The Real Python Podcast

The Real Python Podcast

Real Python

A weekly Python podcast hosted by Christopher Bailey with interviews, coding tips, and conversation with guests from the Python community. The show covers a wide range of topics including Python programming best practices, career tips, and related software development topics. Join us every Friday morning to hear what's new in the world of Python programming and become a more effective Pythonista.

1 hour 13 minutes

Exploring Modern Sentiment Analysis Approaches in Python
What are the current approaches for analyzing emotions within a piece of text? Which tools and Python packages should you use for sentiment analysis? This week, Jodie Burchell, developer advocate for data science at JetBrains, returns to the show to discuss modern sentiment analysis in Python.

Jodie holds a PhD in clinical psychology. We discuss how her interest in studying emotions has continued throughout her career.

In this episode, Jodie covers three ways to approach sentiment analysis. We start by discussing traditional lexicon-based and machine-learning approaches. Then, we dive into how specific types of LLMs can be used for the task. We also share multiple resources so you can continue to explore sentiment analysis on your own.

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

Course Spotlight: Learn Text Classification With Python and Keras

In this course, you’ll learn about Python text classification with Keras, working your way from a bag-of-words model with logistic regression to more advanced methods, such as convolutional neural networks. You’ll see how you can use pretrained word embeddings, and you’ll squeeze more performance out of your model through hyperparameter optimization.

Topics:
- 00:00:00 – Introduction
- 00:02:31 – Conference talks in 2024
- 00:04:23 – Background on sentiment analysis and studying feelings
- 00:07:09 – What led you to study emotions?
- 00:08:57 – Dimensional emotion classification
- 00:10:42 – Different types of sentiment analysis
- 00:14:28 – Lexicon-based approaches
- 00:17:50 – VADER - Valence Aware Dictionary and sEntiment Reasoner
- 00:19:41 – TextBlob and subjectivity scoring
- 00:21:48 – Sponsor: Sentry
- 00:22:52 – Measuring sentiment of New Year’s resolutions
- 00:27:28 – Lexicon-based approaches links for experimenting
- 00:28:35 – Multiple language support in lexicon-based packages
- 00:35:23 – Machine learning techniques
- 00:39:20 – Tools for this approach
- 00:42:54 – Video Course Spotlight
- 00:44:15 – Advantages to the machine learning models approach
- 00:45:55 – Large language model approach
- 00:48:44 – Encoder vs decoder models
- 00:52:09 – Comparing the concept of fine-tuning
- 00:56:49 – Is this a recent development?
- 00:58:08 – Ways to practice with these techniques
- 01:00:10 – Do you find this to be a promising approach?
- 01:07:45 – Resources to practice with all the techniques
- 01:11:06 – Upcoming conference talks
- 01:11:56 – Thanks and goodbye
Show Links:
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20 December 2024, 12:00 pm
51 minutes 26 seconds

Good Python Programming Practices When New to the Language
What advice would you give to someone moving from another language to Python? What good programming practices are inherent to the language? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss an older forum post from a new Python user who came from Perl. We suggest checking out PEP 8, or as it’s commonly known, “The Style Guide for Python Code.” We provide advice about installing Python, avoiding common pitfalls, learning how scope is managed, and taking advantage of a collection of Real Python resources.

We share several other articles and projects from the Python community, including a new Python release, practical NumPy examples and exercises, considering targets of for loops, exploring Python dependency management, checking package compatibility with free-threading and subinterpreters, an experimental filesystem navigator in Textual, and a background workers reference implementation in Django.

This episode is sponsored by AssemblyAI.

Course Spotlight: Writing Beautiful Pythonic Code With PEP 8

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

Topics:
- 00:00:00 – Introduction
- 00:02:17 – Python 3.14.0 Alpha 2 Released
- 00:02:35 – Take the 2024 Django Developers Survey
- 00:03:17 – NumPy Practical Examples: Useful Techniques
- 00:07:09 – Loop Targets
- 00:09:19 – Python Dependency Management Is a Dumpster Fire
- 00:23:15 – Sponsor: AssemblyAI
- 00:24:00 – Package Compatibility With Free-Threading and Subinterpreters
- 00:27:02 – Suggestions for good programming practices?
- 00:37:59 – Video Course Spotlight
- 00:39:24 – terminal-tree: Experimental Filesystem Navigator in Textual
- 00:43:56 – django-tasks: Background Workers Reference Implementation
- 00:49:44 – Thanks and goodbye
News:
- Python 3.14.0 Alpha 2 Released
- Take the 2024 Django Developers Survey
Topics:
- NumPy Practical Examples: Useful Techniques – In this tutorial, you’ll learn how to use NumPy by exploring several interesting examples. You’ll read data from a file into an array and analyze structured arrays to perform a reconciliation. You’ll also learn how to quickly chart an analysis and turn a custom function into a vectorized function.
- Loop Targets – Loop assignment allows you to assign to a dict item in a for loop. This post covers what that means and that it is no more costly than regular assignment.
- Python Dependency Management Is a Dumpster Fire – Managing dependencies in Python can be a bit of a challenge. This deep dive article shows you all the problems and how the problems are mitigated if not solved.
- Package Compatibility With Free-Threading and Subinterpreters – This tracker tests the compatibility of the 500 most popular packages with Python 3.13’s free-threading and subinterpreter features.
Discussion:
Projects:
- terminal-tree: Experimental Filesystem Navigator in Textual
- django-tasks: Background Workers Reference Implementation
Additional Links:
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6 December 2024, 12:00 pm
1 hour 58 seconds

marimo: Reactive Notebooks and Deployable Web Apps in Python
What are common issues with using notebooks for Python development? How do you know the current state, share reproducible results, or create interactive applications? This week on the show, we speak with Akshay Agrawal about the open-source reactive marimo notebook for Python.

Before writing any code, Akshay wrote a 2,500-word design document. He wanted to create a maintainable and reproducible tool that avoided the hidden state of traditional notebooks. We discuss solving the hidden state problem by building the notebook as a directed acyclic graph (DAG).

Akshay shares how marimo notebooks are stored as pure Python files, which makes them easy to read, importable, and git-friendly. We discuss serializing package requirements using PEP 723 inline metadata to create standalone reproducible notebooks. We also cover how marimo notebooks can be deployed as a web app or dashboard using Pyodide.

Course Spotlight: Navigating Namespaces and Scope in Python

In this course, you’ll learn about Python namespaces, the structures used to store and organize the symbolic names created during execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.

Topics:
- 00:00:00 – Introduction
- 00:02:06 – Akshay’s background and studies
- 00:04:14 – Work at Google and PhD program
- 00:06:29 – Sharing notebooks
- 00:08:18 – Starting work on marimo 2 years ago
- 00:12:48 – Avoiding notebook issues and building a DAG
- 00:18:39 – The difference of reactivity
- 00:20:39 – What is a marimo notebook?
- 00:23:39 – Video Course Spotlight
- 00:24:50 – Reproducibility and managing package requirements
- 00:27:49 – Using decorators for cells
- 00:30:23 – Writing a design document before any coding
- 00:34:08 – Interactivity and UI widgets
- 00:38:20 – Design decisions and built-in widgets
- 00:42:05 – Creating a deployable web application
- 00:44:34 – Exploring examples and tutorials
- 00:46:13 – Supporting DataFrame libraries with narwhals
- 00:48:00 – Migrating from a Jupyter notebook
- 00:52:02 – Working with cells and not running code
- 00:54:30 – A couple favorite tutorials
- 00:56:17 – What are you excited about in the world of Python?
- 00:57:39 – What do you want to learn next?
- 00:59:34 – How can people follow the project and yourself?
- 01:00:12 – Thanks and goodbye
Show Links:
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29 November 2024, 12:00 pm
45 minutes 50 seconds

The Joy of Tinkering & Python Free-Threading Performance
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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22 November 2024, 12:00 pm
1 hour 9 minutes

Maintaining the Foundations of Python & Cautionary Tales
How do you build a sustainable open-source project and community? What lessons can be learned from Python’s history and the current mess that the WordPress community is going through? This week on the show, we speak with Paul Everitt from JetBrains about navigating open-source funding and the start of the Python Software Foundation.

Paul has been an organizer in the Python community almost from the beginning. He shares how the project has navigated through multiple sponsors. We talk about the early governance models and the formation of the Python Software Foundation.

We contrast this journey with the current drama unfolding in the WordPress community. We discuss the potential problems of having a benevolent dictator for life. We also dig into sponsorship models and ways to get companies to give back to the open-source projects they rely on.

This episode is sponsored by Sentry.

Course Spotlight: Using pandas to Make a Gradebook in Python

With this course and Python project, you’ll build a script to calculate grades for a class using pandas. The script will quickly and accurately calculate grades from a variety of data sources. You’ll see examples of loading, merging, and saving data with pandas, as well as plotting some summary statistics.

Topics:
- 00:00:00 – Introduction
- 00:01:55 – Meeting Jodie Burchell at PyCon 2022
- 00:02:51 – A non-traditional path into open-source
- 00:07:09 – The current turmoil around WordPress
- 00:13:49 – Keeping things fair in the age of extraction
- 00:16:03 – Sponsor: Sentry
- 00:17:07 – Early Python organizing history and conservation
- 00:20:41 – The Python Software Activity precursor to PSF
- 00:24:14 – Creating the Python Software Foundation
- 00:27:24 – Keeping the perfect distance of business and project
- 00:28:13 – Who gets to capture the value from open-source?
- 00:31:07 – Sponsorships becoming more common
- 00:33:24 – BDFL to a steering council
- 00:34:58 – Video Course Spotlight
- 00:36:16 – What is Plone?
- 00:38:11 – Starting in Python and finding community
- 00:50:07 – Companies contributing
- 00:53:16 – Examples of how JetBrains contributes back
- 00:55:41 – Understanding the support system
- 00:58:09 – Talking to decision makers
- 01:00:07 – Python 1994 talk and continuation
- 01:01:49 – What are you excited about in the world of Python?
- 01:03:06 – What do you want to learn next?
- 01:04:17 – How can people follow your work online?
- 01:07:16 – Thanks and goodbye
Show Links:
Level up your Python skills with our expert-led courses:
Support the podcast & join our community of Pythonistas
15 November 2024, 12:00 pm
47 minutes 58 seconds

New PEPs: Template Strings & External Wheel Hosting
Have you wanted the flexibility of f-strings but need safety checks in place? What if you could have deferred evaluation for logging or avoiding injection attacks? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a set of recent Python Enhancement Proposals (PEPs). The idea of template strings has been under consideration for a while, and PEP 750 describes a new way forward. PEP 759 proposes a way for projects on PyPI to safely host resources on external sites using a new package upload format called a .rim file.

We share several other articles and projects from the Python community, including what didn’t make the headlines about Python 3.13, solving Sudoku with Python packaging, what’s sweet about Python’s syntactic sugar, creating database-generated columns using SQLite and Django, a discussion about mentoring, an adaptive web scraper, and a debugging tool for HTTP(S) client requests.

This episode is sponsored by Sentry.

Course Spotlight: Using Pydantic to Simplify Python Data Validation

Discover the power of Pydantic, Python’s most popular data parsing, validation, and serialization library. In this hands-on video course, you’ll learn how to make your code more robust, trustworthy, and easier to debug with Pydantic.

Topics:
- 00:00:00 – Introduction
- 00:02:08 – Python 3.14.0 Alpha 1 Released
- 00:02:38 – Python 3.13, What Didn’t Make the Headlines
- 00:05:23 – What’s up Python? 3.13 is out, t-strings look awesome
- 00:10:21 – Sponsor: Sentry
- 00:11:25 – Sudoku in Python Packaging
- 00:14:29 – Syntactic Sugar: Why Python Is Sweet and Pythonic
- 00:22:31 – Database generated columns: Django & SQLite
- 00:27:14 – Video Course Spotlight
- 00:28:39 – Mentors
- 00:42:23 – Scrapling: Lightning-Fast, Adaptive Web Scraping for Python
- 00:44:14 – httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests
- 00:46:04 – Request for project submissions to PyCoders
- 00:46:59 – Thanks and goodbye
News:
- Python 3.14.0 Alpha 1 Released
Show Links:
- Python 3.13, What Didn’t Make the Headlines – Bite Code summarizes some of the lesser covered changes to Python in the 3.13 release, including how some of the REPL improvements made it into pdb, improvements to shutil, and small additions to the asyncio library.
- What’s up Python? 3.13 is out, t-strings look awesome, dep groups come in handy…
- Sudoku in Python Packaging – Simon writes about a Sudoku solver written by Konstin that uses the Python packaging mechanisms to do Sudoku puzzles. The results are output using a requirements.txt file, where sudoku-0-3==5 represents the (0,3) cell’s answer of 5.
- Syntactic Sugar: Why Python Is Sweet and Pythonic – In this tutorial, you’ll learn what syntactic sugar is and how Python uses it to help you create more readable, descriptive, clean, and Pythonic code. You’ll also learn how to replace a given piece of syntactic sugar with another syntax construct.
- Database generated columns: Django & SQLite – An introduction to database generated columns, using SQLite and the new GeneratedField added in Django 5.0
Discussion:
- Mentors – Ryan just finished his second round of mentoring with the Djangonaut.Space program. This post talks about how you can help your mentor help you and how to be a good mentor.
Projects:
- Scrapling: Lightning-Fast, Adaptive Web Scraping for Python
- httpdbg: A tool for Python developers to easily debug the HTTP(S) client requests in a Python program
Additional Links:
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8 November 2024, 12:00 pm
1 hour 19 minutes

PySheets: Spreadsheets in the Browser Using PyScript
What goes into building a spreadsheet application in Python that runs in the browser? How do you make it launch quickly, and where do you store the cells of data? This week on the show, we speak with Chris Laffra about his project, PySheets, and his book “Communication for Engineers.”

As a software engineer, Chris has worked at IBM, Google, Uber, and several financial institutions. He speaks about developer productivity and communication skills as an engineer. We begin our conversation by digging into his background, his approach to building engineering teams, and strategies for improving communication.

Chris’ idea for PySheets is to have Excel inside Python with everything running locally in your browser. He was inspired by the success of Jupyter Notebooks but wanted to develop a tool more suited to a spreadsheet’s non-linear graph structure.

PySheets is built to run locally in the user’s browser, taking advantage of PyScript. We discuss finding the right solution for storing data in the browser and developing a graphic toolkit to create the UI. Chris also shares the novel method he found to get the interface up and running while the larger assets are loading.

This episode is sponsored by Sentry.

Course Spotlight: Understanding Python’s Global Interpreter Lock (GIL)

Python’s Global Interpreter Lock, or GIL, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. In this video course, you’ll learn how the GIL affects the performance of your Python programs.

Topics:
- 00:00:00 – Introduction
- 00:02:25 – Background with building engineering teams
- 00:08:43 – Communication for Engineers book
- 00:16:17 – What do customers want and experiences at IBM
- 00:24:28 – Starting the development of PySheets
- 00:27:19 – Working with the DOM
- 00:29:41 – Success of Jupyter notebooks
- 00:35:46 – Sponsor: Sentry
- 00:36:52 – Little Toolkit for PyScript
- 00:43:24 – Finding funding
- 00:46:58 – Building a product before selling
- 00:52:27 – Video Course Spotlight
- 00:53:46 – Finding the right data storage in IndexedDB
- 01:01:57 – Exploring the trial page and extensibility
- 01:08:26 – Contributing to the project or forking
- 01:11:56 – What are you excited about in the world of Python?
- 01:16:20 – What do you want to learn next?
- 01:17:25 – How can people follow your work online?
- 01:18:05 – Thanks and goodbye
Show Links:
Level up your Python skills with our expert-led courses:
Support the podcast & join our community of Pythonistas
1 November 2024, 12:00 pm
43 minutes 4 seconds

Python Getting Faster and Leaner & Ideas for Django Projects
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:
- tinystatus: Tiny Status Page Generated by a Python Script
- srgn: Grep-Like Tool That Understands Code
Additional Links:
Level up your Python skills with our expert-led courses:
Support the podcast & join our community of Pythonistas
25 October 2024, 12:00 pm
1 hour 33 seconds

Narwhals: Expanding DataFrame Compatibility Between Libraries
How does a Python tool support all types of DataFrames and their various features? Could a lightweight library be used to add compatibility for newer formats like Polars or PyArrow? This week on the show, we speak with Marco Gorelli about his project, Narwhals.

Narwhals is a project aimed at library maintainers rather than end users. We discuss how the added compatibility benefits users by supporting modern features like lazy evaluation. We cover several projects Marco has been working with to implement Narwhals, including Altair, scikit-lego, and Ibis.

We also discuss how Marco started contributing to open-source projects. Marco has contributed to both pandas and Polars, which helps explain his interest in growing compatibility between libraries. He also offers advice on making your first contribution.

This episode is sponsored by CodeRabbit.

Course Spotlight: Differences Between Python’s Mutable and Immutable Types

In this video course, you’ll learn how Python’s mutable and immutable data types work internally and how you can take advantage of mutability or immutability to power your code.

Topics:
- 00:00:00 – Introduction
- 00:02:02 – Euro SciPy 2024 and sprints
- 00:04:04 – How did you get involved in open source?
- 00:07:18 – Finding a good issue to get started
- 00:09:25 – Discord and open-source projects
- 00:11:12 – Who would you describe Narwhals?
- 00:16:47 – Working on Polars
- 00:19:17 – Apache Arrow and a data interchange protocol
- 00:22:55 – Sponsor: CodeRabbit
- 00:23:55 – Digging into eager vs lazy
- 00:27:04 – Ibis DataFrame library
- 00:28:57 – What do libraries need from Narwhals?
- 00:34:57 – The scikit-lego library
- 00:37:15 – Video Course Spotlight
- 00:38:45 – Other libraries interested in Narwhals
- 00:41:56 – Compatibility policy
- 00:45:18 – What should an end user expect?
- 00:46:32 – Have other projects that attempted this?
- 00:47:54 – Keeping the project light and pure Python
- 00:49:32 – Contributors and how to get involved
- 00:54:42 – What are you excited about in the world of Python?
- 00:57:18 – What do you want to learn next?
- 00:59:05 – How can people follow your work online?
- 00:59:27 – Thanks and goodbye
Show Links:
Level up your Python skills with our expert-led courses:
Support the podcast & join our community of Pythonistas
18 October 2024, 12:00 pm
55 minutes 24 seconds

Exploring the New Features of Python 3.13
Python 3.13 is here! Our regular guests, Geir Arne Hjelle and Christopher Trudeau, return to discuss the new version. This year, Geir Arne coordinated a series of preview articles with members of the Real Python team and a showcase tutorial, “Python 3.13: Cool New Features for You to Try.” Christopher’s video course “What’s New in Python 3.13” covers the topics from the article and shows the new features in action.

Geir Arne and Christopher dug into the release to create code examples of the new features for the tutorial and course. We look at the options for disabling the Global Interpreter Lock (GIL) and enabling the Just-in-Time (JIT) compiler. We also discuss the new interactive interpreter, better error messages, multiple improvements to static typing, and additional performance improvements.

We share our thoughts on the updates and offer advice about incorporating them into your projects. We also discuss when you should start running Python 3.13.

This is episode is sponsored by Nvidia.

Course Spotlight: What’s New in Python 3.13

In this video course, you’ll learn about the new features in Python 3.13. You’ll take a tour of the new REPL and error messages and see how you can try out the experimental free threading and JIT versions of Python 3.13 yourself.

Topics:
- 00:00:00 – Introduction
- 00:03:14 – A Modern REPL
- 00:08:54 – Making the Global Interpreter Lock Optional in CPython
- 00:11:33 – JIT Compilation
- 00:15:48 – More improved error messages
- 00:18:30 – Sponsor: NVIDIA
- 00:19:13 – Marking deprecations using the type system
- 00:21:09 – Type Defaults for Type Parameters
- 00:22:44 – Narrowing types with TypeIs
- 00:25:24 – TypedDict: Read-only items
- 00:27:50 – Random command line interface
- 00:29:54 – New copy.replace()
- 00:33:43 – Video Course Spotlight
- 00:34:55 – Pathlib and globbing
- 00:39:33 – Stripping docstrings
- 00:41:28 – Import improvements
- 00:41:56 – Dynamically import non-code files
- 00:42:23 – Adding iOS as a supported platform
- 00:43:32 – More consistency with local namespace
- 00:44:30 – Support for deprecation in argparse
- 00:45:00 – Better entry points for breakpoint or set_trace
- 00:46:08 – Removing dead batteries
- 00:47:43 – When to upgrade to 3.13?
- 00:53:19 – core.py podcast
- 00:54:14 – Thanks and goodbye
Show Links:
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11 October 2024, 12:00 pm
55 minutes 46 seconds

Using Virtual Environments in Docker & Comparing Python Dev Tools
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:
- csv_trimming: Remove Common Ugliness From CSV Files
- django-tables2: Create HTML Tables in Django
Additional Links:
Level up your Python skills with our expert-led courses:
Support the podcast & join our community of Pythonistas
27 September 2024, 12:00 pm
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