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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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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PyCoder’s Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2024. Christopher Trudeau is back on the show this week to help wrap it all up by sharing some highlights and uncovering a few missing gems from the pile.

We share the top links that PyCoder’s readers explored this year and uncover trends across all the articles and stories. We also highlight a few gems that we didn’t cover on the show and a couple that explore the overall themes of the year.

We hope you enjoy this review! We look forward to bringing you another year filled with great Python news, articles, topics, and projects.

Course Spotlight: Programming Sockets in Python

In this in-depth video course, you’ll learn how to build a socket server and client with Python. By the end, you’ll understand how to use the main functions and methods in Python’s socket module to write your own networked client-server applications.

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What are the current approaches for analyzing emotions within a piece of text? Which tools and Python packages should you use for sentiment analysis? This week, Jodie Burchell, developer advocate for data science at JetBrains, returns to the show to discuss modern sentiment analysis in Python.

Jodie holds a PhD in clinical psychology. We discuss how her interest in studying emotions has continued throughout her career.

In this episode, Jodie covers three ways to approach sentiment analysis. We start by discussing traditional lexicon-based and machine-learning approaches. Then, we dive into how specific types of LLMs can be used for the task. We also share multiple resources so you can continue to explore sentiment analysis on your own.

This week’s episode is brought to you by Sentry.

Course Spotlight: Learn Text Classification With Python and Keras

In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll see how you can use pretrained word embeddings, and you’ll squeeze more performance out of your model through hyperparameter optimization.

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What advice would you give to someone moving from another language to Python? What good programming practices are inherent to the language? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss an older forum post from a new Python user who came from Perl. We suggest checking out PEP 8, or as it’s commonly known, “The Style Guide for Python Code.” We provide advice about installing Python, avoiding common pitfalls, learning how scope is managed, and taking advantage of a collection of Real Python resources.

We share several other articles and projects from the Python community, including a new Python release, practical NumPy examples and exercises, considering targets of for loops, exploring Python dependency management, checking package compatibility with free-threading and subinterpreters, an experimental filesystem navigator in Textual, and a background workers reference implementation in Django.

This episode is sponsored by AssemblyAI.

Course Spotlight: Writing Beautiful Pythonic Code With PEP 8

Learn how to write high-quality, readable code by using the Python style guidelines laid out in PEP 8. Following these guidelines helps you make a great impression when sharing your work with potential employers and collaborators. This course outlines the key guidelines laid out in PEP 8. It’s aimed at beginner to intermediate programmers.

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What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python.

Before writing any code, Akshay wrote a 2,500-word design document. He wanted to create a maintainable and reproducible tool that avoided the hidden state of traditional notebooks. We discuss solving the hidden state problem by building the notebook as a directed acyclic graph (DAG).

Akshay shares how marimo notebooks are stored as pure Python files, which makes them easy to read, importable, and git-friendly. We discuss serializing package requirements using PEP 723 inline metadata to create standalone reproducible notebooks. We also cover how marimo notebooks can be deployed as a web app or dashboard using Pyodide.

Course Spotlight: Navigating Namespaces and Scope in Python

In this course, you’ll learn about Python namespaces, the structures used to store and organize the symbolic names created during execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.

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What keeps your spark alive for developing software and learning Python? Do you like to try new frameworks, build toy projects, or collaborate with other developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss the joy of tinkering with Python as a way to keep your developer skills sharp. We dig into our techniques for continuing to learn and build projects.

Christopher shares an article that examines the performance of Python 3.13’s free-threading features. This piece uses a clever example to measure how the new features behave with large datasets and parallelization.

We share several other articles and projects from the Python community, including a group of new releases, common use cases and examples for Python closures, finding the opposite of cloud-native, Python’s soft keywords, a command-line utility for taking automated screenshots of websites, and putting the Django admin in the terminal with Textual.

This episode is sponsored by Windsurf.

Course Spotlight: Python Inner Functions

In this step-by-step course, you’ll learn what inner functions are in Python, how to define them, and what their main use cases are. You’ll see how to write helper functions, create closure factory functions, and how to add behavior to existing functions with decorators.

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How do you build a sustainable open-source project and community? What lessons can be learned from Python’s history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation.

Paul has been an organizer in the Python community almost from the beginning. He shares how the project has navigated through multiple sponsors. We talk about the early governance models and the formation of the Python Software Foundation.

We contrast this journey with the current drama unfolding in the WordPress community. We discuss the potential problems of having a benevolent dictator for life. We also dig into sponsorship models and ways to get companies to give back to the open-source projects they rely on.

This episode is sponsored by Sentry.

Course Spotlight: Using pandas to Make a Gradebook in Python

With this course and Python project, you’ll build a script to calculate grades for a class using pandas. The script will quickly and accurately calculate grades from a variety of data sources. You’ll see examples of loading, merging, and saving data with pandas, as well as plotting some summary statistics.

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