docs-v2/content/flux/v0/stdlib/universe/median.md

title	description	menu	weight	flux/v0/tags	introduced
median() function	`median()` returns the median `_value` of an input table or all non-null records in the input table with values that fall within the 0.5 quantile (50th percentile).	flux_v0_ref name parent identifier median universe universe/median	101	transformations aggregates selectors	0.7.0

median() returns the median _value of an input table or all non-null records in the input table with values that fall within the 0.5 quantile (50th percentile).

Function behavior

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

• Aggregate: When using the estimate_tdigest or exact_mean methods, median() acts as an aggregate transformation and outputs the average of non-null records with values that fall within the 0.5 quantile (50th percentile).
• Selector: When using the exact_selector method, meidan() acts as a selector selector transformation and outputs the non-null record with the value that represents the 0.5 quantile (50th percentile).

Function type signature

(<-tables: stream[A], ?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 median. Default is _value.

method

Computation method. Default is estimate_tdigest.

Available methods:

• estimate_tdigest: Aggregate method that uses a t-digest data structure to compute an accurate median estimate on large data sources.
• exact_mean: Aggregate method that takes the average of the two points closest to the median value.
• exact_selector: Selector method that returns the row with the value for which at least 50% of points are less than.

compression

Number of centroids to use when compressing the dataset. Default is 0.0.

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

tables

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

Examples

• Use median as an aggregate transformation
• Use median as a selector transformation

Use median as an aggregate transformation

import "sampledata"

sampledata.float()
    |> median()

{{< 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 >}}

Use median as a selector transformation

import "sampledata"

sampledata.float()
    |> median(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 >}}