The Real Python Podcast

How do you customize a LLM chatbot to address a collection of documents and data? What tools and techniques can you use to build embeddings into a vector database? This week on the show, Calvin Hendryx-Parker is back to discuss developing an AI-powered, Large Language Model-driven chat interface.

Calvin is the co-founder and CTO of Six Feet Up, a Python and AI consultancy. He shares a recent project for a family-owned seed company that wanted to build a tool for customers to access years of farm research. These documents were stored as brochure-style PDFs and spanned 50 years.

We discuss several of the tools used to augment a LLM. Calvin covers working with LangChain and vectorizing data with ChromaDB. We talk about the obstacles and limitations of capturing documentation.

Calvin also shares a smaller project that you can try out yourself. It takes the information from a conference website and creates a chatbot using Django and Python prompt-toolkit.

This episode is sponsored by Mailtrap.

Topics:

• 00:00:00 – Introduction
• 00:02:21 – Background on the project
• 00:03:51 – Complexity of adding documents
• 00:09:01 – Retrieval-augmented generation and providing links
• 00:13:46 – Updating information and larger conversation context
• 00:18:08 – Sponsor: Mailtrap
• 00:18:43 – Working with context
• 00:21:02 – Temperature adjustment
• 00:22:07 – Rally Conference Chatbot Project
• 00:26:20 – Vectorization using ChromaDB
• 00:32:49 – Employing Python prompt-toolkit
• 00:35:07 – Learning libraries on the fly
• 00:37:38 – Video Course Spotlight
• 00:39:00 – Problems with tables in documents
• 00:42:30 – Everything looks like a chat box
• 00:44:26 – Finding the right fit for a client and customer
• 00:49:05 – What are questions you ask a new client now?
• 00:51:54 – Canada Air anecdote
• 00:56:20 – How do you stay up to date on these topics?
• 01:01:03 – What are you excited about in the world of Python?
• 01:03:22 – What do you want to learn next?
• 01:04:58 – How can people follow your work online?
• 01:05:31 – IndyPy
• 01:07:13 – Thanks and goodbye

Show Links:

• Transforming Agricultural Data with AI — Six Feet Up
• Build ChatGPT-like Apps with AI — Six Feet Up
• Innovate with AI: Build ChatGPT-like Apps - YouTube
• What is retrieval-augmented generation? - IBM Research Blog
• rally-llm-presentation - sixfeetup - GitHub
• Python Prompt Toolkit 3.0 — Documentation
• Chroma - the AI-native open-source embedding database
• Embeddings and Vector Databases With ChromaDB – Real Python
• LangChain
• Build an LLM RAG Chatbot With LangChain – Real Python
• Air Canada must pay after chatbot lies to grieving passenger - The Register
• I’d Buy That for a Dollar: Chevy Dealership’s AI Chatbot Goes Rogue
• Omnivore
• TLDR AI - Get smarter about AI in 5 minutes
• Tech Brew
• Simon Willison’s Weblog
• llm: Access large language models from the command-line - simonw - GitHub
• PyCon US 2024
• Syntorial: The Ultimate Synthesizer Tutorial
• Blog — Six Feet Up
• Calvin Hendryx-Parker - LinkedIn
• Eclipse Insights: How AI is Transforming Solar Astronomy - YouTube

Level up your Python skills with our expert-led courses:

• Sneaky REST APIs With Django Ninja
• How to Work With a PDF in Python
• Command Line Interfaces in Python

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Can you build a Space Invaders clone using Python’s built-in turtle module? What advantages does the Seaborn data visualization library provide compared to Matplotlib? Christopher Trudeau is back on the show this week, along with special guest Real Python core team member Bartosz Zaczyński. We’re sharing another batch of PyCoder’s Weekly articles and projects.

Bartosz shares a Real Python step-by-step project for building a video game using the Python turtle module. The turtle module provides an interactive environment that lets users create pictures and shapes on a virtual canvas. The project takes you through game design concepts such as animating sprites, detecting a collision, and building a game loop.

We discuss another Real Python resource, “Visualizing Data in Python With Seaborn.” Seaborn is a significant next step if you’ve already been working with Matplotlib. It produces impressive visualizations and offers a functional or object-based approach to designing plots.

We also share several other articles and projects from the Python community, including a news roundup, finding Python Easter eggs, exploring whether Python has pointers, styling Excel cells with OpenPyXL, using weird tests to capture tacit knowledge, inspecting and running Django commands in a TUI, building reactive web UIs in Python, and a project for predictable Python datetimes.

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

Topics:

• 00:00:00 – Introduction
• 00:03:06 – Python 3.13.0 alpha 5 is now available
• 00:03:38 – Allow disabling the GIL with flags
• 00:04:17 – Django security releases issued: 5.0.3, 4.2.11, and 3.2.25
• 00:04:32 – The Python Coding Book
• 00:05:03 – Finding Python Easter Eggs – Code Conversation
• 00:12:48 – Sponsor: Posit
• 00:13:34 – Visualizing Data in Python With Seaborn
• 00:18:18 – Does Python have pointers?
• 00:21:43 – Build a Python Turtle Game: Space Invaders Clone
• 00:30:42 – Video Course Spotlight
• 00:32:04 – Styling Excel Cells with OpenPyXL and Python
• 00:35:22 – Use weird tests to capture tacit knowledge
• 00:37:09 – whenever: Strict, predictable, and typed datetimes
• 00:42:25 – hyperdiv: Build Reactive Web UIs in Python
• 00:46:19 – django-tui: Inspect and run Django Commands in a TUI
• 00:48:40 – Thanks and goodbye

News:

• Python Insider: Python 3.13.0 alpha 5 is now available
• Allow disabling the GIL with Flags - cpython - GitHub
• Django security releases issued: 5.0.3, 4.2.11, and 3.2.25 - Weblog - Django
• The Python Coding Book – The Python Coding Place

Show Links:

• Finding Python Easter Eggs – Code Conversation - Video Course – Python has its fair share of hidden surprises, commonly known as Easter eggs. From clever jokes to secret messages, these little mysteries are often meant to be discovered by curious developers like you!
• Visualizing Data in Python With Seaborn – In this tutorial, you’ll learn how to use the Python seaborn library to produce statistical data analysis plots to allow you to better visualize your data. You’ll learn how to use both its traditional classic interface and more modern objects interface.
• Does Python have pointers? - Ned Batchelder – Depending on how you’re using the term “pointer” changes the answer to the question. Read on to better understand the programming terminology and whether Python has pointers.
• Build a Python Turtle Game: Space Invaders Clone – In this step-by-step tutorial, you’ll use Python’s turtle module to write a Space Invaders clone. You’ll learn about techniques used in animations and games, and consolidate your knowledge of key Python topics.
• Styling Excel Cells with OpenPyXL and Python - Many Python libraries that deal with Excel only handle data, but OpenPyXL gives you the ability to style your cells in many different ways. Learn how to give your spreadsheets pizazz!
• Use weird tests to capture tacit knowledge - Applied Cartography – Sometimes adding code in one place means configuration elsewhere also needs to be updated. One way of ensuring this is happening properly in a large project is to use unit tests. This post covers a few examples, complete with pytest code.

Projects:

• whenever: ⏰ Strict, predictable, and typed datetimes
• hyperdiv: Build Reactive Web UIs in Python
• django-tui: Inspect and run Django Commands in a text-based user interface (TUI)

Additional Links:

• What’s the Zen of Python? – Real Python
• Using Python for Data Analysis – Real Python
• The Beginner’s Guide to Python Turtle – Real Python
• Roamer - The History of Turtle Robots
• Tutorial — openpyxl 3.1.2 documentation
• Ten Python datetime pitfalls, and what libraries are (not) doing about it - Arie Bovenberg
• The science behind why people hate Daylight Saving Time so much - Ars Technica
• Textual

Level up your Python skills with our expert-led courses:

• Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn
• Python Turtle for Beginners
• Pointers and Objects in Python

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How is Python being used to automate processes in the laboratory? How can it speed up scientific work with DNA sequencing? This week on the show, Chemical Engineering PhD Student Parsa Ghadermazi is here to discuss Python in bioinformatics.

Parsa provides background on his research and the bioinformatic techniques used to discover gut microbes’ role in human health and diseases. We talk about automating lab experiments with liquid handling robots and Python.

We dig into libraries to shatter and reassemble DNA sequences. Parsa also shares current projects from the Chan Lab at Colorado State University and his GitHub repository.

Topics:

• 00:00:00 – Introduction
• 00:01:51 – Engineering Background and Current PhD Program
• 00:05:52 – What is Bioinformatics?
• 00:08:11 – Where do you use Python in the lab?
• 00:10:35 – Using lab robotics
• 00:15:22 – Python development environment
• 00:16:33 – Lab robotics allow for precision
• 00:19:03 – How are using these tools for research?
• 00:22:14 – What are the techniques for measurements?
• 00:26:17 – Video Course Spotlight
• 00:27:33 – How is the data output from the machine?
• 00:29:20 – Moving into DNA sequencing and extraction
• 00:32:08 – Shattering to work with smaller DNA sequences
• 00:34:34 – Python libraries for DNA re-assembly
• 00:36:28 – Building ADToolbox
• 00:40:24 – How do you store the data?
• 00:41:32 – Inferring microbial interactions
• 00:44:02 – Types of hardware used for these projects
• 00:47:07 – What are you excited about in the world of Python?
• 00:48:09 – What do you want to learn next?
• 00:49:16 – How can people follow your work online?
• 00:50:00 – Thanks and goodbye

Show Links:

• ParsaGhadermazi - GitHub
• Bioinformatics - Wikipedia
• Opentrons - Lab Automation - Lab Robots for Life Scientists
• Tutorial — Opentrons Python API V2 Documentation
• Serial Dilutions and Plating: Microbial Enumeration - Microbiology - JoVE
• What is Chromatogram & How to Read a Chromatogram?
• Episode #186: Exploring Python in Excel
• megahit: Ultra-fast and memory-efficient (meta-)genome assembler
• DRAM: Distilled and Refined Annotation of Metabolism - GitHub
• humann: HUMAnN 3.0 - HMP Unified Metabolic Analysis Network
• MetaPhlAn4 – The Huttenhower Lab
• ADToolbox - Tools for modeling and optimizing the anaerobic digestion process
• SPAM-DFBA - Algoritm for inferring microbial interactions
• Microbial interactions from a new perspective - Bioinformatics - Oxford Academic
• Alpine — Research Computing University of Colorado Boulder documentation
• MkDocs
• Build Your Python Project Documentation With MkDocs
• scikit-bio
• Episode #190: Great Starting Points for Contributing to Open Source
• Chan Lab at Colorado State University - GitHub
• ParsaGhadermazi - GitHub
• Parsa Ghadermazi - LinkedIn

Level up your Python skills with our expert-led courses:

• Building Python Project Documentation With MkDocs
• Data Cleaning With pandas and NumPy
• Reading and Writing CSV Files

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What are the advantages of determining the type of an object by how it behaves? What coding circumstances are not a good fit for duck typing? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher covers a recent Real Python tutorial by Leodanis Pozo Ramos titled Duck Typing in Python: Writing Flexible and Decoupled Code. The tutorial explains the concepts of duck typing within object-oriented programming and its use within Python’s built-in tools.

We discuss a recent article on monkey patching in Python. This practice of dynamically modifying a class or module’s behavior at runtime allows for testing, debugging, and experimentation.

We also share several other articles and projects from the Python community, including a news roundup, why names are not the same as objects in Python, using IPython Jupyter magic commands, a discussion about becoming a senior developer, a data exploration challenge, a Python evaluation game, and a terminal UI for regex testing.

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

Topics:

• 00:00:00 – Introduction
• 00:02:38 – Listener feedback
• 00:04:02 – DjangoCon US Call for Proposals
• 00:04:38 – White House Recommends Use of Python
• 00:05:41 – JupyterLab 4.1 and Notebook 7.1 Released
• 00:06:05 – What’s in a Name?
• 00:11:52 – Duck Typing in Python: Writing Flexible and Decoupled Code
• 00:15:07 – Sponsor: Sentry
• 00:16:11 – Using IPython Jupyter Magic Commands
• 00:22:31 – A Guide to Monkey Patching
• 00:25:27 – Falsehoods Junior Developers Believe About Becoming Senior
• 00:33:01 – Video Course Spotlight
• 00:34:11 – Falsehoods continued
• 00:43:43 – Where in the data?
• 00:46:55 – the eval game
• 00:48:03 – rexi: Terminal UI for Regex Testing
• 00:49:43 – Thanks and goodbye

News:

• DjangoCon US Call for Proposals
• White House Recommends Use of Python
• JupyterLab 4.1 and Notebook 7.1 Released

Show Links:

• What’s in a Name? – An article about names in Python, and why they’re not the same as objects. The article discusses reference counts and namespaces.
• Duck Typing in Python: Writing Flexible and Decoupled Code – In this tutorial, you’ll learn about duck typing in Python. It’s a typing system based on objects’ behaviors rather than on inheritance. By taking advantage of duck typing, you can create flexible and decoupled sets of Python classes that you can use together or individually.
• Using IPython Jupyter Magic Commands – “IPython Jupyter Magic commands (e.g. lines in notebook cells starting with % or %%) can decorate a notebook cell, or line, to modify its behavior.” This article shows you how to define them and where they can be useful.
• Monkeying Around With Python: A Guide to Monkey Patching – Monkey patching is the practice of modifying live code. This article shows you how it’s done and why and when to use the practice.

Discussion:

• Falsehoods Junior Developers Believe About Becoming Senior – This opinion piece by Vadim discusses how newer developers perceive what it means to be a senior developer, and how they’re often wrong.

Projects:

• Where in the data?
• the eval game
• rexi: Terminal UI for Regex Testing

Additional Links:

• Pointers in Python: What’s the Point? – Real Python
• Unlock IPython’s Magical Toolbox for Your Coding Journey – Real Python
• Episode #88: Discussing Type Hints, Protocols, and Ducks in Python – The Real Python Podcast
• saul.pw
• BlueBird Shell

Level up your Python skills with our expert-led courses:

• Python Type Checking
• Testing Your Code With pytest
• Pointers and Objects in Python

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How do you get yourself unstuck when facing a programming problem? How do you develop a positive developer mindset while learning Python? This week on the show, Bob Belderbos from Pybites is here to talk about learning Python and building healthy developer habits.

Bob created the Pybites learning platform with his friend Julian Sequeira. They initially focused on exercises and coding challenges to motivate new Python students. As they grew their community, they created a podcast and moved into coaching.

They noticed that most new developers share common struggles of tutorial paralysis, imposter syndrome, and motivation. Bob discusses techniques for developing a positive mindset, overcoming coding blocks, and delivering projects.

Topics:

• 0:00:00 – Introduction
• 0:02:06 – How did you start Pybites?
• 0:04:32 – Building a community through challenges
• 0:06:17 – When did you start your podcast?
• 0:08:22 – Defining developer mindset
• 0:11:31 – Learning Python outside of a classroom
• 0:16:15 – Podcast is a good place to discuss mindset
• 0:19:37 – Video Course Spotlight
• 0:20:56 – Sharing Python tips
• 0:30:14 – Sharing content as a creator
• 0:35:03 – Writing cleaner code
• 0:40:20 – Moving from challenges to projects
• 0:47:08 – Helping yourself when you’re stuck
• 0:51:31 – Dealing with imposter syndrome
• 0:55:50 – What are you excited about in the world of Python?
• 0:57:43 – What do you want to learn next?
• 0:59:15 – How can people follow your work online?
• 0:59:51 – Thanks and goodbye

Show Links:

• Pybites - We Create Python Developers
• Pybites Podcast
• PyBites Platform - Real World Python Exercises
• Pybites Community
• Pybites Python Tips Book - Pybites
• itertools — Functions creating iterators for efficient looping - Python documentation
• string — Common string operations - Python documentation
• Python enumerate(): Simplify Loops That Need Counters – Real Python
• ast — Abstract Syntax Trees - Python documentation
• Building Maintainable Software, Java Edition
• Refactoring - Improving the Design of Existing Code by Martin Fowler
• A Mind For Numbers - Barbara Oakley
• Real Imposters Don’t Experience Imposter Syndrome
• Rust Programming Language
• Bob Belderbos (@bbelderbos) - X
• Bob Belderbos - LinkedIn

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• Grow Your Python Portfolio With 13 Intermediate Project Ideas
• Python Basics: Code Your First Python Program
• Building Python Project Documentation With MkDocs

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What are the typical computer tasks you do manually every week? Could you automate those tasks with a Python script? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent Hacker News thread about frequently used automation scripts. We share the kinds of tasks we’ve automated with Python in our work and personal lives.

Christopher shares a tutorial about building a micro-search engine from scratch using Python. The post takes you through coding the components of a crawler, index, and ranker. The finished engine is designed to search the posts of the blogs you follow.

We also share several other articles and projects from the Python community, including a news roundup, how a Polars query works under the hood, using Python for data analysis, understanding open-source licensing, summarizing the significant changes between Python versions, a robust TUI hex editor, and a lightweight dataframe library with a universal interface for data wrangling.

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

Topics:

• 00:00:00 – Introduction
• 00:02:23 – uv: Python Packaging in Rust
• 00:02:43 – Rye Grows With uv
• 00:03:20 – Python 3.13.0 Alpha 4 Is Now Available
• 00:03:45 – A Bird’s Eye View of Polars
• 00:07:28 – Polars: Why We Have Rewritten the String Data Type
• 00:09:33 – A Search Engine in 80 Lines of Python
• 00:13:14 – Using Python for Data Analysis
• 00:18:22 – Sponsor: Intel
• 00:18:53 – Understanding Open Source Licensing
• 00:21:54 – Summary of Major Changes Between Python Versions
• 00:23:19 – What Python automation scripts do you reuse frequently at work?
• 00:34:21 – Video Course Spotlight
• 00:35:52 – hexabyte: A modern, modular, and robust TUI hex editor
• 00:39:56 – ibis: The Flexibility of Python With the Scale of Modern SQL
• 00:43:31 – Thanks and goodbye

News:

• uv: Python Packaging in Rust – uv is an extremely fast Python package installer and resolver, designed as a drop-in alternative to pip and pip-tools. This post introduces you to uv and shows some of its performance numbers.
• Rye Grows With uv - Armin Ronacher’s Thoughts and Writings
• Python 3.13.0 Alpha 4 Is Now Available

Show Links:

• A Bird’s Eye View of Polars – This post on the Polars blog introduces you to how Polars works, showing the steps from queries, plans, optimizations, and then the final execution.
• Polars: Why We Have Rewritten the String Data Type – A large refactor on the string data type is underway in Polars. This deep dive explains why and what is changing.
• A Search Engine in 80 Lines of Python – In this post Alex explains how he built a micro-search engine from scratch using Python. The resulting search engine is used to search in the posts of the blogs he follows.
• Using Python for Data Analysis – In this tutorial, you’ll learn the importance of having a structured data analysis workflow, and you’ll get the opportunity to practice using Python for data analysis while following a common workflow process.
• Understanding Open Source Licensing – This article discusses the importance of open-source licensing in software development and its implications for stakeholders.
• Summary of Major Changes Between Python Versions – This article is a quick reference covering the major changes introduced with each new version of Python. Can’t remember when the walrus operator was introduced? This is the place to look that up.

Discussion:

• What Python automation scripts do you reuse frequently at work? - Hacker News

Projects

• hexabyte: A modern, modular, and robust TUI hex editor
• ibis: The Flexibility of Python With the Scale of Modern SQL

Additional Links:

• Episode #193: Wes McKinney on Improving the Data Stack & Composable Systems – The Real Python Podcast
• Open Source Licensing: Software Freedom and Intellectual Property Law
• What’s in which Python - Ned Batchelder
• Automate the Boring Stuff with Python - Al Sweigart
• Working With Files in Python – Real Python
• Build Command-Line Interfaces With Python’s argparse – Real Python

Level up your Python skills with our expert-led courses:

• Defining Python Functions With Optional Arguments
• Practical Recipes for Working With Files in Python
• Building Command Line Interfaces With argparse

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How do you avoid the bottlenecks of data processing systems? Is it possible to build tools that decouple storage and computation? This week on the show, creator of the pandas library Wes McKinney is here to discuss Apache Arrow, composable data systems, and community collaboration.

Wes briefly describes the humble beginnings of the pandas project in 2008 and moving the project to open source in 2011. Since then, he’s been thinking about improvements across the data processing ecosystem.

Wes collaborated with members of the broader data science community to build the in-memory analytics infrastructure of Apache Arrow. Arrow avoids the bottlenecks of repeated data serialization and format conversion. He shares examples of Arrow’s use across the spectrum in tools like Polars and DuckDB.

Wes advocates moving from vertically integrated tools toward composable data systems. We discuss his work on Ibis, a portable dataframe API for data manipulation and exploration in Python. Ibis supports multiple backends by decoupling the API from the execution engine.

This week’s episode is brought to you by Posit Connect.

Topics:

• 00:00:00 – Introduction
• 00:02:26 – Dealing with limitations in early data science
• 00:04:53 – Making pandas open source
• 00:07:10 – Making changes to an existing platform
• 00:12:34 – Decoupling storage and computation
• 00:23:04 – Sponsor: Posit Connect
• 00:23:54 – Apache Arrow solving multiple issues
• 00:27:40 – DuckDB efficient analytic SQL database
• 00:30:24 – Polars dataframe library
• 00:31:04 – pandas 2.0 adding Arrow
• 00:35:56 – Video Course Spotlight
• 00:37:20 – Apache Software Foundation background
• 00:41:29 – Shifting from developer to organizer and collaborator
• 00:45:56 – Creating a portable query layer with Ibis
• 00:55:34 – Casualties of the language wars
• 00:57:57 – What’s your role at Posit?
• 01:01:23 – What are you excited about in the world of Python?
• 01:04:52 – What do you want to learn next?
• 01:06:21 – How can people follow your work online?
• 01:08:20 – Thanks and goodbye

Show Links:

• Wes McKinney - Personal Website
• Wes McKinney - The Road to Composable Data Systems: Thoughts on the Last 15 Years and the Future
• Wes McKinney - Leveling Up the Data Stack: Thoughts on the Last 15 Years - YouTube
• Apache Hadoop
• Cloudera - The hybrid data company
• Wes McKinney - Apache Arrow and the “10 Things I Hate About pandas”
• Voltron Data - The Leading Designer and Builder of Enterprise Data Systems
• Apache Arrow
• DuckDB - An in-process SQL OLAP database management system
• DuckDB-Wasm - Efficient Analytical SQL in the Browser
• Polars - Dataframes for the new era
• pandas 2.2.0 documentation
• Episode #167: Exploring pandas 2.0 & Targets for Apache Arrow – The Real Python Podcast
• ASF - Welcome to The Apache Software Foundation!
• Ursa Labs Blog
• Ibis - The Portable Python dataframe Library
• Python dataframe interchange protocol
• Hadley Wickham
• Rust Programming Language
• italki - Best language learning app with certificated tutors
• Wes McKinney - LinkedIn
• Wes McKinney (@wesmckinn) - X
• Posit - The Open-Source Data Science Company

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Unleashing the Power of the Console With Rich
• The pandas DataFrame: Working With Data Efficiently

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What are real-life examples of using Python decorators? How can you harness their power in your code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article series that digs into Python decorators. The first two articles discuss the basics of constructing decorators. The third part describes how popular Python libraries use decorators with call interception, function registration, and enriching the behavior of a function.

Christopher shares a piece about the common pitfalls of working with the Pythondatetime library. The article considers how current third-party libraries don’t address most of these quirks and offers a potential solution with a new library.

We also share several other articles and projects from the Python community, including a couple of news items, a discussion about the popularity of the Rust language, handling unset values in FastAPI with Pydantic, working with Python’s mini-language for formatting strings, mocking Django queryset functions, and a modern replacement for the Requests library.

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

Topics:

• 00:00:00 – Introduction
• 00:02:53 – Django security releases issued: 5.0.2, 4.2.10, and 3.2.24
• 00:03:10 – Python 3.12.2 and 3.11.8 are now available
• 00:03:21 – Introducing PSF Grants Program Office Hours
• 00:04:19 – Python’s Format Mini-Language for Tidy Strings
• 00:12:22 – Ten Python datetime Pitfalls
• 00:18:34 – Sponsor: Sentry
• 00:19:37 – Real Life Use of Decorators
• 00:29:18 – Handling Unset Values in FastAPI With Pydantic
• 00:35:43 – Video Course Spotlight
• 00:37:06 – The Python Rust-Aissance
• 00:50:19 – django-mock-queries: Mock Django Queryset Functions
• 00:53:09 – niquests: Requests but Multiplexed
• 00:55:55 – Thanks and goodbye

News:

• Django security releases issued: 5.0.2, 4.2.10, and 3.2.24
• Python 3.12.2 and 3.11.8 are now available
• Introducing PSF Grants Program Office Hours

Show Links:

• Python’s Format Mini-Language for Tidy Strings – In this tutorial, you’ll learn about Python’s format mini-language. See how to use it for creating working format specifiers and build nicely formatted strings and messages in your code.
• Ten Python datetime Pitfalls – It’s no secret that the Python datetime library has its quirks. Not only are there probably more than you think, but third-party libraries don’t address most of them! Arie created a new library to explore what a better datetime library could look like.
• Real Life Use of Decorators – Part 3 in a series on how Python decorators are used. This part covers real-life use cases including call interception, function registration, and behavioral enrichment.
• Handling Unset Values in FastAPI With Pydantic – When using the HTTP PATCH method only those fields that got changed are updated. Pydantic sets fields not given as arguments as None so there is no way to distinguish between an explicit None value and an unset field. This post explains how you process this scenario.

Discussion:

• The Python Rust-Aissance – Companies like Polars are showing how with Rust, Python developers now have a better, smoother path towards building high-performance libraries.
• Rye: A Python Developer Experience Vision Continued
• PyO3: Rust bindings for the Python interpreter
• Rust in Linux: Where we are and where we’re going next
• RustPython: OSS CPython Written in Rust
• granian: Rust HTTP Server for Python Applications

Projects:

• django-mock-queries: Mock Django Queryset Functions for Testing
• niquests: Requests but Multiplexed

Additional Links:

• Backus–Naur form - Wikipedia
• ANTLR - (ANother Tool for Language Recognition) A Powerful Parser Generator
• Xmas Decoration, Part 1 - Bite code!
• Xmas Decoration, Part 2 - Bite code!
• Falsehoods programmers believe about time - @noahsussman - Infinite Undo
• 10 Reasons You Should Quit Your HTTP Client - Ahmed TAHRI - Dev Genius
• HTTPX - Fully Featured HTTP Client for Python 3

Level up your Python skills with our expert-led courses:

• Using Python's datetime Module
• Python Decorators 101
• Python 3's F-Strings: An Improved String Formatting Syntax

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How do you manage the dependencies of a large-scale data science project? How do you migrate that project from a laptop to cloud infrastructure or utilize GPUs and multiple instances in parallel? This week on the show, Savin Goyal returns to discuss the updates to the open-source framework Metaflow.

Savin briefly describes the Metaflow platform and the goal of simplifying engineering overhead for data scientists and programmers. We discuss how the platform captures snapshots of a project as you work, allowing you to go back in time or share the state of your project with another team member.

We dig into the complicated process of managing dependencies for machine learning and data science projects. Savin describes how the required external libraries can be specified within a flow with the new @pypi or @conda decorators. This allows a project to scale from a local machine to the cloud or multiple instances with all dependencies included.

He talks about starting a new company, Outerbounds, with fellow co-workers from Netflix. Their vision is to continue to build the Metaflow open-source platform and offer customers scalable enterprise-grade infrastructure.

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

Topics:

• 00:00:00 – Introduction
• 00:02:25 – Update on Metaflow
• 00:04:13 – What is Outerbounds?
• 00:07:26 – An ML platform to serve data scientists needs
• 00:13:02 – Dependency reproducibility via @conda and @pypi decorators
• 00:26:18 – Sponsor: Intel
• 00:27:10 – Storing lock files along with snapshots
• 00:29:17 – Working alongside code and dependency management systems
• 00:34:03 – Scaling a project from laptop to the cloud
• 00:40:13 – Video Course Spotlight
• 00:41:41 – Getting visibility on processes
• 00:47:23 – Adjusting your project due to GPU availability
• 00:52:27 – Example of jumping back into a project one year later
• 00:55:54 – What are you excited about in the world of Python?
• 00:57:39 – What do you want to learn next?
• 00:59:35 – How can people follow your work online?
• 01:00:19 – Thanks and goodbye

Show Links:

• Metaflow - a framework for real-life ML, AI, and data science
• Infrastructure for ML, AI, and Data Science - Outerbounds
• Human-Friendly, Production-Ready Data Science with Metaflow- Savin Goyal | SciPy 2022 - YouTube
• Episode #61: Scaling Data Science and Machine Learning Infrastructure Like Netflix – The Real Python Podcast
• New in Metaflow: The Long-Awaited @pypi Decorator - Outerbounds
• Managing Dependencies - Metaflow Docs
• Secure ML with Secure Software Dependencies - Outerbounds
• Directed acyclic graph (DAG) - Wikipedia article
• Visualizing Results - Metaflow Docs
• Seamless Data and ML Pipelines with Airflow and Metaflow - Outerbounds
• Episode #142: Orchestrating Large and Small Projects With Apache Airflow – The Real Python Podcast
• Savin (@SavinGoyal) - X
• Savin Goyal - LinkedIn
• Building the ML-driven future - Outerbounds Blog

Level up your Python skills with our expert-led courses:

• Everyday Project Packaging With pyproject.toml
• Combining Data in pandas With concat() and merge()
• Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn

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What’s it like to sit down for your first developer sprint at a conference? How do you find an appropriate issue to work on as a new open-source contributor? This week on the show, author and software engineer Stefanie Molin is here to discuss starting to contribute to open-source projects.

Stefanie is a data scientist and software engineer on Bloomberg’s Security Data Science team. She recently wrote an article titled “5 Ways to Get Started in Open Source.” We discuss finding ways to contribute that fit your interests and developer skills. We dig into the experience of participating in community sprints at a conference.

Stefanie is the author of Hands-On Data Analysis with Pandas. We also discuss the different processes between writing technical articles and authoring a book.

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

Topics:

• 00:00:00 – Introduction
• 00:02:55 – Being asked about how to get started
• 00:07:13 – Differences in short vs long form writing
• 00:09:03 – What was your introduction to contributing?
• 00:17:52 – What are additional benefits of contributing?
• 00:22:32 – Sponsor: Intel
• 00:23:22 – Sprints as an entry point
• 00:34:36 – Other requirements of a sprint
• 00:36:05 – Differences in conferences
• 00:41:52 – Other sprint experiences
• 00:42:50 – Contributing examples to documentation
• 00:45:59 – Video Course Spotlight
• 00:47:11 – Looking for good first issues
• 00:52:04 – Is this a bug?
• 00:54:10 – Proposing a new feature
• 00:56:36 – Data Morph and working on personal projects
• 01:07:29 – Showing up in the Python community
• 01:12:14 – What are you excited about in the world of Python?
• 01:14:59 – How can people follow the work you do online?
• 01:15:28 – What do you want to learn next?
• 01:18:23 – Thanks and goodbye

Show Links:

• 5 Ways to Get Started in Open Source - by Stefanie Molin - Level Up Coding
• seaborn: Statistical Data Visualization - Documentation
• Docstring Validation using Pre-Commit Hook - numpydoc
• Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition - Amazon.com
• Episode #173: Getting Involved in Open Source & Generating QR Codes With Python – The Real Python Podcast
• Episode #8: Docker + Python for Data Science and Machine Learning With Tania Allard – The Real Python Podcast
• Mentored Sprints for Diverse Beginners - PyCon US 2023
• Episode #177: Welcoming PyPI’s Safety & Security Engineer Mike Fiedler – The Real Python Podcast
• Data Morph: Moving Beyond the Datasaurus Dozen - Level Up Coding
• Data Morph: A Cautionary Tale of Summary Statistics – Slides
• Anscombe’s quartet - Wikipedia
• How to Set Up Pre-Commit Hooks - Stefanie Molin
• Dog Speaks In Italian Accent To Sound Like His Owner - Bored Panda
• Stefanie Molin (@StefanieMolin) / X
• Stefanie Molin - Personal Website

Level up your Python skills with our expert-led courses:

• Building Python Project Documentation With MkDocs
• Data Cleaning With pandas and NumPy
• Documenting Python Projects With Sphinx and Read the Docs

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How does a debugger work? What can you learn about Python by building one from scratch? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a two-part tutorial on building a debugger in Python? Creating a simple one requires less code than you might think.

We also talk about an article from Itamar Turner-Trauring about how to prepare for the upcoming changes to NumPy. The new version is not backward compatible and will require some inspection of your project dependencies. Itamar includes advice, techniques, and tools for updating your code.

We also share several other articles and projects from the Python community, including a couple of news items, a discussion about managing advice as a new developer, moving to Python as a former R developer, building a Markov chain to generate readable nonsense, optimizing fonts to individual glyphs on your website, and a project for working with units of measurement in Python.

This week’s episode is brought to you by Posit Connect.

Topics:

• 00:00:00 – Introduction
• 00:02:18 – DjangoCon Europe 2024 CFP Now Open
• 00:02:38 – Python Insider: Python 3.13.0 alpha 3 is now available
• 00:03:04 – NumPy 2 Is Coming: Preventing Breakage, Updating Your Code
• 00:07:37 – Using a Markov Chain to Generate Readable Nonsense
• 00:12:53 – Sponsor: Posit Connect
• 00:13:43 – Python Rgonomics
• 00:20:10 – Let’s Create a Python Debugger Together
• 00:23:49 – Video Course Spotlight
• 00:25:08 – Advice for New Devs Who’ve Read Other Advice Essays
• 00:42:44 – Fontimize: Optimize Fonts to the Glyphs on Your Site
• 00:44:57 – Pint: Units for Python
• 00:46:55 – Thanks and Goodbye

News:

• DjangoCon Europe 2024 CFP Now Open
• Python Insider: Python 3.13.0 alpha 3 is now available

Show Links:

• NumPy 2 Is Coming: Preventing Breakage, Updating Your Code – NumPy 2 is coming, and it’s backwards incompatible. Learn how to keep your code from breaking, and how to upgrade.
• Using a Markov Chain to Generate Readable Nonsense – Describes a simple Markov chain algorithm to generate reasonable-sounding but utterly nonsensical text, and presents some example outputs as well as a Python implementation in only 20 lines of code.
• Python Rgonomics – If you’re coming to Python from R, this article outlines some libraries that have an R-like feel, helping you make the transition to Pythonic workflows.
• Let’s Create a Python Debugger Together – Ever wondered how a debugger works? Implementing a simple one requires less code than you might think. Read on to find out how.

Discussion:

• Advice for New Devs Who’ve Read Other Advice Essays – After reading some programming advice posts, this author decided a lot of them concentrated on the wrong things. Here is his own take.
• Associated Hacker News Discussion

Projects:

• Fontimize: Optimize Fonts to the Glyphs on Your Site
• Pint: Units for Python

Additional Links:

• Let’s create a Python Debugger together: Part 2 - Mostly nerdless
• “Making Hard Things Easy” by Julia Evans (Strange Loop 2023) - YouTube
• Episode #71: Start Using a Debugger With Your Python Code – The Real Python Podcast
• Debugging Rules! – Find out what’s wrong with anything, fast.

Level up your Python skills with our expert-led courses:

• Python Debugging With pdb
• Using Python's assert to Debug and Test Your Code
• Create a Tic-Tac-Toe Python Game Engine With an AI Player

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How can you measure the quality of a large language model? What tools can measure bias, toxicity, and truthfulness levels in a model using Python? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to discuss techniques and tools for evaluating LLMs With Python.

Jodie provides some background on large language models and how they can absorb vast amounts of information about the relationship between words using a type of neural network called a transformer. We discuss training datasets and the potential quality issues with crawling uncurated sources.

We dig into ways to measure levels of bias, toxicity, and hallucinations using Python. Jodie shares three benchmarking datasets and links to resources to get you started. We also discuss ways to augment models using agents or plugins, which can access search engine results or other authoritative sources.

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

Topics:

• 00:00:00 – Introduction
• 00:02:19 – Testing characteristics of LLMs with Python
• 00:04:18 – Background on LLMs
• 00:08:35 – Training of models
• 00:14:23 – Uncurated sources of training
• 00:16:12 – Safeguards and prompt engineering
• 00:21:19 – TruthfulQA and creating a more strict prompt
• 00:23:20 – Information that is out of date
• 00:26:07 – WinoBias for evaluating gender stereotypes
• 00:28:30 – BOLD dataset for evaluating bias
• 00:30:28 – Sponsor: Intel
• 00:31:18 – Using Hugging Face to start testing with Python
• 00:35:25 – Using the transformers package
• 00:37:34 – Using langchain for proprietary models
• 00:43:04 – Putting the tools together and evaluating
• 00:47:19 – Video Course Spotlight
• 00:48:29 – Assessing toxicity
• 00:50:21 – Measuring bias
• 00:54:40 – Checking the hallucination rate
• 00:56:22 – LLM leaderboards
• 00:58:17 – What helped ChatGPT leap forward?
• 01:06:01 – Improvements of what is being crawled
• 01:07:32 – Revisiting agents and RAG
• 01:11:03 – ChatGPT plugins and Wolfram-Alpha
• 01:13:06 – How can people follow your work online?
• 01:14:33 – Thanks and goodbye

Background Links:

• A Beginner’s Guide to Language Models - Built In
• ChatGPT - Explained! - YouTube

Dataset Links:

• truthful_qa - Datasets at Hugging Face
• wino_bias - Datasets at Hugging Face
• bold - Datasets at Hugging Face

Tutorials and Documentation for Python Packages:

• Evaluating Language Model Bias with 🤗 Evaluate
• Hugging Face - HF_bias_evaluation - Google Colab
• General Usage - Load a Dataset - Hugging Face
• What is Text Generation? - Hugging Face
• 🤗 Evaluate - Library Evaluating ML Models
• Python Quickstart - 🦜️🔗 Langchain

Measurement Links:

• Toxicity - a Hugging Face Space by evaluate-measurement
• Regard - a Hugging Face Space by evaluate-measurement
• Open LLM Leaderboard - a Hugging Face Space

Training Data for LLMs:

• Common Crawl - Open Repository of Web Crawl Data
• The Pile
• The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora

Agents and Plugin Links:

• Transformers Agents - Hugging Face
• Agents - 🦜️🔗 Langchain
• ChatGPT Gets Its “Wolfram Superpowers”! - Stephen Wolfram

Additional Links:

• Inside the AI Factory: The Humans that Make Tech Seem Human - The Verge
• Jodie Burchell - The JetBrains Blog
• Jodie Burchell’s Blog - Standard error
• Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl) - Twitter
• 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
• Creating Web Maps From Your Data With Python Folium
• Learn Text Classification With Python and Keras

Support the podcast & join our community of Pythonistas

Do you need to transfer an extensive data collection for a science project? What’s the best way to send executable code over the wire for distributed processing? What are the different ways to serialize data in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a tutorial by Real Python author Bartosz Zaczyński called “Serialize Your Data With Python.” This comprehensive guide moves beyond XML and JSON to explore multiple data formats and their potential use cases. It’s a deep dive into the topic and provides a thorough resource for future reference.

We also discuss a Real Python tutorial about naming conventions in Python that use single and double underscores. The piece covers differentiating between public and non-public names in APIs, writing safe classes for subclassing purposes, and avoiding name clashes with keywords.

We also share several other articles and projects from the Python community, including a couple of release announcements and news items, a discussion about never being taught how to construct quality software, building a small REPL in Python, using the key parameter in Python functions and methods, a framework for RESTful APIs using Flask and SQLAlchemy, and a Rust-based HTML sanitizer for your Python projects.

Topics:

• 00:00:00 – Introduction
• 00:02:47 – JIT Coming to Python 3.13
• 00:03:13 – A copy-and-patch JIT compiler - Pull Request #113465
• 00:03:54 – Django bugfix releases issued: 4.2.9 and 5.0.1
• 00:04:24 – Single and Double Underscores in Python Names
• 00:16:42 – Building a Small REPL in Python
• 00:20:07 – The Key to the key Parameter in Python
• 00:25:23 – Video Course Spotlight
• 00:26:44 – Serialize Your Data With Python
• 00:30:23 – You Are Never Taught How to Build Quality Software
• 00:48:43 – flask-muck: RESTful APIs Using Flask and SQLAlchemy
• 00:51:26 – nh3: Python binding to Ammonia HTML sanitizer Rust crate
• 00:53:33 – Thanks and goodbye

News:

• JIT Coming to Python 3.13 – Slides related to the upcoming JIT commit for Python 3.13. Note that GitHub paginates the slides if you don’t download them, so click the More Pages button to keep reading.
• A copy-and-patch JIT compiler by brandtbucher - Pull Request #113465
• Django bugfix releases issued: 4.2.9 and 5.0.1 - Weblog Django

Show Links:

• Single and Double Underscores in Python Names – In this tutorial, you’ll learn a few Python naming conventions involving single and double underscores (_). You’ll learn how to use this character to differentiate between public and non-public names in APIs, write safe classes for subclassing purposes, avoid name clashes, and more.
• Building a Small REPL in Python – Learn how to write your own REPL by building on top of the one that comes with Python. With a few lines of code, you can customize Python’s REPL environment as your own.
• The Key to the key Parameter in Python – A parameter named key is present in several Python functions, such as sorted(). This article explores what it is and how to use it.
• Serialize Your Data With Python – In this in-depth tutorial, you’ll explore the world of data serialization in Python. You’ll compare and use different data serialization formats, serialize Python objects and executable code, and handle HTTP message payloads.

Discussion:

• You Are Never Taught How to Build Quality Software – Learning how to build quality software isn’t part of computer science education. How do we learn it?
• Hacker News discussion about the article

Projects:

• flask-muck: RESTful APIs Using Flask and SqlAlchemy
• nh3: Python binding to Ammonia HTML sanitizer Rust crate

Additional Links:

• PEP 8: The Style Guide for Python Code
• Python’s Magic Methods: Leverage Their Power in Your Classes – Real Python
• A Philosophy of Software Design Book - John Ousterhout
• A Philosophy of Software Design - John Ousterhout - Talks at Google - YouTube
• Episode #49: The Challenges of Developing Into a Python Professional – The Real Python Podcast
• Django: Sanitize Incoming HTML Fragments With nh3 – Allowing users to input HTML in comments or blog posts is problematic and can lead to exploits on your site. For years, the Django community used django-bleach, but since its deprecation, Adam has been using nh3, a Rust-based HTML sanitizer.
• nh3 · PyPI

Level up your Python skills with our expert-led courses:

• Working With JSON Data in Python
• Writing Beautiful Pythonic Code With PEP 8
• Serializing Objects With the Python pickle Module

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Are you interested in using your Python skills within Excel? Would you like to share a data science project or visualization as a single Office file? This week on the show, we speak with Principal Architect John Lam and Sr. Cloud Developer Advocate Sarah Kaiser from Microsoft about Python in Excel.

John shares the multi-year journey of adding Python to Excel. He describes how the project moved beyond writing user functions in Python to something much more elaborate. He details assembling a team with diverse skills in interface design, languages, and security.

Sarah discusses the instant convenience of having familiar Python and pandas techniques at your fingertips inside Excel. We cover typical data science workflows and the potential of interactive visualizations within a spreadsheet. We also share multiple resources for you to learn more.

Note: Python in Excel is currently a preview accessible by joining the Microsoft 365 Insider Program and selecting the Beta Channel.

Topics:

• 00:00:00 – Introduction
• 00:01:53 – Sr. Cloud Developer Advocate Sarah Kaiser
• 00:02:46 – Principal Architect John Lam
• 00:04:08 – What is Dev Div?
• 00:04:33 – Python data science inside Excel
• 00:09:05 – Designing features with a focus on sharing
• 00:14:28 – Moving between Excel and Python objects
• 00:18:20 – What libraries are imported by default?
• 00:23:11 – Sharing a workbook with others
• 00:26:12 – Recalculating data workflow
• 00:30:07 – Working in Jupyter Notebook vs Excel
• 00:33:03 – Creating a Python object
• 00:33:38 – Video Course Spotlight
• 00:35:02 – More history and project team
• 00:40:19 – Immediate wins of having Python in Excel
• 00:42:28 – Interactive visualizations
• 00:44:34 – Answering security concerns
• 00:49:17 – Limitations and potential
• 00:54:34 – Creating demo projects
• 01:00:25 – Resources to learn more
• 01:02:59 – What are you excited about in the world of Python?
• 01:10:41 – What do you want to learn next?
• 01:12:09 – How can people follow your work online?
• 01:13:26 – Thanks and goodbye

Show Links:

• Python in Excel – Python to Excel - Microsoft 365
• Get started with Python in Excel - Microsoft Support
• Python in Excel DataFrames - Microsoft Support
• Open-source libraries and Python in Excel - Microsoft Support
• User guide and tutorial - seaborn 0.13.1 documentation
• Assessing and Restoring Reproducibility of Jupyter Notebooks - IEEE Conference Publication - IEEE Xplore
• Book of Python in Excel - John Lam’s Website
• GitHub - microsoft/python-in-excel - Python in Microsoft Excel
• Use Python in Excel to enhance your data science - Python Day - YouTube
• Introducing Python in Excel: The Best of Both Worlds for Data Analysis and Visualization - Microsoft Community Hub
• PEP 703 – Making the Global Interpreter Lock Optional in CPython - peps.python.org
• Dr. Sarah Kaiser (@[email protected]) - Fosstodon
• John Lam (@john_lam) - X

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn
• The pandas DataFrame: Working With Data Efficiently

Support the podcast & join our community of Pythonistas

Three members of the Real Python team are joining us this week: Kate Finegan, Tappan Moore, and Philipp Acsany. We wanted to share a year-end wrap-up with tutorials, step-by-step projects, code conversations, and video courses that showcase what our team created this year.

Kate helps to shepherd articles through the multi-stage editing process. She and the rest of the team ensure these resources impart crucial Python knowledge and provide a thorough didactic experience. Kate was also instrumental in helping introduce a new group of tutorial authors to the Real Python editorial process and house style.

Philipp returns to the podcast after our conversation earlier this year, and it was great to talk to him about onboarding new video instructors. Tappan edits all our video courses and ensures that the sound, picture, and animations are just right. He also helped provide feedback to the new instructors on our video creation process.

We hope you enjoy this review! We look forward to bringing you another year full of great guests, articles, and topics.

Topics:

• 00:00:00 – Introduction
• 00:02:50 – New Video Instructors
• 00:05:31 – New Tutorial Authors
• 00:07:37 – Shout-out to Aldren Santos
• 00:09:27 – Build a Wordle Clone With Python and Rich
• 00:11:37 – Python Classes: The Power of Object-Oriented Programming
• 00:13:43 – SOLID Principles: Improve Object-Oriented Design in Python
• 00:14:47 – Using the NumPy Random Number Generator
• 00:17:58 – Recursion in Python
• 00:19:44 – Filtering Iterables With Python
• 00:21:04 – Creating Web Maps From Your Data With Python Folium
• 00:24:51 – Video Course Spotlight
• 00:26:13 – Python Basics Exercises: Building Systems With Classes
• 00:31:00 – Real Python Quizzes
• 00:33:53 – Process Images Using the Pillow Library and Python
• 00:36:00 – How to Sort Unicode Strings Alphabetically in Python
• 00:38:36 – The Python Rich Package: Unleash the Power of Console Text
• 00:42:31 – Embeddings and Vector Databases With ChromaDB
• 00:46:41 – Advent of Code: Solving Puzzles With Python
• 00:51:07 – Thanks and goodbye

Show Links:

• Build a Wordle Clone With Python and Rich – Step-by-Step Project
• Python Classes: The Power of Object-Oriented Programming – Tutorial
• SOLID Principles: Improve Object-Oriented Design in Python – Tutorial
• Using the NumPy Random Number Generator – Tutorial
• Recursion in Python – Video Course
• Filtering Iterables With Python – Video Course
• Creating Web Maps From Your Data With Python Folium – Video Course
• Python Basics Exercises: Building Systems With Classes – Video Course
• Process Images Using the Pillow Library and Python – Video Course
• How to Sort Unicode Strings Alphabetically in Python – Tutorial
• The Python Rich Package: Unleash the Power of Console Text – Showcase
• Embeddings and Vector Databases With ChromaDB – Tutorial
• Advent of Code: Solving Puzzles With Python – Code Conversation

Additional Links:

• Create a Python Wordle Clone With Rich – Video Course
• Class Concepts: Object-Oriented Programming in Python – Video Course
• Inheritance and Internals: Object-Oriented Programming in Python – Video Course
• Design and Guidance: Object-Oriented Programming in Python – Video Course
• FTX Python code ‘allowed’ Alameda Research to spend deposits - The Register
• Recursion in Python: An Introduction – Tutorial
• Python’s filter(): Extract Values From Iterables – Tutorial
• Python Folium: Create Web Maps From Your Data – Step-by-Step Project
• Episode #12: Web Scraping in Python: Tools, Techniques, and Legality – The Real Python Podcast
• Python Basics: Building Systems With Classes – Video Course
• Image Processing With the Python Pillow Library – Tutorial
• Episode #80: Make Your Python App Interactive With a Text User Interface (TUI) – The Real Python Podcast

Level up your Python skills with our expert-led courses:

• Python Basics Exercises: Building Systems With Classes
• Process Images Using the Pillow Library and Python
• Recursion in Python

Support the podcast & join our community of Pythonistas

It’s been a fascinating year for the Python language and community. PyCoder’s Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2023. Christopher Trudeau is back on the show this week to help wrap up everything by sharing some highlights and Python trends from across the year.

Christopher shares the top five links explored by PyCoder’s readers. We also dig into trends we noticed across all the articles and stories this year, including removing dead batteries from the standard library, continuing the effort to speed up Python, using Rust code in libraries, and moving away from microservices.

We hope you enjoy this review! We look forward to bringing you an upcoming year full of great Python news, articles, topics, and projects.

Topics:

• 00:00:00 – Introduction
• 00:02:21 – Python 3.13.0 Alpha 2 Is Now Available
• 00:02:46 – Welcoming the Supporting Developer in Residence
• 00:03:25 – Django 5.0 Released
• 00:03:34 – Django Ninja 1.0 Released
• 00:04:11 – Top Five PyCoder Links
• 00:05:00 – Python 3.12: Cool New Features for You to Try
• 00:05:27 – Speeding Up Your Code When Multiple Cores Aren’t an Option
• 00:05:46 – Learning About Code Metrics in Python With Radon
• 00:06:07 – Python 3.12 Preview: More Intuitive and Consistent F-Strings
• 00:06:28 – Design and Guidance: Object-Oriented Programming in Python
• 00:07:49 – Python 3.12: What Didn’t Make the Headlines
• 00:11:34 – Python 3.13 Removes 20 Stdlib Modules
• 00:13:32 – Missing Batteries: Essential Libraries You’re Missing Out On
• 00:16:21 – More Batteries Please
• 00:19:19 – Three Python Trends in 2023
• 00:24:36 – Video Course Spotlight
• 00:26:23 – Mojo, a Superset of Python
• 00:27:54 – Why Mojo?
• 00:28:14 – Mojo SDK Released for Linux
• 00:28:19 – Mojo: Head-to-Head With Python and Numba
• 00:29:22 – How We Organize Our Very Large Python Monolith
• 00:34:11 – Python and Folium to Visualize My Outdoor Activities
• 00:37:13 – Thanks and goodbye

News:

• Python 3.13.0 Alpha 2 Is Now Available
• Welcoming the Supporting Developer in Residence
• Django 5.0 Released
• Django Ninja 1.0 Released

Top Five PyCoder Links:

• Python 3.12: Cool New Features for You to Try – In this tutorial, you’ll learn about the new features in Python 3.12. You’ll explore how the new release extends the better error messages and faster code execution found in the previous version, and you’ll try out the improvements to f-strings and type variable syntax.
• Speeding Up Your Code When Multiple Cores Aren’t an Option – Parallelism isn’t the only answer: often you can optimize low-level code to get significant performance improvements.
• Learning About Code Metrics in Python With Radon – Radon is a code metrics tool. This article introduces you to it and explains how you can improve your code based on its measurements.
• Python 3.12 Preview: More Intuitive and Consistent F-Strings – In this tutorial, you’ll preview one of the upcoming features of Python 3.12, which introduces a new f-string syntax formalization and implementation. The new implementation lifts some restrictions and limitations that affect f-string literals in Python versions lower than 3.12.
• Design and Guidance: Object-Oriented Programming in Python – In this video course, you’ll learn about the SOLID principles, which are five well-established standards for improving your object-oriented design in Python. By applying these principles, you can create object-oriented code that is more maintainable, extensible, scalable, and testable.

Topics and 2023 Trends:

• Python 3.12: What Didn’t Make the Headlines – There’s been plenty of coverage about the changes in Python 3.12. This article highlights what fell through the cracks. It talks about performance, pathlib improvements, and a few other changes.
• Python 3.13 Removes 20 Stdlib Modules – Core developers are busy working on PEP 594, removing dead batteries from Python 3.13. This long post in the discussion forum highlights what work has been completed so far.
• Missing Batteries: Essential Libraries You’re Missing Out On – Even though Python’s standard library comes with batteries included, it’s still missing some essentials. This article covers libraries for debugging, testing, CLI, and more.
• More Batteries Please – This brief opinion piece from Carlton Gibson states why he thinks we need more functionality in the Python standard library rather than less.
• Three Python Trends in 2023 – An opinion piece on three trends likely to attract attention in the Python world in 2023: Python/Rust co-projects, web apps, and more typing. Read on for examples in each category.
• Mojo, a Superset of Python – Mojo is a new programming language that’s a superset of Python. It aims to fix Python’s performance and deployment problems. Jeremy Howard - from fast.ai.
• Why Mojo? – “A backstory and rationale for why we created the Mojo language.” Chris Lattner - from Modular.
• Mojo SDK Released for Linux
• Mojo: Head-to-Head With Python and Numba – This article covers a Mandelbrot-based benchmark of Python, variations of Numba, and the newly available Mojo. Although Mojo is fast, it takes a lot more work than the author expected to translate Python to it, and with the right parameters, Numba still beats it.
• How We Organize Our Very Large Python Monolith – Kraken Technologies is an environmental tech company that does a lot of Python development. One of their applications is a monolith with over 27,000 modules. This article outlines how they keep all of this organized and running.

Project:

• Python and Folium to Visualize My Outdoor Activities – Embark on an expedition of exploration and mapping! Learn how to breathe life into your GPX files and create interactive maps using Python and Folium.
• Python Folium: Create Web Maps From Your Data – Real Python
• Creating Web Maps From Your Data With Python Folium – Real Python

Additional Links:

• Sneaky REST APIs With Django Ninja – Real Python Video Course
• Episode #175: Exploring the New Features of Python 3.12 – The Real Python Podcast
• Python 3 Module of the Week - PyMOTW 3
• Episode #171: Making Each Line of Code Efficient & Python In Excel – The Real Python Podcast
• psf/requests: A simple, yet elegant, HTTP library.
• htmx - high power tools for html

Level up your Python skills with our expert-led courses:

• Python Basics Exercises: Building Systems With Classes
• Creating Web Maps From Your Data With Python Folium
• Python Basics: Building Systems With Classes

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What goes into a code review in Python? Is there a difference in how a large organization practices code review compared to a smaller one? What do you do if you’re a solo developer? This week on the show, Brendan Maginnis and Nick Thapen from Sourcery return to talk about code review and automated code assistance.

Nick and Brendan discuss their personal experiences with code reviews. From working together at a large company to starting Sourcery as a pair of developers to growing a team, they’ve experienced how various organizations review code.

We discuss the importance of keeping code reviews positive and maintaining developer momentum. Nick and Brendan share techniques for automating organizational code rules and retaining institutional developer knowledge.

They’ve been busy developing new features for Sourcery, moving beyond the current refactoring features. Their team is leveraging LLMs to build an automated code assistant that can perform code reviews, write tests, and answer questions about your codebase. The goal is for it to act as a personalized computer pair programmer.

Topics:

• 00:00:00 – Introduction
• 00:02:41 – Why discuss code reviewing?
• 00:04:16 – What were your first code reviews like?
• 00:06:31 – Developing positive techniques as a team
• 00:14:07 – Time between reviews
• 00:17:04 – Will the techniques vary based on the organization size?
• 00:18:43 – Things to standardize and remove from code review
• 00:20:06 – Video Course Spotlight
• 00:22:06 – Creating a tool that goes beyond refactoring
• 00:23:42 – Do newer models provide better context for code review?
• 00:25:52 – Working toward an automated pair programmer
• 00:33:42 – Creating rules for an organization’s code standards
• 00:42:55 – Comparing completion tools with code assistance
• 00:48:18 – Working on an existing codebase vs a new project
• 00:52:11 – Current state of Sourcery
• 00:56:19 – What are you excited about in the world of Python?
• 01:00:12 – What do you want to learn next?
• 01:03:41 – How can people follow your work online?
• 01:05:17 – Thanks and goodbye

Show Links:

• Sourcery 1000x Faster Code Review
• Sourcery - GitHub Bot Install
• Sourcery - VSCode Extension - Visual Studio Marketplace
• Sourcery - IntelliJ IDEs Plugin
• Episode #55: Getting Started With Refactoring Your Python Code – The Real Python Podcast
• How to do a code review - eng-practices - Google GitHub
• Generating Code without Generating Technical Debt?
• Episode #117: Measuring Python Code Quality, Simplicity, and Maintainability – The Real Python Podcast
• Writing Custom Rules - Sourcery Documentation
• Nuitka the Python Compiler - Documentation
• Episode #154: Targeting WebAssembly Platforms & Distilling a Minimum Viable Python – The Real Python Podcast
• Rust Programming Language
• Speedsolving the cube : easy-to-follow, step-by-step instructions for many popular 3-D puzzles in SearchWorks catalog
• My Arcade Releases the Atari Gamestation Pro with 200+ Games

Level up your Python skills with our expert-led courses:

• Using the bpython Enhanced REPL
• Testing Your Code With pytest
• Python Basics Exercises: Strings and String Methods

Support the podcast & join our community of Pythonistas

Have you thought of a way to improve the Python language? How do you share your idea with core developers and start a discussion in the Python community? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We consider a couple of Python syntax and functional ideas posted to the discussions on python.org. The first idea is for simplifying the syntax of a function’s keyword arguments, and the second is for the ability to return a NamedTuple from a function. The threads reveal steps within the Python Enhancement Proposal (PEP) process and the goal of finding a sponsor.

Christopher covers a tutorial on building a JSON-like parser in Python. The project is a solid place to start if you want to learn about parsing and developing rules for recognizing syntax and extracting data.

We also share several other articles and projects from the Python community, including a couple of release announcements and news items, a step-by-step guide to building a hangman game for the command line in Python, the reasons why the Django admin is supposedly ugly and ways to customize it to differentiate admin environments, an explanation of confusing git terminology, a project to extract links from a remote HTML resource, and a regex crossword game.

Topics:

• 00:00:00 – Introduction
• 00:02:46 – PyPI has completed its first security audit
• 00:03:55 – Python Developers Survey 2023
• 00:04:45 – Wagtail 5.2 (LTS) Released
• 00:05:02 – How many Python core devs use typing?
• 00:07:51 – Build a Hangman Game for the Command Line in Python
• 00:17:30 – Why Is the Django Admin “Ugly”?
• 00:20:37 – Customize the Django Admin to Differentiate Environments
• 00:22:48 – Confusing git Terminology
• 00:30:01 – Video Course Spotlight
• 00:31:31 – Let’s Make a Silly JSON-like Parser
• 00:34:31 – Idea: Return a NamedTuple
• 00:45:08 – Idea: Syntactic Sugar to Encourage Use of Named Arguments
• 00:50:31 – grablinks: Extract Links From a Remote HTML Resource
• 00:52:43 – Regex Crossword
• 00:55:46 – Thanks and goodbye

News:

• PyPI has completed its first security audit - The Python Package Index
• Python Developers Survey 2023
• Wagtail 5.2 (LTS) Released
• How many Python core devs use typing? - Gram Publishing v2

Show Links:

• Build a Hangman Game for the Command Line in Python – In this step-by-step project, you’ll learn how to write the game of hangman in Python for the command line. You’ll learn how to structure the game as a text-based interface (TUI) application.
• Why Is the Django Admin “Ugly”? – When Vince was talking with people at DjangoCon US, one question kept coming up: Why is the Django admin so “ugly”?
• Customize the Django Admin to Differentiate Environments – A quick post about changing the color scheme of the Django admin depending on what environment the code is deployed within. Although Django specific, this is a great idea—visually reminding your admins and developers whether they’re in dev, staging, or production.
• Confusing git Terminology – Julia is working on a doc that explains git and, in doing so, polled some people about what git terminology they found confusing. This post covers the most common responses and attempts to clear up the confusion.
• Let’s Make a Silly JSON-like Parser – This article goes into deep detail on how you would construct a JSON parser in Python. If you’re new to parsing, then this is a great place to start.

Discussion:

• Idea: Return a NamedTuple
• Idea: Syntactic Sugar to Encourage Use of Named Arguments

Projects:

• grablinks: Extract Links From a Remote HTML Resource
• Regex Crossword
• Regex Crossword Mobile

Additional Links:

• Episode #2: Learn Python Skills While Creating Games – The Real Python Podcast
• Episode #179: Improving Your Git Developer Experience in Python – The Real Python Podcast
• Write Pythonic and Clean Code With namedtuple – Real Python

Level up your Python skills with our expert-led courses:

• Advent of Code: Solving Puzzles With Python
• Everyday Project Packaging With pyproject.toml
• Writing Clean, Pythonic Code With namedtuple

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Has the current growth of artificial intelligence (AI) systems made you wonder what the future holds for Python developers? What are the hidden benefits of learning to program in Python and practicing computational thinking? This week on the show, we speak with author Lawrence Gray about his upcoming book “Mastering Python: A Problem Solving Approach.”

Lawrence shares how learning Python helped him through a dark and trying time. He developed lifelong skills that he wants to pass along through teaching and authoring a book.

We discuss what you can do to prepare for a future where coding jobs are automated through AI. He shares ways that Python can help build the higher-order thinking skills required by future careers. We also talk about how Python can help with computational thinking and promote cognitive development.

This week’s episode is brought to you by Site24x7.

Topics:

• 00:00:00 – Introduction
• 00:02:29 – Learning Python and changing how you think
• 00:05:07 – What is the goal of the book?
• 00:05:59 – Discovering Python during a dark period
• 00:10:32 – What is unique to Python that helped you?
• 00:12:43 – How did you start teaching Python?
• 00:14:40 – Teaching Python to art students
• 00:18:52 – Sponsor: Site24x7.com
• 00:19:48 – Why write about computational thinking?
• 00:21:55 – Why learn Python now?
• 00:25:18 – Multiple modalities for Python
• 00:29:32 – Other optimistic thoughts on the future
• 00:34:15 – Reskilling a workforce
• 00:37:16 – What skills can be developed?
• 00:41:13 – Video Course Spotlight
• 00:42:40 – Bloom’s taxonomy
• 00:48:00 – Sharing the positive impact of Python
• 00:50:26 – What are you excited about in the world of Python?
• 00:51:32 – What do you want to learn next?
• 00:52:45 – How can people follow your work online?
• 00:52:47 – Thanks and goodbye

Show Links:

• The Future of Jobs Report 2023 - World Economic Forum
• Mastering Python: Get Access to Chapter 1
• Computational thinking - Wikipedia
• Bloom’s taxonomy - Wikipedia
• Probably Overthinking It – Data science, Bayesian Statistics, and other ideas
• scikit-learn: machine learning in Python - documentation
• Yellowbrick: Machine Learning Visualization - documentation
• PyCon US 2024 - PyCon US 2024
• Education Summit - PyCon US 2023
• PyTorch - Get Started
• Lawrence Gray Personal Site

Level up your Python skills with our expert-led courses:

• Using k-Nearest Neighbors (kNN) in Python
• How to Write Pythonic Loops
• Looping With Python enumerate()

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Have you moved through the fundamentals of Python, and are you now considering building a more extensive project or complete application? Where can you study the architecture of existing Python projects and learn best practices? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a set of resources that developers can study to learn how to structure projects. The collection was shared in a blog post titled “Great Resources a Beginner Might Not Find So Easily.” It includes a pair of books on the architecture of large software applications and another aimed at more modest projects.

We consider when you should use lambda expressions in your Python code. These one-line expressions create anonymous functions. How do they differ from standard functions, and where is it appropriate to use them?

We also share several other articles and projects from the Python community, including a couple of release announcements, an explanation of Python as a compiled language, a discussion covering the controversy about the recent Flask release, a project for writing less Selenium code, and a project to create ASCII art with Python.

This week’s episode is brought to you by Site24x7.

Topics:

• 00:00:00 – Introduction
• 00:02:50 – PyCon US 2024 Call for Proposals
• 00:03:18 – Django 5.0 Beta 1 Released
• 00:03:29 – Django security releases issued: 4.2.7, 4.1.13, and 3.2.23
• 00:03:51 – The Ruff Formatter: Python’s Fastest Formatter
• 00:04:50 – What Are Lambda Expressions?
• 00:12:01 – Sponsor: Site24x7.com
• 00:12:56 – Python Is a Compiled Language
• 00:16:01 – Great Resources a Beginner Might Not Find So Easily
• 00:27:01 – Video Course Spotlight
• 00:28:32 – We Have to Talk About Flask
• 00:41:46 – selenium-python-helium: Write Less Selenium Code
• 00:45:23 – pyfiglet: An implementation of figlet written in Python
• 00:47:17 – Thanks and goodbye

News:

• PyCon US 2024 Call for Proposals
• Django 5.0 Beta 1 Released
• Django security releases issued: 4.2.7, 4.1.13, and 3.2.23 | Weblog | Django
• The Ruff Formatter: Python’s Fastest Formatter – You may have come across Ruff, a linter that’s been on the scene for about a year. Well, it recently added formatting to its features. This article shows you how, including the configuration option to allow single-quote style.

Show Links:

• What Are Lambda Expressions? – This post teaches you what lambda expressions are and how they get used in Python. It shows several examples and also covers when to avoid lambda expressions.
• Python Is a Compiled Language – Python is interpreted, but it interprets compiled code. This distinction can be confusing to students, and this instructor does a deep dive on where the line between the concepts sits.
• Great Resources a Beginner Might Not Find So Easily – Are you having trouble making the modules work together in a larger project? Have you tried looking at popular projects as models, but did their size and scope put you off? Did you find it hard to see why they did what they did? Resources about this do exist, but they’re scattered all over…unless you know where to look.
• We Have to Talk About Flask – The most recent release of Flask and Werkzeug have introduced backward incompatible changes that are affecting popular add-ons. Miguel writes about how this is a common occurrence and why it keeps happening.

Discussion:

• Armin Ronacher on X: Flask today is a different framework than the one I have created
• Some More To Talk About Flask - miguelgrinberg.com
• We Have to Talk About Flask - Hacker News
• Episode #18: Ten Years of Flask: Conversation With Creator Armin Ronacher - The Real Python Podcast

Projects:

• selenium-python-helium: Write Less Selenium Code
• pyfiglet: An implementation of figlet written in Python - PyPI

Additional Links:

• Lambdas - Expressions - Python 3.12.0 documentation
• How to Use Python Lambda Functions - Real Python
• The Architecture of Open Source Applications
• Episode #9: Leveling Up Your Python Literacy and Finding Python Projects to Study - The Real Python Podcast
• Episode #62: Selecting the Ideal Data Structure & Unravelling Python’s “pass” and “with” - The Real Python Podcast
• asciimatics - PyPI

Level up your Python skills with our expert-led courses:

• Class Concepts: Object-Oriented Programming in Python
• Design and Guidance: Object-Oriented Programming in Python
• Inheritance and Internals: Object-Oriented Programming in Python

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Are you getting by with a few fundamental commands for Git when building your Python projects? Would you like to improve your version control techniques and become more efficient with the Git command line? This week on the show, Adam Johnson is back to talk about his new book, “Boost Your Git DX.”

Adam brings advice about creating a Git configuration, enabling autocorrect, and customizing a global ignore file. He combed through the cryptic documentation and found the valuable developer settings, so you won’t have to.

Adam also shares tools and settings to speed up your terminal workflows. We discuss configuring Zsh, creating aliases, and comparing diffs. We briefly dig into the most recent update to Django and his work on the project.

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

Topics:

• 00:00:00 – Introduction
• 00:02:13 – When did you start working on this book?
• 00:06:11 – Git’s tendency to be inscrutable
• 00:08:57 – Audience for the book
• 00:09:47 – What have you learned about writing?
• 00:13:41 – What were you most excited to share?
• 00:14:34 – Global Git ignore
• 00:19:53 – Sponsor: Taipy
• 00:20:58 – Backing up configuration
• 00:22:37 – Git autocorrect
• 00:24:48 – Recovering from mistakes
• 00:27:58 – Creating terminal aliases in Zsh
• 00:31:59 – Using a Colemak keyboard layout
• 00:34:33 – Changing the diff-options to histogram
• 00:37:22 – Altering the global configuration file
• 00:39:36 – Video Course Spotlight
• 00:41:05 – Brief history of the Git project
• 00:44:27 – delta - syntax-highlighting pager for git, diff, and grep output
• 00:45:22 – pygments-git - Pygments lexers for Git output and files
• 00:46:56 – What are you excited about in the world of Git?
• 00:49:17 – What are you excited about in the world of Python?
• 00:52:51 – What do you want to learn next?
• 00:53:50 – How can people follow your work online?
• 00:55:23 – Thanks and goodbye

Show Links:

• Boost Your Git DX
• “Boost Your Git DX” out now - Adam Johnson
• Boost Your Django DX
• Git: How to set up a global ignore file - Adam Johnson
• Git - git-config Documentation
• Git: How to enable autocorrect - Adam Johnson
• New zine: Oh shit, Git!
• Oh Shit, Git!?!
• Git - git-reflog Documentation
• Oh My Zsh - a delightful & open source framework for Zsh
• Starship: Cross-Shell Prompt
• Git - diff-options Documentation
• Git - Git Configuration
• Colemak - Keyboard Layout
• delta: A syntax-highlighting pager for Git, diff, and grep output
• pygments-git - PyPI
• Sapling from Meta
• Jujutsu: A Git-compatible DVCS that is both simple and powerful
• Django 5.0 beta 1 released - Django
• Adam Johnson’s Blog
• Adam Johnson (@[email protected]) - Fosstodon
• Projects - Adam Johnson

Level up your Python skills with our expert-led courses:

• Introduction to Git and GitHub for Python Developers
• How to Set Up a Django Project
• Building Python Project Documentation With MkDocs

Support the podcast & join our community of Pythonistas

How do you prepare a scientific Python project for sharing with others? Could you use some best practices and guidance for packaging, documentation, and testing? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about the creation of the Scientific Python Library Development Guide. The guide was finalized during the 2023 Scientific Python Developer Summit and is a resource for modern packaging. It includes sections of tutorials, principles, templates, and common patterns.

Christopher shares a recent Real Python tutorial about sorting Unicode strings in Python. He covers some of the pitfalls and ways to avoid them. The tutorial includes multiple third-party libraries to assist in wrangling Unicode characters.

We also share several other articles and projects from the Python community, including a couple of release announcements, information on namespaces and variable scope in Python, benchmark comparisons of Numba and Mojo, a discussion of recent so-called AI fails, a TUI for log files with a merged timeline, a cross-platform GUI building tool similar to HyperCard, and a project for reproducing exact argparse arguments.

Topics:

• 00:00:00 – Introduction
• 00:02:30 – Python 3.13.0 alpha 1 is now available
• 00:03:20 – core.py - Podcast
• 00:03:53 – PEP 730 - Adding iOS as a supported platform
• 00:05:22 – Mojo🔥 is now available on Mac
• 00:06:28 – How to Sort Unicode Strings Alphabetically in Python
• 00:11:48 – Python Variables: Namespaces and Variable Scope
• 00:18:18 – Mojo: Head-to-Head With Python and Numba
• 00:27:25 – Video Course Spotlight
• 00:28:50 – The Scientific Python Library Development Guide
• 00:32:47 – I’m banned for life from advertising on Meta
• 00:48:03 – logmerger: TUI for Log Files With Merged Timeline
• 00:50:34 – CardStock: Cross-Platform GUI Building Tool
• 00:54:24 – reverse_argparse: Tell the User What They Ran
• 00:56:25 – Thanks and goodbye

News:

• Python Insider: Python 3.13.0 alpha 1 is now available
• core.py - Podcast by Pablo Galindo and Łukasz Langa
• PEP 730 - Adding iOS as a supported platform - peps.python.org
• Modular: Mojo🔥 is now available on Mac

Show Links:

• How to Sort Unicode Strings Alphabetically in Python – In this tutorial, you’ll learn how to correctly sort Unicode strings in Python while avoiding common pitfalls. You’ll explore powerful third-party libraries implementing the complete Unicode Collation Algorithm (UCA), as well as standard library modules and a few handmade solutions.
• Python Variables: Namespaces and Variable Scope – This post is a comprehensive guide on namespaces and variable scope. Learn about the four different namespaces and how to access each.
• Mojo: Head-to-Head With Python and Numba – This article covers a Mandelbrot-based benchmark of Python, variations of Numba, and the newly available Mojo. Although Mojo is fast, it takes a lot more work than the author expected to translate Python to it, and with the right parameters, Numba still beats it.
• The Scientific Python Development Guide – This article talks about the release of the Scientific Python Development Guide, which documents Python package development. It includes cookie-cutter templates and repo tools.

Discussion:

• I’m banned for life from advertising on Meta. Because I teach Python. — Reuven Lerner
• ‘Life or Death:’ AI-Generated Mushroom Foraging Books Are All Over Amazon

Projects:

• logmerger: TUI for Log Files With Merged Timeline
• CardStock: Cross-Platform GUI Building Tool
• reverse_argparse: Tell the User What They Ran

Additional Links:

• Write once. Deploy everywhere. - BeeWare
• Python Scope & the LEGB Rule: Resolving Names in Your Code - Real Python
• Exploring Scopes and Closures in Python - Real Python
• Thonny, Python IDE for beginners
• Scikit-HEP - About
• Episode #39: Generators, Coroutines, and Learning Python Through Exercises - The Real Python Podcast
• 30-plus years of HyperCard, the missing link to the Web - Ars Technica
• CardStock.run - Quickly and easily build apps on the web

Level up your Python skills with our expert-led courses:

• Navigating Namespaces and Scope in Python
• Unicode in Python: Working With Character Encodings
• Exploring Scopes and Closures in Python

Support the podcast & join our community of Pythonistas

You may remember a recent Python Package Index (PyPI) announcement about hiring a full-time security engineer. We’ve also mentioned several current security initiatives from PyPI. This week on the show, we talk with Mike Fiedler about accepting this new role and securing accounts on PyPI.

Mike talks about how he started as a contributor to PyPI and eventually became a maintainer. We dig into why he fits this new role well and what his responsibilities are.

We discuss the initiative to secure accounts using two-factor authentication (2FA) methods. Mike also explains how package maintainers can adopt a new, more secure publishing method called trusted publishing that doesn’t require long-lived passwords.

We also discuss Mike’s recent talk called “How to Give Back to Open Source Without Losing Your Mind.” Mike shares advice and resources for finding your own contribution entry points.

Topics:

• 00:00:00 – Introduction
• 00:02:11 – PyPI Safety and Security Engineer
• 00:05:21 – Why did you initially become a PyPI contributor?
• 00:11:26 – What are you most excited about in your new role?
• 00:12:02 – Current security concerns
• 00:15:07 – Focus on malicious package reporting
• 00:16:30 – 2FA enforcement and building trust
• 00:26:51 – Managing credentials and password managers
• 00:29:24 – Forms of 2FA
• 00:31:48 – Trusted publishers
• 00:38:08 – Video Course Spotlight
• 00:39:28 – Updating an older project
• 00:41:44 – Evolution of security
• 00:43:06 – Typosquatting and evolving security
• 00:49:13 – How To Give Back to Open Source Without Losing Your Mind
• 00:52:48 – What are you excited about in the world of Python?
• 00:54:45 – What do you want to learn next?
• 00:57:06 – How can people follow your work online?
• 00:57:37 – Thanks and goodbye

Show Links:

• PyPI hires a Safety & Security Engineer - The Python Package Index
• Inbound Malware Volume Report - The Python Package Index
• 2FA Enforcement for New User Registrations - The Python Package Index
• PyPI 2FA Security Key Giveaway - PyPI
• Software Bill Of Materials - National Telecommunications and Information Administration
• Introducing ‘Trusted Publishers’ - The Python Package Index
• Trusted Publishers - Getting Started - PyPI Docs
• How To Give Back to Open Source Without Losing Your Mind – vBrownBag
• Good First Issues - OpenSauced
• Good First Issues
• Participation - Hacktoberfest 2023
• Python Release Python 3.12.0 - Python.org
• htmx - high power tools for html
• The web framework for perfectionists with deadlines - Django
• The Python Package Index - Blog
• Mike Fiedler, Code Gardener (@[email protected]) - Fosstodon
• Mike Fiedler, Code Gardener (@mikefiedler) / X
• Mike Fiedler’s personal website

Level up your Python skills with our expert-led courses:

• A Beginner's Guide to pip
• Everyday Project Packaging With pyproject.toml
• Publishing Python Packages to PyPI

Support the podcast & join our community of Pythonistas

What fundamental developer skills are new Python users missing? What best practices might developers without a computer science background be lacking? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher opens our discussion by sharing a recent social media thread about teaching software engineering best practices to scientists and others without a computer science background. We talk about software design philosophy and strategies for sharing knowledge within an organization.

We cover the results from the sixth annual official Python Developer Survey. The survey covers Python usage, language versions, frameworks, libraries, and various demographics. We dig into the details and share our insights.

We also share several other articles and projects from the Python community, including a couple of release announcements, so many Python dataframes, ways to speed up your code when multiple cores aren’t an option, Python’s syntactic sugar, a computer algebra system named SymPy, the process of building a blog in Django, code metrics in Python with Radon, and a TUI app for daily writing.

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

Topics:

• 00:00:00 – Introduction
• 00:02:25 – Django 5.0 Alpha 1 Released
• 00:03:45 – Mojo SDK Released for Linux
• 00:04:50 – Python Developers Survey 2022 Results
• 00:13:44 – Why Are There So Many Python Dataframes?
• 00:20:24 – Sponsor: Snyk
• 00:21:15 – Speeding Up Your Code When Multiple Cores Aren’t an Option
• 00:28:41 – Python Is (Mostly) Made of Syntactic Sugar
• 00:32:57 – Towards a New SymPy
• 00:36:48 – Video Course Spotlight
• 00:37:54 – Building a Blog in Django
• 00:41:22 – What are fundamental skills and best-practices that might be lacking?
• 00:55:02 – Learning About Code Metrics in Python With Radon
• 00:59:43 – words-tui: A TUI App for Daily Writing
• 01:02:34 – Thanks and goodbye

News:

• Django 5.0 Alpha 1 Released
• Mojo SDK Released for Linux

Show Links:

• Python Developers Survey 2022 Results
• Why Are There So Many Python Dataframes? – Ever wonder why there are so many ways libraries that have dataframes in Python? This article talks about the different perspectives of popular tool kits to explore why they are what they are.
• Speeding Up Your Code When Multiple Cores Aren’t an Option – Parallelism isn’t the only answer: often you can optimize low-level code to get significant performance improvements.
• Python Is (Mostly) Made of Syntactic Sugar – “Programming languages are often made up of a (mostly) irreducible core, with lots of sugary constructs sprinkled on top–the syntactic sugar.” This article summarizes a lot of Brett Cannon’s recent work exploring what’s sugar and what’s fundamental in Python.
• Towards a New SymPy – SymPy is a computer algebra system, meaning that it does math on symbolic concepts. This can provide a lot more accuracy than typical floating-point mathematics found in numeric systems. This is part one of a multi-part article, explaining SymPy along with its recent improvements. Part two discusses how SymPy handles polynomials.
• Building a Blog in Django – Very little code is needed to get a blog working using the Django framework. This post highlights what you need, including each of the key code components. There’s also an associated Hacker News discussion.

Discussion:

• What are fundamental skills and best-practices that might be lacking? - Matt Harrison
• A Philosophy of Software Design - Book
• Episode #49: The Challenges of Developing Into a Python Professional

Projects:

• Learning About Code Metrics in Python With Radon – Radon is a code metrics tool. This article introduces you to it and teaches you how you can improve your code based on its measurements.
• words-tui: A TUI App for Daily Writing

Additional Links:

• Episode #157: Discussing Mojo & Improving Python Object-Oriented Programming
• Episode #167: Exploring pandas 2.0 & Targets for Apache Arrow
• Episode #47: Unraveling Python’s Syntax to Its Core With Brett Cannon
• Episode #92: Continuing to Unravel Python’s Syntactic Sugar With Brett Cannon
• Episode #154: Targeting WebAssembly Platforms & Distilling a Minimum Viable Python
• SymPy
• Computer algebra system - Wikipedia
• SimPy: Simulating Real-World Processes With Python
• Episode #117: Measuring Python Code Quality, Simplicity, and Maintainability
• Clean Code: Writing Clear, Readable, Understandable & Reliable Quality Code - Sonar
• Anthony Shaw - Wily Python: Writing simpler and more maintainable Python - PyCon 2019 - YouTube
• Textual
• National Novel Writing Month
• Episode #101: Tools for Setting Up Python on a New Machine

Level up your Python skills with our expert-led courses:

• Filtering Iterables With Python
• Defining Python Functions With Optional Arguments
• Simulating Real-World Processes in Python With SimPy

Support the podcast & join our community of Pythonistas

Python 3.12 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. Geir Arne coordinated a series of preview articles with several members of the Real Python team this year, and his showcase tutorial, “Python 3.12: Cool New Features for You to Try,” came out on October 2. Christopher’s video course was posted the next day, covering the topics from the article with visual examples of Python 3.12 in action.

Geir Arne and Christopher collaborated to create code examples of the new features. We discuss better error messaging, more intuitive f-strings, subinterpreters, the Linux perf profiler, improved typing syntax, and more.

We dig into the updates and offer advice on incorporating them into your projects. We also consider when you should start running Python 3.12.

Topics:

• 00:00:00 – Introduction
• 00:02:15 – Preview tutorials
• 00:05:08 – Ever better error messages
• 00:10:03 – More powerful f-strings
• 00:15:46 – Comprehension inlining & additional optimizations
• 00:21:24 – Support for subinterpreters
• 00:27:55 – Support for the Linux perf profiler
• 00:32:02 – Immortal objects
• 00:35:01 – Video Course Spotlight
• 00:36:03 – Calendar constants for days and months
• 00:38:48 – New itertools.batched() for grouping Items
• 00:40:53 – Improved type variable syntax
• 00:50:51 – New Path.walk() for listing files in subdirectories
• 00:53:04 – Override decorator for static typing
• 00:57:36 – Advice on upgrading
• 01:03:08 – Thanks to the core developer team
• 01:04:39 – Thanks to the Real Python team and goodbye

Show Links:

• Python 3.12: Cool New Features for You to Try
• Python Release Python 3.12.0 - Python.org
• Python 3.12 Preview: Ever Better Error Messages
• Python 3.12 Preview: More Intuitive and Consistent F-Strings
• PEP 701 – Syntactic formalization of f-strings
• PEP 709 – Inlined comprehensions
• Python 3.12 Preview: Subinterpreters
• PEP 554 – Multiple Interpreters in the Stdlib
• PEP 684 – A Per-Interpreter GIL
• Python 3.12 Preview: Support For the Linux perf Profiler
• PEP 683 – Immortal Objects, Using a Fixed Refcount
• PEP 695 – Type Parameter Syntax
• Python 3.12 Preview: Static Typing Improvements
• PEP 698 – Override Decorator for Static Typing

Level up your Python skills with our expert-led courses:

• Python Type Checking
• What's New in Python 3.12
• Cool New Features in Python 3.11

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What can you learn from feeding an entire book on Python programming into ChatGPT-4 and asking it to provide a technical review? What are the potential pitfalls of using an LLM as a learning tool? This week on the show, author Al Sweigart talks about his recent experiments using ChatGPT and Python.

Al wanted to test how well an LLM could understand the computer science concepts covered in his recent book, The Recursive Book of Recursion. We talk about the positive and negative results of this technical review. We consider if this would be a valuable tool for technical review of your projects.

Al shares his thoughts on using ChatGPT as a learning tool and its potential pitfalls. We also cover the current strengths of this type of tool for Python developers.

Topics:

• 00:00:00 – Introduction
• 00:01:51 – Reasons for experimenting
• 00:04:24 – Using ChatGPT to review his recursion book
• 00:10:45 – Why do this review?
• 00:11:34 – The trouble of confabulation
• 00:18:45 – Meaning of artificial intelligence over time
• 00:28:21 – Video Course Spotlight
• 00:29:54 – Detailed prompts and asking for specifics
• 00:36:44 – Potential pitfalls for learning
• 00:49:48 – Checking for flaws in generated code
• 00:54:58 – How can it be useful?
• 01:01:33 – Touching briefly on ethics
• 01:02:35 – What are you excited about in the world of Python?
• 01:05:21 – What do you want to learn next?
• 01:07:36 – Python Programming Exercises, Gently Explained
• 01:09:20 – How can people follow your work online?
• 01:09:49 – Thanks and goodbye

Show Links:

• Al Sweigart’s Website
• Using ChatGPT-4 to Review My Recursion Programming Book - The Invent with Python Blog
• Prompt Engineering: A Practical Example – Real Python
• ChatGPT: Your Personal Python Coding Mentor – Real Python
• GitHub Copilot: Fly With Python at the Speed of Thought – Real Python
• ChatGPT: US lawyer admits using AI for case research - BBC News
• If a hammer was like AI…
• The Elements of AI Ethics
• Al Sweigart: An Overview of the Python Code Tool Landscape 2023 - YouTube
• Python Programming Exercises, Gently Explained
• Invent with Python
• Ruff, an extremely fast Python linter - Astral
• Rust Programming Language
• Command Line Applications in Rust

Level up your Python skills with our expert-led courses:

• Python Basics: Strings and String Methods
• Python Basics Exercises: Conditional Logic and Control Flow
• Python Basics: Conditional Logic and Control Flow

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Have you thought about contributing to an open-source Python project? What are possible entry points for intermediate developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article by Stefanie Molin called “5 Ways to Get Started in Open Source.” Christopher shares his experience with suggesting features and potential bug fixes. We talk about common entry points for beginners and provide additional resources.

We cover a recent Real Python tutorial about creating QR codes with Python. The tutorial introduces the library Segno and tours the features. By working through the examples, you’ll be ready to build a QR code project yourself.

We also cover several other articles and projects from the Python community, including a couple of release announcements, an introduction to Python’s functools module, Hatch as an alternative for packaging, options for when NumPy is too slow, a simple diceware generator project, and a collection of machine learning recipes.

Topics:

• 00:00:00 – Introduction
• 00:02:04 – 2023 Django Developers Survey
• 00:02:36 – Python 3.12.0 Release Candidate 2 Available
• 00:03:04 – Pandas 2.1.0 Released
• 00:03:27 – PEP 713: Callable Modules - Rejected
• 00:04:52 – Generate Beautiful QR Codes With Python
• 00:10:17 – Introduction to Python’s Functools Module
• 00:14:00 – Switching to Hatch
• 00:20:08 – Video Course Spotlight
• 00:21:27 – When NumPy is too slow
• 00:26:31 – 5 Ways to Get Started in Open Source
• 00:42:28 – nodice-cli: A simple diceware generator with no dependencies
• 00:44:32 – ML-Recipes: Collection of Machine Learning Recipes
• 00:47:00 – Thanks and goodbye

News:

• 2023 Django Developers Survey
• Python 3.12.0 Release Candidate 2 Available
• Pandas 2.1.0 Released
• PEP 713: Callable Modules - Rejected - PEPs - Discussions on Python.org

Show Links:

• Generate Beautiful QR Codes With Python – In this tutorial, you’ll learn how to use Python to generate QR codes, from your standard black-and-white QR codes to beautiful ones with your favorite colors. You’ll learn how to format QR codes, rotate them, and even replace the static background with moving images.
• Introduction to Python’s Functools Module – This article introduces you to the functions in Python’s functools module with real-world examples to help show you how and when to use each feature.
• Switching to Hatch – Oliver used Poetry for most of his projects, but he recently tried out Hatch instead. This blog post covers what it took to get things going and what features he used, including how he ditched tox.
• When NumPy is too slow – NumPy is typically faster than plain Python for numeric calculations. What should you do when you find your NumPy-based code is too slow?

Discussion:

• 5 Ways to Get Started in Open Source – This article shares ideas for finding and making your first open-source contribution, using examples from contributions the author has made to various projects.
• The In-Person Event Handbook
• Development Sprints - PyCon US 2023
• Hacktoberfest 2023

Projects:

• nodice-cli: A simple diceware generator with no dependencies
• ML-Recipes: Collection of Machine Learning Recipes

Additional Links:

• Episode #157: Discussing Mojo & Improving Python Object-Oriented Programming
• Segno - Python QR Code and Micro QR Code encoder — Segno documentation
• Episode #125: Improve Matplotlib With Style Sheets & Python Async for the Web
• functools — Higher-order functions and operations on callable objects — Python documentation
• About - Hatch
• tox
• xkcd: Password Strength

Level up your Python skills with our expert-led courses:

• Building Python Project Documentation With MkDocs
• Everyday Project Packaging With pyproject.toml
• Caching in Python With lru_cache

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When choosing a tool for profiling Python code performance, should it focus on the CPU, GPU, memory, or individual lines of code? What if it looked at all those factors and didn’t alter code performance while measuring it? This week on the show, we talk about Scalene with Emery Berger, Professor of Computer Science at the University of Massachusetts Amherst.

Emery talks about his background in memory management and his collaboration on Hoard, a scalable memory manager system used in Mac OS X. We discuss the need for improving code performance on modern computer architecture. He highlights this idea by contrasting the familiar limitations of Moore’s law with the lesser-known rule of Dennard scaling.

Working with his students in the university lab, they developed Scalene. Scalene is a high-performance CPU, GPU, and memory profiler. It can look at code from the individual function or line-by-line level and compare time spent in Python vs C code. Emery talks about the recent Scalene feature of AI-powered optimization proposals and covers a couple of examples. He also shares a collection of additional Python code-assistant tools from their lab.

Topics:

• 00:00:00 – Introduction
• 00:02:13 – College of Information and Computer Sciences
• 00:03:25 – Memory management systems background
• 00:05:15 – Dennard Scaling vs Moore’s Law
• 00:10:12 – Starting work on Python profiling
• 00:15:00 – Deciding on a statistical profiler
• 00:17:05 – Wanting to trace memory
• 00:21:21 – Finding memory issues
• 00:23:59 – Line-by-line analysis
• 00:25:56 – Video Course Spotlight
• 00:27:14 – Measuring profiler performance
• 00:30:30 – Memory leak detection
• 00:34:31 – When should you run a profiler?
• 00:37:27 – Considerations for measuring cloud performance
• 00:39:12 – Working with Jupyter and Conda
• 00:42:18 – Common issues and AI solutions
• 00:45:50 – Using a profiler to learn a codebase
• 00:50:48 – Examples of AI-powered optimizations
• 00:55:50 – What are you excited about in the world of Python?
• 00:58:30 – What do you want to learn next?
• 01:01:48 – How can people follow your work online?
• 01:02:56 – Thanks and goodbye

Show Links:

• Emery Berger - Professor of Computer Science, UMass Amherst
• Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals
• Hoard
• Moore’s law - Wikipedia
• Dennard scaling - Wikipedia
• Scalene: A high-performance, high-precision CPU+GPU+memory profiler for Python - PyCon 2021 - YouTube
• Python Performance Matters - Strange Loop 2022 - YouTube
• Triangulating Python Performance Issues with Scalene
• ChatDBG: Puts root causes analysis into your debugger, and suggests fixes
• Commentator: Automatically writes comments and type annotations for your code
• Pythoness: Automatically generates Python code from natural language description
• Slipcover: Near Zero-Overhead Python Code Coverage
• emeryberger - GitHub

Level up your Python skills with our expert-led courses:

• Python Debugging With pdb
• Testing Your Code With pytest
• What Does if name == "main" Mean in Python?

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Are you writing efficient Python with as few lines of code as possible? Are you familiar with the many built-in language features that will simplify your code and make it more Pythonic? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent post from Bob Belderbos titled “Make Each Line Count, Keeping Things Simple in Python.” We provide many of our favorite Pythonic examples and the language mistakes that we’ve learned from. We also share multiple resources to add to your learning path.

Microsoft has announced a limited beta program for Python in Excel. We dig into the current details, requirements, and potential use cases.

We cover several other articles and projects from the Python community, including a group of announcements from the Python Software Foundation, a showcase of the Polars DataFrame library, immortal objects in Python, a code image generator Python project, an MS Paint clone in the terminal, and a Django ORM cheatsheet.

Topics:

• 00:00:00 – Introduction
• 00:02:10 – Python 3.12.0 release candidate 1 released
• 00:02:53 – PSF Announces New PyPI Safety & Security Engineer
• 00:03:44 – 2022 PSF Annual Report
• 00:04:13 – Pydantic has been downloaded … 1 BILLION times!
• 00:04:28 – Python Polars: A Lightning-Fast DataFrame Library
• 00:12:26 – Introducing Immortal Objects for Python
• 00:15:32 – Video Course Spotlight
• 00:17:01 – Introducing Python in Excel
• 00:26:34 – Build a Code Image Generator With Python
• 00:31:49 – Make Each Line Count, Keeping Things Simple in Python
• 00:44:08 – Textual-paint
• 00:46:04 – Django ORM Cheatsheet
• 00:49:48 – Thanks and goodbye

News:

• Python Insider: Python 3.12.0 release candidate 1 released – “The second candidate (and the last planned release preview) is scheduled for Monday, 2023-09-04, while the official release of 3.12.0 is scheduled for Monday, 2023-10-02.”
• Python 3.11.5, 3.10.13, 3.9.18, and 3.8.18 is now available - Python Insider
• PSF Announces New PyPI Safety & Security Engineer
• 2022 PSF Annual Report – The annual report from the Python Software Foundation details all the changes and events at the PSF last year.
• Pydantic has been downloaded … 1 BILLION times! - Twitter

Show Links:

• Python Polars: A Lightning-Fast DataFrame Library – Welcome to the world of Polars, a powerful DataFrame library for Python! In this showcase tutorial, you’ll get a hands-on introduction to Polars’ core features and see why this library is catching so much buzz.
• Introducing Immortal Objects for Python – This article explains immortal objects (PEP 683), which are excluded from garbage collection. This causes performance and shared memory improvements for large architectures.
• Introducing Python in Excel – Microsoft has announced that they’re embedding Python in Excel through a partnership with Anaconda. Read on for details.
• Build a Code Image Generator With Python – In this step-by-step tutorial, you’ll build a code image generator that creates nice-looking images of your code snippets to share on social media. Your code image generator will be powered by the Flask web framework and include exciting packages like Pygments and Playwright.

Discussion:

• Make Each Line Count, Keeping Things Simple in Python – Simplicity is hard. This article talks briefly about how you approach coding while keeping things simple.
• itertools — Functions creating iterators for efficient looping — Python documentation
• More Itertools - more-itertools 10.1.0 documentation
• Python enumerate(): Simplify Loops That Need Counters – Real Python
• Python’s all(): Check Your Iterables for Truthiness – Real Python
• How to Use any() in Python – Real Python

Projects:

• Textual-paint
• Django ORM Cheatsheet

Additional Links:

• Release Python Polars 0.19.0 · pola-rs/polars
• Episode #140: Speeding Up Your DataFrames With Polars – The Real Python Podcast
• Episode #167: Exploring pandas 2.0 & Targets for Apache Arrow – The Real Python Podcast
• Introducing Python in Excel 😱 - YouTube
• Source code beautifier / syntax highlighter – convert code snippets to HTML « hilite.me

Level up your Python skills with our expert-led courses:

• Building a Django User Management System
• Process Images Using the Pillow Library and Python
• Editing Excel Spreadsheets in Python With openpyxl

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What should you consider when picking a font for coding in Python? What characters and their respective glyphs should you check before making your decision? This week on the show, we talk with Real Python author and core team member Philipp Acsany about his recent article, Choosing the Best Coding Font for Programming.

Philipp shares some of his background as a font engineer and graphic designer. We talk about how font design tools were his introduction to programming in Python.

We discuss how the frequent use of underscores, at signs, parentheses, and asterisks in Python’s syntax should affect your decision. Philipp’s tutorial provides several resources to help you find a monospace font that fits your coding requirements.

Topics:

• 00:00:00 – Introduction
• 00:01:32 – Previous podcast appearances
• 00:03:18 – Programming-environment fussiness and monospace fonts
• 00:07:29 – Researching the tutorial and curating the collection
• 00:10:51 – Philipp’s background
• 00:18:07 – Differentiating characters
• 00:21:37 – Monospace, typewriters, and alignment
• 00:25:08 – Character sets to study
• 00:32:38 – The comma and the period
• 00:37:04 – Video Course Spotlight
• 00:38:30 – Python’s use in font development
• 00:42:48 – Different fonts for different languages
• 00:49:27 – Non-English comments
• 00:55:40 – Our font choices
• 00:59:00 – What are you excited about in the world of Python?
• 01:01:35 – What do you want to learn next?
• 01:03:41 – Where can people follow your work online?
• 01:04:38 – Thanks and goodbye

Show Links:

• Choosing the Best Coding Font for Programming - Real Python
• #79 Font-Engineering und Schriftarten fürs Programmieren mit Philipp Acsany - Engineering Kiosk
• Episode #134: Building Python REST APIs With Flask & Structuring Pull Requests – The Real Python Podcast
• Input: Fonts for Code — Info
• Slashed zero - Wikipedia
• Papyrus - SNL - YouTube
• FontLab - Font editors and converters for Mac and Windows
• RoboFont
• Hedy - Textual programming made easy
• Typing Practice - keybr.com
• Monkeytype - A minimalistic, customizable typing test
• Homerow — Keyboard shortcuts for every button in macOS
• About Philipp Acsany – Real Python
• Philipp Acsany - LinkedIn
• Filip Axani (axani) - Chess Profile - Chess.com

Level up your Python skills with our expert-led courses:

• Create a Python Wordle Clone With Rich
• Building Python Project Documentation With MkDocs
• Building Command Line Interfaces With argparse

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How can you improve a classification model while avoiding overfitting? Once you have a model, what tools can you use to explain it to others? This week on the show, we talk with author and Python trainer Matt Harrison about his new book Effective XGBoost: Tuning, Understanding, and Deploying Classification Models.

Matt talks about the process of developing the book and how he wanted it to be an interactive experience for the reader. He explains the concept of gradient boosting and provides metaphors for developing a model. He shares his appreciation for exploratory data analysis as a crucial step in understanding your data.

He also shares additional libraries to help you explain your model. We discuss how difficult it is to develop the story of how the model works to share it with stakeholders.

He illustrates why covering the complete process is essential, from exploring data and building a model to finally deploying it. He shares many of the tools he found along the way.

This week’s episode is brought to you by Scout APM.

Topics:

• 00:00:00 – Introduction
• 00:02:16 – Starting on the book
• 00:04:36 – What is tabular prediction?
• 00:06:50 – Who could leverage XGBoost?
• 00:09:46 – Background to get started
• 00:11:50 – Using XGBoost to explore data
• 00:21:06 – Sponsor: ScoutAPM
• 00:21:54 – Focusing on using the tool
• 00:26:37 – Not being a developer
• 00:30:53 – Contrasting XGBoost and logistic regression
• 00:41:57 – Video Course Spotlight
• 00:43:21 – Using SHAP to explain the model
• 00:48:06 – Working with hyperparameters
• 00:51:40 – Deploying your model
• 00:53:09 – XGBoost Feature Interactions Reshaped (XGBFIR)
• 00:55:47 – Communicating the story of a model
• 00:57:57 – How to find the book
• 00:59:07 – What are you excited about in the world of Python?
• 01:02:46 – What do you want to learn next?
• 01:03:12 – How can people follow what you do online?
• 01:03:59 – Thanks and goodbye

Show Links:

• MetaSnake - Custom Python Training
• Effective XGBoost Book - Store Link (Discount expires end of September 2023)
• XGBoost Documentation — xgboost 1.7.6 documentation
• Gradient boosting - Wikipedia
• SHAP (SHapley Additive exPlanations) Documentation
• Hyperopt Documentation
• MLflow - A platform for the machine learning lifecycle
• xgbfir: XGBoost Feature Interactions Reshaped
• Effective XGBoost Book - Store Link (Discount expires end of September 2023)
• Mojo 🔥: Programming language for all of AI
• MetaSnake - Blog
• 🐍 Matt Harrison - LinkedIn
• Matt Harrison (@__mharrison__) - Twitter

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Using k-Nearest Neighbors (kNN) in Python
• Starting With Linear Regression in Python

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Have you ever encountered strange behavior when trying something new in Python? What are common quirks hiding within the language? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent blog post that lists a collection of quirky Python behaviors. We share a few examples with explanations but leave several as puzzles to dig into.

Christopher transitions our discussion into Python features that can be difficult to explain to a new programmer. We also share some of our own stumbling blocks while learning the language.

We cover several other articles and projects from the Python community, including a news update, previewing Python 3.12’s more intuitive and consistent f-strings, finding performance bottlenecks with profiling, emulating the 6502 processor in Python, using Rich to inspect Python objects, and plotting statistical data with Lets-Plot.

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

Topics:

• 00:00:00 – Introduction
• 00:02:12 – Steering Council Announces Optional GIL
• 00:04:05 – Polars is Starting a Company
• 00:05:13 – Python 3.12 Preview: More Intuitive and Consistent F-Strings
• 00:09:29 – Sponsor: Porkbun
• 00:10:22 – Profiling in Python: How to Find Performance Bottlenecks
• 00:21:01 – Writing a 6502 Emulator in Python
• 00:24:36 – Python Quirks
• 00:32:30 – Video Course Spotlight
• 00:34:02 – What Python feature would you have trouble explaining to a new programmer?
• 00:42:25 – Using Rich Inspect to Interrogate Python Objects
• 00:44:36 – Lets-Plot: Plotting Library for Statistical Data
• 00:48:01 – Thanks and goodbye

News:

• Steering Council Announces Optional GIL
• Polars is Starting a Company

Show Links:

• Python 3.12 Preview: More Intuitive and Consistent F-Strings – In this tutorial, you’ll preview one of the upcoming features of Python 3.12, which introduces a new f-string syntax formalization and implementation. The new implementation lifts some restrictions and limitations that affect f-string literals in Python versions lower than 3.12.
• Profiling in Python: How to Find Performance Bottlenecks – In this tutorial, you’ll learn how to profile your Python programs using numerous tools available in the standard library, third-party libraries, as well as a powerful tool foreign to Python. Along the way, you’ll learn what profiling is and cover a few related concepts.
• Writing a 6502 Emulator in Python – The 6502 processor from Motorola was quite popular and could be found in the Nintendo and Sega consoles as well as the Commodore 64. This very detailed article shows you how to build an emulator for the processor in Python.
• Python Quirks – A straight-out list of code snippets showing off some of the weird and unexpected behavior of your favorite language.

Discussion

• What Python feature would you have trouble explaining to a new programmer? - Trey Hunner on Twitter

Projects:

• Using Rich Inspect to Interrogate Python Objects – You might know the Rich library as the terminal color tool, but it has a few utilities that are generally helpful in your code. This article shows you the inspect feature, which gives you loads of information on an object.
• Lets-Plot: Plotting Library for Statistical Data

Additional Links:

• Polars
• Python 3.12 Preview: Support For the Linux perf Profiler – Real Python
• Python Timer Functions: Three Ways to Monitor Your Code – Real Python
• Episode #128: Using a Memory Profiler in Python & What It Can Teach You – The Real Python Podcast
• Defining Your Own Python Function – Mutable Default Parameters

Level up your Python skills with our expert-led courses:

• Using Jupyter Notebooks
• Creating Web Maps From Your Data With Python Folium
• Graph Your Data With Python and ggplot

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What are the new ways to describe your data in pandas 2.0? Will the addition of Apache Arrow to the data back end foster the growth of data interoperability? This week on the show, we talk with pandas core developer Marc Garcia about the release of pandas 2.0.

Marc shares his background and work on pandas. We discuss the history of data representation in pandas and the need to move beyond NumPy. We also talk about how Apache Arrow only solves some of the issues.

We dig into the potential of an Apache Arrow back end and how it could offer interoperability between data platforms. We also cover the moderate adoption and backward-compatibility concerns. Marc also shares his thoughts on making pandas more extensible.

Topics:

• 00:00:00 – Introduction
• 00:02:07 – Getting involved with the pandas project
• 00:03:48 – Continued growth of the platform
• 00:06:49 – Parallel branch development
• 00:09:19 – The introduction of Apache Arrow
• 00:18:53 – Working with NumPy data in pandas
• 00:30:18 – Arrow data types and strings
• 00:41:23 – Video Course Spotlight
• 00:42:37 – Interoperability of Arrow data back end
• 00:50:36 – Could pandas be more extensible?
• 01:00:49 – Python DataFrame Summit 2023
• 01:08:12 – What are you excited about in the world of Python?
• 01:11:13 – What do you want to learn next?
• 01:12:12 – How can people follow your work online?
• 01:13:46 – Thanks and Goodbye

Show Links:

• Marc Garcia - datapythonista - data engineer, data scientist and pandas core developer
• pandas 2.0 and the Arrow revolution (part I)
• The pandas of the future - Marc Garcia - SciPyLA 2019 - TubEdu
• The deadly consequences of rounding errors - Slate
• Community Blog - pandas - Python Data Analysis Library
• Apache Arrow - Apache Arrow
• Apache Arrow and the “10 Things I Hate About pandas” - Wes McKinney
• I/O Extensions in pandas - PDEP-9
• Extension Arrays for Pandas - Tom’s Blog
• Python Dataframe Summit 2023
• Rust Programming Language
• Freediving - Wikipedia
• Marc Garcia - LinkedIn
• Marc Garcia (@datapythonista) - X

Level up your Python skills with our expert-led courses:

• Reading and Writing Files With pandas
• Explore Your Dataset With pandas
• The pandas DataFrame: Working With Data Efficiently

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What are all the different versions of Python? You may have heard of Cython, Brython, PyPy, or others and wondered where they fit into the Python landscape. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article from the Bite Code blog about all the different forms that Python can take. CPython is the reference implementation of the language, which is what we usually discuss. He lists several alternative projects and the use cases.

We also discuss a recent Real Python tutorial about IPython. IPython is an interactive Python shell from the team that developed Jupyter Notebooks. It includes a set of IDE-like features and unique magic commands. The tutorial digs into using the tool to learn more about Python and explore your code.

We cover several other articles and projects from the Python community, including several news updates, the state of WASI support for CPython, how Python uses garbage collection, a discussion about the current AI echo chamber, an async Python web microframework, a stand-alone CSV editor, and a project for identifying unused dependencies to avoid a bloated virtual environment.

This week’s episode is brought to you by Scout APM.

Topics:

• 00:00:00 – Introduction
• 00:02:19 – Python 3.12.0 Beta 4 Released
• 00:02:53 – Django in Action - Christopher’s Book
• 00:04:28 – State of WASI Support for CPython: June 2023
• 00:08:32 – What’s the Deal With CPython, PyPy, MicroPython, Jython…?
• 00:12:10 – Sponsor: Scout APM
• 00:12:57 – Unlock IPython’s Magical Toolbox for Your Coding Journey
• 00:18:33 – How Python Uses Garbage Collection
• 00:21:31 – Video Course Spotlight
• 00:23:04 – Are People in Tech Inside an AI Echo Chamber?
• 00:39:39 – quart: An Async Python Web Microframework
• 00:41:33 – Modern CSV: CSV Editor/Viewer
• 00:43:09 – creosote: Identify Unused Dependencies
• 00:45:09 – Thanks and Goodbye

News:

• Python 3.12.0 Beta 4 Released
• Django in Action - Christopher’s Book - Manning Early Access Program

Show Links:

• State of WASI Support for CPython: June 2023 – This post from Brett Cannon covers the current state of WebAssembly targets in Python.
• What’s the Deal With CPython, PyPy, MicroPython, Jython…? – This comprehensive article introduces you to all the different ways that you can Python. CPython isn’t the only choice. Learn what else is out there and why you might choose an alternative.
• Unlock IPython’s Magical Toolbox for Your Coding Journey – IPython is a powerful tool that can prove useful on your journey to mastering Python. Its friendly interface will enable you to comfortably take control of your learning. In this tutorial, you’ll cover the basic concepts of using IPython and learn how its features can make coding efficient.
• How Python Uses Garbage Collection – This article outlines how Python stores variables as references and how that relates to memory management

Discussion:

• Are People in Tech Inside an AI Echo Chamber?
• Inside the AI Factory: the humans that make tech seem human - The Verge
• Is ChatGPT getting worse over time? Study claims yes, but others aren’t sure | Ars Technica
• How Is ChatGPT’s Behavior Changing over Time?

Projects:

• quart: An Async Python Web Microframework
• Modern CSV: CSV Editor/Viewer
• creosote: Identify unused dependencies and avoid a bloated virtual environment

Additional Links:

• Episode #154: Targeting WebAssembly Platforms & Distilling a Minimum Viable Python – The Real Python Podcast
• IPython 8.14.0 documentation

Level up your Python skills with our expert-led courses:

• Mazes in Python Part 2: Storing and Solving
• Python Basics: Object-Oriented Programming
• Mazes in Python: Build, Visualize, Store, and Solve

Support the podcast & join our community of Pythonistas

Are you getting the most out of your Postgres database? What features could you leverage to improve your Python project? This week on the show, Craig Kerstiens from Crunchy Data is here to discuss getting the most out of Postgres.

Craig shares his years of PostgreSQL expertise with advice on getting more from the platform. We talk about rich data types for describing ranges, geospatial data, and JSON.

Craig shares tools for accessing performance statistics from the command line and strategies for optimizing your terminal settings for SQL searches. He discusses Postgres extensions for customizing the database to your needs. Craig also provides multiple resources for learning more and an online tool for practicing within a playground environment.

Topics:

• 00:00:00 – Introduction
• 00:02:36 – What are reasons for considering Postgres?
• 00:07:41 – Timeline of recent features
• 00:11:06 – Unique data types
• 00:16:34 – Storing JSON information
• 00:20:19 – Video Course Spotlight
• 00:21:50 – Storing geographic information
• 00:25:16 – What’s the process for adding extensions?
• 00:31:33 – Advice for Python developers using Postgres
• 00:33:31 – Advice on writing SQL
• 00:38:06 – Command-line tools and customizations
• 00:48:18 – Django as an entry to Python
• 00:51:13 – Resources for learning and practicing with Postgres
• 00:53:45 – What are you excited about in the world of Python?
• 00:55:55 – What do you want to learn next?
• 00:58:34 – How can people follow your work online?
• 00:59:20 – Thanks and goodbye

Show Links:

• Craig Kerstiens - Blog
• Trusted Open Source PostgreSQL & Commercial Support for the Enterprise - Crunchy Data
• Why Postgres? - Crunchy Data
• Why Postgres - Craig Kerstiens - YouTube
• A hands on experience with complex SQL - Craig Kerstiens - YouTube
• Ingres (database) - Wikipedia
• PostgreSQL specific model fields - Django documentation
• Psqlrc - PostgreSQL wiki
• The most useful Postgres extension - pg_stat_statements
• High-compression Metrics Storage with Postgres Hyperloglog
• Postgres Playground and Tutorials - Crunchy Data
• PostgreSQL Blog - Crunchy Data
• It’s at least once a week I talk with someone that “loves Postgres” but isn’t sure why… Craig Kerstiens on Twitter:
• The Wok Book: Recipes and Techniques by J. Kenji Lopez-Alt

Level up your Python skills with our expert-led courses:

• Creating Web Maps From Your Data With Python Folium
• Deploying a Flask Application Using Heroku
• SQLite and SQLAlchemy in Python: Moving Your Data Beyond Flat Files

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What principles should you consider when designing a Python library? How do you construct a library API that’s understandable and easy to use? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We share an article about building library APIs. The piece provides advice for package structure, naming, error handling, and more. The author guides you toward Pythonic principles by comparing clunky vs elegant design examples.

Christopher discusses his recent video course on Jinja templating. The course covers creating text files with programmatic content and employing rich templates to structure the front end of Python web applications.

We cover several other articles and projects from the Python community, including several news updates, reasons why membership tests are fast for the range() function, CLI tools hidden in the Python standard library, a thread about the right way to install Python, recipes for using the Polars library, and a project for feature flags within Django.

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

Topics:

• 00:00:00 – Introduction
• 00:02:16 – PyLadies Conference (Dec 2023) Call for Volunteers
• 00:02:32 – PSF Board Election Results
• 00:03:47 – PSF Announces New Security Developer in Residence
• 00:04:39 – Django Security Releases Issued
• 00:04:50 – Deprecation of bdist_egg Uploads to PyPI
• 00:05:54 – Why Are Membership Tests So Fast for range() in Python?
• 00:11:51 – CLI Tools Hidden in the Python Standard Library
• 00:15:59 – Sponsor: Snyk
• 00:16:49 – Designing Pythonic Library APIs
• 00:28:27 – Jinja Templating
• 00:32:49 – Kill a Developer in 4 Words or Less
• 00:37:28 – Video Course Spotlight
• 00:38:51 – What is “the right way” to install Python on a new M2 MacBook?
• 00:44:11 – polars-cookbook: Recipes for Using Python’s Polars Library
• 00:46:48 – waffle: Feature Flags for Django
• 00:49:54 – Thanks and goodbye

News:

• PyLadies Conference (Dec 2023) Call for Volunteers
• PSF Board Election Results
• PSF Announces New Security Developer in Residence
• I Am the First PSF Security Developer-in-Residence – Seth was recently hired as the first security developer in residence at the PSF. His blog post talks about what his responsibilities are and how he defines success for the position.
• Deputy CPython Developer in Residence - Python Software Foundation - Career Page
• Django Security Releases Issued: 4.2.3, 4.1.10, and 3.2.20
• Deprecation of bdist_egg Uploads to PyPI – PEP 715 has been accepted and as of August 1, 2023, the .egg format will no longer be accepted as an upload. Existing eggs on PyPI will remain in place.

Show Links:

• Why Are Membership Tests So Fast for range() in Python? – In Python, range() is most commonly used in for loops. However, ranges have some other use cases too, as they share many properties with lists. In this tutorial, you’ll explore why it’s so fast to perform a membership test on a Python range.
• CLI Tools Hidden in the Python Standard Library – There are several modules in Python that are directly callable from the command line, including the ability to gzip and pretty-print JSON. This article introduces you to what options are available and how Simon discovered them.
• Designing Pythonic Library APIs – This article summarizes principles that Ben has found useful when designing Python library APIs. Topics include structure, naming, error handling, and type annotations.
• Jinja Templating – With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out.

Discussion:

• Kill a Developer in 4 Words or Less - Twitter
• What is “the right way” to install Python on a new M2 MacBook? - Twitter

Projects:

• polars-cookbook: Recipes for Using Python’s Polars Library
• waffle: Feature Flags for Django

Additional Links:

• PSF Board of Directors Nominees - 2023 - YouTube
• pandas-cookbook: Recipes for using Python’s pandas library

Level up your Python skills with our expert-led courses:

• Get Started With Django: Build a Portfolio App
• Deploy Your Python Script on the Web With Flask
• Jinja Templating

Support the podcast & join our community of Pythonistas

How much Python do you need to learn to start creating projects? What’s a good balance of information and hands-on practice? This week on the show, Eric Matthes is here to discuss his book Python Crash Course.

As a former high school science, math, and programming teacher, Eric saw something missing in the programming publishing landscape. We discuss the guiding questions that inspired the book’s development and the title. Eric covers how the crash course takes readers through a fast-paced introduction to Python that culminates in three unique projects.

We also discuss Eric’s blog, Mostly Python, where he digs deeper into technical subjects. He also occasionally shares more topical posts and includes many exercises.

Topics:

• 00:00:00 – Introduction
• 00:02:04 – Why Alaska?
• 00:05:33 – What do you like about Python?
• 00:06:40 – Becoming an author & the intended reader
• 00:15:56 – History of programming books
• 00:17:39 – Having a purpose for learning
• 00:20:22 – Exercising as you go
• 00:24:45 – Including projects in the book
• 00:26:32 – Is the “crash course” a book model?
• 00:29:20 – Video Course Spotlight
• 00:30:29 – How much of a topic to reveal
• 00:35:08 – Guiding questions
• 00:42:43 – Moving from functions to testing in four chapters
• 00:46:21 – What were you excited to update in the book?
• 00:50:28 – Were there doubts about preparing readers for projects?
• 00:53:52 – Space invaders clone pygame project
• 00:55:52 – Data visualization project
• 01:00:38 – Django web app project
• 01:04:15 – Cheat sheets and additional resources
• 01:07:54 – Python flash cards
• 01:09:54 – What are you excited about in the world of Python?
• 01:10:54 – What do you want to learn next?
• 01:11:46 – How can people follow the work you do?
• 01:13:06 – Thanks and goodbye

Show Links:

• Python Crash Course, 3rd Edition - No Starch Press
• Episode #105: Creating Better Error Messages for Python 3.10 & 3.11 – The Real Python Podcast
• Making Python Better One Error Message At A Time - PyCon 2022
• Learning Python, 2nd Edition
• pytest: helps you write better programs — pytest documentation
• Pygame
• Matplotlib — Visualization with Python
• Plotly: Low-Code Data App Development
• django-simple-deploy - readthedocs.io
• Cheat Sheets - Python Crash Course, Third Edition
• Python Flash Cards - No Starch Press
• DjangoCon US 2023 - October 16-20, 2023 - Durham, NC United States
• pandas - Python Data Analysis Library
• Python Crash Course, Third Edition - GitHub Resources
• Mostly Python - Eric Matthes - Substack
• Eric Matthes (@[email protected]) - Fosstodon

Level up your Python skills with our expert-led courses:

• Testing Your Code With pytest
• Python Histogram Plotting: NumPy, Matplotlib, Pandas & Seaborn
• Plot With pandas: Python Data Visualization Basics

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What advice can you extract from the Zen of Python? How can these nineteen guiding principles help you write more idiomatic Python? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a Real Python tutorial by Bartosz Zaczyński titled “What’s the Zen of Python?” We talk about the poem’s origin and ways to access the Zen within Python. We also discuss how different sections provide contradictory advice for what makes good Python code.

We cover a recent post by previous guest Matt Harrison about using Python and pandas for finance. Matt’s article covers methods in the pandas library for aggregation, resampling, and rolling averages.

We cover several other articles and projects from the Python community, including staying up-to-date with news, solving a Legend of Zelda puzzle with Python, avoiding “simply” providing advice, displaying better stack traces, and creating files with fake data.

Topics:

• 00:00:00 – Introduction
• 00:01:49 – Python 3.11.4, 3.10.12, 3.9.17, 3.8.17, 3.7.17, and Python 3.12.0 beta 3 released
• 00:02:24 – Enforcement of 2FA for PyPI Began June 1st
• 00:02:54 – Faster Python 3.13 Plan
• 00:03:14 – PyPy v7.3.12 Released
• 00:03:28 – Migrate to .readthedocs.yaml Configuration
• 00:05:14 – PyCon US 2023 and PyCascades 2023 Videos Are Up
• 00:05:37 – What’s the Zen of Python?
• 00:09:22 – Python for Finance: Pandas Resample, Groupby, and Rolling
• 00:15:24 – Python and the Legend of Zelda
• 00:17:47 – Don’t Say “Simply” Use Pyenv, Poetry or Anaconda
• 00:28:54 – Is Parallel Programming Hard?
• 00:40:54 – Video Course Spotlight
• 00:42:20 – pymg: A Better Display for Stack Traces
• 00:44:58 – faker-file: Create Files With Fake Data
• 00:49:14 – Thanks and goodbye

News:

• Python 3.11.4, 3.10.12, 3.9.17, 3.8.17, 3.7.17, and 3.12.0 Beta 2 Released
• Python Insider: Python 3.12.0 beta 3 released
• Enforcement of 2FA for PyPI Began June 1st – For those accounts that have two-factor authentication turned on for PyPI uploads, the use of 2FA is now required. Users with 2FA who were only using a password in the past will now have to perform 2FA as well. This is all part of the PyPI transition to 2FA across the board.
• Faster Python 3.13 Plan – This brief outline highlights the plan for the faster CPython project for the 3.13 release. It includes PEP 669, PEP 554, improved memory management, and more. Here’s the associated Hacker News discussion.
• PyPy v7.3.12 Released
• Migrate to .readthedocs.yaml Configuration – The Read the Docs site has announced the new requirement that all builds must move to using a .readthedocs.yaml configuration file, version 2. There are some test windows where they’ll be temporarily enforcing the change, but the final release date is September 25, 2023. Read on for details on how to migrate your project.
• PyCascades 2023 Videos Are Up
• PyCon US 2023 Videos Are Up

Topic Links:

• What’s the Zen of Python? – In this tutorial, you’ll be exploring the Zen of Python, a collection of nineteen guiding principles for writing idiomatic Python. You’ll find out how they originated and whether you should follow them. Along the way, you’ll uncover several inside jokes associated with this humorous poem.
• Python for Finance: Pandas Resample, Groupby, and Rolling – When working with time series data such as financial information, the resample, grouping, and rolling features of pandas can make your life easier.
• Python and the Legend of Zelda – The Game Boy Color version of Legend of Zelda: Oracle of Ages contains a grid-based puzzle. Gaz writes about creating a brute-force program to solve the challenge using Python.
• Don’t Say “Simply” Use Pyenv, Poetry or Anaconda – This article talks about the issues that newer Python coders might encounter by adopting more complicated package management mechanisms and explains why sticking with pip is often the better choice.

Discussion:

• Is Parallel Programming Hard?
• AsyncIO: Why I Hate It – Charles is the creator of Peewee ORM and often gets the question when will it support asyncio? In this opinion piece, he talks about why he doesn’t like asyncio and which alternatives he prefers.

Projects:

• pymg: A Better Display for Stack Traces
• faker-file: Create Files With Fake Data

Additional Links:

• Practicality Beats Purity: The Zen of Python’s Escape Hatch - Chris Neugebauer - YouTube
• Relieving your Python packaging pain
• Is Parallel Programming Hard, And, If So, What Can You Do About It? - Book Download

Level up your Python skills with our expert-led courses:

• Threading in Python
• Hands-On Python 3 Concurrency With the asyncio Module
• Speed Up Python With Concurrency

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Are you looking to advance your CircuitPython projects? Would you like a collection of resources and tools to help you along your path? This week on the show, Tod Kurt is here to discuss building projects with CircuitPython.

Tod has been working with embedded electronics for a long time and has been an active member of the Arduino community. He recently started to build projects using CircuitPython, and it has become his preferred prototype method.

He shares software resources, hardware tools, and advice about working with CircuitPython and embedded electronics. We discuss several of his music hardware projects and the libraries and modules that he uses for synthesis. He also shares a powerful online prototyping tool to plan your project before spending money or plugging in a soldering iron.

Topics:

• 00:00:00 – Introduction
• 00:02:17 – Tod’s background and ThingM
• 00:06:37 – The Bootloader Podcast
• 00:09:58 – Why did you move from C to Python?
• 00:13:47 – Starting new projects with CircuitPython instead of Arduino
• 00:17:14 – Advantages of the Arduino language
• 00:21:07 – Modules that extend CircuitPython with C
• 00:25:23 – Synthesis and sample playback in CircuitPython
• 00:32:54 – Video Course Spotlight
• 00:34:13 – ulab for NumPy-like functionality in CircuitPython
• 00:39:20 – Raspberry Pi Pico as inexpensive project board
• 00:46:08 – STEMMA QT and breadboards
• 00:48:40 – Powering projects with a USB power bank
• 00:51:25 – Software tools - tio, circup, and discotool
• 00:56:55 – CircuitPython tricks GitHub repo
• 00:59:44 – Wokwi simulator for CircuitPython and Raspberry Pi Pico
• 01:01:25 – PCB board design resources
• 01:03:27 – todbot projects on Tindie
• 01:06:11 – Hardware tools for projects
• 01:08:21 – What are you excited about in the world of Python?
• 01:09:42 – What do you want to learn next?
• 01:11:30 – How can people follow your work online?
• 01:12:50 – Thanks and goodbye

Show Links:

• todbot blog – Random experiments, circuits, and code by Tod Kurt.
• todbot - YouTube
• ThingM
• The Bootloader Podcast
• The CircuitPython Show Podcast
• CircuitPython
• Arduino - Home
• Extending CircuitPython: An Introduction - Adafruit Learning System
• Adafruit Feather RP2040 with DVI - HDMI Output
• Episode #75: Building With CircuitPython & Constraints of Python for Microcontrollers
• displayio – Native helpers for driving displays - CircuitPython
• Mozzi
• synthio: Support for multi-channel audio synthesis - Adafruit
• ulab: Crunch Numbers fast in CircuitPython - Adafruit Learning System
• Raspberry Pi Pico RP2040 - Adafruit
• What is STEMMA? - Adafruit STEMMA & STEMMA QT
• USB Power bank - Current Favorite - Amazon
• tio: A simple serial device I/O tool - GitHub
• circup: CircuitPython library updater - GitHub
• discotool: USB Arduino type boards discovery library and tool - GitHub
• circuitpython-tricks: Some CircuitPython tricks, mostly reminders to myself - GitHub
• Wokwi Simulator - CircuitPython on Raspberry Pi Pico Project
• EAGLE - PCB Design And Electrical Schematic Software - Autodesk
• KiCad EDA - Schematic Capture & PCB Design Software
• Introducing KiCanvas - Stargirl (Thea) Flowers
• PicoTouch - Capsense MIDI keyboard - Tindie
• PicoStepSeq PCB from todbot synth toys - Tindie
• USB Voltage and Current Tester - Amazon
• USB Hub with Individual Power Switches - Amazon
• CPython Internals Book – Real Python
• ThinkDSP: Free Book on Digital Signal Processing in Python
• todbot blog – Random experiments, circuits, and code by Tod Kurt.
• Tod Kurt (todbot) (@[email protected]) - Mastodon
• Adafruit Discord - @todbot

Level up your Python skills with our expert-led courses:

• Python Basics: Reading and Writing Files
• Getting Started With MicroPython
• Python Basics: File System Operations

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What are the unique challenges of a large Python code base? What techniques can you implement to simplify the management of a big project? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent thread on Hacker News about working with a large Python code base. Christopher advises configuring tests and using tools to keep your code consistent across an organization. He also answers several questions about code complexity, typing, and leveraging third-party libraries.

We cover several other articles and projects from the Python community, including news from the Python Language Summit, Kivy for GUI development, the power of bit manipulation, the removal of unused import statements in your code, and a Python-based iOS project for adding reminders about links from a podcast.

Topics:

• 00:00:00 – Introduction
• 00:01:43 – Python 3.13 Removes 20 Stdlib Modules
• 00:03:20 – Making the Global Interpreter Lock Optional
• 00:04:52 – What Is the Standard Library For?
• 00:06:14 – Python’s .__call__() Method: Creating Callable Instances
• 00:11:40 – The Power of Bit Manipulation
• 00:15:39 – Using Kivy for GUI Development
• 00:21:18 – Video Course Spotlight
• 00:22:43 – How Do You Deal With Large Python Code Bases?
• 00:40:50 – unimport: Remove Unused Import Statements in Your Code
• 00:42:36 – Update: rsync-time-machine.py
• 00:43:20 – memocast: Add links heard in podcasts into iOS reminders
• 00:47:42 – A request to listeners
• 00:48:20 – Thanks and goodbye

Show Links:

• Python 3.13 Removes 20 Stdlib Modules – Core developers are busy working on PEP 594, removing dead batteries from Python 3.13. This long post in the discussion forum highlights what work has been completed so far.
• Making the Global Interpreter Lock Optional – In the past, Sam Gross has outlined how to make the GIL optional in CPython. He presented at the Python Language Summit, providing updates on his progress and future plans for the project.
• PEP 703 – Making the Global Interpreter Lock Optional in CPython | peps.python.org
• PEP 703 - Discussion on Python.org
• What Is the Standard Library For? – This post summarizes a conversation at the Python Language Summit proposing that guidelines be defined for when something should be added to the standard library.
• Python’s .__call__() Method: Creating Callable Instances – In this tutorial, you’ll learn what a callable is in Python and how to create callable instances using the .__call__() special method in your custom classes. You’ll also code several examples of practical use cases for callable instances in Python.
• The Power of Bit Manipulation – In this article, you learn about bit manipulation and how to solve problems efficiently using it in Python.
• Using Kivy for GUI Development – Kivy is an open-source Python library for developing desktop and mobile GUI apps. It’s supported on Windows, Linux, macOS, Android, and iOS. This article introduces you to Kivy and teaches you how to build your first GUI with it.

Discussion:

• How Do You Deal With Large Python Code Bases?

Projects:

• unimport: Remove Unused Import Statements in Your Code
• memocast: 🎧 A small iOS app for e.g. iPhone that allow you to add links heard in podcasts into reminders

Additional Links:

• Binary, Bytes, and Bitwise Operators in Python – Real Python
• Build a Mobile Application With the Kivy Python Framework – Real Python
• Open Source ERP and CRM | Odoo
• Moodle - Open-source learning platform | Moodle.org
• rsync-time-machine.py: Time Machine-style backups using rsync
• Pythonista for iOS

Level up your Python skills with our expert-led courses:

• Python Basics: Object-Oriented Programming
• Simplify Python GUI Development With PySimpleGUI
• Build Cross-Platform GUI Apps With Kivy

Support the podcast & join our community of Pythonistas

Have you thought about getting more involved in the Python community? Are you interested in volunteering for an event or becoming an organizer? This week on the show, we speak with organizers from this year’s PyCascades conference about making connections, learning new skills, and rationing your time.

We have three guests to discuss working on PyCascades 2023 and how they got involved in volunteering. Conference Chair Ben Berry, a site reliability engineer based in Seattle, is currently working on a private platform-as-a-service. Diversity Chair Madison Swain-Bowden is a senior data engineer out of Seattle, currently working at Automattic on the Openverse team. Sponsorship Chair Michael van der Kamp is a back-end engineer at Coffee Meets Bagel.

We discuss finding other volunteers, maintaining motivation, and connecting with sponsors. Our guests also share their stories of overcoming challenges, connecting with community, and finding fulfillment through volunteering.

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

Topics:

• 00:00:00 – Introduction
• 00:02:34 – Conference Chair Ben Berry
• 00:03:58 – Diversity Chair Madison Swain-Bowden
• 00:06:04 – Sponsorship Chair Michael van der Kamp
• 00:08:28 – Scheduling of conferences around one another
• 00:10:41 – How did you get involved in the community?
• 00:20:01 – Sponsor: Proxify
• 00:20:57 – Workplace groups and guilds
• 00:27:41 – How community helps with job referrals
• 00:32:19 – New position and added responsibilities
• 00:41:24 – How would you pitch volunteering?
• 00:44:32 – Video Course Spotlight
• 00:45:45 – How do you avoid overdoing it?
• 00:52:46 – What are you excited about in the world of Python?
• 00:55:16 – What do you want to learn next?
• 00:59:22 – Thanks and goodbye

Show Links:

• The Team - PyCascades 2023
• PyCascades 2023 - YouTube
• PyLadies – Women Who Love Coding in Python
• Puget Sound Programming Python (PuPPy) (Seattle, WA) - Meetup
• PySprings (Colorado Springs, CO) - Meetup
• What is Lean Coffee? - an introduction to agenda-less meetings
• Async IO in Python: A Complete Walkthrough – Real Python
• Structural Pattern Matching – Real Python
• Using FastAPI to Build Python Web APIs – Real Python
• Rust Programming Language
• Rust for Rustaceans - No Starch Press
• CircuitPython
• Michael van der Kamp - LinkedIn
• Madison on Tumblr
• Ben Berry (@[email protected])

Level up your Python skills with our expert-led courses:

• Using pandas to Make a Gradebook in Python
• The pandas DataFrame: Working With Data Efficiently
• Building Python Project Documentation With MkDocs

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Do you need a refresher on using Docker with Python? Would you like to learn how to configure a continuous integration pipeline with modern tools and Docker? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We share a Real Python tutorial from Bartosz Zaczyński about building continuous integration with Docker. Docker provides consistent environments for configuring, testing, and delivering Python applications. In this tutorial, you’ll get up to speed with current Docker and CI techniques.

We also speak with Bill Pollock from No Starch Press about Hacker Initiative. The public nonprofit gives back to and strengthens the hacking community. The 2023 grant cycle is currently open until August 15. We discuss the application process and projects from previous grant recipients.

We cover several other articles and projects from the Python community, including a news update and some resources on implementing metaclasses in Python, creating time machine-style backups, and scanning your project for vulnerabilities.

Topics:

• 00:00:00 – Introduction
• 00:02:26 – PyPI Temporarily Suspended New Registrations
• 00:03:38 – PyPI was subpoenaed
• 00:04:54 – Python 3.12.0 beta 1 released
• 00:05:19 – Build Robust Continuous Integration With Docker and Friends
• 00:13:02 – Metaclasses in Python
• 00:20:03 – Pronouncing SQL - Is there life after SQL?
• 00:22:52 – Video Course Spotlight
• 00:24:25 – pyscan: Python dependency vulnerability scanner, written in Rust
• 00:26:28 – rsync-time-machine.py: Time Machine-Style Backups
• 00:29:20 – Bill Pollock and Hacker Initiative
• 00:34:18 – Previous grant recipients
• 00:36:58 – First Tech Challenge
• 00:43:18 – Large vs small nonprofits
• 00:45:48 – Applying for the 2023 grant cycle
• 00:49:01 – Underserved areas
• 00:51:45 – Thanks and goodbye

News:

• PyPI Temporarily Suspended New Registrations – Due to a large volume of traffic from malicious users, PyPI temporarily suspended new account and project registrations on May 20. The suspension was lifted thirty hours later on May 21.
• PyPI was subpoenaed - The Python Package Index – The PSF received three subpoenas from the US Department of Justice for PyPI user data in March and April of 2023.
• Python Insider: Python 3.12.0 beta 1 released

Show Links:

• Build Robust Continuous Integration With Docker and Friends – In this tutorial, you’ll use Docker and GitHub Actions to build a robust continuous integration pipeline for a multi-container web application consisting of Flask and Redis. Along the way, you’ll learn how to dockerize a Python web application.
• Metaclasses in Python - Real Python Video Course – Metaclasses are an important but mysterious behind-the-scenes mechanism for instantiating classes in Python. In this video course, you’ll learn how Python’s metaclasses work in object-oriented programming.
• Is there life after SQL? - YouTube

Projects:

• pyscan: Python dependency vulnerability scanner, written in Rust - GitHub
• rsync-time-machine.py: Time Machine-Style Backups

Hacker Initiative Links:

• Hacker Initiative – By hackers. For hackers.
• Apply for the 2023 Grant Cycle – Hacker Initiative
• 2022 Grant Recipients – Hacker Initiative

Additional Links:

• Release CPython 3.12.0b1 w/ WASI SDK 20 - brettcannon/cpython-wasi-build
• Episode #149: Coding With namedtuple & Python’s Dynamic Superpowers
• What is FIRST Tech Challenge? - FIRST
• No Starch Press - “The finest in geek entertainment”

Level up your Python skills with our expert-led courses:

• Continuous Integration With Python
• Metaclasses in Python
• Writing Clean, Pythonic Code With namedtuple

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Would you like to speed up your Python machine-learning code dramatically? What if you only had to change a few keywords and add a couple of type hints on portions of your code? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a new programming language named Mojo, which is a superset of Python. It aims to fix Python’s performance and deployment problems. The project has many interesting ideas and a leader who has helped to shape modern compiler technology.

We also share a pair of Real Python tutorials from Leodanis Pozo Ramos about object-oriented programming in Python. The first one is a deep dive into the creation of classes. It’s an excellent refresher for anyone looking to hone their OOP skills in Python.

The second tutorial covers the SOLID principles, which are five well-established standards for improving your object-oriented design. These principles guide you to create object-oriented code that is more maintainable, extensible, scalable, and testable.

We cover several other articles and projects from the Python community, including a news update, showing warnings when running Django, tracking the progress of your Python program, and a Markdown browser for your terminal.

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

Topics:

• 00:00:00 – Introduction
• 00:02:39 – PEP 713: Callable Modules
• 00:04:22 – PEP 712: “Converter” Parameter for dataclasses.field
• 00:06:49 – Python Classes: The Power of Object-Oriented Programming
• 00:16:44 – Sponsor: Koyeb
• 00:17:33 – SOLID Principles: Improve Object-Oriented Design in Python
• 00:22:00 – Mojo, a Superset of Python
• 00:34:31 – Mojo might be the biggest thing to happen in programming for decades
• 00:41:48 – Mojo – a new programming language for AI developers
• 00:44:03 – Video Course Spotlight
• 00:45:23 – Have Python Show Warnings When Running Django
• 00:47:52 – TQDM: Tracking the Progress of Your Python Program
• 00:50:34 – frogmouth: A Markdown Browser for Your Terminal
• 00:53:59 – Thanks and goodbye

News:

• PEP 713: Callable Modules
• PEP 712: “Converter” Parameter for dataclasses.field

Show Links:

• Python Classes: The Power of Object-Oriented Programming – In this tutorial, you’ll learn how to create and use full-featured classes in your Python code. Classes provide a great way to solve complex programming problems by approaching them through models that represent real-world objects.
• SOLID Principles: Improve Object-Oriented Design in Python – In this tutorial, you’ll learn about the SOLID principles, which are five well-established standards for improving your object-oriented design in Python. By applying these principles, you can create object-oriented code that is more maintainable, extensible, scalable, and testable.
• Mojo, a Superset of Python – Mojo is a new programming language, which is a superset of Python. It aims to fix Python’s performance and deployment problems.
• Have Python Show Warnings When Running Django – How to show warnings when running Python, and Django, during local development. The easy, yet not well-known, way.

Discussion:

• Mojo might be the biggest thing to happen in programming for decades - Hacker News
• Mojo, a new programming language for AI developers - Hacker News

Projects:

• TQDM: Tracking the Progress of Your Python Program
• frogmouth: A Markdown Browser for Your Terminal

Additional Links:

• Video - tqdm documentation
• tqdm documentation
• pipx
• Episode #101: Tools for Setting Up Python on a New Machine – The Real Python Podcast

Level up your Python skills with our expert-led courses:

• Using Jupyter Notebooks
• Using k-Nearest Neighbors (kNN) in Python
• Providing Multiple Constructors in Your Python Classes

Support the podcast & join our community of Pythonistas

How do Python virtual environments work under the hood? How does understanding these concepts help you with managing them for your projects? This week on the show, CPython core developer Brett Cannon returns to discuss his recent articles about virtual environments and the Python packaging landscape.

Brett talks about his recent article “How Virtual Environments Work.” He was researching the topic to solve an issue with a Linux Python distribution that doesn’t provide the tools to create virtual environments. We talk about how he solved the problem by creating a tiny library named microvenv.

We also take a look at the Python packaging ecosystem. Brett talks about the early days of Python, when these tools didn’t exist. He contrasts that with the current packaging solution explosion and how each one attempts to solve unique problems. We also discuss the Python Packaging User Survey and the plans for packaging summits at PyCon US. Note that we recorded this episode two weeks before PyCon US 2023.

Topics:

• 00:00:00 – Introduction
• 00:01:58 – Virtual environments
• 00:07:10 – PEP 704 & virtual environment workflows
• 00:26:57 – Experimenting with not including pip
• 00:34:44 – Video Course Spotlight
• 00:36:35 – What are you using for packaging?
• 00:43:32 – Python Packaging User Survey & the history of packaging
• 00:48:40 – Packing only gets complex when it’s not pure Python code
• 01:01:22 – Outcomes after the survey and the packaging summit
• 01:03:56 – What are you excited about in the world of Python?
• 01:05:28 – What’s something you want to learn next?
• 01:07:12 – How can people follow the work that you do?
• 01:08:14 – Thanks and goodbye

Show Links:

• How virtual environments work
• PEP 704 – Require virtual environments by default for package installers - peps.python.org
• microvenv - PyPI
• python-launcher: Python launcher for Unix - GitHub
• bootstrap.pypa.io
• Classifying Python virtual environment workflows
• Python Packaging User Survey - Results PDF
• Thoughts on the Python packaging ecosystem - Pradyun Gedam
• pypa/packaging: Core utilities for Python packages - GitHub
• Flit 3.8.0 - Flit 3.8.0 documentation
• hatch - PyPI
• hatchling - PyPI
• Episode #93: Launching Python, Virtual Environments, and Locking Dependencies With Brett Cannon – The Real Python Podcast
• PEP 665 – A file format to list Python dependencies for reproducibility of an application - peps.python.org
• Programming Rust, 2nd Edition
• Tall, Snarky Canadian - Brett’s Blog
• Microsoft Python Blog
• Visual Studio Code Blog
• Brett Cannon (@[email protected]) - Mastodon

Level up your Python skills with our expert-led courses:

• Getting the Most Out of the Python Standard REPL
• Working With Python Virtual Environments
• Publishing Python Packages to PyPI

Support the podcast & join our community of Pythonistas

How can you ensure that you’ve appropriately declared your project’s required dependencies? How do you determine what dependencies are missing from a third-party project that you can’t run? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a new Python dependency checker called FawltyDeps. The tool helps you determine if you’ve declared too few or too many packages for your project.

Christopher has brought several developer resource collections: a list of assured open-source Python packages from Google, test databases with interesting datasets, and multiple Django third-party packages.

We cover several other articles and projects from the Python community, including a news update, advice on how to pitch yourself as a guest to a podcast, how to submit articles and projects to PyCoders Weekly, PyPI’s introduction of trusted publishers and organizations, a tool for tracking package history, a pixel art paint program written in Python, and a project for efficient string matching with regular expressions.

Topics:

• 00:00:00 – Introduction
• 00:02:19 – PEP 684 – A Per-Interpreter GIL Accepted
• 00:03:59 – Python 3.10 Runtime Now Available in AWS Lambda
• 00:04:19 – PyCon US 2023 Follow Up
• 00:08:05 – Podcast Topic & Guest Submissions
• 00:13:45 – Submissions to PyCoders Weekly
• 00:16:49 – Google’s Assured OSS Python Packages
• 00:18:27 – Groovy Datasets for Test Databases
• 00:19:10 – Top 10 Django Third-Party Packages
• 00:20:27 – Video Course Spotlight
• 00:21:46 – FawltyDeps - A Dependency Checker for Your Python
• 00:26:58 – PyPI Introduces “Trusted Publishers”
• 00:28:27 – Introducing PyPI Organizations
• 00:29:02 – pypi-diff: PyPI Package History Tracking
• 00:30:29 – PyDPainter: A usable pixel art paint program written in Python
• 00:35:19 – trrex: Efficient String Matching With Regular Expressions
• 00:37:16 – Thanks and Goodbye

News:

• PEP 684 – A Per-Interpreter GIL Accepted
• Python 3.10 Runtime Now Available in AWS Lambda

Show Links:

• PyCon US 2023 - Follow Up
• The Real Python Podcast ([email protected])
• PyCoder’s Weekly - Submit a Link
• Google’s Assured OSS Python Packages – Google publishes a list of the open-source packages that it uses and secures within its own software supply chain. The list is public, so you can take advantage of their assessment.
• Groovy Datasets for Test Databases – When you experiment with a new-to-you data science skill, you need some sort of data to work with. Why be boring? This article talks about several available datasets that you can use when you practice your coding skills.
• Top 10 Django Third-Party Packages – Will covers a list of his favorite third-party packages for Django. This includes old favorites, like Django REST framework, and lesser-known packages, like django-filter and django-environ.
• FawltyDeps: A Dependency Checker for Your Python – FawltyDeps is a new tool to help you identify undeclared and unused dependencies in your Python code, making your projects leaner and more reproducible.
• PyPI Introduces “Trusted Publishers” – PyPI package maintainers can adopt a new, more secure, OIDC-authenticated publishing method that doesn’t require sharing long-lived passwords or API tokens with external systems.
• Introducing PyPI Organizations - The Python Package Index
• pypi-diff: PyPI Package History Tracking

Projects

• PyDPainter: A usable pixel art paint program written in Python - GitHub
• trrex: Efficient String Matching With Regular Expressions

Additional Links:

• Data Is Plural
• FawltyDeps
• Quick PyDPainter Demo - YouTube
• Having a look at PyDPainter and comparing it to Deluxe Paint on the Amiga - YouTube

Level up your Python skills with our expert-led courses:

• Everyday Project Packaging With pyproject.toml
• Publishing Python Packages to PyPI
• Documenting Python Projects With Sphinx and Read the Docs

Support the podcast & join our community of Pythonistas

Are you familiar with the different versions of WebAssembly? Could WASM be the “write once, run everywhere” solution that developers have searched for? Where does distributing Python applications fit in the narrative? This week on the show, we have CPython core developer Brett Cannon to discuss his recent articles about WebAssembly and MVPy.

Brett has completed his syntactic sugar series, which we discussed in a previous episode. He details the origin of the series and his process of unearthing a minimum viable version of Python. Brett shares how he updated his PyCon US talk on the subject after feedback from presenting it at PyCascades.

We also dig deep into WebAssembly, specifically WebAssembly System Interface (WASI). Brett explains the concept of a “platform target triple” and the importance of defining which system CPython is compiled for. We also discuss WebAssembly becoming a ubiquitous distribution system.

Topics:

• 00:00:00 – Introduction
• 00:02:05 – PyCascades 2023
• 00:02:56 – Using social media for polls and checking interest
• 00:06:02 – Completing the syntactic sugar blog series
• 00:15:29 – Minimum Viable Python (MVPy) and WebAssembly
• 00:19:29 – Other teams focusing on WebAssembly
• 00:21:31 – Sponsor: Courier
• 00:22:13 – Stack of technology
• 00:26:50 – WebAssembly and its platform targets
• 00:32:35 – WASI and connecting to a runtime
• 00:38:33 – Extension modules and dynamic libraries
• 00:47:29 – Overcoming road blocks and envisioning a new WASI assignment
• 00:51:51 – Video Course Spotlight
• 00:53:26 – PEP 11 & CPython platform support for WASI
• 01:03:11 – Machine-specific runtime
• 01:04:57 – Write once, run everywhere
• 01:13:14 – Talks and summits planned for PyCon 2023
• 01:18:00 – Thanks and goodbye

Show Links:

• MVPy: Minimum Viable Python
• Python’s Syntactic Sugar - PyCon US 2023
• Episode #47: Unraveling Python’s Syntax to Its Core With Brett Cannon – The Real Python Podcast
• syntactic sugar - Tall, Snarky Canadian
• Brett Cannon (@[email protected]) - Fosstodon
• WASI - wasi.dev
• WebAssembly and its platform targets
• Introducing the Disney+ Application Development Kit (ADK) - Mike Hanley
• Compute@Edge services using WebAssembly - Fastly Developer Hub
• Experimental - Python for the Web - Visual Studio Marketplace
• PEP 11 – CPython platform support - peps.python.org
• Testing a Python project using the WASI build of CPython with pytest
• The rise of WebAssembly - InfoWorld
• Can WASM become the new Docker?
• bytecodealliance/wasmtime: A fast and secure runtime for WebAssembly
• Emscripten - Dev Documentation
• PyScript - Run Python in your HTML

Level up your Python skills with our expert-led courses:

• Python Basics: Object-Oriented Programming
• Inheritance and Composition: A Python OOP Guide
• Python Basics: Building Systems With Classes

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What can you do if your text manipulation in Python is slowing you down? Are there faster alternatives using a compiled extension? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent article by Itamar Turner-Trauring called “Speeding Up Text Processing in Python (Is Hard).” The piece compares the performance of string-matching scenarios using several alternatives to pure Python that rely on compiled extensions.

We also discuss a recent Real Python tutorial by Stephen Gruppetta on when to use .__repr__() vs .__str__() in Python. We cover the use cases for these special methods and the intended audiences for the strings they produce.

We share several other articles and projects from the Python community, including a news update, an article on the functional power of Python’s reduce(), a call to ban 1+N in Django, a friendly project to fetch your data files, and a tool for tracking your work from the shell.

Topics:

• 00:00:00 – Introduction
• 00:02:11 – The Python Package Index Launches a Blog
• 00:03:11 – PEP 582 (Python Local Packages Directory) Rejected
• 00:05:00 – Django 4.2 Release Candidate 1 Released
• 00:05:34 – Want to Host DjangoCon Europe 2024?
• 00:06:23 – When Should You Use .__repr__() vs .__str__() in Python?
• 00:14:16 – Sponsor: Snyk
• 00:15:06 – Speeding Up Text Processing in Python (Is Hard)
• 00:22:21 – reduce() - The Power of a Single Python Function
• 00:30:27 – Video Course Spotlight
• 00:32:04 – Ban 1+N in Django
• 00:35:26 – Pooch - A Friend to Fetch Your Data Files
• 00:39:11 – workedon - Track Your Work From the Shell
• 00:41:53 – Thanks and Goodbye

News:

• The Python Package Index Launches a Blog
• PEP 582 (Python Local Packages Directory) Rejected
• Django 4.2 Release Candidate 1 Released
• Want to Host DjangoCon Europe 2024?

Show Links:

• When Should You Use .__repr__() vs .__str__() in Python? – In this tutorial, you’ll learn the difference between the string representations returned by .__repr__() vs .__str__() and understand how to use them effectively in classes that you define.
• Speeding Up Text Processing in Python (Is Hard) – If you need to speed up string parsing and formatting in Python, you have many choices. This article covers the uses of Cython, mypyc, Rust, and PyPy and considers how to choose between them.
• reduce() - The Power of a Single Python Function – “While Python is not a pure functional programming language, you still can do a lot of functional programming in it. In fact, just one function - reduce() - can do most of it.” This article introduces you to reduce().
• Ban 1+N in Django – The 1+N database anti-pattern is common: fetch some rows from the database then re-fetch specific rows to get all the items. An ORM can hide this away and make you fail to realize that it’s happening. This article discusses how to avoid this anti-pattern in Django. It also has an added meta-bonus: a link to the attempt to write the article with ChatGPT.

Projects:

• Pooch: A Friend to Fetch Your Data Files
• workedon: Track Your Work From the Shell

Additional Links:

• Python’s reduce(): From Functional to Pythonic Style – Real Python
• Episode #116: Exploring Functional Programming in Python With Bruce Eckel – The Real Python Podcast
• Lightning talk at PyCascades 2023 - Pooch: A friend to fetch your data files - YouTube
• Secure copy protocol - Wikipedia

Level up your Python skills with our expert-led courses:

• Python's map() Function: Transforming Iterables
• Python Basics: Strings and String Methods
• Unicode in Python: Working With Character Encodings

Support the podcast & join our community of Pythonistas

Are you exploring automation of your repetitive business tasks with Python? How are you going to share your helpful tools with co-workers? This week on the show, Sampo Ahokas from Robocorp is here to discuss robotic process automation (RPA) and distribution of these robots.

Sampo is a co-founder and VP of engineering at Robocorp. We talk about using Robot Framework, an open-source RPA tool, to develop bots that implement your existing Python skills. Sampo shares example projects and additional resources for new users.

We discuss the typical difficulties of sharing automation tools with a team and trying to avoid the dreaded “works on my machine” problem. Sampo describes how their group worked to develop a Conda-based tool for creating shareable packages and environments.

Topics:

• 00:00:00 – Introduction
• 00:02:25 – What is robotic process automation (RPA)?
• 00:03:55 – What do you mean by automation?
• 00:05:56 – Additional examples of RPA
• 00:07:41 – What is the RPA platform?
• 00:10:06 – What is the Robot Framework?
• 00:12:42 – Robocorp portal
• 00:14:09 – Python integration
• 00:17:06 – Sponsor: REVSYS
• 00:17:56 – Distribution with RCC
• 00:20:24 – Why does the system use conda under the hood?
• 00:24:12 – What hurdles did you face creating RCC?
• 00:27:51 – Steps for the end user
• 00:30:52 – Making the project open source
• 00:35:20 – Video Course Spotlight
• 00:36:42 – Tips for someone starting with automation
• 00:42:17 – Integration with VSCode
• 00:44:18 – Intelligent document processing (IDP)
• 00:45:36 – What are you excited about in the world of Python?
• 00:47:46 – What do you want to learn next?
• 00:48:13 – How can people follow the project online?
• 00:48:46 – Thanks and goodbye

Show Links:

• Open Source RPA - Intelligent Automation Software - Robocorp
• What is RPA? A breakdown of RPA and its benefits - Robocorp
• Robocorp Portal
• RPA Documentation, Training Courses, Certificates - Robocorp documentation
• rpaframework: Collection of open-source libraries and tools for Robotic Process Automation (RPA), designed to be used with both Robot Framework and Python
• rcc: RCC is a set of tooling that allows you to create, manage, and distribute Python-based self-contained automation packages - or ‘robots’ as we call them.
• Conda - documentation
• QuantStack
• micromamba - documentation
• Low-code RPA Development Solution | Automation Studio - Robocorp
• Bolster IDP With Robotic Process Automation - DZone
• Visual Studio Code - Code Editing. Redefined
• Welcome to LangChain - 🦜🔗 LangChain 0.0.131
• Sampo Ahokas - LinkedIn
• Robocorp (@RobocorpInc) - Twitter
• Community for Software Robot Developers
• RPA Resources, White Papers and Case Studies - Robocorp

Level up your Python skills with our expert-led courses:

• Reading and Writing Files in Python
• Testing Your Code With pytest
• Manipulating ZIP Files With Python

Support the podcast & join our community of Pythonistas

Have you ever installed a Python package without knowing anything about it? What best practices should you employ to ensure the quality of your next package installation? Christopher Trudeau is back this week, bringing another batch of PyCoder’s Weekly articles and projects. We also have Python Software Foundation executive director, Deb Nicholson, to share details about PyCon US 2023.

We cover a recent Real Python tutorial by Philipp Acsany on evaluating the quality of Python packages. The piece provides a tool kit for researching the traits, history, software license, and current condition of external Python packages. We also discuss the techniques that we personally use before selecting a package for our Python projects.

We share several other articles and projects from the Python community, with topics such as the underlying structure of virtual environments, the overhead of Python asyncio tasks, documentation for Python projects with Sphinx and Read the Docs, a project for creating argparse boilerplate, and a way to generate seemingly realistic fake numbers using Benford’s law.

Deb Nicholson is also here to talk about the 20th anniversary of PyCon US, hosted in Salt Lake City. We dig into the details of the upcoming conference, including keynote speakers, tutorials, scheduled talks, and improvements to the hybrid online experience.

Topics:

• 00:00:00 – Introduction
• 00:02:46 – How to Evaluate the Quality of Python Packages
• 00:11:35 – Overhead of Python asyncio Tasks
• 00:14:53 – Sponsor: Courier
• 00:15:37 – How Virtual Environments Work
• 00:23:48 – Documenting Python Projects With Sphinx and Read the Docs
• 00:28:29 – duckargs: Code Generator for argparse Boilerplate
• 00:30:46 – Video Course Spotlight
• 00:32:04 – Are Those Numbers Realistic or Fake? Try Using Benford’s Law
• 00:34:37 – Introduction for Deb Nicholson
• 00:36:33 – What is your role with PyCon US?
• 00:37:28 – Hybrid conference and dates
• 00:39:07 – Tutorials
• 00:40:30 – Education Summit and Typing Summit
• 00:42:06 – Keynote speakers
• 00:42:57 – Lightning talks, posters, and job fair
• 00:45:04 – 20th anniversary of PyCon US
• 00:46:56 – Resources for proposals and talks
• 00:49:22 – Previous podcast guests and talks
• 00:51:26 – Mentored sprints for diverse beginners
• 00:53:12 – PyLadies auction
• 00:54:29 – COVID policy
• 00:56:50 – What are you excited about in the world of Python?
• 00:58:07 – What do you want to learn next?
• 00:59:24 – How to follow the PSF and PyCon US?
• 00:59:55 – Thanks and goodbye

Show Links:

• How to Evaluate the Quality of Python Packages – Just like you shouldn’t download any file from the Internet, you shouldn’t install third-party Python packages without evaluating them first. This tutorial will give you the tool set to evaluate the quality of external Python packages before you incorporate them into your Python projects.
• Overhead of Python Asyncio Tasks – The Textual library uses a lot of asyncio tasks. In order to determine whether to spend time optimizing them, Will measured the cost of creating asyncio tasks. TLDR; optimize something else. This article also spawned a conversation on Hacker News.
• How Virtual Environments Work – This article attempts to demystify virtual environments, specifically why they exist and how they work. It even delves into why Brett is heading down this alley and how running into challenges with cross-platform tools has prompted the creation of microvenv.
• Documenting Python Projects With Sphinx and Read the Docs – In this video series, you’ll create project documentation from scratch using Sphinx, the de facto standard for Python. You’ll also hook your code repository up to Read The Docs to automatically build and publish your code documentation.

Projects:

• duckargs: Code Generator for argparse Boilerplate
• Are Those Numbers Realistic or Fake? Try Using Benford’s Law – How can you tell whether a set of figures is trustworthy? It’s not always simple, but Benford’s Law gives you one way to find out. There’s even a Python Package to help you check: randalyze.

PyCon US 2023 Links:

• Welcome to PyCon US 2023
• Python Software Foundation
• Registration Information - PyCon US 2023
• Talks Schedule - PyCon US 2023
• Tutorials Schedule - PyCon US 2023
• Education Summit - PyCon US 2023
• PyCon US Stories Slideshow
• Proposal Guidelines - PyCon US 2023
• PyLadies Auction - PyCon US 2023
• Volunteering - PyCon US 2023

Additional Links:

• Libraries.io - The Open Source Discovery Service
• Licenses - Choose a License
• Python Virtual Environments: A Primer – Real Python
• EU Cyber Resilience Act - Shaping Europe’s digital future
• Python Software Foundation News: Where is the PSF?
• Signup for the Python Software Foundation Newsletter
• The Boston Python User Group (Cambridge, MA) - Meetup
• PyLadies – Women Who Love Coding in Python

Level up your Python skills with our expert-led courses:

• Building Python Project Documentation With MkDocs
• Documenting Python Projects With Sphinx and Read the Docs
• Documenting Python Code: A Complete Guide

Support the podcast & join our community of Pythonistas

What are the core lessons you’ve learned along your Python development journey? What are key takeaways you would share with new users of the language? This week on the show, Duarte Oliveira e Carmo is here to discuss his recent talk, “Four Years of Python.”

Duarte works at the crossroads of machine learning, data science, and software engineering. He began using Python in his graduate studies and never looked back. In 2021, he wrote a blog post about some of the valuable lessons he’s learned. Then he decided the lessons and concepts in the post might make a good conference talk.

We cover the steps in his process of crafting the presentation, practicing it at a smaller conference, and finally presenting it at PyCon Italia last year. We also dig into the four major themes of the talk. Along the way, we share a collection of resources to help you continue learning on your Python journey.

Topics:

• 00:00:00 – Introduction
• 00:02:38 – Four years of Python
• 00:04:18 – Why did you create a blog?
• 00:06:19 – A singular vs wide focus for the blog
• 00:09:19 – Pitching the talk to conferences
• 00:13:02 – Resources for preparing your talk
• 00:16:03 – What was your programming and Python background?
• 00:19:00 – Sponsor: InfluxData
• 00:19:47 – Reading is better than Googling
• 00:26:23 – What are some of your favorite docs?
• 00:28:48 – Thoughts on GPT and Copilot
• 00:31:45 – Keep it stupid simple
• 00:36:07 – What’s extensible code?
• 00:38:29 – Video Course Spotlight
• 00:39:54 – Learning testing techniques & testing data science code
• 00:46:05 – Continuous learning
• 00:51:46 – What do you use for RSS?
• 00:53:06 – Resources for machine learning
• 00:57:20 – What are you excited about in the world of Python?
• 00:58:57 – What do you want to learn next?
• 01:00:55 – How can people follow the work you do?
• 01:01:20 – Thanks and goodbye

Show Links:

• Four years of Python - Duarte O.Carmo
• Four years of Python - Duarte Carmo - YouTube
• Practices of the Python Pro
• Pelican 4.8.0
• “One for Them, One for Me” - Blank Check Movies From Famous Directors
• PyData
• NumFOCUS: A Nonprofit Supporting Open Code for Better Science
• Proposing a Talk - PyCon US 2023
• pandas documentation - pandas 1.5.3 documentation
• scikit-learn 1.2.2 - User guide - documentation
• FastAPI - Tutorial - User Guide
• Using FastAPI to Build Python Web APIs - Real Python
• Python 3.11.2 Documentation
• Kindle Highlights Newsletter
• Reeder 5
• Welcome to Feedly
• Normconf: The Normcore Tech Conference
• Tech Blog - ★❤✰ Vicki Boykis ★❤✰
• Sebastian Raschka - Blog
• Blog of a data person. - koaning.io
• Machine Learning Design Patterns - Book
• The Practical AI Podcast - Changelog
• tidytuesday: Official repo for the #tidytuesday project
• PyCon.DE & PyData Berlin, 2023 - PyConDE & PyData Berlin 2023
• PyCon Italia - 2023
• ruff - PyPI
• Effective Python › The Book: Second Edition
• Episode #3: Effective Python and Python at Google Scale - The Real Python Podcast
• Duarte O.Carmo
• Talks - Duarte O.Carmo
• Duarte O.Carmo - LinkedIn

Level up your Python skills with our expert-led courses:

• Python REST APIs With FastAPI
• Splitting Datasets With scikit-learn and train_test_split()
• Building a URL Shortener With FastAPI and Python

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Have you explored Python’s collections module? Within it, you’ll find a powerful factory function called namedtuple(), which provides multiple enhancements over the standard tuple for writing clearer and cleaner code. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher discusses his Real Python video course about writing more Pythonic code using namedtuple(). With namedtuple(), you can create immutable sequence types that allow you to access their values using descriptive field names and dot notation instead of clunky integer indices.

We also discuss metaprogramming and the unique advantages of Python’s dynamism. Christopher shares potential paths for this type of coding from web applications, testing, and AST techniques.

We share several other articles and projects from the Python community, including a news update, the Arrow revolution happening in pandas 2.0, a new PEP for inlined comprehensions, tips and techniques for modern Flask apps, a Python helper tool for building and running a REPL with custom commands, and a project to turn a pandas DataFrame into a Tableau-style UI.

Topics:

• 00:00:00 – Introduction
• 00:02:17 – Python 3.12.0 alpha 6 released
• 00:02:40 – Django Developers Survey 2022 Results
• 00:03:12 – Writing Clean, Pythonic Code With namedtuple
• 00:07:40 – pandas 2.0 and the Arrow Revolution (Part I)
• 00:17:21 – Sponsor: RevSys
• 00:18:10 – PEP 709: Inlined Comprehensions
• 00:20:51 – 13 Tips and Techniques for Modern Flask Apps
• 00:25:54 – Video Course Spotlight
• 00:27:24 – Discussion: Python’s “Disappointing” Superpowers
• 00:47:54 – replbuilder: Python helper tool for building and running a REPL with custom commands
• 00:49:58 – pygwalker: Turn pandas Into a Tableau-Style UI
• 00:52:15 – Thanks and goodbye

News:

• Python Insider: Python 3.12.0 alpha 6 released
• Django Developers Survey 2022 Results - Django Weblog

Show Links:

• Writing Clean, Pythonic Code With namedtuple – In this video course, you’ll learn what Python’s namedtuple is and how to use it in your code. You’ll also learn about the main differences between named tuples and other data structures, such as dictionaries, data classes, and typed named tuples.
• pandas 2.0 and the Arrow Revolution (Part I) – This article details the changes in the pandas 2.0 release, with emphasis on the underlying adoption of Apache Arrow.
• PEP 709: Inlined Comprehensions – Python Enhancement Proposal 709 covers a change to how comprehensions are handled. Currently, they’re compiled as nested functions. Benchmarking shows that treating list, dict, and set comprehensions as inline code can result in a 2x speedup on the comprehension.
• 13 Tips and Techniques for Modern Flask Apps – Flask is approaching its 13th birthday, and to celebrate, Phillip has written 13 tips for writing modern Flask apps. It covers dealing with JSON, environment-based configuration, auto-generated docs, and more.

Discussion:

• Python’s “Disappointing” Superpowers - lukeplant.me.uk
• I am disappointed by dynamic typing - Buttondown
• Python’s “Disappointing” superpowers - Hacker News
• Python’s “Disappointing” Superpowers - Lobsters

Projects:

• replbuilder: Python helper tool for building and running a repl with custom commands
• pygwalker: Turn pandas Into a Tableau-Style UI

Additional Links:

• Write Pythonic and Clean Code With namedtuple – Real Python
• Episode #146: Using NumPy and Linear Algebra for Faster Python Code – The Real Python Podcast
• Apache Arrow and the “10 Things I Hate About pandas” - Wes McKinney
• Data science without borders - Wes McKinney (Two Sigma Investments) - YouTube
• Joining Forces for an Arrow-Native Future - Wes McKinney
• pandas arrays, scalars, and data types — pandas 2.1.0.dev0+171.gc293caf2e9 documentation
• Episode #18: Ten Years of Flask: Conversation With Creator Armin Ronacher – The Real Python Podcast
• Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 1 – Real Python
• Python Metaclasses – Real Python
• Kanaries AI enhanced data exploration

Level up your Python skills with our expert-led courses:

• Data Cleaning With pandas and NumPy
• Lists and Tuples in Python
• Writing Clean, Pythonic Code With namedtuple

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Are you interested in deploying your Python project everywhere? This week on the show, Russell Keith-Magee, founder and maintainer of the BeeWare project, returns. Russell shares recent updates to Briefcase, a tool that converts a Python application into native installers on macOS, Windows, Linux, and mobile devices.

We cover how Anaconda hired him last year to work full-time on the BeeWare project. He shares how this has helped him focus his efforts and move the project forward.

We also discuss his recent talk at DjangoCon US 2022 on how to turn your website into an app (and why maybe you shouldn’t). Russell details the problems of converting from the Web to a mobile platform. We also contrast WebAssembly System Interface (WASI) with the tools that his team works on.

Topics:

• 00:00:00 – Introduction
• 00:02:06 – BeeWare project update and open-source funding
• 00:06:47 – What are BeeWare and Briefcase?
• 00:08:19 – Toga GUI and contributions
• 00:10:47 – Pace of the project now
• 00:12:47 – PEP 517 and binary packages with C or Rust
• 00:17:29 – WASM and Briefcase for Web
• 00:22:22 – Sponsor: InfluxData
• 00:23:10 – How to turn your Website into an App - Talk
• 00:28:14 – Bridging libraries that access platform hardware
• 00:40:56 – Video Course Spotlight
• 00:42:27 – WASI - WebAssembly System Interface
• 00:48:18 – Do you need an app or a website?
• 00:54:23 – Getting started with BeeWare
• 01:00:06 – What to do first if interested in contributing?
• 01:02:33 – Channels for the project
• 01:04:55 – Upcoming conference talks
• 01:05:26 – What are you excited about in the world of Python?
• 01:06:42 – What do you want to learn next?
• 01:09:28 – How can people follow your work online?
• 01:10:10 – Thanks and goodbye

Show Links:

• Write once. Deploy everywhere. — BeeWare
• Russell Keith-Magee - Keynote - PyCon 2019 - YouTube
• Anaconda - Open Source
• PyScript - Run Python in your HTML
• Episode #22: Create Cross-Platform Python GUI Apps With BeeWare – The Real Python Podcast
• How to turn your Website into an App (and why maybe you shouldn’t!) with Russell Keith Magee - YouTube
• PEP 517 – A build-system independent format for source trees - peps.python.org
• rubicon-objc - PyPI
• Chaquopy – Python SDK for Android
• WASI.dev
• CAP theorem - Wikipedia
• BeeWare Tutorial
• The Buzz - BeeWare Blog
• You can take it with you: Packaging your Python code with Briefcase - PyCon US 2023
• BeeWare (@PyBeeWare) - Twitter
• Russell Keith-Magee (@[email protected]) - Mastodon

Level up your Python skills with our expert-led courses:

• Managing Attributes With Python's property()
• Everyday Project Packaging With pyproject.toml
• Getters and Setters in Python

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Have you decided how you’re going to deploy your Django project? Should you use a VPS or a PaaS? Christopher Trudeau is back this week, bringing another batch of PyCoder’s Weekly articles and projects. We also have organizers from PyCascades to share details about this year’s hybrid in-person and virtual conference.

Christopher shares an article about selecting an appropriate Django project deployment strategy. The guide compares VPS (virtual private server) and PaaS (platform as a service) systems. He also covers hosting providers for each and highlights potential pitfalls.

We share several other articles and projects from the Python community, including a news update, what’s new in SQLAlchemy 2.0, how to flush the output of the Python print function, the dangers behind image resizing for machine learning, a project that visualizes pathfinding algorithms, and a runtime executor project.

We also have three special guests from PyCascades 2023 to dig into the details of the conference. Conference chair Eliza Sarobhasa is CTO at Women Who Drone and Leadership Fellow (Python Track) & Python Developer Advocate at Women Who Code. Sprints chair Chethana Gopinath is a Software Engineer at realtor.com and a Senior Lead at Women Who Code Python. Speaker Support Chair Jolene Wong is a Senior Software Engineer at Cisco based in Vancouver. We discuss hosting a hybrid conference, participating in open-source sprints, and finding a local Python community.

Topics:

• 00:00:00 – Introduction
• 00:02:47 – Django 4.2 beta 1 released
• 00:03:04 – What’s New in SQLAlchemy 2.0?
• 00:06:42 – How to Flush the Output of the Python Print Function
• 00:14:30 – The Essential Django Deployment Guide
• 00:21:37 – Sponsor: Snyk
• 00:22:30 – The Dangers Behind Image Resizing
• 00:29:40 – Pathfinding-Visualizer: Visualize Pathfinding With Pygame
• 00:32:40 – rtx: Runtime Executor (asdf Rust Clone)
• 00:36:19 – Video Course Spotlight
• 00:37:31 – PyCascades 2023 Details
• 00:38:53 – Hybrid conference
• 00:41:11 – How did Chethana get involved?
• 00:42:30 – Open-source sprints
• 00:45:19 – How did Jolene get involved?
• 00:46:31 – How did Eliza get involved?
• 00:50:21 – Venue details
• 00:52:32 – Scheduled talks
• 00:56:13 – Conference sponsors
• 00:57:48 – Advice for attendees
• 01:00:46 – Tickets and virtual platform
• 01:03:01 – What are you excited about in the world of Python?
• 01:06:13 – Thanks and goodbye

News:

• Django 4.2 beta 1 released | Weblog | Django
• SQLAlchemy 2.0 Released

Show Links:

• What’s New in SQLAlchemy 2.0? – SQLAlchemy 2.0 was launched in January. This article reviews the latest changes, whether it is worth the upgrade, and how to go about it.
• How to Flush the Output of the Python Print Function – In this tutorial, you’ll learn how to flush the output of Python’s print function. You’ll explore output stream buffering in Python using code examples and learn that output streams are block-buffered by default, and that print() with its default arguments executes line-buffered when interactive.
• The Essential Django Deployment Guide – Going from “it works on my machine” to deploying to the public can be a daunting task. This guide details the choices between VPS and PaaS systems, how to choose, what the options are, and what you need to know to get your Django code live.
• The Dangers Behind Image Resizing – When training an ML model on image data you likely want smaller, consistently sized images. That means image processing in your pipeline, but the expectation that image resizing is the same across libraries can cause unforeseen problems.

Projects:

• Pathfinding-Visualizer: Visualize Pathfinding With Pygame
• rtx: Runtime Executor (asdf Rust Clone)

PyCascades Links:

• Home - PyCascades 2023
• The Team - PyCascades 2023
• Schedule - PyCascades 2023
• Sprints - PyCascades 2023
• PyCascades 2023 - Sprints Sign Up Form
• COVID Policy - PyCascades 2023
• Venueless - PyCascades 2023
• Become A Sponsor - PyCascades 2023
• PyCascades - YouTube
• Episode #44: Creating an Interactive Online Python Conference for PyCascades 2021 – The Real Python Podcast

Additional Links:

• File Object - Glossary - Python Documentation
• The Python print() Function: Go Beyond the Basics – Real Python
• functools — Higher-order functions and operations on callable objects — Python 3.11.2 documentation
• Python - Women Who Code
• WWCodePython - Tiktok Creator & Bio Links - Beacons
• PyLadies – Women Who Love Coding in Python
• PyLadies Vancouver (Vancouver, BC) | Meetup

Level up your Python skills with our expert-led courses:

• Deploy a Django App With Gunicorn and Nginx
• The Python print() Function: Go Beyond the Basics
• SQLite and SQLAlchemy in Python: Moving Your Data Beyond Flat Files

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Are you still using loops and lists to process your data in Python? Have you heard of a Python library with optimized data structures and built-in operations that can speed up your data science code? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to share secrets for harnessing linear algebra and NumPy for your projects.

Jodie details how most people begin their data science journey using loops to iterate over values and apply operations sequentially. We talk about how loops are friendly for beginners, being clear to read and easy to debug, but unfortunately don’t scale well, especially with large amounts of data.

Jodie shares some of the basics of linear algebra and how to organize data into vectors. We talk about how the NumPy library leverages those concepts to improve data processing. We discuss how the library includes operations for vector and matrix addition and subtraction, and why these operations are more efficient than loops. We also cover how NumPy stores arrays in memory and when working with them is faster vs when it’s not.

Topics:

• 00:00:00 – Introduction
• 00:02:35 – Vectorize all the things! - PyCon UK 2022 Talk
• 00:06:39 – Becoming familiar with linear algebra
• 00:09:05 – Beginners start with loops
• 00:11:25 – Starting with basic linear algebra
• 00:12:25 – The basic unit of a vector
• 00:18:06 – NumPy representing vectors in Python
• 00:23:25 – Sponsor: InfluxDB
• 00:24:13 – Block management
• 00:25:54 – Replacing a loop with vector-based operations
• 00:34:06 – NumPy broadcasting
• 00:38:52 – Approximating nearest neighbors
• 00:43:49 – Video Course Spotlight
• 00:45:15 – Solving the problem
• 00:46:44 – Getting rid of nested loops
• 00:48:54 – A peek under the hood
• 00:53:28 – How arrays vs lists are stored in memory
• 01:00:24 – Considering a GPU
• 01:03:37 – Real Python resources on the subject
• 01:04:08 – Upcoming talks and conferences
• 01:07:31 – Thanks and goodbye