Christopher Trudeau returns to share his expertise on Python development while discussing the integration of virtual environments within Docker. He emphasizes the importance of maintaining consistent development practices and highlights the benefits of isolation in code structure. Listeners learn about the latest Python releases and best practices for dependency management. Trudeau explores the evolving relationship between Python and R in data science, and shares insights on code commenting and preferred development tools. This engaging conversation underscores the significance of efficient workflows.
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volunteer_activism ADVICE
Virtual Environments in Docker
Use virtual environments for Python projects, even within Docker containers.
This practice isolates dependencies and improves project structure and maintainability.
volunteer_activism ADVICE
Avoid pip install --user
Avoid using pip install --user, it complicates dependency management and troubleshooting.
Install packages within project-specific virtual environments for better isolation.
insights INSIGHT
Avoid Blindly Following Advice
Don't blindly adopt development practices without considering project needs.
Microservices and complex scaling solutions are not always necessary and can introduce unnecessary complexity.
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Logic for Programmers is designed to introduce programmers to the fundamentals of logic and its practical applications in software development. The book covers topics such as property testing, functional correctness, formal verification, and data modeling. It is currently in early access and includes exercises to help readers master the material.
Should you use a Python virtual environment in a Docker container? What are the advantages of using the same development practices locally and inside a container? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
We share a recent post by Hynek Schlawack about building Python projects using Docker containers. Hynek argues for using virtual environments for these projects, like developing a local one. He’s found that keeping your code in an isolated, well-defined location and structure avoids confusion and complexity.
We also discuss our development setups, including Python versions, code editors, virtual environment practices, terminals, and customizations. We dig into how your programming history affects the tools you use.
We share several other articles and projects from the Python community, including a group of new releases, addressing the “why” in comments, comparing a data science workflow in Python and R, removing common problems from CSV files, and a project for creating HTML tables in Django.
The Python import system is as powerful as it is useful. In this in-depth video course, you’ll learn how to harness this power to improve the structure and maintainability of your code.
Topics:
00:00:00 – Introduction
00:02:55 – Python Releases 3.12.6, 3.11.10, 3.10.15, 3.9.20, and 3.8.20
00:03:26 – Python Release Python 3.13.0rc2
00:04:07 – Django Security Releases Issued: 5.1.1, 5.0.9, and 4.2.16
00:04:36 – Polars Has a New Lightweight Plotting Backend
00:05:49 – Why I Still Use Python Virtual Environments in Docker
00:11:37 – How to Use Conditional Expressions With NumPy where()
00:15:55 – Sponsor: InfluxData
00:16:39 – PythonistR: A Match Made in Data Heaven
00:23:44 – Why Not Comments
00:26:48 – Video Course Spotlight
00:28:10 – Discussion: Personal development setups
00:51:01 – csv_trimming: Remove Common Ugliness From CSV Files
00:53:01 – django-tables2: Create HTML Tables in Django
Polars Has a New Lightweight Plotting Backend – Polars 1.6 allows you to natively create beautiful plots without pandas, NumPy, or PyArrow. This is enabled by Narwhals, a lightweight compatibility layer between dataframe libraries.
How to Use Conditional Expressions With NumPy where() – This tutorial teaches you how to use the where() function to select elements from your NumPy arrays based on a condition. You’ll learn how to perform various operations on those elements and even replace them with elements from a separate array or arrays.
PythonistR: A Match Made in Data Heaven – In data science you’ll sometimes hear a debate between R and Python. Cosima says ‘why not choose both?’ She outlines a data pipeline that uses the best tool for each job.
Why Not Comments – This post talks about why you might want to include information in your code comments about why you didn’t take a particular approach.
The Real Python Podcast 
