Python's statistics.quantiles in TypeScript

✓ Verified: Python 3.12

How to use

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

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

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	`quantiles([1, 2, 3, 4, 5])`	`[1.5, 3, 4.5]`
2	`quantiles([1, 2, 3, 4, 5], 4, 'inclusive')`	`[2, 3, 4]`
3	`quantiles([1, 2], 4)`	`[0.75, 1.5, 2.25]`

Notes

Divides numeric sample data into equally probable intervals using Python’s inclusive or exclusive interpolation.

Dependencies

This function uses the following Locutus functions:

_statistics (python/_helpers)

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

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

export function quantiles(data: unknown, n = 4, method: 'exclusive' | 'inclusive' = 'exclusive'): number[] {
  //      discuss at: https://locutus.io/python/statistics/quantiles/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Divides numeric sample data into equally probable intervals using Python's inclusive or exclusive interpolation.
  //       example 1: quantiles([1, 2, 3, 4, 5])
  //       returns 1: [1.5, 3, 4.5]
  //       example 2: quantiles([1, 2, 3, 4, 5], 4, 'inclusive')
  //       returns 2: [2, 3, 4]
  //       example 3: quantiles([1, 2], 4)
  //       returns 3: [0.75, 1.5, 2.25]

  if (n < 1) {
    throw new Error('n must be at least 1')
  }

  const sorted = sortStatisticsValues(assertStatisticsArray(data, 'quantiles'), 'quantiles')
  if (sorted.length < 2) {
    throw new Error('must have at least two data points')
  }

  const values = sorted.map((value) => {
    if (typeof value === 'string') {
      throw new TypeError("unsupported operand type(s) for /: 'str' and 'int'")
    }
    return typeof value === 'boolean' ? (value ? 1 : 0) : Number(value)
  })

  const result: number[] = []
  if (method === 'inclusive') {
    const m = values.length - 1
    for (let i = 1; i < n; i += 1) {
      const product = i * m
      const j = Math.floor(product / n)
      const delta = product % n
      const interpolated = ((values[j] ?? 0) * (n - delta) + (values[j + 1] ?? 0) * delta) / n
      result.push(interpolated)
    }
    return result
  }

  if (method === 'exclusive') {
    const ld = values.length
    const m = ld + 1
    for (let i = 1; i < n; i += 1) {
      let j = Math.floor((i * m) / n)
      j = j < 1 ? 1 : j > ld - 1 ? ld - 1 : j
      const delta = i * m - j * n
      const interpolated = ((values[j - 1] ?? 0) * (n - delta) + (values[j] ?? 0) * delta) / n
      result.push(interpolated)
    }
    return result
  }

  throw new TypeError(`Unknown method: '${String(method)}'`)
}

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

export function quantiles(data, n = 4, method = 'exclusive') {
  //      discuss at: https://locutus.io/python/statistics/quantiles/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Divides numeric sample data into equally probable intervals using Python's inclusive or exclusive interpolation.
  //       example 1: quantiles([1, 2, 3, 4, 5])
  //       returns 1: [1.5, 3, 4.5]
  //       example 2: quantiles([1, 2, 3, 4, 5], 4, 'inclusive')
  //       returns 2: [2, 3, 4]
  //       example 3: quantiles([1, 2], 4)
  //       returns 3: [0.75, 1.5, 2.25]

  if (n < 1) {
    throw new Error('n must be at least 1')
  }

  const sorted = sortStatisticsValues(assertStatisticsArray(data, 'quantiles'), 'quantiles')
  if (sorted.length < 2) {
    throw new Error('must have at least two data points')
  }

  const values = sorted.map((value) => {
    if (typeof value === 'string') {
      throw new TypeError("unsupported operand type(s) for /: 'str' and 'int'")
    }
    return typeof value === 'boolean' ? (value ? 1 : 0) : Number(value)
  })

  const result = []
  if (method === 'inclusive') {
    const m = values.length - 1
    for (let i = 1; i < n; i += 1) {
      const product = i * m
      const j = Math.floor(product / n)
      const delta = product % n
      const interpolated = ((values[j] ?? 0) * (n - delta) + (values[j + 1] ?? 0) * delta) / n
      result.push(interpolated)
    }
    return result
  }

  if (method === 'exclusive') {
    const ld = values.length
    const m = ld + 1
    for (let i = 1; i < n; i += 1) {
      let j = Math.floor((i * m) / n)
      j = j < 1 ? 1 : j > ld - 1 ? ld - 1 : j
      const delta = i * m - j * n
      const interpolated = ((values[j - 1] ?? 0) * (n - delta) + (values[j] ?? 0) * delta) / n
      result.push(interpolated)
    }
    return result
  }

  throw new TypeError(`Unknown method: '${String(method)}'`)
}

// 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 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/quantiles (target function module)
function quantiles(data: unknown, n = 4, method: 'exclusive' | 'inclusive' = 'exclusive'): number[] {
  //      discuss at: https://locutus.io/python/statistics/quantiles/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Divides numeric sample data into equally probable intervals using Python's inclusive or exclusive interpolation.
  //       example 1: quantiles([1, 2, 3, 4, 5])
  //       returns 1: [1.5, 3, 4.5]
  //       example 2: quantiles([1, 2, 3, 4, 5], 4, 'inclusive')
  //       returns 2: [2, 3, 4]
  //       example 3: quantiles([1, 2], 4)
  //       returns 3: [0.75, 1.5, 2.25]

  if (n < 1) {
    throw new Error('n must be at least 1')
  }

  const sorted = sortStatisticsValues(assertStatisticsArray(data, 'quantiles'), 'quantiles')
  if (sorted.length < 2) {
    throw new Error('must have at least two data points')
  }

  const values = sorted.map((value) => {
    if (typeof value === 'string') {
      throw new TypeError("unsupported operand type(s) for /: 'str' and 'int'")
    }
    return typeof value === 'boolean' ? (value ? 1 : 0) : Number(value)
  })

  const result: number[] = []
  if (method === 'inclusive') {
    const m = values.length - 1
    for (let i = 1; i < n; i += 1) {
      const product = i * m
      const j = Math.floor(product / n)
      const delta = product % n
      const interpolated = ((values[j] ?? 0) * (n - delta) + (values[j + 1] ?? 0) * delta) / n
      result.push(interpolated)
    }
    return result
  }

  if (method === 'exclusive') {
    const ld = values.length
    const m = ld + 1
    for (let i = 1; i < n; i += 1) {
      let j = Math.floor((i * m) / n)
      j = j < 1 ? 1 : j > ld - 1 ? ld - 1 : j
      const delta = i * m - j * n
      const interpolated = ((values[j - 1] ?? 0) * (n - delta) + (values[j] ?? 0) * delta) / n
      result.push(interpolated)
    }
    return result
  }

  throw new TypeError(`Unknown method: '${String(method)}'`)
}

// 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 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/quantiles (target function module)
function quantiles(data, n = 4, method = 'exclusive') {
  //      discuss at: https://locutus.io/python/statistics/quantiles/
  // parity verified: Python 3.12
  //     original by: Kevin van Zonneveld (https://kvz.io)
  //          note 1: Divides numeric sample data into equally probable intervals using Python's inclusive or exclusive interpolation.
  //       example 1: quantiles([1, 2, 3, 4, 5])
  //       returns 1: [1.5, 3, 4.5]
  //       example 2: quantiles([1, 2, 3, 4, 5], 4, 'inclusive')
  //       returns 2: [2, 3, 4]
  //       example 3: quantiles([1, 2], 4)
  //       returns 3: [0.75, 1.5, 2.25]

  if (n < 1) {
    throw new Error('n must be at least 1')
  }

  const sorted = sortStatisticsValues(assertStatisticsArray(data, 'quantiles'), 'quantiles')
  if (sorted.length < 2) {
    throw new Error('must have at least two data points')
  }

  const values = sorted.map((value) => {
    if (typeof value === 'string') {
      throw new TypeError("unsupported operand type(s) for /: 'str' and 'int'")
    }
    return typeof value === 'boolean' ? (value ? 1 : 0) : Number(value)
  })

  const result = []
  if (method === 'inclusive') {
    const m = values.length - 1
    for (let i = 1; i < n; i += 1) {
      const product = i * m
      const j = Math.floor(product / n)
      const delta = product % n
      const interpolated = ((values[j] ?? 0) * (n - delta) + (values[j + 1] ?? 0) * delta) / n
      result.push(interpolated)
    }
    return result
  }

  if (method === 'exclusive') {
    const ld = values.length
    const m = ld + 1
    for (let i = 1; i < n; i += 1) {
      let j = Math.floor((i * m) / n)
      j = j < 1 ? 1 : j > ld - 1 ? ld - 1 : j
      const delta = i * m - j * n
      const interpolated = ((values[j - 1] ?? 0) * (n - delta) + (values[j] ?? 0) * delta) / n
      result.push(interpolated)
    }
    return result
  }

  throw new TypeError(`Unknown method: '${String(method)}'`)
}

Improve this function

Locutus is a community effort following The McDonald's Theory: we ship first iterations, hoping others will improve them. If you see something that could be better, we'd love your contribution.

View on GitHub · Edit on GitHub · View Raw

Help us add more

Got a rainy Sunday afternoon and a taste for a porting puzzle?

Get inspiration from the Python 3 standard library string page.
Click "New file" in the appropriate folder on GitHub. This will fork the project to your account, directly add the file to it, and send a Pull Request to us.

We will then review it. If it's useful to the project and in line with our contributing guidelines your work will become part of Locutus and you'll be automatically credited in the authors section accordingly.

« More Python statistics functions

Star