Podd: The Real Python Podcast

Telling Effective Stories With Your Python Visualizations

21 februari 2025 | 69 min

Behavior-Driven vs Test-Driven Development & Using Regex in Python

14 februari 2025 | 57 min

What is behavior-driven development, and how does it work alongside test-driven development? How do you communicate requirements between teams in an organization? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

In this episode, we expand on our software testing discussion from two weeks ago by adding behavior-driven development concepts. Christopher describes how BDD correlates with test-driven development and how it fosters collaboration within a team. We discuss building acceptance tests written in plain language and a handy tool for creating them.

We also share several other articles and projects from the Python community, including a news roundup, using regular expressions in Python, dealing with missing data in Polars, monkey patching in Django, first steps with Playwright, 3D printing giant things with a Python jigsaw generator, and a query language for JSON.

This episode is sponsored by Postman.

Course Spotlight: Regular Expressions and Building Regexes in Python

In this course, you’ll learn how to perform more complex string pattern matching using regular expressions, or regexes, in Python. You’ll also explore more advanced regex tools and techniques that are available in Python.

Topics:

00:00:00 – Introduction
00:02:21 – PyOhio 2025 July 26-27, 2025 Announced
00:02:38 – Python 3.13.2 and 3.12.9 now available!
00:02:52 – Django bugfix releases issued: 5.1.6, 5.0.12, and 4.2.19
00:03:04 – DjangoCon Europe 2025 - Real Python Podcast
00:05:24 – How to Deal With Missing Data in Polars
00:10:29 – Monkey Patching Django
00:15:50 – Sponsor: Postman
00:16:42 – My First Steps With Playwright
00:20:48 – How to Use Regular Expressions in Python
00:25:55 – Video Course Spotlight
00:27:25 – TDD vs. BDD: What’s the Difference?
00:50:13 – 3D Printing Giant Things With a Python Jigsaw Generator
00:53:58 – jmespath.py: Query Language for JSON
00:55:58 – Thanks and goodbye

News:

Topics:

How to Deal With Missing Data in Polars – In this tutorial, you’ll learn how to deal with missing data in Polars to ensure it doesn’t interfere with your data analysis. You’ll discover how to check for missing values, update them, and remove them.
Monkey Patching Django – The nanodjango project is a modification to the Django framework that lets you get started with a single file instead of the usual cookie-cutter directory structure. This is a detailed post explaining how nanodjango monkey patches Django to achieve this result.
Fake Django Objects With Factory Boy – The
My First Steps With Playwright – Playwright is a browser-based automation tool that can be used for web scraping or testing. This intro article shows you how to use the Python interface to access a page including using cookies.
How to Use Regular Expressions in Python – This post explores the basics of regular expressions in Python, as well as more advanced techniques. It includes real-world use cases and performance optimization strategies.

Discussion:

TDD vs. BDD: What’s the Difference? – Discover the key differences between TDD vs BDD, their workflows, tools, and best practices for developers.
Cucumber

Projects:

3D Printing Giant Things With a Python Jigsaw Generator – This is a long, detailed article on 3D printing objects too large for the printer bed. The author has created dovetail joints to assemble pieces together. He wrote a Python program to automatically split up the larger model files into the jigsaw pieces needed to build a final result.
jmespath.py: Query Language for JSON

Additional Links:

Polars — DataFrames for the new era
nanodjango: Full Django in a single file - views, models, API ,with async support. Automatically convert it to a full project.
factory_boy library is a tool for managing fixtures for your tests. This article shows you how to use it with Django.
trimesh 4.6.2 documentation
Email::RFC822::Address - Regex Recipe

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Charlie Marsh: Accelerating Python Tooling With Ruff and uv

7 februari 2025 | 91 min

Testing Your Python Code Base: Unit vs. Integration

31 januari 2025 | 54 min

What goes into creating automated tests for your Python code? Should you focus on testing the individual code sections or on how the entire system runs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article from Semaphore about unit testing vs. integration testing. Christopher shares his experiences setting up automated tests for his own smaller projects. He also answers questions about building tests in an existing codebase and integrating tests across systems.

We also share several other articles and projects from the Python community, including a news roundup, improving default line charts to journal-quality infographics, why hash(-1) == hash(-2) in Python, data cleaning in data science, ways to work with large files in Python, a lightweight CLI viewer for log files, and a tool for mocking the datetime module for testing.

This episode is sponsored by Postman.

Course Spotlight: Testing Your Code With pytest

In this video course, you’ll learn how to take your testing to the next level with pytest. You’ll cover intermediate and advanced pytest features such as fixtures, marks, parameters, and plugins. With pytest, you can make your test suites fast, effective, and less painful to maintain.

Topics:

00:00:00 – Introduction
00:02:28 – Python news and releases
00:04:02 – From Default Line Charts to Journal-Quality Infographics
00:07:25 – PyViz: Python Tools for Data Visualization
00:09:25 – Why Is hash(-1) == hash(-2) in Python?
00:12:40 – Sponsor: Postman
00:13:32 – Data Cleaning in Data Science
00:19:29 – 10 Ways to Work With Large Files in Python
00:23:40 – Unit Testing vs. Integration Testing
00:29:17 – Does university curriculum cover this?
00:31:22 – Building tests into smaller projects
00:36:04 – Video Course Spotlight
00:37:30 – How does the approach differ with clients or larger-scale projects?
00:40:45 – How do tests act as documentation?
00:42:02 – Difficulties in building integration tests
00:45:24 – How do you limit the results of tests?
00:47:52 – klp: Lightweight CLI Viewer for Log Files
00:50:54 – freezegun: Mocks the datetime Module for Testing
00:53:11 – Thanks and goodbye

News:

Topics:

From Default Line Charts to Journal-Quality Infographics – “Everyone who has used Matplotlib knows how ugly the default charts look like.” In this series of posts, Vladimir shares some tricks to make your visualizations stand out and reflect your individual style.
PyViz: Python Tools for Data Visualization – This site contains an overview of all the different visualization libraries in the Python ecosystem. If you’re trying to pick a tool, this is a great place to better understand the pros and cons of each.
Why Is hash(-1) == hash(-2) in Python? – Somewhat surprisingly, hash(-1) == hash(-2) in CPython. This post examines how and discovers why this is the case.
Data Cleaning in Data Science – “Real-world data needs cleaning before it can give us useful insights. Learn how you can perform data cleaning in data science on your dataset.”
10 Ways to Work With Large Files in Python – “Handling large text files in Python can feel overwhelming. When files grow into gigabytes, attempting to load them into memory all at once can crash your program.” This article covers different ways of dealing with this challenge.

Discussion:

Unit Testing vs. Integration Testing – Discover the key differences between unit testing vs. integration testing and learn how to automate both with Python.

Project:

Additional Links:

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Simon Willison: Using LLMs for Python Development

24 januari 2025 | 82 min

Principles for Considering Your Python Tooling

17 januari 2025 | 47 min

What are the principles you should consider when making decisions about which Python tools to use? What anti-patterns get in the way of making the right choices for your team? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article about effective Python developer tooling. Instead of digging into a list of current libraries, we talk about the principles you must consider before making decisions for your team. We cover common pitfalls teams get mired in and how to avoid them.

We also share several other articles and projects from the Python community, including a news roundup, a huge collection of the top Python libraries of 2024, programming sockets in Python, merging dictionaries, a Django quiz, mistakes to avoid in production, building a Portal sentry turret, a powerful TUI expense tracker, and a pure-Python async rendering engine.

Course Spotlight: Managing Dependencies With Python Poetry

Learn how Python Poetry can help you start new projects, maintain existing ones, and master dependency management.

Topics:

00:00:00 – Introduction
00:01:53 – DjangoCon US 2025 (Chicago, Sept 8-12) Announced
00:02:38 – Textualize 1.0 Released
00:03:15 – Top Python Libraries of 2024
00:07:07 – Programming Sockets in Python
00:11:56 – Merging Dictionaries in Python
00:17:03 – Django Quiz 2024
00:17:55 – Confessions of a Django Dev: Mistakes To Avoid in Production
00:18:40 – Sentry Turret Straight Out of the ‘Portal’ Franchise
00:20:00 – Video Course Spotlight
00:21:26 – Effective Python Developer Tooling in December 2024
00:41:13 – Bagels: Powerful TUI Expense Tracker
00:43:42 – htmy: Async, Pure-Python Rendering Engine
00:45:41 – Thanks and goodbye

News:

Show Links:

Top Python Libraries of 2024 – For the past ten years, Tyrolabs has put together a list of their favorite Python libraries of the year. This list includes ten general purpose libraries and ten more specific to AI/ML and Data.
Programming Sockets in Python – In this in-depth video course, you’ll learn how to build a socket server and client with Python. By the end, you’ll understand how to use the main functions and methods in Python’s socket module to write your own networked client-server applications.
Merging Dictionaries in Python – There are multiple ways of merging two or more dictionaries in Python. This post teaches you how to do it and how to deal with corner cases like duplicate keys.
Django Quiz 2024 – Adam runs a quiz on Django at his Django London meetup. He’s shared it so you can try it yourself. Test how much you know about your favorite web framework.
Confessions of a Django Dev: Mistakes To Avoid in Production – This post covers some of the common mistakes you might make when taking a Django project into production.
Sentry Turret Straight Out of the ‘Portal’ Franchise – “Reckless_commenter has created a Raspberry Pi-powered sentry turret that looks and sounds just like the creepy machines found in the ‘Portal’ franchise.” Logic and sound effects managed through the PyGame library.

Discussion:

Effective Python Developer Tooling in December 2024 – This post talks about how tooling doesn’t solve all your problems when you code, especially with a team. It outlines some principles to implement, and bad practices to avoid when writing Python.
Mistakes engineers make in large established codebases - Sean Goedecke

Projects:

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Building New Structures for Learning Python

10 januari 2025 | 52 min

What are the new ways we can teach and share our knowledge about Python? How can we improve the structure of our current offerings and build new educational resources for our audience of Python learners? This week on the show, Real Python core team members Stephen Gruppetta and Martin Breuss join us to discuss enhancements to the site and new ways to learn Python.

Stephen has recently joined the team, bringing years of online training expertise. He discusses our new offering of cohort-based courses, which combine live expert instruction, hands-on exercises, and a supportive community.

Martin has been busy leading the effort to create quizzes for our written tutorials to test your knowledge and Python skills. He’s also restructuring the learning paths to provide a more consistent way to navigate your journey learning Python.

Stephen is currently working on new Real Python books. These books will be collections of our tutorials based on specific Python topics and edited to provide a more structured learning experience. The first book, which covers object-oriented programming in Python, will be available in the next few months.

This episode is sponsored by Sentry.

Course Spotlight: Handling or Preventing Errors in Python: LBYL vs EAFP

In this video course, you’ll explore two popular coding styles in Python: Look Before You Leap (LBYL) and Easier to Ask Forgiveness than Permission (EAFP). These approaches help you handle errors and exceptional situations in your code effectively. You’ll dive into the key differences between LBYL and EAFP and learn when to use each one.

Topics:

00:00:00 – Introduction
00:02:29 – What Stephen has been up to
00:03:31 – What’s new for Martin
00:04:07 – Bringing on new team members
00:06:09 – Cohort-based courses
00:19:25 – Sponsor: Sentry
00:20:27 – Restructured and new learning paths
00:30:50 – Video Course Spotlight
00:32:19 – New Real Python Books
00:38:57 – A destination for learning
00:40:46 – Quizzes for tutorials and courses
00:44:58 – Video courses and updating content
00:47:52 – Code Mentor
00:49:45 – Code challenges
00:51:06 – Thanks and goodbye

Show Links:

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PyCoder's Weekly 2024 Top Articles & Missing Gems

3 januari 2025 | 41 min

Exploring Modern Sentiment Analysis Approaches in Python

20 december 2024 | 73 min

Good Python Programming Practices When New to the Language

6 december 2024 | 51 min

marimo: Reactive Notebooks and Deployable Web Apps in Python

29 november 2024 | 61 min

The Joy of Tinkering & Python Free-Threading Performance

22 november 2024 | 46 min

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

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

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

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

This episode is sponsored by Windsurf.

Course Spotlight: Python Inner Functions

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

Topics:

00:00:00 – Introduction
00:02:18 – Django Bugfix Release Issued: 5.1.3
00:02:46 – Pillow Release 11.0.0
00:03:14 – Flask Version 3.1.0
00:03:30 – PyCon US 2025 (Pittsburgh) Call for Proposals
00:03:46 – Python Closures: Common Use Cases and Examples
00:09:20 – State of Python 3.13 Performance: Free-Threading
00:15:42 – Sponsor: Windsurf
00:16:32 – Opposite of Cloud Native Is…?
00:22:36 – Python’s Soft Keywords
00:24:50 – Video Course Spotlight
00:26:11 – The Joy of Tinkering
00:38:33 – shot-scraper: A command-line utility for taking automated screenshots of websites
00:41:13 – django-admin-tui: Django Admin in the Terminal!
00:42:37 – django-admin-dracula: Dracula Themes for the Django Admin
00:44:21 – Thanks and goodbye

News:

Show Links:

Python Closures: Common Use Cases and Examples – In this tutorial, you’ll learn about Python closures. A closure is a function-like object with an extended scope. You can use closures to create decorators, factory functions, stateful functions, and more.
State of Python 3.13 Performance: Free-Threading – This article does a comparison between code in single threaded, threaded, and multi-process versions under Python 3.12, 3.13, and 3.13 free-threaded with the GIL on and off.
Opposite of Cloud Native Is…? – Michael (from Talk Python fame) introduces the concept of “stack-native” as the opposite of “cloud-native”, and how it applies to Python web apps. Building applications with just enough full-stack building blocks to run reliably with minimal complexity, rather than relying on a multitude of cloud services.
Python’s soft keywords – Python includes soft keywords: tokens that are important to the parser but can also be used as variable names. This article shows you what a soft keyword is and how to find them in Python 3.12 (both the easy and hard way).

Discussion:

Habits of Great Software Engineers - The Joy of Tinkering

Projects:

Additional Projects:

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Maintaining the Foundations of Python & Cautionary Tales

15 november 2024 | 69 min

New PEPs: Template Strings & External Wheel Hosting

8 november 2024 | 48 min

PySheets: Spreadsheets in the Browser Using PyScript

1 november 2024 | 80 min

Python Getting Faster and Leaner & Ideas for Django Projects

25 oktober 2024 | 43 min

What changes are happening under the hood in the latest versions of Python? How are these updates laying the groundwork for a faster Python in the coming years? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article about Python’s recent performance improvements. The piece covers the specialized adaptive interpreter and explains what those terms mean. It also includes details about the experimental feature of the Just-In-Time (JIT) compiler added in 3.13.

We dig into a collection of Django projects you can use to practice and develop your skills. The projects ramp up from detailed beginner tutorials to more advanced projects with guidelines on how to get started. We also discuss a collection of popular websites that use Django.

We share several other articles and projects from the Python community, including a batch of recent Python Enhancement Protocols (PEPs), a couple of Python releases, using DuckDB in the browser with Pyodide, building a contact book app with Textual, generating a tiny status page with a Python script, and a grep-like tool that understands code.

This episode is sponsored by AssemblyAI.

Course Spotlight: Building a Site Connectivity Checker

In this video course, you’ll build a Python site connectivity checker for the command line. While building this app, you’ll integrate knowledge related to making HTTP requests with standard-library tools, creating command-line interfaces, and managing concurrency with asyncio and aiohttp.

Topics:

00:00:00 – Introduction
00:03:11 – PEP 777: How to Re-Invent the Wheel
00:04:22 – PEP 758: Allow except and except* Expressions Without Parentheses
00:04:51 – PEP 760: No More Bare Excepts (Withdrawn)
00:05:42 – PEP 735: Dependency Groups in pyproject.toml
00:06:29 – PEP 761: Deprecating PGP Signatures for CPython Artifacts
00:06:59 – Python 3.12.7 Released
00:07:12 – Incremental GC and Pushing Back the 3.13.0 Release
00:09:10 – DuckDB in the Browser With Pyodide
00:15:35 – Sponsor: AssemblyAI
00:16:18 – Build a Contact Book App With Python, Textual, and SQLite
00:21:55 – Django Project Ideas
00:28:42 – Video Course Spotlight
00:30:00 – In the Making of Python Fitter and Faster
00:35:13 – tinystatus: Tiny Status Page Generated by a Python Script
00:38:06 – srgn: Grep-Like Tool That Understands Code
00:42:01 – Thanks and goodbye

News:

PEP 777: How to Re-Invent the Wheel – “The current wheel 1.0 specification was written over a decade ago, and has been extremely robust to changes in the Python packaging ecosystem… this PEP prescribes compatibility requirements on future wheel revisions.”
PEP 758: Allow except and except* Expressions Without Parentheses – “This PEP proposes to allow unparenthesized except and except* blocks in Python’s exception handling syntax. Currently, when catching multiple exceptions, parentheses are required around the exception types.”
PEP 760: No More Bare Excepts (Withdrawn)
PEP 735: Dependency Groups in pyproject.toml (Accepted)
PEP 761: Deprecating PGP Signatures for CPython Artifacts – Since Python 3.11.0, CPython has provided two verifiable digital signatures for all CPython artifacts: PGP and sigstore. This PEP proposes moving to sigstore as the only way of signing artifacts.
Python 3.12.7 Released
Python 3.13.0 Released
Incremental GC and Pushing Back the 3.13.0 Release – Some last minute performance considerations delayed the release of Python 3.13 with one of the features being backed out.

Show Links:

DuckDB in the Browser With Pyodide – Learn how to run DuckDB in an in-browser Python environment to enable simple querying on remote files, interactive documentation, and easy to use training materials.
Build a Contact Book App With Python, Textual, and SQLite – In this tutorial, you’ll be guided step by step through the process of building a basic contact book application. You’ll use Python and Textual to build the application’s text-based user interface (TUI), and then use SQLite to manage the database.
Django Project Ideas – Looking to experiment or build your portfolio? Discover creative Django project ideas for all skill levels, from beginner apps to advanced full-stack projects.
In the Making of Python Fitter and Faster – This post details how Python’s recent performance improvements work under the hood. It covers changes to the interpreter, better memory management, and the newly experimental JIT compiler.

Projects:

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Narwhals: Expanding DataFrame Compatibility Between Libraries

18 oktober 2024 | 61 min

Exploring the New Features of Python 3.13

11 oktober 2024 | 55 min

Using Virtual Environments in Docker & Comparing Python Dev Tools

27 september 2024 | 56 min

Should you use a Python virtual environment in a Docker container? What are the advantages of using the same development practices locally and inside a container? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We share a recent post by Hynek Schlawack about building Python projects using Docker containers. Hynek argues for using virtual environments for these projects, like developing a local one. He’s found that keeping your code in an isolated, well-defined location and structure avoids confusion and complexity.

We also discuss our development setups, including Python versions, code editors, virtual environment practices, terminals, and customizations. We dig into how your programming history affects the tools you use.

We share several other articles and projects from the Python community, including a group of new releases, addressing the “why” in comments, comparing a data science workflow in Python and R, removing common problems from CSV files, and a project for creating HTML tables in Django.

This episode is sponsored by InfluxData.

Course Spotlight: Advanced Python import Techniques

The Python import system is as powerful as it is useful. In this in-depth video course, you’ll learn how to harness this power to improve the structure and maintainability of your code.

Topics:

00:00:00 – Introduction
00:02:55 – Python Releases 3.12.6, 3.11.10, 3.10.15, 3.9.20, and 3.8.20
00:03:26 – Python Release Python 3.13.0rc2
00:04:07 – Django Security Releases Issued: 5.1.1, 5.0.9, and 4.2.16
00:04:36 – Polars Has a New Lightweight Plotting Backend
00:05:49 – Why I Still Use Python Virtual Environments in Docker
00:11:37 – How to Use Conditional Expressions With NumPy where()
00:15:55 – Sponsor: InfluxData
00:16:39 – PythonistR: A Match Made in Data Heaven
00:23:44 – Why Not Comments
00:26:48 – Video Course Spotlight
00:28:10 – Discussion: Personal development setups
00:51:01 – csv_trimming: Remove Common Ugliness From CSV Files
00:53:01 – django-tables2: Create HTML Tables in Django
00:54:39 – Thanks and goodbye

News:

Show Links:

Polars Has a New Lightweight Plotting Backend – Polars 1.6 allows you to natively create beautiful plots without pandas, NumPy, or PyArrow. This is enabled by Narwhals, a lightweight compatibility layer between dataframe libraries.
Why I Still Use Python Virtual Environments in Docker – Hynek often gets challenged when he suggests the use of virtual environments within Docker containers, and this post explains why he still does.
How to Use Conditional Expressions With NumPy where() – This tutorial teaches you how to use the where() function to select elements from your NumPy arrays based on a condition. You’ll learn how to perform various operations on those elements and even replace them with elements from a separate array or arrays.
PythonistR: A Match Made in Data Heaven – In data science you’ll sometimes hear a debate between R and Python. Cosima says ‘why not choose both?’ She outlines a data pipeline that uses the best tool for each job.
Why Not Comments – This post talks about why you might want to include information in your code comments about why you didn’t take a particular approach.

Discussion:

Projects:

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Thriving as a Developer With ADHD

20 september 2024 | 67 min

What are strategies for being a productive developer with ADHD? How can you help your team members with ADHD to succeed and complete projects? This week on the show, we speak with Chris Ferdinandi about his website and podcast “ADHD For the Win!”

Chris struggled with productivity early in his career as a developer. He shares systems and strategies he’s discovered to harness the focusing power of ADHD.

We discuss time management, meetings, and maintaining productivity in a hectic world. Chris also shares resources for learning more about defining ADHD, self-evaluation, and how to keep getting things done.

This episode is sponsored by InfluxData.

Course Spotlight: Build a GUI Calculator With PyQt and Python

In this video course, you’ll learn how to create graphical user interface (GUI) applications with Python and PyQt. Once you’ve covered the basics, you’ll build a fully functional desktop calculator that can respond to user events with concrete actions.

Topics:

00:00:00 – Introduction
00:02:30 – Defining ADHD and how it aligns with coding
00:05:47 – Analogy for focus
00:06:51 – Can you sense the change in focus?
00:07:46 – The challenge of meetings
00:11:45 – Tips for managing time
00:15:44 – Capturing notes and defragging
00:18:48 – Sponsor: InfluxData
00:19:33 – Downtime and interruptions
00:25:26 – Remote work and focus
00:33:16 – Sitting still and meetings
00:37:39 – Video Course Spotlight
00:39:13 – Anything worth doing is worth doing poorly
00:47:36 – Prototypes and working on interesting things
00:50:26 – Deadlines and pomodoro timers
00:54:21 – Have your symptoms changed over time?
00:56:18 – Starting ADHDftw.com
00:59:12 – Decision to keep podcast episodes short
01:00:01 – Deciding on medication
01:02:02 – Resources available
01:03:29 – What motivates you to continue to learn programming?
01:04:06 – What do you want to learn next?
01:04:55 – What are other ways to follow your work online?
01:05:28 – Thanks and goodbye

Show Links:

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Configuring Git Pre-Commit Hooks & Estimating Software Projects

13 september 2024 | 54 min

Astrophysics and Astronomy With Python & PyCon Africa 2024

6 september 2024 | 66 min

Are you interested in practicing your Python skills while learning how to solve astrophysics and astronomy problems? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a pair of his recent Real Python video courses about exploring astronomy and astrophysics with Python. Throughout the courses, you’ll get to practice using a variety of data science libraries, such as NumPy, Matplotlib, pandas, pint, and Astropy.

We speak with Mannie Young who is the Organizing Committee Chair of PyCon Africa. Real Python is excited to be a contributing sponsor of this year’s conference. Mannie discusses reinvigorating a continent-spanning conference after a multiyear hiatus. He also talks about introducing Python to students and new developers across Africa through PyClubs, PyLadies, and PyData programs.

We share several other articles and projects from the Python community, including a news roundup, logging in Python, understanding operator precedence, reconciling why it only works on your machine, a fast way to create an HTML app, and a tool for deep inspection of Python objects.

This episode is sponsored by InfluxData.

Course Spotlight: Sorting Dictionaries in Python: Keys, Values, and More

In this video course, you’ll learn how to sort Python dictionaries. By the end, you’ll be able to sort by key, value, or even nested attributes. But you won’t stop there, you’ll also measure the performance of variations when sorting and compare different key-value data structures.

Topics:

00:00:00 – Introduction
00:03:05 – PEP 750: Tag Strings for Domain-Specific Languages
00:05:19 – PEP 752: Package Repository Namespaces
00:07:32 – PyCon US 2024 Recap and Recording Release
00:08:01 – Logging in Python
00:14:57 – It Works on My Machine. Why?
00:17:33 – Python’s Operator Precedence
00:20:54 – Exploring Astrophysics in Python With pandas and Matplotlib
00:24:03 – Using Astropy for Astronomy With Python
00:26:37 – Sponsor: InfluxData
00:27:22 – fasthtml: The Fastest Way to Create an HTML App
00:32:50 – wat: Deep Inspection of Python Objects
00:38:08 – PyCon Africa 2024
00:40:47 – What goes into re-energizing a conference?
00:44:20 – Talks and speakers
00:46:58 – Video Course Spotlight
00:48:29 – How did you get involved?
00:52:19 – PyClubs and growing Python education
00:58:41 – What industries are using Python in Ghana?
01:00:20 – Sponsorship and support
01:01:51 – Travel in and outside the continent
01:04:23 – Call to action
01:05:05 – Thanks and goodbye

News:

PEP 750: Tag Strings for Domain-Specific Languages (Added)
PEP 752: Package Repository Namespaces (Added)
PyCon US 2024 Recap and Recording Release – PyCon US 2024 had a record breaking attendance with over 2,700 in-person tickets sold. This article is a recap from the conference runners and links to all the available recordings.

Show Links:

Logging in Python – If you use Python’s print() function to get information about the flow of your programs, then logging is the natural next step for you. This tutorial will guide you through creating your first logs and show you ways to curate them to grow with your projects.
It Works on My Machine. Why? – A list of things to check when something works on your computer but not on someone else’s.
Python’s Operator Precedence – Stephen uses a story-telling style to explain how operator precedence works in Python.
Exploring Astrophysics in Python With pandas and Matplotlib – This course uses three problems often covered in introductory astro-physics courses to play in Python. Along the way you’ll learn some astronomy and how to use a variety of datascience libraries like NumPy, Matplotlib, pandas, and pint.
Using Astropy for Astronomy With Python – This course covers two problems from introductory astronomy to help you play with some Python libraries. You’ll use NumPy, Matplotlib, and pandas to find planet conjunctions, and graph the best viewing times for a star.

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Exploring Robotics and Python Through Electronic Projects

23 augusti 2024 | 66 min

Packaging Data Analyses & Using pandas GroupBy

16 augusti 2024 | 55 min

What are the best practices for organizing data analysis projects in Python? What are the advantages of a more package-centric approach to data science? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss Joshua Cook’s recent article “How I Use Python to Organize My Data Analyses.” The article covers how his process for building data analysis projects has evolved and now incorporates modern Python packaging techniques.

Christopher shares his recent video course on grouping real-world data with pandas. The course offers a quick refresher before digging into how to use pandas GroupBy to manipulate, transform, and summarize data.

We also share several other articles and projects from the Python community, including a news roundup, working with JSON data in Python, running an Asyncio event loop in a separate thread, knowing the why behind a system’s code, a retro game engine for Python, and a project for vendorizing packages from PyPI.

This episode is sponsored by Mailtrap.

Course Spotlight: pandas GroupBy: Grouping Real World Data in Python

In this course, you’ll learn how to work adeptly with the pandas GroupBy while mastering ways to manipulate, transform, and summarize data. You’ll work with real-world datasets and chain GroupBy methods together to get data into an output that suits your needs.

Topics:

00:00:00 – Introduction
00:02:18 – Setuptools Breaks Things, Then Fixes Them
00:04:57 – PEP 751: A File Format to List Python Dependencies
00:07:04 – Python 3.13.0 Release Candidate 1 Released
00:07:15 – Python Insider: Python 3.12.5 released
00:07:22 – Django 5.1 released - Django Weblog
00:07:27 – Django security releases issued: 5.0.8 and 4.2.15
00:07:49 – How I Use Python to Organize My Data Analyses
00:13:45 – Sponsor: Mailtrap
00:14:21 – pandas GroupBy: Grouping Real World Data in Python
00:20:33 – Working With JSON Data in Python
00:25:01 – Asyncio Event Loop in Separate Thread
00:30:33 – Video Course Spotlight
00:31:47 – Habits of great software engineers
00:49:17 – pyxel: A Retro Game Engine for Python
00:52:36 – python-vendorize: Vendorize Packages From PyPI
00:54:18 – Thanks and goodbye

News:

Setuptools Breaks Things, Then Fixes Them – This post is Bite Code’s monthly summary, but the lead story happened just days ago. In line with a 7 year old deprecation, setuptools finally removed the ability to call its test command. Many packages promptly broke. The following day the change was undone.
PEP 751: A File Format to List Python Dependencies for Installation Reproducibility (New) – This PEP proposes a new file format for dependency specification to enable reproducible installation in a Python environment.
Python 3.13.0 Release Candidate 1 Released
Python Insider: Python 3.12.5 released
Django 5.1 released - Django Weblog
Django security releases issued: 5.0.8 and 4.2.15 - Django Weblog

Show Links:

How I Use Python to Organize My Data Analyses – This is a description of how Joshua uses Python in a package-centric way to organize his approach to data analyses. This is a system he has evolved while working on his computational biology Ph.D. and working in industry.
pandas GroupBy: Grouping Real World Data in Python – In this course, you’ll learn how to work adeptly with the pandas GroupBy while mastering ways to manipulate, transform, and summarize data. You’ll work with real-world datasets and chain GroupBy methods together to get data into an output that suits your needs.
Working With JSON Data in Python – In this tutorial, you’ll learn how to read and write JSON-encoded data in Python. You’ll begin with practical examples that show how to use Python’s built-in “json” module and then move on to learn how to serialize and deserialize custom data.
Asyncio Event Loop in Separate Thread – Typically, the asyncio event loop runs in the main thread, but as that is the one used by the interpreter, sometimes you want the event loop to run in a separate thread. This article talks about why and how to do just that.

Discussion:

Habits of great software engineers

Projects:

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Learning Through Building the Black Python Devs Community

9 augusti 2024 | 72 min

Using GraphQL in Django With Strawberry & Prototype Purgatory

2 augusti 2024 | 49 min

How do you integrate GraphQL into your Python web development? How about quickly building graph-based APIs inside Django’s battery-included framework? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent tutorial for building GraphQL APIs in Django using the Python library Strawberry. The tutorial digs into creating a project, defining models, and creating GraphQL queries and mutations using Strawberry.

We discuss a blog post from Nat Bennet titled “Why do prototypes suck?” We dig into the common pitfalls of building prototypes and the misconceptions between developers and end users.

We also share several other articles and projects from the Python community, including a news roundup, using HTMX with FastAPI, creating an unbelievably stupid airline Wi-Fi package, extracting wisdom from conference videos, writing pixel images to the terminal, and a macOS app for Jupyter Notebooks.

This episode is sponsored by Mailtrap.

Course Spotlight: Building a URL Shortener With FastAPI and Python

In this video course, you’ll build an app to create and manage shortened URLs. Your Python URL shortener can receive a full target URL and return a shortened URL. You’ll also use the automatically created documentation of FastAPI to try out your API endpoints.

Topics:

00:00:00 – Introduction
00:02:27 – Python 3.13.0 Beta 4 Released
00:03:15 – Using HTMX With FastAPI
00:09:51 – Free, Unbelievably Stupid Wi-Fi on Long-Haul Flights
00:13:37 – Sponsor: Mailtrap
00:14:13 – “Extracting Wisdom” From Conference Videos
00:22:34 – Developing GraphQL APIs in Django With Strawberry
00:30:01 – Video Course Spotlight
00:31:33 – Why do prototypes suck?
00:42:53 – Satyrn: macOS App for Jupyter Notebooks
00:46:41 – rich-pixels: A Rich-compatible library for writing pixel images
00:48:23 – Thanks and goodbye

News:

Python 3.13.0 Beta 4 Released

Topics:

Using HTMX With FastAPI – This tutorial looks at how use HTMX with FastAPI by creating a simple todo web app and deploying it on Render.
Free, Unbelievably Stupid Wi-Fi on Long-Haul Flights – Deep in a need to procrastinate on a flight between London and San Francisco, Robert discovered that changing his name on an airline’s frequent flyer account was free over the plane’s WiFi. What’s a developer to do? Work on their tickets? No, create an entire TCP/IP protocol using this loophole. The result is the PySkyWiFi package.
“Extracting Wisdom” From Conference Videos – There are so many conferences and so many videos, you can’t possibly watch them all. This post shows you how to extract information to summarize a talk so you can quickly decide what you want to watch.
Developing GraphQL APIs in Django With Strawberry – This tutorial details how to integrate GraphQL with Django using Strawberry.

Discussion:

Why do prototypes suck?

Project:

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Build Captivating Display Tables in Python With Great Tables

26 juli 2024 | 71 min

Constraint Programming & Exploring Python's Built-in Functions

19 juli 2024 | 48 min

What are discrete optimization problems? How do you solve them with constraint programming in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects

Christopher discusses an article about constraint programming using Python. He describes the fundamentals and how the problems resemble logic problems you may have experienced in school. The article shows how to solve a weekly work scheduling problem using the open-source CP-SAT package.

We discuss Leodanis Pozo Ramos’s recent tutorial, “Python’s Built-in Functions: A Complete Exploration.” These functions are available for use directly in your code without importing.

We also share several other articles and projects from the Python community, including a news roundup, spotting ships with satellites, grappling with Apple’s App Store rejecting Python applications, considering changes to Python’s security model, discussing pivoting from one development path to another, prettifying Jinja and Django templates, and generating static sites with Python.

This episode is sponsored by Sentry.

Course Spotlight: Parallel Iteration With Python’s zip() Function

In this course, you’ll learn how to use the Python zip() function to solve common programming problems. You’ll learn how to traverse multiple iterables in parallel and create dictionaries with just a few lines of code.

Topics:

00:00:00 – Introduction
00:02:35 – Polars 1.0 Released
00:03:26 – Psycopg 3.2 Released
00:04:06 – Django security releases issued: 5.0.7 and 4.2.14
00:04:40 – PyBay 2024 Call for Proposals
00:05:16 – Python’s Built-in Functions: A Complete Exploration
00:12:10 – Satellites Spotting Ships
00:16:02 – Sponsor: Sentry
00:17:09 – Python Grapples With Apple App Store Rejections
00:20:27 – Python’s Security Model After the xz-utils Backdoor
00:25:38 – Video Course Spotlight
00:26:56 – Constraint Programming Using CP-SAT and Python
00:31:40 – Any Web Devs Successfully Pivoted to AI/ML Development?
00:43:12 – aurora: Static Site Generator Implemented in Python
00:45:14 – Running Prettier Against Django or Jinja Templates
00:46:58 – Thanks and goodbye

News:

Show Links:

Python’s Built-in Functions: A Complete Exploration – In this tutorial, you’ll learn the basics of working with Python’s numerous built-in functions. You’ll explore how you can use these predefined functions to perform common tasks and operations, such as mathematical calculations, data type conversions, and string manipulations.
Satellites Spotting Ships – Umbra Space has released a data set consisting of satellite based radar images of shipping. This article from Mark shows you how to grab the data, visualize, and annotate it.
Python Grapples With Apple App Store Rejections – A string that is part of the urllib parser module in Python references a scheme for apps that use the iTunes feature to install other apps, which is disallowed. Auto scanning by Apple is rejecting any app that uses Python 3.12 underneath. A solution has been proposed for Python 3.13.
Python’s Security Model After the xz-utils Backdoor – The backdoor introduced to the xz-utils compression project through social engineering was one of the topics at the Python Language Summit. Participants discussed what can be done to prevent similar social engineering attacks on the Python source.
Constraint Programming Using CP-SAT and Python – Constraint programming is the process of looking for solutions based on a series of restrictions, like employees over 18 who have worked the cash before. This article introduces the concept and shows you how to use open source libraries to write constraint solving code.

Discussion:

Any Web Devs Successfully Pivoted to AI/ML Development?

Projects:

aurora: Static Site Generator Implemented in Python
Running Prettier Against Django or Jinja Templates – “Prettier” is a JavaScript based linting tool for templates. For folks not familiar with the world of npm, it can be a bit daunting to get it going. Simon fiddled with it so you don’t have to and posted how he got it working on his system.

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Digging Into Graph Theory in Python With David Amos

12 juli 2024 | 64 min

Python Doesn't Round Numbers the Way You Might Think

5 juli 2024 | 49 min

Does Python round numbers the same way you learned back in math class? You might be surprised by the default method Python uses and the variety of ways to round numbers in Python. Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher discusses his recent video course, “Rounding Numbers in Python.” He covers rounding bias and how to avoid introducing it into your dataset. We dig into the various rounding strategies and how to implement them in Python.

We also share several other articles and projects from the Python community, including a news roundup, a fast Python linter for error-free and maintainable code, the decline of the user interface, more thoughts on Python in Excel, a discussion about calendar versioning for Python, a financial database as a Python module, and a project to prettify the colors of your terminal user interfaces.

This episode is sponsored by Sentry.

Course Spotlight: Rounding Numbers in Python

In this video course, you’ll learn about the mistakes you might make when rounding numbers and how to best manage or avoid them. It’s a great place to start for the early to intermediate Python developer who’s interested in using Python for finance, data science, or scientific computing.

Topics:

00:00:00 – Introduction
00:02:06 – NumPy 2.0.0 Release Notes
00:02:52 – Python 3.13.0 beta 3 released
00:03:05 – Announcing the PSF Board Candidates for 2024!
00:03:27 – Prohibiting Outlook Email Domains
00:04:31 – Ruff: A Python Linter for Error-Free and Maintainable Code
00:09:31 – Sponsor: Sentry
00:10:35 – The Decline of the User Interface
00:19:14 – My Thoughts on Python in Excel
00:26:30 – Rounding Numbers in Python
00:30:53 – Video Course Spotlight
00:32:13 – PEP 2026: Calendar Versioning for Python
00:42:37 – Financial Database as a Python Module
00:45:34 – prettypretty: Build Awesome Terminal User Interfaces
00:47:48 – Thanks and goodbye

News:

NumPy 2.0.0 Release Notes — NumPy v2.0 Manual – The long awaited 2.0 release of NumPy landed this week. Not all the docs are up to date yet, but this final draft of the release notes shows you what is included.
Python Insider: Python 3.13.0 beta 3 released
Python Software Foundation News: Announcing the PSF Board Candidates for 2024!
Prohibiting Outlook Email Domains – Due to an inordinate amount of bot accounts coming from outlook.com and hotmail.com, PyPI has disallowed new account sign-ups with email addresses from these domains.

Show Links:

Ruff: A Python Linter for Error-Free and Maintainable Code – Ruff is an extremely fast, modern linter with a simple interface, making it straightforward to use. It also aims to be a drop-in replacement for other linting and formatting tools, like Pylint, isort, and Black. It’s no surprise it’s quickly becoming one of the most popular Python linters.
The Decline of the User Interface – “Software has never looked cooler, but user interface design and user experience have taken a sharp turn for the worse.”
My Thoughts on Python in Excel – Microsoft’s new Python in Excel functionality was released almost a year ago. Having now had time to play with it, Felix gives his take.
Rounding Numbers in Python – In this video course, you’ll learn about the mistakes you might make when rounding numbers and how to best manage or avoid them. It’s a great place to start for the early to intermediate Python developer who’s interested in using Python for finance, data science, or scientific computing.

Discussion:

PEP 2026: Calendar Versioning for Python – This PEP proposes updating the versioning scheme for Python to include the calendar year. This aims to make the support lifecycle clear by making it easy to see when a version was first released, and easier to work out when it will reach end of life (EOL).
Associated discussion
Semantic Versioning 2.0.0 - Semantic Versioning
Calendar Versioning — CalVer

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Creating a Guitar Synthesizer & Generating WAV Files With Python

28 juni 2024 | 56 min

Python's Command-Line Utilities & Music Information Retrieval Tools

21 juni 2024 | 51 min

Detecting Outliers in Your Data With Python

14 juni 2024 | 67 min

Decomposing Software Problems & Avoiding the Trap of Clever Code

7 juni 2024 | 55 min

How do you effectively break a software problem into individual steps? What are signs you’re writing overly clever code? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss an article about de-warping images of book pages. We both found the piece a good study on decomposing a complex software problem.

Christopher discusses an article titled “Clever code is probably the worst code you could write.” Early in a programming career, it’s easier to write complex and difficult-to-read code. The real challenge is progressing towards writing clearer and readable code.

We also share several other articles and projects from the Python community, including a news roundup, what the __pycache__ folder is for in Python, what’s new in Django 5.1, a discussion about software engineering hiring and firing, a project for setting up repeated tasks, and a simple way to create reusable template components in Django.

This episode is sponsored by Sentry.

Course Spotlight: Efficient Iterations With Python Iterators and Iterables

In this video course, you’ll learn what iterators and iterables are in Python. You’ll learn how they differ and when to use them in your code. You’ll also learn how to create your own iterators and iterables to make data processing more efficient.

Topics:

00:00:00 – Introduction
00:02:18 – PEP 667: Consistent Views of Namespaces (Accepted)
00:03:08 – PEP 649 Re-targeted to 3.14
00:03:50 – Untold Stories From 6 Years Working on Python Packaging
00:04:38 – What Is the __pycache__ Folder in Python?
00:09:57 – Sponsor: Sentry
00:11:04 – What’s New in Django 5.1
00:17:48 – Page Dewarping
00:26:55 – Video Course Spotlight
00:28:26 – Clever Code Is Probably the Worst Code You Could Write
00:33:19 – Software Engineering Hiring and Firing
00:51:22 – Metronomes: An Easy Way to Set Up Regular Tasks
00:52:26 – django-web-components: Create reusable template components in Django
00:54:03 – Thanks and goodbye

News:

PEP 667: Consistent Views of Namespaces (Accepted)
PEP 649 Re-targeted to 3.14 – Python Enhancement Proposal 649: Deferred Evaluation Of Annotations Using Descriptors has been re-targeted to the Python 3.14 release
Untold Stories From 6 Years Working on Python Packaging – Sumana gave the closing keynote address at PyCon US this year and this posting shares all the links and references from the talk.

Show Links:

What Is the __pycache__ Folder in Python? – In this tutorial, you’ll explore Python’s __pycache__ folder. You’ll learn about when and why the interpreter creates these folders, and you’ll customize their default behavior. Finally, you’ll take a look under the hood of the cached .pyc files.
What’s New in Django 5.1 – Django 5.1 has gone alpha so the list of features targeting this release has more or less solidified. This article introduces you to what is coming in Django 5.1.
Page Dewarping – This article shows the techniques behind a page flattening algorithm. It starts with images of a book’s page which are curled from the spine of the book, and creates a resulting PDF that is a flat version.
Clever Code Is Probably the Worst Code You Could Write – When you come across a clever bit of code, it is hard not to admire it, but often times, clear, readable code is the hardest code to write.

Discussion:

Software Engineering Hiring and Firing – This article is a deep dive on the hiring and firing practices in the software field, and unlike most articles focuses on senior engineering roles. It isn’t a “first job” post, but a “how the decision process works” article.

Projects:

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Building Python Unit Tests & Exploring a Data Visualization Gallery

31 maj 2024 | 43 min

How do you start adding unit tests to your Python code? Can the built-in unittest framework cover most or all of your needs? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We dig into a recent tutorial by Leodanis Pozo Ramos about writing unit tests using Python’s unittest. The tutorial covers organizing your tests, exploring assert methods, creating test fixtures, and debugging failing tests.

We explore a collection of Python data visualizations and tutorials from the Python Graph Gallery. The website features hundreds of charts and graphs built using popular plotting libraries. Each chart type features a foundational tutorial that introduces the structure and application.

We also share several other articles and projects from the Python community, including a news roundup, the new REPL coming in Python 3.13, a pytest daemon to 10X test iteration speed, a discussion about software friction, a Raspberry Pi document scanner, and a project for controlling time per iteration loop.

Course Spotlight: Building a Python GUI Application With Tkinter

In this video course, you’ll learn the basics of GUI programming with Tkinter, the de facto Python GUI framework. Master GUI programming concepts such as widgets, geometry managers, and event handlers. Then, put it all together by building two applications: a temperature converter and a text editor.

Topics:

00:00:00 - Introduction
00:02:08 - Python Software Foundation Board Election Dates for 2024
00:02:35 - 2023 PSF Annual Impact Report
00:03:03 - Python’s unittest: Writing Unit Tests for Your Code
00:09:41 - What’s New in Python 3.13
00:10:38 - The New REPL in Python 3.13
00:13:39 - Best Python Chart Examples
00:15:27 - Animation with text that highlights important events
00:16:39 - Sankey Diagram with Python and Plotly
00:18:55 - Video Course Spotlight
00:20:25 - Pytest Daemon: 10X Local Test Iteration Speed
00:23:58 - Software Friction
00:35:41 - A Raspberry Pi Document Scanner
00:39:00 - pacemaker: For Controlling Time Per Iteration Loop in Python
00:41:55 - Thanks and goodbye

News:

Show Links:

Python’s unittest: Writing Unit Tests for Your Code – In this tutorial, you’ll learn how to use the unittest framework to create unit tests for your Python code. Along the way, you’ll also learn how to create test cases, fixtures, test suites, and more.
What’s New in Python 3.13 – Python 3.13 has gone into beta, which means the feature freeze is now in place. This is the official listing of the new features in 3.13. This release includes changes to the REPL, new typing features, experimental support for disabling the GIL, dead battery removal, and more.
The New REPL in Python 3.13 – Python 3.13 just hit feature freeze with the first beta release, and it includes a host of improvements to the REPL. Automatic indenting, block-level editing, and more make the built-in REPL more powerful and easier to use.
Best Python Chart Examples
Animation with text that highlights important events - Python Graph Gallery
Sankey Diagram with Python and Plotly - Python Graph Gallery
Pytest Daemon: 10X Local Test Iteration Speed – Discord has a large Python monolith with lots of imports, which now takes 13 seconds to start up. On the server that’s not a problem but to run a test it is. Ruby’s solution is to have a daemon that hot loads a test on a process that already has the imports completed.

Discussion:

Software Friction – Friction is everywhere in software development. Two setbacks are more than twice as bad as one setback. This article discusses the sources of software friction and what you can do about it.

Projects:

Additional Links:

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Considering Accessibility & Assistive Tech as a Python Developer

17 maj 2024 | 61 min

Querying OpenStreetMaps via API & Lazy Evaluation in Python

10 maj 2024 | 53 min

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:

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:

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Embarking on a Relaxed and Friendly Python Coding Journey

3 maj 2024 | 64 min

Pydantic Data Validation & Python Web Security Practices

26 april 2024 | 59 min

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:

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.

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Decoupling Systems to Get Closer to the Data

19 april 2024 | 69 min

Avoiding Error Culture and Getting Help Inside Python

12 april 2024 | 65 min

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:

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Leveraging Documents and Data to Create a Custom LLM Chatbot

5 april 2024 | 68 min

Build a Video Game With Python Turtle & Visualize Data in Seaborn

29 mars 2024 | 50 min

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:

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.

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Using Python in Bioinformatics and the Laboratory

22 mars 2024 | 51 min

Exploring Duck Typing in Python & Dynamics of Monkey Patching

15 mars 2024 | 51 min

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:

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:

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Building a Healthy Developer Mindset While Learning Python

8 mars 2024 | 61 min

Automate Tasks With Python & Building a Small Search Engine

1 mars 2024 | 45 min

Wes McKinney on Improving the Data Stack & Composable Systems

23 februari 2024 | 69 min

Practical Python Decorator Uses & Avoiding datetime Pitfalls

16 februari 2024 | 57 min

Focusing on Data Science & Less on Engineering and Dependencies

9 februari 2024 | 61 min

Great Starting Points for Contributing to Open Source

2 februari 2024 | 79 min

Building a Python Debugger & Preparing for NumPy 2.0

26 januari 2024 | 48 min

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.

Course Spotlight: Create a Tic-Tac-Toe Python Game Engine With an AI Player

In this video course, you’ll create a universal game engine in Python for tic-tac-toe with two computer players, one of which will be an AI player using the powerful minimax algorithm. You’ll give your game library a text-based graphical interface and explore two front ends.

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:

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

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Measuring Bias, Toxicity, and Truthfulness in LLMs With Python

19 januari 2024 | 76 min

Serializing Data With Python & Underscore Naming Conventions

12 januari 2024 | 54 min

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.

Course Spotlight: Writing Beautiful Pythonic Code With PEP 8

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

Topics:

00:00:00 – Introduction
00:02:47 – 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:

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

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Exploring Python in Excel

5 januari 2024 | 74 min

2023 Real Python Tutorial & Video Course Wrap-Up

29 december 2023 | 53 min

PyCoder's Weekly 2023 Wrap Up

22 december 2023 | 38 min

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.

Course Spotlight: Python Basics: Building Systems With Classes

In this video course, you’ll learn how to work with classes to build complex systems in Python. By composing classes, inheriting from other classes, and overriding class behavior, you’ll harness the power of object-oriented programming (OOP).

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:

Exploring Code Reviews in Python and Automating the Process

8 december 2023 | 66 min

Building a Python JSON Parser & Discussing Ideas for PEPs

1 december 2023 | 57 min

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.

Course Spotlight: Advent of Code: Solving Puzzles With Python

Advent of Code is an online advent calendar that shares new programming puzzles each day from December 1 to the 25. In this Code Conversation, you’ll learn why solving programming puzzles can be beneficial and how you can get started with Advent of Code using Python.

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:

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:

Projects:

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Computational Thinking & Learning Python During an AI Revolution

17 november 2023 | 54 min

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.

Course Spotlight: Looping With Python enumerate()

Once you learn about for loops in Python, you know that using an index to access items in a sequence isn’t very Pythonic. So what do you do when you need that index value? In this course, you’ll learn all about Python’s built-in enumerate(), where it’s used, and how you can emulate its behavior.

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:

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Support the podcast & join our community of Pythonistas

Studying Python Software Architecture & Creating Lambda Expressions

10 november 2023 | 48 min

Improving Your Git Developer Experience in Python

3 november 2023 | 56 min

Guiding Scientific Python Library Development

27 oktober 2023 | 57 min

Welcoming PyPI's Safety & Security Engineer Mike Fiedler

20 oktober 2023 | 59 min

Building Python Best Practices and Fundamental Skills

13 oktober 2023 | 64 min

Exploring the New Features of Python 3.12

6 oktober 2023 | 66 min

Considering ChatGPT's Technical Review of a Programming Book

29 september 2023 | 71 min

Getting Involved in Open Source & Generating QR Codes With Python

22 september 2023 | 48 min

Measuring Multiple Facets of Python Performance With Scalene

15 september 2023 | 64 min

Making Each Line of Code Efficient & Python In Excel

8 september 2023 | 51 min

Finding the Right Coding Font for Programming in Python

1 september 2023 | 65 min

Improving Classification Models With XGBoost

25 augusti 2023 | 65 min

Common Python Stumbling Blocks & Quirky Behaviors

11 augusti 2023 | 49 min

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.

Course Spotlight: Graph Your Data With Python and ggplot

In this course, you’ll learn how to use ggplot in Python to build data visualizations with plotnine. You’ll discover what a grammar of graphics is and how it can help you create plots in a very concise and consistent way.

Topics:

00:00:00 – Introduction
00:02:12 – 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:

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:

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Exploring pandas 2.0 & Targets for Apache Arrow

4 augusti 2023 | 75 min

Differentiating the Versions of Python & Unlocking IPython's Magic

28 juli 2023 | 46 min

Leveraging the Features of Your Database With Postgres and Python

21 juli 2023 | 61 min

Constructing Python Library APIs & Tackling Jinja Templating

14 juli 2023 | 51 min

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.

Course Spotlight: 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.

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:

Projects:

Additional Links:

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Python Crash Course & Learning Enough to Start Creating

7 juli 2023 | 74 min

Exploring the Zen of Python & pandas Features for Finance

30 juni 2023 | 50 min

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.

Course Spotlight: Speed Up Python With Concurrency

Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.

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:

Additional Links:

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Resources and Advice for Building CircuitPython Projects

23 juni 2023 | 74 min

Inheriting a Large Python Code Base & Building a GUI With Kivy

16 juni 2023 | 49 min

Volunteering, Organizing, and Finding a Python Community

9 juni 2023 | 61 min

Building Python CI With Docker & Applying for a Hacker Initiative Grant

2 juni 2023 | 53 min

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.

Course Spotlight: Metaclasses in Python

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.

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:

Hacker Initiative Links:

Additional Links:

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Discussing Mojo & Improving Python Object-Oriented Programming

26 maj 2023 | 55 min

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.

Course Spotlight: Using k-Nearest Neighbors (kNN) in Python

In this video course, you’ll learn all about the k-nearest neighbors (kNN) algorithm in Python, including how to implement kNN from scratch. Once you understand how kNN works, you’ll use scikit-learn to facilitate your coding process.

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:

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:

Projects:

Additional Links:

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Virtual Environment Structure & Surveying the Packaging Ecosystem

12 maj 2023 | 69 min

Checking Project Dependencies & Python Dev Resource Collections

5 maj 2023 | 38 min

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.

Course Spotlight: Publishing Python Packages to PyPI

In this video course, you’ll learn how to create a Python package for your project and how to publish it to PyPI, the Python Package Index. Quickly get up to speed on everything from naming your package to configuring it using setup.cfg.

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:

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

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Targeting WebAssembly Platforms & Distilling a Minimum Viable Python

28 april 2023 | 79 min

Seeking Faster Text Processing & Python's .repr() vs .str()

14 april 2023 | 43 min

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.

Course Spotlight: Unicode in Python: Working With Character Encodings

In this course, you’ll get a Python-centric introduction to character encodings and Unicode. Handling character encodings and numbering systems can at times seem painful and complicated, but this guide is here to help with easy-to-follow Python examples.

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:

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:

Additional Links:

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Automate Processes and Distribute Python Tools With RPA and RCC

7 april 2023 | 50 min

Evaluating Python Packages & Celebrating 20 Years of PyCon US

31 mars 2023 | 61 min

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.

Course Spotlight: 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.

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:

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Support the podcast & join our community of Pythonistas

Lessons Learned From Four Years Programming With Python

24 mars 2023 | 62 min

Coding With namedtuple & Python's Dynamic Superpowers

17 mars 2023 | 53 min

Sharing Your Python App Across Platforms With BeeWare

10 mars 2023 | 71 min

Django Deployment Strategies & Preparing for PyCascades 2023

3 mars 2023 | 68 min

Using NumPy and Linear Algebra for Faster Python Code

24 februari 2023 | 69 min

Creating a Python Wordle Clone & Testing Environments With Nox

17 februari 2023 | 59 min

Would you like to practice your Python skills while building a challenging word game? Have you been wanting to learn more about creating command-line interfaces and making them colorful and interactive? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We share a recent Real Python step-by-step project about creating a clone of Wordle. In the project, you’ll practice building a terminal application, validating user input, and refactoring code into functions.

Christopher shares an article that compares two popular testing tools, Nox and Tox. He discusses how each framework approaches test environment configuration and why the author leans toward using Nox’s Python decorator–based format.

We share several other articles and projects from the Python community, including a news update, a guide to trying out code and ideas quickly with the Python REPL, a PEP about requiring virtual environments by default, a discussion about lessons learned in twenty years as a software engineer, a project for a spreadsheet GUI inside of JupyterLab notebooks, and adding C-style for loops to Python.

Course Spotlight: Getters and Setters in Python

In this video course, you’ll learn what getter and setter methods are, how Python properties are preferred over getters and setters when dealing with attribute access and mutation, and when to use getter and setter methods instead of properties in Python.

Topics:

00:00:00 – Introduction
00:02:21 – Django Security Releases Issued
00:02:43 – PSF Is Hiring a Security Developer-in-Residence
00:03:44 – Python 3.11.2, Python 3.10.10 and 3.12.0 alpha 5 are available
00:04:02 – Build a Wordle Clone With Python and Rich
00:10:05 – Why I Like Nox
00:16:44 – Sponsor: Anaconda Cloud
00:17:26 – PEP 704: Require Virtual Environments by Default
00:27:17 – The Python Standard REPL: Try Out Code and Ideas Quickly
00:33:08 – Video Course Spotlight
00:34:35 – 20 Things I’ve Learned in My 20 Years as a Software Engineer
00:47:50 – Mito: A Spreadsheet Inside Your JupyterLab Notebooks
00:51:37 – How I Added C-Style for-Loops to Python
00:58:08 – Thanks and goodbye

News:

Show Links:

Build a Wordle Clone With Python and Rich – In this step-by-step project, you’ll build your own Wordle clone with Python. Your game will run in the terminal, and you’ll use Rich to ensure your word-guessing app looks good. Learn how to build a command-line application from scratch and then challenge your friends to a wordly competition!
Why I Like Nox – Both Nox and Tox are multi-environment testing tools. This opinion piece by Hynek compares and contrasts them and explains why he is increasingly using Nox.
PEP 704: Require Virtual Environments by Default
The Python Standard REPL: Try Out Code and Ideas Quickly – In this tutorial, you’ll learn how to use the Python standard REPL (Read-Eval-Print Loop) to run your code interactively. This tool will allow you to test new ideas, explore and experiment with new tools and libraries, refactor and debug your code, try out examples, and more.

Discussion

20 Things I’ve Learned in My 20 Years as a Software Engineer – Justin writes a list of things he’s learned over his past twenty years in development. He starts by stating how context is important and that his lessons are from small teams that emphasize productivity and are tool agnostic.
The 10x Programmer Myth - Simple Thread

Projects:

Mito: A Spreadsheet Inside Your JupyterLab Notebooks
How I Added C-Style for-Loops to Python – Ever wanted a C-style for loop in Python? No? Well, you can have one anyway. See how Tushar implemented with for (i := var(0), i < 10, i + 2):

Additional Links:

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Wrangling Business Process Models With Python and SpiffWorkflow

10 februari 2023 | 52 min

Create Interactive Maps & Geospatial Data Visualizations With Python

3 februari 2023 | 63 min

Orchestrating Large and Small Projects With Apache Airflow

27 januari 2023 | 54 min

Exploring Python With bpython & Formalizing f-String Grammar

20 januari 2023 | 56 min

Have you used the Python Read-Eval-Print Loop (REPL) to explore the language and learn about how it operates? Would it help if it provided syntax highlighting, definitions, and code completion and behaved more like an IDE? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss the drop-in REPL replacement bpython. bpython enhances the interactivity of a Python REPL session. It’s also a powerful teaching tool for instructors and students to experiment with and explore Python code.

Christopher shares a recent Python Enhancement Proposal (PEP) about formalizing the grammar for f-strings. The PEP describes a reduction in the underlying parser code complexity and provides for future features like comments in multiline f-strings.

We share several other articles and projects from the Python community, including a news roundup, a collection of surveys to classify Python virtual environment workflows, a course about context managers and Python’s with statement, a discussion about microfeatures that we would like to see adopted in Python, a Python terminal music player, and an infinite array powered by AI.

Course Spotlight: Context Managers and Python’s with Statement

In this video course, you’ll learn what the Python with statement is and how to use it with existing context managers. You’ll also learn how to create your own context managers.

Topics:

00:00:00 – Introduction
00:02:21 – Pillow 9.4.0 Released
00:02:47 – Django Bugfix Release: 4.1.5
00:02:56 – Plone 6.0 Released
00:03:16 – PyCon Italia 2023
00:03:54 – Discover bpython: A Python REPL With IDE-Like Features
00:13:55 – PEP 701: Syntactic Formalization of f-Strings
00:17:07 – Sponsor: Influx Data
00:17:57 – Classifying Python Virtual Environment Workflows
00:30:26 – Context Managers and Python’s with Statement
00:36:32 – Video Course Spotlight
00:37:47 – Microfeatures I’d Like to See in More Languages
00:49:34 – Python Terminal Music Player
00:51:19 – Infinite AI Array
00:55:05 – Thanks and goodbye

News:

Show Links:

Discover bpython: A Python REPL With IDE-Like Features – In this tutorial, you’ll learn about bpython, an alternative Python REPL that brings code suggestions and many other IDE-like features to the terminal. Once you discover how much bpython can improve your productivity, you’ll never want to return to using the vanilla Python REPL again.
PEP 701: Syntactic Formalization of f-Strings – This Python Enhancement Proposal describes the formalization of a grammar for f-strings, allowing a reduction in the underlying parser code complexity and providing future features like comments in multiline f-strings.
Classifying Python Virtual Environment Workflows – This article discusses the various ways of creating and managing Python virtual environments, including what kinds of tools you could use. It categorizes the different styles and describes how the choices that you make affect your workflow.
Context Managers and Python’s with Statement – In this video course, you’ll learn what the Python with statement is and how to use it with existing context managers. You’ll also learn how to create your own context managers.
Microfeatures I’d Like to See in More Languages – Some language features are intrinsic to the language. Others are syntactic sugar that other programming languages could easily borrow. This opinion piece from Hillel highlights some features that the mainstream should steal from more obscure languages. Two Python features that he’d like to see in more languages are chained evaluations (2 <= x < 10) and numbers with separators (1000000 == 1_000_000).

Projects:

Python Terminal Music Player
Infinite AI Array – Learn about an insane library containing special lists and dicts so that any missed calls automatically go to GPT3 and add a predictive value in its place.

Additional Links:

Christopher Trudeau’s - Act function for virtual environments

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Speeding Up Your DataFrames With Polars

13 januari 2023 | 58 min

Surveying Comprehension Constructs & Python Parallelism Infighting

6 januari 2023 | 45 min

Have you embraced the use of comprehensions in your Python journey? Are you familiar with all the varieties of comprehension constructs? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article that surveys Python’s comprehensions and generators. This overview includes code snippets and the fundamentals of creating list, set, and dictionary comprehensions. We weigh the advantages of using a comprehension versus the more familiar for loops that they replace.

Christopher shares an article about how there may be infighting between the parallelism in your Python code and the parallelism within the libraries that you’re using. These complex system interactions can cause processing slowdowns and hard-to-trace bottlenecks.

We share several other articles and projects from the Python community, including a news roundup, a Python linter comparison, an overview of multiprocessing race conditions in Python, a discussion covering import statement styles, a project for WASM-powered Jupyter tools running in the browser, and a collection of easter eggs and jokes hidden inside Python itself.

Course Spotlight: Understanding Python List Comprehensions

Python list comprehensions make it easy to create lists while performing sophisticated filtering, mapping, and conditional logic on their members. In this course, you’ll learn when to use list comprehensions in Python and how to create them effectively.

Topics:

00:00:00 – Introduction
00:02:41 – PyPy v7.3.10 Release
00:03:17 – Django Bugfix Release: 4.1.4
00:03:28 – Python 3.11.1, 3.10.9, 3.9.16, 3.8.16, 3.7.16 Released
00:03:56 – Python Linter Comparison 2022
00:11:51 – Who Controls Parallelism? A Disagreement That Leads to Slower Code
00:15:58 – Sponsor: InfluxDB
00:16:47 – A Crash Course in Comprehensions and Generators
00:24:23 – Multiprocessing Race Conditions in Python
00:27:46 – Video Course Spotlight
00:28:55 – What Style of import Statement Do You Use?
00:36:54 – jupyterlite: WASM Powered Jupyter Running in the Browser
00:40:35 – python-easter-eggs: Easter Eggs and Hidden Jokes in Python
00:43:18 – PyCoder’s Weekly: Submit a Link
00:43:46 – Thanks and goodbye

News:

Show Links:

Python Linter Comparison 2022 – There are many linter choices for Python. This article covers a lot of them: Pylint, Pyflakes, Flake8, autopep8, Bandit, Prospector, Pylama, Pyroma, Black, Mypy, Radon, and mccabe.
Who Controls Parallelism? A Disagreement That Leads to Slower Code – In complex systems, there may be a fight between the parallelism in your code vs the parallelism in the libraries that you’re using. This fight can cause things to slow down. This article shows some examples and explores what you can do about the issue.
A Crash Course in Comprehensions and Generators – A great collection of code snippets that showcase the power and flexibility of list comprehensions, generators, and related constructs.
Multiprocessing Race Conditions in Python – A race condition happens when parallel tasks attempt to execute code at the same time and the results are dependent on order of execution. Finding race conditions can be challenging. This article gives some hints as to how to find the different kinds of race conditions when coding with the multiprocessing module.

Discussion:

Projects:

Additional Links:

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Support the podcast & join our community of Pythonistas

2022 Real Python Tutorial & Video Course Wrap Up

23 december 2022 | 76 min

Start Using a Build System & Continuous Integration in Python

16 december 2022 | 60 min

Package Python Code With pyproject.toml & Listing Files With pathlib

9 december 2022 | 54 min

Preparing Data to Measure True Machine Learning Model Performance

2 december 2022 | 58 min

Building Python REST APIs With Flask & Structuring Pull Requests

25 november 2022 | 58 min

Moving Projects Away From Passwords With WebAuthn and Python

18 november 2022 | 45 min

Creating Tic-Tac-Toe With an AI Player & Shortcuts for Python Decorators

11 november 2022 | 54 min

How do you create a computer opponent for a simple game within Python? Would you also like to learn how to adapt the game to run in a web browser or graphical user interface (GUI)? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares a recent Real Python step-by-step project for creating a tic-tac-toe game engine. He talks about how to build the game engine and adapt it for different front ends. The tutorial also shows how to implement an unbeatable computer player using the minimax algorithm.

We discuss an article about how to avoid repeating yourself when creating decorators with multiple parameters. We talk about how you can stop copying and pasting code several times by assigning the decorator to a new variable.

We share several other articles and projects from the Python community, including a news roundup, a deep dive into Python’s doctest, several Python command line tricks, type annotations via automated refactoring, a new way to draw boxes in the terminal, a collection of projects for beginners with source code, a minimalist PDF creation library, and a tool for sensible logging in Python.

Course Spotlight: Python Decorators 101

In this course on Python decorators, you’ll learn what they are and how to create and use them. Decorators provide a simple syntax for calling higher-order functions in Python. By definition, a decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it.

Topics:

00:00:00 – Introduction
00:02:25 – Python 3.12.0 Alpha 1 Released
00:02:45 – PyCon US 2023 Call for Proposals
00:03:27 – Python’s doctest: Document and Test Your Code at Once
00:13:59 – Build a Tic-Tac-Toe Game Engine With an AI Player in Python
00:22:12 – Sponsor: InfluxDB
00:22:59 – Python Command Line Tricks
00:30:50 – Type Annotation via Automated Refactoring
00:34:29 – A New (?) Way of Drawing Boxes in the Terminal
00:37:10 – Decorator Shortcuts
00:39:36 – Video Course Spotlight
00:41:02 – 190 Python Projects With Source Code
00:47:58 – fpdf2: Minimalist PDF Creation Library
00:50:04 – Simple, Sane, and Sensible Logging in Python
00:53:05 – Thanks and goodbye

News:

Topics:

Python’s doctest: Document and Test Your Code at Once – In this tutorial, you’ll learn how to add usage examples to your code’s documentation and docstrings and how to use these examples to test your code. To run your usage examples as automated tests, you’ll use Python’s doctest module from the standard library.
Build a Tic-Tac-Toe Game Engine With an AI Player in Python – In this step-by-step tutorial, you’ll build a universal game engine in Python with tic-tac-toe rules and two computer players, including an unbeatable AI player using the minimax algorithm. You’ll also create a text-based graphical front end for your library and explore two alternative front ends.
Python Command Line Tricks – Using python -m you can do all sorts of things from the command line, including starting a webserver, opening a browser, parsing JSON, compressing files, and much more.
Type Annotation via Automated Refactoring – Jimmy’s team at Carta decided they wanted to add type annotations to their large codebase, but doing so manually would’ve taken a very long time. This post shows you how they built automated refactoring tools to add type annotations to their code.
A New (?) Way of Drawing Boxes in the Terminal – With clever use of some of the Unicode border characters, you can build a better box around your text, without any color bleeding.
Decorator Shortcuts – “When using many decorators in code, there’s a shortcut you can use if you find yourself repeating them. They can be assigned to a variable just like any other Python expression.”

Discussion:

190 Python Projects With Source Code

Projects:

fpdf2: Minimalist PDF Creation Library
Simple, Sane, and Sensible Logging in Python – Get started with logging in Python or deploy advanced, flexible loggers without the boilerplate code. Learn all about log2d, a third-party wrapper for the Python logging library.

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Exploring the New Features of Python 3.11

4 november 2022 | 62 min

Fostering an Internal Python Community & Managing the 3.11 Release

21 oktober 2022 | 69 min

Using an Ellipsis in Python & Goals for CPython 3.12

14 oktober 2022 | 57 min

Where should you use an ellipsis in Python? How does it behave as a placeholder in a script, project, or stub file? What are the next goals for the Faster CPython project? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We talk about a Real Python article that covers when you should use an ellipsis in Python. We discuss the similarities with the pass keyword and how it’s used for type hints within stub files.

Christopher shares resources covering the goals of the Faster CPython project. We’re on the cusp of the release of Python 3.11, but the project keeps moving forward as they look at ways to continue speeding up Python.

We share several other articles and projects from the Python community, including a news roundup, alternatives for hosting Python-based applications, ways to create custom Python strings, a discussion about aging programmers, a structural diff that understands syntax, and a project for refurbishing and modernizing Python codebases.

Course Spotlight: Providing Multiple Constructors in Your Python Classes

In this video course, you’ll learn how to provide multiple constructors in your Python classes. To this end, you’ll learn different techniques, such as checking argument types, using default argument values, writing class methods, and implementing single-dispatch methods.

Topics:

00:00:00 – Introduction
00:02:26 – Django security releases issued
00:02:44 – PEP 698: Override Decorator for Static Typing
00:03:37 – Heroku Alternatives for Python-Based Applications
00:14:34 – Python 3.12 Goals: Faster-CPython Ideas Wiki
00:20:29 – Sponsor: InfluxDB
00:21:19 – When Do You Use an Ellipsis in Python?
00:28:18 – Custom Python Strings: Inheriting From str vs UserString
00:32:52 – Aging Programmer
00:46:32 – Video Course Spotlight
00:47:48 – difftastic: A Structural Diff That Understands Syntax
00:50:44 – refurb: Refurbish and Modernize Python Codebases
00:55:44 – Thanks and goodbye

News:

Django security releases issued: 4.1.2, 4.0.8, and 3.2.16 | Weblog | Django
PEP 698: Override Decorator for Static Typing – This Python Enhancement Proposal describes the use of a new decorator, @override, which would be used as a type hint for methods in a subclass that override a parent’s method. This type hint would introduce a level of safety if the parent method is refactored without corresponding changes to the child method.

Show Links:

Heroku Alternatives for Python-Based Applications – Learn about alternatives to Heroku and their pros and cons. Platforms discussed include Digital Ocean, Google App Engine, AWS, Azure, PythonAnywhere, and half a dozen more.
Python 3.12 Goals: Faster-CPython Ideas Wiki – A summary of the goals for the Faster CPython initiative within the Python 3.12 release. Includes trace optimizations, shrinking object sizes, improving memory management overhead, and more. See also the associated Workflow for 3.12 cycle checklist.
When Do You Use an Ellipsis in Python? – You may have seen three dots in Python scripts. Although this syntax may look odd, using an ellipsis is valid Python code. In this tutorial, you’ll learn when Python’s Ellipsis constant can come in handy for you.
Custom Python Strings: Inheriting From str vs UserString – In this tutorial, you’ll learn how to create custom string-like classes in Python by inheriting from the built-in str class or by subclassing UserString from the collections module.

Discussion:

Aging Programmer

Projects:

Additional Links:

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

Support the podcast & join our community of Pythonistas

Using a Memory Profiler in Python & What It Can Teach You

7 oktober 2022 | 64 min

Explaining Access Control Using Python & Cautiously Handling Pickles

30 september 2022 | 59 min

Python as an Efficiency Tool for Non-Developers

23 september 2022 | 70 min

Improve Matplotlib With Style Sheets & Python Async for the Web

16 september 2022 | 53 min

Exploring Recursion in Python With Al Sweigart

9 september 2022 | 81 min

Creating a Python Code Completer & More Abstract Syntax Tree Projects

2 september 2022 | 74 min

Configuring a Coding Environment on Windows & Using TOML With Python

19 augusti 2022 | 62 min

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

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

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

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

Course Spotlight: Python Basics: Finding and Fixing Code Bugs

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

Topics:

00:00:00 – Introduction
00:02:13 – Python 3.10.6 Released
00:02:41 – Python 3.11.0rc1 Released
00:03:13 – Django 4.1 Released
00:04:07 – 10 malicious Python packages exposed in latest repository attack
00:05:12 – Protestware: Why Developers Sabotage Their Own Code
00:06:41 – Python and TOML: New Best Friends
00:16:19 – Say Goodbye to These Obsolete Python Libraries
00:25:51 – Video Course Spotlight
00:27:26 – Uncommon Uses of Python in Commonly Used Libraries
00:37:56 – Your Python Coding Environment on Windows: Setup Guide
00:48:20 – Technical Writing for Developers
00:55:24 – reloadium: Advanced Hot Reloading for Python
00:58:07 – pls: A Prettier ‘ls’
01:00:56 – Thanks and goodbye

News:

Topic Links:

Python and TOML: New Best Friends – TOML is a configuration file format that’s becoming increasingly popular in the Python community. In this tutorial, you’ll learn the syntax of TOML and explore how you can work with TOML files in your own projects.
Say Goodbye to These Obsolete Python Libraries – It’s time to say goodbye to os.path, random, pytz, namedtuple and many more obsolete Python libraries. Start using the latest and greatest ones instead.
Uncommon Uses of Python in Commonly Used Libraries – To learn more about writing maintainable Python, Eugene has been reading code from some of the more popular Python libraries. This blog post talks about some of the coding patterns he has encountered along the way.
Your Python Coding Environment on Windows: Setup Guide – With this opinionated guide to setting up a basic, fully featured, and flexible setup for Python coding and contributing to open-source projects when working from Windows, you’ll go from a fresh install to ready to contribute, and even check out a PowerShell script to automate much of the process.

Discussion:

Technical Writing for Developers – “The way we write about and around code is arguably as important as the code itself.” This article outlines how programming and writing come together to take your developer skills to the next level.

Projects:

Additional Links:

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Moving NLP Forward With Transformer Models and Attention

12 augusti 2022 | 51 min

Inspiring Young People to Learn Python With Mission Encodeable

5 augusti 2022 | 43 min

Natural Language Processing and How ML Models Understand Text

29 juli 2022 | 59 min

Creating Documentation With MkDocs & When to Use a Python dict

22 juli 2022 | 55 min

Measuring Python Code Quality, Simplicity, and Maintainability

15 juli 2022 | 67 min

Exploring Functional Programming in Python With Bruce Eckel

1 juli 2022 | 74 min

Digging Into PyScript & Preventing or Handling Python Errors

24 juni 2022 | 56 min

Getting Started in Python Cybersecurity and Forensics

17 juni 2022 | 62 min

Build Streamlit Data Science Dashboards & Verbose Regex f-Strings

10 juni 2022 | 50 min

Managing Large Python Data Science Projects With Dask

3 juni 2022 | 46 min

Questions for New Dependencies & Comparing Python Game Libraries

27 maj 2022 | 52 min

Advantages of Protobuf for Serialization in Python

20 maj 2022 | 58 min

Start Testing Your Python with doctest & Pagination in Django

13 maj 2022 | 57 min

Did you know you can add testing to your Python code while simultaneously documenting it? Using docstrings, you can create examples of how your functions should interact in a Python REPL and test them with the built-in doctest module. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article by previous guest Mike Driscoll about testing with doctest. This is a great way to get started with testing your own code, and it offers the added benefit of documenting functionality.

We talk about the recent Real Python article “Pagination for a User-Friendly Django App.” Spreading your content across multiple pages can significantly improve the user experience of your web application. This article takes you through configuring Django’s built-in pagination tool and how to combine it with other web tools.

We discuss a recent article about Python type hints and the author’s disappointment. We also include reactions from a couple of online communities.

We cover several other articles and projects from the Python community, including why it’s important to close files in Python, how dunder methods are awesome, a bidirectional Python dictionary, prettier git diffs, and a command-line game to learn git.

Spotlight: Python Coding Interviews: Tips & Best Practices

In this step-by-step course, you’ll learn how to take your Python coding interview skills to the next level and use Python’s built-in functions and modules to solve problems faster and more easily.

Topics:

00:00:00 – Introduction
00:02:15 – PyCon US 2022 Follow-up
00:07:57 – Why Is It Important to Close Files in Python?
00:15:17 – Dunder Methods in Python: The Ugliest Awesome Sauce
00:24:26 – Sponsor: Mailtrap
00:25:08 – Python Testing With doctest
00:28:20 – Python Bidirectional Dictionary
00:30:27 – Pagination for a User-Friendly Django App
00:36:07 – Video Course Spotlight
00:37:27 – Python’s “Type Hints” are a bit of a disappointment to me
00:52:25 – dunk: Prettier Git Diffs
00:53:43 – git-gud: Command-Line Game to Learn git
00:55:40 – Thanks and goodbye

Topic Links:

Why Is It Important to Close Files in Python? – Model citizens use context managers to open and close file resources in Python, but have you ever wondered why it’s important to close files? In this tutorial, you’ll take a deep dive into the reasons why it’s important to close files and what can happen if you dont.
Dunder Methods in Python: The Ugliest Awesome Sauce – Double-underscore methods, also known as “dunder methods” or “magic methods” are an ugly way of bringing beauty to your code. Learn about constructors, __repr__, __str__, operator overloading, and getting your classes working with Python functions like len().
Python Testing With doctest – Python’s doctest module allows you to write unit tests through REPL-like sessions in your docstrings. Learn how to write and execute doctest code. Also available in video.
Python Bidirectional Dictionary – Learn about the Bidict library, a bidirectional dictionary where your keys and your values can both be used to look up an item. This can be a useful tool when dealing with mapped data, like country code to country name, where you want to look up either side of the relationship.
Pagination for a User-Friendly Django App – In this tutorial, you’ll learn how to serve paginated content in your Django apps. Using Django pagination can significantly improve your website’s performance and give your visitors a better user experience.

Discussion:

Projects:

Additional Links:

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

Support the podcast & join our community of Pythonistas

Run Python in a Browser With Pyodide & The Power of f-Strings

6 maj 2022 | 56 min

Have you heard about the projects working toward getting Python to run in the browser? Maybe you would like to try it out for yourself, by building an interactive Python REPL with Pyodide and WebAssembly (WASM). This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We talk about a step-by-step project that shows you how to build a Python code editor in the browser using WebAssembly through Pyodide and CodeMirror. You’re going to be hearing a lot about Pyodide in the coming months, and here’s a chance for you to play around while building a small project.

Christopher shares an article about the power of Python f-strings. It covers some lesser-known features like variable debugging, nesting, and detailed formatting.

We also have a couple of topics up for discussion this week. They’re both related to finding work as a Python developer.

We cover several other articles and projects from the Python community, including the 2038 date problem, a primer about Python virtual environments, how to build a site connectivity checker in Python, a free book on digital signal processing in Python, and a project to build a voice-activated, password-protected wooden box.

Spotlight: Using Python’s datetime Module

Have you ever wondered about working with dates and times in Python? In this video course, you’ll learn all about the built-in Python datetime library. You’ll also learn about how to manage time zones and daylight saving time, and how to do accurate arithmetic on dates and times.

Topics:

00:00:00 – Introduction
00:02:38 – 2038 Date Problem
00:07:46 – Python Virtual Environments: A Primer
00:16:06 – Python f-Strings Are More Powerful Than You Might Think
00:20:19 – Sponsor: CData Software
00:21:01 – Build an Editor in Python and WebAssembly
00:28:19 – Build a Site Connectivity Checker in Python
00:30:21 – What Jobs Can I Have Knowing Python?
00:39:53 – Video Course Spotlight
00:41:12 – Projects for a Self-Taught Dev to Help Get a Job?
00:50:46 – ThinkDSP: Free Book on Digital Signal Processing in Python
00:53:14 – Durin’s Box: Voice-Activated, Password-Protected Wooden Box
00:55:05 – Thanks and goodbye

Topic Links:

2038 Date Problem (Funny, but True) – Coders old enough to remember Y2K are already dreading 2038. Join the conversation.
Python Virtual Environments: A Primer – In this tutorial, you’ll learn how to use a Python virtual environment to manage your Python projects. You’ll also dive deep into the structure of virtual environments built using the venv module, as well as the reasoning behind using virtual environments.
Python f-Strings Are More Powerful Than You Might Think – Learn about the lesser-known features of Python’s f-strings, including date formatting, variable debugging, nested f-strings, and conditional formatting.
Build an Editor in Python and WebAssembly – Step-by-step instructions on how to build a code editor in the browser using Python and WebAssembly (WASM), via Pyodide and CodeMirror.
Build a Site Connectivity Checker in Python – In this step-by-step project, you’ll build a Python site connectivity checker for the command line. While building this app, you’ll integrate knowledge related to making HTTP requests with standard-library tools, creating command-line interfaces, and managing concurrency with asyncio and aiohttp.

Discussions:

Projects:

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Type-Safe ORM With Prisma Client & Real Python at PyCon US 2022

22 april 2022 | 59 min

Are you using an Object-Relational Mapper (ORM) for your Python projects? What if it could work with SQL or No-SQL databases and be fully type-safe? This week on the show, Robert Craigie talks about Prisma Client Python.

Prisma Client Python is built on top of Prisma, which was created for TypeScript and Node.js. It uses a schema file to declare your application’s data models and relationships in a human-readable form. The schema file allows you to easily switch the database type.

Prisma Client is different from other Python ORMs. It is fully type-safe and can be used with or without async.

We talk about how Robert started the project and what types of challenges he’s faced. He also shares areas of improvement and how to contribute to the project.

We also have a conversation with several Real Python core team members about PyCon US 2022. We will have a booth at the conference where we hope you’ll come and connect with us. The team also shares what to expect from PyCon and what they’re excited about this year.

Course Spotlight: Python REST APIs With FastAPI

In this course, you’ll learn the main concepts of FastAPI and how to use it to quickly create web APIs that implement best practices by default. By the end of it, you will be able to start creating production-ready web APIs.

Topics:

00:00:00 – Introduction
00:02:15 – Have you worked on other open-source projects?
00:02:59 – What is Prisma?
00:05:00 – What are advantages of using an ORM?
00:06:52 – What problem is Prisma Client solving?
00:08:43 – What was involved in porting the project over?
00:09:55 – Creating a Prisma schema
00:12:09 – Other challenges along the way
00:14:57 – Dangers of not having type safety
00:16:27 – Sponsor: Linear B
00:17:06 – How long have you been working on the project?
00:18:57 – How could someone contribute to the project?
00:21:03 – In what situations does Prisma Client excel?
00:24:21 – What other projects do you currently work on?
00:24:57 – What are you excited about in the world of Python?
00:28:01 – What do you want to learn next?
00:29:52 – Thanks and Goodbye
00:30:14 – Introduction of RP team
00:31:09 – What are we doing at PyCon 2022?
00:35:55 – How to Get the Most Out of PyCon US
00:37:42 – Tutorials at PyCon US 2022
00:40:24 – Video Course Spotlight
00:41:49 – Talks at PyCon US 2022
00:50:16 – Sprints at PyCon US 2022
00:54:38 – Final thoughts
00:57:30 – Thanks and goodbyes

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Class Constructors & Pythonic Image Processing

15 april 2022 | 58 min

Creating Better Error Messages for Python 3.10 & 3.11

8 april 2022 | 82 min

Building a Hash Table in Python and Thoughtful REST API Design

1 april 2022 | 53 min

Do you understand how a hash table works? What if you could learn about building one while practicing test-driven development? What are best practices when designing a REST API? This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We talk about the recent Real Python article “Build a Hash Table in Python With TDD.” The tutorial shows how to implement a hash table prototype from scratch in Python. It also provides a hands-on crash course in test-driven development.

Christopher shares an article on designing REST APIs and provides some of his own best practices. We cover authentication implementation, good naming conventions, versioned APIs, and ways to specify dates.

We cover several other articles and projects from the Python community, including a news roundup, a PEP on removing dead batteries from the standard library, a comparison of the Python list vs tuple, a guide to writing user-friendly CLIs in Python, just enough Cython to be useful, a cross-platform TUI and ASCII animation package, and code for running black on Python code blocks in documentation files.

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
00:02:23 – PEP 594: Removing Dead Batteries From the Standard Library
00:05:33 – Python 3.10.3, 3.9.11, 3.8.13, and 3.7.13 Now Available
00:08:37 – EuroPython 2022: Ticket Sales Open
00:09:34 – Python list vs tuple Comparison
00:12:19 – How to Write User-Friendly CLIs in Python
00:20:14 – Sponsor: Anvil
00:20:55 – Build a Hash Table in Python With TDD
00:26:11 – Just Enough Cython to Be Useful
00:36:21 – Video Course Spotlight
00:37:45 – How to Design Better REST APIs
00:47:10 – blacken-docs: Run black on Python Code Blocks within Documentation Files
00:49:09 – asciimatics: Cross Platform TUI and ASCII Animation Package
00:52:03 – Thanks and goodbye

News:

Topic Links:

Python list vs tuple Comparison – Learn how list and tuple are similar and how they’re different, including storage and speed differences and how to choose between them.
How to Write User-Friendly CLIs in Python – Learn how to write user-friendly command-line interface applications and an overview of several of the popular CLI libraries: argparse, Click, Typer, Docopt, and Fire.
Build a Hash Table in Python With TDD – In this step-by-step tutorial, you’ll implement the classic hash table data structure using Python. Along the way, you’ll learn how to cope with various challenges such as hash code collisions while practicing test-driven development (TDD).
Just Enough Cython to Be Useful – Cython is a superset of Python designed to give C-like performance. Ever wanted to learn the basics? This article shows you how to get started.
How to Design Better REST APIs – Fifteen language-agnostic tips on REST API design, including good naming conventions, ways to specify dates, versioned APIs, authentication keys, pagination, and when to use which HTTP methods.

Projects:

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Becoming More Effective at Manipulating Data With Pandas

25 mars 2022 | 60 min

Do you wonder if you’re taking the right approach when shaping data in pandas? Is your Jupyter workflow getting out of hand? This week on the show, Matt Harrison talks about his new book, “Effective Pandas: Patterns for Data Manipulation.”

Matt discusses working as a corporate consultant and migrating Excel users toward Python. We explore several “NumPy-isms” that beginners get stuck on. Matt shares advice about chaining operations in pandas, which some developers find controversial.

Course Spotlight: Sorting Data in Python With Pandas

In this video course, you’ll learn how to sort data in a pandas DataFrame using the pandas sort functions sort_values() and sort_index(). You’ll learn how to sort by one or more columns and by index in ascending or descending order.

Topics:

00:00:00 – Introduction
00:01:32 – Working as a consultant
00:03:39 – Moving from Excel to Python
00:06:50 – Who is the book for?
00:10:15 – Using real data for examples
00:16:16 – Sponsor: CData Software
00:16:58 – What are patterns for data manipulation?
00:18:38 – Cleaning and preparing data
00:21:33 – What concepts were you most eager to share?
00:26:57 – An example of chaining operations in pandas
00:33:20 – NumPy-isms and other challenges in learning pandas
00:40:20 – The use of exercises throughout the book
00:43:50 – Video Course Spotlight
00:45:01 – Challenges of using color throughout the book
00:51:40 – Avoiding the slow path in pandas
00:56:03 – What are you excited about in the world of Python?
00:56:58 – What would you like to learn next?
00:58:16 – Effective Pandas book
00:58:38 – Social connections
00:58:58 – Thanks and goodbye

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Making Your Notebook Interactive and Using Python's Assert

18 mars 2022 | 47 min

Tools for Setting Up Python on a New Machine

11 mars 2022 | 63 min

Defining Optional Arguments and Moving Beyond "Beginner" Python

4 mars 2022 | 53 min

How do you define Python functions that accept optional arguments or default values? Are you wondering how to go beyond being a beginner with Python? This week on the show, Christopher Trudeau is here, and he’s brought another batch of PyCoder’s Weekly articles and projects.

Christopher shares a Real Python article, “Defining Python Functions With Optional Arguments.” We talk about function flexibility, specifying default values, and using args & kwargs.

We discuss resources for a Python beginner to move beyond the basics and become more competent. Both of us share our experiences moving past these hurdles.

We cover several other articles and projects from the Python community, including a news roundup, ten tools you may have wished you knew when you started working with Python, Python’s zipfile for manipulating your zip files efficiently, how one company optimized Python API server code by 100x, a dependency-free Python library for downloading YouTube videos, and how to use Python on the command line with other shell commands.

Course Spotlight: Defining Python Functions With Optional Arguments

In this video course, you’ll learn about Python optional arguments and how to define functions with default values. You’ll also learn how to create functions that accept any number of arguments using args and kwargs.

Topics:

00:00:00 – Introduction
00:03:08 – Django Project Reformats Entire Codebase With Black
00:03:53 – PEP 673: Self Type Was Accepted
00:05:16 – PEP 654 Accepted: Exception Groups
00:07:45 – Upcoming Python Feature PEPs
00:10:01 – Defining Python Functions With Optional Arguments
00:15:12 – Sponsor: CData Software
00:15:53 – 10 Tools I Wish I Knew When I Started Working With Python
00:23:22 – How We Optimized Python API Server Code 100x
00:29:47 – Python’s zipfile: Manipulate Your ZIP Files Efficiently
00:32:47 – Video Course Spotlight
00:33:59 – How Do I Go From Being a Beginner to Competent in Python?
00:47:02 – pytube: Dependency-Free Python Library for Downloading – YouTube Videos
00:49:30 – pz: Use Python on the Command Line With Other Shell Commands
00:52:23 – Thanks and goodbye

News:

Django Project Reformats Entire Codebase With Black – Related discussion on Hacker News.
PEP 673: Self Type Was Accepted – This PEP introduces a simple and intuitive way to annotate methods that return an instance of their class. This behaves the same as the TypeVar-based approach specified in PEP 484 but is more concise and easier to follow. Related Twitter thread by core dev Raymond Hettinger.
PEP 654 Accepted: Exception Groups – New standard for grouping exceptions together
Upcoming Python Feature PEPs – “These PEPs are a great way of getting the freshest info about what might be included in the upcoming Python releases. So, in this article we will go over all the proposals that are going to bring some exciting new Python features in a near future!”

Topics:

Defining Python Functions With Optional Arguments – Learn about Python optional arguments and how to define functions with default values. You’ll also see how to create functions that accept any number of arguments using *args and **kwargs.
10 Tools I Wish I Knew When I Started Working With Python – Learn about how venvs, flake8, black, isort, pytest, commitizen, semantic-release, pre-commit hooks, and Github Actions work together!
How We Optimized Python API Server Code 100x – Tricks we used to speed up calls to our analytical API written in Python: asyncio, SQLAlchemy, asyncpg, Cython plugins, improved data structures, and replaced some Pandas with NumPy.
Python’s zipfile: Manipulate Your ZIP Files Efficiently – In this guided tutorial, you’ll learn how to manipulate ZIP files using Python’s zipfile module from the standard library. Through hands-on examples, you’ll learn how to read, write, compress, and extract files from your ZIP files quickly.
How Do I Go From Being a Beginner to Competent in Python?

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OAuth 2 and Authentication Choices for Your Python Project

25 februari 2022 | 59 min

Drawing Fractals With Python and Working With a Weather API

18 februari 2022 | 51 min

Have you been wanting to explore fractals and complex numbers in Python? Would you like to practice working with APIs in Python through a new project? This week on the show, Christopher Trudeau is here, and he’s taking on the task of curating new issues of PyCoder’s Weekly going forward. He’ll be joining me as a cohost every other week and bringing a fresh batch of PyCoder’s Weekly articles and projects.

We discuss a Real Python article about drawing the Mandelbrot set in Python. The tutorial guides you through creating fractal art using Matplotlib and Pillow. We also share a new step-by-step project building a weather-reporting command-line interface (CLI) app.

We cover several other articles and projects from the Python community, including a news roundup, a better Pygame main loop, ways to work with static and media files in Django, and a library for pleasing console output.

Course Spotlight: Make a 2D Side-Scroller Game With Pygame

In this step-by-step course, you’ll learn how to use Pygame. This library allows you to create games and rich multimedia programs in Python. You’ll learn how to draw items on your screen, implement collision detection, handle user input, and much more!

Topics:

00:00:00 – Introduction
00:02:15 – Black, the Python Code Formatter, Is Stable
00:04:07 – Apple Removes Python 2.7 in macOS Monterey 12.3
00:06:36 – GitHub Survey on Languages and More Developer Things
00:10:14 – CPython 3.11.0a5 Is Available
00:11:48 – Django Security Releases Issued: 4.0.2, 3.2.12, and 2.2.27
00:12:43 – A Better Pygame Main Loop
00:21:22 – Sponsor: CData Software
00:22:04 – Draw the Mandelbrot Set in Python
00:29:12 – The Fastest Way to Read a CSV in Pandas
00:35:16 – Video Course Spotlight
00:36:26 – Working With Static and Media Files in Django
00:41:42 – Raining Outside? Build a Weather CLI App With Python
00:48:15 – konsole: Readable, Pleasing Console Output
00:50:27 – Thanks and Goodbye

News:

Black, the Python Code Formatter, Is Stable – The popular Python autoformatter Black is finally non-beta software. Related discussion on Hacker News and Black’s stability policy doc.
Apple Removes Python 2.7 in macOS Monterey 12.3 – “Python 2.7 was removed from macOS in this update. Developers should use Python 3 or an alternative language instead.” Also see the related discussion on Hacker News.
GitHub Survey on Languages and More Developer Things – JavaScript retains the top spot, Python keeps second place gained in 2019 over Java, which holds in third. TypeScript continues fourth after racing up tenth in 2017 to fourth in 2020. JavaScript + TypeScript seem to put that way ahead in terms of amount of code on GitHub.
CPython 3.11.0a5 Is Available – “We needed to tame some angry buildbots, but after a small fight, we won with just some scratches! Here you have a shiny new alpha release: Python 3.11.0a5.”
Django Security Releases Issued: 4.0.2, 3.2.12, and 2.2.27 – Includes fixes for a possible XSS via debug template tag and a denial-of-service possibility in file uploads.

Articles:

A Better Pygame Main Loop – Improving your game’s main loop for smoother gameplay that takes less battery power: “Now your players’ laptops run cool while playing, and the graphics don’t have ugly tearing artifacts any more!”
Draw the Mandelbrot Set in Python – In this tutorial, you’ll visualize the famous Mandelbrot set using Python’s Matplotlib and Pillow libraries. You’ll learn how to draw the fractal in black and white, grayscale, and color.
The Fastest Way to Read a CSV in Pandas – This article covers pandas’ default CSV reading, the faster and more parallel CSV reader introduced in v1.4, and a different approach that can make things even faster.
Working With Static and Media Files in Django – A look at how to work with static and media files in a Django project, locally and in production.

Projects:

Raining Outside? Build a Weather CLI App With Python – In this tutorial, you’ll write a nicely formatted Python CLI app that displays information about the current weather in any city that you provide the name for.
konsole: Readable, Pleasing Console Output

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Improving Your Django and Python Developer Experience

11 februari 2022 | 50 min

Manipulating and Analyzing Audio in Python

4 februari 2022 | 59 min

What Is a JIT and How Can Pyjion Speed Up Your Python?

28 januari 2022 | 67 min

Designing for Users and Building a Social Network With Django

21 januari 2022 | 59 min

Launching Python, Virtual Environments, and Locking Dependencies With Brett Cannon

14 januari 2022 | 51 min

Continuing to Unravel Python's Syntactic Sugar With Brett Cannon

7 januari 2022 | 59 min

2021 Real Python Articles Wrap Up

24 december 2021 | 64 min

A Python Journey: Cyber Security, Automating AWS, and TDD

17 december 2021 | 54 min

Solving Advent of Code Puzzles With Python

10 december 2021 | 57 min

Discussing Type Hints, Protocols, and Ducks in Python

3 december 2021 | 81 min

Building a Content Aggregator and Working With RSS in Python

19 november 2021 | 58 min

The Legacy of OLPC and Charismatic Pitfalls in Teaching Programming

12 november 2021 | 82 min

Exploring Django Templates, Tags, and Filters

5 november 2021 | 62 min

Creating and Manipulating PDFs in Python With borb

29 oktober 2021 | 62 min

Have you wanted to generate PDFs from your Python project? Many of the current libraries require designing the document down at the pixel level. Would you be interested in a tool that lets you specify the page layout while it handles the specific details of laying out the text? This week on the show, we talk with Joris Schellekens about his library for creating and manipulating PDFs named borb.

borb is a pure Python library that can read, write and manipulate PDFs. You can use it to build fillable forms, invoices with attached data files, and multiple column document layouts. We discuss the extensive example repository Joris has created for the library.

Joris shares his background in working with PDFs. He talks about starting the project and the challenges he had to overcome. We also talk about licensing and maintaining an open-source library.

Course Spotlight: Writing Idiomatic Python

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

Topics:

00:00:00 – Introduction
00:01:58 – Articles about borb
00:03:25 – History of the project
00:07:26 – Background in PDFs and Postscript
00:09:18 – Signatures and other challenges of working in PDFs
00:11:33 – Reading from PDFs and standards of versions
00:14:54 – Features of the library and creating documents
00:18:25 – Creating layout features
00:20:42 – How are fonts handled in borb?
00:21:19 – Sponsor: Cloudsmith
00:22:04 – Why use JSON across the library?
00:22:55 – Embedding data and files within a PDF
00:25:12 – What features were crucial for you to include in borb?
00:28:48 – Why creating a separate examples repository?
00:31:04 – Article series about borb
00:32:25 – Writing a book about borb
00:33:44 – Python 3.10 and borb
00:34:19 – Video Course Spotlight
00:35:39 – Licensing borb and AGPL
00:45:14 – Other open-source projects and Stack Overflow answers
00:46:37 – Working with forms in borb
00:47:55 – Additional tools for working with PDFs
00:50:15 – Different users of the library
00:53:36 – Thoughts on the future of PDFs
00:58:10 – What are you excited about in the world of Python?
00:58:40 – What do you want to learn next?
01:00:25 – Social connection info
01:00:46 – Thanks and goodbye

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Ready to Publish Your Python Packages?

22 oktober 2021 | 62 min

Welcoming the CPython Developer in Residence

15 oktober 2021 | 93 min

Exploring the New Features of Python 3.10

8 oktober 2021 | 54 min

Make Your Python App Interactive With a Text User Interface (TUI)

1 oktober 2021 | 63 min

Measuring Your Python Learning Progress

24 september 2021 | 56 min

Learning Python Through Illustrated Stories

17 september 2021 | 48 min

Are you a visual learner? Does it help to have programming concepts shared with concrete examples and images? Would you like to see if your child might be interested in programming? This week on the show, we talk with author Shari Eskenas about her books, “A Day in Code - Python: Learn to Code in Python Through an Illustrated Story” and “Learn Python Through Nursery Rhymes & Fairy Tales.”

We talk about the books and what inspired her to bring programming to picture books. Shari discusses her goal of providing a fun way for beginners to experience learning to code.

Shari is also an electrical engineer with multiple patents and the founder of Sundae Electronics. We talk briefly about SoundBrake, which is an audio device that alerts headphone users to outside sounds.

Shari’s programming background is primarily in C, and we cover her path to Python. We also discuss how she is using Python and the Raspberry Pi to prototype new projects.

Course Spotlight: Reading and Writing Files With Pandas

In this course, you’ll learn about the Pandas IO tools API and how you can use it to read and write files. You’ll use the Pandas read_csv() function to work with CSV files. You’ll also cover similar methods for efficiently working with Excel, CSV, JSON, HTML, SQL, pickle, and big data files.

Topics:

00:00:00 – Introduction
00:02:05 – A Day in Code Books
00:05:32 – Showing Python classes and object oriented concepts
00:08:02 – What Python concepts did you want to cover in the book?
00:09:17 – Translating the book and concepts from C to Python
00:11:04 – Reception of the book
00:12:10 – Using real objects and names in code
00:14:05 – Sponsor: DataStax Astra DB
00:14:38 – Learn Python Through Nursery Rhymes & Fairy Tales
00:20:25 – Sundae Electronics and SoundBrake
00:26:47 – Video Course Spotlight
00:27:57 – Prototyping with Python and Raspberry Pi
00:38:19 – When did you start to learn Python?
00:39:31 – Real Python community
00:41:33 – How do you see yourself using Python in the future?
00:43:39 – What are you excited about in the world of Python?
00:45:02 – What do you want to learn next?
00:45:51 – Shout outs and plugs: Kickstarter campaign
00:46:40 – Social media connections
00:47:08 – Thanks and goodbye

Show Links:

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Advantages of Completing Small Python Projects

10 september 2021 | 70 min

Harnessing Python's math Module and Exposing Practical Pandas Functions

3 september 2021 | 50 min

How well do you know Python’s math module? Maybe you’ve used a few of the constants or arithmetic functions. You may be surprised by the amount of functionality hiding within this built-in library, and perhaps you don’t need to reach for an additional outside library. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We discuss a recent video course about the math module. David shares a recent article about implementing efficient queues and stacks with Python’s deque (double-ended queue) class. We also talk about an article that shares 25 Pandas functions you may not have known to exist.

We cover several other articles and projects from the Python community including, visualization and interactive dashboards in Python with HoloViz, designing a camera with Python and PyRayT, graphing data science with Python and networkx, another useful Python pdf library, and runtime software verification and automated testing for scientific software in Python with a project named paranoidscientist.

Course Spotlight: Exploring the Python math Module

In this step-by-step course, you’ll learn all about Python’s math module for higher-level mathematical functions. Whether you’re working on a scientific project, a financial application, or any other type of programming endeavor, you just can’t escape the need for math!

Topics:

00:00:00 – Introduction
00:02:09 – Python’s deque: Implement Efficient Queues and Stacks
00:07:31 – Visualization and Interactive Dashboard in Python
00:14:11 – Sponsor: DataStax Astra DB
00:14:42 – Design a Camera with Python and PyRayT
00:19:39 – 25 Pandas Functions You Didn’t Know Existed
00:27:03 – Graph Data Science With Python and NetworkX
00:33:01 – Exploring the Python math Module
00:40:51 – Video Course Spotlight
00:42:12 – paranoidscientist: Runtime Software Verification and Automated Testing for Scientific Software in Python
00:45:33 – borb: A Python PDF library
00:49:01 – Thanks and goodbye

Show Links:

Python’s deque: Implement Efficient Queues and Stacks – In this step-by-step tutorial, you’ll learn about Python’s deque and how to use it to perform efficient pop and append operations on both ends of your sequences. Deques are commonly used to build queues and stacks.

Visualization and Interactive Dashboard in Python – Have you ever heard of HoloViz? It’s a set of Python visualization and plotting tools for browser-based data visualization and presentation. In this article, Sophia Yang — a senior data scientist at Anaconda — explains why she loves HoloViz and what her workflow looks like when using it.

Design a Camera with Python and PyRayT – Camera lenses are more complex than you might think. If you cut open a camera to look at a cross-section, you’d see that a lens is actually made up of several smaller lenses. This article explores how camera lenses work using a new Python raytracing project called PyRayT that’s designed for designing optical systems.

25 Pandas Functions You Didn’t Know Existed – Did you know that you can style the pandas DataFrame output in a notebook? This listicle covers twenty-five pandas functions that you may not have heard of, including .explode(), .squeeze(), and the pandas DataFrame styler. There’s something in this article for everyone, so read it to find out how you can take your pandas skills to the next level.

Graph Data Science With Python and NetworkX – Graph theory network analysis can yield deep data insights that are difficult to tease out via alternative methods. Get a taste of what graph analysis can do in this quick, hands-on tutorial that uses NetworkX to create and analyze a graph.

Exploring the Python math Module – In this step-by-step course, you’ll learn all about Python’s math module for higher-level mathematical functions. Whether you’re working on a scientific project, a financial application, or any other type of programming endeavor, you just can’t escape the need for math!

Projects:

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Building With CircuitPython & Constraints of Python for Microcontrollers

27 augusti 2021 | 85 min

Python's Assignment Expressions and Fixing a Botched Release to PyPI

20 augusti 2021 | 58 min

Have you started to use Python’s assignment expression in your code? Maybe you have heard them called the walrus operator. Now that the controversy over the introduction in Python 3.8 has settled down, how can you use assignment expressions effectively in your code? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares a recent article by previous guest Brett Cannon about what to do if you botch a release to PyPI. It’s a valuable resource to keep bookmarked for when things go sideways. We also talk about a recent project by Brett, a Python launcher for Unix based operating systems.

We cover several other articles and projects from the Python community including, a Python framework with a built-in database and authorization support from Replit, do coders learn how to use entire libraries just from the documentation, how to use sleep() to code a Python uptime bot, monitor your home’s temperature and humidity with Raspberry Pis and Prometheus, and a fast Settlers of Catan Python implementation with a strong AI player.

Course Spotlight: Using sleep() to Code a Python Uptime Bot

In this course, you’ll learn how to add time delays to your Python programs. You’ll use the built-in time module to add Python sleep() calls to your code. To practice, you’ll use time.sleep() when making an uptime bot that checks whether a website is still live.

Topics:

00:00:00 – Introduction
00:02:43 – The Walrus Operator: Python 3.8 Assignment Expressions
00:12:41 – Replit.web: Python Framework With Built-in Database and Auth
00:16:55 – Sponsor: Gather
00:17:39 – Do Coders Learn How to Use Entire Libraries Just From the Docs?
00:26:15 – Using sleep() to Code a Python Uptime Bot
00:31:53 – Monitor Home Temperature and Humidity With Raspberry Pis and Prometheus
00:39:51 – Video Course Spotlight
00:40:57 – What to Do When You Botch a Release on PyPI
00:47:25 – catanatron: Fast Settlers of Catan Python Implementation
00:50:37 – python-launcher: Python Launcher for Unix
00:56:59 – Thanks and goodbye

Show Links:

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

Replit.web: A Python Framework With Built-in Database and Auth Support – The folks over at replit have released a new Python web framework with built-in authentication and database support and, more interestingly, hosting. In a few lines of code, you can have a Python web app connected to a database, authenticating users, and hosted on replit. This could be a great tool for quickly building and hosting prototypes or experimental projects.

Do Coders Really Learn How to Use Entire Libraries Just From the Documentation? – How do you learn a new library? Do you start with the docs? What do you do if the documentation is lacking? Or do you first search for video lessons or written tutorials?

Using sleep() to Code a Python Uptime Bot – Learn how to add time delays to your Python programs. You’ll use the built-in time module to add Python sleep() calls to your code. To practice, you’ll use time.sleep() when making an uptime bot that checks whether a website is still live.

Monitor Your Home’s Temperature and Humidity With Raspberry Pis and Prometheus – Do you enjoy collecting and analyzing data, or are you looking for a fun project to improve your data skills? Do you also enjoy tinkering with hardware? Then this project might be a fun one for you to check out! Learn how to set up a RaspberryPi using Prometheus to collect and monitor temperature sensor data.

What to Do When You Botch a Release on PyPI – Mistakes happen to everyone. But what do you do if you make a mistake when releasing a package to PyPI? Don’t panic! There are a number of things you can do to fix a bad release. This article walks you through several scenarios and suggested solutions.

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Supporting Python Open Source Projects and Maintainers

13 augusti 2021 | 61 min

Starting With FastAPI and Examining Python's Import System

6 augusti 2021 | 46 min

Have you heard of FastAPI? An application programming interface is vital to make your software accessible to users across the internet. FastAPI is an excellent option for quickly creating a web API that implements best practices. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We share an introduction to FastAPI written by the framework’s author, Sebastián Ramírez. The goal behind the article is to get you started creating production-ready APIs.

David covers an article about the Python import system and how it remains a mystery for many Python developers. We share some additional Real Python resources on the import system and statements.

We cover several other articles and projects from the Python community including, a buffet of specialized data types with Python’s collections module, maps with Django using GeoDjango, PostGIS, and Leaflet, moving SciPy to the Meson build system, what’s new in Python 3.11, a community-maintained Python framework for creating mathematical animations, and easily make PDFs with pdfme.

Course Spotlight: Python Modules and Packages: An Introduction

In this course, you’ll explore Python modules and Python packages, two mechanisms that facilitate modular programming. See how to write and import modules so you can optimize the structure of your own programs and make them more maintainable.

Topics:

00:00:00 – Introduction
00:02:18 – Python’s collections: A Buffet of Specialized Data Types
00:08:07 – Maps With Django: GeoDjango, PostGIS, and Leaflet
00:12:12 – Moving SciPy to the Meson Build System
00:18:16 – Sponsor: Sentry
00:19:18 – What’s New In Python 3.11
00:24:32 – Behind the Scenes: How the Python Import System Works
00:31:34 – Video Course Spotlight
00:32:40 – Using FastAPI to Build Python Web APIs
00:38:42 – manim: A Community-Maintained Python Framework for Creating Mathematical Animations
00:41:56 – pdfme: Make PDFs Easily
00:44:42 – Thanks and goodbye

Show Links:

Python’s collections: A Buffet of Specialized Data Types – Python has a number of useful data types beyond the built-in lists, tuples, dicts, and sets. In this tutorial, you’ll learn all about the series of specialized container data types in the collections module from the Python standard library. Learning the collections module is a great way to level up your Python programming knowledge!

Maps With Django: GeoDjango, PostGIS, and Leaflet – This quickstart guide shows you how to create a web map using Django’s GeoDjango module. Data for the map is stored in a PostgreSQL database using the PostGIS extension, and Leaflet, a lightweight JavaScript library for interactive maps, is used on the front-end. You’ll not only learn how to set up the Django application and display the map but also add markers to the map and automatically center the map on the application user’s location.

Moving SciPy to the Meson Build System – In accordance with PEP 632, distutils will be deprecated in Python 3.10 and in Python 3.12 it will be removed. This posed a big problem for SciPy, since it’s build system depends on NumPy’s distutils module — an extension of Python’s built-in distutils. The SciPy maintainers set out to find a new build system and settled on Meson, which solves a number of build issues and even scores a 4x speed-up on build times!

What’s New In Python 3.11 – Python 3.10 is still in beta, but work on Python 3.11 has already begun. Big changes include some major improvements to tracebacks as well as a new cube root function in the math module.

Behind the Scenes: How the Python Import System Works – Importing a Python module is probably one of the most used language features. But Python’s import system remains a mystery to many Python developers, even folks with years of experience. This in-depth article explores how the import system works from the top down. You’ll learn everything from the difference is between absolute and relative imports to how Python searches for modules and packages and resolves naming conflicts.

Using FastAPI to Build Python Web APIs – In this guide, written by FastAPI creator Sebastián Ramírez, you’ll learn the main concepts of FastAPI and how to use it to quickly create web APIs that implement best practices by default. By the end of it, you will be able to start creating production-ready web APIs.

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Start Using a Debugger With Your Python Code

30 juli 2021 | 66 min

What Can You Do With Python and Counting Objects Using "Counter"

23 juli 2021 | 57 min

Planning a Faster Future at the Python Language Summit

16 juli 2021 | 58 min

Exploring the functools Module and Complex Numbers in Python

9 juli 2021 | 55 min

Are you ready to expand your Python knowledge into the intermediate to advanced territory? What tools are awaiting your discovery inside Python’s functools module? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We discuss an article about the functools module, which adds functionality for caching, function overloading, better definitions for decorated functions, and more. David talks about a new Real Python article about working with complex numbers in Python. We also cover a tutorial about troubleshooting memory problems in Python.

We cover several other articles and projects from the Python community including, DevOps interview questions, correlation analysis in Python, pivot and plot data with pandas, how to use Python and OpenCV to play online chess with a real chessboard, and generating hardware pinout diagrams as SVG images.

Course Spotlight: Python Decorators 101

In this course on Python decorators, you’ll learn what they are and how to create and use them. Decorators provide a simple syntax for calling higher-order functions in Python. By definition, a decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it.

Topics:

00:00:00 – Introduction
00:01:56 – The Future of FastAPI and Pydantic Is Bright
00:04:33 – Simplify Complex Numbers With Python
00:10:37 – Functools: The Power of Higher-Order Functions in Python
00:18:40 – Sponsor: Sentry
00:19:42 – How to Pivot and Plot Data With Pandas
00:23:06 – devops-exercises: DevOps Interview Questions
00:32:09 – Video Course Spotlight
00:33:25 – Correlation Analysis 101 in Python
00:39:28 – How to Troubleshoot Memory Problems in Python
00:46:16 – Use Python and OpenCV to Play Online Chess With a Real Chessboard
00:49:28 – pinout: Generate Hardware Pinout Diagrams as SVG Images
00:54:21 – Thanks and goodbyes

Show Links:

The Future of FastAPI and Pydantic Is Bright – Not long ago there was some chatter on the internet about a change in Python 3.10 that would impact Python projects that check types at runtime. The discussion centered around FastAPI and Pydantic and had some folks worried about the future of those projects. In this article, FastAPI’s creator explains what the discussion was all about and why the future of FastAPI and Pydantic remains bright.

Simplify Complex Numbers With Python – In this tutorial, you’ll learn about the unique treatment of complex numbers in Python. Complex numbers are a convenient tool for solving scientific and engineering problems. You’ll experience the elegance of using complex numbers in Python with several hands-on examples.

Functools: The Power of Higher-Order Functions in Python – The functools module is one of the “hidden gems” of the Python standard library. This article takes you on a tour of everything in functools. You’ll learn how to implement caching, function overloading, and a whole lot more.

How to Pivot and Plot Data With Pandas – One of the challenges of working with data is knowing how to manipulate the data format for a particular analysis. And there’s no single correct format. You need to know how to melt, pivot, and transpose data into a format that fits whatever you’re analyzing. If you enjoy this article, be sure to also check out Stefanie’s Pandas Workshop.

devops-exercises: DevOps Interview Questions

Correlation Analysis 101 in Python – Correlation analysis is a useful part of exploratory data analysis. It can help you identify potential relationships between various features of your data. In this helpful guide, you’ll learn how to do correlation analysis in a pandas DataFrame. You’ll see how to display a correlation matrix as a heatmap and explore some guidelines for identifying when correlation might imply causation.

How to Troubleshoot Memory Problems in Python – Memory problems can be frustrating. They’re hard to diagnose and fix, and memory issues in Python applications can be especially frustrating thanks to the language’s garbage collection system. In this article, you’ll learn a six-step process for troubleshooting memory problems that the EvalML team used to solve a tricky problem with their library.

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Securing Your Python Software Supply Chain With Dustin Ingram

2 juli 2021 | 71 min

Practicing Python With CSV Files and Extracting Values With "filter()"

25 juni 2021 | 56 min

Are you ready to practice your Python skills some more? There is a new set of practice problems prepared for you to tackle, and this time they’re based on working with CSV files. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares an article about functional programming with a focus on the “filter” function. The tutorial covers how to process an iterable and extract the items that satisfy a given condition. It also covers combining filter with other functional tools, and compares it to coding with Pythonic tools like list comprehensions and generator expressions.

We cover several other articles and projects from the Python community including, Excel, Python, and the future of data science, a Bayesian analysis of Lego prices in Python, why can’t comments appear after a line continuation character, teaching Python on the Raspberry Pi400 at the public library, a cross-platform editor designed for writing novels built with Python and Qt, and a text user interface with rich as the renderer.

Spotlight: Python vs JavaScript for Python Developers

Python and JavaScript are two of the most popular programming languages in the world. In this course, you’ll take a deep dive into the JavaScript ecosystem by comparing Python vs JavaScript. You’ll learn the jargon, language history, and best practices from a Python developer’s perspective.

Topics:

00:00:00 – Introduction
00:02:29 – Excel, Python, and the Future of Data Science
00:07:50 – Python Practice Problems: Parsing CSV Files
00:17:09 – Sponsor: Digital Ocean’s App Platform
00:17:45 – A Bayesian Analysis of Lego Prices in Python With PyMC3
00:23:02 – Why Can’t Comments Appear After a Line Continuation Character?
00:28:40 – Python’s filter(): Extract Values From Iterables
00:34:57 – Video Course Spotlight
00:36:24 – How I Teach Python on the Raspberry Pi 400 at the Public Library
00:46:23 – novelWriter: Cross-Platform Editor Designed for Writing Novels Built With Python and Qt
00:48:02 – textual: A Text User Interface With Rich as the Renderer
00:54:58 – Thanks and goodbye

Show Links:

Excel, Python, and the Future of Data Science – What’s the most widely used tool in data science? Is it pandas or NumPy? Is it the Python language itself? Not really. It’s Excel. You might argue that data scientists aren’t using Excel as their primary tool, and you might be right. But Excel enables non-technical users, like small business owners, to gain insights into their data. In this article, Anaconda CEO Peter Wang discusses his goal of making Python and PyData the “conceptual successor” to Excel.

Python Practice Problems: Parsing CSV Files – In this tutorial, you’ll prepare for future interviews by working through a set of Python practice problems that involve CSV files. You’ll work through the problems yourself and then compare your results with solutions developed by the Real Python team.

A Bayesian Analysis of Lego Prices in Python With PyMC3 – Follow along with this in-depth analysis of LEGO prices to see Bayesian analysis in action. Along the way, you’ll how pooled and unpooled linear models can be used to determine if a LEGO set is fairly priced. The article is quite technical, so experience with Bayesian statistics is recommended.

Why Can’t Comments Appear After a Line Continuation Character? – Chaining together many object methods can create long tines that break the PEP 8 79-character line length recommendation. You can use \ to break the chain of methods onto individual lines, but if you want to leave comments at the end of some of the lines, you’re out of luck. There’s another pattern, though, that solves this.

Python’s filter(): Extract Values From Iterables – In this step-by-step tutorial, you’ll learn how Python’s filter() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace filter() and make your code more Pythonic.

How I Teach Python on the Raspberry Pi 400 at the Public Library – Community-based programming courses are a great way to introduce folks to computer programming that otherwise may not have the means to do so. One of the barriers to learning to code is cost. You need a computer to program on, after all. But with the advent of tiny computers like the Raspberry Pi, computers aimed at education are more affordable than ever.

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Expanding the International Python Community With the PSF

18 juni 2021 | 61 min

The popularity of Python is continuing to grow Developers across the globe are embracing the language. How is Python being used in all of these different countries? How does an organization like the Python Software Foundation (PSF) work toward the goals in its mission statement for supporting and growing this international community? This week on the show, we have Marlene Mhangami, a PSF board member and part of the Diversity and Inclusion Work Group.

Marlene lives in Zimbabwe on the continent of Africa. She has been organizing events not only locally in Zimbabwe but across all of Africa. She is the chair of Pycon Africa and has given talks at Pycon US, Pycon UK, and Pycon India.

She has been working locally as an organizer and educator. We talk about the challenges of teaching technology and programming to a population of young people. Some of these students don’t have access to computers.

She is also currently pursuing a computer science degree with the University of London. Along with her studies, she is also interning with NVidia. She is working with them on the RAPIDS project with a focus on the cuDF library.

Spotlight: Introduction to Sorting Algorithms in Python

In this course, you’ll learn all about five different sorting algorithms in Python from both a theoretical and a practical standpoint. You’ll also learn several related and important concepts, including Big O notation and recursion.

Topics:

00:00:00 – Introduction
00:02:05 – Connecting during PyCon 2021
00:03:08 – Roles with the Python Software Foundation (PSF)
00:05:39 – Python in Africa
00:10:31 – School overseas and return to Zimbabwe to build a Python community
00:13:47 – Teaching technology and Python to students who don’t have computers
00:22:14 – Sponsor: Digital Ocean’s App Platform
00:22:50 – Work with the PSF and building geographic diversity
00:27:49 – PSF work groups
00:32:10 – Organizing PyCon Africa 2019 and bringing a continent of communities together
00:35:51 – How is Python being used in Africa?
00:38:05 – Video Course Spotlight
00:39:24 – Working with NVidia RAPIDS and cuDF
00:43:57 – What are you excited about in the world of Python?
00:46:12 – What do you want to learn next?
00:52:45 – What is something you thought you knew about Python but were wrong about it?
00:59:41 – Thanks and goodbye

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Detecting Deforestation With Python & Using GraphQL With Django and Vue

11 juni 2021 | 61 min

Are you looking for an in-depth data science project to practice your skills on? Perhaps you would like to add new tools to your Python web development projects instead? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares an article about how to go about detecting deforestation from satellite images. He covers how a data science team built a machine learning (ML) solution to do just that, using FastAI for the modeling and Streamlit to create a dashboard.

We also discuss a Real Python article about building a blog using Django, Vue.js, and GraphQL. GraphQL is a great tool to enhance your API to make it more flexible. The step by step project walks you through turning your Django blog data models into a GraphQL API.

We cover several other articles and projects from the Python community including, the tools and tech used to run a one-woman hardware company, visualizing data in Python using plt.scatter(), why the sad face when using Black, how to iterate over dataframe rows (and should you?), pipx is now a PyPA member project, and real-time lossless audio compression in Python with pyFLAC.

Spotlight: Explore Your Dataset With Pandas

In this step-by-step course, you’ll learn how to start exploring a dataset with Pandas and Python. You’ll learn how to access specific rows and columns to answer questions about your data. You’ll also see how to handle missing values and prepare to visualize your dataset in a Jupyter Notebook.

Topics:

00:00:00 – Introduction
00:02:11 – Build a Blog Using Django, Vue, and GraphQL
00:10:06 – Detecting Deforestation From Satellite Images
00:16:35 – Sponsor: Digital Ocean’s App Platform
00:17:11 – The Tools and Tech I Use to Run a One-Woman Hardware Company
00:29:13 – Visualizing Data in Python Using plt.scatter()
00:34:24 – Why the Sad Face?
00:40:20 – Video Course Spotlight
00:41:26 – How to Iterate Over DataFrame Rows (And Should You?)
00:48:31 – pyFLAC: Real-Time Lossless Audio Compression in Python
00:53:47 – pipx: Install and Run Python Applications in Isolated Environments
00:59:59 – Thanks and goodbye

Show Links:

Build a Blog Using Django, Vue, and GraphQL – In this step-by-step project, you’ll build a blog from the ground up. You’ll turn your Django blog data models into a GraphQL API and consume it in a Vue application for users to read. You’ll end up with an admin site and a user-facing site you can continue to refine for your own use.

Detecting Deforestation From Satellite Images – How would you go about detecting deforestation — a contributor to climate change — from satellite images? In this article, you’ll learn how one team built a machine learning (ML) solution to do just that, using FastAI for the modeling and Streamlit to create a dashboard. The article discusses methodology and results, and is a great read about building an ML solution. The project code is available on GitHub.

The Tools and Tech I Use to Run a One-Woman Hardware Company – Winterbloom makes open-source, boutique synthesizers. There’s a lot that goes into running a hardware company. Someone has to design the hardware, code the firmware, write the documentation, not to mention administrate the company. Winterbloom does all of this with just one engineer — Stargirl Flowers. Learn what tools and tech Stargirl uses to run her company, and how Python fits into the big picture in more ways than one.

Visualizing Data in Python Using plt.scatter() – In this tutorial, you’ll learn how to create scatter plots in Python, which are a key part of many data visualization applications. You’ll get an introduction to plt.scatter(), a versatile function in the Matplotlib module for creating scatter plots.

Why the Sad Face? – The Black autoformatter adopts some conventions that might surprise you the first time you use it. One of those conventions — the “sadface dedent” — moves closing parentheses in function signatures and other block headers to their own lines. This creates a line containing nothing but “):”, which looks like a sad face emoji. Łukasz Langa, Black’s creator, explains why Black does this.

How to Iterate Over DataFrame Rows (And Should You?) – How to iterate over pandas DataFrame rows is one of the top voted questions with the pandas tag on Stack Overflow. That question is also the most copied answer with a code block on the entire site. Clearly, lots of people want to iterate over the rows in a DataFrame. But should you do this, or are there better options?

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Create Web Applications Using Only Python With Anvil

4 juni 2021 | 71 min

What if you could create an application and deploy it to the web with just Python? Wouldn’t it be nice to skip the additional full-stack development steps of learning three different languages in addition to Python? That’s the idea behind Anvil. This week on the show, we have Meredydd Luff, co-founder of Anvil.

We talk about the history of Anvil and how the founders wanted to simplify web app creation. We discuss their choice to make the project open source and how it benefited the project’s development. We also cover creating a portfolio of projects and things that employers look for in the hiring process.

Course Spotlight: Python’s map() Function: Transforming Iterables

In this step-by-step course, you’ll learn how Python’s map() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace map() in a Pythonic and efficient way.

Topics:

00:00:00 – Introduction
00:01:33 – What is Anvil?
00:03:16 – Why did you choose Python for Anvil?
00:06:47 – The three general groups of developers
00:17:04 – Sponsor: Digital Ocean’s App Platform
00:17:40 – What Python libraries are available to developers?
00:19:48 – Working with PDFs and files
00:22:17 – Free accounts scaling to business accounts and self hosting
00:24:32 – Modularity of code and user management
00:31:12 – High school students creating projects and starting programming
00:35:33 – Video Course Spotlight
00:36:45 – What is server code vs client code?
00:41:35 – Designing the UI and responsive web design
00:48:10 – Working with APIs and modularity of code
00:49:47 – Building a portfolio of projects as a employment strategy
00:52:29 – If you were to start this project over from scratch, what would you change?
00:55:01 – What were the benefits to Anvil of making it open source?
00:57:10 – What has been your biggest challenge?
01:01:16 – Anvil is hiring
01:05:18 – What are you excited about in the world of Python?
01:07:35 – What do you want to learn next?
01:09:54 – Thanks and goodbye

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Selecting the Ideal Data Structure & Unravelling Python's "pass" and "with"

28 maj 2021 | 49 min

Scaling Data Science and Machine Learning Infrastructure Like Netflix

21 maj 2021 | 59 min

Would you move your data science project from a laptop to the cloud? Would you also like to have snapshots of your project saved along the way so that you can go back in time or share the state of your project with another team member? This week on the show, we have Savin Goyal from Netflix. Savin is the technical lead for machine learning infrastructure at Netflix. He joins us to talk about Metaflow, an open-source tool to simplify building, managing, and scaling data science projects.

Metaflow addresses the needs of the numerous data scientists who work at Netflix. Machine learning is key strength for the streaming service. They tried several existing tools to scale their own internal infrastructure and after this experimentation developed Metaflow.

We talk about the history of the project and how someone could get started with the open-source version. Savin also contrasts the cost of infrastructure as compared to data scientists and the cost of their time.

Course Spotlight: Simplify Python GUI Development With PySimpleGUI

In this step-by-step course, you’ll learn how to create a cross-platform graphical user interface (GUI) using Python and PySimpleGUI. A graphical user interface is an application that has buttons, windows, and lots of other elements that the user can use to interact with your application.

Topics:

00:00:00 – Introduction
00:01:53 – What is Metaflow?
00:04:15 – Savin’s background in data science and infrastructure
00:06:06 – Democratization of infrastructure and iteration of tools
00:10:34 – What information is saved about the infrastructure requirements for a project?
00:17:17 – How are the requirements annotated?
00:18:39 – Sponsor: Digital Ocean’s App Platform
00:19:15 – How do project snapshots work?
00:29:33 – Cost of infrastructure vs data scientists
00:32:28 – Working with data at Netflix scale
00:37:55 – Video Course Spotlight
00:39:06 – Getting an organization to use new tools and then making open-source
00:49:51 – Documentation of Metaflow and getting started on solving infrastructure problems
00:53:57 – What made you interested in working on infrastructure tools?
00:55:13 – What is something you are excited about in the world of Python?
00:56:18 – What do you want to learn next?
00:58:14 – Thanks and goodbye

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Building a Platform Game With Arcade and Covering Python News Monthly

14 maj 2021 | 55 min

Did you know the Python Software Foundation is hiring! With the recent support of three Visionary Sponsors, the PSF has been able to open positions for a developer-in-residence and a Python packaging project manager. Real Python now has a monthly Python news article. Frequent guest of the show, David Amos compiles and summarizes the biggest Python news from the past month.

This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects. We discuss David’s news article from the last month. We also discuss previous guest Jon Fincher’s new step-by-step tutorial about creating a platform game with the arcade framework.

We cover several other articles and projects from the Python community including, how to use ipywidgets to make your Jupyter notebook interactive, the hidden performance overhead of Python C extensions, adding else to for loops, film simulations from scratch using Python, a gradual programming language named Hedy, and a Python raytracer.

Spotlight: CPython Internals: Your Guide to the Python 3 Interpreter

Unlock the Inner Workings of the Python Language, Compile the Python Interpreter From Source Code, And Participate in the Development of CPython

Topics:

00:00:00 – Introduction
00:02:04 – How to Use ipywidgets to Make Your Jupyter Notebook Interactive
00:06:07 – Build a Platform Game in Python With arcade
00:12:35 – Sponsor: Digital Ocean’s App Platform
00:13:11 – The Hidden Performance Overhead of Python C Extensions
00:21:17 – For-Else: A Weird but Useful Feature in Python
00:25:42 – Python News: What’s New From April 2021?
00:39:43 – Spotlight: CPython Internals Now in Paperback!
00:41:15 – Film Simulations From Scratch Using Python
00:47:44 – hedy: Hedy Is a Gradual Programming Language, Which Increases in Syntactic Elements Level by Level
00:50:27 – Python-Raytracer: A Basic Ray Tracer That Exploits NumPy Arrays and Functions to Work Fast
00:53:31 – Thanks and goodbye

Show Links:

How to Use ipywidgets to Make Your Jupyter Notebook Interactive – Jupyter Notebooks are great for exploratory data analysis. They’re also a good way to share results and analysis with other people, who can alter the notebook to further explore the data themselves. But there are some limitations to notebook interactivity. That’s where ipywidgets comes in! In this tutorial you’ll learn how to create widgets like check boxes, drop-down menus, sliders, and how to handle events like button clicks.

Build a Platform Game in Python With arcade – Building games can be a fun way to learn new Python concepts and practice techniques you’ve already learned. Plus, they make for great projects to share! This step-by-step tutorial shows you how to build a platform game using the arcade library. You’ll learn techniques for designing levels, sourcing assets, and implementing advanced features

The Hidden Performance Overhead of Python C Extensions – It’s no secret that Python is slower than compiled languages like C, C++, and Rust. If you need a performance boost, you can write compiled Python C extensions. But there are some hidden performance costs that you should be aware of if you decide to do this. This article explains two ways that Python C extensions can actually be slower than pure Python and discusses some solutions and work around for them.

For-Else: A Weird but Useful Feature in Python – Python for loops have an unusual feature: they support an else block that only executes if there is no break in the loop. The pattern isn’t used very often with the argument against it being that it is a bit weird and potentially difficult to understand. But there may be times when for/else makes sense. This article presents three situations where for/else is useful and argues that, in these situations, the pattern makes the code more readable.

Python News: What’s New From April 2021? – April 2021 was an eventful month in the world of Python. In this article, you’ll get up to speed on everything that happened in the past month, including new sponsorships for the PSF, changes to Python error messages, and a community-led discussion over the future of type annotations.

Film Simulations From Scratch Using Python – In analog photography, you can achieve different “looks” for your photographs by selecting different kinds of film to shoot with. Digital camera manufacturers often include different presets to simulate different kinds of film. In this article, you’ll learn how to simulate different films on your own images using color lookup tables, or CLUTs, using NumPy and the Pillow image library.

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Organizing and Restructuring DjangoCon Europe 2021

7 maj 2021 | 54 min

Podcast Rewind With Guest Highlights for 2020-2021

30 april 2021 | 45 min

Taking the Next Step in Python Game Development

23 april 2021 | 76 min

OrderedDict vs dict and Object Oriented Programming in Python vs Java

16 april 2021 | 51 min

Are you looking for a bit of order when working with dictionaries in Python? Are you aware that the Python dict has changed over the last several versions and now keeps items in order? Could you learn more about object-oriented programming in Python by comparing it to another language? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David covers a Real Python article about the differences between the OrderedDict versus a standard dictionary. We discuss a recent video course about how object-oriented programming in Python compares to Java.

We cover several other articles and projects from the Python community including, what is Werkzeug, building a full-text search engine in 150 lines of Python code, loading SQL data into pandas without running out of memory, how to beat the Berlin rental market with a Python script, replacing print with ice cream, and the new version of CircuitPython and Mu.

Course Spotlight: Python vs Java: Object Oriented Programming

In this step-by-step course, you’ll learn about the practical differences in Python vs Java for object-oriented programming. By the end, you’ll be able to apply your knowledge to Python, understand how to reinterpret your understanding of Java objects to Python, and use objects in a Pythonic way.

Topics:

00:00:00 – Introduction
00:02:07 – OrderedDict vs dict in Python: The Right Tool for the Job
00:09:30 – What is Werkzeug?
00:13:08 – Sponsor: Digital Ocean
00:13:47 – Building a Full-Text Search Engine in 150 Lines of Python Code
00:19:53 – Loading SQL Data Into Pandas Without Running Out of Memory
00:25:35 – How I Beat the Berlin Rental Market With a Python Script
00:34:36 – Python vs Java: Object Oriented Programming
00:38:42 – Video Course Spotlight
00:40:11 – Upcoming video courses
00:41:17 – icecream: Never Use print() to Debug Again
00:45:28 – CircuitPython 6.2.0 Released
00:49:40 – Thanks and goodbye

Show Links:

OrderedDict vs dict in Python: The Right Tool for the Job – In this step-by-step tutorial, you’ll learn what Python’s OrderedDict is and how to use it in your code. You’ll also learn about the main differences between regular dictionaries and ordered dictionaries.

What is Werkzeug? – Have you ever noticed that when you install Flask a dependency called Werkzeug is also installed? Werkzeug provides a set of utilities for building a WSGI interface in Python, which is an important part of any web application. This article will take you on a deep dive of Werkzeug and show you exactly how it works so you can have a deeper understanding of Flask applications.

Building a Full-Text Search Engine in 150 Lines of Python Code – Go from data preparation to search engine in just a few lines of Python.

Loading SQL Data Into Pandas Without Running Out of Memory – If you need to load a bunch of SQL query results into a Pandas DataFrame, then you might run into a problem if there are enough rows in the SQL query’s results: it won’t fit in RAM. Panda’s read_sql() function has a batching option, but it loads all of the data into memory, too. So, how do you handle larger-than-memory queries with Pandas? This article will show you how!

How I Beat the Berlin Rental Market With a Python Script – Learn how one Python developer used a Python script to analyze the housing market in Berlin and predict when a property would be sold or when the price would be decreased. While the article doesn’t include a lot of technical details, it’s a great case study of how Python, its rich ecosystem, and a little creativity can turn solving a banal problem — like searching for a new house in a crowded market — into something fun and intellectually rewarding!

Python vs Java: Object Oriented Programming – You may have heard that “everything is an object in Python.” But what does that mean for doing object oriented programming? If you’re coming to Python with a Java background, you’ll want to check out this course to learn how to reinterpret your understanding of Java objects to Python, and use objects in a Pythonic way.

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Getting Started With Refactoring Your Python Code

9 april 2021 | 58 min

Building a Neural Network and How to Write Tests in Python

2 april 2021 | 46 min

Improving the Learning Experience on Real Python

26 mars 2021 | 70 min

If you haven’t visited the website lately, then you’re missing out on the updates to realpython.com! The site features a completely refreshed layout with multiple sections to help you take advantage of even more great educational Python content. This week on the show, we have Dan Bader, the person behind Real Python, and all these architectural changes.

Among the features changed are a new bookmarking system, a section to keep track of what you’ve been learning lately, and a much more advanced way to search the site. A new tile system makes it easier to explore learning paths, quizzes, office hours, and other sections of the site.

Dan shares details about the website technology stack and why he started using Python for the core content management system. He also talks about the struggle of being the sole maintainer and feature architect.

Spotlight: Python Basics: A Practical Introduction to Python 3

Go from beginner to intermediate in Python with this complete curriculum, up-to-date for Python 3.9. Python Basics includes exercises, interactive quizzes, and sample projects, so you’ll always know what to focus on next in order to build a strong Python foundation. Paperback copies are available now.

Topics:

00:00:00 – Introduction
00:01:44 – Welcome to the show Dan!
00:03:12 – What updates are happening on the website?
00:04:40 – The “Continue Learning” section
00:16:02 – Updating how search works
00:21:41 – Sponsor: PyCharm
00:22:22 – Implementing bookmarking of articles
00:26:39 – A development team of one
00:28:16 – Surfacing features with explore
00:35:39 – How to take advantage of the new features?
00:39:26 – Spotlight: Python Basics in paper back available now!
00:41:12 – What did it take to implement progress system?
00:47:27 – Closed captions and transcripts complete across all courses
00:48:47 – Python Basics and CPython Internals books
00:53:38 – What are you excited about in the world of Python?
00:58:40 – What do you want to learn next?
01:02:21 – What is something you thought you knew about Real Python but were wrong about it?
01:06:00 – Request for reviews, feedback, and questions
01:09:06 – Thanks and goodbye

Show Links:

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Connecting to MongoDB and Updates on the Python Packaging Landscape

19 mars 2021 | 45 min

Navigating Options for Deploying Your Python Application

12 mars 2021 | 63 min

Consuming APIs With Python and Building Microservices With gRPC

5 mars 2021 | 54 min

Have you wanted to get your Python code to consume data from web-based APIs? Maybe you’ve dabbled with the requests package, but you don’t know what steps to take next. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We discuss an article titled, “Python’s APIs: A Winning Combo for Reading Public Data”. David shares another Real Python article about creating microservices using Google Remote Procedure Calls (gRPC).

We also cover several other articles and projects from the Python community including, making a difficult data analysis question easy with pandas, efficiently cleaning text with pandas, the tricky bits of Python concurrency, building rich terminal dashboards, making better assertions for Python tests, and building and managing real-life data science projects with metaflow.

Course Spotlight: Making HTTP Requests With Python

The “requests” library is the de facto standard for making HTTP requests in Python. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. This course shows you how to work effectively with “requests”, from start to finish.

Topics:

00:00:00 – Introduction
00:01:46 – Python Microservices With gRPC
00:07:49 – Python’s APIs: A Winning Combo for Reading Public Data
00:15:07 – Making a Difficult Data Analysis Question Easy With Pandas
00:21:07 – Efficiently Cleaning Text With Pandas
00:34:20 – Video Course Spotlight
00:35:27 – Python Concurrency: The Tricky Bits
00:41:49 – Building Rich Terminal Dashboards
00:45:08 – python-precisely: Better Assertions for Python Tests
00:48:45 – metaflow: Build and Manage Real-Life Data Science Projects With Ease
00:52:35 – Thanks and goodbye

Show Links:

Python Microservices With gRPC – Learn how to build a robust and developer-friendly Python microservices infrastructure using gRPC and Kubernetes. You’ll also explore advanced topics such as interceptors and integration testing.

Python’s APIs: A Winning Combo for Reading Public Data – Learn what APIs are and how to consume them using Python. You’ll also learn some core concepts for working with APIs, such as status codes, HTTP methods, using the requests library, and much more.

Making a Difficult Data Analysis Question Easy With Pandas – A great strategy to use when faced with a tricky data analysis problem is to reshape the dataset into a format that turns it into an easy problem. In this article, you’ll look at an example involving a simple calculation and extensive reshaping in pandas.

Efficiently Cleaning Text With Pandas – In this article, you’ll see some examples of cleaning text fields in a large data file and learn several strategies for efficiently cleaning unstructured text fields using Python and pandas.

Python Concurrency: The Tricky Bits – An exploration of threads, processes, and coroutines in Python, with interesting examples that illuminate the differences between each.

Building Rich Terminal Dashboards – Learn how to use the Rich CLI library’s new terminal dashboard feature.

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The Challenges of Developing Into a Python Professional

26 februari 2021 | 52 min

Stochastic Gradient Descent and Deploying Your Python Scripts on the Web

19 februari 2021 | 61 min

Do you know the initial steps to get your Python script hosted on the web? You may have built something with Flask, but how would you stand it up so that you can share it with others? This week on the show, we have the previous guest Martin Breuss back on the show. Martin shares his recent article titled, “Python Web Applications: Deploy Your Script as a Flask App”. David Amos also returns, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares a recent mathematical Real Python article about the stochastic gradient descent algorithm with Python. Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find ideal model parameters.

We also cover several other articles and projects from the Python community including, property-based testing with hypothesis, Python’s tug of war between beginner-friendly features and support for advanced users, how Python integers work, the steering council accepts PEP 634, a magical full-stack framework for Django named django-unicorn, and a visual programming environment called Math Inspector.

Course Spotlight: Simulating Real-World Processes in Python With SimPy

In this step-by-step course, you’ll see how you can use the SimPy package to model real-world processes with a high potential for congestion. You’ll create an algorithm to approximate a complex system, and then you’ll design and run a simulation of that system in Python.

Topics:

00:00:00 – Introduction
00:02:44 – Property-Based Testing With hypothesis, and Associated Use Cases
00:09:55 – Python’s Tug of War Between Beginner-Friendly Features and Support for Advanced Users
00:18:50 – Sponsor: Scout APM
00:19:54 – How Python Integers Work
00:26:53 – Python Steering Council Accepts PEP 634
00:32:48 – Stochastic Gradient Descent Algorithm With Python and NumPy
00:38:36 – Video Course Spotlight
00:39:39 – Martin Breuss - Followup about Stay at Home Mentorship Program
00:42:13 – Python Web Applications: Deploy Your Script as a Flask App
00:52:25 – django-unicorn: A Magical Full-Stack Framework for Django
00:55:15 – Math Inspector: A Visual Programming Environment for Scientific Computing With NumPy and SciPy
01:00:21 – Thanks and goodbye

Show Links:

Property-Based Testing With hypothesis, and Associated Use Cases – Testing software is hard. Property-based testing can help you create more effective tests. Learn how to do property-based testing with the hypothesis framework by looking at some real-world use cases.

Python’s Tug of War Between Beginner-Friendly Features and Support for Advanced Users – Python has made some big improvements to tracebacks in recent versions. See how tracebacks have evolved over the last couple of major releases and where there’s still some work left to be done. Check out the discussion on Hacker News.

How Python Integers Work – Python’s integer datatype is pretty different from most other languages because they allow arbitrary precision. Learn how integers work under the hood in this in-depth article.

Python Steering Council Accepts PEP 634 – Pattern matching, which adds a kind of switch-case statement to Python, has been accepted.

Stochastic Gradient Descent Algorithm With Python and NumPy – Learn what the stochastic gradient descent algorithm is, how it works, and how to implement it with Python and NumPy.

Python Web Applications: Deploy Your Script as a Flask App – In this tutorial, you’ll learn how to go from a local Python script to a fully deployed Flask web application that you can share with the world.

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Unraveling Python's Syntax to Its Core With Brett Cannon

12 februari 2021 | 93 min

C for Python Developers and Data Visualization With Dash

5 februari 2021 | 45 min

Processing Images in Python With Pillow

29 januari 2021 | 47 min

Creating an Interactive Online Python Conference for PyCascades 2021

22 januari 2021 | 64 min

Deep Reinforcement Learning in a Notebook With Jupylet + Gaming and Synthesis

15 januari 2021 | 62 min

What Is Data Engineering and Researching 10 Million Jupyter Notebooks

8 januari 2021 | 56 min

Are you familiar with the role data engineers play in the modern landscape of data science and Python? Data engineering is a sub-discipline that focuses on the transportation, transformation, and storage of data. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article on data engineering, we talk about a project where researchers downloaded 10 million Jupyter notebooks from Github to gather insights about the current state of data science technology.

We also discuss an article about validating data in Python with the package Cerberus. And this led us to a conversation about a set of coding challenges from Advent of Code.

We also cover several other articles and projects from the Python community including, building my own chess engine, the visual guide to NumPy, a free and open-source alternative to SAP, a library for working with STL files and 3D objects, and is Python really a bottleneck?

Course Spotlight: Building With Django REST Framework

This course will get you ready to build with Django REST Framework. The Django REST framework (DRF) is a toolkit built on top of the Django web framework that reduces the amount of code you need to write to create REST interfaces.

Topics:

00:00:00 – Introduction
00:01:51 – What Is Data Engineering and Is It Right for You?
00:12:07 – Building My Own Chess Engine
00:17:52 – We Downloaded 10,000,000 Jupyter Notebooks From Github: This Is What We Learned
00:28:12 – Video Course Spotlight
00:29:20 – Is Python Really a Bottleneck?
00:34:01 – Validating Data in Python With Cerberus
00:39:04 – NumPy Illustrated: The Visual Guide to NumPy
00:42:54 – erpnext: Free and Open Source Alternative to SAP
00:48:49 – numpy-stl: Library for Working With STL Files and 3D Objects
00:54:54 – Thanks and goodbye

Show Links:

What Is Data Engineering and Is It Right for You? — In this article, you’ll get an overview of the discipline of data engineering. You’ll learn what is and isn’t part of a data engineer’s job, who data engineers work with, and why data engineers play a crucial role in many industries.

Building My Own Chess Engine — Writing your own chess engine is a great way to explore computational complexity and combinatorial aspects of programming. Not to mention it’s pretty fun! Follow along with this reflection on how one coder created his own Chess engine from scratch.

We Downloaded 10,000,000 Jupyter Notebooks From Github: This Is What We Learned — The JetBrains Datalore team downloaded ten million Jupyter Notebooks and analyzed them to determine things like which languages were the most popular, what kinds of content are in notebook cells, and how consistently notebooks can be reproduced. It’s a fascinating look into trends in data science technology!

Is Python Really a Bottleneck? — Python is slow. From one perspective, that is. But what are the true bottlenecks in the data engineering/data processing space, and how does Python compare to other technologies when those factors are considered?

Validating Data in Python With Cerberus — Thanks to an Advent of Code challenge, author Hector Castro was exposed to the Cerberus Python package for data validation. Get a quick introduction to Cerberus and see Hector’s solution to an Advent of Code challenge in this quick-yet-informative read.

NumPy Illustrated: The Visual Guide to NumPy — This illustrated guide to NumPy is a great way to learn NumPy or brush up on the package. Full of great visual aides, this tutorial covers all the basics and more!

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2020 Real Python Articles in Review

25 december 2020 | 48 min

How Python Manages Memory and Creating Arrays With np.linspace

18 december 2020 | 58 min

Have you wondered how Python manages memory? How are your variables stored in memory, and when do they get deleted? This week on the show, David Amos is here, and he has brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article on Python memory management, we also talk about another article about creating even and non-even spaced arrays in Python with np.linspace.

We share an article titled “The Unholy Way of Using Virtual Environments”. This leads to a discussion on how to structure the directories around a virtual environment.

We also cover several other articles and projects from the Python community including, storing a list in an int, why you should use an ORM (Object Relational Manager), unraveling not in Python, an open-source Python fuzzer, and Python static website generators.

Course Spotlight: How Python Manages Memory

Get ready for a deep dive into the internals of Python to understand how it handles memory management. By the end of this course, you’ll know more about low-level computing, understand how Python abstracts lower-level operations, and find out about Python’s internal memory management algorithms.

Topics:

00:00:00 – Introduction
00:01:38 – np.linspace(): Create Evenly or Non-Evenly Spaced Arrays
00:05:20 – The Unholy Way of Using Virtual Environments
00:17:33 – Storing a list in an int
00:24:48 – Why Should You Use an ORM (Object Relational Mapper)?
00:32:30 – Video Course Spotlight
00:33:24 – Unravelling not in Python
00:40:03 – How Python Manages Memory
00:45:28 – Follow-up from Jupylet
00:46:51 – Announcing the Atheris Python Fuzzer
00:50:42 – nikola: Static Website and Blog Generator

Show Links:

np.linspace(): Create Evenly or Non-Evenly Spaced Arrays – In this tutorial, you’ll learn how to use NumPy’s np.linspace() effectively to create an evenly or non-evenly spaced range of numbers. You’ll explore several practical examples of the function’s many uses in numerical applications.

The Unholy Way of Using Virtual Environments – If you’ve used virtual environments before, you may have created a venv/ folder inside the root directory of your project. This is standard, but has some downsides. Have you every thought about reversing this and putting your project inside your venv/ folder?

Storing a list in an int – For a fun exercise, learn how you can leverage Python’s unlimited integer precision to encode and store lists of any size as a single integer. Because, why not?

Why Should You Use an ORM (Object Relational Mapper)? – Budding web developers learning Model-View-Controller frameworks are taught that they should use an Object Relational Mapper (ORM) to interface with their databases. But the “why” is often brushed aside or omitted entirely, leaving a fledgling programmer with burning questions like “What are ORMs, anyway?” and “What problems do they solve?”

Unravelling not in Python – In the next blog post in his series about Python’s syntactic sugar, Brett Cannon tackles what would seem to be a very simple bit of syntax, but which actually requires diving into multiple layers to fully implement: not.

How Python Manages Memory – Get ready for a deep dive into the internals of Python to understand how it handles memory management. By the end of this course, you’ll know more about low-level computing, understand how Python abstracts lower-level operations, and find out about Python’s internal memory management algorithms.

Announcing the Atheris Python Fuzzer – Get started with fuzzing, a technique for automatically finding bugs in Python code by repeatedly trying various inputs to your program, using Google’s newly open-sourced Python fuzzer called Atheris.

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Generators, Coroutines, and Learning Python Through Exercises

11 december 2020 | 66 min

Have you started to use generators in Python? Are you unsure why you would even use one over a regular function? How do you use the special “send” method and the “yield from” syntax? This week on the show, we have Reuven Lerner to talk about his PyCon Africa 2020 talk titled “Generators, coroutines, and nanoservices.”

Reuven helps developers around the world become more fluent in Python. We talk about some of his teaching techniques and also how he continues to learn. Reuven is a believer in the continued practice of Python through exercises. We discuss his book “Python Workout” and his Weekly Python Exercise courses.

Course Spotlight: Python Generators 101

In this step-by-step course, you’ll learn about generators and yielding in Python. You’ll create generator functions and generator expressions using multiple Python yield statements. You’ll also learn how to build data pipelines that take advantage of these Pythonic tools.

Topics:

00:00:00 – Introduction
00:01:38 – Reuven’s web site: Teaching Python and Data Science around the world
00:03:39 – Training remotely and screen-time overload
00:07:03 – Info-tainment and sparking engagement
00:11:05 – PyCon Africa 2020 - Generators, Coroutines, and Nanoservices
00:13:34 – Using dis() - disassembler for Python bytecode
00:15:09 – Exceptions as signals, not exclusively for errors
00:17:39 – What makes generator functions different?
00:22:05 – Using .send() and creating a coroutine
00:23:39 – Sponsor: Scout APM
00:24:40 – Stopping the generator with .throw()
00:26:28 – Additional uses for generators
00:30:03 – Python Workout: 50 ten-minute exercises
00:34:22 – A combination of techniques and sources for continued learning
00:40:15 – Weekly Python Exercise
00:42:02 – Video Course Spotlight
00:43:08 – Bachelor degree in CS, but a Doctorate in learning sciences
00:48:26 – What are additional techniques you use to keep learning?
00:50:18 – Teaching is a great way to keep learning
00:51:28 – What areas of data science are you focusing on?
00:53:22 – The Business of Freelancing: podcast
00:56:26 – What is something you thought you knew about Python, but were wrong about?
00:59:24 – What is something you are excited about in the world of Python?
01:02:18 – Conferences and user groups
01:05:03 – Thanks and goodbye

Show Links:

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Support the podcast & join our community of Pythonistas

Looping With enumerate() and Python GUIs With PyQt

4 december 2020 | 44 min

If you’re coming to Python from a different language, you may not know about a useful tool for working with loops, Python’s built-in enumerate function. This week on the show, David Amos is here, and he has brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article covering the details of the enumerate function, we also talk about another article about constructing Python graphical user interface elements in PyQt.

David shares a couple of resources for data scientists, including an article about skills not taught in data science boot camps, and a project for creating synthetic data.

We also cover several other articles and projects from the Python community including, an update about youtubedl, hunting for malicious packages on PyPI, using Python’s bisect module, 73 examples to help you master f-strings, and game programming in Jupyter notebooks.

Course Spotlight: Formatting Python Strings

In this course, you’ll see two items to add to your Python string formatting toolkit. You’ll learn about Python’s string format method and the formatted string literal, or f-string. You’ll learn about these formatting techniques in detail and add them to your Python string formatting toolkit.

Topics:

00:00:00 – Introduction
00:01:53 – The youtube-dl Repository Has Been Restored on GitHub With Help From the Electronic Frontier Foundation
00:04:12 – Python enumerate(): Simplify Looping With Counters
00:07:24 – Hunting for Malicious Packages on PyPI
00:14:31 – Sponsor: Scout APM
00:15:31 – Using Python’s bisect module
00:19:00 – 73 Examples to Help You Master Python’s f-Strings
00:21:35 – 10 Python Skills They Don’t Teach in Bootcamp
00:27:32 – Video Course Spotlight
00:28:28 – Python and PyQt: Creating Menus, Toolbars, and Status Bars
00:33:51 – SDV: Synthetic Data Generation for Tabular, Relational, Time Series Data
00:38:19 – jupylet: Game Programming in Jupyter Notebooks
00:42:59 – Thanks and goodbye

Show Links:

The youtube-dl Repository Has Been Restored on GitHub With Help From the Electronic Frontier Foundation

Python enumerate(): Simplify Looping With Counters – Once you learn about for loops in Python, you know that using an index to access items in a sequence isn’t very Pythonic. So what do you do when you need that index value? In this tutorial, you’ll learn all about Python’s built-in enumerate(), where it’s used, and how you can emulate its behavior.

Hunting for Malicious Packages on PyPI – Jordan Wright installed every package on PyPI to look for malicious content. And he didn’t just inspect code, he actually ran the packages. Brave soul! Learn how he set-up this project and what he learned on his adventure.

Using Python’s bisect module – Python’s bisect module has tools for searching and inserting values into sorted lists. It’s one of his “batteries-included” features that often gets overlooked, but can be a great tool for optimizing certain kinds of code.

73 Examples to Help You Master Python’s f-Strings – f-Strings might be one of the most beloved features in Python 3.6+. Here are 73 examples of how to use f-strings to improve your Python code.

10 Python Skills They Don’t Teach in Bootcamp – Here are ten practical and little-known pandas tips to help you take your skills to the next level.

Python and PyQt: Creating Menus, Toolbars, and Status Bars – In this step-by-step tutorial, you’ll learn how to create, customize, and use Python menus, toolbars, and status bars for creating GUI applications using PyQt.

Projects:

Additional Links:

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Support the podcast & join our community of Pythonistas

Teaching Python and Finding Resources for Students

27 november 2020 | 50 min

Sentiment Analysis, Fourier Transforms, and More Python Data Science

20 november 2020 | 57 min

Security and Authorization in Your Python Web Applications

13 november 2020 | 52 min

The Python Modulo Operator & Managing Data With SQLite and SQLAlchemy

6 november 2020 | 53 min

Are you ready to move beyond flat files for your data in Python? Maybe you’re not sure where to start with databases and SQL. This week on the show, David Amos returns with another batch of PyCoder’s Weekly articles and projects. We cover a Real Python article about managing data with SQLite and SQLAlchemy.

David explores the intricacies of using the modulo operator (%). We also cover several other articles and projects from the Python community including, how to shoot yourself in the foot with python, exploring fractals on a cloud computer, the DMCA takedown request for youtube-dl, python for feature film, an online multiplayer text-based game framework, and a sorting algorithms visualizer.

Course Spotlight: Playing and Recording Sound in Python

In this course, you’ll learn about libraries that can be used for playing and recording sound in Python, such as PyAudio and python-sounddevice. You’ll also see code snippets for playing and recording sound files and arrays, as well as for converting between different sound file formats.

Topics:

00:00:00 – Introduction
00:01:35 – How to Shoot Yourself in the Foot With Python, Part 1
00:11:31 – Data Management With Python, SQLite, and SQLAlchemy
00:19:00 – Sponsor: Techmeme Ride Home Podcast
00:19:29 – Exploring Fractals on a Cloud Computer
00:23:28 – The youtube-dl GitHub Repo Has Received a DMCA Takedown Request From the RIAA
00:29:09 – Video Course Spotlight
00:30:12 – Python Modulo in Practice: How to Use the % Operator
00:36:29 – Python For Feature Film
00:44:01 – evennia: Online Multiplayer Text-Based Game Framework
00:47:11 – Sorting-Algorithms-Visualizer: See How Sorting Algorithm Works With Pygame
00:51:55 – Thanks and goodbye

Show Links:

How to Shoot Yourself in the Foot With Python, Part 1 – If you’re new to Python, you might find yourself confused by some of the situations described in this article. Learn about five mistakes you could make, why they happen, and how to fix them.

Data Management With Python, SQLite, and SQLAlchemy – In this tutorial, 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.

Exploring Fractals on a Cloud Computer – Fractals might be some of the most interesting mathematical structures to study and to visualize. Learn what fractals are and how to create beautiful fractal animations with Python.

The youtube-dl GitHub Repo Has Received a DMCA Takedown Request From the RIAA

Python Modulo in Practice: How to Use the % Operator – In this tutorial, you’ll learn about the Python modulo operator (%). You’ll look at the mathematical concepts behind the modulo operation and how the modulo operator is used with Python’s numeric types. You’ll also see ways to use the modulo operator in your own code.

Python For Feature Film – A look into how Python is used to bring your favorite movies to the big screen.

Projects:

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Going Beyond the Basic Stuff With Python and Al Sweigart

30 oktober 2020 | 87 min

Our New "Python Basics" Book & Filling the Gaps in Your Learning Path

23 oktober 2020 | 51 min

Do you have gaps in your Python learning path? If you’re like me, you may have followed a completely random route to learn Python. This week on the show, David Amos is here to talk about the release of the Real Python book, “Python Basics: A Practical Introduction to Python 3”. The book is designed not only to get beginners up to speed but also to help fill in the gaps many intermediate learners may still have.

David has been working on the book for the last two years, and we dive into all the resources that come with it. These include code challenges, quizzes, and multiple projects that are designed to help you cement your learning. We also discuss the people and processes involved in creating, reviewing, and updating the book.

Spotlight: Python Basics: A Practical Introduction to Python 3

Go from beginner to intermediate in Python with this complete curriculum, up-to-date for Python 3.9. Python Basics includes exercises, interactive quizzes, and sample projects, so you’ll always know what to focus on next in order to build a strong Python foundation.

Topics:

00:00:00 – Introduction
00:01:36 – Python Basics: A Practical Introduction to Python 3
00:02:39 – How has feedback helped the process?
00:04:17 – Who is the intended audience?
00:06:02 – Covering how to get Python installed on different platforms?
00:11:05 – What topics does the book cover?
00:14:12 – What can be previewed on Real Python?
00:15:03 – What format can you get the book in?
00:18:30 – Code challenges included!
00:21:33 – What other resources are provided to help with cementing your learning?
00:22:41 – What versions of Python are covered?
00:23:08 – How does the book fit into the Real Python learning eco-system?
00:29:35 – Spotlight: How to get a preview of the book!
00:30:38 – What has the writing process been like?
00:33:21 – What does didactic mean, in terms of reviewing materials?
00:39:23 – What were areas you were excited about updating?
00:41:29 – Were there important things you felt needed to be added?
00:45:55 – Who worked on the book?
00:47:13 – What are you excited about in the world of Python?
00:48:13 – What do you want to learn next?
00:49:40 – Thanks and goodbye

Show Links:

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Python Return Statement Best Practices and Working With the map() Function

16 oktober 2020 | 47 min

The Python return statement is such a fundamental part of writing functions. Is it possible you missed some best practices when writing your own return statements? This week on the show, David Amos returns with another batch of PyCoder’s Weekly articles and projects. We also talk functional programming again with an article on the Python map function and processing iterables without a loop.

We cover several other articles and projects from the Python community including, interactive data visualization with Pygal, everything you need to know about namedtuples, PEP 638 syntactic macros, python for kids, the new Nvidia Jetson board, and a reinforcement learning project named football.

Topics:

00:00:00 – Introduction
00:01:33 – Interactive Data Visualization in Python With Pygal
00:06:40 – Python’s map(): Processing Iterables Without a Loop
00:12:51 – Everything You Need to Know About Python’s NamedTuples
00:19:46 – PEP 638: Syntactic Macros
00:26:43 – Video Course Spotlight
00:27:57 – The Python return Statement: Usage and Best Practices
00:34:42 – Python for Kids
00:38:03 – Build a Face Recognition System With the Nvidia Jetson Nano
00:42:32 – football: Reinforcement Learning Environment
00:45:51 – Thanks and goodbye

Show Links:

Interactive Data Visualization in Python With Pygal – Pygal is an overlooked library for creating interactive plots that can be turned into SVGs with an optimal resolution for printing or displaying on webpages. Learn how it works in this introductory tutorial.

Python’s map(): Processing Iterables Without a Loop – In this step-by-step tutorial, you’ll learn how Python’s map() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace map() in a Pythonic and efficient way.

Everything You Need to Know About Python’s NamedTuples – Are you using NamedTuple in your code? If you aren’t, learn what they are and why you should consider using them in this comprehensive tutorial.

PEP 638: Syntactic Macros – This brand new PEP, which is still in draft mode, proposes adding support for syntactic macros to Python. Syntactic macros are compile-time functions that extend the language’s syntax without adding any new complexity to the language as a whole.

The Python return Statement: Usage and Best Practices – In this step-by-step tutorial, you’ll learn how to use the Python return statement when writing functions. Additionally, you’ll cover some good programming practices related to the use of return. With this knowledge, you’ll be able to write readable, robust, and maintainable functions in Python.

Python for Kids – In this ten part series, senior software engineer Kevin Thomas presents a kid-friendly comprehensive Python development tutorial utilizing a micro:bit development board. The GitHub repo contains all of the sample code as well as links to each tutorial in the series on LinkedIn. The first seven parts are published and the last three are coming soon!

Projects:

Build a Face Recognition System With the Nvidia Jetson Nano 2GB and Python – The Nvidia Jetson Nano is a single board computer similar to a Raspberry Pi. The Jetson Nano really packs a punch, however, thanks to its onboard Nvidia Maxwell GPU.

football: Reinforcement Learning Environment Where Agents Learn to Play Football

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Exploring the New Features of Python 3.9

9 oktober 2020 | 75 min

Resolving Package Dependencies With the New Version of Pip

2 oktober 2020 | 69 min

Using Pylance to Write Better Python Inside of Visual Studio Code

25 september 2020 | 50 min

Preparing for an Interview With Python Practice Problems

18 september 2020 | 47 min

What is an effective way to prepare for a Python interview? Would you like a set of problems that increase in difficulty to practice and hone your Python skills? This week on the show, we have Jim Anderson to talk about his new Real Python article, “Python Practice Problems: Get Ready for Your Next Interview.” This article provides several problems, which include skeleton code, unit tests, and solutions for you to compare your work.

David Amos also joins us this week, and he has brought another batch of PyCoder’s Weekly articles and projects from the Python community. We cover these topics: Structural Pattern Matching, Common Python Data Structures, A Tax Attorney Uses Python, Discover the Role of Python in Space Exploration, and Five Pairs of Magic Methods in Python That You Should Know.

Course Spotlight: How to Implement a Python Stack

In this course, you’ll learn how to implement a Python stack. You’ll see how to recognize when a stack is a good choice for data structures, how to decide which implementation is best for a program, and what extra considerations to make about stacks in a threading or multiprocessing environment.

Topics:

00:00:00 – Introduction
00:01:37 – Python Practice Problems
00:08:19 – Structural Pattern Matching
00:14:16 – Common Python Data Structures
00:17:49 – Video Course Spotlight
00:18:42 – A Tax Attorney Uses Python
00:23:36 – Discover the Role of Python in Space Exploration
00:26:33 – Thanks for the Reviews!
00:27:28 – 5 Pairs of Magic Methods in Python That You Should Know
00:36:56 – python-adventure: Original Colossal Caves Adventure Game
00:41:06 – clifford: Geometric Algebra for Python
00:43:45 – pippi: Computer Music With Python
00:46:24 – Thanks and Goodbye

Show Links:

Python Practice Problems: Get Ready for Your Next Interview – Are you a Python developer brushing up on your skills before an interview? If so, then this tutorial will usher you through a series of Python practice problems meant to simulate common coding test scenarios.

“Structural Pattern Matching”; for Python, Part 2 – The saga of PEP 622 continues with updates to the proposed structure of the match statement—which has some similarities to a switch statement—and a discussion on the best way to document objections to a PEP.

Common Python Data Structures (Guide) – In this tutorial, you’ll learn about Python’s data structures. You’ll look at several implementations of abstract data types and learn which implementations are best for your specific use cases.

A Tax Attorney Uses Python – See how one tax attorney uses Python to automate grueling and repetitive tasks and improve his business. While the article is non-technical, it’s always fun to see how Python is used in diverse fields.

Discover the Role of Python in Space Exploration – In this learning path from Microsoft, you’ll get an introduction to Python, and be inspired to learn, discover, and create using Python-based data science and machine learning to help generate knowledge about the world beyond Earth.

5 Pairs of Magic Methods in Python That You Should Know – Magic, or “dunder,” methods are an important part of creating custom classes in Python. Learn about some commonly used magic methods by exploring hem in pairs that are frequently used together.

python-adventure: Original Colossal Caves Adventure Game, but in Python 3

clifford: Geometric Algebra for Python

pippi: Computer Music With Python

Additional Links:

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5 Years Podcasting Python With Michael Kennedy: Growth, GIL, Async, and More

11 september 2020 | 88 min

Data Version Control in Python and Real Python Video Transcripts

4 september 2020 | 60 min

Wouldn’t it be nice to a use a form of version control for data? Something that would allow you to track and version your datasets and models. Well, that’s what the tool called DVC is designed to do. This week on the show, David Amos is here and he’s brought another batch of PyCoder’s Weekly articles and projects.

David starts with a Real Python article titled, “Data Version Control With Python and DVC”. We also cover several other articles and projects from the Python community including: where to get exposure to well-written code, delegation – composition and inheritance, good Python project ideas for high school students, never run Python in your downloads folder, and more.

We also have a special guest this week. I talk to Sadie Parker, who recently joined the Real Python team to help create and edit transcripts for all the Real Python video courses. We talk about how to take advantage of all the features this new resource provides. Sadie also discusses how she uses Python to speed up and simplify the editing process. The transcripts and closed captions are now live on the website for all new courses, and we are working through the back catalog.

Course Spotlight: Parallel Iteration With Python’s zip() Function

In this course, you’ll learn how to use the Python zip() function to solve common programming problems. You’ll learn how to traverse multiple iterables in parallel and create dictionaries with just a few lines of code.

Topics:

00:00:00 – Introduction
00:02:01 – Data Version Control With Python and DVC
00:08:57 – Where Do You Go to Get Exposure to Well-Written Code?
00:13:13 – Delegation: Composition and Inheritance in Object-Oriented Programming
00:21:46 – What Are Some Good Python Project Ideas for High School Students?
00:26:53 – Video Course Spotlight
00:27:48 – Never Run Python in Your Downloads Folder
00:35:55 – present: A Terminal-Based Presentation Tool With Colors and Effects
00:37:36 – Mini Raspberry Pi Boston Dynamics–inspired Robot
00:41:48 – Sadie Parker and Transcripts for Real Python Video Courses
00:59:26 – Thanks and Goodbye

Show Links:

Data Version Control With Python and DVC – In this tutorial, you’ll learn to use DVC, a powerful tool that solves many problems encountered in machine learning and data science. You’ll find out how data version control helps you to track your data, share development machines with your team, and create easily reproducible experiments!

Where Do You Go to Get Exposure to Well-Written Code? – Maybe someone needs to start a Python reading club…

Delegation: Composition and Inheritance in Object-Oriented Programming – Delegation is often considered one of the three pillars of object-oriented programming. Learn how to use this powerful concept in Python.

What Are Some Good Python Project Ideas for High School Students? – Honestly, some of these ideas are good for any beginning Pythonista!

Never Run Python in Your Downloads Folder – Learn about security issues that exploit how Python interacts with PATH and why you should always think twice about your current working directory.

present: A Terminal-Based Presentation Tool With Colors and Effects

Mini Raspberry Pi Boston Dynamics–inspired Robot – See how one Redditor taught themselves robotics by building a miniature version of a Boston Dynamic’s robot dog “Spot” using a Raspberry Pi, C++, and Python.

Additional Links:

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Options for Packaging Your Python Application: Wheels, Docker, and More

28 augusti 2020 | 75 min

Python Wheels and Pass by Reference in Python

21 augusti 2020 | 65 min

Have you wondered what are Python wheels? How are they used to package Python code? Does Python use pass by value or pass by reference? This week on the show, David Amos is here to help answer these questions, and he has brought another batch of PyCoder’s Weekly articles and projects.

We talk about an article called “What are Python Wheels, and Why Should You Care.” David talks about a Real Python article about pass by reference in Python. We cover several other articles and projects from the Python community including: transcribing speech to text, 4 powerful features Python is still missing, 10 awesome pythonic one-liners, and even more options for packaging your Python code.

Course Spotlight: Practical Recipes for Working With Files in Python

In this course, you’ll learn how you can work with files in Python by using built-in modules to perform practical tasks that involve groups of files, like renaming them, moving them around, archiving them, and getting their metadata.

Topics:

00:00:00 – Introduction
00:01:25 – 4 Powerful Features Python Is Still Missing
00:14:41 – What Are Python Wheels and Why Should You Care?
00:23:58 – 10 Awesome Pythonic One-Liners Explained
00:38:11 – Video Course Spotlight
00:39:14 – How to Transcribe Speech Recordings Into Text With Python
00:43:34 – Pass by Reference in Python: Background and Best Practices
00:49:54 – Options for Packaging Your Python Code: Wheels, Conda, Docker, and More
00:55:53 – PyOxidizer: A Modern Python Application Packaging and Distribution Tool
01:00:13 – Python and PDF: A Review of Existing Tools
01:04:01 – Thanks and Goodbye

Show Links:

4 Powerful Features Python Is Still Missing – Python doesn’t have true constants, nor does it implement features like tail recursion optimization that many compiled languages employ. Find out what other features Python is “missing” when compared to other languages, and why the core developers haven’t added these features to the language.

What Are Python Wheels and Why Should You Care? – In this tutorial, you’ll learn what Python wheels are and why you should care as both a developer and end user of Python packages. You’ll see how the wheel format has gained momentum over the last decade and how it has made the package installation process faster and more stable.

10 Awesome Pythonic One-Liners Explained – Some things in Python are just better on one line.

How to Transcribe Speech Recordings Into Text With Python – Learn to transcribe speech in recordings like MP3s into text with Python and AssemblyAI’s API

Pass by Reference in Python: Background and Best Practices – In this tutorial, you’ll explore the concept of passing by reference and learn how it relates to Python’s own system for handling function arguments. You’ll look at several use cases for passing by reference and learn some best practices for implementing pass-by-reference constructs in Python.

Options for Packaging Your Python Code: Wheels, Conda, Docker, and More – There’s a lot of ways to package your Python code. Find out which one is right for you.

PyOxidizer: A Modern Python Application Packaging and Distribution Tool

Python and PDF: A Review of Existing Tools – The ultimate list of PDF tools in Python.

Additional Links:

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Create Cross-Platform Python GUI Apps With BeeWare

14 augusti 2020 | 84 min

Exploring K-means Clustering and Building a Gradebook With Pandas

7 augusti 2020 | 61 min

Building PDFs in Python with ReportLab

31 juli 2020 | 53 min

Advanced Python Import Techniques and Managing Users in Django

24 juli 2020 | 52 min

Ten Years of Flask: Conversation With Creator Armin Ronacher

17 juli 2020 | 77 min

Linear Programming, PySimpleGUI, and More

10 juli 2020 | 50 min

Are you familiar with linear programming, and how it can be used to solve resource optimization problems? Would you like to free your Python code from a clunky command line and start making convenient graphical interfaces for your users? This week on the show, David Amos is back with another batch of PyCoder’s Weekly articles and projects.

David talks about a recent Real Python article about linear programming in Python. We discuss an article titled “PySimpleGUI: The Simple Way to Create a GUI With Python.” We also cover several other articles and projects from the Python community including: Python’s reduce() function, flaws in the pickle module, advanced pytest techniques, and how to trick a neural network.

Course Spotlight: Parallel Iteration With Python’s zip() Function

This course will get you up to speed with Python’s zip() function. In this course, you’ll discover the logic behind zip() and how you can use it to consistently solve common programming problems, like creating dictionaries.

Topics:

00:00:00 – Introduction
00:01:34 – Python’s reduce(): From Functional to Pythonic Style
00:07:46 – Hands-On Linear Programming: Optimization With Python
00:15:07 – Pickle’s Nine Flaws
00:22:31 – Video Course Spotlight
00:23:33 – Advanced pytest Techniques I Learned While Contributing to pandas
00:33:41 – PySimpleGUI: The Simple Way to Create a GUI With Python
00:38:20 – How to Trick a Neural Network in Python 3
00:43:31 – TextAttack: A Python Framework for Adversarial Attacks, Data Augmentation, and Model Training in NLP
00:46:09 – byob: BYOB (Build Your Own Botnet)
00:49:09 – Thanks and Goodbye

Show Links:

Python’s reduce(): From Functional to Pythonic Style – In this step-by-step tutorial, you’ll learn how Python’s reduce() works and how to use it effectively in your programs. You’ll also learn some more modern, efficient, and Pythonic ways to gently replace reduce() in your programs.

Hands-On Linear Programming: Optimization With Python – In this tutorial, you’ll learn about implementing optimization in Python with linear programming libraries. Linear programming is one of the fundamental mathematical optimization techniques. You’ll use SciPy and PuLP to solve linear programming problems.

Pickle’s Nine Flaws – “Python’s pickle module is a very convenient way to serialize and de-serialize objects. It needs no schema, and can handle arbitrary Python objects. But it has problems. This post briefly explains the problems.”

Advanced pytest Techniques I Learned While Contributing to pandas – Contributing to open-source projects is a great way to learn new techniques and level up your skills. Martin Winkel shares five advanced pytest techniques he learned while contributing to the pandas project.

PySimpleGUI: The Simple Way to Create a GUI With Python – In this step-by-step tutorial, you’ll learn how to create a cross-platform graphical user interface (GUI) using Python and PySimpleGUI. A graphical user interface is an application that has buttons, windows, and lots of other elements that the user can use to interact with your application.

How to Trick a Neural Network in Python 3 – Is that a corgi or a goldfish?

Projects:

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Thinking in Pandas: Python Data Analysis the Right Way

3 juli 2020 | 62 min

Python Regular Expressions, Views vs Copies in Pandas, and More

26 juni 2020 | 45 min

Have you wanted to learn Regular Expressions in Python, but don’t know where to start? Have you stumbled into the dreaded pink SettingWithCopyWarning in Pandas? This week on the show, we have David Amos from the Real Python team to discuss a recent two-part series on Regex in Python. We also talk about another recent article on the site about views vs copies in Pandas. David also brings a few other articles and projects from the wider Python community for us to discuss.

David searches for the latest Python news, links, and articles to produce PyCoder’s Weekly with Dan Bader. PyCoder’s Weekly is a free email newsletter for those interested in Python development. Along with the previously mentioned Real Python articles, we also discuss articles from the community about: getting machine learning to production, combining Flask and Vue, space science with Python, and the fastest way to flatten a list in Python.

Course Spotlight: Reading and Writing Files in Python

This course will get you up to speed with reading and writing files in Python. You’ll cover everything from what a file is made up of, to which libraries can help you along that way. You’ll also take a look at some basic scenarios of file usage as well as some advanced techniques.

Topics:

00:00:00 – Introduction
00:01:40 – SettingWithCopyWarning in Pandas
00:06:17 – Fastest Way to Flaten a List in Python
00:12:12 – Combining Flask and Vue
00:15:33 – Regular Expressions in Python
00:23:08 – Video Course Spotlight
00:24:00 – Space Science With Python
00:30:13 – Getting Machine Learning to Production
00:37:15 – genetic-drawing
00:39:42 – MicroscoPy
00:43:55 – Thanks and Goodbye

Show Links:

SettingWithCopyWarning in Pandas: Views vs Copies – In this tutorial, you’ll learn about views and copies in NumPy and Pandas. You’ll see why the SettingWithCopyWarning occurs in Pandas and how to properly write code that avoids it.

Fastest Way to Flatten a List in Python – Explore six different was to flatten a list of lists in Python and how their performance compares. The fastest of the six methods mentioned might surprise you!

Combining Flask and Vue – Learn about three ways to combine Flask and Vue, the pros and cons of each, and some guidelines for when to use each method.

Regular Expressions: Regexes in Python (Part 2) – In the previous tutorial in this series, you learned how to perform sophisticated pattern matching using regular expressions, or regexes, in Python. This tutorial explores more regex tools and techniques that are available in Python.

Space Science With Python – Explore and analyze the wonders and mysteries of space… with Python!

Getting Machine Learning to Production – Millions of web apps get deployed to production every day. But machine learning models aren’t web apps. And very few people are talking about deployment. Learn how tools like Streamlit can help take the edge off deploying your machine learning models.

Projects:

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Going Serverless with Python

19 juni 2020 | 55 min

PDFs in Python and Projects on the Raspberry Pi

12 juni 2020 | 45 min

Have you wanted to work with PDF files in Python? Maybe you want to extract text, merge and concatenate files, or even create PDFs from scratch. Are you interested in building hardware projects using a Raspberry Pi? This week on the show we have David Amos from the Real Python team to discuss his recent article on working with PDFs. David also brings a few other articles from the wider Python community for us to discuss.

David searches for the latest Python news, links, and articles to produce PyCoder’s Weekly with Dan Bader. PyCoder’s Weekly is a free email newsletter for those interested in Python development. Along with David’s article on PDFs, we discuss another recent Real Python article about building physical projects with the Raspberry Pi. We also discuss articles from the community about: the PEPs of Python 3.9, why you should stop using datetime.now, Python dependency tools, and several ways to pass code to Python from the terminal.

Course Spotlight: Cool New Features in Python 3.8

This course will get you up to speed with the new features of the latest release of Python. You’ll learn about using assignment expressions, how to enforce postional-only arguments, more precise type hints, and using f-strings for simpler debugging. It’s a worthy investment of your time to understand what the most recent release of Python provides before moving on to the next version this fall.

Topics:

00:00:00 – Introduction
00:02:06 – Ways to Pass Code to Python From the Terminal
00:05:54 – The PEPs of Python 3.9
00:10:54 – Creating and Modifying PDF Files in Python
00:18:51 – Video Course Spotlight
00:19:56 – An Overview of Python Dependency Tools
00:26:55 – Stop Using datetime.now
00:31:44 – Build Physical Projects With Python on the Raspberry Pi
00:38:18 – What are you excited about in the world of Python?
00:42:29 – What do you want to learn next in Python?
00:44:31 – Thanks and Good Bye

Topic Links:

PyCoder’s Weekly

The Many Ways to Pass Code to Python From the Terminal – You might know about pointing Python to a file path, or using -m to execute a module. But did you know that Python can execute a directory? Or a .zip file?

The PEPs of Python 3.9 – The first Python 3.9 beta release is upon us! Learn what to expect in the final October release by taking a tour of the Python Enhancement Proposals (PEPs) that were accepted for Python 3.9.

Creating and Modifying PDF Files in Python – Explore the different ways of creating and modifying PDF files in Python. You’ll learn how to read and extract text, merge and concatenate files, crop and rotate pages, encrypt and decrypt files, and even create PDFs from scratch.

Overview of Python Dependency Management Tools – While pip is often considered the de facto Python package manager, the dependency management ecosystem has really grown over that last few years. Learn about the different tools available and how they fit into this ecosystem.

Stop Using datetime.now! (With Dependency Injection) – How do you test a function that relies on datetime.now() or date.today()? You could use libraries like FreezeGun or libfaketime, but not every project can afford the luxury of reaching for third-party solutions. Learn how dependency injection can help you write code that is more testable, maintainable, and practical.

Build Physical Projects With Python on the Raspberry Pi – In this tutorial, you’ll learn to use Python on the Raspberry Pi. The Raspberry Pi is one of the leading physical computing boards on the market and a great way to get started using Python to interact with the physical world.

Additional Links:

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Web Scraping in Python: Tools, Techniques, and Legality

5 juni 2020 | 50 min

Advice on Getting Started With Testing in Python

29 maj 2020 | 58 min

Python Job Hunting in a Pandemic

22 maj 2020 | 80 min

Leveling Up Your Python Literacy and Finding Python Projects to Study

15 maj 2020 | 76 min

Docker + Python for Data Science and Machine Learning

8 maj 2020 | 56 min

AsyncIO + Music, Origins of Black, and Managing Python Releases

1 maj 2020 | 87 min

Python REST APIs and The Well-Grounded Python Developer

24 april 2020 | 55 min

Exploring CircuitPython

17 april 2020 | 63 min

Have you ever wanted to explore using Python with electronics? CircuitPython is a great platform to get started with. This week we have Thea Flowers on the show. Thea has been creating several hardware projects based around CircuitPython, and she talks about getting started on the platform.

She also answers questions about how she taught herself to design and prototype printed circuit boards. Thea discusses several of her open source projects, including Nox, ConductHotline, and getting involved with CircuitPython.

Thea was the conference co-chair for PyCascades, and we talk about how someone could get involved in volunteering for conferences. We also discuss building diversity in the community.

This episode was initially recorded at an earlier date, so we asked Thea to come back for a few minutes to discuss updates on her projects and about a recent honor she received.

Topics:

00:00:00 – Introduction
00:01:25 – Thea’s programming background
00:02:45 – Working with Google Cloud Platform
00:04:10 – Flutter developer relations
00:04:52 – Learning Python
00:06:07 – Working on open source projects
00:06:33 – Nox - Automated Python testing
00:07:03 – ConductHotline
00:07:38 – Contributing to CircuitPython
00:07:53 – More background on Nox and Tox
00:10:03 – Getting involved with CircuitPython
00:12:38 – MicroPython and CircuitPython
00:14:20 – Suggestions for starter board or kit
00:15:49 – What are you excited about in CircuitPython?
00:16:31 – Nina Zakharenko CircuitPython project
00:17:47 – Things you’d like to see improved in CircuitPython?
00:21:30 – Working toward consensus in open source projects?
00:25:41 – Winterbloom - Big Honking Button
00:30:25 – Creating circuit boards
00:34:32 – Winterbloom - Sol
00:38:49 – Code editor for CircuitPython
00:40:08 – Something you thought you knew about Python, but were wrong about?
00:42:14 – What are you excited about in the world of Python?
00:44:21 – Do you listen to music when coding?
00:45:29 – Being an organizer for PyCascades
00:46:53 – Getting involved and volunteering for events
00:48:16 – Ways to increase diversity
00:53:51 – Extended episode conversation
00:54:25 – Updates on the WInterbloom projects
00:55:24 – 2020 Q1 PSF Fellow Member!
00:56:32 – PyCon 2020 moves to online only
00:58:45 – How would you learn Python if starting from scratch?
01:02:10 – Thanks and ending

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Learning Python Through Errors

10 april 2020 | 70 min

Effective Python and Python at Google Scale

3 april 2020 | 43 min

Learn Python Skills While Creating Games

27 mars 2020 | 55 min

Python Decorators and Writing for Real Python

20 mars 2020 | 50 min

About the Show

6 mars 2020 | 2 min

The Real Python Podcast

A weekly Python podcast hosted by Christopher Bailey with interviews, coding tips, and conversation with guests from the Python community.

Om podden

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Telling Effective Stories With Your Python Visualizations

Behavior-Driven vs Test-Driven Development & Using Regex in Python

Charlie Marsh: Accelerating Python Tooling With Ruff and uv

Testing Your Python Code Base: Unit vs. Integration

Simon Willison: Using LLMs for Python Development

Principles for Considering Your Python Tooling

Building New Structures for Learning Python

PyCoder's Weekly 2024 Top Articles & Missing Gems

Exploring Modern Sentiment Analysis Approaches in Python

Good Python Programming Practices When New to the Language

marimo: Reactive Notebooks and Deployable Web Apps in Python

The Joy of Tinkering & Python Free-Threading Performance

Maintaining the Foundations of Python & Cautionary Tales

New PEPs: Template Strings & External Wheel Hosting

PySheets: Spreadsheets in the Browser Using PyScript

Python Getting Faster and Leaner & Ideas for Django Projects

Narwhals: Expanding DataFrame Compatibility Between Libraries

Exploring the New Features of Python 3.13

Using Virtual Environments in Docker & Comparing Python Dev Tools

Thriving as a Developer With ADHD

Configuring Git Pre-Commit Hooks & Estimating Software Projects

Astrophysics and Astronomy With Python & PyCon Africa 2024

Exploring Robotics and Python Through Electronic Projects

Packaging Data Analyses & Using pandas GroupBy

Learning Through Building the Black Python Devs Community

Using GraphQL in Django With Strawberry & Prototype Purgatory

Build Captivating Display Tables in Python With Great Tables

Constraint Programming & Exploring Python's Built-in Functions

Digging Into Graph Theory in Python With David Amos

Python Doesn't Round Numbers the Way You Might Think

Creating a Guitar Synthesizer & Generating WAV Files With Python

Python's Command-Line Utilities & Music Information Retrieval Tools

Detecting Outliers in Your Data With Python

Decomposing Software Problems & Avoiding the Trap of Clever Code

Building Python Unit Tests & Exploring a Data Visualization Gallery

Considering Accessibility & Assistive Tech as a Python Developer

Querying OpenStreetMaps via API & Lazy Evaluation in Python

Embarking on a Relaxed and Friendly Python Coding Journey

Pydantic Data Validation & Python Web Security Practices

Decoupling Systems to Get Closer to the Data

Avoiding Error Culture and Getting Help Inside Python

Leveraging Documents and Data to Create a Custom LLM Chatbot

Build a Video Game With Python Turtle & Visualize Data in Seaborn

Using Python in Bioinformatics and the Laboratory

Exploring Duck Typing in Python & Dynamics of Monkey Patching

Building a Healthy Developer Mindset While Learning Python

Automate Tasks With Python & Building a Small Search Engine

Wes McKinney on Improving the Data Stack & Composable Systems

Practical Python Decorator Uses & Avoiding datetime Pitfalls

Focusing on Data Science & Less on Engineering and Dependencies

Great Starting Points for Contributing to Open Source

Building a Python Debugger & Preparing for NumPy 2.0

Measuring Bias, Toxicity, and Truthfulness in LLMs With Python

Serializing Data With Python & Underscore Naming Conventions

Exploring Python in Excel

2023 Real Python Tutorial & Video Course Wrap-Up

PyCoder's Weekly 2023 Wrap Up

Exploring Code Reviews in Python and Automating the Process

Building a Python JSON Parser & Discussing Ideas for PEPs

Computational Thinking & Learning Python During an AI Revolution

Studying Python Software Architecture & Creating Lambda Expressions

Improving Your Git Developer Experience in Python

Guiding Scientific Python Library Development

Welcoming PyPI's Safety & Security Engineer Mike Fiedler

Building Python Best Practices and Fundamental Skills

Exploring the New Features of Python 3.12

Considering ChatGPT's Technical Review of a Programming Book

Getting Involved in Open Source & Generating QR Codes With Python

Measuring Multiple Facets of Python Performance With Scalene

Making Each Line of Code Efficient & Python In Excel

Finding the Right Coding Font for Programming in Python

Improving Classification Models With XGBoost

Common Python Stumbling Blocks & Quirky Behaviors

Exploring pandas 2.0 & Targets for Apache Arrow

Differentiating the Versions of Python & Unlocking IPython's Magic

Leveraging the Features of Your Database With Postgres and Python

Seeking Faster Text Processing & Python's .repr() vs .str()