# 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  or utilizing conda, with the conda-forge channel: $ conda install -c conda-forge 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. Unit name translation is available if Babel is installed.

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

Awesome 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.

Dependency free: it depends only on Python and its standard library. It interacts with other packages like numpy and uncertainties if they are installed

Pandas integration: Thanks to Pandas Extension Types it is now possible to use Pint with Pandas. Operations on DataFrames and between columns are units aware, providing even more convenience for users of Pandas DataFrames. For full details, see the pint-pandas Jupyter notebook.

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.