Do you know how a neural network functions? What goes into building one from scratch using Python? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David talks about a recent Real Python article titled “Python AI: How to Build a Neural Network & Make Predictions.” This article covers how to train a neural network and create a linear regression model.

We also cover several articles about testing in Python including, writing unit tests, testing code in Jupyter notebooks, and a testing style guide.

We cover several other articles and projects from the Python community including, how to build an Asteroids game with Python and Pygame, a 5-point framework for Python performance management, how it helps to know a Python programmer if you want a vaccination appointment, a Flask mega-tutorial, and the new release of SQLAlchemy.

Course Spotlight: Python Coding Interviews: Tips & Best Practices

In this step-by-step course, you’ll learn how to take your Python coding interview skills to the next level and use Python’s built-in functions and modules to solve problems faster and more easily.

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Build an Asteroids Game With Python and Pygame – Build a clone of the Asteroids game in Python using Pygame. Step by step, you’ll add images, input handling, game logic, sounds, and text to your program.

Python AI: How to Build a Neural Network & Make Predictions – Build a neural network from scratch as an introduction to the world of artificial intelligence (AI) in Python. You’ll learn how to train your neural network and make accurate predictions based on a given dataset.

How to Write Unit Tests in Python, Part 1: Fizz Buzz – Get an introduction to unit testing in Python from the author of the Flask Megatutorial.

A 5-Point Framework For Python Performance Management – “Performance testing — like sailboat racing — depends on the conditions along the racecourse.”

Unit Testing Python Code in Jupyter Notebooks – Even if you code in Jupyter notebooks, there’s no excuse to not be testing your code!

Python Testing Style Guide – Need a quick yet thorough guide to testing? This excellent resource is for you.

Want a vaccination appointment? It helps to know a Python programmer – Programmers are writing scripts to help find vaccine appointments for those who are eligible.

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If you haven’t visited the website lately, then you’re missing out on the updates to realpython.com! The site features a completely refreshed layout with multiple sections to help you take advantage of even more great educational Python content. This week on the show, we have Dan Bader, the person behind Real Python, and all these architectural changes.

Among the features changed are a new bookmarking system, a section to keep track of what you’ve been learning lately, and a much more advanced way to search the site. A new tile system makes it easier to explore learning paths, quizzes, office hours, and other sections of the site.

Dan shares details about the website technology stack and why he started using Python for the core content management system. He also talks about the struggle of being the sole maintainer and feature architect.

Spotlight: Python Basics: A Practical Introduction to Python 3

Go from beginner to intermediate in Python with this complete curriculum, up-to-date for Python 3.9. Python Basics includes exercises, interactive quizzes, and sample projects, so you’ll always know what to focus on next in order to build a strong Python foundation. Paperback copies are available now.

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Have you heard about NoSQL databases, or wondered how to use one with Python? How does MongoDB store information and what packages can you use to connect this type of database to your Python project? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David talks about a recent Real Python video course about managing namespaces in Python. We also look at a few recent stories about the Python packaging ecosystem.

We cover several other articles and projects from the Python community including, generating customizable PDF reports with Python, how semantic versioning will not save you, PEP 621 is final, a user hits the Python community with 4,000 fake modules, making a synth with Python, and what is running on the Mars helicopter.

Course Spotlight: Navigating Namespaces and Scope in Python

In this course, you’ll learn about Python namespaces, the structures used to store and organize the symbolic names created during execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.

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Generate Customizable PDF Reports With Python – Learn how to generate custom PDF reports using reportlab and pdfrw with a PyQt GUI.

Semantic Versioning Will Not Save You – Semantic versioning aims to both communicate the version of software as well as promise that certain versions won’t break anything. Sounds great, right? In a lot of cases it is, but a blind reliance on semantic versioning can come back to haunt you.

PEP 621 Is Final – In the near future, you’ll be able to store project metadata in pyproject.toml.

Poison Packages: User Hits Python Community With 4000 Fake Modules – Recently, a PyPI user going by the name “Remind Supply Chain Risks” uploaded nearly 4,000 fake modules to the index, many of which were named as common misspellings of popular packages. Learn about the incident in this article, and read all the way to the end for four tips every Python developer should follow.

Python and MongoDB: Connecting to NoSQL Databases – Learn how to use Python to interface with the NoSQL database system MongoDB. You’ll get an overview of the differences between SQL and NoSQL, and you’ll also learn about related tools, including PyMongo and MongoEngine.

Navigating Namespaces and Scope in Python – Learn about Python namespaces, the structures used to store and organize the symbolic names created during the execution of a Python program. You’ll learn when namespaces are created, how they are implemented, and how they define variable scope.

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Making a Synth With Python: Oscillators – Learn how to create oscillators using Python as a foundation for creating your own software synthesizers. This article is one of a three-part series. The other articles cover modulators and controllers.

Python Is Running on the Mars Helicopter

How the First Helicopter on Mars Uses Off-the-Shelf Hardware and Linux

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What goes into the decision of how to host your Python code or application in the cloud? Which technology stack is the right size for your project? This week on the show, we have Calvin Hendryx-Parker. Calvin talks about cloud hosting options, infrastructure choices, and deployment tools.

Calvin is the co-founder and CTO of Six Feet Up, and co-organizer of the Python Web Conference. We talk about finding the right tools for clients. He also discusses the Python platform they created for hosting a virtual conference.

We also discuss hosting personal portfolio projects. That conversation leads to the question, what types of skills you can showcase through creating a hosted project.

Course Spotlight: Creating PyQt Layouts for GUI Applications

In this step-by-step course, you’ll learn how to use PyQt layouts to arrange and manage the graphical components on your GUI applications. With the help of PyQt’s layout managers, you’ll be able to create polished and professional GUIs with minimal effort.

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Have you wanted to get your Python code to consume data from web-based APIs? Maybe you’ve dabbled with the requests package, but you don’t know what steps to take next. This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

We discuss an article titled, “Python’s APIs: A Winning Combo for Reading Public Data”. David shares another Real Python article about creating microservices using Google Remote Procedure Calls (gRPC).

We also cover several other articles and projects from the Python community including, making a difficult data analysis question easy with pandas, efficiently cleaning text with pandas, the tricky bits of Python concurrency, building rich terminal dashboards, making better assertions for Python tests, and building and managing real-life data science projects with metaflow.

Course Spotlight: Making HTTP Requests With Python

The “requests” library is the de facto standard for making HTTP requests in Python. It abstracts the complexities of making requests behind a beautiful, simple API so that you can focus on interacting with services and consuming data in your application. This course shows you how to work effectively with “requests”, from start to finish.

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Python Microservices With gRPC – Learn how to build a robust and developer-friendly Python microservices infrastructure using gRPC and Kubernetes. You’ll also explore advanced topics such as interceptors and integration testing.

Python’s APIs: A Winning Combo for Reading Public Data – Learn what APIs are and how to consume them using Python. You’ll also learn some core concepts for working with APIs, such as status codes, HTTP methods, using the requests library, and much more.

Making a Difficult Data Analysis Question Easy With Pandas – A great strategy to use when faced with a tricky data analysis problem is to reshape the dataset into a format that turns it into an easy problem. In this article, you’ll look at an example involving a simple calculation and extensive reshaping in pandas.

Efficiently Cleaning Text With Pandas – In this article, you’ll see some examples of cleaning text fields in a large data file and learn several strategies for efficiently cleaning unstructured text fields using Python and pandas.

Python Concurrency: The Tricky Bits – An exploration of threads, processes, and coroutines in Python, with interesting examples that illuminate the differences between each.

Building Rich Terminal Dashboards – Learn how to use the Rich CLI library’s new terminal dashboard feature.

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What’s the difference between writing code for yourself and developing for others? What new considerations do you need to take into account as a professional Python developer? This week on the show, we talk to Dane Hillard about his book “Practices of the Python Pro”.

Dane discusses his philosophy on the design principles that go into writing code. We talk about namespaces, object-oriented design, and how to keep your code extensible. We also consider the how and when of code optimization.

Course Spotlight: Dictionaries and Arrays: Selecting the Ideal Data Structure

In this course, you’ll learn about two of Python’s data structures: dictionaries and arrays. You’ll look at multiple types and classes for both of these and learn which implementations are best for your specific use cases.

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Do you know the initial steps to get your Python script hosted on the web? You may have built something with Flask, but how would you stand it up so that you can share it with others? This week on the show, we have the previous guest Martin Breuss back on the show. Martin shares his recent article titled, “Python Web Applications: Deploy Your Script as a Flask App”. David Amos also returns, and he’s brought another batch of PyCoder’s Weekly articles and projects.

David shares a recent mathematical Real Python article about the stochastic gradient descent algorithm with Python. Stochastic gradient descent is an optimization algorithm often used in machine learning applications to find ideal model parameters.

We also cover several other articles and projects from the Python community including, property-based testing with hypothesis, Python’s tug of war between beginner-friendly features and support for advanced users, how Python integers work, the steering council accepts PEP 634, a magical full-stack framework for Django named django-unicorn, and a visual programming environment called Math Inspector.

Course Spotlight: Simulating Real-World Processes in Python With SimPy

In this step-by-step course, you’ll see how you can use the SimPy package to model real-world processes with a high potential for congestion. You’ll create an algorithm to approximate a complex system, and then you’ll design and run a simulation of that system in Python.

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Property-Based Testing With hypothesis, and Associated Use Cases – Testing software is hard. Property-based testing can help you create more effective tests. Learn how to do property-based testing with the hypothesis framework by looking at some real-world use cases.

Python’s Tug of War Between Beginner-Friendly Features and Support for Advanced Users – Python has made some big improvements to tracebacks in recent versions. See how tracebacks have evolved over the last couple of major releases and where there’s still some work left to be done. Check out the discussion on Hacker News.

How Python Integers Work – Python’s integer datatype is pretty different from most other languages because they allow arbitrary precision. Learn how integers work under the hood in this in-depth article.

Python Steering Council Accepts PEP 634 – Pattern matching, which adds a kind of switch-case statement to Python, has been accepted.

Stochastic Gradient Descent Algorithm With Python and NumPy – Learn what the stochastic gradient descent algorithm is, how it works, and how to implement it with Python and NumPy.

Python Web Applications: Deploy Your Script as a Flask App – In this tutorial, you’ll learn how to go from a local Python script to a fully deployed Flask web application that you can share with the world.

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Do you feel like you understand how Python works under the hood? What is syntactic sugar, and how much of it should be in Python? This week on the show, we have Brett Cannon. Brett is a Python core developer and he’s been working on a series of articles where he is unraveling the syntax of Python. His series is a fantastic resource for those wanting to learn how Python is structured and works at its core.

Brett wants to see a version of Python that can run in web browsers, so he started to breakdown Python into its syntactic elements to try to answer the question, what are core elements of Python? His detailed series takes the reader along for the ride.

Brett also works at Microsoft as the dev manager for the Python extension for VS Code. Brett is also serving his third term on the Python steering council, and we discuss recent Python enhancement proposals (PEP) that the council is considering.

Course Spotlight: Cool New Features in Python 3.9

In this course, you’ll explore some of the coolest and most useful features in the newly released Python 3.9. You’ll learn how Python 3.9 makes it easier to work with time zones, dictionaries, decorators, and several other techniques that will make your code cleaner and more efficient.

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Are you interested in building interactive dashboards with Python? How about a project that takes a flat data file all the way to a web-hosted interactive dashboard? This week on the show, David Amos is back, and he’s brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article about data visualizations using Dash, David covers an article designed to help Python developers understand the fundamentals of C. We discuss a couple of articles about Excel and using Python with Microsoft Office.

We also cover several other articles and projects from the Python community including, out-of-memory crashes in Python, updating all packages with pip-review, data science notebooks for teams, and a command-line tool for looking up colors, shades, and palettes.

Course Spotlight: Command Line Interfaces in Python

Command line arguments are the key to converting your programs into useful and enticing tools that are ready to be used in the terminal of your operating system. In this course, you’ll learn their origins, standards, and basics, and how to implement them in your program.

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C for Python Programmers – In this tutorial, you’ll learn the basics of the C language, which is used in the source code for CPython, the most popular Python implementation. Learning C is important for Python programmers interested in contributing to CPython.

Dying, Fast and Slow: Out-Of-Memory Crashes in Python – Learn about the different ways that memory issues can manifest in your Python programs, and how you can debug and fix them.

Automating Excel File Creation and Distribution With Pandas And Outlook – See how a little bit of Python can go a long way to automating manual processes and save businesses valuable time.

Update All Packages With pip-review – Keeping track of Python dependencies and updates can be tricky. The pip-review tool automates a lot of this process in a convenient command-line interface.

Ditching Excel for Python: Lessons Learned From a Legacy Industry – Learn how Python is revolutionizing an industry that’s notoriously resistant to change and fraught with every programmer’s most dreaded tool: Excel spreadsheets.

Develop Data Visualization Interfaces in Python With Dash – Learn how to build a dashboard using Python and Dash. Dash is a framework for building data visualization interfaces. It helps data scientists build fully interactive web applications quickly.

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Are you interested in processing images in Python? Do you need to load and modify images for your Flask or Django website or CMS? Then you most likely will be working with Pillow, the friendly fork of PIL, the Python imaging library. This week on the show, we have Mike Driscoll, who is writing a new book about image processing in Python.

We dive deep into the types of processing Pillow provides. Mike talks about creating Python GUI applications to take advantage of all the library has to offer. We also talk about his PyDev of the week series and his Python Interviews book.

Course Spotlight: Editing Excel Spreadsheets in Python With openpyxl

In this course, you’ll learn how to handle spreadsheets in Python using the openpyxl package. You’ll learn how to manipulate Excel spreadsheets, extract information from spreadsheets, create simple or more complex spreadsheets, including adding styles, charts, and so on.

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