home**.

**What does any or all mean? ›**

ANY OR ALL means **readers may choose any item(s) (they choose which and how many) OR all of them, whichever they prefer**. If you have used this phrase, you probably meant "each," "every," or "each and every," which is a phrase of emphasis often used by lawyers.

### How many days it will take to learn DAX? ›

Becoming proficient with essential DAX functions is estimated to take approximately **two weeks**. Understanding how to make dashboards and reports takes most people three days.

**Should I learn DAX or M? ›**

So the answer is; **there is no best language between these two**. The type of usage identifies which one is best. Normally any changes to prepare the data for the model is best to be done in M, and any analysis calculation on top of the model is best to be done in DAX.

**How many DAX formulas are there? ›**

The DAX function reference provides detailed information including syntax, parameters, return values, and examples for each of the **over 250** functions used in Data Analysis Expression (DAX) formulas.

**Is DAX language hard to learn? ›**

**DAX is a really easy language to write but a bit hard to grasp in the beginning**. Once you learn DAX underlying theory then it is easy to write any formula and you can play with multiple nested contexts. With every topic, first, we will discuss hidden concepts and then we will do its implementation.

**Which is faster DAX or power query? ›**

PQ calculation slows down data load, where as DAX calculated column will slow down visual update on interaction with slicer etc. Most cases, if you do data load during after hours (or early in the morning) once. **It's more desirable to have better performance during interaction with report**.

**What time do most traders wake up? ›**

The opening **9:30 a.m.** to 10:30 a.m. Eastern time (ET) period is often one of the best hours of the day for day trading, offering the biggest moves in the shortest amount of time. A lot of professional day traders stop trading around 11:30 a.m. because that is when volatility and volume tend to taper off.

**What time frame is good for day trading? ›**

The **15-minute** time frame is probably the most popular interval for day traders focusing on multiple stocks throughout the day. The longer the watchlist, the higher the chart interval should be. You need to have a realistic chance to scan and analyze the current market behavior.

**Which time frame is best for week trading? ›**

Hence, this makes the time frame **between 9:30 am to 10:30 am** the ideal time to make trades. Intraday trading in the first few hours of the market opening has many benefits: – The first hour is usually the most volatile, providing ample opportunity to make the best trades of the day.

**What language is DAX? ›**

**Data Analysis Expressions** (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.

**What is the difference between distinct () and values () in DAX? ›**

**The DISTINCT function allows a column name or any valid table expression to be its argument but the VALUES function only accepts a column name or a table name as the argument**.

### How do you use a large function with multiple conditions? ›

We can find the largest value based on multiple criteria as well. To do that we need to extend the formula based on boolean logic. The formula would have the syntax **{=LARGE(IF((criteria 1)*(criteria 2),value),n)}** . This will find the nth largest value based on multiple criteria.

**Can I write 2 conditions in if statement? ›**

Here we'll study how can we check multiple conditions in a single if statement. This can be done by **using 'and' or 'or' or BOTH in a single statement**. and comparison = for this to work normally both conditions provided with should be true. If the first condition falls false, the compiler doesn't check the second one.

**What does Crossjoin do in DAX? ›**

**Returns a table that contains the Cartesian product of all rows from all tables in the arguments**.

**Is || before &&? ›**

**The logical-AND operator ( && ) has higher precedence than the logical-OR operator ( || )**, so q && r is grouped as an operand. Since the logical operators guarantee evaluation of operands from left to right, q && r is evaluated before s-- .

**Is && a boolean? ›**

**The logical AND ( && ) (logical conjunction) operator for a set of boolean operands** will be true if and only if all the operands are true . Otherwise it will be false .

**What is the difference between all and Removefilters in DAX? ›**

REMOVEFILTERS is like ALL, but it can only be used as a filter argument in CALCULATE. **While REMOVEFILTERS can replace ALL, there is not replacement for ALLEXCEPT and ALLSELECTED used as CALCULATE modifiers**. In this case, ALL is a filter parameter of CALCULATE. As such, it acts as a REMOVEFILTERS, not as an ALL.

**What are the 3 types of function *? ›**

Types of Functions

**Many – one function**. Onto – function (Surjective Function) Into – function. Polynomial function.

**What is the difference between all and Allselected? ›**

ALL – Returns all the rows in a table or all the values in a column, ignoring any filters which might have been applied. ALLSELECTED- Returns all the rows in a table or all the values in a column, ignoring any filters which might have been applied on the columns or the rows but keeping all other explicit filters.

**What does SUMX do in DAX? ›**

**Returns the sum of an expression evaluated for each row in a table**.

**What is difference between sum and SUMX? ›**

SUMX is an iterator function and takes a different approach. Unlike SUM, **SUMX is capable of performing row-by-row calculations and iterates through every row of a specified table to complete the calculation**. SUMX then adds all the row-wise results of the iterations of the given expression.

### What is the difference between summarize and Summarizecolumns in DAX? ›

Another difference between SUMMARIZE and SUMMARIZECOLUMNS is that SUMMARIZE keeps both a row context and a filter context active in the expression where you specify the aggregation (more details in All the secrets of SUMMARIZE), whereas SUMMARIZECOLUMNS provides a filter context only and no row context.

**What are the two types of DAX functions? ›**

You can create two types of expressions or calculations using DAX in Power BI; **calculated columns and calculated measures**.

**How do I improve my DAX query performance? ›**

Before you run your queries, you have to **clear the cache** first because other users may not use the cache that is embedded in the calculation. This will make your DAX work faster in providing results. One of the best practices is to always clear the cache before doing any type of optimization.

**What are the 8 basic functions? ›**

The eight types are **linear, power, quadratic, polynomial, rational, exponential, logarithmic, and sinusoidal**.