Pint: makes units easy¶

Pint is a Python package to define, operate and manipulate physical quantities: the product of a numerical value and a unit of measurement. It allows arithmetic operations between them and conversions from and to different units.

It is distributed with a comprehensive list of physical units, prefixes and constants. Due to its modular design, you can extend (or even rewrite!) the complete list without changing the source code. It supports a lot of numpy mathematical operations without monkey patching or wrapping numpy.

It is extremely easy and natural to use:

>>> import pint
>>> ureg = pint.UnitRegistry()
>>> 3 * ureg.meter + 4 * ureg.cm
<Quantity(3.04, 'meter')>


and you can make good use of numpy if you want:

>>> import numpy as np
>>> [3, 4] * ureg.meter + [4, 3] * ureg.cm
<Quantity([ 3.04  4.03], 'meter')>
>>> np.sum(_)
<Quantity(7.07, 'meter')>


Quick Installation¶

To install Pint, simply:

\$ pip install pint


and then simply enjoy it!

Design principles¶

Although there are already a few very good Python packages to handle physical quantities, no one was really fitting my needs. Like most developers, I programmed Pint to scratch my own itches.

Unit parsing: prefixed and pluralized forms of units are recognized without explicitly defining them. In other words: as the prefix kilo and the unit meter are defined, Pint understands kilometers. This results in a much shorter and maintainable unit definition list as compared to other packages.

Standalone unit definitions: units definitions are loaded from a text file which is simple and easy to edit. Adding and changing units and their definitions does not involve changing the code.

Advanced string formatting: a quantity can be formatted into string using PEP 3101 syntax. Extended conversion flags are given to provide symbolic, LaTeX and pretty formatting.

Free to choose the numerical type: You can use any numerical type (fraction, float, decimal, numpy.ndarray, etc). NumPy is not required but supported.

NumPy integration: When you choose to use a NumPy ndarray, its methods and ufuncs are supported including automatic conversion of units. For example numpy.arccos(q) will require a dimensionless q and the units of the output quantity will be radian.

Uncertainties integration: transparently handles calculations with quantities with uncertainties (like 3.14±0.01) meter via the uncertainties package.

Handle temperature: conversion between units with different reference points, like positions on a map or absolute temperature scales.

Small codebase: easy to maintain codebase with a flat hierarchy.

Dependency free: it depends only on Python and its standard library.

Python 2 and 3: a single codebase that runs unchanged in Python 2.7+ and Python 3.0+.
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