lenient_parse - v1.3.2

lenient_parse

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A Gleam library that replicates the functionality of Python’s built-in float() and int() functions for parsing strings into float and integer values. This package offers more flexible parsing than the standard Gleam functions.

Installation

gleam add lenient_parse

Usage

import gleam/float
import gleam/int
import gleam/io
import lenient_parse

pub fn main() {
  // Parse a string containing an integer value into a float

  "1" |> lenient_parse.to_float |> io.debug // Ok(1.0)
  "1" |> float.parse |> io.debug // Error(Nil)

  // Parse a string containing a negative float

  "-5.001" |> lenient_parse.to_float |> io.debug // Ok(-5.001)
  "-5.001" |> float.parse |> io.debug // Ok(-5.001)

  // Parse a string containing a complex float with scientific notation

  "-1_234.567_8e-2" |> lenient_parse.to_float |> io.debug // Ok(-12.345678)
  "-1_234.567_8e-2" |> float.parse |> io.debug // Error(Nil)

  // Parse a string containing an integer

  "123" |> lenient_parse.to_int |> io.debug // Ok(123)
  "123" |> int.parse |> io.debug // Ok(123)

  // Parse a string containing a negative integer with surrounding whitespace

  "  -123  " |> lenient_parse.to_int |> io.debug // Ok(-123)
  "  -123  " |> int.parse |> io.debug // Error(Nil)

  // Parse a string containing an integer with underscores

  "1_000_000" |> lenient_parse.to_int |> io.debug // Ok(1000000)
  "1_000_000" |> int.parse |> io.debug // Error(Nil)

  // Parse a string containing a binary number with underscores

  "1000_0000" |> lenient_parse.to_int_with_base(2) |> io.debug // Ok(128)
  "1000_0000" |> int.base_parse(2) |> io.debug // Error(Nil)

  // Parse a string containing a hexadecimal number with underscores

  "DEAD_BEEF" |> lenient_parse.to_int_with_base(16) |> io.debug // Ok(3735928559)
  "DEAD_BEEF" |> int.base_parse(16) |> io.debug // Error(Nil)

  // Use base 0 to automatically detect the base when parsing strings with prefix indicators

  let dates = [
    "0b11011110000", "0o3625", "1865", "0x7bc", "0B11110110001", "1929",
    "0O3507", "0X7a9", "0b11011111011",
  ]

  dates
  |> list.map(lenient_parse.to_int_with_base(_, 0))
  |> io.debug()
  // [Ok(1776), Ok(1941), Ok(1865), Ok(1980), Ok(1969), Ok(1929), Ok(1863), Ok(1961), Ok(1787)]

  dates
  |> list.map(int.base_parse(_, 0))
  |> io.debug()
  // [Error(Nil), Error(Nil), Error(Nil), Error(Nil), Error(Nil), Error(Nil), Error(Nil), Error(Nil), Error(Nil)]

  // Nice errors

  "12.3e_3" |> lenient_parse.to_float |> io.debug // Error(InvalidUnderscorePosition(5))
  "12.3e_3" |> float.parse |> io.debug // Error(Nil)
}

Rigorous Testing

lenient_parse’s testing is extensive. We test the tokenization step, the overall parse procedure, as well as various intermediate layers. We currently have 460+ passing tests. Regressions are not welcome here.

Backed by Python

Each test input is also processed using Python’s (3.13) float() and int() functions. We verify that lenient_parse produces the same output as Python. If Python’s built-ins succeed, lenient_parse should also succeed with identical results. If Python’s built-ins fail to parse, lenient_parse should also fail. This ensures that lenient_parse behaves consistently with Python’s built-ins for all supplied test data.

If you run into a case where lenient_parse and Python’s built-ins disagree, please open an issue - we aim to be 100% consistent with Python’s built-ins and we will fix any reported discrepancies.

Development

To run the tests for this package, you’ll need to install uv.

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