Have you wondered what are Python wheels? How are they used to package Python code? Does Python use pass by value or pass by reference? This week on the show, David Amos is here to help answer these questions, and he has brought another batch of PyCoder’s Weekly articles and projects.

We talk about an article called “What are Python Wheels, and Why Should You Care.” David talks about a Real Python article about pass by reference in Python. We cover several other articles and projects from the Python community including: transcribing speech to text, 4 powerful features Python is still missing, 10 awesome pythonic one-liners, and even more options for packaging your Python code.

Course Spotlight: Practical Recipes for Working With Files in Python

In this course, you’ll learn how you can work with files in Python by using built-in modules to perform practical tasks that involve groups of files, like renaming them, moving them around, archiving them, and getting their metadata.

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4 Powerful Features Python Is Still Missing – Python doesn’t have true constants, nor does it implement features like tail recursion optimization that many compiled languages employ. Find out what other features Python is “missing” when compared to other languages, and why the core developers haven’t added these features to the language.

What Are Python Wheels and Why Should You Care? – In this tutorial, you’ll learn what Python wheels are and why you should care as both a developer and end user of Python packages. You’ll see how the wheel format has gained momentum over the last decade and how it has made the package installation process faster and more stable.

10 Awesome Pythonic One-Liners Explained – Some things in Python are just better on one line.

How to Transcribe Speech Recordings Into Text With Python – Learn to transcribe speech in recordings like MP3s into text with Python and AssemblyAI’s API

Pass by Reference in Python: Background and Best Practices – In this tutorial, you’ll explore the concept of passing by reference and learn how it relates to Python’s own system for handling function arguments. You’ll look at several use cases for passing by reference and learn some best practices for implementing pass-by-reference constructs in Python.

Options for Packaging Your Python Code: Wheels, Conda, Docker, and More – There’s a lot of ways to package your Python code. Find out which one is right for you.

PyOxidizer: A Modern Python Application Packaging and Distribution Tool

Python and PDF: A Review of Existing Tools – The ultimate list of PDF tools in Python.

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Do you want to distribute your Python applications to other users who don’t have or even use Python? Maybe you’re interested in seeing your Python application run on iOS or Android mobile devices. This week on the show we have Russell Keith-Magee, the founder and maintainer of the BeeWare project. Russell talks about Briefcase, a tool that converts a Python application into native installers on macOS, Windows, Linux, and mobile devices.

We spend some time digging into BeeWare’s cross-platform widget toolkit named Toga. Russell talks about some of the intricacies of converting graphical user interface components from across multiple computing platforms. If you’re interested in contributing to an open source project, he discusses how you could get involved in the project. We also talk about the struggle of getting funding for open source projects.

Course Spotlight: Python Decorators 101

In this course on Python decorators, you’ll learn what they are and how to create and use them. Decorators provide a simple syntax for calling higher-order functions in Python. By definition, a decorator is a function that takes another function and extends the behavior of the latter function without explicitly modifying it.

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Do you want to learn the how and when of implementing K-means clustering in Python? Would you like to practice your pandas skills with a real-world project? This week on the show, David Amos is back with another batch of PyCoder’s Weekly articles and projects.

David talks about a Real Python article about how to perform K-means clustering in Python. We also talk about a new project based article on the site about how to create a gradebook using pandas, practicing the skills of importing, merging, and calculating across groups of data. We cover several other articles and projects from the Python community including: JPEG image decoding, object-oriented development with interfaces and mixins, sparking joy with Python, five package picks from Real Python authors, and more.

Course Spotlight: Reading and Writing CSV Files

This course teaches how to read and write data to CSV files using Python’s built in csv module and the pandas library. You’ll learn how to handle standard and non-standard data such as CSV files without headers, or files containing delimiters in the data.

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Understanding and Decoding a JPEG Image Using Python – Learn about the JPEG compression algorithm in this comprehensive guide to decoding JPEGs with Python.

K-Means Clustering in Python: A Practical Guide – Learn how to perform k-means clustering in Python. You’ll review evaluation metrics for choosing an appropriate number of clusters and build an end-to-end k-means clustering pipeline in scikit-learn.

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

Interfaces, Mixins and Building Powerful Custom Data Structures in Python – How to supercharge Python’s built-in data structures and build powerful custom data structures with mixin classes.

Sparking Joy With Python – After a fling with TypeScript, one Python programmer shares some thoughts on keeping the flame alive with Python.

Python Packages: Five Real Python Favorites – In this tutorial, several Real Python authors share Python packages we like to use as alternatives to modules in the standard library. You’ll get to know a number of useful packages, including pudb, requests, parse, dateutil, and typer.

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Have you wanted to generate advanced reports as PDFs using Python? Maybe you want to build documents with tables, images, or fillable forms. This week on the show we have Mike Driscoll to talk about his book “ReportLab - PDF Processing with Python.”

Mike is an author of multiple books about Python, and has recently re-written his Python 101 book. He is also a member of the Real Python team and has written several articles for the site. Along with our discussion about ReportLab and PDFs, Mike talks about being a self-published author. We also talk briefly about his favorite Python GUI framework.

Course Spotlight: How to Work With a PDF in Python

In this step-by-step course, you’ll learn how to work with a PDF in Python. You’ll see how to extract metadata from preexisting PDFs. You’ll also learn how to merge, split, watermark, and rotate pages in PDFs using Python and PyPDF2.

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Would you like to clearly understand what’s happening when you use the Python import keyword? Do you want to use modules more effectively to structure your code? Or maybe you’re ready to move to the next level with your Django project by adding user management. This week on the show, David Amos is back with another batch of PyCoder’s Weekly articles and projects.

We discuss a Real Python article about advanced techniques and tips for using the Python import keyword. David also talks about another recent article on the site about managing users in Django. We cover several other articles and projects from the Python community including: robot programming in Python, f-strings vs .format(), the rise of Python malware, a hardware Python keyboard, and more.

Course Spotlight: Grow Your Python Portfolio With 13 Intermediate Project Ideas

Get started on 13 Python project ideas that are just right for intermediate Python developers. They’ll challenge you enough to help you become a better Pythonista.

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The (Non-)Return of the Python Print Statement – Guido van Rossum recently proposed re-introducing the Python print statement. He was completely serious and even though the idea didn’t gain traction, it’s interesting to know why he made the proposal.

Python Malware on the Rise – Python’s low barrier to entry, enormous ecosystem, and rapid development process has made it one of he most desired programming languages for millions of developers around he globe—including malicious actors. Read the article at the link above and follow the discussion on Hacker News.

Get Started With Django Part 2: Django User Management – In this step-by-step tutorial, you’ll learn how to extend your Django application with a user management system, complete with email sending and third-party authentication.

Why Do People Use .format() When f-Strings Exist? – f-Strings aren’t exactly a drop-in replacement for .format().

A Beginner’s Guide to Robot Programming With Python – Get a crash course in programming autonomous robots with Python. Don’t have a robot laying around? No problem! Use this open-source simulator to get started.

Python import: Advanced Techniques and Tips – The Python import system is as powerful as it is useful. In this in-depth tutorial, you’ll learn how to harness this power to improve the structure and maintainability of your code.

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This week on the show we have Armin Ronacher to talk about the first 10 years of Flask. Armin talks about the origins of Flask and the components that make up the framework. He talks about what goes into documenting a framework or API. He also talks about the community working on the ongoing development of Flask.

He also shares his thoughts about Python, and how it contrasts with Rust and TypeScript. Armin talks about what he would do differently if he were to start development of a project like Flask now.

Course Spotlight: Documenting Python Code: A Complete Guide

This course will get you up to speed with how to document your Python code. Documenting your code is an important step to help developers and users fully understand its usage and purpose.

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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.

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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?

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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.

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Have you wanted to learn Regular Expressions in Python, but don’t know where to start? Have you stumbled into the dreaded pink SettingWithCopyWarning in Pandas? This week on the show, we have David Amos from the Real Python team to discuss a recent two-part series on Regex in Python. We also talk about another recent article on the site about views vs copies in Pandas. David also brings a few other articles and projects 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 the previously mentioned Real Python articles, we also discuss articles from the community about:

Course Spotlight: Reading and Writing Files in Python

This course will get you up to speed with reading and writing files in Python. You’ll cover everything from what a file is made up of, to which libraries can help you along that way. You’ll also take a look at some basic scenarios of file usage as well as some advanced techniques.

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SettingWithCopyWarning in Pandas: Views vs Copies – In this tutorial, you’ll learn about views and copies in NumPy and Pandas. You’ll see why the SettingWithCopyWarning occurs in Pandas and how to properly write code that avoids it.

Fastest Way to Flatten a List in Python – Explore six different was to flatten a list of lists in Python and how their performance compares. The fastest of the six methods mentioned might surprise you!

Combining Flask and Vue – Learn about three ways to combine Flask and Vue, the pros and cons of each, and some guidelines for when to use each method.

Regular Expressions: Regexes in Python (Part 2) – In the previous tutorial in this series, you learned how to perform sophisticated pattern matching using regular expressions, or regexes, in Python. This tutorial explores more regex tools and techniques that are available in Python.

Space Science With Python – Explore and analyze the wonders and mysteries of space… with Python!

Getting Machine Learning to Production – Millions of web apps get deployed to production every day. But machine learning models aren’t web apps. And very few people are talking about deployment. Learn how tools like Streamlit can help take the edge off deploying your machine learning models.

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Would you like to run your Python code in the cloud without having to become an infrastructure engineer? Do you want to have Python functions that run when triggered by specific events? This week on the show we have Anthony Chu to discuss serverless computing and running python functions in the cloud. Anthony Chu is program manager for Microsoft’s Azure Functions.

We discuss the advantages of serverless computing over virtual machines, containers, and other infrastructure options for running your Python code in the cloud. Anthony also talks about the types of projects suited for this type of platform, including data science, machine learning, and creating APIs.

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.

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