Hi, We are happy to announce Pint 0.2. Pint is a Python package to define, operate and manipulate physical quantities: the product of a numerical value and a unit of measurement. This release brings a lot of new exciting features including extended NumPy support, temperature conversion, implementation of the Buckingham Pi Theorem and support for values with uncertainties. Check out the blog post for more details: http://python-in-the-lab.blogspot.com.ar/2013/05/a-pint-day.html You can get pint using pip: $ pip install pint or get the source code: https://github.com/hgrecco/pint and check the docs: http://pint.readthedocs.org/ What is Pint? --- Pint is 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 it’s modular design, you can extend (or even rewrite!) the complete list without changing the source code. It has a complete test coverage. It runs in Python 2.7 and 3.X with no other dependency. It licensed under BSD. Highlights --- * 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. * 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 it’s standard library. * Python 2 and 3: a single codebase that runs unchanged in Python 2.7+ and Python 3.0+. Thanks to the people that contributed bug reports, suggestions and patches. In particular to: Richard Barnes, Alexander Böhn, Dave Brooks, Giel van Schijndel, Brend Wanders Enjoy! Hernán