#214: Dive into CPython 3.8 and beyond
Python 3.8 is coming soon. It's scheduled for release at the end of October 2019 and you can already download test versions today. Given that Python ships on an 18-month cycle, it's time to talk about what's coming for us Python developers in the fall.
31 Maj 20191h
#213: WebAssembly and CPython
On the last episode, we explored Pyodide. A project whose goal is to bring the CPython scientific stack to the browser via WebAssembly.
25 Maj 201949min
#212: Python in Web Assembly with Pyodide
It's been said that JavaScript is the assembly language of the web. But should you be required to write code in assembly language or JavaScript?
17 Maj 201957min
#211: Classic CS problems in Python
Many of you studied computer science at a University to get into programming and your careers. But I bet most of you came through some self-study or some sort of back door into the industry. I count myself among that crowd.
11 Maj 20191h 8min
#210: Making the most out of in-person training
How do you stay up on your Python skills. Many of us are self-starters and good at learning on our own or online with the video courses like the ones we have over at Talk Python. But sometimes, having everyone on your team go from zero to ready to work on a project is the best path. And that usually means in-person training.
2 Maj 20191h 7min
#209: Inside Python's new governance model
We all got a bit of a shock to the system when Guido van Rossum decided to step down as the leader and top decider of the Python language and CPython runtime. This happened due to many factors but was precipitated by the so- called walrus operator (PEP 572).
28 Apr 20191h 7min
#208: Packaging, Making the most of PyCon, and more
Are you going to PyCon (or a similar conference)? Join me and Kenneth Retiz as we discuss how to make the most of PyCon and what makes it special for each of us.
21 Apr 20191h 10min
#207: Parallelizing computation with Dask
What if you could write standard numpy and pandas code but have it run on a distributed computing grid for incredible parallel processing right from Python? How about just splitting it across multiprocessing to escape the limitations of the GIL on your local machine? That's what Dask was built to do.
14 Apr 201957min
