docs-v2/content/flux/v0.x/stdlib/universe/quantile.md

title	description	menu	weight	flux/v0.x/tags	introduced
quantile() function	`quantile()` returns rows from each input table with values that fall within a specified quantile or returns the row with the value that represents the specified quantile.	flux_0_x_ref name parent identifier quantile universe universe/quantile	101	transformations aggregates selectors	0.24.0

quantile() returns rows from each input table with values that fall within a specified quantile or returns the row with the value that represents the specified quantile.

quantile() supports columns with float values.

Function behavior

quantile() acts as an aggregate or selector transformation depending on the specified method.

• Aggregate: When using the estimate_tdigest or exact_mean methods, quantile() acts as an aggregate transformation and outputs the average of non-null records with values that fall within the specified quantile.
• Selector: When using the exact_selector method, quantile() acts as a selector selector transformation and outputs the non-null record with the value that represents the specified quantile.

Function type signature

(
    <-tables: stream[A],
    q: float,
    ?column: string,
    ?compression: float,
    ?method: string,
) => stream[A] where A: Record

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

Parameters

column

Column to use to compute the quantile. Default is _value.

q

({{< req >}}) Quantile to compute. Must be between 0.0 and 1.0.

method

Computation method. Default is estimate_tdigest.

Avaialable methods:

• estimate_tdigest: Aggregate method that uses a t-digest data structure to compute an accurate quantile estimate on large data sources.
• exact_mean: Aggregate method that takes the average of the two points closest to the quantile value.
• exact_selector: Selector method that returns the row with the value for which at least q points are less than.

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

• Quantile as an aggregate
• Quantile as a selector

Quantile as an aggregate

import "sampledata"

sampledata.float()
    |> quantile(q: 0.99, method: "estimate_tdigest")

{{< 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	17.53

*tag	_value
t2	19.85

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

Quantile as a selector

import "sampledata"

sampledata.float()
    |> 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 >}}