The Real Python Podcast

Are you familiar with linear programming, and how it can be used to solve resource optimization problems? Would you like to free your Python code from a clunky command line and start making convenient graphical interfaces for your users? This week on the show, David Amos is back with another batch of PyCoder’s Weekly articles and projects. David talks about a recent Real Python article about linear programming in Python. We discuss an article titled “PySimpleGUI: The Simple Way to Create a GUI With Python.” We also cover several other articles and projects from the Python community including: Python’s reduce() function, flaws in the pickle module, advanced pytest techniques, and how to trick a neural network. Course Spotlight: Parallel Iteration With Python’s zip() Function This course will get you up to speed with Python’s zip() function. In this course, you’ll discover the logic behind zip() and how you can use it to consistently solve common programming problems, like creating dictionaries. Topics: 00:00:00 – Introduction 00:01:34 – Python’s reduce(): From Functional to Pythonic Style 00:07:46 – Hands-On Linear Programming: Optimization With Python 00:15:07 – Pickle’s Nine Flaws 00:22:31 – Video Course Spotlight 00:23:33 – Advanced pytest Techniques I Learned While Contributing to pandas 00:33:41 – PySimpleGUI: The Simple Way to Create a GUI With Python 00:38:20 – How to Trick a Neural Network in Python 3 00:43:31 – TextAttack: A Python Framework for Adversarial Attacks, Data Augmentation, and Model Training in NLP 00:46:09 – byob: BYOB (Build Your Own Botnet) 00:49:09 – Thanks and Goodbye Show Links: Python’s reduce(): From Functional to Pythonic Style – In this step-by-step tutorial, you’ll learn how Python’s reduce() works and how to use it effectively in your programs. You’ll also learn some more modern, efficient, and Pythonic ways to gently replace reduce() in your programs. Hands-On Linear Programming: Optimization With Python – In this tutorial, you’ll learn about implementing optimization in Python with linear programming libraries. Linear programming is one of the fundamental mathematical optimization techniques. You’ll use SciPy and PuLP to solve linear programming problems. Pickle’s Nine Flaws – “Python’s pickle module is a very convenient way to serialize and de-serialize objects. It needs no schema, and can handle arbitrary Python objects. But it has problems. This post briefly explains the problems.” Advanced pytest Techniques I Learned While Contributing to pandas – Contributing to open-source projects is a great way to learn new techniques and level up your skills. Martin Winkel shares five advanced pytest techniques he learned while contributing to the pandas project. PySimpleGUI: The Simple Way to Create a GUI With Python – In this step-by-step tutorial, you’ll learn how to create a cross-platform graphical user interface (GUI) using Python and PySimpleGUI. A graphical user interface is an application that has buttons, windows, and lots of other elements that the user can use to interact with your application. How to Trick a Neural Network in Python 3 – Is that a corgi or a goldfish? Projects: TextAttack: A Python Framework for Adversarial Attacks, Data Augmentation, and Model Training in NLP byob: Build Your Own Botnet Additional Links: PyCoder’s Weekly Functional Programming in Python Linear Programming: Wikipedia article The Python pickle Module: How to Persist Objects in Python Marshmallow Python REST APIs With Flask, Connexion, and SQLAlchemy – Part 2 Effective Python Testing With Pytest Getting Started With Testing in Python Practical Text Classification With Python and Keras PySimpleGUI Level up your Python skills with our expert-led courses: Supercharge Your Classes With Python super() Functional Programming in Python Parallel Iteration With Python's zip() Function Support the podcast & join our community of Pythonistas

Are you using the Python library Pandas the right way? Do you wonder about getting better performance, or how to optimize your data for analysis? What does normalization mean? This week on the show we have Hannah Stepanek to discuss her new book “Thinking in Pandas”. The inspiration behind Hannah’s book came out of her talk at PyCon US 2019 titled “Thinking Like a Panda: Everything You Need to Know to Use Pandas the Right Way.” We discuss several core concepts covered in the book. She shares techniques for getting more performance when working with your data in Pandas. We also talk about her recent PyCon US 2020 online presentation about databases and migration. Course Spotlight: Finding the Perfect Python Code Editor Find your perfect Python development setup with this review of Python IDEs and code editors. With this course you’ll get an overview of the most common Python coding environments to help you make an informed decision. Topics: 00:00:00 – Introduction 00:01:36 – Working for New Relic 00:03:14 – Thinking in Pandas book release 00:03:27 – Who is the intended reader? 00:05:27 – What is the underlying tech for Pandas? 00:09:04 – Why you shouldn’t use apply? 00:13:00 – When you have to use apply 00:16:06 – Normalizing your data 00:17:05 – Do you have a preferred format for a dataframe? 00:18:17 – More on multi-index dataframes 00:24:50 – Creating NumPy types 00:28:30 – Loading in your data 00:30:33 – Video Course Spotlight 00:31:41 – Pivoting data 00:34:34 – Considering outside libraries and performance 00:35:41 – What topic were you eager to share in the book? 00:37:52 – What resources did you use to learn pandas? 00:40:53 – PyCon 2020 talk about databases and migration 00:45:34 – Delving into migration and Alembic 00:53:15 – Speaking opportunities 00:56:13 – What are you excited about in the world of Python? 00:57:32 – What do you want to learn next? 00:58:49 – Do you read source code to learn? 01:00:16 – Is there a particularly well-written library? 01:01:28 – Final Thanks Links: Thinking in Pandas: How to Use the Python Data Analysis Library the Right Way - Apress Thinking like a Panda: Everything you need to know to use pandas the right way - PyCon 2019 - Hannah Stepanek pandas CPython Internals: Your Guide to the Python 3 Interpreter MultiIndex / advanced indexing: pandas documentation NumPy Data type objects (dtype) pandas.DataFrame.pivot: pandas documentation Let’s talk Databases in Python: SQLAlchemy and Alembic - PyCon 2020 - Hannah Stepanek SQLAlchemy: The Python SQL Toolkit and Object Relational Mapper Alembic: A database migration tool for SQLAlchemy import asyncio: Learn Python’s AsyncIO #1 - The Async Ecosystem Level up your Python skills with our expert-led courses: Finding the Perfect Python Code Editor Histogram Plotting in Python: NumPy, Matplotlib, Pandas & Seaborn Idiomatic pandas: Tricks & Features You May Not Know Support the podcast & join our community of Pythonistas

This podcast covers a range of interesting topics including regular expressions in Python, views vs copies in Pandas, and methods for flattening a list in Python. They also discuss combining Flask and Vue, machine learning production, space science with Python, and a video course on reading and writing files in Python.

Learn about the advantages of serverless computing with Python in the cloud and how it is suitable for data science, machine learning, and API creation. Discover how to use Azure Functions and VS Code for serverless development. Explore topics such as blob storage integration, working with service principles, and setting up and running serverless functions locally. The podcast also discusses real-time functionality with Flask Socket.IO and delves into the fascination with threading in Python.

Have you wanted to work with PDF files in Python? Maybe you want to extract text, merge and concatenate files, or even create PDFs from scratch. Are you interested in building hardware projects using a Raspberry Pi? This week on the show we have David Amos from the Real Python team to discuss his recent article on working with PDFs. David also brings a few other articles from the wider Python community for us to discuss. David searches for the latest Python news, links, and articles to produce PyCoder’s Weekly with Dan Bader. PyCoder’s Weekly is a free email newsletter for those interested in Python development. Along with David’s article on PDFs, we discuss another recent Real Python article about building physical projects with the Raspberry Pi. We also discuss articles from the community about: the PEPs of Python 3.9, why you should stop using datetime.now, Python dependency tools, and several ways to pass code to Python from the terminal. Course Spotlight: Cool New Features in Python 3.8 This course will get you up to speed with the new features of the latest release of Python. You’ll learn about using assignment expressions, how to enforce postional-only arguments, more precise type hints, and using f-strings for simpler debugging. It’s a worthy investment of your time to understand what the most recent release of Python provides before moving on to the next version this fall. Topics: 00:00:00 – Introduction 00:02:06 – Ways to Pass Code to Python From the Terminal 00:05:54 – The PEPs of Python 3.9 00:10:54 – Creating and Modifying PDF Files in Python 00:18:51 – Video Course Spotlight 00:19:56 – An Overview of Python Dependency Tools 00:26:55 – Stop Using datetime.now 00:31:44 – Build Physical Projects With Python on the Raspberry Pi 00:38:18 – What are you excited about in the world of Python? 00:42:29 – What do you want to learn next in Python? 00:44:31 – Thanks and Good Bye Topic Links: PyCoder’s Weekly The Many Ways to Pass Code to Python From the Terminal – You might know about pointing Python to a file path, or using -m to execute a module. But did you know that Python can execute a directory? Or a .zip file? The PEPs of Python 3.9 – The first Python 3.9 beta release is upon us! Learn what to expect in the final October release by taking a tour of the Python Enhancement Proposals (PEPs) that were accepted for Python 3.9. Creating and Modifying PDF Files in Python – Explore the different ways of creating and modifying PDF files in Python. You’ll learn how to read and extract text, merge and concatenate files, crop and rotate pages, encrypt and decrypt files, and even create PDFs from scratch. Overview of Python Dependency Management Tools – While pip is often considered the de facto Python package manager, the dependency management ecosystem has really grown over that last few years. Learn about the different tools available and how they fit into this ecosystem. Stop Using datetime.now! (With Dependency Injection) – How do you test a function that relies on datetime.now() or date.today()? You could use libraries like FreezeGun or libfaketime, but not every project can afford the luxury of reaching for third-party solutions. Learn how dependency injection can help you write code that is more testable, maintainable, and practical. Build Physical Projects With Python on the Raspberry Pi – In this tutorial, you’ll learn to use Python on the Raspberry Pi. The Raspberry Pi is one of the leading physical computing boards on the market and a great way to get started using Python to interact with the physical world. Additional Links: Python Basics: A Practical Introduction to Python 3 PEG Parsers -Guido van Rossum - Medium article Code with Mu: a simple Python editor for beginner programmers SSH (Secure Shell) Visual Studio Code VSCode - Remote Development using SSH VIM and Python – A Match Made in Heaven - Real Python article How to Build a Python GUI Application With wxPython - Real Python article import asyncio: Learn Python’s AsyncIO #1 - The Async Ecosystem python-rtmidi - A Python binding for the RtMidi C++ library Level up your Python skills with our expert-led courses: Arduino With Python: Getting Started Finding the Perfect Python Code Editor Cool New Features in Python 3.8 Support the podcast & join our community of Pythonistas

Do you want to get started with web scraping using Python? Are you concerned about the potential legal implications? What are the tools required and what are some of the best practices? This week on the show we have Kimberly Fessel to discuss her excellent tutorial created for PyCon 2020 online titled “It’s Officially Legal so Let’s Scrape the Web.” We discuss getting started with web scraping, and cover tools and techniques. Kimberly gives advice on finding elements inside of the html, and techniques for cleaning your data. She also notes a recent change to the legal landscape regarding scraping the web. Kimberly is a Senior Data Scientist at Metis Data Science Bootcamp in New York City. She holds a Ph.D. in applied mathematics. We talk about her switch from academia to data science, and discuss her passion for data storytelling and visualizations. Course Spotlight: Defining Main Functions in Python This course will get you up to speed with defining a starting point for the execution of a program, and helps you to understand what goes into the main() function. Prepare for a deep dive as you go through the sections. It’s a worthy investment of your time to understand this vital entry point for your Python scripts and applications! Topics: 00:00:00 – Introduction 00:01:31 – Kimberly’s background and Metis Data Science Bootcamp 00:02:19 – NLP and work in advertising 00:03:27 – Changes in the legality of web scraping 00:06:12 – What are good projects for web scraping? 00:06:56 – Tools to start web scraping 00:07:51 – How to find the elements you want? 00:09:00 – How much HTML should you know? 00:10:49 – Inspecting elements in the browser 00:14:30 – What are good sites to practice on? 00:16:20 – Pausing between requests 00:19:02 – Saving as you go 00:20:54 – Real Python Video Course Spotlight 00:21:55 – Navigating the DOM 00:23:10 – Data cleaning and formatting 00:28:26 – Dynamic sites and Selenium 00:32:16 – Scrapy 00:33:55 – PyOhio 2020 00:35:40 – Transition out of academia 00:38:40 – What are you excited about in the world of Python? 00:41:05 – What do you want to learn next in Python? 00:48:00 – What is a less known Python tip or trick? 00:49:17 – Thanks and Goodbye Show Links: Kimberly Fessel, PHD - Blog Metis: Data Science Training It’s Officially Legal so Let’s Scrape the Web: PyCon 2020 online - Tutorial Victory! Ruling in hiQ v. Linkedin Protects Scraping of Public Data: EFF.org Computer Fraud and Abuse Act - Wikipedia Article Box Office Mojo Sports Reference | Sports Stats, fast, easy, and up-to-date Springfield! Springfield! - TV & Movie Scripts - Archive.org Jupyter Notebook: An Introduction - Real Python Article The Python pickle Module: How to Persist Objects in Python - Real Python Article A Practical Introduction to Web Scraping in Python - Real Python Article Beautiful Soup: Build a Web Scraper With Python - Real Python Article Making HTTP Requests With Python - Real Python Video Course Natural Language Processing With spaCy in Python - Real Python Article Delorean: Time Travel Made Easy Maya: Datetimes for Humans Regular Expressions: Regexes in Python (Part 1) - Real Python Article Selenium: Automates browsers. That’s it! Scrapy: Framework for extracting the data you need from websites PyOhio 2020 ODSC: Open Data Science Conference Slides from Kimberly’s talk - Level Up: Fancy NLP with Straightforward Tools Tonks: A general purpose deep learning library Tonks: Building One (Multi-Task) Model to Rule Them All! - Medium Article Plotly | Dash geoplotlib: Python toolbox for visualizing geographical data and making map GeoPandas: Make working with geospatial data in Python easier Altair: Declarative Visualization in Python Understanding the Transform Function in Pandas: Practical Business Python JavaScript charting detour: Down and Up: A Puzzle Illustrated with D3.js - Kimberly’s blog d3js - Data-Driven Documents Crossfilter: Fast Multidimensional Filtering for Coordinated Views dc.js - Dimensional Charting JavaScript Library Level up your Python skills with our expert-led courses: Defining Main Functions in Python Making HTTP Requests With Python Strings and Character Data in Python Support the podcast & join our community of Pythonistas

Have you wanted to get started with testing in Python? Maybe you feel a little nervous about diving in deeper than just confirming your code runs. What are the tools needed and what would be the next steps to level up your Python testing? This week on the show we have Anthony Shaw to discuss his article on this subject. Anthony is a member of the Real Python team and has written several articles for the site. We discuss getting started with built-in Python features for testing and the advantages of a tool like pytest. Anthony talks about his plug-ins for pytest, and we touch on the next level of testing involving continuous integration. Anthony recently finished a talk for PyCon 2020 Online, titled “Why is Python Slow?” He had the idea for the talk while he was working on his upcoming book about the CPython source code. I also want to give an update on last weeks episode with Kyle Stratis, where we discussed Kyle being let go from his job due to the pandemic. Here’s some good news, Kyle will be joining a Boston startup called Vizit, as a senior data engineer. Congratulations Kyle! Course Spotlight: The Python print() Function: Go Beyond the Basics This course will get you up to speed with using Python print() effectively. Prepare for a deep dive as you go through the sections. You may be surprised how much print() has to offer! Topics: 00:00:00 – Introduction 00:01:46 – PyCon 2020 Online Talk - Why is Python slow? 00:04:05 – CPython Internals Book 00:07:08 – Attending Conferences 00:09:01 – Getting Started with Testing in Python 00:12:32 – Unittest 00:17:16 – What does a tool like pytest add? 00:19:53 – pytest plugins 00:21:03 – Anthony’s pytest plugins 00:21:58 – What does coverage mean? 00:25:23 – Test runners 00:27:12 – Testing environments with Tox 00:30:50 – Real Python Video Course Spotlight 00:31:49 – More on continuous integration (CI) 00:37:21 – Recent changes to GitHub 00:38:21 – PSF to move issue tracker to GitHub 00:41:01 – DRY (Don’t Repeat Yourself) 00:43:46 – Benefits of linters and code formatting 00:48:00 – What is a little known part of Python? 00:52:16 – What are you excited about in the world of Python? 00:56:06 – What is something you thought you knew about Python, but were wrong about it? 00:57:27 – Goodbye and thanks Show links: Why is Python slow?: PyCon 2020 Online Talk Your Guide to the CPython Source Code: Real Python article TalkPython Podcast Episode #265: Why is Python slow? Getting Started With Testing in Python: Real Python article pytest: helps you write better programs pytest-azurepipelines: Plugin for pytest that makes it simple to work with Azure Pipelines Effective Python Testing With Pytest tox automation project: Command line driven CI frontend GitHub Actions: Automate your workflow from idea to production Continuous Integration With Python: An Introduction: Real Python article Brian K Okken - Multiply your Testing Effectiveness with Parameterized Testing: PyCon 2020 Online Talk Python Testing with pytest: Brian Okken - The Pragmatic Bookshelf Test & Code: Python Testing for Software Engineering: Podcast Python’s migration to GitHub Refactoring Python Applications for Simplicity: Real Python article Black: The uncompromising code formatter Wily: A command-line application for tracking, reporting on complexity of Python tests and applications PEP 554 – Multiple Interpreters in the Stdlib Python Insider: Python core development news and information Level up your Python skills with our expert-led courses: Continuous Integration With Python Test-Driven Development With pytest The Python print() Function: Go Beyond the Basics Support the podcast & join our community of Pythonistas

Do you know someone in the Python community who recently was let go from their job due to the pandemic? What does the job landscape currently look like? What are skills and techniques that will help you in your job search? This week we have Kyle Stratis on the show to discuss how he is managing his job search after just being let go from his data engineering job. Kyle is a member of the Real Python team and has written several articles for the site. We discuss Kyle’s career and the skills that he’s developed, which are currently helping him in his job search. Kyle left academia to work as a data engineer. His background helps him to communicate between teams of scientists and engineers. We also talk about Kyle’s recent article on combining data in Pandas. Kyle shares a tip on Pandas efficiency, and hints at some lesser known features of Python generators. Topics: 00:00:00 – Introduction 00:01:27 – Kyle’s background on being let go 00:04:17 – Programming background and building connections 00:10:18 – Becoming a Data Engineer 00:15:59 – Translating between science and data teams 00:20:35 – Every job has different language requirements 00:23:44 – Getting out of your Python language comfort zone 00:27:08 – NASDANQ project - a stockmarket for Memes 00:30:34 – Learning the power of building a network 00:35:13 – Using skills developed in outside projects 00:38:45 – What does the job landscape look like currently? 00:49:52 – Writing for Real Python 00:52:53 – Combining data in Pandas article 00:55:22 – Merging in Pandas 01:03:05 – Feedback and community 01:10:37 – What are you excited ab out in the world of Python? 01:12:12 – What is something you thought you knew about Python but were wrong about it? 01:14:01 – What is a little known Python trick or tip? 01:14:33 – More efficient Pandas 01:15:52 – Using more of the advanced features of generators 01:18:55 – Thanks and Goodbye Show Links: Kyle’s Blog Kyle’s LinkedIn A MongoDB Optimization: Kyle Stratis’ Blog Memes are serious business with their own stock exchange: CNET How a group of Redditors is creating a fake stock market to figure out the value of memes: The Verge The joke Meme Economy is a now real thing called NASDANQ: AV Club Forbes Did A V. Serious Analysis Of NASDANQ, The Stock Market For Memes: Pedestrian Domi Station in Tallahassee Combining Data in Pandas With merge(), .join(), and concat(): Real Python article A Visual Explanation of SQL Joins: Coding Horror Wily: A command-line application for tracking, reporting on complexity of Python tests and applications Refactoring Python Applications for Simplicity: Real Python article Fast, Flexible, Easy and Intuitive: How to Speed Up Your Pandas Projects: Real Python article How to Use Generators and yield in Python: Real Python article Level up your Python skills with our expert-led courses: Python Coding Interviews: Tips & Best Practices Sorting Data With Python Idiomatic pandas: Tricks & Features You May Not Know Support the podcast & join our community of Pythonistas

In your quest to become a better developer, how do you find Python code that is at your reading level? What are good code bases or projects to study? What are the things holding you back from leveling up your Python literacy? This week we have Cecil Phillip on the show to discuss all of these common questions. Cecil is a Senior Cloud Advocate at Microsoft. Cecil has been learning Python in the open on Twitch with Brian Clark. They run a weekly event on Twitch, where they are live-streaming an interactive Python course. Cecil has a background in multiple languages and technologies, and now he’s learning Python, bringing an audience along the way! We start things off with a listener question and jump into a conversation about building up your Python skills. Then we’ll discuss common Python language stumbling blocks. Next we consider the importance of making personal projects, and documenting that code. We also touch on some unique skills employers are looking for. And we discuss working through impostor syndrome. Cecil talks about his podcast “Away from the Keyboard” and his plans to start it back up. In the show notes this week you’ll find links to resources we discuss, and several more that we didn’t have time to cover individually. Want your question featured on the show? Send us your question at realpython.com/podcast-question and we might feature it on a future episode of the show. Topics: 00:00:00 – Intro 00:01:52 – Cecil’s role at Microsoft 00:03:35 – Twitch Stream with Brian Clark 00:05:07 – Learning in front of an audience 00:13:05 – Listener’s question 00:14:46 – Finding code that’s at your level 00:20:31 – Understanding more complex syntax in Python 00:23:40 – Breaking down complexity 00:29:17 – Translation of code 00:31:55 – Importance of making projects and comments 00:36:28 – Finding community 00:41:23 – Open source contributing 00:42:25 – Dealing with impostor syndrome 00:49:09 – Looking for that first position 01:00:58 – More project resources in show notes 01:02:55 – Cecil’s podcast - Away from the keyboard 01:08:29 – What are you excited about in the world of Python? 01:10:14 – What is something you thought you knew about Python but were wrong about it? 01:12:01 – What’s the next thing you want to learn in Python? 01:13:37 – Read the actual Python docs 01:15:24 – Thanks and goodbye Show links: Microsoft Developer Channel Cecil Phillip’s Twitter Cecil’s Github Microsoft Developer Twitch Official Microsoft Python Discord Away from the Keyboard: Podcast Python Decorators 101: Real Python video course Python Type Checking: Real Python video course 13 Project Ideas for Intermediate Python Developers: Real Python article Suggested project reading list: Flask: The Python micro framework for building web applications. Django: The Web framework for perfectionists with deadlines Howdoi: instant coding answers via the command line Curio: A coroutine-based library for concurrent Python systems programming scikit-learn: machine learning in Python SQLAlchemy: The Database Toolkit for Python Requests: A simple, yet elegant HTTP library Markupsafe: Safely add untrusted strings to HTML/XML markup Ask HN: Good Python codebases to read? The Hitchhiker’s Guide to Python: Reading Great Code Welcome! This is the documentation for Python 3.8 Level up your Python skills with our expert-led courses: Intro to Object-Oriented Programming (OOP) in Python Python Decorators 101 Python Type Checking Support the podcast & join our community of Pythonistas

Docker is a common tool for Python developers creating and deploying applications, but what do you need to know if you want to use Docker for data science and machine learning? What are the best practices if you want to start using containers for your scientific projects? This week we have Tania Allard on the show. She is a Sr. Developer Advocate at Microsoft focusing on Machine Learning, scientific computing, research and open source. Tania has created a talk for the PyCon US 2020 which is now online. The talk is titled “Docker and Python: Making them Play Nicely and Securely for Data Science and ML.” Her talk draws on her expertise in the improvement of processes, reproducibility and transparency in research and data science. We discuss a variety of tools for making your containers more secure and results reproducible. Tania is passionate about mentoring, open-source, and its community. She is an organizer for Mentored Sprints for Diverse Beginners, and she talks about the upcoming online sprints for PyCon US 2020. We also discuss her plans to start a podcast. Topics: 00:00:00 – Introduction 00:01:43 – Microsoft Senior Developer Advocate Role 00:04:07 – PyCon 2020 Talk - Docker and Python: making them play nicely 00:05:34 – What is Docker? 00:10:08 – Reproducibility of project results 00:12:03 – What are the challenges of using Docker for machine learning? 00:15:06 – Getting started suggestions 00:16:26 – What metadata should be included? 00:17:48 – Creating images through stages 00:21:16 – What about your data? 00:22:40 – Kubernetes: Orchestrating containers 00:24:37 – Continuing stages into testing 00:25:37 – What are tools for testing security? 00:27:07 – Challenges in using containers for ML 00:28:52 – What types of databases? 00:29:39 – Are you doing initial research on a local machine? 00:30:59 – An example of a recent ML project 00:32:16 – Papermill: parameterizing and executing notebooks 00:33:16 – NLP: Natural Language Processing 00:33:58 – Kaggle: Help us better understand COVID-19 00:34:42 – What are other best practices for data intensive projects? 00:39:13 – Resources to get started in machine learning? 00:40:30 – Mentored Sprints for Diverse Beginners 00:45:34 – Tania’s upcoming podcast 00:48:38 – A visiting fellow at the Alan Turing Institute 00:49:08 – Weight lifting 00:50:16 – Craft beer 00:52:09 – What is something you thought you knew in Python but were wrong about? 00:53:50 – What are excited about in the world of Python? 00:54:42 – Thank you and Goodbye Show links: Tania Allard: Personal site Docker and Python: making them play nicely and securely for Data Science and ML - Tania Allard Slides for Docker and Python Talk Docker XKCD: Python Superfund Site Best practices for writing Dockerfiles Run Python Versions in Docker: How to Try the Latest Python Release Kubernetes: Production-Grade Container Orchestration Snyk: Securing open source and containers papermill: A tool for parameterizing and executing Jupyter Notebooks Natural Language Processing: Wikipedia article Natural Language Processing With spaCy in Python: Real Python article Kaggle: Help us better understand COVID-19 datree.io: Scale Engineering organization repo2docker: Build, Run, and Push Docker Images from Source Code Repositories Jupyter Docker Stacks: A set of ready-to-run Docker images binder: Turn a Git Repo into a Collection of Interactive Notebooks Hands-On Machine Learning with Scikit-Learn and TensorFlow: O’Reilly Data Science from Scratch: O’Reilly Python for Data Analysis: Wes McKinney - Creator of Pandas Mentored Sprints for Diverse Beginners The Alan Turing Institute Easy Data Processing With Azure Fun - Tania Allard - PyCon 2020 PEP 581 – Using GitHub Issues for CPython Python’s migration to GitHub - Request for Project Manager Resumes Level up your Python skills with our expert-led courses: Using Jupyter Notebooks Histogram Plotting in Python: NumPy, Matplotlib, Pandas & Seaborn Idiomatic pandas: Tricks & Features You May Not Know Support the podcast & join our community of Pythonistas