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

title	description	menu	weight	flux/v0/tags	introduced
experimental.histogram() function	`experimental.histogram()` approximates the cumulative distribution of a dataset by counting data frequencies for a list of bins.	flux_v0_ref name parent identifier experimental.histogram experimental experimental/histogram	101	transformations	0.112.0

experimental.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.

Function behavior

• Outputs a single table for each input table.
• Each output table represents a unique histogram.
• Output tables have the same group key as the corresponding input table.
• Drops columns that are not part of the group key.
• Adds an le column to store upper bound values.
• Stores bin counts in the _value column.

Function type signature

(<-tables: stream[{A with _value: float}], bins: [float], ?normalize: bool) => stream[{A with le: float, _value: float}]

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Parameters

bins

({{< req >}}) List of upper bounds to use when computing histogram frequencies, including the maximum value of the data set.

This value can be set to positive infinity (float(v: "+Inf")) if no maximum is known.

Bin helper functions

The following helper functions can be used to generated bins.

• linearBins()
• logarithmicBins()

normalize

Convert count values 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 histogram from input data

import "experimental"
import "sampledata"

sampledata.float()
    |> experimental.histogram(
        bins: [
            0.0,
            5.0,
            10.0,
            15.0,
            20.0,
        ],
    )

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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	15	4
t1	20	6

*tag	le	_value
t2	0	1
t2	5	3
t2	10	3
t2	15	4
t2	20	6

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