The Real Python Podcast 8m20

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. Topics: 00:00:00 – Introduction 00:02:31 – Conference talks in 2024 00:04:23 – Background on sentiment analysis and studying feelings 00:07:09 – What led you to study emotions? 00:08:57 – Dimensional emotion classification 00:10:42 – Different types of sentiment analysis 00:14:28 – Lexicon-based approaches 00:17:50 – VADER - Valence Aware Dictionary and sEntiment Reasoner 00:19:41 – TextBlob and subjectivity scoring 00:21:48 – Sponsor: Sentry 00:22:52 – Measuring sentiment of New Year’s resolutions 00:27:28 – Lexicon-based approaches links for experimenting 00:28:35 – Multiple language in lexicon-based packages 00:35:23 – Machine learning techniques 00:39:20 – Tools for this approach 00:42:54 – Video Course Spotlight 00:44:15 – Advantages to the machine learning models approach 00:45:55 – Large language model approach 00:48:44 – Encoder vs decoder models 00:52:09 – Comparing the concept of fine-tuning 00:56:49 – Is this a recent development? 00:58:08 – Ways to practice with these techniques 01:00:10 – Do you find this to be a promising approach? 01:07:45 – Resources to practice with all the techniques 01:11:06 – conference talks 01:11:56 – Thanks and goodbye Show Links: Introduction to Sentiment Analysis in Python - The PyCharm Blog How to Do Sentiment Analysis With Large Language Models - The PyCharm Blog Talks - Jodie Burchell: Lies, damned lies and large language models - YouTube Mirror, mirror: LLMs and the illusion of humanity - Jodie Burchell - YouTube Separating fact from fiction in a world of AI fairytales - Jodie Burchell - NDC London 2024 - YouTube Hurt Feelings (Rap Version) - Flight Of The Conchords (Lyrics) - YouTube Universal Emotions - What are Emotions? - Paul Ekman Group VADER - nltk.sentiment.vader module clips/pattern: Web mining module for Python, with tools for scraping, natural language processing, machine learning TextBlob: Simplified Text Processing — TextBlob documentation Power vs. Force: The Hidden Determinants of Human Behavior by David R. Hawkins - Goodreads Episode #36: Sentiment Analysis, Fourier Transforms, and More Python Data Science – The Real Python Podcast Use Sentiment Analysis With Python to Classify Movie Reviews – Real Python Sentiment Analysis: First Steps With Python’s NLTK Library – Real Python Sentiment Analysis in DataSpell with @JetBrainsTV - YouTube Episode #119: Natural Language Processing and How ML Models Understand Text – The Real Python Podcast spaCy - Industrial-strength Natural Language Processing in Python amazon_polarity - Datasets at Hugging Face Introduction to Sentiment Analysis in Python - The PyCharm Blog Kaggle: Your Machine Learning and Data Science Community ZS BIT AI Community Day - 10 December 2024 Jodie Burchell - The JetBrains Blog Jodie Burchell’s Blog - Standard error Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl) - Twitter Jodie Burchell (@t-redactyl.bsky.social) — Bluesky Jodie Burchell 🇦🇺🇩🇪 (@[email protected]) - Fosstodon JetBrains: Essential tools for software developers and teams Level up your Python skills with our expert-led courses: Data Cleaning With pandas and NumPy Learn Text Classification With Python and Keras Exploring Astrophysics in Python With pandas and Matplotlib the podcast & our community of Pythonistas

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 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. Topics: 00:00:00 – Introduction 00:02:17 – Python 3.14.0 Alpha 2 Released 00:02:35 – Take the 2024 Django Developers Survey 00:03:17 – NumPy Practical Examples: Useful Techniques 00:07:09 – Loop Targets 00:09:19 – Python Dependency Management Is a Dumpster Fire 00:23:15 – Sponsor: AssemblyAI 00:24:00 – Package Compatibility With Free-Threading and Subinterpreters 00:27:02 – Suggestions for good programming practices? 00:37:59 – Video Course Spotlight 00:39:24 – terminal-tree: Experimental Filesystem Navigator in Textual 00:43:56 – django-tasks: Background Workers Reference Implementation 00:49:44 – Thanks and goodbye News: Python 3.14.0 Alpha 2 Released Take the 2024 Django Developers Survey Topics: NumPy Practical Examples: Useful Techniques – In this tutorial, 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. Loop Targets – Loop assignment allows you to assign to a dict item in a for loop. This post covers what that means and that it is no more costly than regular assignment. Python Dependency Management Is a Dumpster Fire – Managing dependencies in Python can be a bit of a challenge. This deep dive article shows you all the problems and how the problems are mitigated if not solved. Package Compatibility With Free-Threading and Subinterpreters – This tracker tests the compatibility of the 500 most popular packages with Python 3.13’s free-threading and subinterpreter features. Discussion: Suggestions for good programming practices? Python Best Practices – Real Python PEP 8 – Style Guide for Python Code Projects: terminal-tree: Experimental Filesystem Navigator in Textual django-tasks: Background Workers Reference Implementation Additional Links: Episode #146: Using NumPy and Linear Algebra for Faster Python Code – The Real Python Podcast How to Write Beautiful Python Code With PEP 8 – Real Python Writing Idiomatic Python – Real Python Namespaces and Scope in Python – Real Python How to Install Python on Your System: A Guide – Real Python Python Virtual Environments: A Primer – Real Python Sourcery - Instant Code Review for Faster Velocity Episode #183: Exploring Code Reviews in Python and Automating the Process Textual uv - An extremely fast Python package and project manager, written in Rust. DEP 0014: Background workers - GitHub PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link Level up your Python skills with our expert-led courses: Navigating Namespaces and Scope in Python Writing Idiomatic Python Writing Beautiful Pythonic Code With PEP 8 the podcast & our community of Pythonistas

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. Topics: 00:00:00 – Introduction 00:02:06 – Akshay’s background and studies 00:04:14 – Work at Google and PhD program 00:06:29 – Sharing notebooks 00:08:18 – Starting work on marimo 2 years ago 00:12:48 – Avoiding notebook issues and building a DAG 00:18:39 – The difference of reactivity 00:20:39 – What is a marimo notebook? 00:23:39 – Video Course Spotlight 00:24:50 – Reproducibility and managing package requirements 00:27:49 – Using decorators for cells 00:30:23 – Writing a design document before any coding 00:34:08 – Interactivity and UI widgets 00:38:20 – Design decisions and built-in widgets 00:42:05 – Creating a deployable web application 00:44:34 – Exploring examples and tutorials 00:46:13 – ing DataFrame libraries with narwhals 00:48:00 – Migrating from a Jupyter notebook 00:52:02 – Working with cells and not running code 00:54:30 – A couple favorite tutorials 00:56:17 – What are you excited about in the world of Python? 00:57:39 – What do you want to learn next? 00:59:34 – How can people follow the project and yourself? 01:00:12 – Thanks and goodbye Show Links: marimo - a next-generation Python notebook marimo: an open-source reactive notebook for Python - Akshay Agrawal (Nbpy2024) - YouTube TensorFlow Made with marimo - marimo FAQ - marimo Pluto.jl — interactive Julia programming environment Observable: Build expressive charts and dashboards with code We ed 10,000,000 Jupyter Notebooks From Github – This Is What We Learned - The Datalore Blog A Large-scale Study about Quality and Reproducibility of Jupyter Notebooks Lessons learned reinventing the Python notebook - marimo Episode #226: PySheets: Spreadsheets in the Browser Using PyScript PEP 723 – Inline script metadata Inline script metadata - Python Packaging Guide Serializing package requirements in marimo notebooks - marimo uv: Unified Python packaging marimo Newsletter 7 - Jupyter to marimo Custom UI elements - marimo anywidget - anywidget Interactive elements - marimo Episode #224: Narwhals: Expanding DataFrame Compatibility Between Libraries Calmcode - marimo: Introduction the marimo Discord marimo newsletter marimo on Twitter marimo on LinkedIn Akshay Agrawal’s website Aksahy on Twitter Level up your Python skills with our expert-led courses: Navigating Namespaces and Scope in Python Python Decorators 101 Using Jupyter Notebooks the podcast & our community of Pythonistas

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 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. Topics: 00:00:00 – Introduction 00:02:18 – Django Bugfix Release Issued: 5.1.3 00:02:46 – Pillow Release 11.0.0 00:03:14 – Flask Version 3.1.0 00:03:30 – PyCon US 2025 (Pittsburgh) Call for Proposals 00:03:46 – Python Closures: Common Use Cases and Examples 00:09:20 – State of Python 3.13 Performance: Free-Threading 00:15:42 – Sponsor: Windsurf 00:16:32 – Opposite of Cloud Native Is…? 00:22:36 – Python’s Soft Keywords 00:24:50 – Video Course Spotlight 00:26:11 – The Joy of Tinkering 00:38:33 – shot-scraper: A command-line utility for taking automated screenshots of websites 00:41:13 – django--tui: Django in the Terminal! 00:42:37 – django--dracula: Dracula Themes for the Django 00:44:21 – Thanks and goodbye News: Django Bugfix Release Issued: 5.1.3 Pillow Release 11.0.0 Flask Version 3.1.0 PyCon US 2025 (Pittsburgh) Call for Proposals Show Links: Python Closures: Common Use Cases and Examples – In this tutorial, you’ll learn about Python closures. A closure is a function-like object with an extended scope. You can use closures to create decorators, factory functions, stateful functions, and more. State of Python 3.13 Performance: Free-Threading – This article does a comparison between code in single threaded, threaded, and multi-process versions under Python 3.12, 3.13, and 3.13 free-threaded with the GIL on and off. Opposite of Cloud Native Is…? – Michael (from Talk Python fame) introduces the concept of “stack-native” as the opposite of “cloud-native”, and how it applies to Python web apps. Building applications with just enough full-stack building blocks to run reliably with minimal complexity, rather than relying on a multitude of cloud services. Python’s soft keywords – Python includes soft keywords: tokens that are important to the parser but can also be used as variable names. This article shows you what a soft keyword is and how to find them in Python 3.12 (both the easy and hard way). Discussion: Habits of Great Software Engineers - The Joy of Tinkering Projects: shot-scraper: A command-line utility for taking automated screenshots of websites django--tui: Django in the terminal! django--dracula: 🦇 Dracula themes for the Django Additional Projects: Primer on Python Decorators – Real Python We’ve moved to Hetzner - Talk Python Blog Talk Python rewritten in Quart (async Flask) - Talk Python Blog PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link Level up your Python skills with our expert-led courses: Python Decorators 101 Python Inner Functions Defining and Calling Python Functions the podcast & our community of Pythonistas

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. Topics: 00:00:00 – Introduction 00:01:55 – Meeting Jodie Burchell at PyCon 2022 00:02:51 – A non-traditional path into open-source 00:07:09 – The current turmoil around WordPress 00:13:49 – Keeping things fair in the age of extraction 00:16:03 – Sponsor: Sentry 00:17:07 – Early Python organizing history and conservation 00:20:41 – The Python Software Activity precursor to PSF 00:24:14 – Creating the Python Software Foundation 00:27:24 – Keeping the perfect distance of business and project 00:28:13 – Who gets to capture the value from open-source? 00:31:07 – Sponsorships becoming more common 00:33:24 – BDFL to a steering council 00:34:58 – Video Course Spotlight 00:36:16 – What is Plone? 00:38:11 – Starting in Python and finding community 00:50:07 – Companies contributing 00:53:16 – Examples of how JetBrains contributes back 00:55:41 – Understanding the system 00:58:09 – Talking to decision makers 01:00:07 – Python 1994 talk and continuation 01:01:49 – What are you excited about in the world of Python? 01:03:06 – What do you want to learn next? 01:04:17 – How can people follow your work online? 01:07:16 – Thanks and goodbye Show Links: JetBrains: Essential tools for software developers and teams PyCharm: the Python IDE for data science and web development PyCon - us at PyCon Benevolent dictator for life - Wikipedia The messy WordPress drama, explained - The Verge WordPress.org’s latest move involves taking control of a WP Engine plugin - The Verge WP Engine asks court to stop Matt Mullenweg from blocking access to WordPress resources - The Verge Podcast: Why the WordPress Chaos Matters - 404 Media Zope - Wikipedia Python Software Foundation PyLadies – Women Who Love Coding in Python Django Software Foundation - Django OpenCV - About Page Plone Foundation FastHTML - Modern web applications in pure Python Paul Everitt - Python 1994 - YouTube A Team at Microsoft is Helping Make Python Faster - Python Velda Kiara JetBrains Blog: The Drive to Develop Paul Everitt (@[email protected]) - Fosstodon Guido van Rossum - Wikipedia The History of Python: Personal History - part 1, CWI Oral History of Guido van Rossum, part 1 - YouTube Level up your Python skills with our expert-led courses: Building Python Project Documentation With MkDocs The pandas DataFrame: Working With Data Efficiently Using pandas to Make a Gradebook in Python the podcast & our community of Pythonistas

Have you wanted the flexibility of f-strings but need safety checks in place? What if you could have deferred evaluation for logging or avoiding injection attacks? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a set of recent Python Enhancement Proposals (PEPs). The idea of template strings has been under consideration for a while, and PEP 750 describes a new way forward. PEP 759 proposes a way for projects on PyPI to safely host resources on external sites using a new package format called a .rim file. We share several other articles and projects from the Python community, including what didn’t make the headlines about Python 3.13, solving Sudoku with Python packaging, what’s sweet about Python’s syntactic sugar, creating database-generated columns using SQLite and Django, a discussion about mentoring, an adaptive web scraper, and a debugging tool for HTTP(S) client requests. This episode is sponsored by Sentry. Course Spotlight: Using Pydantic to Simplify Python Data Validation Discover the power of Pydantic, Python’s most popular data parsing, validation, and serialization library. In this hands-on video course, you’ll learn how to make your code more robust, trustworthy, and easier to debug with Pydantic. Topics: 00:00:00 – Introduction 00:02:08 – Python 3.14.0 Alpha 1 Released 00:02:38 – Python 3.13, What Didn’t Make the Headlines 00:05:23 – What’s up Python? 3.13 is out, t-strings look awesome 00:10:21 – Sponsor: Sentry 00:11:25 – Sudoku in Python Packaging 00:14:29 – Syntactic Sugar: Why Python Is Sweet and Pythonic 00:22:31 – Database generated columns: Django & SQLite 00:27:14 – Video Course Spotlight 00:28:39 – Mentors 00:42:23 – Scrapling: Lightning-Fast, Adaptive Web Scraping for Python 00:44:14 – httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests 00:46:04 – Request for project submissions to PyCoders 00:46:59 – Thanks and goodbye News: Python 3.14.0 Alpha 1 Released Show Links: Python 3.13, What Didn’t Make the Headlines – Bite Code summarizes some of the lesser covered changes to Python in the 3.13 release, including how some of the REPL improvements made it into pdb, improvements to shutil, and small additions to the asyncio library. What’s up Python? 3.13 is out, t-strings look awesome, dep groups come in handy… Sudoku in Python Packaging – Simon writes about a Sudoku solver written by Konstin that uses the Python packaging mechanisms to do Sudoku puzzles. The results are output using a requirements.txt file, where sudoku-0-3==5 represents the (0,3) cell’s answer of 5. Syntactic Sugar: Why Python Is Sweet and Pythonic – In this tutorial, you’ll learn what syntactic sugar is and how Python uses it to help you create more readable, descriptive, clean, and Pythonic code. You’ll also learn how to replace a given piece of syntactic sugar with another syntax construct. Database generated columns: Django & SQLite – An introduction to database generated columns, using SQLite and the new GeneratedField added in Django 5.0 Discussion: Mentors – Ryan just finished his second round of mentoring with the Djangonaut.Space program. This post talks about how you can help your mentor help you and how to be a good mentor. Projects: Scrapling: Lightning-Fast, Adaptive Web Scraping for Python httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests in a Python program Additional Links: PEP 750 – Template Strings PEP 735 – Dependency Groups in pyproject.toml PEP 759 – External Wheel Hosting Episode #47: Unraveling Python’s Syntax to Its Core With Brett Cannon – The Real Python Podcast Episode #92: Continuing to Unravel Python’s Syntactic Sugar With Brett Cannon – The Real Python Podcast Episode #4: Learning Python Through Errors – The Real Python Podcast PyCoder’s Weekly - Have a Project You Want to Share? - Submit a Link Level up your Python skills with our expert-led courses: Using Pydantic to Simplify Python Data Validation Python Type Checking Using Type Hints for Multiple Return Types in Python the podcast & our community of Pythonistas

What goes into building a spreadsheet application in Python that runs in the browser? How do you make it launch quickly, and where do you store the cells of data? This week on the show, we speak with Chris Laffra about his project, PySheets, and his book “Communication for Engineers.” As a software engineer, Chris has worked at IBM, Google, Uber, and several financial institutions. He speaks about developer productivity and communication skills as an engineer. We begin our conversation by digging into his background, his approach to building engineering teams, and strategies for improving communication. Chris’ idea for PySheets is to have Excel inside Python with everything running locally in your browser. He was inspired by the success of Jupyter Notebooks but wanted to develop a tool more suited to a spreadsheet’s non-linear graph structure. PySheets is built to run locally in the ’s browser, taking advantage of PyScript. We discuss finding the right solution for storing data in the browser and developing a graphic toolkit to create the UI. Chris also shares the novel method he found to get the interface up and running while the larger assets are loading. This episode is sponsored by Sentry. Course Spotlight: Understanding Python’s Global Interpreter Lock (GIL) Python’s Global Interpreter Lock, or GIL, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. In this video course, you’ll learn how the GIL affects the performance of your Python programs. Topics: 00:00:00 – Introduction 00:02:25 – Background with building engineering teams 00:08:43 – Communication for Engineers book 00:16:17 – What do customers want and experiences at IBM 00:24:28 – Starting the development of PySheets 00:27:19 – Working with the DOM 00:29:41 – Success of Jupyter notebooks 00:35:46 – Sponsor: Sentry 00:36:52 – Little Toolkit for PyScript 00:43:24 – Finding funding 00:46:58 – Building a product before selling 00:52:27 – Video Course Spotlight 00:53:46 – Finding the right data storage in IndexedDB 01:01:57 – Exploring the trial page and extensibility 01:08:26 – Contributing to the project or forking 01:11:56 – What are you excited about in the world of Python? 01:16:20 – What do you want to learn next? 01:17:25 – How can people follow your work online? 01:18:05 – Thanks and goodbye Show Links: Chris Laffra C4E - Communication for Engineers (ePUB) PySheets - Spreadsheet UI for Python PySheets: Source for PySheets PyScript - Python in the browser - Chris Laffra - YouTube Python in Excel - Microsoft 365 pyscript/ltk: LTK is a little toolkit for writing UIs in PyScript LTK - Little Toolkit PROCOL: a parallel object language with protocols - ACM SIGPLAN IndexedDB API - MDN First steps - PyScript Pyodide — Version 0.26.3 PyScript Updates: Bytecode Alliance, Pyodide, and MicroPython MicroPython - Python for microcontrollers FreeCAD: Your own 3D parametric modeler Chris Laffra - How to become a Happy and Productive Engineer - YouTube Level up your Python skills with our expert-led courses: What's New in Python 3.13 Python Plotting With Matplotlib Understanding Python's Global Interpreter Lock (GIL) the podcast & our community of Pythonistas

What changes are happening under the hood in the latest versions of Python? How are these updates laying the groundwork for a faster Python in the coming years? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares an article about Python’s recent performance improvements. The piece covers the specialized adaptive interpreter and explains what those mean. It also includes details about the experimental feature of the Just-In-Time (JIT) compiler added in 3.13. We dig into a collection of Django projects you can use to practice and develop your skills. The projects ramp up from detailed beginner tutorials to more advanced projects with guidelines on how to get started. We also discuss a collection of popular websites that use Django. We share several other articles and projects from the Python community, including a batch of recent Python Enhancement Protocols (PEPs), a couple of Python releases, using DuckDB in the browser with Pyodide, building a book app with Textual, generating a tiny status page with a Python script, and a grep-like tool that understands code. This episode is sponsored by AssemblyAI. Course Spotlight: Building a Site Connectivity Checker In this video course, you’ll build a Python site connectivity checker for the command line. While building this app, you’ll integrate knowledge related to making HTTP requests with standard-library tools, creating command-line interfaces, and managing concurrency with asyncio and aiohttp. Topics: 00:00:00 – Introduction 00:03:11 – PEP 777: How to Re-Invent the Wheel 00:04:22 – PEP 758: Allow except and except* Expressions Without Parentheses 00:04:51 – PEP 760: No More Bare Excepts (Withdrawn) 00:05:42 – PEP 735: Dependency Groups in pyproject.toml 00:06:29 – PEP 761: Deprecating PGP Signatures for ython Artifacts 00:06:59 – Python 3.12.7 Released 00:07:12 – Incremental GC and Pushing Back the 3.13.0 Release 00:09:10 – DuckDB in the Browser With Pyodide 00:15:35 – Sponsor: AssemblyAI 00:16:18 – Build a Book App With Python, Textual, and SQLite 00:21:55 – Django Project Ideas 00:28:42 – Video Course Spotlight 00:30:00 – In the Making of Python Fitter and Faster 00:35:13 – tinystatus: Tiny Status Page Generated by a Python Script 00:38:06 – srgn: Grep-Like Tool That Understands Code 00:42:01 – Thanks and goodbye News: PEP 777: How to Re-Invent the Wheel – “The current wheel 1.0 specification was written over a decade ago, and has been extremely robust to changes in the Python packaging ecosystem… this PEP prescribes compatibility requirements on future wheel revisions.” PEP 758: Allow except and except* Expressions Without Parentheses – “This PEP proposes to allow unparenthesized except and except* blocks in Python’s exception handling syntax. Currently, when catching multiple exceptions, parentheses are required around the exception types.” PEP 760: No More Bare Excepts (Withdrawn) PEP 735: Dependency Groups in pyproject.toml (Accepted) PEP 761: Deprecating PGP Signatures for ython Artifacts – Since Python 3.11.0, ython has provided two verifiable digital signatures for all ython artifacts: PGP and Sigstore. This PEP proposes moving to Sigstore as the only way of g artifacts. Python 3.12.7 Released Python 3.13.0 Released Incremental GC and Pushing Back the 3.13.0 Release – Some last minute performance considerations delayed the release of Python 3.13 with one of the features being backed out. Show Links: DuckDB in the Browser With Pyodide – Learn how to run DuckDB in an in-browser Python environment to enable simple querying on remote files, interactive documentation, and easy to use training materials. Build a Book App With Python, Textual, and SQLite – In this tutorial, you’ll be guided step by step through the process of building a basic book application. You’ll use Python and Textual to build the application’s text-based interface (TUI), and then use SQLite to manage the database. Django Project Ideas – Looking to experiment or build your portfolio? Discover creative Django project ideas for all skill levels, from beginner apps to advanced full-stack projects. In the Making of Python Fitter and Faster – This post details how Python’s recent performance improvements work under the hood. It covers changes to the interpreter, better memory management, and the newly experimental JIT compiler. Projects: tinystatus: Tiny Status Page Generated by a Python Script srgn: Grep-Like Tool That Understands Code Additional Links: What Are Python Wheels and Why Should You Care? – Real Python Deploy your first JupyterLite website on GitHub Pages — JupyterLite 0.4.3 documentation rich: Python library for rich text and beautiful formatting in the terminal The 10 Most Popular Websites Using Django Django in Action Django and htmx Tutorial: Easier Web Development - YouTube Build a Site Connectivity Checker in Python – Real Python Refactoring Python with 🌳 Tree-sitter & Jedi | Jack’s blog Level up your Python skills with our expert-led courses: Building a Site Connectivity Checker How to Set Up a Django Project Building Command Line Interfaces With argparse the podcast & our community of Pythonistas

How does a Python tool all types of DataFrames and their various features? Could a lightweight library be used to add compatibility for newer formats like Polars or PyArrow? This week on the show, we speak with Marco Gorelli about his project, Narwhals. Narwhals is a project aimed at library maintainers rather than end s. We discuss how the added compatibility benefits s by ing modern features like lazy evaluation. We cover several projects Marco has been working with to implement Narwhals, including Altair, scikit-lego, and Ibis. We also discuss how Marco started contributing to open-source projects. Marco has contributed to both pandas and Polars, which helps explain his interest in growing compatibility between libraries. He also offers advice on making your first contribution. This episode is sponsored by CodeRabbit. Course Spotlight: Differences Between Python’s Mutable and Immutable Types In this video course, you’ll learn how Python’s mutable and immutable data types work internally and how you can take advantage of mutability or immutability to power your code. Topics: 00:00:00 – Introduction 00:02:02 – Euro SciPy 2024 and sprints 00:04:04 – How did you get involved in open source? 00:07:18 – Finding a good issue to get started 00:09:25 – Discord and open-source projects 00:11:12 – Who would you describe Narwhals? 00:16:47 – Working on Polars 00:19:17 – Apache Arrow and a data interchange protocol 00:22:55 – Sponsor: CodeRabbit 00:23:55 – Digging into eager vs lazy 00:27:04 – Ibis DataFrame library 00:28:57 – What do libraries need from Narwhals? 00:34:57 – The scikit-lego library 00:37:15 – Video Course Spotlight 00:38:45 – Other libraries interested in Narwhals 00:41:56 – Compatibility policy 00:45:18 – What should an end expect? 00:46:32 – Have other projects that attempted this? 00:47:54 – Keeping the project light and pure Python 00:49:32 – Contributors and how to get involved 00:54:42 – What are you excited about in the world of Python? 00:57:18 – What do you want to learn next? 00:59:05 – How can people follow your work online? 00:59:27 – Thanks and goodbye Show Links: Narwhals EuroSciPy narwhals: Lightweight and Extensible Compatibility Layer Between DataFrame Libraries! - GitHub DataFrame Interoperability - What’s Been Achieved, and What Comes Next? - PyCon Lithuania - YouTube How Narwhals Has Many End s … That Never Use It Directly - YouTube Polars Has a New Lightweight Plotting Backend - Altair pandas - Python Data Analysis Library Polars — DataFrames for the new era great-tables - PyPI Episode #214: Build Captivating Display Tables in Python With Great Tables Ibis Episode #201: Decoupling Systems to Get Closer to the Data Great Tables is Now BYODF (Bring Your Own DataFrame) How Narwhals and scikit-lego Came Together to Achieve DataFrame-Agnosticism Explore Using Narwhals in Plotly Express · Issue #4749 - GitHub Fairlearn Perfect Backwards Compatibility Policy - Narwhals uv: Unified Python packaging pixi - Powerful Development Environments Narwhals - Discord marcogorelli (@[email protected]) - Fosstodon Marco Gorelli - Quansight - LinkedIn Level up your Python skills with our expert-led courses: What's New in Python 3.13 pandas GroupBy: Grouping Real World Data in Python The pandas DataFrame: Working With Data Efficiently the podcast & our community of Pythonistas

Python 3.13 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. This year, Geir Arne coordinated a series of preview articles with of the Real Python team and a showcase tutorial, “Python 3.13: Cool New Features for You to Try.” Christopher’s video course “What’s New in Python 3.13” covers the topics from the article and shows the new features in action. Geir Arne and Christopher dug into the release to create code examples of the new features for the tutorial and course. We look at the options for disabling the Global Interpreter Lock (GIL) and enabling the Just-in-Time (JIT) compiler. We also discuss the new interactive interpreter, better error messages, multiple improvements to static typing, and additional performance improvements. We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.13. This is episode is sponsored by Nvidia. Course Spotlight: What’s New in Python 3.13 In this video course, you’ll learn about the new features in Python 3.13. You’ll take a tour of the new REPL and error messages and see how you can try out the experimental free threading and JIT versions of Python 3.13 yourself. Topics: 00:00:00 – Introduction 00:03:14 – A Modern REPL 00:08:54 – Making the Global Interpreter Lock Optional in ython 00:11:33 – JIT Compilation 00:15:48 – More improved error messages 00:18:30 – Sponsor: NVIDIA 00:19:13 – Marking deprecations using the type system 00:21:09 – Type Defaults for Type Parameters 00:22:44 – Narrowing types with TypeIs 00:25:24 – TypedDict: Read-only items 00:27:50 – Random command line interface 00:29:54 – New copy.replace() 00:33:43 – Video Course Spotlight 00:34:55 – Pathlib and globbing 00:39:33 – Stripping docstrings 00:41:28 – Import improvements 00:41:56 – Dynamically import non-code files 00:42:23 – Adding iOS as a ed platform 00:43:32 – More consistency with local namespace 00:44:30 – for deprecation in argparse 00:45:00 – Better entry points for breakpoint or set_trace 00:46:08 – Removing dead batteries 00:47:43 – When to 3.13? 00:53:19 – core.py podcast 00:54:14 – Thanks and goodbye Show Links: Python 3.13: Cool New Features for You to Try What’s New in Python 3.13 Python 3.13 Preview: A Modern REPL Python 3.13 Preview: Free Threading and a JIT Compiler What’s New In Python 3.13 — Python 3.13.0rc2 documentation PEP 703 – Making the Global Interpreter Lock Optional in ython PEP 744 – JIT Compilation PEP 702 – Marking deprecations using the type system PEP 696 – Type Defaults for Type Parameters PEP 742 – Narrowing types with TypeIs PEP 705 – TypedDict: Read-only items PEP 730 – Adding iOS as a ed platform PEP 738 – Adding Android as a ed platform PEP 667 – Consistent views of namespaces PEP 594 – Removing dead batteries from the standard library The Python Standard REPL: Try Out Code and Ideas Quickly Unlock IPython’s Magical Toolbox for Your Coding Journey core.py Podcast - Episode 14: Integration Events Level up your Python skills with our expert-led courses: What's New in Python 3.13 Python Type Checking What's New in Python 3.12 the podcast & our community of Pythonistas

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. This episode is sponsored by InfluxData. 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. 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 00:54:39 – Thanks and goodbye News: Python Releases 3.12.6, 3.11.10, 3.10.15, 3.9.20, and 3.8.20 Python Release Python 3.13.0rc2 Django Security Releases Issued: 5.1.1, 5.0.9, and 4.2.16 Show Links: 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. Why I Still Use Python Virtual Environments in Docker – Hynek often gets challenged when he suggests the use of virtual environments within Docker containers, and this post explains why he still does. 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. Discussion: Editors & IDEs – Real Python Visual Studio Code - Code Editing. Redefined Project Jupyter - Home vim online: welcome home iTerm2 - macOS Terminal Replacement Projects: csv_trimming: Remove Common Ugliness From CSV Files django-tables2: Create HTML Tables in Django Additional Links: virtualenv Lives! - Hynek Schlawack - 2014 Production-ready Python Docker Containers with uv - Hynek Schlawack r-python-talk: 🦸🏼‍♀️ Contains material for talk on how to use Python and R together RStudio - Posit Logic for Programmers by Hillel Wayne - Leanpub Level up your Python skills with our expert-led courses: Using Jupyter Notebooks Absolute vs Relative Imports in Python Advanced Python import Techniques the podcast & our community of Pythonistas

What are strategies for being a productive developer with ADHD? How can you help your team with ADHD to succeed and complete projects? This week on the show, we speak with Chris Ferdinandi about his website and podcast “ADHD For the Win!” Chris struggled with productivity early in his career as a developer. He shares systems and strategies he’s discovered to harness the focusing power of ADHD. We discuss time management, meetings, and maintaining productivity in a hectic world. Chris also shares resources for learning more about defining ADHD, self-evaluation, and how to keep getting things done. This episode is sponsored by InfluxData. Course Spotlight: Build a GUI Calculator With PyQt and Python In this video course, you’ll learn how to create graphical interface (GUI) applications with Python and PyQt. Once you’ve covered the basics, you’ll build a fully functional desktop calculator that can respond to events with concrete actions. Topics: 00:00:00 – Introduction 00:02:30 – Defining ADHD and how it aligns with coding 00:05:47 – Analogy for focus 00:06:51 – Can you sense the change in focus? 00:07:46 – The challenge of meetings 00:11:45 – Tips for managing time 00:15:44 – Capturing notes and defragging 00:18:48 – Sponsor: InfluxData 00:19:33 – Downtime and interruptions 00:25:26 – Remote work and focus 00:33:16 – Sitting still and meetings 00:37:39 – Video Course Spotlight 00:39:13 – Anything worth doing is worth doing poorly 00:47:36 – Prototypes and working on interesting things 00:50:26 – Deadlines and pomodoro timers 00:54:21 – Have your symptoms changed over time? 00:56:18 – Starting ADHDftw.com 00:59:12 – Decision to keep podcast episodes short 01:00:01 – Deciding on medication 01:02:02 – Resources available 01:03:29 – How motivates you to continue to learn programming? 01:04:06 – What do you want to learn next? 01:04:55 – What are other ways to follow your work online? 01:05:28 – Thanks and goodbye Show Links: ADHD ftw! - Resources ADHD isn’t a deficit of attention (and doesn’t necessarily mean you’re hyperactive) Do I have ADHD? Snoot - Wikipedia Anything worth doing is worth doing poorly Getting stuff done with ADHD: defrag your notebook - ADHD ftw! Go Make Things - About Chris Ferdinandi ⚓️ (@[email protected]) - Fosstodon Level up your Python skills with our expert-led courses: Creating PyQt Layouts for GUI Applications Build a GUI Calculator With PyQt and Python HTML and CSS Foundations for Python Developers the podcast & our community of Pythonistas

How do you take advantage of Git pre-commit hooks? How do you build custom software checks and rules that run every time you commit your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We share a trio of articles by previous guest Stefanie Molin about Git pre-commit hooks. Across the series, she provides step-by-step instructions for building your own hooks, managing them, and learning how they operate. We discuss the process of estimating software development projects. We dig into the art of “guesstimation,” rough calculation, and napkin math. Christopher shares his experience in agile scenarios and measuring projects by story counts. We share several other articles and projects from the Python community, including a news roundup, 10 Python programming optimization techniques, and building a b Django using GraphQL & Vue. We also explore experimenting with Python’s preprocessor, a toolkit for writing UIs in PyScript, and a couple of projects for working with Django . This episode is sponsored by InfluxData. Course Spotlight: Python mmap: Doing File I/O With Memory Mapping In this video course, you’ll learn how to use Python’s mmap module to improve your code’s performance when you’re working with files. You’ll get a quick overview of the different types of memory before diving into how and why memory mapping with mmap can make your file I/O operations faster. Topics: 00:00:00 – Introduction 00:02:28 – Python Top Language of 2024 00:02:59 – Python Developers Survey 2023 Results 00:03:46 – How Pre-Commit Works 00:10:00 – Build a Blog Using Django, GraphQL, and Vue 00:13:38 – Sponsor: InfluxData 00:14:23 – 10 Python Programming Optimization Techniques 00:22:45 – Python’s Preprocessor 00:26:42 – Video Course Spotlight 00:28:16 – You don’t have to guess to estimate 00:44:18 – LTK is a little toolkit for writing UIs in PyScript 00:49:06 – django--action-forms: Forms for Django 00:52:09 – django-public-: A Public and Read-Only Django 00:53:24 – Thanks and goodbye News: Python Top Language of 2024 – “Python continues to cement its overall dominance, buoyed by things like popular libraries for hot fields such as A.I.” Read the article to see where other languages have placed. Python Developers Survey 2023 Results – Official Python Developers Survey 2023 Results by Python Software Foundation and JetBrains: more than 25k responses from almost 200 countries. Show Links: How Pre-Commit Works – As a of pre-commit hooks, do you know what happens when you run pre-commit install or why you have to run it in the first place? How does pre-commit actually work with Git? In this article, Stefanie takes you behind the scenes of how your pre-commit setup works. How to Set Up Pre-Commit Hooks - Stefanie Molin How to Create a Pre-Commit Hook - Stefanie Molin Build a Blog Using Django, GraphQL, and Vue – In this step-by-step project, you’ll build a blog from the ground up. You’ll turn your Django blog data models into a GraphQL API and consume it in a Vue application for s to read. You’ll end up with an site and a -facing site you can continue to refine for your own use. 10 Python Programming Optimization Techniques – Optimization should be your last step, but once you’re there, just what can you do? This article covers ten different techniques that address memory size and code performance. Python’s Preprocessor – Every now and then you hear outrageous claims such as “Python has no preprocessor,” well it is there if you’re willing to dig deep enough. Learn how to hack Python’s compile step. Discussion You Don’t Have to Guess to Estimate Habits of Great Software Engineers Projects: pyscript/ltk: LTK Is a Little Toolkit for Writing UIs in PyScript django--action-forms: Forms for Django django-public-: A Public and Read-Only Django Additional Links: Python mmap: Doing File I/O With Memory Mapping – Real Python Caching in Python With lru_cache – Real Python Episode #128: Using a Memory Profiler in Python & What It Can Teach You Episode #172: Measuring Multiple Facets of Python Performance With Scalene – The Real Python Podcast software development - Why are estimates treated like deadlines? - Project Management Stack Exchange Software Estimation Without Guessing: Effective Planning in an Imperfect World by George Dinwiddie Guesstimation - Princeton University Press Anaconda Toolbox for Excel — Anaconda documentation Anaconda Code — Anaconda documentation PySheets - Spreadsheet UI for Python Level up your Python skills with our expert-led courses: Python mmap: Doing File I/O With Memory Mapping Caching in Python With lru_cache How Python Manages Memory the podcast & our community of Pythonistas

Are you interested in practicing your Python skills while learning how to solve astrophysics and astronomy problems? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares a pair of his recent Real Python video courses about exploring astronomy and astrophysics with Python. Throughout the courses, you’ll get to practice using a variety of data science libraries, such as NumPy, Matplotlib, pandas, pint, and Astropy. We speak with Mannie Young who is the Organizing Committee Chair of PyCon Africa. Real Python is excited to be a contributing sponsor of this year’s conference. Mannie discusses reinvigorating a continent-spanning conference after a multiyear hiatus. He also talks about introducing Python to students and new developers across Africa through PyClubs, PyLadies, and PyData programs. We share several other articles and projects from the Python community, including a news roundup, logging in Python, understanding operator precedence, reconciling why it only works on your machine, a fast way to create an HTML app, and a tool for deep inspection of Python objects. This episode is sponsored by InfluxData. Course Spotlight: Sorting Dictionaries in Python: Keys, Values, and More In this video course, you’ll learn how to sort Python dictionaries. By the end, you’ll be able to sort by key, value, or even nested attributes. But you won’t stop there, you’ll also measure the performance of variations when sorting and compare different key-value data structures. Topics: 00:00:00 – Introduction 00:03:05 – PEP 750: Tag Strings for Domain-Specific Languages 00:05:19 – PEP 752: Package Repository Namespaces 00:07:32 – PyCon US 2024 Recap and Recording Release 00:08:01 – Logging in Python 00:14:57 – It Works on My Machine. Why? 00:17:33 – Python’s Operator Precedence 00:20:54 – Exploring Astrophysics in Python With pandas and Matplotlib 00:24:03 – Using Astropy for Astronomy With Python 00:26:37 – Sponsor: InfluxData 00:27:22 – fasthtml: The Fastest Way to Create an HTML App 00:32:50 – wat: Deep Inspection of Python Objects 00:38:08 – PyCon Africa 2024 00:40:47 – What goes into re-energizing a conference? 00:44:20 – Talks and speakers 00:46:58 – Video Course Spotlight 00:48:29 – How did you get involved? 00:52:19 – PyClubs and growing Python education 00:58:41 – What industries are using Python in Ghana? 01:00:20 – Sponsorship and 01:01:51 – Travel in and outside the continent 01:04:23 – Call to action 01:05:05 – Thanks and goodbye News: PEP 750: Tag Strings for Domain-Specific Languages (Added) PEP 752: Package Repository Namespaces (Added) PyCon US 2024 Recap and Recording Release – PyCon US 2024 had a record breaking attendance with over 2,700 in-person tickets sold. This article is a recap from the conference runners and links to all the available recordings. Show Links: Logging in Python – If you use Python’s print() function to get information about the flow of your programs, then logging is the natural next step for you. This tutorial will guide you through creating your first logs and show you ways to curate them to grow with your projects. It Works on My Machine. Why? – A list of things to check when something works on your computer but not on someone else’s. Python’s Operator Precedence – Stephen uses a story-telling style to explain how operator precedence works in Python. Exploring Astrophysics in Python With pandas and Matplotlib – This course uses three problems often covered in introductory astro-physics courses to play in Python. Along the way you’ll learn some astronomy and how to use a variety of datascience libraries like NumPy, Matplotlib, pandas, and pint. Using Astropy for Astronomy With Python – This course covers two problems from introductory astronomy to help you play with some Python libraries. You’ll use NumPy, Matplotlib, and pandas to find planet conjunctions, and graph the best viewing times for a star. Projects: fasthtml: The Fastest Way to Create an HTML App wat: Deep Inspection of Python Objects Additional Links: PyCon Africa 2024 - Home Announcing PyCon Africa 2024 Blog: A Return to Accra and a Look Ahead Episode #65: Expanding the International Python Community With the PSF Django Girls - Start your journey with programming PyLadies Ghana - Python Ghana’s Blog PyData Ghana - Python Ghana’s Blog PyClubs - Home Quiz: Logging in Python Mannie Young - LinkedIn Mannie Young (@mawy_7) - X Level up your Python skills with our expert-led courses: Using Astropy for Astronomy With Python Sorting Dictionaries in Python: Keys, Values, and More Exploring Astrophysics in Python With pandas and Matplotlib the podcast & our community of Pythonistas

Are you interested in learning robotics with Python? Can physical electronics-based projects grow a child’s interest in coding? This week on the show, we speak with author Marwan Alsabbagh about his book “Build Your Own Robot - Using Python, CRICKIT, and Raspberry Pi.” Marwan discusses his two conferences talks about building electronics projects with his children. He provides advice on equipment and techniques to make learning Python engaging. We explore his robotics project and the literal balancing act of deg a robot around the Raspberry Pi. Marwan shares his successes and disappointments while working to incorporate computer vision, joystick controls, and voice commands. This episode is sponsored by Mailtrap. Course Spotlight: Python Debugging With pdb In this hands-on course, you’ll learn the basics of using pdb, Python’s interactive source code debugger. pdb is a great tool for tracking down hard-to-find bugs, and it allows you to fix faulty code more quickly. Topics: 00:00:00 – Introduction 00:02:14 – How did you get started with Python and electronics? 00:04:27 – Snow globe intruder alert system 00:06:57 – Things to keep in mind with a child 00:12:50 – Challenges in teaching a child Python 00:16:34 – Sponsor: Mailtrap 00:17:11 – What are other projects you’ve tried? 00:21:12 – Powering the robot project 00:24:56 – Putting together the robot librarian talk 00:29:47 – Was there any friction teaching kids robotics? 00:32:47 – Adding the complexity of a Raspberry Pi 00:38:27 – Video Course Spotlight 00:39:48 – Hardware components of the robot 00:41:51 – Thinking about access to the equipment 00:45:37 – Assembling the robot project? 00:49:14 – Various control systems 00:54:42 – What experience level is required with Python? 00:55:40 – What concepts were you excited to share? 00:57:59 – Do you think Python is a good language for robotics? 00:59:21 – MicroPython Cookbook 01:00:07 – What are projects you tried that didn’t work out? 01:03:01 – What are you excited about in the world of Python? 01:04:04 – What do you want to learn next? 01:04:56 – How can people follow your work online? 01:05:19 – Thanks and goodbye Show Links: Build Your Own Robot - Using Python, CRICKIT, and Raspberry PI Snow globe intruder alert system Snow globe intruder alert system - Marwan Alsabbagh - PyLondinium18 - YouTube Adafruit Industries, Unique & fun DIY electronics and kits Nina Zakharenko - Keynote - PyCon 2019 - YouTube Episode #86: The Legacy of OLPC and Charismatic Pitfalls in Teaching Programming Episode #161: Resources and Advice for Building CircuitPython Projects Episode #75: Building With CircuitPython & Constraints of Python for Microcontrollers MicroPython Cookbook: Marwan Alsabbagh - Amazon.com: Books WebAssembly htmx - high power tools for html Marwan Alsabbagh - personal website marwano (Marwan Alsabbagh) - GitHub Level up your Python skills with our expert-led courses: Using Pygame to Build an Asteroids Game in Python Python Debugging With pdb Using Python's assert to Debug and Test Your Code the podcast & our community of Pythonistas

What are the best practices for organizing data analysis projects in Python? What are the advantages of a more package-centric approach to data science? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We discuss Joshua Cook’s recent article “How I Use Python to Organize My Data Analyses.” The article covers how his process for building data analysis projects has evolved and now incorporates modern Python packaging techniques. Christopher shares his recent video course on grouping real-world data with pandas. The course offers a quick refresher before digging into how to use pandas GroupBy to manipulate, transform, and summarize data. We also share several other articles and projects from the Python community, including a news roundup, working with JSON data in Python, running an Asyncio event loop in a separate thread, knowing the why behind a system’s code, a retro game engine for Python, and a project for vendorizing packages from PyPI. This episode is sponsored by Mailtrap. Course Spotlight: pandas GroupBy: Grouping Real World Data in Python In this course, you’ll learn how to work adeptly with the pandas GroupBy while mastering ways to manipulate, transform, and summarize data. You’ll work with real-world datasets and chain GroupBy methods together to get data into an output that suits your needs. Topics: 00:00:00 – Introduction 00:02:18 – Setuptools Breaks Things, Then Fixes Them 00:04:57 – PEP 751: A File Format to List Python Dependencies 00:07:04 – Python 3.13.0 Release Candidate 1 Released 00:07:15 – Python Insider: Python 3.12.5 released 00:07:22 – Django 5.1 released - Django Weblog 00:07:27 – Django security releases issued: 5.0.8 and 4.2.15 00:07:49 – How I Use Python to Organize My Data Analyses 00:13:45 – Sponsor: Mailtrap 00:14:21 – pandas GroupBy: Grouping Real World Data in Python 00:20:33 – Working With JSON Data in Python 00:25:01 – Asyncio Event Loop in Separate Thread 00:30:33 – Video Course Spotlight 00:31:47 – Habits of great software engineers 00:49:17 – pyxel: A Retro Game Engine for Python 00:52:36 – python-vendorize: Vendorize Packages From PyPI 00:54:18 – Thanks and goodbye News: Setuptools Breaks Things, Then Fixes Them – This post is Bite Code’s monthly summary, but the lead story happened just days ago. In line with a 7 year old deprecation, setuptools finally removed the ability to call its test command. Many packages promptly broke. The following day the change was undone. PEP 751: A File Format to List Python Dependencies for Installation Reproducibility (New) – This PEP proposes a new file format for dependency specification to enable reproducible installation in a Python environment. Python 3.13.0 Release Candidate 1 Released Python Insider: Python 3.12.5 released Django 5.1 released - Django Weblog Django security releases issued: 5.0.8 and 4.2.15 - Django Weblog Show Links: How I Use Python to Organize My Data Analyses – This is a description of how Joshua uses Python in a package-centric way to organize his approach to data analyses. This is a system he has evolved while working on his computational biology Ph.D. and working in industry. pandas GroupBy: Grouping Real World Data in Python – In this course, you’ll learn how to work adeptly with the pandas GroupBy while mastering ways to manipulate, transform, and summarize data. You’ll work with real-world datasets and chain GroupBy methods together to get data into an output that suits your needs. Working With JSON Data in Python – In this tutorial, you’ll learn how to read and write JSON-encoded data in Python. You’ll begin with practical examples that show how to use Python’s built-in “json” module and then move on to learn how to serialize and deserialize custom data. Asyncio Event Loop in Separate Thread – Typically, the asyncio event loop runs in the main thread, but as that is the one used by the interpreter, sometimes you want the event loop to run in a separate thread. This article talks about why and how to do just that. Discussion: Habits of great software engineers Projects: pyxel: A Retro Game Engine for Python python-vendorize: Vendorize Packages From PyPI Additional Links: Everyday Project Packaging With pyproject.toml – Real Python Packaging Your Python Code With pyproject.toml - Complete Code Conversation - YouTube Episode #197: Using Python in Bioinformatics and the Laboratory – The Real Python Podcast Level up your Python skills with our expert-led courses: Everyday Project Packaging With pyproject.toml Working With JSON Data in Python pandas GroupBy: Grouping Real World Data in Python the podcast & our community of Pythonistas

What hurdles must be cleared when starting an international organization? How do you empower others in a community by sharing responsibilities? This week on the show, we speak with Jay Miller about Black Python Devs. Jay shares how the idea of forming a community began through attending conferences. They wanted to welcome more black developers into the Python community. We discuss the introduction of Black Python Devs as part of their PyCon 2024 keynote presentation. Jay explains working with a few key people to build the group’s foundations. They talk about the difficulty of letting other people share in the responsibilities and ownership as the hip grew. We also discuss the advantages of partnering with a non-profit organization. This episode is sponsored by InfluxData. Course Spotlight: Interacting With REST APIs and Python In this video course, you’ll learn how to use Python to communicate with REST APIs. You’ll learn about REST architecture and how to use the requests library to get data from a REST API. You’ll also explore different Python tools you can use to build REST APIs. Topics: 00:00:00 – Introduction 00:02:50 – PyCon 2024 Keynote 00:06:02 – New role at Aiven 00:11:32 – Nobody knows what Dev Rel is 00:19:43 – Podcasting about productivity 00:24:12 – Sponsor: InfluxData 00:24:57 – Starting Black Python Devs 00:33:11 – Distinct perspectives and problems 00:37:10 – Partnering with Gnome Foundation 00:40:31 – What were hurdles in starting Black Python Devs? 00:45:31 – Video Course Spotlight 00:47:01 – What do you wish you knew before you started? 00:50:56 – What’s your latest win? 00:53:28 – Helping people prepare for jobs and new roles 00:58:03 – What’s your call to action? 01:00:26 – What are you excited about in the world of Python? 01:03:48 – How do you stay motivated to keep learning Python? 01:06:19 – What do you want to learn next? 01:09:02 – How can people follow your work online? 01:11:02 – Thanks and goodbye Show Links: Black Python Devs - Home PyCon 2024 Keynote Speaker - Jay Miller - YouTube PyCon 2024 Keynote Speaker - Sumana Harihareswara - YouTube Conduit - Relay FM Aiven - Your Trusted Data & AI Platform Abigail Mesrenyame Dogbe Honored with Inaugural Outstanding PyLady Award Episode #86: The Legacy of OLPC and Charismatic Pitfalls in Teaching Programming – The Real Python Podcast Black Python Devs the GNOME Foundation Nonprofit Umbrella – The GNOME Foundation Applying for a Hacker Initiative Grant With Bill Pollock of No Starch Press – The Real Python Podcast Jay Miller - Personal Website kjaymiller - Jay Miller’s GitHub Jay Miller (@[email protected]) - Fosstodon Jay Miller - LinkedIn Render Engine - read the docs Level up your Python skills with our expert-led courses: Sneaky REST APIs With Django Ninja Unleashing the Power of the Console With Rich Interacting With REST APIs and Python the podcast & our community of Pythonistas

How do you integrate GraphQL into your Python web development? How about quickly building graph-based APIs inside Django’s battery-included framework? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares a recent tutorial for building GraphQL APIs in Django using the Python library Strawberry. The tutorial digs into creating a project, defining models, and creating GraphQL queries and mutations using Strawberry. We discuss a blog post from Nat Bennet titled “Why do prototypes suck?” We dig into the common pitfalls of building prototypes and the misconceptions between developers and end s. We also share several other articles and projects from the Python community, including a news roundup, using HTMX with FastAPI, creating an unbelievably stupid airline Wi-Fi package, extracting wisdom from conference videos, writing pixel images to the terminal, and a macOS app for Jupyter Notebooks. This episode is sponsored by Mailtrap. Course Spotlight: Building a URL Shortener With FastAPI and Python In this video course, you’ll build an app to create and manage shortened URLs. Your Python URL shortener can receive a full target URL and return a shortened URL. You’ll also use the automatically created documentation of FastAPI to try out your API endpoints. Topics: 00:00:00 – Introduction 00:02:27 – Python 3.13.0 Beta 4 Released 00:03:15 – Using HTMX With FastAPI 00:09:51 – Free, Unbelievably Stupid Wi-Fi on Long-Haul Flights 00:13:37 – Sponsor: Mailtrap 00:14:13 – “Extracting Wisdom” From Conference Videos 00:22:34 – Developing GraphQL APIs in Django With Strawberry 00:30:01 – Video Course Spotlight 00:31:33 – Why do prototypes suck? 00:42:53 – Satyrn: macOS App for Jupyter Notebooks 00:46:41 – rich-pixels: A Rich-compatible library for writing pixel images 00:48:23 – Thanks and goodbye News: Python 3.13.0 Beta 4 Released Topics: Using HTMX With FastAPI – This tutorial looks at how use HTMX with FastAPI by creating a simple todo web app and deploying it on Render. Free, Unbelievably Stupid Wi-Fi on Long-Haul Flights – Deep in a need to procrastinate on a flight between London and San Francisco, Robert discovered that changing his name on an airline’s frequent flyer was free over the plane’s WiFi. What’s a developer to do? Work on their tickets? No, create an entire T/IP protocol using this loophole. The result is the PySkyWiFi package. “Extracting Wisdom” From Conference Videos – There are so many conferences and so many videos, you can’t possibly watch them all. This post shows you how to extract information to summarize a talk so you can quickly decide what you want to watch. Developing GraphQL APIs in Django With Strawberry – This tutorial details how to integrate GraphQL with Django using Strawberry. Discussion: Why do prototypes suck? Project: Satyrn: macOS App for Jupyter Notebooks darrenburns/rich-pixels: A Rich-compatible library for writing pixel images and ASCII art to the terminal. Additional Links: htmx - high power tools for html Using FastAPI to Build Python Web APIs – Real Python Ollama fabric: An open-source framework for augmenting humans using AI A modern GraphQL library for Python - 🍓 Strawberry GraphQL Satyrn Discord Level up your Python skills with our expert-led courses: Sneaky REST APIs With Django Ninja Building a URL Shortener With FastAPI and Python Python REST APIs With FastAPI the podcast & our community of Pythonistas

Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library. Michael and Richard discuss the design philosophy and history behind creating display tables. We dig into the grammar of tables, the background of the project, and an ingenious way to build a collection of examples for a library. We briefly cover how Richard and Michael started contributing to open source. We also discuss practicing data skills with challenges and resources like Tidy Tuesday. This episode is sponsored by Mailtrap. Course Spotlight: Graph Your Data With Python and ggplot In this course, you’ll learn how to use ggplot in Python to build data visualizations with plotnine. You’ll discover what a grammar of graphics is and how it can help you create plots in a very concise and consistent way. Topics: 00:00:00 – Introduction 00:02:00 – Michael’s background in open source 00:04:07 – Rich’s background in open source 00:05:27 – Advice for someone starting out 00:08:55 – What do you mean by the term “display” table 00:11:32 – What components were missing from other tables? 00:13:31 – Using examples to explain features 00:16:09 – Why was there an absence of this functionality in Python? 00:19:35 – A progressive approach and the grammar of tables 00:21:26 – Sponsor: Mailtrap 00:22:01 – The design philosophy of great tables 00:25:31 – Nanoplots, spark lines, and column spanners 00:27:06 – Building a gallery of examples 00:28:56 – Heat mapping cells and automatically adjusting text color 00:32:54 – Output formats for the tables 00:34:46 – Building in accessibility 00:36:55 – Dependencies 00:37:42 – What is the common workflow? 00:41:39 – Video Course Spotlight 00:43:15 – Adding graphics 00:46:41 – Using a table contest to get examples 00:49:47 – quartodoc and documenting the project 00:55:00 – Tidy Tuesday and data science community 01:00:29 – What are you excited about in the world of Python? 01:03:46 – What do you want to learn next? 01:08:05 – How can people follow the work you do online? 01:09:57 – Thanks and goodbye Show Links: Great Tables - Intro Examples – great_tables great-tables: Make awesome display tables using Python. - GitHub siuba: Python library for using dplyr like syntax with pandas and SQL The Design Philosophy of Great Tables – great_tables Richard Iannone - Using Great Tables to Make Presentable Tables in Python - YouTube Evaluation of the players of #LigaEndesa this week in Europe - Great Tables Example - X quartodoc: Generate API documentation with quarto Tidy Tuesday R Screencasts - YouTube Polars — DataFrames for the new era narwhals-dev/narwhals: Lightweight and extensible compatibility layer between dataframe libraries! A Grammar of Graphics for Python – plotnine 0.13.6 Richard Iannone - GitHub Michael Chow - GitHub) Richard Iannone - LinkedIn Michael Chow - LinkedIn Level up your Python skills with our expert-led courses: Using Jupyter Notebooks pandas GroupBy: Grouping Real World Data in Python Graph Your Data With Python and ggplot the podcast & our community of Pythonistas

What are discrete optimization problems? How do you solve them with constraint programming in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects Christopher discusses an article about constraint programming using Python. He describes the fundamentals and how the problems resemble logic problems you may have experienced in school. The article shows how to solve a weekly work scheduling problem using the open-source -SAT package. We discuss Leodanis Pozo Ramos’s recent tutorial, “Python’s Built-in Functions: A Complete Exploration.” These functions are available for use directly in your code without importing. We also share several other articles and projects from the Python community, including a news roundup, spotting ships with satellites, grappling with Apple’s App Store rejecting Python applications, considering changes to Python’s security model, discussing pivoting from one development path to another, prettifying Jinja and Django templates, and generating static sites with Python. This episode is sponsored by Sentry. Course Spotlight: Parallel Iteration With Python’s zip() Function In this course, you’ll learn how to use the Python zip() function to solve common programming problems. You’ll learn how to traverse multiple iterables in parallel and create dictionaries with just a few lines of code. Topics: 00:00:00 – Introduction 00:02:35 – Polars 1.0 Released 00:03:26 – Psycopg 3.2 Released 00:04:06 – Django security releases issued: 5.0.7 and 4.2.14 00:04:40 – PyBay 2024 Call for Proposals 00:05:16 – Python’s Built-in Functions: A Complete Exploration 00:12:10 – Satellites Spotting Ships 00:16:02 – Sponsor: Sentry 00:17:09 – Python Grapples With Apple App Store Rejections 00:20:27 – Python’s Security Model After the xz-utils Backdoor 00:25:38 – Video Course Spotlight 00:26:56 – Constraint Programming Using -SAT and Python 00:31:40 – Any Web Devs Successfully Pivoted to AI/ML Development? 00:43:12 – aurora: Static Site Generator Implemented in Python 00:45:14 – Running Prettier Against Django or Jinja Templates 00:46:58 – Thanks and goodbye News: Polars 1.0 Released Psycopg 3.2 Released Django security releases issued: 5.0.7 and 4.2.14 PyBay 2024 Call for Proposals Show Links: Python’s Built-in Functions: A Complete Exploration – In this tutorial, you’ll learn the basics of working with Python’s numerous built-in functions. You’ll explore how you can use these predefined functions to perform common tasks and operations, such as mathematical calculations, data type conversions, and string manipulations. Satellites Spotting Ships – Umbra Space has released a data set consisting of satellite based radar images of shipping. This article from Mark shows you how to grab the data, visualize, and annotate it. Python Grapples With Apple App Store Rejections – A string that is part of the urllib parser module in Python references a scheme for apps that use the iTunes feature to install other apps, which is disallowed. Auto scanning by Apple is rejecting any app that uses Python 3.12 underneath. A solution has been proposed for Python 3.13. Python’s Security Model After the xz-utils Backdoor – The backdoor introduced to the xz-utils compression project through social engineering was one of the topics at the Python Language Summit. Participants discussed what can be done to prevent similar social engineering attacks on the Python source. Constraint Programming Using -SAT and Python – Constraint programming is the process of looking for solutions based on a series of restrictions, like employees over 18 who have worked the cash before. This article introduces the concept and shows you how to use open source libraries to write constraint solving code. Discussion: Any Web Devs Successfully Pivoted to AI/ML Development? Projects: aurora: Static Site Generator Implemented in Python Running Prettier Against Django or Jinja Templates – “Prettier” is a JavaScript based linting tool for templates. For folks not familiar with the world of npm, it can be a bit daunting to get it going. Simon fiddled with it so you don’t have to and posted how he got it working on his system. Additional Links: Episode #209: Python’s Command-Line Utilities & Music Information Retrieval Tools – The Real Python Podcast Python Module Index — Python 3.12.4 documentation Built-in Functions — Python 3.12.4 documentation Briefcase— BeeWare Ask HN: What’s Prolog like in 2024? - Hacker News Episode #199: Leveraging Documents and Data to Create a Custom LLM Chatbot – The Real Python Podcast PEP 730 – Adding iOS as a ed platform | peps.python.org Level up your Python skills with our expert-led courses: Parallel Iteration With Python's zip() Function Python Inner Functions Jinja Templating the podcast & our community of Pythonistas