The Real Python Podcast

Topics include Pythonic examples, language mistakes, Microsoft's Python in Excel beta program, PSF announcements, Polars DataFrame library showcase, immortal objects in Python, code image generator project, MS Paint clone in terminal, Django ORM cheatsheet.

Philipp Acsany, a Real Python author and core team member, talks about choosing the right coding font for programming in Python. They discuss the importance of differentiating characters, the impact of syntax on font choice, and provide resources to find the perfect monospace font. The podcast also mentions a course on building a Python wordle clone and a discussion about the Haiti programming language.

Author and Python trainer Matt Harrison discusses his new book on improving classification models with XGBoost. He emphasizes the importance of exploratory data analysis and provides tools to explain models to stakeholders. The podcast also covers the popularity of XGBoost, the concept of prediction in data science, and the application of XGBoost in classification models.

Have you ever encountered strange behavior when trying something new in Python? What are common quirks hiding within the language? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects. We discuss a recent blog post that lists a collection of quirky Python behaviors. We share a few examples with explanations but leave several as puzzles to dig into. Christopher transitions our discussion into Python features that can be difficult to explain to a new programmer. We also share some of our own stumbling blocks while learning the language. We cover several other articles and projects from the Python community, including a news update, previewing Python 3.12’s more intuitive and consistent f-strings, finding performance bottlenecks with profiling, emulating the 6502 processor in Python, using Rich to inspect Python objects, and plotting statistical data with Lets-Plot. This week’s episode is brought to you by Porkbun. Course Spotlight: Graph Your Data With Python and ggplot In this course, you’ll learn how to use ggplot in Python to build data visualizations with plotnine. You’ll discover what a grammar of graphics is and how it can help you create plots in a very concise and consistent way. Topics: 00:00:00 – Introduction 00:02:12 – Steering Council Announces Optional GIL 00:04:05 – Polars is Starting a Company 00:05:13 – Python 3.12 Preview: More Intuitive and Consistent F-Strings 00:09:29 – Sponsor: Porkbun 00:10:22 – Profiling in Python: How to Find Performance Bottlenecks 00:21:01 – Writing a 6502 Emulator in Python 00:24:36 – Python Quirks 00:32:30 – Video Course Spotlight 00:34:02 – What Python feature would you have trouble explaining to a new programmer? 00:42:25 – Using Rich Inspect to Interrogate Python Objects 00:44:36 – Lets-Plot: Plotting Library for Statistical Data 00:48:01 – Thanks and goodbye News: Steering Council Announces Optional GIL Polars is Starting a Company Show Links: Python 3.12 Preview: More Intuitive and Consistent F-Strings – In this tutorial, you’ll preview one of the upcoming features of Python 3.12, which introduces a new f-string syntax formalization and implementation. The new implementation lifts some restrictions and limitations that affect f-string literals in Python versions lower than 3.12. Profiling in Python: How to Find Performance Bottlenecks – In this tutorial, you’ll learn how to profile your Python programs using numerous tools available in the standard library, third-party libraries, as well as a powerful tool foreign to Python. Along the way, you’ll learn what profiling is and cover a few related concepts. Writing a 6502 Emulator in Python – The 6502 processor from Motorola was quite popular and could be found in the Nintendo and Sega consoles as well as the Commodore 64. This very detailed article shows you how to build an emulator for the processor in Python. Python Quirks – A straight-out list of code snippets showing off some of the weird and unexpected behavior of your favorite language. Discussion What Python feature would you have trouble explaining to a new programmer? - Trey Hunner on Twitter Projects: Using Rich Inspect to Interrogate Python Objects – You might know the Rich library as the terminal color tool, but it has a few utilities that are generally helpful in your code. This article shows you the inspect feature, which gives you loads of information on an object. Lets-Plot: Plotting Library for Statistical Data Additional Links: Polars Python 3.12 Preview: Support For the Linux perf Profiler – Real Python Python Timer Functions: Three Ways to Monitor Your Code – Real Python Episode #128: Using a Memory Profiler in Python & What It Can Teach You – The Real Python Podcast Defining Your Own Python Function – Mutable Default Parameters Level up your Python skills with our expert-led courses: Using Jupyter Notebooks Creating Web Maps From Your Data With Python Folium Graph Your Data With Python and ggplot Support the podcast & join our community of Pythonistas

What are the new ways to describe your data in pandas 2.0? Will the addition of Apache Arrow to the data back end foster the growth of data interoperability? This week on the show, we talk with pandas core developer Marc Garcia about the release of pandas 2.0. Marc shares his background and work on pandas. We discuss the history of data representation in pandas and the need to move beyond NumPy. We also talk about how Apache Arrow only solves some of the issues. We dig into the potential of an Apache Arrow back end and how it could offer interoperability between data platforms. We also cover the moderate adoption and backward-compatibility concerns. Marc also shares his thoughts on making pandas more extensible. Course Spotlight: The pandas DataFrame: Working With Data Efficiently In this course, you’ll get started with pandas DataFrames, which are powerful and widely used two-dimensional data structures. You’ll learn how to perform basic operations with data, handle missing values, work with time-series data, and visualize data from a pandas DataFrame. Topics: 00:00:00 – Introduction 00:02:07 – Getting involved with the pandas project 00:03:48 – Continued growth of the platform 00:06:49 – Parallel branch development 00:09:19 – The introduction of Apache Arrow 00:18:53 – Working with NumPy data in pandas 00:30:18 – Arrow data types and strings 00:41:23 – Video Course Spotlight 00:42:37 – Interoperability of Arrow data back end 00:50:36 – Could pandas be more extensible? 01:00:49 – Python DataFrame Summit 2023 01:08:12 – What are you excited about in the world of Python? 01:11:13 – What do you want to learn next? 01:12:12 – How can people follow your work online? 01:13:46 – Thanks and Goodbye Show Links: Marc Garcia - datapythonista - data engineer, data scientist and pandas core developer pandas 2.0 and the Arrow revolution (part I) The pandas of the future - Marc Garcia - SciPyLA 2019 - TubEdu The deadly consequences of rounding errors - Slate Community Blog - pandas - Python Data Analysis Library Apache Arrow - Apache Arrow Apache Arrow and the “10 Things I Hate About pandas” - Wes McKinney I/O Extensions in pandas - PDEP-9 Extension Arrays for Pandas - Tom’s Blog Python Dataframe Summit 2023 Rust Programming Language Freediving - Wikipedia Marc Garcia - LinkedIn Marc Garcia (@datapythonista) - X Level up your Python skills with our expert-led courses: Reading and Writing Files With pandas Explore Your Dataset With pandas The pandas DataFrame: Working With Data Efficiently Support the podcast & join our community of Pythonistas

What are all the different versions of Python? You may have heard of Cython, Brython, PyPy, or others and wondered where they fit into the Python landscape. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares an article from the Bite Code blog about all the different forms that Python can take. CPython is the reference implementation of the language, which is what we usually discuss. He lists several alternative projects and the use cases. We also discuss a recent Real Python tutorial about IPython. IPython is an interactive Python shell from the team that developed Jupyter Notebooks. It includes a set of IDE-like features and unique magic commands. The tutorial digs into using the tool to learn more about Python and explore your code. We cover several other articles and projects from the Python community, including several news updates, the state of WASI support for CPython, how Python uses garbage collection, a discussion about the current AI echo chamber, an async Python web microframework, a stand-alone CSV editor, and a project for identifying unused dependencies to avoid a bloated virtual environment. This week’s episode is brought to you by Scout APM. Course Spotlight: Mazes in Python Part 1: Building and Visualizing In this two-part video course project, you’ll build a maze solver in Python using graph algorithms from the NetworkX library. Along the way, you’ll design a binary file format for the maze, represent it in an object-oriented way, and visualize the solution using scalable vector graphics (SVG). Topics: 00:00:00 – Introduction 00:02:19 – Python 3.12.0 Beta 4 Released 00:02:53 – Django in Action - Christopher’s Book 00:04:28 – State of WASI Support for CPython: June 2023 00:08:32 – What’s the Deal With CPython, PyPy, MicroPython, Jython…? 00:12:10 – Sponsor: Scout APM 00:12:57 – Unlock IPython’s Magical Toolbox for Your Coding Journey 00:18:33 – How Python Uses Garbage Collection 00:21:31 – Video Course Spotlight 00:23:04 – Are People in Tech Inside an AI Echo Chamber? 00:39:39 – quart: An Async Python Web Microframework 00:41:33 – Modern CSV: CSV Editor/Viewer 00:43:09 – creosote: Identify Unused Dependencies 00:45:09 – Thanks and Goodbye News: Python 3.12.0 Beta 4 Released Django in Action - Christopher’s Book - Manning Early Access Program Show Links: State of WASI Support for CPython: June 2023 – This post from Brett Cannon covers the current state of WebAssembly targets in Python. What’s the Deal With CPython, PyPy, MicroPython, Jython…? – This comprehensive article introduces you to all the different ways that you can Python. CPython isn’t the only choice. Learn what else is out there and why you might choose an alternative. Unlock IPython’s Magical Toolbox for Your Coding Journey – IPython is a powerful tool that can prove useful on your journey to mastering Python. Its friendly interface will enable you to comfortably take control of your learning. In this tutorial, you’ll cover the basic concepts of using IPython and learn how its features can make coding efficient. How Python Uses Garbage Collection – This article outlines how Python stores variables as references and how that relates to memory management Discussion: Are People in Tech Inside an AI Echo Chamber? Inside the AI Factory: the humans that make tech seem human - The Verge Is ChatGPT getting worse over time? Study claims yes, but others aren’t sure | Ars Technica How Is ChatGPT’s Behavior Changing over Time? Projects: quart: An Async Python Web Microframework Modern CSV: CSV Editor/Viewer creosote: Identify unused dependencies and avoid a bloated virtual environment Additional Links: Episode #154: Targeting WebAssembly Platforms & Distilling a Minimum Viable Python – The Real Python Podcast IPython 8.14.0 documentation Level up your Python skills with our expert-led courses: Mazes in Python Part 2: Storing and Solving Python Basics: Object-Oriented Programming Mazes in Python: Build, Visualize, Store, and Solve Support the podcast & join our community of Pythonistas

Are you getting the most out of your Postgres database? What features could you leverage to improve your Python project? This week on the show, Craig Kerstiens from Crunchy Data is here to discuss getting the most out of Postgres. Craig shares his years of PostgreSQL expertise with advice on getting more from the platform. We talk about rich data types for describing ranges, geospatial data, and JSON. Craig shares tools for accessing performance statistics from the command line and strategies for optimizing your terminal settings for SQL searches. He discusses Postgres extensions for customizing the database to your needs. Craig also provides multiple resources for learning more and an online tool for practicing within a playground environment. Course Spotlight: Creating Web Maps From Your Data With Python Folium You’ll learn how to create web maps from data using Folium. The package combines Python’s data-wrangling strengths with the data-visualization power of the JavaScript library Leaflet. In this video course, you’ll create and style a choropleth world map showing the ecological footprint per country. Topics: 00:00:00 – Introduction 00:02:36 – What are reasons for considering Postgres? 00:07:41 – Timeline of recent features 00:11:06 – Unique data types 00:16:34 – Storing JSON information 00:20:19 – Video Course Spotlight 00:21:50 – Storing geographic information 00:25:16 – What’s the process for adding extensions? 00:31:33 – Advice for Python developers using Postgres 00:33:31 – Advice on writing SQL 00:38:06 – Command-line tools and customizations 00:48:18 – Django as an entry to Python 00:51:13 – Resources for learning and practicing with Postgres 00:53:45 – What are you excited about in the world of Python? 00:55:55 – What do you want to learn next? 00:58:34 – How can people follow your work online? 00:59:20 – Thanks and goodbye Show Links: Craig Kerstiens - Blog Trusted Open Source PostgreSQL & Commercial Support for the Enterprise - Crunchy Data Why Postgres? - Crunchy Data Why Postgres - Craig Kerstiens - YouTube A hands on experience with complex SQL - Craig Kerstiens - YouTube Ingres (database) - Wikipedia PostgreSQL specific model fields - Django documentation Psqlrc - PostgreSQL wiki The most useful Postgres extension - pg_stat_statements High-compression Metrics Storage with Postgres Hyperloglog Postgres Playground and Tutorials - Crunchy Data PostgreSQL Blog - Crunchy Data It’s at least once a week I talk with someone that “loves Postgres” but isn’t sure why… Craig Kerstiens on Twitter: The Wok Book: Recipes and Techniques by J. Kenji Lopez-Alt Level up your Python skills with our expert-led courses: Creating Web Maps From Your Data With Python Folium Deploying a Flask Application Using Heroku SQLite and SQLAlchemy in Python: Moving Your Data Beyond Flat Files Support the podcast & join our community of Pythonistas

What principles should you consider when designing a Python library? How do you construct a library API that’s understandable and easy to use? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects. We share an article about building library APIs. The piece provides advice for package structure, naming, error handling, and more. The author guides you toward Pythonic principles by comparing clunky vs elegant design examples. Christopher discusses his recent video course on Jinja templating. The course covers creating text files with programmatic content and employing rich templates to structure the front end of Python web applications. We cover several other articles and projects from the Python community, including several news updates, reasons why membership tests are fast for the range() function, CLI tools hidden in the Python standard library, a thread about the right way to install Python, recipes for using the Polars library, and a project for feature flags within Django. This week’s episode is brought to you by Snyk. Course Spotlight: Jinja Templating With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out. Topics: 00:00:00 – Introduction 00:02:16 – PyLadies Conference (Dec 2023) Call for Volunteers 00:02:32 – PSF Board Election Results 00:03:47 – PSF Announces New Security Developer in Residence 00:04:39 – Django Security Releases Issued 00:04:50 – Deprecation of bdist_egg Uploads to PyPI 00:05:54 – Why Are Membership Tests So Fast for range() in Python? 00:11:51 – CLI Tools Hidden in the Python Standard Library 00:15:59 – Sponsor: Snyk 00:16:49 – Designing Pythonic Library APIs 00:28:27 – Jinja Templating 00:32:49 – Kill a Developer in 4 Words or Less 00:37:28 – Video Course Spotlight 00:38:51 – What is “the right way” to install Python on a new M2 MacBook? 00:44:11 – polars-cookbook: Recipes for Using Python’s Polars Library 00:46:48 – waffle: Feature Flags for Django 00:49:54 – Thanks and goodbye News: PyLadies Conference (Dec 2023) Call for Volunteers PSF Board Election Results PSF Announces New Security Developer in Residence I Am the First PSF Security Developer-in-Residence – Seth was recently hired as the first security developer in residence at the PSF. His blog post talks about what his responsibilities are and how he defines success for the position. Deputy CPython Developer in Residence - Python Software Foundation - Career Page Django Security Releases Issued: 4.2.3, 4.1.10, and 3.2.20 Deprecation of bdist_egg Uploads to PyPI – PEP 715 has been accepted and as of August 1, 2023, the .egg format will no longer be accepted as an upload. Existing eggs on PyPI will remain in place. Show Links: Why Are Membership Tests So Fast for range() in Python? – In Python, range() is most commonly used in for loops. However, ranges have some other use cases too, as they share many properties with lists. In this tutorial, you’ll explore why it’s so fast to perform a membership test on a Python range. CLI Tools Hidden in the Python Standard Library – There are several modules in Python that are directly callable from the command line, including the ability to gzip and pretty-print JSON. This article introduces you to what options are available and how Simon discovered them. Designing Pythonic Library APIs – This article summarizes principles that Ben has found useful when designing Python library APIs. Topics include structure, naming, error handling, and type annotations. Jinja Templating – With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out. Discussion: Kill a Developer in 4 Words or Less - Twitter What is “the right way” to install Python on a new M2 MacBook? - Twitter Projects: polars-cookbook: Recipes for Using Python’s Polars Library waffle: Feature Flags for Django Additional Links: PSF Board of Directors Nominees - 2023 - YouTube pandas-cookbook: Recipes for using Python’s pandas library Level up your Python skills with our expert-led courses: Getting Started With Django: Building a Portfolio App Deploy Your Python Script on the Web With Flask Jinja Templating Support the podcast & join our community of Pythonistas

How much Python do you need to learn to start creating projects? What’s a good balance of information and hands-on practice? This week on the show, Eric Matthes is here to discuss his book Python Crash Course. As a former high school science, math, and programming teacher, Eric saw something missing in the programming publishing landscape. We discuss the guiding questions that inspired the book’s development and the title. Eric covers how the crash course takes readers through a fast-paced introduction to Python that culminates in three unique projects. We also discuss Eric’s blog, Mostly Python, where he digs deeper into technical subjects. He also occasionally shares more topical posts and includes many exercises. Course Spotlight: Plot With pandas: Python Data Visualization Basics In this course, you’ll get to know the basic plotting possibilities that Python provides in the popular data analysis library pandas. You’ll learn about the different kinds of plots that pandas offers, how to use them for data exploration, and which types of plots are best for certain use cases. Topics: 00:00:00 – Introduction 00:02:04 – Why Alaska? 00:05:33 – What do you like about Python? 00:06:40 – Becoming an author & the intended reader 00:15:56 – History of programming books 00:17:39 – Having a purpose for learning 00:20:22 – Exercising as you go 00:24:45 – Including projects in the book 00:26:32 – Is the “crash course” a book model? 00:29:20 – Video Course Spotlight 00:30:29 – How much of a topic to reveal 00:35:08 – Guiding questions 00:42:43 – Moving from functions to testing in four chapters 00:46:21 – What were you excited to update in the book? 00:50:28 – Were there doubts about preparing readers for projects? 00:53:52 – Space invaders clone pygame project 00:55:52 – Data visualization project 01:00:38 – Django web app project 01:04:15 – Cheat sheets and additional resources 01:07:54 – Python flash cards 01:09:54 – What are you excited about in the world of Python? 01:10:54 – What do you want to learn next? 01:11:46 – How can people follow the work you do? 01:13:06 – Thanks and goodbye Show Links: Python Crash Course, 3rd Edition - No Starch Press Episode #105: Creating Better Error Messages for Python 3.10 & 3.11 – The Real Python Podcast Making Python Better One Error Message At A Time - PyCon 2022 Learning Python, 2nd Edition pytest: helps you write better programs — pytest documentation Pygame Matplotlib — Visualization with Python Plotly: Low-Code Data App Development django-simple-deploy - readthedocs.io Cheat Sheets - Python Crash Course, Third Edition Python Flash Cards - No Starch Press DjangoCon US 2023 - October 16-20, 2023 - Durham, NC United States pandas - Python Data Analysis Library Python Crash Course, Third Edition - GitHub Resources Mostly Python - Eric Matthes - Substack Eric Matthes (@ehmatthes@fosstodon.org) - Fosstodon Level up your Python skills with our expert-led courses: Testing Your Code With pytest Histogram Plotting in Python: NumPy, Matplotlib, Pandas & Seaborn Plot With pandas: Python Data Visualization Basics Support the podcast & join our community of Pythonistas

What advice can you extract from the Zen of Python? How can these nineteen guiding principles help you write more idiomatic Python? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects. Christopher shares a Real Python tutorial by Bartosz Zaczyński titled “What’s the Zen of Python?” We talk about the poem’s origin and ways to access the Zen within Python. We also discuss how different sections provide contradictory advice for what makes good Python code. We cover a recent post by previous guest Matt Harrison about using Python and pandas for finance. Matt’s article covers methods in the pandas library for aggregation, resampling, and rolling averages. We cover several other articles and projects from the Python community, including staying up-to-date with news, solving a Legend of Zelda puzzle with Python, avoiding “simply” providing advice, displaying better stack traces, and creating files with fake data. Course Spotlight: Speed Up Python With Concurrency Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing. Topics: 00:00:00 – Introduction 00:01:49 – Python 3.11.4, 3.10.12, 3.9.17, 3.8.17, 3.7.17, and Python 3.12.0 beta 3 released 00:02:24 – Enforcement of 2FA for PyPI Began June 1st 00:02:54 – Faster Python 3.13 Plan 00:03:14 – PyPy v7.3.12 Released 00:03:28 – Migrate to .readthedocs.yaml Configuration 00:05:14 – PyCon US 2023 and PyCascades 2023 Videos Are Up 00:05:37 – What’s the Zen of Python? 00:09:22 – Python for Finance: Pandas Resample, Groupby, and Rolling 00:15:24 – Python and the Legend of Zelda 00:17:47 – Don’t Say “Simply” Use Pyenv, Poetry or Anaconda 00:28:54 – Is Parallel Programming Hard? 00:40:54 – Video Course Spotlight 00:42:20 – pymg: A Better Display for Stack Traces 00:44:58 – faker-file: Create Files With Fake Data 00:49:14 – Thanks and goodbye News: Python 3.11.4, 3.10.12, 3.9.17, 3.8.17, 3.7.17, and 3.12.0 Beta 2 Released Python Insider: Python 3.12.0 beta 3 released Enforcement of 2FA for PyPI Began June 1st – For those accounts that have two-factor authentication turned on for PyPI uploads, the use of 2FA is now required. Users with 2FA who were only using a password in the past will now have to perform 2FA as well. This is all part of the PyPI transition to 2FA across the board. Faster Python 3.13 Plan – This brief outline highlights the plan for the faster CPython project for the 3.13 release. It includes PEP 669, PEP 554, improved memory management, and more. Here’s the associated Hacker News discussion. PyPy v7.3.12 Released Migrate to .readthedocs.yaml Configuration – The Read the Docs site has announced the new requirement that all builds must move to using a .readthedocs.yaml configuration file, version 2. There are some test windows where they’ll be temporarily enforcing the change, but the final release date is September 25, 2023. Read on for details on how to migrate your project. PyCascades 2023 Videos Are Up PyCon US 2023 Videos Are Up Topic Links: What’s the Zen of Python? – In this tutorial, you’ll be exploring the Zen of Python, a collection of nineteen guiding principles for writing idiomatic Python. You’ll find out how they originated and whether you should follow them. Along the way, you’ll uncover several inside jokes associated with this humorous poem. Python for Finance: Pandas Resample, Groupby, and Rolling – When working with time series data such as financial information, the resample, grouping, and rolling features of pandas can make your life easier. Python and the Legend of Zelda – The Game Boy Color version of Legend of Zelda: Oracle of Ages contains a grid-based puzzle. Gaz writes about creating a brute-force program to solve the challenge using Python. Don’t Say “Simply” Use Pyenv, Poetry or Anaconda – This article talks about the issues that newer Python coders might encounter by adopting more complicated package management mechanisms and explains why sticking with pip is often the better choice. Discussion: Is Parallel Programming Hard? AsyncIO: Why I Hate It – Charles is the creator of Peewee ORM and often gets the question when will it support asyncio? In this opinion piece, he talks about why he doesn’t like asyncio and which alternatives he prefers. Projects: pymg: A Better Display for Stack Traces faker-file: Create Files With Fake Data Additional Links: Practicality Beats Purity: The Zen of Python’s Escape Hatch - Chris Neugebauer - YouTube Relieving your Python packaging pain Is Parallel Programming Hard, And, If So, What Can You Do About It? - Book Download Level up your Python skills with our expert-led courses: Threading in Python Hands-On Python 3 Concurrency With the asyncio Module Speed Up Python With Concurrency Support the podcast & join our community of Pythonistas