# distinct_count

The distinct_count function calculates the number of distinct values in a dimension or measure, grouped by the chosen dimension or dimensions. For example, distinct_count(product type) returns the total number of unique product types grouped by the (optional) chosen dimension, without any duplicates. The distinct_count(ship date) function returns the total number of dates when products were shipped grouped by the (optional) chosen dimension, for example region.

## Syntax

`distinct_count(dimension or measure, [group-by level])`

## Arguments

dimension or measure

The argument must be a measure or a dimension. Null values are omitted from the results. Literal values don't work. The argument must be a field.

group-by level

(Optional) Specifies the level to group the aggregation by. The level added can be any dimension or dimensions independent of the dimensions added to the visual.

The argument must be a dimension field. The group-by level must be enclosed in square brackets [ ]. For more information, see LAC-A functions.

## Example

The following example calculates the total number of dates when products were ordered grouped by the (optional) chosen dimension in the visual, for example region.

`distinct_count({Order Date})`

You can also specify at what level to group the computation using one or more dimensions in the view or in your dataset. This is called a LAC-A function. For more information about LAC-A functions, see LAC-A functions. The following example calculates the average sales at the Country level, but not across other dimensions (Region) in the visual.

`distinct_count({Order Date}, [Country])`