# percentileCont


The `percentileCont` function calculates percentile based on a continuous distribution of the numbers in the measure. It uses the grouping and sorting that are applied in the field wells. It answers questions like: What values are representative of this percentile? To return an exact percentile value that might not be present in your dataset, use `percentileCont`. To return the nearest percentile value that is present in your dataset, use `percentileDisc` instead.

## Syntax


```
percentileCont(expression, percentile, [group-by level])
```

## Arguments


 *measure*   
Specifies a numeric value to use to compute the percentile. The argument must be a measure or metric. Nulls are ignored in the calculation. 

 *percentile*   
The percentile value can be any numeric constant 0–100. A percentile value of 50 computes the median value of the measure. 

 *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).

## Returns


The result of the function is a number. 

## Usage notes


The `percentileCont` function calculates a result based on a continuous distribution of the values from a specified measure. The result is computed by linear interpolation between the values after ordering them based on settings in the visual. It's different from `percentileDisc`, which simply returns a value from the set of values that are aggregated over. The result from `percentileCont` might or might not exist in the values from the specified measure.

## Examples of percentileCont
Examples

The following examples help explain how percentileCont works.

**Example Comparing median, `percentileCont`, and `percentileDisc`**  
The following example shows the median for a dimension (category) by using the `median`, `percentileCont`, and `percentileDisc` functions. The median value is the same as the percentileCont value. `percentileCont` interpolates a value, which might or might not be in the data set. However, because `percentileDisc` always displays a value that exists in the dataset, the two results might not match. The last column in this example shows the difference between the two values. The code for each calculated field is as follows:  
+ `50%Cont = percentileCont( example , 50 )`
+ `median = median( example )`
+ `50%Disc = percentileDisc( example , 50 )`
+ `Cont-Disc = percentileCont( example , 50 ) − percentileDisc( example , 50 )`
+ `example = left( category, 1 )` (To make a simpler example, we used this expression to shorten the names of categories down to their first letter.)

```
  example     median       50%Cont      50%Disc      Cont-Disc
 -------- ----------- ------------ -------------- ------------ 
 A          22.48          22.48          22.24          0.24
 B          20.96          20.96          20.95          0.01
 C          24.92          24.92          24.92          0
 D          24.935         24.935         24.92          0.015
 E          14.48          14.48          13.99          0.49
```

**Example 100th percentile as maximum**  
The following example shows a variety of `percentileCont` values for the `example` field. The calculated fields `n%Cont` are defined as `percentileCont( {example} ,n)`. The interpolated values in each column represent the numbers that fall into that percentile bucket. In some cases, the actual data values match the interpolated values. For example, the column `100%Cont` shows the same value for every row because 6783.02 is the highest number.  

```
 example      50%Cont     75%Cont      99%Cont    100%Cont  
 --------- ----------- ----------- ------------ ----------- 

 A             20.97       84.307      699.99      6783.02  
 B             20.99       88.84       880.98      6783.02  
 C             20.99       90.48       842.925     6783.02  
 D             21.38       85.99       808.49      6783.02
```

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 30th percentile based on a continuous distribution of the numbers at the Country level, but not across other dimensions (Region) in the visual.

```
percentileCont({Sales}, 30, [Country])
```

![\[The percentile of sales in each country.\]](http://docs.amazonaws.cn/en_us/quick/latest/userguide/images/percentileCont-function-example-lac.png)