Deciphering Python Jargon & Compiling Python 1.0

How do you learn the terms commonly used when speaking about Python? How is the jargon similar to other programming languages? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a Python glossary recently created by Trey Hunner. Trey describes it as an unofficial glossary and Python jargon file. We dig into the terms and colloquial language often used when describing Python.

We cover a blog post celebrating 31 years of Python by compiling Python 1.0. The piece walks through the hoops of finding the source code and standing up an old version of Debian. Once compiled, they open the REPL and find it surprisingly capable.

We also share several other articles and projects from the Python community, including release news, a Python enhancement proposal roundup, managing Django’s queue, a course about NumPy techniques including practical examples, getting platform-specific directories, detecting which shell is in use, and a project for sorted container types.

This episode is sponsored by Postman.

Course Spotlight: NumPy Techniques and Practical Examples

In this video course, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function.

Topics:

• 00:00:00 – Introduction
• 00:02:42 – Python Release 3.14.0a5
• 00:02:54 – PyPy v7.3.18 Released
• 00:03:32 – Beautifulsoup 4.13 Released
• 00:04:13 – PEP 759: External Wheel Hosting (Withdrawn)
• 00:04:54 – PEP 2026: Calendar Versioning for Python (Rejected)
• 00:06:48 – PEP 739: Static Description File for Build Details (Accepted)
• 00:07:51 – PEP 765: Disallow Return/Break/Continue That Exit a Finally Block (Accepted)
• 00:09:01 – Python Terminology: An Unofficial Glossary
• 00:19:32 – Sponsor: Postman
• 00:20:28 – NumPy Techniques and Practical Examples
• 00:24:12 – Let’s Compile Python 1.0
• 00:28:55 – Video Course Spotlight
• 00:30:14 – Managing Django’s Queue
• 00:36:41 – platformdirs: Get Platform-Specific Dirs
• 00:39:57 – shellingham: Tool to Detect Surrounding Shell
• 00:41:02 – python-sortedcontainers: Python Sorted Container Type
• 00:41:58 – Thanks and goodbye

News:

• Python Release 3.14.0a5
• PyPy v7.3.18 Released
• Beautifulsoup 4.13 Released
• PEP 759: External Wheel Hosting (Withdrawn)
• PEP 2026: Calendar Versioning for Python (Rejected)
• PEP 739: Static Description File for Build Details (Accepted)
• PEP 765: Disallow Return/Break/Continue That Exit a Finally Block (Accepted)

Topics:

• Python Terminology: An Unofficial Glossary – “Definitions for colloquial Python terminology (effectively an unofficial version of the Python glossary).”
• NumPy Techniques and Practical Examples – In this video course, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function.
• Let’s Compile Python 1.0 – As part of the celebration of 31 years of Python, Bite Code compiles the original Python 1.0 and plays around with it.
• Managing Django’s Queue – Carlton is one of the core developers of Django. This post talks about staying on top of the incoming pull-requests, bug fixes, and everything else in the development queue.

Projects:

• platformdirs: Get Platform-Specific Dirs, e.g. “User Data Dir”
• shellingham: Tool to Detect Surrounding Shell
• python-sortedcontainers: Python Sorted Container Types

Additional Links:

• Reference: Concise definitions for common Python terms – Real Python
• NumPy Practical Examples: Useful Techniques – Tutorial
• NumPy Practical Examples: Useful Techniques Quiz
• Python 1.0.0 is out!
• Podman
• OrbStack · Fast, light, simple Docker & Linux

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Building Command Line Interfaces With argparse
• NumPy Techniques and Practical Examples

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