Python's statistics.geometric_mean in TypeScript

How to use

Install via yarn add locutus and import: import { geometric_mean } from 'locutus/python/statistics/geometric_mean'.

Or with CommonJS: const { geometric_mean } = require('locutus/python/statistics/geometric_mean')

Use a bundler that supports tree-shaking so you only ship the functions you actually use. Vite, webpack, Rollup, and Parcel all handle this. For server-side use this is less of a concern.

Examples

These examples are extracted from test cases that automatically verify our functions against their native counterparts.

#	code	expected result
1	geometric_mean([54, 24, 36])	36.000000000000014
2	geometric_mean([1, 3, 9])	3
3	geometric_mean([2.5, 6.25])	3.9528470752104745

Notes

• Returns the geometric mean for a non-empty dataset of positive numbers.

Dependencies

This function uses the following Locutus functions:

• _statistics (python/_helpers)

Here's what our current TypeScript equivalent to Python's statistics.geometric_mean looks like.

TSTypeScript JSJavaScript TSStandalone TS JSStandalone JS

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import { assertStatisticsArray, toStatisticNumber } from '../_helpers/_statistics.ts'

export function geometric_mean(data: unknown): number {
  //      discuss at: https://locutus.io/python/statistics/geometric_mean/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Returns the geometric mean for a non-empty dataset of positive numbers.
  //       example 1: geometric_mean([54, 24, 36])
  //       returns 1: 36.000000000000014
  //       example 2: geometric_mean([1, 3, 9])
  //       returns 2: 3
  //       example 3: geometric_mean([2.5, 6.25])
  //       returns 3: 3.9528470752104745

  const values = assertStatisticsArray(data, 'geometric_mean').map((value) =>
    toStatisticNumber(value, 'geometric_mean'),
  )
  if (values.length === 0 || values.some((value) => value <= 0)) {
    throw new Error('geometric mean requires a non-empty dataset containing positive numbers')
  }

  const logSum = values.reduce((sum, value) => sum + Math.log(value), 0)
  return Math.exp(logSum / values.length)
}

import { assertStatisticsArray, toStatisticNumber } from '../_helpers/_statistics.ts'

export function geometric_mean(data) {
  //      discuss at: https://locutus.io/python/statistics/geometric_mean/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Returns the geometric mean for a non-empty dataset of positive numbers.
  //       example 1: geometric_mean([54, 24, 36])
  //       returns 1: 36.000000000000014
  //       example 2: geometric_mean([1, 3, 9])
  //       returns 2: 3
  //       example 3: geometric_mean([2.5, 6.25])
  //       returns 3: 3.9528470752104745

  const values = assertStatisticsArray(data, 'geometric_mean').map((value) =>
    toStatisticNumber(value, 'geometric_mean'),
  )
  if (values.length === 0 || values.some((value) => value <= 0)) {
    throw new Error('geometric mean requires a non-empty dataset containing positive numbers')
  }

  const logSum = values.reduce((sum, value) => sum + Math.log(value), 0)
  return Math.exp(logSum / values.length)
}

// python/_helpers/_statistics (Locutus helper dependency)
function assertStatisticsArray(data: unknown, functionName: string): unknown[] {
  if (!Array.isArray(data)) {
    throw new TypeError(`${functionName}() data must be an array`)
  }

  return data
}

function toStatisticNumber(value: unknown, functionName: string): number {
  if (typeof value === 'number') {
    return value
  }

  if (typeof value === 'boolean') {
    return value ? 1 : 0
  }

  if (typeof value === 'bigint') {
    const numericValue = Number(value)
    if (!Number.isSafeInteger(numericValue)) {
      throw new RangeError(`${functionName}() bigint values must fit within JS safe integers`)
    }

    return numericValue
  }

  throw new TypeError(`${functionName}() data must contain only real numbers`)
}

// python/statistics/geometric_mean (target function module)
function geometric_mean(data: unknown): number {
  //      discuss at: https://locutus.io/python/statistics/geometric_mean/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Returns the geometric mean for a non-empty dataset of positive numbers.
  //       example 1: geometric_mean([54, 24, 36])
  //       returns 1: 36.000000000000014
  //       example 2: geometric_mean([1, 3, 9])
  //       returns 2: 3
  //       example 3: geometric_mean([2.5, 6.25])
  //       returns 3: 3.9528470752104745

  const values = assertStatisticsArray(data, 'geometric_mean').map((value) =>
    toStatisticNumber(value, 'geometric_mean'),
  )
  if (values.length === 0 || values.some((value) => value <= 0)) {
    throw new Error('geometric mean requires a non-empty dataset containing positive numbers')
  }

  const logSum = values.reduce((sum, value) => sum + Math.log(value), 0)
  return Math.exp(logSum / values.length)
}

// python/_helpers/_statistics (Locutus helper dependency)
function assertStatisticsArray(data, functionName) {
  if (!Array.isArray(data)) {
    throw new TypeError(`${functionName}() data must be an array`)
  }

  return data
}

function toStatisticNumber(value, functionName) {
  if (typeof value === 'number') {
    return value
  }

  if (typeof value === 'boolean') {
    return value ? 1 : 0
  }

  if (typeof value === 'bigint') {
    const numericValue = Number(value)
    if (!Number.isSafeInteger(numericValue)) {
      throw new RangeError(`${functionName}() bigint values must fit within JS safe integers`)
    }

    return numericValue
  }

  throw new TypeError(`${functionName}() data must contain only real numbers`)
}

// python/statistics/geometric_mean (target function module)
function geometric_mean(data) {
  //      discuss at: https://locutus.io/python/statistics/geometric_mean/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Returns the geometric mean for a non-empty dataset of positive numbers.
  //       example 1: geometric_mean([54, 24, 36])
  //       returns 1: 36.000000000000014
  //       example 2: geometric_mean([1, 3, 9])
  //       returns 2: 3
  //       example 3: geometric_mean([2.5, 6.25])
  //       returns 3: 3.9528470752104745

  const values = assertStatisticsArray(data, 'geometric_mean').map((value) =>
    toStatisticNumber(value, 'geometric_mean'),
  )
  if (values.length === 0 || values.some((value) => value <= 0)) {
    throw new Error('geometric mean requires a non-empty dataset containing positive numbers')
  }

  const logSum = values.reduce((sum, value) => sum + Math.log(value), 0)
  return Math.exp(logSum / values.length)
}

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