# Find percentile and quantile values

Use the `quantile()` function to return a value representing the `q` quantile or percentile of input data.

## Percentile versus quantile

Percentiles and quantiles are very similar, differing only in the number used to calculate return values. A percentile is calculated using numbers between `0` and `100`. A quantile is calculated using numbers between `0.0` and `1.0`. For example, the `0.5` quantile is the same as the 50th percentile.

## Select a method for calculating the quantile

Select one of the following methods to calculate the quantile:

### estimate_tdigest

(Default) An aggregate method that uses a t-digest data structure to compute a quantile estimate on large data sources. Output tables consist of a single row containing the calculated quantile.

If calculating the `0.5` quantile or 50th percentile:

Given the following input table:

_time_value
2020-01-01T00:01:00Z1.0
2020-01-01T00:02:00Z1.0
2020-01-01T00:03:00Z2.0
2020-01-01T00:04:00Z3.0

`estimate_tdigest` returns:

_value
1.5

### exact_mean

An aggregate method that takes the average of the two points closest to the quantile value. Output tables consist of a single row containing the calculated quantile.

If calculating the `0.5` quantile or 50th percentile:

Given the following input table:

_time_value
2020-01-01T00:01:00Z1.0
2020-01-01T00:02:00Z1.0
2020-01-01T00:03:00Z2.0
2020-01-01T00:04:00Z3.0

`exact_mean` returns:

_value
1.5

### exact_selector

A selector method that returns the data point for which at least `q` points are less than. Output tables consist of a single row containing the calculated quantile.

If calculating the `0.5` quantile or 50th percentile:

Given the following input table:

_time_value
2020-01-01T00:01:00Z1.0
2020-01-01T00:02:00Z1.0
2020-01-01T00:03:00Z2.0
2020-01-01T00:04:00Z3.0

`exact_selector` returns:

_time_value
2020-01-01T00:02:00Z1.0

The examples below use the example data variable.

## Find the value representing the 99th percentile

Use the default method, `"estimate_tdigest"`, to return all rows in a table that contain values in the 99th percentile of data in the table.

``````data
|> quantile(q: 0.99)
``````

## Find the average of values closest to the quantile

Use the `exact_mean` method to return a single row per input table containing the average of the two values closest to the mathematical quantile of data in the table. For example, to calculate the `0.99` quantile:

``````data
|> quantile(q: 0.99, method: "exact_mean")
``````

## Find the point with the quantile value

Use the `exact_selector` method to return a single row per input table containing the value that `q * 100`% of values in the table are less than. For example, to calculate the `0.99` quantile:

``````data
|> quantile(q: 0.99, method: "exact_selector")
``````

## Use quantile() with aggregateWindow()

`aggregateWindow()` segments data into windows of time, aggregates data in each window into a single point, and then removes the time-based segmentation. It is primarily used to downsample data.

To specify the quantile calculation method in `aggregateWindow()`, use the full function syntax:

``````data
|> aggregateWindow(
every: 5m,
fn: (tables=<-, column) => tables
|> quantile(q: 0.99, method: "exact_selector"),
)
``````

Thank you for your feedback!

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