A design issue we've found when building large Django applications is that model instances lack any real encapsulation. As codebases grow it becomes difficult to make any cast-iron guarantees that you really are enforcing application-level data integrity. We'll take an example of user accounts to demonstrate the issue ...
Data analysis libraries for Python keep getting better and better. pandas is now on 0.15, scikit-learn continues to pick up converts, and a number of visualization libraries have started to emerge: for example, our own baby, ggplot, and others like seaborn. But there's still one subject that's practically synonymous with data analysis where there aren't any new, killer libraries: databases. There are some great Python database libraries (SQLAlchemy instantly comes to mind), but none are focu
There are several repositories for Python language in GitHub and we are providing you with a list of top 30 among them. GitHub, GitHub projects, GitHub Python projects, top 30 Python projects in GitHub, django, httpie, flask, ansible, python-guide, sentry, scrapy, Mailpile, youtube-dl, sshuttle, fabric
Inspired by the "Java Puzzlers" book and series of talks, this is a set of 6 puzzles that expose some pitfalls and oddities in the Python programming language. For each puzzle, you're given some Python code, and your task is to figure out what happens when the code is run. I added in a few annotations to make the slides a little more self-contained.
There are other similar talks on the internet also called "Python Puzzlers", and this talk is unrelated to all of those.