Measuring Python Code Quality, Simplicity, and Maintainability

The Real Python Podcast

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Python

تمت الإضافة منذ قبل six عام

المحتوى المقدم من Real Python. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Real Python أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

Ready For Love with Hilary Silver

1
#80: Dating Over 50 is Actually The Best Time 11:59

منذ 13 يومًا11:59

التشغيل لاحقا

قوائم

إعجاب

احب

11:59

Most women I talk to over 40, 50, and 60 say the same thing: “I never thought I’d be here, dating at this stage of life.” And let’s be honest, midlife dating often gets a bad rap. But the truth? It can be the BEST time in your life to find real love… if you know how to approach it. In this episode, we talk about why dating now can be easier, more authentic, and more fulfilling than ever—and how shifting your perspective can change everything. Episode Highlights: Why dating now can feel easier and more authentic than ever The freedom that comes from wanting—not needing—a partner How showing up as your true self changes who you attract Why self-acceptance is magnetic How trusting yourself shapes every choice you make in love Episode Breakdown: 00:00 Dating Over 50: Why It’s the Best Time for Love 00:53 Meet Hilary 01:47 Midlife Dating Truths 02:07 Reason 1: Maturity & Clarity 03:44 Reason 2: Desire, Not Need 05:08 Reason 3: Authentic Self 07:06 Reason 4: Confidence & Magnetism 08:52 Reason 5: Self-Trust & Boundaries 10:28 Why It’s Never Too Late for Love Dating now isn’t about chasing chemistry or proving your worth—it’s about finding a true, equal partnership that adds to your already beautiful life. ✨ I’m Hilary Silver, LCSW, former psychotherapist turned master coach and founder of Ready for Love. I help high-achieving women show up in love as confidently as they do in their careers. 💡 Through this podcast, I share my WOMAN-centered, SELF-centered approach—time-tested methods that blend psychology, brain science, relationship skills, and no-BS dating advice. 🎙️ Since 2017, we’ve helped over 10,000 women with a 98% success rate, making Ready for Love the #1 program in the world for women who’ve tried everything else. 🎯 Watch my free masterclass to learn the proven 4-step Ready for Love Method: https://readyforloveinc.com/masterclass 💬 Apply for a free Love Breakthrough Call with my team: https://readyforloveinc.com/apply…

منذ 3 أعوام 1:06:32

MP3•منزل الحلقة

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

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

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

Course Spotlight: Writing Idiomatic Python

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

Topics:

00:00:00 – Introduction
00:01:54 – Reka’s tutorials on Real Python
00:02:45 – PyCon US 2022 Talk
00:06:01 – Code reviews and metrics
00:09:42 – Trying to make things more objective
00:13:24 – Sponsor: CData Software
00:14:07 – Measuring WTFs/min and magical code
00:16:39 – Pythonic, idiomatic, and clean code
00:21:17 – Sourcery.ai and refactoring
00:24:04 – Four metrics to measure
00:24:56 – Function length
00:30:05 – Cyclomatic complexity
00:36:49 – Video Course Spotlight
00:38:21 – Cognitive complexity
00:44:38 – Working memory
00:51:49 – Suggestions on how to use the metrics
00:58:34 – Generating measurements
01:01:36 – What are you excited about in the world of Python?
01:03:30 – What do you want to learn next?
01:05:31 – Thanks and goodbye

Show Links:

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

Support the podcast & join our community of Pythonistas

266 حلقات

#Real Python #Data Science #Python2 #Python3 #Learn Python #Django #Programming #Development #Dev #Open Source #Software #Python #Tech #Prog Languages #Code #Test

Measuring Python Code Quality, Simplicity, and Maintainability

The Real Python Podcast

557 subscribers

published منذ 3 أعوام

MP3•منزل الحلقة

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

Course Spotlight: Writing Idiomatic Python

Topics:

00:00:00 – Introduction
00:01:54 – Reka’s tutorials on Real Python
00:02:45 – PyCon US 2022 Talk
00:06:01 – Code reviews and metrics
00:09:42 – Trying to make things more objective
00:13:24 – Sponsor: CData Software
00:14:07 – Measuring WTFs/min and magical code
00:16:39 – Pythonic, idiomatic, and clean code
00:21:17 – Sourcery.ai and refactoring
00:24:04 – Four metrics to measure
00:24:56 – Function length
00:30:05 – Cyclomatic complexity
00:36:49 – Video Course Spotlight
00:38:21 – Cognitive complexity
00:44:38 – Working memory
00:51:49 – Suggestions on how to use the metrics
00:58:34 – Generating measurements
01:01:36 – What are you excited about in the world of Python?
01:03:30 – What do you want to learn next?
01:05:31 – Thanks and goodbye

Show Links:

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

Support the podcast & join our community of Pythonistas

266 حلقات

#Real Python #Data Science #Python2 #Python3 #Learn Python #Django #Programming #Development #Dev #Open Source #Software #Python #Tech #Prog Languages #Code #Test

كل الحلقات

The Real Python Podcast

1
Python App Hosting Choices & Documenting Python's History 43:50

منذ 5 أيام43:50

43:50

What are your options for hosting your Python application or scripts? What are the advantages of a platform as a service, container-based hosts, or setting up a virtual machine? 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 hosting Python applications. The piece digs into the differences between using containers or platform-provided tools to automatically configure your project. We also cover an older article listing several free hosting options for smaller projects or Python scripts. The Python documentary premiered this past week, and we provide a synopsis and our thoughts. Following a similar theme, we cover our recent code conversation video course, which explores the history of Python versions and the features added across the releases. We also share several other articles and projects from the Python community, including a news roundup, regex affordances, four different ways to speed up your code, a Python-based music library manager, a tool to visualize the structure of your data in Python, and a project for exploring color science in Python. This episode is sponsored by Influxdata. Course Spotlight: A History of Python Versions and Features Explore Python’s evolution from the 1990s to today with a brief history and demos of key features added throughout its lifetime. Topics: 00:00:00 – Introduction 00:03:02 – PEP 728: TypedDict With Typed Extra Items 00:04:09 – Django security releases issued: 5.2.6, 5.1.12, and 4.2.24 00:04:37 – Python Type System and Tooling Survey 2025 00:04:56 – Python: The Documentary - An Origin Story 00:14:11 – A History of Python Versions and Features 00:16:58 – Sponsor: Influxdata 00:17:48 – Regex Affordances 00:21:34 – Where to Host Your Python App 00:26:48 – Best hosting platforms for Python applications and Python scripts 00:30:18 – Video Course Spotlight 00:31:59 – 330× Faster: Four Different Ways to Speed Up Your Code 00:37:18 – beets: Music Library Manager 00:39:12 – Memory Graph - GitHub 00:40:49 – colour: Colour Science for Python 00:42:35 – Thanks and goodbye News: PEP 728: TypedDict With Typed Extra Items (Accepted) Django security releases issued: 5.2.6, 5.1.12, and 4.2.24 Python Type System and Tooling Survey 2025 Show Links: Python: The Documentary - An Origin Story - YouTube – “This is the story of the world’s most beloved programming language: Python. What began as a side project in Amsterdam during the 1990s became the software powering artificial intelligence, data science and some of the world’s biggest companies.” A History of Python Versions and Features – Video Course Regex Affordances – A tour of some real code showing little-used power features of the Python regular expression module, including verbose regex syntax, calling re.sub() with a function reference, and more. Where to Host Your Python App – Whether it’s Django, Flask, FastAPI, or some other Python web framework, your hosting options are plenty. This guide will show you how to choose. Best hosting platforms for Python applications and Python scripts 330× Faster: Four Different Ways to Speed Up Your Code – There are many approaches to speeding up Python code; applying multiple approaches can make your code even faster. This post talks about four different ways you can achieve speed-up. Projects: beets: Music Library Manager Memory Graph - GitHub colour: Colour Science for Python Additional Links: What’s in which Python - Ned Batchelder Anvil Opalstack: Managed Hosting Level up your Python skills with our expert-led courses: Handling Missing Keys With the Python defaultdict Type Using Structural Pattern Matching in Python A History of Python Versions and Features Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Large Language Models on the Edge of the Scaling Laws 1:28:34

منذ 12 يومًا1:28:34

1:28:34

What’s happening with the latest releases of large language models? Is the industry hitting the edge of the scaling laws, and do the current benchmarks provide reliable performance assessments? This week on the show, Jodie Burchell returns to discuss the current state of LLM releases. The most recent release of GPT-5 has been a wake-up call for the LLM industry. We discuss how the current scaling of these systems is reaching a diminishing edge. Jodie also shares how many AI model assessments and benchmarks are flawed. We also take a sober look at the productivity gains from using these tools for software development within companies. We discuss how newer developers should consider additional factors when looking at the current job market. Jodie digs into how economic changes and rising interest rates are influencing layoffs and hiring freezes. Then we share a wide collection of resources for you to continue exploring these topics. This episode is sponsored by Influxdata. Course Spotlight: Exploring Python Closures: Examples and Use Cases Learn about Python closures: function-like objects with extended scope used for decorators, factories, and stateful functions. Topics: 00:00:00 – Introduction 00:03:00 – Recent conferences and talks 00:04:18 – What’s going on with LLMs? 00:06:06 – What happened with the GPT-5 release? 00:08:14 – Simon Willison - 2025 in LLMs so far 00:09:00 – How did we get here? 00:10:37 – OpenAI’s and scaling laws 00:12:25 – Pivoting to post-training 00:16:01 – Some history of AI eras 00:17:54 – Issues with measuring performance and benchmarks 00:22:19 – Chatbot Arena 00:24:06 – Languages are finite 00:26:22 – LLMs and the illusion of humanity 00:30:41 – Sponsor: Influxdata 00:31:34 – Types of solutions to move past these limits 00:36:57 – Does AI actually boost developer productivity? 00:44:19 – Agentic Al Programming with Python 00:48:02 – Results of non-programmers vibe coding 00:50:18 – Back to the concept of overfitting 00:52:52 – The money involved in training 00:56:50 – Video Course Spotlight 00:58:21 – Deepseek and new methods of training 01:01:02 – Quantizing and fitting on a local machine 01:04:48 – The layoffs and the economic changes 01:10:32 – AI implementation failures 01:21:01 – Don’t doubt yourself as a developer 01:24:06 – What are you excited about in the world of Python? 01:25:39 – What do you want to learn next? 01:26:42 – What’s the best way to follow your work online? 01:27:04 – Thanks and goodbye Survey: Listener Survey - Help Shape the Future of the Real Python Podcast Show Links: EuroPython 2025 - July 14th-20th 2025 - Prague, Czech Republic & Remote Episode #232: Exploring Modern Sentiment Analysis Approaches in Python GPT-5: Overdue, overhyped and underwhelming. And that’s not the worst of it. GPT 5’s Rocky Launch Highlights AI Disillusionment - IEEE Spectrum 2025 in LLMs so far, illustrated by Pelicans on Bicycles — Simon Willison Attention is All You Need - Google Scaling laws for neural language models - OpenAI What if AI Doesn’t Get Much Better Than This? - Cal Newport Hiltzik: AI hype is fading fast - Los Angeles Times Does AI Actually Boost Developer Productivity? (100k Devs Study) - Yegor Denisov-Blanch, Stanford - YouTube Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity - METR Amazon Cloud Chief: Replacing Junior Staff With AI Is ‘Dumbest’ Idea - Business Insider 20 LLM evaluation benchmarks and how they work MMLU - Measuring Massive Multitask Language Understanding HellaSwag: Can a Machine Really Finish Your Sentence? Mechanical Turk - Wikipedia Amazon Mechanical Turk Chatbot Arena - LMArena LLMs Can’t Reason - The Reversal Curse, The Alice In Wonderland Test, And The ARC - AGI Challenge - CustomGPT Mirror, mirror: LLMs and the illusion of humanity - Jodie Burchell - NDC Oslo 2024 - YouTube Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens Context Rot: How Increasing Input Tokens Impacts LLM Performance - YouTube Does AI Actually Boost Developer Productivity? (100k Devs Study) - Yegor Denisov-Blanch, Stanford - YouTube AWS CEO says no more programmers in 2 years - Tech Industry - Blind MIT report: 95% of generative AI pilots at companies are failing - Fortune Agentic Al Programming with Python - Talk Python To Me Podcast Vibe coding through the GPT-5 mess - The Verge Overfitting - Wikipedia Andrej Karpathy - Busy Person’s Intro to LLMs - YouTube AI Isn’t Taking Your Job – The Economy Is - Andrew Stiefel Commonwealth Bank backtracks on AI job cuts, apologizes for ‘error’ as call volumes rise - ABC News Klarna CEO Reverses Course By Hiring More Humans, Not AI | Entrepreneur Has Duolingo Lost Its Streak? - Matt Jones - Medium McDonald’s removes AI drive-throughs after order errors OpenAI Usage Plummets in the Summer, When Students Aren’t Cheating on Homework What Happened When I Tried to Replace Myself with ChatGPT in My English Classroom - Literary Hub Learning to code in the age of AI — Sheena O’Connell - YouTube Jodie Burchell - The JetBrains Blog Jodie Burchell’s Blog - Standard error Jodie Burchell (@t-redactyl.bsky.social) — Bluesky Jodie Burchell 🇦🇺🇩🇪 (@t_redactyl@fosstodon.org) - Fosstodon JetBrains: Essential tools for software developers and teams Level up your Python skills with our expert-led courses: Python Decorators 101 A History of Python Versions and Features Exploring Python Closures: Examples and Use Cases Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Exploring Mixin Classes in Python 50:15

منذ 19 يومًا50:15

50:15

What is a good way to add isolated, reusable functionality to Python classes? 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 about developing mixin classes to reuse code across multiple Python classes. He describes how mixins rely on multiple inheritance to combine features from different classes, enhancing flexibility and code reuse. We also share several other articles and projects from the Python community, including a news roundup, highlights from the 2024 Python Developers Survey, reasons you might not need a Python class, an exploration of asyncio’s sharp corners, an explanation of how JIT builds of CPython work, a web-based GUI library, and a project for quickly querying Python lists. This episode is sponsored by Influxdata. Course Spotlight: Design and Guidance: Object-Oriented Programming in Python In this video course, you’ll learn about the SOLID principles, which are five well-established standards for improving your object-oriented design in Python. By applying these principles, you can create object-oriented code that’s more maintainable, extensible, scalable, and testable. Topics: 00:00:00 – Introduction 00:03:14 – Python 3.13.7 Released 00:03:38 – Python 3.14.0rc2 Released 00:04:10 – PEP 802: Display Syntax for the Empty Set 00:04:59 – Announcing the PSF Board Candidates for 2025 00:05:26 – Rodrigo - PSF - Community Service Award for Q2 2025 00:06:06 – Python Developers Survey 2024 Results 00:13:27 – pyx: A Python-Native Package Registry 00:15:12 – Test & Code Final Episode 00:15:48 – You Might Not Need a Python Class 00:20:52 – Sponsor: Influxdata 00:21:44 – asyncio: A Library With Too Many Sharp Corners 00:25:43 – How JIT Builds of CPython Actually Work 00:35:21 – Video Course Spotlight 00:37:12 – What Are Mixin Classes in Python? 00:44:05 – nicegui: Create Web-Based UI With Python 00:46:51 – leopards: Quickly query your Python lists 00:49:00 – Thanks and goodbye Survey: Listener Survey - Help Shape the Future of the Real Python Podcast News: Python 3.13.7 Released Python 3.14.0rc2 Released PEP 802: Display Syntax for the Empty Set (Added) Announcing the PSF Board Candidates for 2025 – The Python Software Foundation elections are upon us, and this post announces this year’s candidates. Voting is September 2nd to 16th. To vote, you must be registered by August 26th. Rodrigo - PSF - Community Service Award for Q2 2025 Python Developers Survey 2024 Results The State of Python 2025 - The PyCharm Blog pyx: A Python-Native Package Registry, Now in Beta – The folks at Astral, who brought you uv and more, have created a new commercial Python-native package registry called “pyx”. Test & Code Final Episode – After 10 years and 237 episodes, Brian Okken has decided to stop recording Test & Code. He’ll still be contributing to Python Bytes. Here’s to all his work on a great podcast over the last decade. Show Links: You Might Not Need a Python Class – If you’re coming from other languages, you might think a class is the easiest way to do something, but Python has other options. This post shows you some alternatives and why you might choose them. asyncio : A Library With Too Many Sharp Corners – asyncio has a few gotchas and this post describes five different problems, including: cancellation, disappearing tasks, and more. How JIT Builds of CPython Actually Work – You don’t have to be a compiler engineer to understand how your code runs in a JIT build of CPython. This article runs you through just what happens under the covers. What Are Mixin Classes in Python? – Learn how to use Python mixin classes to write modular, reusable, and flexible code with practical examples and design tips. Projects: nicegui: Create Web-Based UI With Python leopards: Quickly query your Python lists Additional Links: Episode #259: Design Patterns That Don’t Translate to Python Episode #123: Creating a Python Code Completer & More Abstract Syntax Tree Projects Specializing Adaptive Interpreter The LLVM Compiler Infrastructure Project PEP 774 – Removing the LLVM requirement for JIT builds Building a JIT compiler for CPython What they don’t tell you about building a JIT compiler for CPython Quiz: What Are Mixin Classes in Python? NiceGUI Level up your Python skills with our expert-led courses: Class Concepts: Object-Oriented Programming in Python Inheritance and Internals: Object-Oriented Programming in Python Design and Guidance: Object-Oriented Programming in Python Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Travis Oliphant: SciPy, NumPy, and Fostering Scientific Python 1:11:20

منذ 26 يومًا1:11:20

1:11:20

What went into developing the open-source Python tools data scientists use every day? This week on the show, we talk with Travis Oliphant about his work on SciPy, NumPy, Numba, and many other contributions to the Python scientific community. Travis discusses his initial involvement in the open-source community and how he discovered Python while working in biomedical imaging. He was trying to find ways to manage large sets of numerical data, which led to his initial contributions and collaborations in building scientific libraries. His appearance on the show coincides with the release of the Python documentary, in which he’s featured. We discuss the myriad organizations Travis founded, including Quansight, OpenTeams, and Anaconda. We dig into his underlying mission to continue fostering the growth of the open-source scientific computing community. This episode is sponsored by Influxdata. Course Spotlight: NumPy Techniques and Practical Examples In this video course, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function. Topics: 00:00:00 – Introduction 00:02:41 – Python documentary 00:07:44 – Getting involved in open source 00:12:04 – Numeric Python 00:15:36 – SciPy and the SciPy community 00:17:35 – Starting to think about entrepreneurship 00:18:16 – NumPy evolving from the work of Numeric 00:22:01 – Sponsor: Influxdata 00:22:53 – Python as controlling code for lower-level libraries 00:23:37 – Numba open-source JIT compiler 00:30:09 – Starting to build in Python before learning it all 00:34:45 – Python as the language AI generates 00:36:31 – Guilds and sharing knowledge 00:40:15 – More NumPy backstory 00:46:36 – Contributing to Python 00:48:24 – Video Course Spotlight 00:49:41 – The investment of companies in Python 00:51:22 – Quansight and businesses in open source 00:53:09 – Open Teams and Quansight details 00:57:14 – NumFOCUS and Anaconda 00:58:51 – FairOSS 01:02:36 – Documenting these efforts 01:05:37 – What are you excited about in the world of Python? 01:07:12 – What do you want to learn next? 01:08:10 – How can people follow your work online? 01:10:03 – Thanks and goodbye Show Links: Python: The Documentary - OFFICIAL TRAILER - Coming August 28 - YouTube The Python Matrix Object: Extending Python for Numerical Computation Jim Fulton Jim Hugunin - Home History of SciPy - SciPy wiki dump SciPy NumPy Numba: A High Performance Python Compiler LPython - High performance typed Python compiler OpenTeams: Open SaaS AI Solutions Quansight Consulting OpenTeams Incubator NumFOCUS: A Nonprofit Supporting Open Code for Better Science Anaconda FairOSS faster-cpython Travis Oliphant (@teoliphant) / X Travis Oliphant - LinkedIn Level up your Python skills with our expert-led courses: Data Cleaning With pandas and NumPy Stacks and Queues: Selecting the Ideal Data Structure NumPy Techniques and Practical Examples Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Selecting Inheritance or Composition in Python 46:02

منذ 5 weeks46:02

46:02

When considering an object-oriented programming problem, should you prefer inheritance or composition? Why wouldn’t it just be simpler to use functions? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares an article about structuring code using inheritance, composition, or only functions. We discuss how the piece is a good exploration of the trade-offs of each solution. Unlike the tutorials beginners typically encounter while learning the fundamentals, the article goes much deeper into the “why” of object-oriented programming. We also share several other articles and projects from the Python community, including a news roundup, processing audio in Python, reasons why you shouldn’t call dunder methods, smuggling arbitrary data through an emoji, an HTML to markdown converter, and a library to convert Python requests into curl commands. Course Spotlight: Single and Double Underscore Naming Conventions in Python In this video course, 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. Topics: 00:00:00 – Introduction 00:02:23 – Python 3.13.6 Released 00:02:47 – Django 5.2.5 Released 00:02:55 – Django REST Framework Release v3.16.1 00:03:09 – Narwhals v2.0.0 Released 00:03:22 – mypy 1.17 Released 00:03:42 – PEP 798: Unpacking in Comprehensions 00:04:11 – PEP 799: A Dedicated Profilers Package for Organizing Python Profiling Tools 00:06:12 – PyPI Users Email Phishing Attack 00:07:33 – Django in Action 00:08:00 – Call for proposals deadline - PyCon NL 00:08:59 – Python Audio Processing With pedalboard 00:18:49 – Smuggling Arbitrary Data Through an Emoji 00:21:53 – Don’t Call Dunder Methods 00:28:51 – Video Course Spotlight 00:30:27 – Inheritance Over Composition, Sometimes 00:40:03 – html-to-markdown: HTML to Markdown Converter 00:42:20 – curlify: A library to convert Python requests request object into curl commands 00:44:18 – transfunctions: Support Both Sync and Async 00:45:18 – Thanks and goodbye Survey: Listener Survey - Help Shape the Future of the Real Python Podcast News: Python 3.13.6 Released Django 5.2.5 Released Django REST Framework Release v3.16.1 Narwhals v2.0.0 Released Mypy 1.17 Released PEP 798: Unpacking in Comprehensions (Added) PEP 799: A Dedicated Profilers Package for Organizing Python Profiling Tools (Added) PyPI Users Email Phishing Attack – PyPI users are being targeted by an email phishing attack attempting to trick them into logging into a fake PyPI site. This post from the Security Engineer at PyPI discusses what’s happening and what you should do about it. There’s also a follow-up post . PyCon NL 2025 - Call for Papers Django in Action - Christopher Trudeau - Code ladjango40 Topics: Python Audio Processing With pedalboard – The pedalboard library for Python is aimed at audio processing of various sorts, from converting between formats to adding audio effects. This post summarizes a PyCon US talk on pedalboard and its uses. Smuggling Arbitrary Data Through an Emoji – Unicode includes flexibility through the use of variation selectors. These include the ability to change characters through a consecutive series of coding points. But, when used with code points that don’t need them, they’re ignored, so you can hide data in them. Don’t Call Dunder Methods – It’s best to avoid calling dunder methods. It’s common to define dunder methods, but uncommon to call them directly. Inheritance Over Composition, Sometimes – In an older post, Adrian wrote some code using inheritance. He got questions from his readers asking why it wouldn’t just be simpler to use functions. This post re-implements the code with inheritance, composition, and plain old functions, then compares the approaches. Projects: html-to-markdown: HTML to Markdown Converter curlify: A library to convert Python requests request object to curl command transfunctions: Support Both Sync and Async Additional Links: Working with Audio in Python (feat. Pedalboard) - Peter Sobot - YouTube Python’s Magic Methods: Leverage Their Power in Your Classes – Tutorial Level up your Python skills with our expert-led courses: Class Concepts: Object-Oriented Programming in Python Single and Double Underscore Naming Conventions in Python Inheritance and Internals: Object-Oriented Programming in Python Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Harnessing the Power of Python Polars 1:14:59

منذ 6 weeks1:14:59

1:14:59

What are the advantages of using Polars for your Python data projects? When should you use the lazy or eager APIs, and what are the benefits of each? This week on the show, we speak with Jeroen Janssens and Thijs Nieuwdorp about their new book, Python Polars: The Definitive Guide . Jeroen and Thijs describe how they were introduced to Polars while working at Xomnia. They were converting a large data project to Python and saw surprising speed increases using the new library. We discuss converting projects from pandas to Polars, getting away from indexes, consistent syntax, and using lazy vs eager APIs. Along the way, Jeroen and Thijs offer tips for getting the most out of Polars in your code. We dig into the process of writing a definitive guide and the advantages of working collaboratively on a book project. They also share resources for practicing data wrangling and building visualizations with Pydy Tuesday. Course Spotlight: Working With Python Polars Welcome to the world of Polars, a powerful DataFrame library for Python. In this video course, you’ll get a hands-on introduction to Polars’ core features and see why this library is catching so much buzz. Topics: 00:00:00 – Introduction 00:02:47 – Polars start at Xomnia 00:04:08 – Putting Polars into production 00:07:18 – Realizing the speed differences 00:08:49 – Converting the project from R to Python 00:14:34 – How did Polars improve the project? 00:16:34 – Making the code more ergonomic and readable 00:19:21 – Only grabbing the data that is needed 00:20:37 – Titling and deciding to write the book 00:24:40 – Advantages to collaboration 00:29:34 – What were you excited to include in the book? 00:31:55 – Working with different engines and Nvidia’s Cuda 00:35:05 – Defining a Polars expression 00:36:11 – Transitioning from pandas to Polars 00:37:34 – Not needing an index 00:39:56 – What inspired the syntax? 00:45:01 – Defining lazy vs eager workflows 00:49:16 – Examples covered in first chapter preview 00:51:51 – Video Course Spotlight 00:53:14 – Data formats and Arrow 00:55:41 – Working with NaN, null, or None 00:58:11 – Measuring performance through a benchmark 00:59:12 – Advantages to working with the Discord community 01:02:32 – Code examples and applying the techniques 01:03:34 – Pydy Tuesday 01:05:47 – What are you excited about in the world of Python? 01:09:21 – What do you want to learn next? 01:13:26 – What’s the best way to follow your work online? 01:14:14 – Thanks and goodbye Survey: Listener Survey - Help Shape the Future of the Real Python Podcast Show Links: Python Polars: The Definitive Guide Janssens & Nieuwdorp - What we learned by converting a large codebase from Pandas to Polars - YouTube Polars — DataFrames for the new era polars · PyPI Xomnia - Home Page Episode #140: Speeding Up Your DataFrames With Polars Data Science at the Command Line - Jeroen Janssens Tidyverse PySpark Overview — PySpark 4.0.0 documentation Episode #193: Wes McKinney on Improving the Data Stack & Composable Systems Apache Arrow TPC-H Homepage Community – Python Polars: The Definitive Guide pydytuesday: A Python package to download TidyTuesday datasets PydyTuesday - Python How-to Videos - YouTube Astral: High-performance Python tooling Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv uv: An extremely fast Python package and project manager, written in Rust. PEP 723 – Inline script metadata Inline script metadata - Python Packaging User Guide Package Your Python Code as a CLI - PyData London 25 - YouTube marimo - A next-generation Python notebook The Rust Programming Language Book Pimsleur - Learn New Languages Online Official Rosetta Stone - How Language Is Learned Thijs Nieuwdorp Jeroen Janssens Python Polars: The Definitive Guide Level up your Python skills with our expert-led courses: Working With Python Polars Graph Your Data With Python and ggplot Working With Missing Data in Polars Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Design Patterns That Don't Translate to Python 49:12

منذ 7 weeks49:12

49:12

Do the design patterns learned in other programming languages translate to coding in Python? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher dives into an article that tackles popular object-oriented design patterns from the famous Gang of Four book. These patterns offer solutions to common coding problems, but as Christopher explores, Python often doesn’t even have the problems these solutions try to fix. He discusses several common design patterns and the simpler, more Pythonic ways to achieve the same goals. We also share several other articles and projects from the Python community, including an exceptionally robust news roundup, running coverage on tests, an exploration of expert generalists, a preview of template strings from Python 3.14, a quiz on f-strings, and a project that calculates the complexity of your Python code. Course Spotlight: Working With Python’s Built-in Exceptions Learn the most common built-in Python exceptions, when they occur, how to handle them, and how to raise them properly in your code. Topics: 00:00:00 – Introduction 00:02:03 – Python 3.14.0b4 Released 00:02:11 – Python 3.14 release candidate 1 is go! 00:02:48 – PyPy v7.3.20 Release 00:03:00 – Textual 4.0.0 Released 00:03:23 – Announcing Toad - a universal UI for agentic coding in the terminal 00:03:42 – uv 0.8.0 Released 00:03:56 – Django Bugfix Release 5.2.4 00:04:14 – Django Community Ecosystem 00:04:52 – Happy 20th Birthday Django! 00:05:31 – PyData London 2025 Videos 00:05:48 – PEP 792: Project Status Markers in the Simple Index 00:06:09 – PEP 800 – Solid bases in the type system 00:07:06 – Run Coverage on Tests 00:14:32 – Design Patterns You Should Unlearn in Python 00:18:13 – Video Course Spotlight 00:19:24 – Expert Generalists 00:34:42 – Python 3.14 Preview: Template Strings (T-Strings) 00:41:00 – fstrings.wtf - Python F-String Quiz 00:43:09 – complexipy: Calculate Complexity of Your Python 00:48:18 – Thanks and goodbye Survey: Listener Survey - Help Shape the Future of the Real Python Podcast News: Python 3.14.0b4 Released Python 3.14 release candidate 1 is go! - Core Development - Discussions on Python.org PyPy v7.3.20 Release Textual 4.0.0 Released Announcing Toad - a universal UI for agentic coding in the terminal – Will McGugan uv 0.8.0 Released Django Bugfix Release 5.2.4 Django Community Ecosystem - Django Happy 20th Birthday Django! Django Origins (and some things I have built with Django) - YouTube PyData London 2025 Videos PEP 792: Project Status Markers in the Simple Index (Accepted) PEP 800 – Solid bases in the type system Show Topics: Run Coverage on Tests – Code coverage tools tell you which parts of your programs got executed during test runs. They’re an important part of your test suite, and without them, you may miss errors in your tests themselves. This post has two quick examples of just why you should use a coverage tool. Design Patterns You Should Unlearn in Python – The Gang of Four design patterns specify object-oriented solutions to common issues in code, but Python doesn’t have many of the problems the solutions are aiming to solve. This article talks about some of the common patterns and the easier ways to solve the problems they intend to address in Python. See also Part 2 . Expert Generalists – MartinFowler.com – “As computer systems get more sophisticated we’ve seen a growing trend to value deep specialists. But we’ve found that our most effective colleagues have a skill in spanning many specialties.” Python 3.14 Preview: Template Strings (T-Strings) – Python 3.14 introduces t-strings: a safer, more flexible alternative to f-strings. Learn how to process templates securely and customize string workflows. Projects: fstrings.wtf - Python F-String Quiz complexipy: Calculate Complexity of Your Python Additional Links: Design Patterns - Gang of Four - Wikipedia Episode #117: Measuring Python Code Quality, Simplicity, and Maintainability Episode #176: Building Python Best Practices and Fundamental Skills Cyclomatic complexity - Wikipedia Cognitive Complexity: A new way of measuring understandability - SonarSource Listener Survey - Help Shape the Future of the Real Python Podcast Level up your Python skills with our expert-led courses: Working With Python's Built-in Exceptions Testing Your Code With pytest Using Python's assert to Debug and Test Your Code Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Supporting the Python Package Index 49:49

منذ 8 weeks49:49

49:49

What goes into supporting more than 650,000 projects and nearly a million users of the Python Package Index? This week on the show, we speak with Maria Ashna about her first year as the inaugural PyPI Support Specialist. Maria has a varied background in creative arts and neuroscience. She decided to apply for the PyPI support position, defying common misconceptions about who can take on roles inside the Python Software Foundation, and challenging imposter syndrome along the way. Her recent talks at PyCon US 2025 and EuroPython 2025 were about her experiences in the role. She describes tackling the backlogs of account recovery and PEP 541 requests, and we also discuss PyPI community and company organizations. 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 00:01:42 – What led you to learn Python? 00:08:09 – PyCon 2025 talk about the first year at PyPI 00:11:06 – Embracing asking questions 00:13:55 – Being willing to say “I don’t know, let’s find out” 00:15:06 – What is PEP 541 and resolving name retention issues 00:23:22 – Video Course Spotlight 00:24:40 – Addressing the account recovery backlog 00:26:43 – PyPI Organizations 00:30:54 – Moving beyond the hesitancy to submit a package to PyPI 00:40:43 – Getting past imposter syndrome and applying 00:45:07 – What are you excited about in the world of Python? 00:46:10 – What do you want to learn next? 00:47:52 – How can people follow your work online? 00:49:03 – Thanks and goodbye Show Links: Adventures in Account Recovery, PEP 541 & More As Inaugural PyPI Support Specialist - Maria Ashna - YouTube PyCon US 2025 - A PEP Talk: Adventures in Account Recovery, PEP 541, And More As the Inaugural PyPI Support Specialist EuroPython 2025 - July 14th-20th 2025 - Prague, Czech Republic & Remote PyPI - The Python Package Index PEP 541 – Package Index Name Retention Introducing PyPI Organizations - The Python Package Index Blog Packaging Python Projects - Python Packaging User Guide The Traveling Guitar Maria Ashna (@thespi_brain) - Instagram Thespi-Brain (thespibrain) - GitHub Level up your Python skills with our expert-led courses: Documenting Python Projects With Sphinx and Read the Docs Publishing Python Packages to PyPI Exploring Python Closures: Examples and Use Cases Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Comparing Real-World Python Performance Against Big O 45:01

منذ 10 weeks45:01

45:01

How does the performance of an algorithm hold up when you put it into a realistic context? Where might Python code defy Big O notation expectations when using a profiler? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares an article about why real-world performance often defies Big O expectations. The piece starts with a task coded in Go and then optimized from O(n²) to O(n) . Can an interpreted language like Python compete with a compiled language? Profiling the performance of both versions provides some interesting results. We also share several other articles and projects from the Python community, including a news roundup, the fastest way to detect a vowel in a string, whether Python dictionaries are ordered data structures, an overview of Python’s enum module, a Python client library for Google Data Commons, and a project to convert plain ASCII to “smart” punctuation. Course Spotlight: Building Enumerations With Python’s enum In this video course, you’ll discover the art of creating and using enumerations of logically connected constants in Python. To accomplish this, you’ll explore the Enum class and other associated tools and types from the enum module in the Python standard library. Topics: 00:00:00 – Introduction 00:02:12 – ruff Release 0.12.0 00:02:38 – streamlit Release 1.46.0 00:02:48 – lxml 6.0.0 Released 00:03:00 – PSF Board Election Schedule 00:03:26 – Are Python Dictionaries Ordered Data Structures? 00:08:59 – The Fastest Way to Detect a Vowel in a String 00:16:37 – Module enum Overview 00:24:46 – Video Course Spotlight 00:26:24 – O(no) You Didn’t 00:38:34 – New Python Client Library for Google Data Commons 00:41:55 – smartypants.py: Plain ASCII to “Smart” Punctuation 00:44:07 – Thanks and goodbye News: ruff Release 0.12.0 streamlit Release 1.46.0 lxml 6.0.0 Released PSF Board Election Schedule – It is time for the Python Software Foundation Board elections. Nominations are due by July 29th. See the article for the full election schedule and deadlines. Show Links: Are Python Dictionaries Ordered Data Structures? – Although dictionaries have maintained insertion order since Python 3.6, they aren’t strictly speaking ordered data structures. Read on to find out why and how the edge cases can be important depending on your use case. The Fastest Way to Detect a Vowel in a String – If you need to find the vowels in a string there are several different approaches you could take. This article covers 11 different ways and how each performs. Module enum Overview – This article gives an overview of the tools available in the module enum and how to use them, including Enum, auto, StrEnum, Flag, and more. O(no) You Didn’t – A deep dive into why real-world performance often defies Big-O expectations, and why context and profiling matter more than theoretical complexity. Projects: New Python Client Library for Google Data Commons – Google Data Commons announced the general availability of its new Python client library for the Data Commons. The goal of the library is to enhance how students, researchers, analysts, and data scientists access and leverage Data Commons. smartypants.py: Plain ASCII to “Smart” Punctuation Additional Links: Betteridge’s Law collections — Container datatypes — Python 3.13.5 documentation OrderedDict vs dict in Python: The Right Tool for the Job – Real Python Build Enumerations of Constants With Python’s Enum – Tutorial Building Enumerations With Python’s enum - Video Course Python client library for the Data Commons PyCoder’s Weekly - Submit a Link Level up your Python skills with our expert-led courses: Looping With Python enumerate() Sorting Dictionaries in Python: Keys, Values, and More Building Enumerations With Python's enum Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Solving Problems and Saving Time in Chemistry With Python 1:13:10

منذ 11 weeks1:13:10

1:13:10

What motivates someone to learn how to code as a scientist? How do you harness the excitement of solving problems quickly and make the connection to the benefits of coding in your scientific work? This week on the show, we speak with Ben Lear and Christopher Johnson about their book “Coding For Chemists.” Christopher is an associate professor of chemistry at Stony Brook University. Ben is a professor of chemistry at Penn State’s Eberly College of Science. They’re long-time friends who decided to collaborate on a book after discussing the challenges of teaching coding to chemistry students. The book targets chemists and other researchers who want to streamline common workflows with Python. It covers core Python concepts, data visualization, and data analysis topics by sharing common problems encountered in chemical research and presenting a complete Python-based solution for each problem. We discuss how they collaborated on the book and decided what libraries and tools to include. We cover how LLM tools have affected classroom teaching and require new techniques to reinforce learning. We also dig into what motivates students to learn how to code. 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 00:02:06 – Ben’s background and starting with Python 00:04:34 – Chris’ background and starting with Python 00:07:16 – What has sped up Python for your use? 00:08:22 – How did idea for the book start? 00:11:30 – Shifting publisher and new release time frame 00:12:24 – Three potential audiences 00:13:20 – The ubiquitous need for programming skills in science 00:15:05 – Difficult workflows with chemistry equipment 00:16:06 – What is a chart recorder? 00:16:34 – Working with proprietary equipment and exporting data 00:23:37 – Explaining how programming will help chemists 00:27:31 – Finding the problems to solve 00:29:09 – The classic common chemistry workflow 00:30:48 – Teaching Python in a classroom and starting with functions 00:35:05 – Helping students cultivate inspiration 00:37:06 – LLM and AI use by students 00:41:19 – Video Course Spotlight 00:42:36 – Using Spyder IDE and Positron 00:45:29 – How does the book cover notebooks and managing packages? 00:48:08 – Using marimo for archiving and sharing projects 00:50:25 – What was difficult to put into the book? 00:54:04 – What were you eager to share in the book? 00:55:54 – Teaching students about file management 00:58:13 – Sharing tools to plot data 01:01:45 – Choosing not to teach pandas and using NumPy arrays instead 01:04:03 – How can people learn more about the book? 01:05:20 – What are you excited about in the world of Python? 01:07:58 – What do you want to learn next? 01:10:50 – How can people follow your work? 01:12:15 – Thanks and goodbye Show Links: Coding For Chemists - Getting Started Ben Lear - Eberly College of Science Christopher Johnson - Department of Chemistry Chart recorder - Wikipedia pandas - Python Data Analysis Library NumPy Spyder - The Python IDE that scientists and data analysts deserve Positron marimo - A next-generation Python notebook Streamlit - A faster way to build and share data apps Plotly - Data Apps for Production Bokeh Vega-Altair: Declarative Visualization in Python codechembook - PyPI CodeChemBook: Companion library for Coding for Chemists Book - GitHub Data Meets Design The Lear Laboratory Statistical Inference - 2nd Edition - George Casella - Roger Berger Johnson Lab @SBU Christopher J. Johnson - Google Scholar Benjamin Lear - Google Scholar Level up your Python skills with our expert-led courses: Defining Python Functions With Optional Arguments Data Visualization Interfaces in Python With Dash NumPy Techniques and Practical Examples Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Structuring Python Scripts & Exciting Non-LLM Software Trends 54:07

منذ 12 weeks54:07

54:07

What goes into crafting an effective Python script? How do you organize your code, manage dependencies with PEP 723, and handle command-line arguments for the best results? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We dig into a recent Real Python article about how to structure your Python scripts. It includes advice for adding inline script metadata as defined in PEP 723, which helps tools automatically create an environment and install dependencies when the script is run. The piece also covers choosing appropriate data structures, improving runtime feedback, and making your code more maintainable with constants and entry points. We discuss a collection of software trends happening behind the scenes of the constant LLM news. The piece starts with local-first software, prioritizing processing and storing private data on personal devices rather than relying on the cloud. The other trends include common themes and tools we’ve shared over the past few years, including WebAssembly, SQLite’s renaissance, and improvements to cross-platform mobile development. We also share several other articles and projects from the Python community, including a news roundup, the state of free-threaded Python, tips for improving Django management commands, advice for time management as a manager, a data science-focused IDE, and a project to check for multiple patterns in a single string. 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, SQLAlchemy, and 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 00:02:26 – Followup - marimo and LaTeX 00:03:26 – PEP 734: Multiple Interpreters in the Stdlib (Accepted) 00:03:52 – Python 3.13.4, 3.12.11, 3.11.13, 3.10.18 and 3.9.23 Security Releases 00:04:21 – Python Insider: Python 3.14.0 beta 3 is here! 00:04:30 – Django Bugfix Releases: 5.2.3, 5.1.11, and 4.2.23 00:04:52 – NumPy v2.3.0 Released 00:05:02 – scikit-learn 1.7 Released 00:05:12 – PyData Virginia 2025 Talks 00:05:52 – How Can You Structure Your Python Script? 00:12:08 – State of Free-Threaded Python 00:18:23 – 5 Non-LLM Software Trends to Be Excited About 00:29:50 – Video Course Spotlight 00:31:23 – Better Django Management Commands 00:33:56 – Advice for time management as a manager 00:46:49 – positron: Data Science IDE 00:50:05 – ahocorasick_rs: Check for Multiple Patterns in a Single String 00:52:41 – 10 Polars Tools and Techniques To Level Up Your Data Science - Podcast Episode 00:53:22 – Thanks and goodbye News: PEP 734: Multiple Interpreters in the Stdlib (Accepted) Python 3.13.4, 3.12.11, 3.11.13, 3.10.18 and 3.9.23 Security Releases Python 3.13.5 Released Python Insider: Python 3.14.0 beta 3 is here! Django Bugfix Releases: 5.2.3, 5.1.11, and 4.2.23 NumPy v2.3.0 Released scikit-learn 1.7 Released PyData Virginia 2025 Talks – A list of the recorded talks from PyData Virginia 2025. Show Links: How Can You Structure Your Python Script? – Structure your Python script like a pro. This guide shows you how to organize your code, manage dependencies with PEP 723, and handle command-line arguments. State of Free-Threaded Python – This is a blog post from the Python Language Summit 2025 giving an update on the progress of free-threaded Python. You may also be interested in the complete list of Language Summit Blogs . PEP 779 – Criteria for supported status for free-threaded Python 5 Non-LLM Software Trends to Be Excited About – Tired of reading about AI and LLMs? This post talks about other tech that is rapidly changing in the software world, including local-first applications, web assembly, the improvement of cross-platform tools, and more. Better Django Management Commands – Writing Django management commands can involve a ton of boilerplate code. This article shows you how to use two libraries that could cut your management command code in half: django-click and django-typer. Discussion: Advice for time management as a manager - benkuhn.net Projects: positron: Data Science IDE ahocorasick_rs: Check for Multiple Patterns in a Single String Additional Links: Visualize outputs - Mardown editor and LaTeX - marimo PyCon US 2025 - YouTube DjangoCon Europe 2025 Dublin - YouTube Executing Python Scripts With a Shebang Local-first software: You own your data, in spite of the cloud Code OSS Episode #510 - 10 Polars Tools and Techniques To Level Up Your Data Science - Talk Python To Me Podcast PyCoder’s Weekly - Submit a Link Level up your Python skills with our expert-led courses: Execute Your Python Scripts With a Shebang Django Admin Customization SQLite and SQLAlchemy in Python: Move Your Data Beyond Flat Files Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Scaling Python Web Applications With Kubernetes and Karpenter 1:04:47

منذ 13 weeks1:04:47

1:04:47

What goes into scaling a web application today? What are resources for learning and practicing DevOps skills? This week on the show, Calvin Hendryx-Parker is back to discuss the tools and infrastructure for autoscaling web applications with Kubernetes and Karpenter. Calvin is the co-founder and CTO of Six Feet Up, a Python and AI consultancy. He shares how they recently helped a client scale a web application that employs video, audio, and chat sessions. We dig deep into the tooling behind modern Kubernetes systems management and performance monitoring. Calvin shares a project bootstrap tool for streamlining the development and deployment of a web application. The tool includes a complete blueprint for the infrastructure needed to get started. We also dig into a collection of coding tools Calvin has been experimenting with. We discuss his recent IndyPy presentation, “Battle of the Bots,” which put several AI code assistants through their paces. This episode is sponsored by AMD. Course Spotlight: First Steps With LangChain Large language models (LLMs) have taken the world by storm. In this step-by-step video course, you’ll learn to use the LangChain library to build LLM-assisted applications. Topics: 00:00:00 – Introduction 00:02:23 – Scaling a Django project using Kubernetes 00:05:35 – Elastic Kubernets Service (EKS) 00:09:10 – Kubernetes terminology and improvements in tooling 00:11:29 – The Control Plane and the API 00:14:06 – Video Course Spotlight 00:15:11 – scaf: providing DevOps engineers a blueprint for new projects 00:17:21 – What have been the benefits of scaf for internal teams? 00:20:18 – How do you identify and reproduce scaling issues? 00:22:44 – Dealing with IP address scaling 00:26:03 – Why use other observability tools beyond AWS internal ones? 00:29:22 – Other lessons learned and moving toward refactoring code 00:33:53 – Scaling a voice-based LLM application 00:35:35 – Sponsor: AMD 00:36:11 – Looking at limitations and bottlenecks 00:38:08 – Configuring a Kubernetes operator to act on itself 00:39:47 – What project components are within a pod of containers? 00:42:31 – Budgeting for scale using Karpenter 00:43:58 – Tools for running containers locally 00:46:01 – Are containers still a primary development tool for you? 00:50:58 – Resources for learning DevOps and Kubernetes 00:52:54 – Conferences and talks 00:53:56 – Battle of the Bots: comparing coding agents 00:55:15 – What are you excited about in the world of Python? 00:56:20 – What do you want to learn next? 01:02:42 – What’s the best way for people to follow your work online? 01:03:33 – Thanks and goodbye Show Links: Six Feet Up - Python and AI for Good, Custom Software Development Kubernetes - Tutorials Managed Kubernetes Service - Amazon EKS - AWS Karpenter Kustomize - Kubernetes native configuration management Kubernetes Components - Control Plane Components Continuous Integration and Deployment for Python With GitHub Actions Argo CD scaf: Provides developers and DevOps engineers with a complete blueprint for a new project Streamline the Dev Experience with Kubernetes and Scaf™ Scaf™ — Six Feet Up Scaf: Complete blueprint for new Python Kubernetes projects - Talk Python To Me Podcast E496 kind Locust - A modern load testing framework Grafana: The open and composable observability platform Grafana Loki OSS - Log aggregation system Prometheus - client_python Elastic network interfaces - Amazon Elastic Compute Cloud eks-node-viewer: EKS Node Viewer k9s: 🐶 Kubernetes CLI To Manage Your Clusters In Style! OrbStack · Fast, light, simple Docker & Linux NixOS Wiki - Python TechWorld with Nana - YouTube Python: The Documentary [OFFICIAL TRAILER] - YouTube Calvin Hendryx-Parker - LinkedIn Conferences and Meetups: All Things Open 2025 - All Things Open All Things Open AI Conference All Things Open AI 2025 - AI Builders Track - YouTube Rolling out Enterprise AI: Tools, Insights, & Team Empowerment - Calvin Hendryx-Parker, Six Feet Up - YouTube PyCon US 2025 - PyCon US 2025 PyOhio 2025 IndyPy Events AI Coding Tools: Battle of the Bots - Developer Tools Showdown - YouTube Aider - AI Pair Programming in Your Terminal codename goose An entirely open-source AI code assistant inside your editor - Ollama Blog Devstral - Mistral AI 10 LLM Observability Tools to Know in 2025 - Coralogix How often do LLMs snitch? Recreating Theo’s SnitchBench with LLM The lethal trifecta for AI agents: private data, untrusted content, and external communication vllm-proxy: Proxy for vLLM enabling multi-model operation, cache-aware routing, and load balancing. Level up your Python skills with our expert-led courses: Python Continuous Integration and Deployment Using GitHub Actions First Steps With LangChain Managing Dependencies With Python Poetry Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Starting With marimo Notebooks & Python App Config Management 51:41

منذ 14 weeks51:41

51:41

Looking for a guide on getting started with marimo notebooks? How do you build a reproducible notebook for sharing or create a dashboard with interactive UI elements? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. We cover a recent Real Python article by Ian Eyre about using narimo notebooks. The tutorial covers installing Marimo, taking advantage of reactivity, building interactive dashboards, and managing a notebook’s environment through sandboxing. The piece ends by examining the limitations of traditional linear notebooks and how Marimo addresses them. Christopher discusses an article about how to store the configurations for your Python scripts and projects. Whether you’re managing resource handles to a database, deployment variables, or credentials to external services, you’ll need a way to save and load the details into your Python project. The piece compares saving configurations in several common file formats or through environment variables. We also share several other articles and projects from the Python community, including a news roundup, the discourse between generative AI coding proponents and detractors, catching memory leaks with your test suite, epigrams on programming, a command line tool to check packages on PyPI, and a collection of string, file, and object utilities. This episode is sponsored by Six Feet Up. Course Spotlight: Python Continuous Integration and Deployment Using GitHub Actions With most software following agile methodologies, it’s essential to have robust DevOps systems in place to manage, maintain, and automate common tasks with a continually changing codebase. By using GitHub Actions, you can automate your workflows efficiently, especially for Python projects. Topics: 00:00:00 – Introduction 00:02:44 – Python Release 3.14.0b2 00:03:09 – Django security releases issued: 5.2.2, 5.1.10, and 4.2.22 00:03:27 – PyBay 2025 00:03:43 – PyCon NL 2025 - Call for Proposals 00:04:05 – Django Forum: Supporting t-strings 00:04:44 – Ruff Users: What Rules Are You Using and What Are You Ignoring? 00:05:19 – My Shot at Real Python 00:06:03 – My AI Skeptic Friends Are All Nuts 00:10:06 – I Think I’m Done Thinking About genAI For Now 00:11:12 – AI Changes Everything 00:23:01 – Video Course Spotlight 00:24:14 – Configuration of Python Applications 00:29:15 – marimo: A Reactive, Reproducible Notebook 00:35:15 – Sponsor: Six Feet Up 00:36:02 – Catching memory leaks with your test suite 00:41:45 – Epigrams on Programming 00:46:54 – whatsonpypi: Check PyPI From the Command Line 00:48:18 – strif: String, File, and Object Utilities 00:49:44 – PyCoder’s Weekly - Submit a Link 00:50:13 – Thanks and goodbye News: Python Release 3.14.0b2 Django security releases issued: 5.2.2, 5.1.10, and 4.2.22 PyBay 2025 PyCon NL 2025 - Call for Proposals Django Forum: Supporting t-strings Ruff Users: What Rules Are You Using and What Are You Ignoring? My Shot at Real Python – Amanda has recently written her first article for Real Python and this post talks about her experience doing so. If you want to check out the article, it’s on Nested Loops . Show Links: My AI Skeptic Friends Are All Nuts I Think I’m Done Thinking About genAI For Now AI Changes Everything AI Blog Comparison - Armin Ronacher Configuration of Python Applications – This post talks about how to store configuration for your script and how and when to load the information into your program. marimo: A Reactive, Reproducible Notebook – Discover how marimo notebook simplifies coding with reactive updates, UI elements, and sandboxing for safe, sharable notebooks. Catching memory leaks with your test suite – “If you have a good test suite, you may be able to use pytest fixtures to identify memory and other resource leaks.” Discussion: Epigrams on Programming Alan Perlis - Wikipedia Projects: whatsonpypi: Check PyPI From the Command Line strif: String, File, and Object Utilities Additional Links: Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv Episode #250: DjangoCon Europe 2025: Live Recording From Dublin Episode #236: Simon Willison: Using LLMs for Python Development GenAI Criticism and Moral Quandaries - Armin Ronacher Math support in Markdown - The GitHub Blog Quiz: marimo: A Reactive, Reproducible Notebook Episode #42: What Is Data Engineering and Researching 10 Million Jupyter Notebooks We Downloaded 10,000,000 Jupyter Notebooks From Github – This Is What We Learned Episode #230: marimo: Reactive Notebooks and Deployable Web Apps in Python Episode #501 - marimo - Reactive Notebooks for Python - Talk Python To Me Podcast Episode #24: Options for Packaging Your Python Application: Wheels, Docker, and More - Itamar Turner-Trauring Episode #128: Using a Memory Profiler in Python & What It Can Teach You Episode #172: Measuring Multiple Facets of Python Performance With Scalene PyCoder’s Weekly - Submit a Link Level up your Python skills with our expert-led courses: Python Continuous Integration and Deployment Using GitHub Actions Python mmap: Doing File I/O With Memory Mapping Testing Your Code With pytest Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Rodrigo Girão Serrão: Python Training, itertools, and Idioms 1:02:49

منذ 15 weeks1:02:49

1:02:49

Once you’ve learned the vocabulary and syntax of the Python language, how do you progress into learning the right combinations to put into your code? How can Python’s built-in itertools library enhance your skills? This week on the show, we speak with Rodrigo Girão Serrão about teaching Python through his blog and his passion for the itertools library. We discuss Rodrigo’s different approaches to writing on his blog. He likes to document smaller concepts about Python and building code in his “Today I Learned” series. He’s also been collecting advice about the best way to use core Python features in another series called “Pydon’ts.” We cover his recent PyCon US tutorial about the built-in itertools module. The functions contained in the module create iterators for efficient looping. We discuss the categories of tools inside the collection and ways to simplify your code. We also explore the concept of vocabulary versus idioms in writing. Idioms are a group of words that hold a symbolic meaning that goes beyond the literal meaning of the individual words. We dig into how that applies to learning Python and building a personal collection of programming idioms. This episode is sponsored by AMD. Course Spotlight: Working With Missing Data in Polars In this video course, 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. Topics: 00:00:00 – Introduction 00:02:34 – Creating Polars video course 00:03:27 – How did you start programming and teaching Python? 00:04:59 – Where did mathspp come from? 00:05:38 – Exploring math and programming in university 00:07:48 – Learning APL 00:09:24 – What goes into building the blog? 00:15:05 – The Pydon’ts and writing books 00:18:37 – PyCon US 2025 00:20:46 – Sponsor: AMD 00:21:23 – Teaching a tutorial about itertools 00:28:58 – Categorizing itertools 00:40:39 – Video Course Spotlight 00:41:55 – The difference between me and Shakespeare 00:46:51 – Learning and practicing with idioms 00:51:01 – TIL and asking questions 00:53:54 – What are you excited about in the world of Python? 00:55:40 – What do you want to learn next? 00:57:35 – How can people follow your work online? 01:01:19 – Thanks and goodbye Show Links: mathspp blog TIL (Today I Learned) - mathspp Working With Missing Data in Polars Paul Valéry - “A poem is never finished” - Oxford Reference Personal highlights of PyCon US 2025 - mathspp PyCon US 2025 Lightning Talks - Friday, May 16th, 2025 PM - YouTube PyCon US 2025 Tutorial Sneak Peek: “Reimplement itertools for fun & profit” Rodrigo Girão Serrão - YouTube Alan Perlis - Wikipedia Epigrams on Programming What learning APL taught me about Python - mathspp What APL taught me about Python ⚡️ – lightning talk by Rodrigo Girão Serrão at EuroPython 2023 - YouTube itertools — Functions creating iterators for efficient looping — Python 3.13.4 documentation Module itertools overview - mathspp The little book of itertools - mathspp Python itertools By Example – Real Python What’s new in Python 3.14 — Python 3.15.0a0 documentation beehiiv — The newsletter platform built for growth Python drops 🐍💧 newsletter - mathspp Books - mathspp Rodrigo Girão Serrão 🐍🚀 (@mathspp.com) — Bluesky Rodrigo Girão Serrão - LinkedIn Rodrigo 🐍🚀 (@mathsppblog@fosstodon.org) - Fosstodon Rodrigo 🐍🚀 (@mathsppblog) / X Level up your Python skills with our expert-led courses: Efficient Iterations With Python Iterators and Iterables Working With Python Polars Working With Missing Data in Polars Support the podcast & join our community of Pythonistas…

The Real Python Podcast

1
Python Thread Safety & Managing Projects With uv 34:48

منذ 16 weeks34:48

34:48

What are the ways you can manage multithreaded code in Python? What synchronization techniques are available within Python’s threading module? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects. Christopher discusses his recent Real Python video course about thread safety. The course provides a quick overview of race conditions and how to use locks in your code. It then goes on to share a collection of additional synchronization primitives to make your code thread-safe. We dig into a tutorial by Leodanis Pozo Ramos about managing Python projects with uv. The tutorial shows you how to quickly initialize a project, build the directory structure, add dependencies, and publish your package while practicing the commands inside uv. We also share several other articles and projects from the Python community, including a news roundup, unraveling t-strings, what’s new in pip 25.1, an SVG-first plotting library, and a data modeling tool built on top of Polars and Pydantic. Course Spotlight: Thread Safety in Python: Locks and Other Techniques In this video course, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe. Topics: 00:00:00 – Introduction 00:02:23 – PEP 773: A Python Installation Manager for Windows 00:03:09 – PEP 784: Adding Zstandard to the Standard Library 00:03:28 – Python Insider: Python 3.14.0 Beta 1 Is Here! 00:03:48 – Django Security Releases Issued: 5.2.1, 5.1.9 and 4.2.2 00:04:09 – ty: New Type Checker and Language Server by Astral 00:05:01 – pyrefly: A Fast Type Checker and IDE for Python 00:06:03 – The Future of Textualize 00:07:08 – Managing Python Projects With uv 00:12:20 – pre-commit: Install With uv 00:13:03 – Python’s New t-strings 00:16:38 – Unraveling t-strings 00:18:33 – Video Course Spotlight 00:19:50 – What’s New in Pip 25.1 00:24:30 – Thread Safety in Python: Locks and Other Techniques 00:28:40 – glyphx: SVG-first Plotting Library 00:31:20 – patito: A data modeling layer built on top of Polars and Pydantic 00:34:02 – Thanks and goodbye News: PEP 773: A Python Installation Manager for Windows (Accepted) PEP 784: Adding Zstandard to the Standard Library (Accepted) Python Insider: Python 3.14.0 Beta 1 Is Here! Django Security Releases Issued: 5.2.1, 5.1.9 and 4.2.21 ty: New Type Checker and Language Server by Astral pyrefly: A Fast Type Checker and IDE for Python The Future of Textualize – Will McGugan, founder of Textualize the company has announced that they will be closing their doors. Textualize the open source project will remain. Show Links: Managing Python Projects With uv – In this tutorial, you’ll learn how to create and manage your Python projects using uv, an extremely fast Python package and project manager written in Rust. pre-commit : Install With uv – pre-commit is Adam’s favorite Git-integrated “run things on commit” tool. It acts as a kind of package manager, installing tools as necessary from their Git repositories. This post explains how to use it with uv . Python’s New t-strings – Using f-strings is a readable way of building output, but there are situations where they can’t be used because the contents need to be verified before being string-ified. The new t-strings, coming in 3.14, are a solution to this problem. Unraveling t-strings – PEP 750 introduced t-strings for Python 3.14. These are a template string mechanism similar to f-strings. Although they are in 3.14.0b1, there isn’t any documentation yet, so this post explains what they are how they can be used. What’s New in Pip 25.1 – pip 25.1 introduces support for Dependency Groups (PEP 735), resumable downloads, and an installation progress bar. Dependency resolution has also received a raft of bugfixes and improvements. Thread Safety in Python: Locks and Other Techniques – In this video course, you’ll learn about the issues that can occur when your code is run in a multithreaded environment. Then you’ll explore the various synchronization primitives available in Python’s threading module, such as locks, which help you make your code safe. Projects: glyphx: SVG-first Plotting Library JakobGM/patito: A data modeling layer built on top of Polars and Pydantic Additional Links: Episode #238: Charlie Marsh: Accelerating Python Tooling With Ruff and uv pgjones/sql-tstring: SQL-tString allows for f-string like construction of sql queries PEP 787: Safer Subprocess Usage Using t-strings (Postponed to 3.15) davepeck/pep750-examples: Examples of using t-strings as defined in PEP 750 xkcd: Exploits of a Mom Little Bobby Tables - explain xkcd Level up your Python skills with our expert-led courses: Threading in Python Thread Safety in Python: Locks and Other Techniques Python Basics Exercises: Installing Packages With pip Support the podcast & join our community of Pythonistas…

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