Would you like to get more practice working with APIs in Python? How about exploring the globe using the data from OpenStreetMap? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share an article from the Pybites blog about building queries using the Overpass API for OpenStreetMap. The post explores the data structures, tags, query formats, and how to use Overpass in Python.

Christopher discusses a Real Python article by recent guest Stephen Gruppetta about lazy evaluation in Python. The piece covers the advantages of generator expressions or functions and the potential disadvantages of using lazy versus eager evaluation methods.

We also share several other articles and projects from the Python community, including a news roundup, handling control-c in asyncio, preventing data leakage in pandas and scikit-learn, discussing the Django developer survey results, asking developers why they aren’t shipping faster, using UV to install into isolated environments, and a couple of tools for retrying in Python.

This episode is sponsored by Sentry.

Course Spotlight: How to Set Up a Django Project

In this course, you’ll learn the necessary steps that you’ll need to take to set up a new Django project. You’ll learn the basic setup for any new Django project, which needs to happen before programming the specific functionality of your project.

Topics:

• 00:00:00 – Introduction
• 00:02:33 – PyPy v7.3.16 Release
• 00:02:54 – PEP 745: Python 3.14 Release Schedule
• 00:03:31 – The BASIC programming language turns 60
• 00:05:24 – Asyncio Handle Control-C (SIGINT)
• 00:07:37 – OpenStreetMaps, Overpass API and Python
• 00:11:53 – What’s Lazy Evaluation in Python?
• 00:16:10 – Sponsor: Sentry
• 00:17:17 – How to Prevent Data Leakage in pandas & scikit-learn
• 00:24:09 – Django Developers Survey 2023 Results
• 00:33:07 – Video Course Spotlight
• 00:34:21 – I Asked 100 Devs Why They Aren’t Shipping Faster?
• 00:47:03 – pipxu: Install in Isolated Environments Using UV
• 00:49:05 – tenacity: Retrying Library for Python
• 00:50:04 – stamina: Production-Grade Retries for Python
• 00:52:00 – Thanks and goodbye

News:

• PyPy v7.3.16 Release
• PEP 745: Python 3.14 Release Schedule
• The BASIC programming language turns 60 - Ars Technica

Show Links:

• Asyncio Handle Control-C (SIGINT) – When the user presses CTRL-C on the keyboard, the OS raises an interrupt signal to your program. When writing concurrent code this can get complicated as the signal goes to the process. This article shows you how to handle capturing CTRL-C elegantly when using asyncio.
• OpenStreetMaps, Overpass API and Python – OpenStreetMaps (OSM) is an open source mapping project that allows people to browse the world map and to plan routes. Not only does it have the expected web interface, but it also has an API known as Overpass. This article shows you two ways to use Python to query Overpass.
• What’s Lazy Evaluation in Python? – This tutorial explores lazy evaluation in Python and looks at the advantages and disadvantages of using lazy and eager evaluation methods. By the end of this tutorial, you’ll clearly understand which approach is best for you, depending on your needs.
• How to Prevent Data Leakage in pandas & scikit-learn – How you impute missing values in machine learning data sets can affect the quality of your training. This article teaches you what data leakage is and what steps you should take to avoid it.

Discussion

• Django Developers Survey 2023 Results
• I Asked 100 Devs Why They Aren’t Shipping Faster? – Daksh asked 100 developers why they aren’t shipping faster and this blog post shares what he learned. Problems include dependency bugs, overly complicated code bases, waiting on requirements, and more.

Projects:

• pipxu: Install in Isolated Environments Using UV
• tenacity: Retrying Library for Python
• stamina: Production-Grade Retries for Python

Additional Links:

• NASA’s Voyager 1 spacecraft finally phones home after 5 months of no contact - Space
• Python & APIs: A Winning Combo for Reading Public Data – Real Python
• Loading Data from OpenStreetMap with Python and the Overpass API - Nikolai Janakiev
• Leakage (machine learning) - Wikipedia
• Djangonaut Space - Where Contributors launch!
• Debris That Fell Off a Boeing 767 Found Outside the House of Lawyer

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

• Get Started With Django: Build a Portfolio App
• Python Generators 101
• How to Set Up a Django Project

Support the podcast & join our community of Pythonistas

Do you get stressed while trying to learn Python? Do you prefer to build small programs or projects as you continue your coding journey? This week on the show, Real Python author Stephen Gruppetta is here to talk about his new book, “The Python Coding Book.”

Stephen has been teaching Python to adults and children for many years. With his new book, he wants to share the relaxed and friendly learning environment he’s developed. We discuss using analogies to explain programming concepts and constructing complete programs as chapter goals.

Over the last year, Stephen focused on writing. He started his newsletter, The Python Coding Stack, wrote more tutorials for Real Python and authored the book.

This episode is sponsored by Mailtrap.

Course Spotlight: Python Basics: Code Your First Python Program

In this video course, you’ll write your first Python program. Along the way, you’ll learn about errors, declare variables and inspect their values, and try your hand at writing comments.

Topics:

• 00:00:00 – Introduction
• 00:02:32 – Education and programming background
• 00:05:50 – Developing a teaching style
• 00:10:36 – A friendly and relaxed programming book
• 00:14:31 – Making mistakes
• 00:18:29 – Sponsor: Mailtrap
• 00:19:03 – What was your curation process like?
• 00:21:22 – First chapter building an actual program
• 00:25:08 – Glossary terms and exercises
• 00:27:48 – Why feature an IDE?
• 00:34:07 – Monty and the White Room analogy
• 00:37:46 – What, no turtle?
• 00:42:21 – Video Course Spotlight
• 00:44:00 – Shift toward teaching
• 00:46:50 – Teaching adults and children
• 00:51:23 – Python sequences tutorial
• 00:53:48 – Building community and social media
• 00:58:12 – What are you excited about in the world of Python?
• 01:00:49 – What do you want to learn next?
• 01:03:07 – Thanks and goodbye

Show Links:

• Learn Python Coding - The Python Coding Book
• Rambling Reflections - Twelve Months of The Python Coding Stack
• On Writing: A Memoir of the Craft - Stephen King - Wikipedia
• Episode #4: Learning Python Through Errors – The Real Python Podcast
• Build a Python Turtle Game: Space Invaders Clone – Real Python
• Python Sequences: A Comprehensive Guide – Real Python
• Django in Action
• The Python Coding Place – The Place to Learn Python
• Stephen Gruppetta (@s_gruppetta_ct) / X

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

• Python Basics: Setting Up Python
• Python Turtle for Beginners
• Python Basics: Code Your First Python Program

Support the podcast & join our community of Pythonistas

How do you verify and validate the data coming into your Python web application? What tools and security best practices should you consider as a developer? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss the recent Real Python tutorial “Pydantic: Simplifying Data Validation in Python.” The piece covers installing the library with optional dependencies, working with base models, validating functions, and managing environment variables.

We continue our conversation about web development with another article about Python security best practices. This article covers several Python libraries and crucial steps you can take to help make your web-based applications more secure.

We also share several other articles and projects from the Python community, including a news roundup, why Python lists multiply oddly, inline run dependencies in pipx, a discussion about open-source contribution assignments, playing sounds in Python, and a Python library to access ISO country definitions.

This episode is sponsored by Mailtrap.

Course Spotlight: Using raise for Effective Exceptions

In this video course, you’ll learn how to raise exceptions in Python, which will improve your ability to efficiently handle errors and exceptional situations in your code. This way, you’ll write more reliable, robust, and maintainable code.

Topics:

• 00:00:00 – Introduction
• 00:02:40 – Python 3.12.3, Python 3.11.9, and 3.13.0a6 Released
• 00:03:43 – Django Bugfix Release Issued: 5.0.4
• 00:04:48 – PEP 738 Accepted: Adding Android as a Supported Platform
• 00:05:53 – EuroPython Tickets on Sale: Prague/Remote July 8-14
• 00:06:38 – PyCon Portugal 2024
• 00:07:17 – Pydantic: Simplifying Data Validation in Python
• 00:15:24 – Sponsor: Mailtrap
• 00:15:58 – Why Do Python Lists Multiply Oddly?
• 00:22:21 – Best Python Security Practices for Web Developers
• 00:34:13 – Video Course Spotlight
• 00:35:38 – Inline Run Dependencies in pipx 1.4.2
• 00:40:16 – So Your Teacher Wants You to Do Open Source
• 00:54:49 – nava: Play Sounds in Python
• 00:56:25 – pycountry: A Python library to access ISO country definitions
• 00:58:18 – Thanks and goodbye

News:

• Python 3.12.3 and 3.13.0a6 Released
• Python 3.11.9 Released
• Django Bugfix Release Issued: 5.0.4
• PEP 738 Accepted: Adding Android as a Supported Platform
• PEP 742 Accepted: Narrowing Types With TypeIs
• EuroPython Tickets on Sale: Prague/Remote July 8-14
• PyCon Portugal 2024

Show Links:

• Pydantic: Simplifying Data Validation in Python – Discover the power of Pydantic, Python’s most popular data parsing, validation, and serialization library. In this hands-on tutorial, you’ll learn how to make your code more robust, trustworthy, and easier to debug with Pydantic.
• Why Do Python Lists Multiply Oddly? – In Python you can use the multiplication operator on sequences to return a repeated version of the value. When you do this with a list containing an empty list you get what might be unexpected behavior. This article explains what happens and why.
• Best Python Security Practices for Web Developers – Coding on the web means you have to be more security conscious as everyone has access to your software. This article discusses key steps you can take to help make your code more secure.
• Inline Run Dependencies in pipx 1.4.2 – PEP 723 adds the ability to specify dependencies within a Python script itself. The folks who write pipx have added an experimental feature that takes advantage of this future language change. This article shows you how the new feature looks and what pipx does with it.
• Install and Execute Python Applications Using pipx – In this tutorial, you’ll learn about a tool called pipx, which lets you conveniently install and run Python packages as standalone command-line applications in isolated environments. In a way, pipx turns the Python Package Index (PyPI) into an app marketplace for Python programmers.

Discussion:

• So Your Teacher Wants You to Do Open Source – Sometimes teachers or mentors ask students to contribute to an open source project, without the context of what that entails. This opinion piece covers just how much noise that causes for the projects and why you shouldn’t do it unless you truly mean to contribute.
• 503 Days Working Full-Time on FOSS: Lessons Learned – For a year and a half, Rodrigo worked at Textualize the company behind the popular open source Python projects Rich and Textual. This blog post talks about what he learned while he was there.

Projects:

• nava: Play Sounds in Python
• pycountry: A Python library to access ISO country, subdivision, language, currency and script definitions and their translations

Additional Links:

• Pydantic
• François Fleuret on X: “2h of debugging. Whatever you say, that’s counter intuitive.”
• bandit: Security oriented static analyzer for Python code - PyPI
• Dependency Management With Python Poetry – Real Python
• OWASP Top Ten - OWASP Foundation
• pipx
• Governance - The Pallets Projects
• Textual
• How to Contribute to Open Source - Open Source Guides
• Djangonaut Space - Where contributors launch!

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

• Lists and Tuples in Python
• Using raise for Effective Exceptions
• Sorting Data in Python With pandas

Support the podcast & join our community of Pythonistas

What are the benefits of using a decoupled data processing system? How do you write reusable queries for a variety of backend data platforms? This week on the show, Phillip Cloud, the lead maintainer of Ibis, will discuss this portable Python dataframe library.

Phillip contrasts Ibis’s workflow with other Python dataframe libraries. We discuss how “getting close to the data” speeds things up and conserves memory.

He describes the different approaches Ibis provides for querying data and how to select a specific backend. We discuss ways to get started with the library and how to access example data sets to experiment with the platform.

Phillip discovered Ibis while looking for a tool that allowed him to reuse SQL queries written for a specific data platform on a different one. He recounts how he got involved with the Ibis project, sharing his background in open source and learning how to contribute to a first project.

This episode is sponsored by Mailtrap.

Course Spotlight: Creating Web Maps From Your Data With Python Folium

You’ll learn how to create web maps from data using Folium. The package combines Python’s data-wrangling strengths with the data-visualization power of the JavaScript library Leaflet. In this video course, you’ll create and style a choropleth world map showing the ecological footprint per country.

Topics:

• 00:00:00 – Introduction
• 00:02:18 – How did you get started with Ibis?
• 00:08:10 – First contribution to open source
• 00:13:46 – Comparing Ibis to other dataframe libraries
• 00:20:09 – Sponsor: Mailtrap
• 00:20:43 – What goes into the selection of backend?
• 00:27:07 – Database connections vs SQL compilers
• 00:30:03 – Raw SQL approach
• 00:34:06 – Dataframe approach
• 00:38:31 – What does “getting close to the data” mean?
• 00:41:52 – Video Course Spotlight
• 00:43:24 – Phillip in the cloud - YouTube channel
• 00:44:56 – Access to sample data sets
• 00:50:11 – Additional resources
• 00:52:50 – What are some of the backends Ibis supports?
• 00:54:13 – Entry points to the platform
• 00:55:00 – How are you supported?
• 00:57:10 – Exporting a SQL query
• 00:59:23 – What are you excited about in the world of Python?
• 01:04:28 – What do you want to learn next?
• 01:07:12 – How can people follow your work online?
• 01:08:00 – Thanks and goodbye

Show Links:

• Ibis - the portable Python dataframe library
• The Leading Designer and Builder of Enterprise Data Systems - Voltron Data
• PEP 249 – Python Database API Specification v2.0
• sqlglot: Python SQL Parser and Transpiler - GitHub
• Ibis – getting_started
• ibis-examples: A repository of runnable examples using ibis
• Ibis – Reference Documentation
• PyScript - Run Python in your HTML
• pixi - Prefix.dev
• uv: An extremely fast Python package installer and resolver, written in Rust
• PyCon US 2024
• LearnCraft Spanish – Fluency for Serious Learners
• ibis: the portable Python dataframe library - GitHub
• Ibis – Blog Posts
• Phillip in the Cloud - YouTube
• Phillip Cloud (@cpcloudy) / X
• cpcloud (Phillip Cloud) · GitHub

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

• Building Python Project Documentation With MkDocs
• Creating Web Maps From Your Data With Python Folium
• Using raise for Effective Exceptions

Support the podcast & join our community of Pythonistas

What is error culture, and how do you avoid it within your organization? How do you navigate alert and notification fatigue? Hey, it’s episode #200! Real Python’s editor-in-chief, Dan Bader, joins us this week to celebrate. Christopher Trudeau also returns to bring another batch of PyCoder’s Weekly articles and projects.

We discuss an article series about error culture. We dig into false positives, hero culture, and the tendency to start ignoring alerts. We contrast our personal experiences and propose possible remedies. Dan describes configuring Real Python’s alert system.

We also share several other articles and projects from the Python community, including a news roundup, reading and writing WAV files in Python, moving beyond flat files toward SQLite and SQLAlchemy, getting help in Python, exploring four kinds of optimization, a framework for building web scrapers, and a project to simply subprocesses.

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

Course Spotlight: SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files

In this video course, you’ll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.

Topics:

• 00:00:00 – Introduction
• 00:02:42 – PyPI Hiring a Support Specialist
• 00:03:19 – PyPI Temporarily Halted New Users and Projects
• 00:04:14 – What we know about the xz Utils backdoor
• 00:05:38 – CPython, PyPI, and the backdoor of xz
• 00:07:18 – Episode 200 appreciation and the journey
• 00:09:18 – A visit from Dan
• 00:14:14 – Reading and Writing WAV Files in Python
• 00:19:56 – Sponsor: Sentry
• 00:21:03 – SQLite and SQLAlchemy in Python
• 00:27:36 – Getting Help (In Python)
• 00:32:49 – Laurence Tratt: Four Kinds of Optimization
• 00:40:54 – Video Course Spotlight
• 00:42:26 – Discussion: Error Culture
• 00:58:03 – botasaurus: The All in One Framework to Build Awesome Scrapers
• 01:01:04 – suby: Slightly Simplified Subprocesses
• 01:02:50 – Thanks and goodbye

News:

• PyPI Hiring a Support Specialist (Remote)
• PyPI Temporarily Halted New Users and Projects – To fend off a supply-chain attack, PyPI temporarily halted new users and projects for about 10 hours last week. This article discusses why, and the scourge of supply-chain attacks.
• What we know about the xz Utils backdoor that almost infected the world - Ars Technica
• CPython, PyPI, and many Python packages are not affected by the backdoor of xz - Core Development - Discussions on Python.org

Topics:

• Reading and Writing WAV Files in Python – In this tutorial, you’ll learn how to work with WAV audio files in Python using the standard-library wave module. Along the way, you’ll synthesize sounds from scratch, visualize waveforms in the time domain, animate real-time spectrograms, and apply special effects to widen the stereo field.
• SQLite and SQLAlchemy in Python: Beyond Flat Files – In this video course, you’ll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.
• How SQLite Is Tested – The page describes how SQLite is rigorously tested using four test harnesses, fuzz testing, anomaly testing like crash and I/O error simulations, and other techniques to ensure reliability.
• Getting Help (In Python) – When trying to remember just where sleep() was in the Python standard library, Ishaan stumbled through the built-in help and learned how to use it to answer just these kinds of questions.
• Laurence Tratt: Four Kinds of Optimization – “Premature optimization might be the root of all evil, but overdue optimization is the root of all frustration. No matter how fast hardware becomes, we find it easy to write programs which run too slow.” Read on to learn what to do about it.

Discussion:

• Error Culture
• Error Culture Part II
• Error Culture Part III

Projects:

• botasaurus: The All in One Framework to Build Awesome Scrapers
• suby: Slightly Simplified Subprocesses

Additional Links:

• xkcd: Exploits of a Mom
• How SQLite Is Tested
• SettingWithCopyWarning in pandas: Views vs Copies – Real Python
• 99% Invisible - Mini-Stories: Volume 4

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

• Playing and Recording Sound in Python
• Exploring Scopes and Closures in Python
• SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files

Support the podcast & join our community of Pythonistas

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.

Course Spotlight: Command Line Interfaces in Python

Command line arguments are the key to converting your programs into useful and enticing tools that are ready to be used in the terminal of your operating system. In this course, you’ll learn their origins, standards, and basics, and how to implement them in your program.

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

Support the podcast & join our community of Pythonistas

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.

Course Spotlight: Python Turtle for Beginners

In this step-by-step course, you’ll learn the basics of Python programming with the help of a simple and interactive Python library called turtle. If you’re a beginner to Python, then this tutorial will definitely help you on your journey as you take your first steps into the world of programming.

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

Support the podcast & join our community of Pythonistas

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.

Course Spotlight: Building Python Project Documentation With MkDocs

In this video course, you’ll learn how to build professional documentation for a Python package using MkDocs and mkdocstrings. These tools allow you to generate nice-looking and modern documentation from Markdown files and, more importantly, from your code’s docstrings.

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.

Course Spotlight: Pointers and Objects in Python

In this video course, you’ll learn about Python’s object model and see why pointers don’t really exist in Python. You’ll also cover ways to simulate pointers in Python without managing memory.

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

Support the podcast & join our community of Pythonistas

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.

Course Spotlight: Grow Your Python Portfolio With 13 Intermediate Project Ideas

Get started on 13 Python project ideas that are just right for intermediate Python developers. They’ll challenge you enough to help you become a better Pythonista.

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

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

• 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.

Course Spotlight: Building Command Line Interfaces With argparse

In this step-by-step Python video course, you’ll learn how to take your command line Python scripts to the next level by adding a convenient command line interface that you can write with argparse.

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

Support the podcast & join our community of Pythonistas