Python's statistics.median_grouped in TypeScript

How to use

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

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

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	median_grouped([52, 52, 53, 54])	52.5
2	median_grouped([52, 52, 53, 54], 2)	52
3	median_grouped([true, false, true])	0.75

Notes

• Estimates the median for data binned around interval midpoints using grouped interpolation.

Dependencies

This function uses the following Locutus functions:

• _statistics (python/_helpers)

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

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

export function median_grouped(data: unknown, interval = 1): number {
  //      discuss at: https://locutus.io/python/statistics/median_grouped/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Estimates the median for data binned around interval midpoints using grouped interpolation.
  //       example 1: median_grouped([52, 52, 53, 54])
  //       returns 1: 52.5
  //       example 2: median_grouped([52, 52, 53, 54], 2)
  //       returns 2: 52
  //       example 3: median_grouped([true, false, true])
  //       returns 3: 0.75

  const values = sortStatisticsValues(assertStatisticsArray(data, 'median_grouped'), 'median_grouped')
  const n = values.length
  if (n === 0) {
    throw new Error('no median for empty data')
  }

  const x = values[Math.floor(n / 2)]
  let i = 0
  while (i < values.length && values[i] !== x) {
    i += 1
  }

  let j = i
  while (j < values.length && values[j] === x) {
    j += 1
  }

  const numericInterval = toFloatStatisticNumber(interval, 'median_grouped')
  const numericX = toFloatStatisticNumber(x, 'median_grouped')
  const lowerLimit = numericX - numericInterval / 2
  const cumulativeFrequency = i
  const frequency = j - i

  return lowerLimit + (numericInterval * (n / 2 - cumulativeFrequency)) / frequency
}

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

export function median_grouped(data, interval = 1) {
  //      discuss at: https://locutus.io/python/statistics/median_grouped/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Estimates the median for data binned around interval midpoints using grouped interpolation.
  //       example 1: median_grouped([52, 52, 53, 54])
  //       returns 1: 52.5
  //       example 2: median_grouped([52, 52, 53, 54], 2)
  //       returns 2: 52
  //       example 3: median_grouped([true, false, true])
  //       returns 3: 0.75

  const values = sortStatisticsValues(assertStatisticsArray(data, 'median_grouped'), 'median_grouped')
  const n = values.length
  if (n === 0) {
    throw new Error('no median for empty data')
  }

  const x = values[Math.floor(n / 2)]
  let i = 0
  while (i < values.length && values[i] !== x) {
    i += 1
  }

  let j = i
  while (j < values.length && values[j] === x) {
    j += 1
  }

  const numericInterval = toFloatStatisticNumber(interval, 'median_grouped')
  const numericX = toFloatStatisticNumber(x, 'median_grouped')
  const lowerLimit = numericX - numericInterval / 2
  const cumulativeFrequency = i
  const frequency = j - i

  return lowerLimit + (numericInterval * (n / 2 - cumulativeFrequency)) / frequency
}

// python/_helpers/_statistics (Locutus helper dependency)
type StatisticsNumericInput = number | boolean | bigint

type StatisticsSortableInput = StatisticsNumericInput | string

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

  return data
}

function sortStatisticsValues(data: unknown, functionName: string): StatisticsSortableInput[] {
  const values = assertStatisticsArray(data, functionName) as StatisticsSortableInput[]
  if (values.length === 0) {
    return []
  }

  const firstKind = getStatisticsSortKind(values[0], functionName)
  for (let index = 1; index < values.length; index += 1) {
    const currentKind = getStatisticsSortKind(values[index], functionName)
    if (currentKind !== firstKind) {
      throw new TypeError(`${functionName}() requires data values that can be ordered together`)
    }
  }

  return [...values].sort((left, right) => compareStatisticsValues(left, right, firstKind))
}

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

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

  if (typeof value === 'bigint') {
    return Number(value)
  }

  if (typeof value === 'string') {
    const parsed = Number(value)
    if (!Number.isNaN(parsed)) {
      return parsed
    }
    throw new TypeError('Value cannot be converted to a float')
  }

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

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

  if (typeof value === 'number' || typeof value === 'boolean' || typeof value === 'bigint') {
    return 'number'
  }

  throw new TypeError(`${functionName}() requires sortable scalar values`)
}

function compareStatisticsValues(
  left: StatisticsSortableInput,
  right: StatisticsSortableInput,
  kind: 'number' | 'string',
): number {
  if (kind === 'string') {
    return String(left).localeCompare(String(right))
  }

  return toNumericSortValue(left) - toNumericSortValue(right)
}

function toNumericSortValue(value: StatisticsSortableInput): number {
  return typeof value === 'boolean' ? (value ? 1 : 0) : Number(value)
}

// python/statistics/median_grouped (target function module)
function median_grouped(data: unknown, interval = 1): number {
  //      discuss at: https://locutus.io/python/statistics/median_grouped/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Estimates the median for data binned around interval midpoints using grouped interpolation.
  //       example 1: median_grouped([52, 52, 53, 54])
  //       returns 1: 52.5
  //       example 2: median_grouped([52, 52, 53, 54], 2)
  //       returns 2: 52
  //       example 3: median_grouped([true, false, true])
  //       returns 3: 0.75

  const values = sortStatisticsValues(assertStatisticsArray(data, 'median_grouped'), 'median_grouped')
  const n = values.length
  if (n === 0) {
    throw new Error('no median for empty data')
  }

  const x = values[Math.floor(n / 2)]
  let i = 0
  while (i < values.length && values[i] !== x) {
    i += 1
  }

  let j = i
  while (j < values.length && values[j] === x) {
    j += 1
  }

  const numericInterval = toFloatStatisticNumber(interval, 'median_grouped')
  const numericX = toFloatStatisticNumber(x, 'median_grouped')
  const lowerLimit = numericX - numericInterval / 2
  const cumulativeFrequency = i
  const frequency = j - i

  return lowerLimit + (numericInterval * (n / 2 - cumulativeFrequency)) / frequency
}

// 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 sortStatisticsValues(data, functionName) {
  const values = assertStatisticsArray(data, functionName)
  if (values.length === 0) {
    return []
  }

  const firstKind = getStatisticsSortKind(values[0], functionName)
  for (let index = 1; index < values.length; index += 1) {
    const currentKind = getStatisticsSortKind(values[index], functionName)
    if (currentKind !== firstKind) {
      throw new TypeError(`${functionName}() requires data values that can be ordered together`)
    }
  }

  return [...values].sort((left, right) => compareStatisticsValues(left, right, firstKind))
}

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

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

  if (typeof value === 'bigint') {
    return Number(value)
  }

  if (typeof value === 'string') {
    const parsed = Number(value)
    if (!Number.isNaN(parsed)) {
      return parsed
    }
    throw new TypeError('Value cannot be converted to a float')
  }

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

function getStatisticsSortKind(value, functionName) {
  if (typeof value === 'string') {
    return 'string'
  }

  if (typeof value === 'number' || typeof value === 'boolean' || typeof value === 'bigint') {
    return 'number'
  }

  throw new TypeError(`${functionName}() requires sortable scalar values`)
}

function compareStatisticsValues(left, right, kind) {
  if (kind === 'string') {
    return String(left).localeCompare(String(right))
  }

  return toNumericSortValue(left) - toNumericSortValue(right)
}

function toNumericSortValue(value) {
  return typeof value === 'boolean' ? (value ? 1 : 0) : Number(value)
}

// python/statistics/median_grouped (target function module)
function median_grouped(data, interval = 1) {
  //      discuss at: https://locutus.io/python/statistics/median_grouped/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Estimates the median for data binned around interval midpoints using grouped interpolation.
  //       example 1: median_grouped([52, 52, 53, 54])
  //       returns 1: 52.5
  //       example 2: median_grouped([52, 52, 53, 54], 2)
  //       returns 2: 52
  //       example 3: median_grouped([true, false, true])
  //       returns 3: 0.75

  const values = sortStatisticsValues(assertStatisticsArray(data, 'median_grouped'), 'median_grouped')
  const n = values.length
  if (n === 0) {
    throw new Error('no median for empty data')
  }

  const x = values[Math.floor(n / 2)]
  let i = 0
  while (i < values.length && values[i] !== x) {
    i += 1
  }

  let j = i
  while (j < values.length && values[j] === x) {
    j += 1
  }

  const numericInterval = toFloatStatisticNumber(interval, 'median_grouped')
  const numericX = toFloatStatisticNumber(x, 'median_grouped')
  const lowerLimit = numericX - numericInterval / 2
  const cumulativeFrequency = i
  const frequency = j - i

  return lowerLimit + (numericInterval * (n / 2 - cumulativeFrequency)) / frequency
}

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