Most Python developers have written at least one tool, script, library or framework that others would find useful. My goal in this article is to make the process of open-sourcing existing Python code as clear and painless as possible. And I don't simply mean, "create GitHub repo, git push, post on Reddit, and call it a day." By the end of this article, you'll be able to take an existing code base and transform it into an open source project that encourages both use and contribution.

While every project is different, there are some parts of the process of open-sourcing existing code that are common to all Python projects. In the vein of another popular series I've written, "Starting a Django Project The Right Way," I'll outline the steps I've found to be necessary when open-sourcing a Python project.

This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python’s most noteworthy features, and will give you a good idea of the language’s flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in The Python Standard Library.

This book contains a collection of relevant data structures, algorithms, and programming tips written for University students who want to be more competitive in the ACM International Collegiate Programming Contest (ICPC), high school students who are aspiring to be competitive in the International Olympiad in Informatics (IOI), coaches for these competitions, those who love problem solving using computer programs, and those who go for interviews in big IT-companies.

“Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.” – Brian W. Kernighan.

Everyone tests their software to some extent, if only by running it and trying it out (technically known as “smoke testing”). Most programmers do a certain amount of exploratory testing, which involves running through various functional paths in your code and seeing if they work.

Systematic testing, however, is a different matter. Systematic testing simply cannot be done properly without a certain (large!) amount of automation, because every change to the software means that the software needs to be tested all over again.

This is an introduction to some lower level automated testing concepts, and how to use built-in Python constructs to start writing tests.

In the world of software management there exists a dread place called "dependency hell." The bigger your system grows and the more packages you integrate into your software, the more likely you are to find yourself, one day, in this pit of despair.

This page provides guidelines for version numbering your software.

Ventiquattro ore di programmazione non stop per oltre 2mila studenti di informatica di tutto il mondo. Alla IEEExtreme programming organizzata dall’Istituto degli ingegneri elettrici e elettronici, i pavesi se la sono cavata bene. « Il team dell’università di Pavia miglior classificato è jOmegaTeam – spiega Tullio Facchinetti, professore di programmazione e del laboratorio di robotica del dipartimento di ingegneria industriale e dell’informazione –