The Real Python Podcast

How well do you know your software supply chain? When you PIP install a package, what steps can you take to minimize the risk of installing something malicious? This week on the show, we have Dustin Ingram, a director of the Python Software Foundation (PSF) and a maintainer of the Python Package Index (PyPI). We talk about Dustin’s PyCon 2021 talk titled “Secure Software Supply Chains for Python”. Dustin shares the types of attacks you should be aware of and how you can make your supply chain more trustworthy. We cover tools, techniques, and best practices. Dustin also discusses what it takes to keep the Python Package Index running and the players working to keep it going into the future. Course Spotlight: A Beginner’s Guide to Pip This course is a great introduction to pip for those who are getting started Python, and for those who want to understand more about what is happening when you install new packages into your environment. It’s a worthy investment of your time to understand the fundamentals of pip. Topics: 00:00:00 – Introduction 00:01:51 – Developer Advocate at Google 00:04:34 – A director of the PSF 00:06:27 – A maintainer of PyPI 00:12:29 – Secure Software Supply Chains for Python - PyCon 2021 00:15:53 – Do I need to be a security expert as a Python developer? 00:17:23 – Typo-squatting of package names 00:19:46 – Sponsor: Scout APM 00:20:52 – Dependency confusion and private repos 00:26:00 – What are some best practices? 00:31:55 – How to lessen the scale of “I don’t know what I don’t know”? 00:36:33 – Tools and techniques that can help 00:44:11 – Video Course Spotlight 00:45:30 – Namespaces on PyPI 00:53:03 – What does it take to power the Python Package Index? 01:01:57 – What are you excited about in the world of Python? 01:03:55 – What do you want to learn next? 01:05:52 – What is something you thought you knew about Python, but were wrong about it? 01:08:46 – Shout outs and social information 01:10:16 – Thanks and goodbye Show Links: Dustin Ingram: Personal Website Python on Google Cloud Cloud Run: Develop and deploy highly scalable containerized applications on a fully managed serverless platform Python Software Foundation PSF Membership FAQ PyPI: The Python Package Index Secure Software Supply Chains for Python: PyCon 2021 - YouTube pip Documentation: Requirements Files pip Documentation: Hash-Checking Mode PEP-0440: Direct references for pip pip-tools: pip-tools keeps your pinned dependencies fresh PyPA: Python Packaging User Guide The Update Framework (TUF) tuf: A secure updater framework for Python pipx: Install and Run Python Applications in Isolated Environments How to Publish an Open-Source Python Package to PyPI - Real Python Article Poetry: Python packaging and dependency management made easy PyUp: Python Dependency Security Dependabot: Automated dependency updates Why Package Signing is not the Holy Grail: Donald Stufft Dependency Confusion: How I Hacked Into Apple, Microsoft and Dozens of Other Companies What Is Pip? A Guide for New Pythonistas - Real Python Article A ‘Worst Nightmare’ Cyberattack: The Untold Story Of The SolarWinds Hack Security scanners for Python and Docker: from code to dependencies What does it take to power the Python Package Index? Level up your Python skills with our expert-led courses: A Beginner's Guide to pip How to Publish Your Own Python Package to PyPI Python Modules and Packages: An Introduction Support the podcast & join our community of Pythonistas

Are you ready to practice your Python skills some more? There is a new set of practice problems prepared for you to tackle, and this time they’re based on working with CSV files. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects. David shares an article about functional programming with a focus on the “filter” function. The tutorial covers how to process an iterable and extract the items that satisfy a given condition. It also covers combining filter with other functional tools, and compares it to coding with Pythonic tools like list comprehensions and generator expressions. We cover several other articles and projects from the Python community including, Excel, Python, and the future of data science, a Bayesian analysis of Lego prices in Python, why can’t comments appear after a line continuation character, teaching Python on the Raspberry Pi400 at the public library, a cross-platform editor designed for writing novels built with Python and Qt, and a text user interface with rich as the renderer. Spotlight: Python vs JavaScript for Python Developers Python and JavaScript are two of the most popular programming languages in the world. In this course, you’ll take a deep dive into the JavaScript ecosystem by comparing Python vs JavaScript. You’ll learn the jargon, language history, and best practices from a Python developer’s perspective. Topics: 00:00:00 – Introduction 00:02:29 – Excel, Python, and the Future of Data Science 00:07:50 – Python Practice Problems: Parsing CSV Files 00:17:09 – Sponsor: Digital Ocean’s App Platform 00:17:45 – A Bayesian Analysis of Lego Prices in Python With PyMC3 00:23:02 – Why Can’t Comments Appear After a Line Continuation Character? 00:28:40 – Python’s filter(): Extract Values From Iterables 00:34:57 – Video Course Spotlight 00:36:24 – How I Teach Python on the Raspberry Pi 400 at the Public Library 00:46:23 – novelWriter: Cross-Platform Editor Designed for Writing Novels Built With Python and Qt 00:48:02 – textual: A Text User Interface With Rich as the Renderer 00:54:58 – Thanks and goodbye Show Links: Excel, Python, and the Future of Data Science – What’s the most widely used tool in data science? Is it pandas or NumPy? Is it the Python language itself? Not really. It’s Excel. You might argue that data scientists aren’t using Excel as their primary tool, and you might be right. But Excel enables non-technical users, like small business owners, to gain insights into their data. In this article, Anaconda CEO Peter Wang discusses his goal of making Python and PyData the “conceptual successor” to Excel. Python Practice Problems: Parsing CSV Files – In this tutorial, you’ll prepare for future interviews by working through a set of Python practice problems that involve CSV files. You’ll work through the problems yourself and then compare your results with solutions developed by the Real Python team. A Bayesian Analysis of Lego Prices in Python With PyMC3 – Follow along with this in-depth analysis of LEGO prices to see Bayesian analysis in action. Along the way, you’ll how pooled and unpooled linear models can be used to determine if a LEGO set is fairly priced. The article is quite technical, so experience with Bayesian statistics is recommended. Why Can’t Comments Appear After a Line Continuation Character? – Chaining together many object methods can create long tines that break the PEP 8 79-character line length recommendation. You can use \ to break the chain of methods onto individual lines, but if you want to leave comments at the end of some of the lines, you’re out of luck. There’s another pattern, though, that solves this. Python’s filter(): Extract Values From Iterables – In this step-by-step tutorial, you’ll learn how Python’s filter() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace filter() and make your code more Pythonic. How I Teach Python on the Raspberry Pi 400 at the Public Library – Community-based programming courses are a great way to introduce folks to computer programming that otherwise may not have the means to do so. One of the barriers to learning to code is cost. You need a computer to program on, after all. But with the advent of tiny computers like the Raspberry Pi, computers aimed at education are more affordable than ever. Projects: novelWriter: Cross-Platform Editor Designed for Writing Novels Built With Python and Qt textual: A Text User Interface With Rich as the Renderer Additional Links: Episode 27: Preparing for an Interview With Python Practice Problems PyMC3: Probabilistic Programming in Python Black: The uncompromising code formatter My review of the Raspberry Pi 400 Will McGugan’s Blog: Introducing Textual Level up your Python skills with our expert-led courses: Python vs JavaScript for Python Developers Python's map() Function: Transforming Iterables Functional Programming in Python Support the podcast & join our community of Pythonistas

The popularity of Python is continuing to grow Developers across the globe are embracing the language. How is Python being used in all of these different countries? How does an organization like the Python Software Foundation (PSF) work toward the goals in its mission statement for supporting and growing this international community? This week on the show, we have Marlene Mhangami, a PSF board member and part of the Diversity and Inclusion Work Group. Marlene lives in Zimbabwe on the continent of Africa. She has been organizing events not only locally in Zimbabwe but across all of Africa. She is the chair of Pycon Africa and has given talks at Pycon US, Pycon UK, and Pycon India. She has been working locally as an organizer and educator. We talk about the challenges of teaching technology and programming to a population of young people. Some of these students don’t have access to computers. She is also currently pursuing a computer science degree with the University of London. Along with her studies, she is also interning with NVidia. She is working with them on the RAPIDS project with a focus on the cuDF library. Spotlight: Introduction to Sorting Algorithms in Python In this course, you’ll learn all about five different sorting algorithms in Python from both a theoretical and a practical standpoint. You’ll also learn several related and important concepts, including Big O notation and recursion. Topics: 00:00:00 – Introduction 00:02:05 – Connecting during PyCon 2021 00:03:08 – Roles with the Python Software Foundation (PSF) 00:05:39 – Python in Africa 00:10:31 – School overseas and return to Zimbabwe to build a Python community 00:13:47 – Teaching technology and Python to students who don’t have computers 00:22:14 – Sponsor: Digital Ocean’s App Platform 00:22:50 – Work with the PSF and building geographic diversity 00:27:49 – PSF work groups 00:32:10 – Organizing PyCon Africa 2019 and bringing a continent of communities together 00:35:51 – How is Python being used in Africa? 00:38:05 – Video Course Spotlight 00:39:24 – Working with NVidia RAPIDS and cuDF 00:43:57 – What are you excited about in the world of Python? 00:46:12 – What do you want to learn next? 00:52:45 – What is something you thought you knew about Python but were wrong about it? 00:59:41 – Thanks and goodbye Show Links: Marlene Mhangami: Personal Website Python Software Foundation PSF Membership PyCon US 2021: YouTube Playlist Real Python Interview With Marlene Mhangami PyCon Africa Conference 2019 Highlights & Interviews: YouTube Django Girls RAPIDS: Open GPU Data Science 10 Minutes to Data Science: Transitioning Between RAPIDS cuDF and CuPy Libraries Level up your Python skills with our expert-led courses: Using pandas to Make a Gradebook in Python Hands-On Python 3 Concurrency With the asyncio Module Introduction to Sorting Algorithms in Python Support the podcast & join our community of Pythonistas

Are you looking for an in-depth data science project to practice your skills on? Perhaps you would like to add new tools to your Python web development projects instead? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects. David shares an article about how to go about detecting deforestation from satellite images. He covers how a data science team built a machine learning (ML) solution to do just that, using FastAI for the modeling and Streamlit to create a dashboard. We also discuss a Real Python article about building a blog using Django, Vue.js, and GraphQL. GraphQL is a great tool to enhance your API to make it more flexible. The step by step project walks you through turning your Django blog data models into a GraphQL API. We cover several other articles and projects from the Python community including, the tools and tech used to run a one-woman hardware company, visualizing data in Python using plt.scatter(), why the sad face when using Black, how to iterate over dataframe rows (and should you?), pipx is now a PyPA member project, and real-time lossless audio compression in Python with pyFLAC. Spotlight: Explore Your Dataset With Pandas In this step-by-step course, you’ll learn how to start exploring a dataset with Pandas and Python. You’ll learn how to access specific rows and columns to answer questions about your data. You’ll also see how to handle missing values and prepare to visualize your dataset in a Jupyter Notebook. Topics: 00:00:00 – Introduction 00:02:11 – Build a Blog Using Django, Vue, and GraphQL 00:10:06 – Detecting Deforestation From Satellite Images 00:16:35 – Sponsor: Digital Ocean’s App Platform 00:17:11 – The Tools and Tech I Use to Run a One-Woman Hardware Company 00:29:13 – Visualizing Data in Python Using plt.scatter() 00:34:24 – Why the Sad Face? 00:40:20 – Video Course Spotlight 00:41:26 – How to Iterate Over DataFrame Rows (And Should You?) 00:48:31 – pyFLAC: Real-Time Lossless Audio Compression in Python 00:53:47 – pipx: Install and Run Python Applications in Isolated Environments 00:59:59 – Thanks and goodbye Show Links: Build a Blog Using Django, Vue, and GraphQL – In this step-by-step project, you’ll build a blog from the ground up. You’ll turn your Django blog data models into a GraphQL API and consume it in a Vue application for users to read. You’ll end up with an admin site and a user-facing site you can continue to refine for your own use. Detecting Deforestation From Satellite Images – How would you go about detecting deforestation — a contributor to climate change — from satellite images? In this article, you’ll learn how one team built a machine learning (ML) solution to do just that, using FastAI for the modeling and Streamlit to create a dashboard. The article discusses methodology and results, and is a great read about building an ML solution. The project code is available on GitHub. The Tools and Tech I Use to Run a One-Woman Hardware Company – Winterbloom makes open-source, boutique synthesizers. There’s a lot that goes into running a hardware company. Someone has to design the hardware, code the firmware, write the documentation, not to mention administrate the company. Winterbloom does all of this with just one engineer — Stargirl Flowers. Learn what tools and tech Stargirl uses to run her company, and how Python fits into the big picture in more ways than one. Visualizing Data in Python Using plt.scatter() – In this tutorial, you’ll learn how to create scatter plots in Python, which are a key part of many data visualization applications. You’ll get an introduction to plt.scatter(), a versatile function in the Matplotlib module for creating scatter plots. Why the Sad Face? – The Black autoformatter adopts some conventions that might surprise you the first time you use it. One of those conventions — the “sadface dedent” — moves closing parentheses in function signatures and other block headers to their own lines. This creates a line containing nothing but “):”, which looks like a sad face emoji. Łukasz Langa, Black’s creator, explains why Black does this. How to Iterate Over DataFrame Rows (And Should You?) – How to iterate over pandas DataFrame rows is one of the top voted questions with the pandas tag on Stack Overflow. That question is also the most copied answer with a code block on the entire site. Clearly, lots of people want to iterate over the rows in a DataFrame. But should you do this, or are there better options? Projects pyFLAC: Real-Time Lossless Audio Compression in Python pipx: Install and Run Python Applications in Isolated Environments Additional Links: Django: The Web Framework for Perfectionists With Deadlines Vue.js: The Progressive JavaScript Framework GraphQL: A query language for your API GraphQL? Here is what you need to know! - Syntax FM Episode FastAI: Simplifies training fast and accurate neural nets using modern best practices Streamlit: The fastest way to build and share data apps Winterbloom: Magical Musical Machines Black: The uncompromising code formatter pyFLAC: Real-Time Lossless Audio Compression in Python pipx Is Now a PyPA Member Project Level up your Python skills with our expert-led courses: Using Jupyter Notebooks How to Set Up a Django Project Explore Your Dataset With pandas Support the podcast & join our community of Pythonistas

This podcast discusses Anvil, a platform that allows Python developers to create web applications without learning additional languages. They cover the history of Anvil and its open-source nature. They also talk about Python libraries available to developers, working with PDFs and files, and the modularity of code and user management. The episode also mentions high school students creating projects, a video course spotlight, and the difference between server code and client code.

Discussions on selecting the ideal data structure in Python, using named tuples for cleaner code, unraveling the 'pass' and 'with' statements, typing code in Pallets projects, the value of reading and understanding other people's code, and using Python as a calculator and with new TI 84 calculator. Also explores Python usage on different devices and building a spell-checking library.

Would you move your data science project from a laptop to the cloud? Would you also like to have snapshots of your project saved along the way so that you can go back in time or share the state of your project with another team member? This week on the show, we have Savin Goyal from Netflix. Savin is the technical lead for machine learning infrastructure at Netflix. He joins us to talk about Metaflow, an open-source tool to simplify building, managing, and scaling data science projects. Metaflow addresses the needs of the numerous data scientists who work at Netflix. Machine learning is key strength for the streaming service. They tried several existing tools to scale their own internal infrastructure and after this experimentation developed Metaflow. We talk about the history of the project and how someone could get started with the open-source version. Savin also contrasts the cost of infrastructure as compared to data scientists and the cost of their time. Course Spotlight: Simplify Python GUI Development With PySimpleGUI In this step-by-step course, you’ll learn how to create a cross-platform graphical user interface (GUI) using Python and PySimpleGUI. A graphical user interface is an application that has buttons, windows, and lots of other elements that the user can use to interact with your application. Topics: 00:00:00 – Introduction 00:01:53 – What is Metaflow? 00:04:15 – Savin’s background in data science and infrastructure 00:06:06 – Democratization of infrastructure and iteration of tools 00:10:34 – What information is saved about the infrastructure requirements for a project? 00:17:17 – How are the requirements annotated? 00:18:39 – Sponsor: Digital Ocean’s App Platform 00:19:15 – How do project snapshots work? 00:29:33 – Cost of infrastructure vs data scientists 00:32:28 – Working with data at Netflix scale 00:37:55 – Video Course Spotlight 00:39:06 – Getting an organization to use new tools and then making open-source 00:49:51 – Documentation of Metaflow and getting started on solving infrastructure problems 00:53:57 – What made you interested in working on infrastructure tools? 00:55:13 – What is something you are excited about in the world of Python? 00:56:18 – What do you want to learn next? 00:58:14 – Thanks and goodbye Show Links: Metaflow: A framework for real-life data science Metaflow: Tutorials More Data Science, Less Engineering with Netflix’s Metaflow By Savin Goyal - YouTube R: The R Project for Statistical Computing Tidyverse: R packages for data science Anything you can do, I can do (kinda). Tidyverse pipes in Pandas reticulate: R Interface to Python Apache Airflow: Programmatically author, schedule and monitor workflows Directed acyclic graph (DAG) - Wikipedia article Serializing Objects With the Python pickle Module - Real Python Course Level up your Python skills with our expert-led courses: Learn Text Classification With Python and Keras Using Jupyter Notebooks Simplify Python GUI Development With PySimpleGUI Support the podcast & join our community of Pythonistas

Did you know the Python Software Foundation is hiring! With the recent support of three Visionary Sponsors, the PSF has been able to open positions for a developer-in-residence and a Python packaging project manager. Real Python now has a monthly Python news article. Frequent guest of the show, David Amos compiles and summarizes the biggest Python news from the past month. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects. We discuss David’s news article from the last month. We also discuss previous guest Jon Fincher’s new step-by-step tutorial about creating a platform game with the arcade framework. We cover several other articles and projects from the Python community including, how to use ipywidgets to make your Jupyter notebook interactive, the hidden performance overhead of Python C extensions, adding else to for loops, film simulations from scratch using Python, a gradual programming language named Hedy, and a Python raytracer. Spotlight: CPython Internals: Your Guide to the Python 3 Interpreter Unlock the Inner Workings of the Python Language, Compile the Python Interpreter From Source Code, And Participate in the Development of CPython Topics: 00:00:00 – Introduction 00:02:04 – How to Use ipywidgets to Make Your Jupyter Notebook Interactive 00:06:07 – Build a Platform Game in Python With arcade 00:12:35 – Sponsor: Digital Ocean’s App Platform 00:13:11 – The Hidden Performance Overhead of Python C Extensions 00:21:17 – For-Else: A Weird but Useful Feature in Python 00:25:42 – Python News: What’s New From April 2021? 00:39:43 – Spotlight: CPython Internals Now in Paperback! 00:41:15 – Film Simulations From Scratch Using Python 00:47:44 – hedy: Hedy Is a Gradual Programming Language, Which Increases in Syntactic Elements Level by Level 00:50:27 – Python-Raytracer: A Basic Ray Tracer That Exploits NumPy Arrays and Functions to Work Fast 00:53:31 – Thanks and goodbye Show Links: How to Use ipywidgets to Make Your Jupyter Notebook Interactive – Jupyter Notebooks are great for exploratory data analysis. They’re also a good way to share results and analysis with other people, who can alter the notebook to further explore the data themselves. But there are some limitations to notebook interactivity. That’s where ipywidgets comes in! In this tutorial you’ll learn how to create widgets like check boxes, drop-down menus, sliders, and how to handle events like button clicks. Build a Platform Game in Python With arcade – Building games can be a fun way to learn new Python concepts and practice techniques you’ve already learned. Plus, they make for great projects to share! This step-by-step tutorial shows you how to build a platform game using the arcade library. You’ll learn techniques for designing levels, sourcing assets, and implementing advanced features The Hidden Performance Overhead of Python C Extensions – It’s no secret that Python is slower than compiled languages like C, C++, and Rust. If you need a performance boost, you can write compiled Python C extensions. But there are some hidden performance costs that you should be aware of if you decide to do this. This article explains two ways that Python C extensions can actually be slower than pure Python and discusses some solutions and work around for them. For-Else: A Weird but Useful Feature in Python – Python for loops have an unusual feature: they support an else block that only executes if there is no break in the loop. The pattern isn’t used very often with the argument against it being that it is a bit weird and potentially difficult to understand. But there may be times when for/else makes sense. This article presents three situations where for/else is useful and argues that, in these situations, the pattern makes the code more readable. Python News: What’s New From April 2021? – April 2021 was an eventful month in the world of Python. In this article, you’ll get up to speed on everything that happened in the past month, including new sponsorships for the PSF, changes to Python error messages, and a community-led discussion over the future of type annotations. Film Simulations From Scratch Using Python – In analog photography, you can achieve different “looks” for your photographs by selecting different kinds of film to shoot with. Digital camera manufacturers often include different presets to simulate different kinds of film. In this article, you’ll learn how to simulate different films on your own images using color lookup tables, or CLUTs, using NumPy and the Pillow image library. Projects: hedy: Hedy Is a Gradual Programming Language, Which Increases in Syntactic Elements Level by Level Python-Raytracer: A Basic Ray Tracer That Exploits NumPy Arrays and Functions to Work Fast Additional Links: The Python Arcade Library Arcade: A Primer on the Python Game Framework Kenney: Free game assets, no strings attached Tiled: Free and Open Source, Flexible Level Editor Episode 24: Options for Packaging Your Python Application: Wheels, Docker, and More Your Code Is Without a Doubt the Worst I Have Ever Run Welcoming Google as a Visionary Sponsor of the PSF Welcoming Microsoft as a Visionary Sponsor Python Job Board: Project Manager - Python Packaging PEP 563 – Postponed Evaluation of Annotations Episode 45: Processing Images in Python With Pillow RawTherapee: A free, cross-platform raw image processing program Level up your Python skills with our expert-led courses: Using Jupyter Notebooks For Loops in Python (Definite Iteration) Make a 2D Side-Scroller Game With PyGame Support the podcast & join our community of Pythonistas

Are you interested in learning more about Django? Would you like to meet other professionals and learn how they are using Django? DjangoCon Europe 2021 is virtual this year, and you can join in from anywhere in the world. This week on the show, we have Miguel Magalhães and David Vaz, two of the organizers of the conference. We discuss what makes DjangoCon Europe unique. David and Miguel talk about how they got involved and how the conference passes between different countries. They also cover the struggle of upending their plans for hosting the conference in Porto Portugal last year and how this year could use some extra support. Tickets are available now. DjangoCon Europe is looking for additional sponsors. If you work for an organization that can help, get in contact with them. Course Spotlight: Get Started With Django: Build a Portfolio App In this course, you’ll learn the basics of creating powerful web applications with Django, a Python web framework. You’ll build a portfolio website to showcase your web development projects, complete with a fully functioning blog. Topics: 00:00:00 – Introduction 00:01:43 – How did you get involved with DjangoCon Europe? 00:10:42 – European vs US conference differences 00:12:24 – What makes a DjangoCon unique? 00:13:27 – What are some examples of projects using Django? 00:15:02 – Sponsor: Digital Ocean’s App Platform 00:15:38 – What are types of talks will the conference have? 00:20:04 – Conference schedule during the week 00:24:40 – Who is the intended audience? 00:25:29 – Video Course Spotlight 00:26:43 – Sharing your project and lightning talks 00:28:56 – What tools are you using to facilitate a virtual event? 00:33:32 – Ticket grants and sponsors 00:38:55 – What is your background with and use of Django? 00:43:45 – What are you excited about in the world of Python? 00:46:05 – What do you want to learn next? 00:52:25 – Thanks and goodbye Show Links: DjangoCon Europe 2021 Sponsors: DjangoCon Europe 2021 DjangoCon Europe: Twitter Account LoudSwarm: Virtual Event Hosting Lightning ⚡ Talks - DjangoCon Europe 2021 Opportunity Grants: DjangoCon Europe 2021 pretalx: From Call for Papers to schedule – build your conference! pretalx: GitHub pretix: Event Ticketing Software pretix: GitHub Wagtail: The powerful CMS for modern websites Gather Town: Better spaces to gather around The Future of Web Software Is HTML-over-WebSockets: A List Apart The WebSocket API (WebSockets) WebAssembly (Wasm) PyTorch: open source machine learning framework Keras: the Python deep learning API Learn Text Classification With Python and Keras: Real Python Course Cozmo: Digital Dream Labs CUDA: Parallel Computing Platform Level up your Python skills with our expert-led courses: Learn Text Classification With Python and Keras Building HTTP APIs With Django REST Framework Getting Started With Django: Building a Portfolio App Support the podcast & join our community of Pythonistas

This week’s show is a bit different. We are taking a well-deserved short break, but we still wanted to share an episode with you. This rewind episode highlights clips from the many interviews over the past year or so of the show. We also hear from many new listeners who have just discovered the show. Welcome aboard! We wanted to provide a sample of guests, topics, and questions we feature on the show. For long-time listeners, this will be a brisk walk through past episodes and guests. We’ve talked with many guests, and it was hard to narrow it down to the sample provided here. We hope you enjoy this podcast rewind, and look forward to sharing a fantastic slate of upcoming guests. 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 00:01:28 – E03 Brett Slatkin: Is Python a good tool for infrastructure? 00:03:30 – E07 Łukasz Langa: Origins of Black 00:10:55 – E08 Tania Allard: Reproducibility of project results 00:13:06 – Sponsor: Digital Ocean 00:13:47 – E11 Anthony Shaw: DRY (Don’t Repeat Yourself) 00:16:38 – E16 Hannah Stepanek: Creating NumPy types 00:20:02 – E18 Armin Ronacher: What would you change if you started the Flask project from scratch? 00:23:20 – E22 Russell Keith-Magee: Funding open-source projects 00:26:27 – E26 Michael Kennedy: How is the GIL part of the problem? 00:29:18 – Video Course Spotlight 00:30:27 – E30 Christopher Trudeau - The PEG parser 00:33:00 – E39 Reuven Lerner: What makes generator functions different? 00:37:35 – E47 Brett Cannon: Unravelling Python’s syntatic sugar series 00:43:43 – Thanks and goodbye Show Links: Episode 3: Effective Python and Python at Google Scale - With Brett Slatkin Episode 7: AsyncIO + Music, Origins of Black, and Managing Python Releases - With Łukasz Langa Episode 8: Docker + Python for Data Science and Machine Learning - With Tania Allard Episode 11: Advice on Getting Started With Testing in Python - With Anthony Shaw Episode 16: Thinking in Pandas: Python Data Analysis the Right Way - With Hannah Stepanek Episode 18: Ten Years of Flask: Conversation With Creator Armin Ronacher Episode 22: Create Cross-Platform Python GUI Apps With BeeWare - With Russell Keith-Magee Episode 26: 5 Years Podcasting Python with Michael Kennedy: Growth, GIL, Async, and More Episode 30: Exploring the New Features of Python 3.9 - With Geir Arne Hjelle and Christopher Trudeau Episode 39: Generators, Coroutines, and Learning Python Through Exercises - With Reuven Lerner Episode 47: Unraveling Python’s Syntax to Its Core With Brett Cannon Level up your Python skills with our expert-led courses: Python Generators 101 Plot With pandas: Python Data Visualization Basics Cool New Features in Python 3.9 Support the podcast & join our community of Pythonistas