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

title	description	menu	weight	flux/v0/tags	introduced
histogram() function	`histogram()` approximates the cumulative distribution of a dataset by counting data frequencies for a list of bins.	flux_v0_ref name parent identifier histogram universe universe/histogram	101	transformations	0.7.0

histogram() approximates the cumulative distribution of a dataset by counting data frequencies for a list of bins.

A bin is defined by an upper bound where all data points that are less than or equal to the bound are counted in the bin. Bin counts are cumulative.

Each input table is converted into a single output table representing a single histogram. Each output table has the same group key as the corresponding input table. Columns not part of the group key are dropped. Output tables include additional columns for the upper bound and count of bins.

Function type signature

(
    <-tables: stream[A],
    bins: [float],
    ?column: string,
    ?countColumn: string,
    ?normalize: bool,
    ?upperBoundColumn: string,
) => stream[B] where A: Record, B: Record

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

Parameters

column

Column containing input values. Column must be of type float. Default is _value.

upperBoundColumn

Column to store bin upper bounds in. Default is le.

countColumn

Column to store bin counts in. Default is _value.

bins

({{< req >}}) List of upper bounds to use when computing the histogram frequencies.

Bins should contain a bin whose bound is the maximum value of the data set. This value can be set to positive infinity if no maximum is known.

Bin helper functions

The following helper functions can be used to generated bins.

• linearBins()
• logarithmicBins()

normalize

Convert counts into frequency values between 0 and 1. Default is false.

Note: Normalized histograms cannot be aggregated by summing their counts.

tables

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

Examples

• Create a cumulative histogram
• Create a cumulative histogram with dynamically generated bins

Create a cumulative histogram

import "sampledata"

sampledata.float()
    |> histogram(bins: [0.0, 5.0, 10.0, 20.0])

{{< 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	le	_value
t1	0	1
t1	5	2
t1	10	3
t1	20	6

*tag	le	_value
t2	0	1
t2	5	3
t2	10	3
t2	20	6

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

Create a cumulative histogram with dynamically generated bins

import "sampledata"

sampledata.float()
    |> histogram(bins: linearBins(start: 0.0, width: 4.0, count: 3))

{{< 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	le	_value
t1	0	1
t1	4	1
t1	8	3
t1	+Inf	6

*tag	le	_value
t2	0	1
t2	4	2
t2	8	3
t2	+Inf	6

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