# distinct\$1count


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 [Level-aware calculation - aggregate (LAC-A) functions](https://docs.amazonaws.cn/quicksight/latest/user/level-aware-calculations-aggregate.html).

## 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})
```

![\[The total number of dates when products were ordered in each region.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/distinct_count-function-example.png)


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 [Level-aware calculation - aggregate (LAC-A) functions](https://docs.amazonaws.cn/quicksight/latest/user/level-aware-calculations-aggregate.html). 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])
```

![\[The total number of dates when products were ordered in each country.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/distinct_count-function-example2.png)