# percentileContOver

The percentileContOver function calculates the percentile based on the actual numbers in measure. It uses the grouping and sorting that are applied in the field wells. The result is partitioned by the specified dimension at the specified calculation level.

Use this function to answer the following question: Which actual data points are present in this percentile? To return the nearest percentile value that is present in your dataset, use percentileDiscOver. To return an exact percentile value that might not be present in your dataset, use percentileContOver instead.

## Syntax

Copy
percentileDiscOver (
measure
, percentile-n
, [partition-by, …]
, calculation-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-n

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

partition-by

(Optional) One or more dimensions that you want to partition by, separated by commas. Each field in the list is enclosed in { } (curly braces), if it is more than one word. The entire list is enclosed in [ ] (square brackets).

calculation-level

Specifies where to perform the calculation in relation to the order of evaluation. There are three supported calculation levels:

• PRE_FILTER

• PRE_AGG

• POST_AGG_FILTER (default) – To use this calculation level, specify an aggregation on measure, for example sum(measure).

PRE_FILTER and PRE_AGG are applied before the aggregation occurs in a visualization. For these two calculation levels, you can't specify an aggregation on measure in the calculated field expression. To learn more about calculation levels and when they apply, see Order of evaluation in Insights and Using level-aware calculations in Insights.

## Returns

The result of the function is a number.

## Example of percentileContOver

The following example helps explain how percentileContOver works.

Example Comparing calculation levels for the median

The following example shows the median for a dimension (category) by using different calculation levels with the percentileContOver function. The percentile is 50. The dataset is filtered by a region field. The code for each calculated field is as follows:

• example = left( category, 1 ) (A simplified example.)

• pre_agg = percentileContOver ( {Revenue} , 50 , [ example ] , PRE_AGG)

• pre_filter = percentileContOver ( {Revenue} , 50 , [ example ] , PRE_FILTER)

• post_agg_filter = percentileContOver ( sum ( {Revenue} ) , 50 , [ example ], POST_AGG_FILTER )

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example   pre_filter     pre_agg      post_agg_filter
------------------------------------------------------
0            106,728     119,667            4,117,579
1            102,898      95,946            2,307,547
2             97,807      93,963              554,570
3            101,043     112,585            2,709,057
4             96,533      99,214            3,598,358
5            106,293      97,296            1,875,648
6             97,118      69,159            1,320,672
7            100,201      90,557              969,807