Reuven Lerner (that’s me) gave this talk at the online hack.summit() in December 2014. In this talk, I compare the syntax of Ruby and Python, and consider the language design trade-offs that have been made, as well as the relative strengths of each language.
The GIL (global interpreter lock) surprises and frustrates many Python developers. This talk by famed Python developer and trainer David Beazley describes the GIL, and some of the efforts Beazley has made to reduce its influence on multithreaded Python code.
I remember the days when O’Reilly was the only publishing house with Python books. Moreover, I remember when O’Reilly claimed they would only write a single book on a topic, so that there would be no overlap among their titles. Nowadays, there are many different O’Reilly books about Python, and there seems to be a great deal of overlap. In this talk, Tanya Schlusser tries to cast some light on the differences between these books, and who might benefit from each one.
Kenneth, the author of the Requests library for Python, describes where Python’s APIs could be improved — and more importantly, how to write an API that is useful for programmers, and not just for programs.
Celery allows Python programs to offload processing needs onto a background queue. It thus provides an alternative concurrency solution to threads and processes, and can be particularly useful with Django.
In this talk from PyCon 2014, Jessica McKellar introduces the idea of a software sandbox, and reviews the ways in which we can and should (and also shouldn’t) create such a system.
Yesterday, we learned how garbage collection is implemented in Ruby. Today’s video is about garbage collection in Python, another high-level, object-oriented language.
IPython is quite popular as an interactive shell, or even for providing shared Web-based notebooks. However, it can also be used for simple parallel computing tasks. In this talk, Min Ragen-Kelley introduces IPython’s parallel-processing capabilities.
The previous video introduced Pandas. In this relatively short video,Taavi Burns show us how we can use Pandas for a specific task, namely the analysis of logfiles.
Wes McKinney describes Pandas, a library for data analysis written in Python. Pandas sits on top of NumPy, and provides many of the same manipulation possibilities as the R language.