Stay ConnectedA 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.
Copyright: © 2020 Real Python
What can you do if your text manipulation in Python is slowing you down? Are there faster alternatives using a compiled extension? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
Christopher shares a recent article by Itamar Turner-Trauring called “Speeding Up Text Processing in Python (Is Hard).” The piece compares the performance of string-matching scenarios using several alternatives to pure Python that rely on compiled extensions.
We also discuss a recent Real Python tutorial by Stephen Gruppetta on when to use .__repr__() vs .__str__() in Python. We cover the use cases for these special methods and the intended audiences for the strings they produce.
We share several other articles and projects from the Python community, including a news update, an article on the functional power of Python’s reduce(), a call to ban 1+N in Django, a friendly project to fetch your data files, and a tool for tracking your work from the shell.
Course Spotlight: Unicode in Python: Working With Character Encodings
In this course, you’ll get a Python-centric introduction to character encodings and Unicode. Handling character encodings and numbering systems can at times seem painful and complicated, but this guide is here to help with easy-to-follow Python examples.
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Our TOPPODCAST Picks .__repr__() vs .__str__() in Python?reduce() - The Power of a Single Python FunctionNews:
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.__repr__() vs .__str__() in Python? – In this tutorial, you’ll learn the difference between the string representations returned by .__repr__() vs .__str__() and understand how to use them effectively in classes that you define.reduce() - The Power of a Single Python Function – “While Python is not a pure functional programming language, you still can do a lot of functional programming in it. In fact, just one function - reduce() - can do most of it.” This article introduces you to reduce().Projects:
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reduce(): From Functional to Pythonic Style – Real PythonLevel up your Python skills with our expert-led courses:
Are you exploring automation of your repetitive business tasks with Python? How are you going to share your helpful tools with co-workers? This week on the show, Sampo Ahokas from Robocorp is here to discuss robotic process automation (RPA) and distribution of these robots.
Sampo is a co-founder and VP of engineering at Robocorp. We talk about using Robot Framework, an open-source RPA tool, to develop bots that implement your existing Python skills. Sampo shares example projects and additional resources for new users.
We discuss the typical difficulties of sharing automation tools with a team and trying to avoid the dreaded “works on my machine” problem. Sampo describes how their group worked to develop a Conda-based tool for creating shareable packages and environments.
Course Spotlight: Manipulating ZIP Files With Python
In this video course, you’ll learn how to manipulate ZIP files using Python’s zipfile module from the standard library. Through hands-on examples, you’ll learn how to read, write, compress, and extract files from your ZIP files quickly.
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Have you ever installed a Python package without knowing anything about it? What best practices should you employ to ensure the quality of your next package installation? Christopher Trudeau is back this week, bringing another batch of PyCoder’s Weekly articles and projects. We also have Python Software Foundation executive director, Deb Nicholson, to share details about PyCon US 2023.
We cover a recent Real Python tutorial by Philipp Acsany on evaluating the quality of Python packages. The piece provides a tool kit for researching the traits, history, software license, and current condition of external Python packages. We also discuss the techniques that we personally use before selecting a package for our Python projects.
We share several other articles and projects from the Python community, with topics such as the underlying structure of virtual environments, the overhead of Python asyncio tasks, documentation for Python projects with Sphinx and Read the Docs, a project for creating argparse boilerplate, and a way to generate seemingly realistic fake numbers using Benford’s law.
Deb Nicholson is also here to talk about the 20th anniversary of PyCon US, hosted in Salt Lake City. We dig into the details of the upcoming conference, including keynote speakers, tutorials, scheduled talks, and improvements to the hybrid online experience.
Course Spotlight: Documenting Python Projects With Sphinx and Read the Docs
In this video series, you’ll create project documentation from scratch using Sphinx, the de facto standard for Python. You’ll also hook your code repository up to Read The Docs to automatically build and publish your code documentation.
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asyncio Tasksargparse BoilerplateShow Links:
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What are the core lessons you’ve learned along your Python development journey? What are key takeaways you would share with new users of the language? This week on the show, Duarte Oliveira e Carmo is here to discuss his recent talk, “Four Years of Python.”
Duarte works at the crossroads of machine learning, data science, and software engineering. He began using Python in his graduate studies and never looked back. In 2021, he wrote a blog post about some of the valuable lessons he’s learned. Then he decided the lessons and concepts in the post might make a good conference talk.
We cover the steps in his process of crafting the presentation, practicing it at a smaller conference, and finally presenting it at PyCon Italia last year. We also dig into the four major themes of the talk. Along the way, we share a collection of resources to help you continue learning on your Python journey.
Course Spotlight: Building a URL Shortener With FastAPI and Python
In this video course, you’ll build an app to create and manage shortened URLs. Your Python URL shortener can receive a full target URL and return a shortened URL. You’ll also use the automatically created documentation of FastAPI to try out your API endpoints.
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Have you explored Python’s collections module? Within it, you’ll find a powerful factory function called namedtuple(), which provides multiple enhancements over the standard tuple for writing clearer and cleaner code. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
Christopher discusses his Real Python video course about writing more Pythonic code using namedtuple(). With namedtuple(), you can create immutable sequence types that allow you to access their values using descriptive field names and dot notation instead of clunky integer indices.
We also discuss metaprogramming and the unique advantages of Python’s dynamism. Christopher shares potential paths for this type of coding from web applications, testing, and AST techniques.
We share several other articles and projects from the Python community, including a news update, the Arrow revolution happening in pandas 2.0, a new PEP for inlined comprehensions, tips and techniques for modern Flask apps, a Python helper tool for building and running a REPL with custom commands, and a project to turn a pandas DataFrame into a Tableau-style UI.
Course Spotlight: Writing Clean, Pythonic Code With namedtuple
In this video course, you’ll learn what Python’s namedtuple is and how to use it in your code. You’ll also learn about the main differences between named tuples and other data structures, such as dictionaries, data classes, and typed named tuples.
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namedtupleNews:
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namedtuple – In this video course, you’ll learn what Python’s namedtuple is and how to use it in your code. You’ll also learn about the main differences between named tuples and other data structures, such as dictionaries, data classes, and typed named tuples.list, dict, and set comprehensions as inline code can result in a 2x speedup on the comprehension.Discussion:
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Are you interested in deploying your Python project everywhere? This week on the show, Russell Keith-Magee, founder and maintainer of the BeeWare project, returns. Russell shares recent updates to Briefcase, a tool that converts a Python application into native installers on macOS, Windows, Linux, and mobile devices.
We cover how Anaconda hired him last year to work full-time on the BeeWare project. He shares how this has helped him focus his efforts and move the project forward.
We also discuss his recent talk at DjangoCon US 2022 on how to turn your website into an app (and why maybe you shouldn’t). Russell details the problems of converting from the Web to a mobile platform. We also contrast WebAssembly System Interface (WASI) with the tools that his team works on.
Course Spotlight: Managing Attributes With Python’s property()
In this video course, you’ll learn how to create managed attributes, also known as properties, using Python’s property() in your custom classes.
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Have you decided how you’re going to deploy your Django project? Should you use a VPS or a PaaS? Christopher Trudeau is back this week, bringing another batch of PyCoder’s Weekly articles and projects. We also have organizers from PyCascades to share details about this year’s hybrid in-person and virtual conference.
Christopher shares an article about selecting an appropriate Django project deployment strategy. The guide compares VPS (virtual private server) and PaaS (platform as a service) systems. He also covers hosting providers for each and highlights potential pitfalls.
We share several other articles and projects from the Python community, including a news update, what’s new in SQLAlchemy 2.0, how to flush the output of the Python print function, the dangers behind image resizing for machine learning, a project that visualizes pathfinding algorithms, and a runtime executor project.
We also have three special guests from PyCascades 2023 to dig into the details of the conference. Conference chair Eliza Sarobhasa is CTO at Women Who Drone and Leadership Fellow (Python Track) & Python Developer Advocate at Women Who Code. Sprints chair Chethana Gopinath is a Software Engineer at realtor.com and a Senior Lead at Women Who Code Python. Speaker Support Chair Jolene Wong is a Senior Software Engineer at Cisco based in Vancouver. We discuss hosting a hybrid conference, participating in open-source sprints, and finding a local Python community.
Course Spotlight: The Python print() Function: Go Beyond the Basics
In this step-by-step course, you’ll learn about the print() function in Python and discover some of its lesser-known features. Avoid common mistakes, take your “hello world” to the next level, and know when to use a better alternative.
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asdf Rust Clone)News:
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Are you still using loops and lists to process your data in Python? Have you heard of a Python library with optimized data structures and built-in operations that can speed up your data science code? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to share secrets for harnessing linear algebra and NumPy for your projects.
Jodie details how most people begin their data science journey using loops to iterate over values and apply operations sequentially. We talk about how loops are friendly for beginners, being clear to read and easy to debug, but unfortunately don’t scale well, especially with large amounts of data.
Jodie shares some of the basics of linear algebra and how to organize data into vectors. We talk about how the NumPy library leverages those concepts to improve data processing. We discuss how the library includes operations for vector and matrix addition and subtraction, and why these operations are more efficient than loops. We also cover how NumPy stores arrays in memory and when working with them is faster vs when it’s not.
Course Spotlight: Data Cleaning With pandas and NumPy
In this video course, you’ll learn how to clean up messy data using pandas and NumPy. You’ll become equipped to deal with a range of problems, such as missing values, inconsistent formatting, malformed records, and nonsensical outliers.
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Would you like to practice your Python skills while building a challenging word game? Have you been wanting to learn more about creating command-line interfaces and making them colorful and interactive? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
We share a recent Real Python step-by-step project about creating a clone of Wordle. In the project, you’ll practice building a terminal application, validating user input, and refactoring code into functions.
Christopher shares an article that compares two popular testing tools, Nox and Tox. He discusses how each framework approaches test environment configuration and why the author leans toward using Nox’s Python decorator–based format.
We share several other articles and projects from the Python community, including a news update, a guide to trying out code and ideas quickly with the Python REPL, a PEP about requiring virtual environments by default, a discussion about lessons learned in twenty years as a software engineer, a project for a spreadsheet GUI inside of JupyterLab notebooks, and adding C-style for loops to Python.
Course Spotlight: Getters and Setters in Python
In this video course, you’ll learn what getter and setter methods are, how Python properties are preferred over getters and setters when dealing with attribute access and mutation, and when to use getter and setter methods instead of properties in Python.
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for loop in Python? No? Well, you can have one anyway. See how Tushar implemented with for (i := var(0), i < 10, i + 2):Additional Links:
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Can you describe your business processes with flowcharts? What if you could define the steps in a standard notation and implement the workflows in pure Python? This week on the show, Dan Funk from Sartography is here to discuss SpiffWorkflow.
SpiffWorkflow is a Python tool for translating Business Process Model and Notation (BPMN) diagrams into a workflow engine. You can manipulate this visual chain of events to suit your team’s business requirements. Individual events in the workflow can contain blocks or scripts of Python code to be executed.
We discuss the concept of low-code software tools. Dan also talks about how SpiffWorkflow aims at getting non-developers within an organization involved in development.
Course Spotlight: Cool New Features in Python 3.11 – Real Python
In this video course, you’ll explore what Python 3.11 brings to the table. You’ll learn how Python 3.11 is the fastest and most user-friendly version of CPython yet, and learn about improvements to the typing system and to the asynchronous features of Python.
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