Stay ConnectedA weekly Python podcast hosted by Christopher Bailey with interviews, coding tips, and conversation with guests from the Python community.
The show covers a wide range of topics including Python programming best practices, career tips, and related software development topics. Join us every Friday morning to hear what’s new in the world of Python programming and become a more effective Pythonista.
Copyright: © 2020 Real Python
How can you simplify the management of your Python projects with one file? What are the advantages of using LazyFrames in Polars? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
We share a recent Real Python tutorial by Ian Currie about managing projects with a pyproject.toml file. This file simplifies Python project configuration by unifying package setup, managing dependencies, and streamlining builds.
Christopher continues his exploration of the Polars library by covering another Real Python tutorial about working with LazyFrames. He describes how LazyFrames don’t contain data but instead store a set of instructions known as a query plan.
We also share several other articles and projects from the Python community, including a news roundup, building a to-do app with Python and Kivy, working with DuckDB directly instead of using a DataFrame library, a discussion on fiction and nonfiction books about computer science, a terminal visual effects engine, and a full-stack platform for interactive data apps.
Course Spotlight: Everyday Project Packaging With pyproject.toml
In this Code Conversation video course, you’ll learn how to package your everyday projects with pyproject.toml. Playing on the same team as the import system means you can call your project from anywhere, ensure consistent imports, and have one file that’ll work for many build systems.
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pyproject.toml – Learn how to manage Python projects with the pyproject.toml configuration file. In this tutorial, you’ll explore key use cases of the pyproject.toml file, including configuring your build, installing your package locally, managing dependencies, and publishing your package to PyPI.Discussion:
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Should you always start testing your code with unit tests? When does it make sense to look at integration or end-to-end testing as a first step instead? This week on the show, we speak with previous guest Eric Matthes about where to begin testing your code.
Eric is the author of the popular book Python Crash Course. Early in the development of the book, he decided to introduce testing and added a chapter on testing code with pytest.
Over the past couple of years, Eric has continued to consider when and where to test a project’s code. He thinks there are hazards to always starting with unit tests. The type of project and its audience should determine what kind of testing to employ initially.
We discuss using pytest to develop integration tests on multiple types of projects. We also explore fixtures and what goes into building a test suite. Eric also shares criteria for when and where it makes sense to add unit tests to a project.
Course Spotlight: Using Python’s assert to Debug and Test Your Code
In this course, you’ll learn how to use Python’s assert statement to document, debug, and test code in development. You’ll learn how assertions might be disabled in production code, so you shouldn’t use them to validate data. You’ll also learn about a few common pitfalls of assertions in Python.
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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.
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How do you make compelling visualizations that best convey the story of your data? What methods can you employ within popular Python tools to improve your plots and graphs? This week on the show, Matt Harrison returns to discuss his new book “Effective Visualization: Exploiting Matplotlib & Pandas.”
As a data scientist and instructor, Matt has been teaching the concepts of managing tabular data and making visualizations for over 20 years. Matt shares his methodology for taking a basic plot and then telling a compelling story with it. We discuss why you should limit your plot types to a few that your audience is familiar with.
We cover the resources built into pandas and Matplotlib and some of the libraries’ limitations. Matt talks about the professionally produced plots that inspired him and the process of recreating them. He also answers questions about finding data sources to practice these techniques with.
This episode is sponsored by Postman.
Course Spotlight: Using plt.scatter() to Visualize Data in Python
In this course, you’ll learn how to create scatter plots in Python, which are a key part of many data visualization applications. You’ll get an introduction to plt.scatter(), a versatile function in the Matplotlib module for creating scatter plots.
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What is behavior-driven development, and how does it work alongside test-driven development? How do you communicate requirements between teams in an organization? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
In this episode, we expand on our software testing discussion from two weeks ago by adding behavior-driven development concepts. Christopher describes how BDD correlates with test-driven development and how it fosters collaboration within a team. We discuss building acceptance tests written in plain language and a handy tool for creating them.
We also share several other articles and projects from the Python community, including a news roundup, using regular expressions in Python, dealing with missing data in Polars, monkey patching in Django, first steps with Playwright, 3D printing giant things with a Python jigsaw generator, and a query language for JSON.
This episode is sponsored by Postman.
Course Spotlight: Regular Expressions and Building Regexes in Python
In this course, you’ll learn how to perform more complex string pattern matching using regular expressions, or regexes, in Python. You’ll also explore more advanced regex tools and techniques that are available in Python.
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Are you looking for fast tools to lint your code and manage your projects? How is the Rust programming language being used to speed up Python tools? This week on the show, we speak with Charlie Marsh about his company, Astral, and their tools, uv and Ruff.
Charlie started working on Ruff as a proof of concept, stating that Python tooling could be much faster. He had seen similar gains in JavaScript tools written in Rust. The project started as a speedy linter with a small ruleset. It’s grown to include code formatting and over 800 built-in linting rules.
Last year, the team at Astral started working on a Python package and project manager written in Rust. As a single tool, uv can replace pip, pip-tools, pipx, poetry, pyenv, and more. We discuss how uv can install and manage versions of Python and run scripts without thinking about virtual environments or dependencies.
Charlie talks about growing the team at Astral over the past couple of years. We also discuss the funding model Astral has adopted and sustaining open-source software.
This episode is sponsored by Postman.
Course Spotlight: Python Basics: Installing Packages With pip
Python’s standard library includes a whole buffet of useful packages, but sometimes you need to reach for a third-party library. That’s where pip comes in handy. In this video course, you’ll learn how to pip install packages.
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What goes into creating automated tests for your Python code? Should you focus on testing the individual code sections or on how the entire system runs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
We discuss a recent article from Semaphore about unit testing vs. integration testing. Christopher shares his experiences setting up automated tests for his own smaller projects. He also answers questions about building tests in an existing codebase and integrating tests across systems.
We also share several other articles and projects from the Python community, including a news roundup, improving default line charts to journal-quality infographics, why hash(-1) == hash(-2) in Python, data cleaning in data science, ways to work with large files in Python, a lightweight CLI viewer for log files, and a tool for mocking the datetime module for testing.
This episode is sponsored by Postman.
Course Spotlight: Testing Your Code With pytest
In this video course, you’ll learn how to take your testing to the next level with pytest. You’ll cover intermediate and advanced pytest features such as fixtures, marks, parameters, and plugins. With pytest, you can make your test suites fast, effective, and less painful to maintain.
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hash(-1) == hash(-2) in Python? – Somewhat surprisingly, hash(-1) == hash(-2) in CPython. This post examines how and discovers why this is the case.Discussion:
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What are the current large language model (LLM) tools you can use to develop Python? What prompting techniques and strategies produce better results? This week on the show, we speak with Simon Willison about his LLM research and his exploration of writing Python code with these rapidly evolving tools.
Simon has been researching LLMs over the past two and a half years and documenting the results on his blog. He shares which models work best for writing Python versus JavaScript and compares coding tools and environments.
We discuss prompt engineering techniques and the first steps to take. Simon shares his enthusiasm for the usefulness of LLMs but cautions about the potential pitfalls.
Simon also shares how he got involved in open-source development and Django. He’s a proponent of starting a blog and shares how it opened doors for his career.
This episode is sponsored by Postman.
Course Spotlight: Advanced Python import Techniques
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.
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What are the principles you should consider when making decisions about which Python tools to use? What anti-patterns get in the way of making the right choices for your team? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
We discuss a recent article about effective Python developer tooling. Instead of digging into a list of current libraries, we talk about the principles you must consider before making decisions for your team. We cover common pitfalls teams get mired in and how to avoid them.
We also share several other articles and projects from the Python community, including a news roundup, a huge collection of the top Python libraries of 2024, programming sockets in Python, merging dictionaries, a Django quiz, mistakes to avoid in production, building a Portal sentry turret, a powerful TUI expense tracker, and a pure-Python async rendering engine.
Course Spotlight: Managing Dependencies With Python Poetry
Learn how Python Poetry can help you start new projects, maintain existing ones, and master dependency management.
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What are the new ways we can teach and share our knowledge about Python? How can we improve the structure of our current offerings and build new educational resources for our audience of Python learners? This week on the show, Real Python core team members Stephen Gruppetta and Martin Breuss join us to discuss enhancements to the site and new ways to learn Python.
Stephen has recently joined the team, bringing years of online training expertise. He discusses our new offering of cohort-based courses, which combine live expert instruction, hands-on exercises, and a supportive community.
Martin has been busy leading the effort to create quizzes for our written tutorials to test your knowledge and Python skills. He’s also restructuring the learning paths to provide a more consistent way to navigate your journey learning Python.
Stephen is currently working on new Real Python books. These books will be collections of our tutorials based on specific Python topics and edited to provide a more structured learning experience. The first book, which covers object-oriented programming in Python, will be available in the next few months.
This episode is sponsored by Sentry.
Course Spotlight: Handling or Preventing Errors in Python: LBYL vs EAFP
In this video course, you’ll explore two popular coding styles in Python: Look Before You Leap (LBYL) and Easier to Ask Forgiveness than Permission (EAFP). These approaches help you handle errors and exceptional situations in your code effectively. You’ll dive into the key differences between LBYL and EAFP and learn when to use each one.
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