docs-v2/content/flux/v0/stdlib/experimental/quantile.md

title	description	menu	weight	flux/v0/tags	introduced
experimental.quantile() function	`experimental.quantile()` returns non-null records with values in the `_value` column that fall within the specified quantile or represent the specified quantile.	flux_v0_ref name parent identifier experimental.quantile experimental experimental/quantile	101	transformations aggregates selectors	0.107.0

experimental.quantile() returns non-null records with values in the _value column that fall within the specified quantile or represent the specified quantile.

The _value column must contain float values.

Computation methods and behavior

experimental.quantile() behaves like an aggregate function or a selector function depending on the method parameter. The following computation methods are available:

estimate_tdigest

An aggregate method that uses a t-digest data structure to compute an accurate quantile estimate on large data sources. When used, experimental.quantile() outputs non-null records with values that fall within the specified quantile.

exact_mean

An aggregate method that takes the average of the two points closest to the quantile value. When used, experimental.quantile() outputs non-null records with values that fall within the specified quantile.

exact_selector

A selector method that returns the data point for which at least q points are less than. When used, experimental.quantile() outputs the non-null record with the value that represents the specified quantile.

Function type signature

(
    <-tables: stream[{A with _value: float}],
    q: float,
    ?compression: float,
    ?method: string,
) => stream[{A with _value: float}]

{{% caption %}} For more information, see Function type signatures. {{% /caption %}}

Parameters

q

({{< req >}}) Quantile to compute ([0 - 1]).

method

Computation method. Default is estimate_tdigest.

Supported methods:

• estimate_tdigest
• exact_mean
• exact_selector

compression

Number of centroids to use when compressing the dataset. Default is 1000.0.

A larger number produces a more accurate result at the cost of increased memory requirements.

tables

Input data. Default is piped-forward data (<-).

Examples

• Return values in the 50th percentile of each input table
• Return a value representing the 50th percentile of each input table

Return values in the 50th percentile of each input table

import "experimental"
import "sampledata"

sampledata.float()
    |> experimental.quantile(q: 0.5)

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time	*tag	_value
2021-01-01T00:00:00Z	t1	-2.18
2021-01-01T00:00:10Z	t1	10.92
2021-01-01T00:00:20Z	t1	7.35
2021-01-01T00:00:30Z	t1	17.53
2021-01-01T00:00:40Z	t1	15.23
2021-01-01T00:00:50Z	t1	4.43

_time	*tag	_value
2021-01-01T00:00:00Z	t2	19.85
2021-01-01T00:00:10Z	t2	4.97
2021-01-01T00:00:20Z	t2	-3.75
2021-01-01T00:00:30Z	t2	19.77
2021-01-01T00:00:40Z	t2	13.86
2021-01-01T00:00:50Z	t2	1.86

Output data

*tag	_value
t1	9.135

*tag	_value
t2	9.415

{{% /expand %}} {{< /expand-wrapper >}}

Return a value representing the 50th percentile of each input table

import "experimental"
import "sampledata"

sampledata.float()
    |> experimental.quantile(q: 0.5, method: "exact_selector")

{{< expand-wrapper >}} {{% expand "View example input and output" %}}

Input data

_time	*tag	_value
2021-01-01T00:00:00Z	t1	-2.18
2021-01-01T00:00:10Z	t1	10.92
2021-01-01T00:00:20Z	t1	7.35
2021-01-01T00:00:30Z	t1	17.53
2021-01-01T00:00:40Z	t1	15.23
2021-01-01T00:00:50Z	t1	4.43

_time	*tag	_value
2021-01-01T00:00:00Z	t2	19.85
2021-01-01T00:00:10Z	t2	4.97
2021-01-01T00:00:20Z	t2	-3.75
2021-01-01T00:00:30Z	t2	19.77
2021-01-01T00:00:40Z	t2	13.86
2021-01-01T00:00:50Z	t2	1.86

Output data

_time	*tag	_value
2021-01-01T00:00:20Z	t1	7.35

_time	*tag	_value
2021-01-01T00:00:10Z	t2	4.97

{{% /expand %}} {{< /expand-wrapper >}}