ALL(), ALLEXCEPT(), ALLSELECTED() & REMOVEFILTERS()

One of the greatest strengths of DAX is its ability to control filter context.

Functions such as ALL(), ALLEXCEPT(), ALLSELECTED(), and REMOVEFILTERS() allow you to ignore, preserve, or modify filters while evaluating a calculation.

These functions are commonly used for:

• Percent of Total calculations
• Running Totals
• Ranking
• Year-over-Year comparisons
• Dynamic KPIs
• Dashboard summaries

Although these functions appear similar, each serves a different purpose.

Understanding the differences is essential for writing advanced DAX.

Why Remove Filters?

Suppose a report is filtered to:

Category = Bikes

Your measure:

Total Sales =
SUM(FactSales[SalesAmount])

returns only Bike sales.

Sometimes, however, you need to compare Bike sales to all products.

That's where filter removal functions become useful.

The ALL() Function

ALL() removes filters from a table or column before evaluating an expression.

General syntax:

ALL(Table)

ALL(Column)

When used inside CALCULATE(), the specified filters are removed.

Example:

Total Sales All Products =
CALCULATE(
    [Total Sales],
    ALL(DimProduct)
)

Even if the report is filtered to:

Category = Bikes

the measure returns sales for every product.

Removing a Single Column Filter

ALL() can also remove filters from a specific column.

Example:

Sales All Categories =
CALCULATE(
    [Total Sales],
    ALL(DimProduct[Category])
)

Only the Category filter is removed.

Other filters, such as:

• Brand
• Color
• Size

continue to affect the calculation.

This makes column-level filter removal much more targeted than removing filters from an entire table.

How ALL() Changes Filter Context

Normal report:

Report Filter

↓

Category = Bikes

↓

FactSales

↓

$150,000

Using ALL():

Report Filter

↓

ALL(DimProduct)

↓

All Products

↓

FactSales

↓

$500,000

The original report filter is ignored, allowing the measure to calculate against the complete dataset.

A Common Use Case

One of the most common applications of ALL() is calculating percentages of the grand total.

Example:

Percent of Total =
DIVIDE(
    [Total Sales],
    CALCULATE(
        [Total Sales],
        ALL(DimProduct)
    )
)

The numerator respects the current report filters.

The denominator removes the product filter and calculates the overall total.

This pattern appears in countless Power BI reports and dashboards.

ALLEXCEPT()

ALLEXCEPT() removes all filters from a table except the columns you specify.

General syntax:

ALLEXCEPT(
    Table,
    Column1,
    Column2
)

Example:

Sales by Category =
CALCULATE(
    [Total Sales],
    ALLEXCEPT(
        DimProduct,
        DimProduct[Category]
    )
)

In this example, every filter on DimProduct is removed except Category.

If the report contains filters for:

• Brand
• Color
• Size

those filters are ignored, while the Category filter remains active.

ALLSELECTED()

ALLSELECTED() removes filters applied inside the visual while preserving filters selected by the user.

This makes it especially useful for interactive reports.

Example:

Visual Total Sales =
CALCULATE(
    [Total Sales],
    ALLSELECTED(DimProduct)
)

Suppose a report has a slicer selecting:

Category = Bikes

Inside a table visual, each row displays an individual product.

ALLSELECTED() removes the row-level product filter but keeps the slicer selection.

The result is the total sales for all Bikes, not just the current product.

REMOVEFILTERS()

REMOVEFILTERS() explicitly removes filters from a table or column.

General syntax:

REMOVEFILTERS(Table)

REMOVEFILTERS(Column)

Example:

Overall Sales =
CALCULATE(
    [Total Sales],
    REMOVEFILTERS(DimProduct)
)

This produces the same result as:

CALCULATE(
    [Total Sales],
    ALL(DimProduct)
)

Many developers prefer REMOVEFILTERS() because its intent is immediately clear.

Comparing the Filter Functions

Although these functions appear similar, they behave differently.

Choosing the correct function depends on the type of report you are building and how you want filters to behave.

Choosing the Right Function

Use ALL() when you need to ignore all filters.

Example:

Percent of Total =
DIVIDE(
    [Total Sales],
    CALCULATE(
        [Total Sales],
        ALL(DimProduct)
    )
)

Use ALLEXCEPT() when one or more filters should remain active.

Use ALLSELECTED() when calculations should respect slicers but ignore the current visual's row context.

Use REMOVEFILTERS() when your goal is simply to remove filters and make the code easier to read.

Real Business Examples

These functions appear in many Power BI reports.

Understanding the differences between these functions is essential for building accurate and interactive reports.

Percent of Total

One of the most common uses of filter functions is calculating a percentage of the overall total.

Example:

Percent of Total =
DIVIDE(
    [Total Sales],
    CALCULATE(
        [Total Sales],
        ALL(DimProduct)
    )
)

How it works:

1. The numerator calculates sales for the current filter context.
2. The denominator removes the Product filter.
3. The result is the percentage of overall sales.

Example:

Ranking Products

Filter functions are commonly used with RANKX().

Example:

Product Rank =
RANKX(
    ALL(DimProduct),
    [Total Sales]
)

Using ALL() removes the current product filter so every product is ranked against the complete list.

Without ALL(), every product would rank as 1 because only the current row would be visible.

Common Mistakes

The most common mistakes include:

• Using ALL() when ALLSELECTED() is required.
• Removing more filters than intended.
• Forgetting that ALL(Table) removes every filter on the table.
• Applying ALL() outside of CALCULATE().
• Expecting ALLSELECTED() to ignore slicers.

Understanding which filters remain active is the key to writing correct DAX.

Best Practices

When working with filter functions:

• Use REMOVEFILTERS() when your intention is simply to remove filters.
• Use ALL() for grand totals and rankings.
• Use ALLEXCEPT() when one or more filters should remain.
• Use ALLSELECTED() for interactive reports that use slicers.
• Keep measures simple by combining filter functions with reusable base measures.

Filter functions become much easier to understand when each measure has a single purpose.

Performance Considerations

Filter functions are generally efficient because they modify filter context rather than looping through rows.

However, performance can decrease when they are combined with:

• Large iterator functions (SUMX(), FILTER())
• Complex virtual tables
• Multiple nested CALCULATE() statements

When working with large models, test measures using Performance Analyzer to identify expensive calculations.

Summary

Filter functions control how DAX evaluates filter context.

The four most common functions are:

Mastering these functions is essential for building percentages, rankings, KPIs, and advanced analytical measures.

Next Steps

Continue learning advanced DAX functions:

• FILTER
• RELATED & RELATEDTABLE
• LOOKUPVALUE
• SELECTEDVALUE
• KEEPFILTERS()
• CROSSFILTER()

These functions are frequently combined with filter functions to create powerful business calculations.

Want a fast side-by-side reference instead? See the DAX CALCULATE Modifiers Cheat Sheet.