experimental.histogram() function

The experimental.histogram() function 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.

import "experimental"

experimental.histogram(
    bins: [50.0, 75.0, 90.0],
    normalize: false,
)

Output schema

experimental.histogram() 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.

The function does the following:

• 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.

Parameters

bins

(Required) A list of upper bounds to use when computing the histogram frequencies, including 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 count values into frequency values between 0 and 1. Default is false.

tables

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

Examples

Histogram with dynamically generated bins

import "experimental"

data
    |> experimental.histogram(bins: linearBins(start: 0.0, width: 20.0, count: 5))

Input data

Output data