Have you wanted to understand recursion and how to use it in Python? Are you familiar with the call stack and how it relates to tracebacks? This week on the show, Al Sweigart talks about his new book, “The Recursive Book of Recursion.”

Recursion is one of those concepts held as a tenet of high-level computer science priesthood. Al explains the fundamentals of writing recursive functions and a critical missing piece in understanding how they operate, the call stack. After completing his research, he concluded that it’s a technique that you should understand but rarely use.

He also shares the few cases where recursion is an appropriate solution. Along the way, we talk about directed acyclic graphs, solving mazes, exploring file trees, and creating fractal images.

Course Spotlight: Caching in Python With lru_cache

Caching is an essential optimization technique. In this video course, you’ll learn how to use Python’s @lru_cache decorator to cache the results of your functions using the LRU cache strategy. This is a powerful technique you can use to leverage the power of caching in your implementations.

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How does a code completion tool work? What is an Abstract Syntax Tree, and how is it created in Python? How does an AST help you write programs and projects that inspect and modify your Python code? This week on the show, Meredydd Luff, co-founder of Anvil, shares his PyCon talk, “Building a Python Code Completer.”

Meredydd talks about his experience building a code completion engine for the Anvil platform. The conversation leads us to discuss how Python parses the code that you type. We examine tokenization, abstract syntax trees, and how parsing has changed in Python.

We cover additional projects that you can explore once you have a tool that inspects the Python code you’re writing. Join us as we dive into multiple rabbit holes of research and exploration.

Course Spotlight: Python Assignment Expressions and Using the Walrus Operator

In this course, you’ll learn about assignment expressions and the walrus operator. The biggest change in Python 3.8 was the inclusion of the := operator, which you can use to assign variables in the middle of expressions. You’ll see several examples of how to take advantage of this feature.

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Have you attempted to set up a Python development environment on Windows before? Would it be helpful to have an easy-to-follow guide to get you started? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python tutorial that covers configuring a Windows coding environment. The guide contains valuable suggestions, best practices, and powerful coding tools. It also covers how to use a package manager, the new Windows Terminal, PowerShell Core, and a program to manage multiple versions of Python.

Christopher covers another Real Python tutorial about using TOML in Python. TOML is a configuration format for building and distributing your own packages. We discuss how TOML parsing will be added to Python’s standard library in version 3.11.

We cover several other articles and projects from the Python community, on topics including technical writing for developers, a news round-up, a farewell to obsolete Python libraries, uncommon uses of Python in commonly used libraries, a prettier ls, and a project for advanced hot reloading in Python.

Course Spotlight: Python Basics: Finding and Fixing Code Bugs

In this Python Basics video course, you’ll learn how to identify and fix logic errors, or bugs, in your Python code. You’ll use the built-in debugging tools in Python’s Integrated Development and Learning Environment to practice locating and resolving bugs in an example function.

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What’s the big breakthrough for Natural Language Processing (NLP) that has dramatically advanced machine learning into deep learning? What makes these transformer models unique, and what defines “attention?” This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, continues our talk about how machine learning (ML) models understand and generate text.

This episode is a continuation of the conversation in episode #119. Jodie builds on the concepts of bag-of-words, word2vec, and simple embedding models. We talk about the breakthrough mechanism called “attention,” which allows for parallelization in building models.

We also discuss the two major transformer models, BERT and GPT3. Jodie continues to share multiple resources to help you continue exploring modeling and NLP with Python.

Course Spotlight: Building a Neural Network & Making Predictions With Python AI

In this step-by-step course, you’ll build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You’ll learn how to train your neural network and make predictions based on a given dataset.

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Is there someone in your life you’d like to inspire to learn Python? Mission Encodeable is a website designed to teach people to code, built by two high-school students. This week on the show, Anna and Harry Wake talk about creating their site and motivating people to start coding.

We discuss why they decided to build the site. Anna and Harry initially felt that the site would be for other students but soon realized it could be helpful for anyone interested in starting to code in Python. We cover the project-based approach and how they implemented the interactive browser-based tool replit.com.

We talk about learning Python in the classroom and how they found additional books and tutorials to supplement their coding education. Anna and Harry also created a resource hub to help teachers take advantage of the site.

Course Spotlight: Rock, Paper, Scissors With Python: A Command Line Game

In this course, you’ll learn to program rock paper scissors in Python from scratch. You’ll learn how to take in user input, make the computer choose a random action, determine a winner, and split your code into functions.

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How do you process and classify text documents in Python? What are the fundamental techniques and building blocks for Natural Language Processing (NLP)? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, talks about how machine learning (ML) models understand text.

Jodie explains how ML models require data in a structured format, which involves transforming text documents into columns and rows. She covers the most straightforward approach, called binary vectorization. We discuss the bag-of-words method and the tools of stemming, lemmatization, and count vectorization.

We jump into word embedding models next. Jodie talks about WordNet, Natural Language Toolkit (NLTK), word2vec, and Gensim. Our conversation lays a foundation for starting with text classification, implementing sentiment analysis, and building projects using these tools. Jodie also shares multiple resources to help you continue exploring NLP and modeling.

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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How do you start building your project documentation? What if you had a tool that could do the heavy lifting and automatically write large portions directly from your code? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python step-by-step project from Martin Breuss about MkDocs. The project walks you through generating nice-looking and modern documentation from Markdown files and your existing code’s docstrings. The final step is to deploy your freshly generated documentation to a GitHub repository.

Christopher talks about a pair of articles arguing for and against using Python dictionaries. The first article, “Just Use Dictionaries,” pushes to keep things simple, while the second article, “Don’t Let Dicts Spoil Your Code,” contends that complex projects require something more specific.

We cover several other articles and projects from the Python community, including discussing the recent beta release of Python 3.11, 2FA for PyPI, procedural music composition with arvo, building a tic-tac-toe game with Python and Tkinter, common issues encountered while coding in Python, a type-safe library to generate SVG files, and a lightweight static analysis tool for your projects.

Course Spotlight: Dictionaries and Arrays: Selecting the Ideal Data Structure

In this course, you’ll learn about two of Python’s data structures: dictionaries and arrays. You’ll look at multiple types and classes for both of these and learn which implementations are best for your specific use cases.

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How maintainable is your Python code? Is it possible to hold the code for your functions in your head? When is it appropriate to use measurements in a code review? This week on the show, Reka Horvath and Ben Martineau from Sourcery are here to discuss their recent PyCon talk, “Actionable insights vs ranking: How to use and how NOT to use code quality metrics.”

Reka and Ben share their thoughts on how metrics can provide insights into your Python code. We discuss four measurements of code complexity and what factors into each.

We also talk about deciding whether to refactor or rewrite code. Ben and Reka share their experience in code review situations and the importance of shifting the conversation from subjective opinions toward objective measurements.

Course Spotlight: Writing Idiomatic Python

What are the programming idioms unique to Python? This course is a short overview for people coming from other languages and an introduction for beginners to the idiomatic practices within Python. You’ll cover truth values, looping, DRY principles, and the Zen of Python.

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Would you like to explore the functional programming side of Python? What are the advantages of this approach, and what tools are built into the language? This week on the show, author Bruce Eckel talks about functional programming in Python.

Bruce is the author of several programming books, including Thinking in Java, Thinking in C++, Thinking in Python, Atomic Scala, and most recently, Atomic Kotlin. He’s been an explorer of programming languages over his career. Functional programming—with its lack of side effects, its transparency, and its potential for parallelization—has recently caught Bruce’s attention.

Bruce’s talk “Making Data Classes Work for You” at PyCon US 2022 explored the idea of the invariance of objects.

We also discuss his next book project, the Python community, and his affection for “un-conferences”. Bruce is hosting the upcoming Summer Tech Forum in Crested Butte, Colorado, this August.

Course Spotlight: Using Data Classes in Python

Data classes were introduced in Python 3.7. When using data classes, you don’t have to write boilerplate code to get proper initialization, representation, and comparisons for your objects.

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Have you heard about PyScript? The brand-new framework has the community excited about building interactive Python applications that run entirely within the user’s browser. Would you like to dig into the details beyond the “Hello World” examples? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python tutorial from Bartosz Zaczyński about PyScript. The article provides an initial look at the framework and then takes you deep into the intricacies. We also share additional resources to help familiarize you with the project.

Christopher talks about another Real Python article, this one on how to approach managing errors in Python. The tutorial “LBYL vs EAFP: Preventing or Handling Errors in Python” is from frequent contributor Leodanis Pozo Ramos.

We cover several other articles and projects from the Python community, including discussing the PSF’s Python Developers Survey 2021 Results, Django static files and templates, how to profile Python code, a launcher for penetration testing, and a project for confirming Python versions through syntax errors.

Course Spotlight: Building a Django User Management System

In this video course, you’ll learn how to extend your Django application with a user management system, complete with email sending and third-party authentication.

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