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.

Topics:

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

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.

Topics:

Show Links:

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.

Projects:

Additional Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

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.

Topics:

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

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.

Topics:

Show Links:

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.

Projects:

Additional Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

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.

Topics:

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

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.

Topics:

Show Links:

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.

Projects:

Additional Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

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.

Topics:

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

How do you create a virtual conference that retains the interactivity of an in-person event? What are the tools needed for talk submissions, ticketing, and live hosting? Can you find those tools written in Python?

Nina Zakharenko and Seb Vetter are co-chairs, and Ashia Zawaduk is the conference program chair. PyCascades will be held online from February 19th through 21st, with a day of virtual social events, one of live-streamed talks, and another of mentored sprints.

We discuss ways to recreate the elusive feel of the “hallway” track virtually. They share advice about submitting a talk proposal and ways that you can volunteer for conferences.

Tickets are available now. PyCascades is looking for additional sponsors. If you work for an organization that can help, get in contact with them.

Course Spotlight: Speed Up Python With Concurrency

Learn what concurrency means in Python and why you might want to use it. You’ll see a simple, non-concurrent approach and then look into why you’d want threading, asyncio, or multiprocessing.

Topics:

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

What is it like to design a Python library for three different audiences? This week on the show, we have Nir Aides, creator of Jupylet. His new library is designed for deep reinforcement learning researchers, musicians interested in live music coding, and kids interested in learning to program. Everything is designed to run inside of a Jupyter notebook.

Nir’s initial goal was to create a framework to study deep reinforcement learning, and this led to building a framework for 2D and 3D games and graphics. As he continued the development, he realized that this interactive environment could be a useful tool for learning Python.

We also talk about how he got interested in live music coding and the advanced mathematics of sound synthesis. Nir also shares some resources for finding graphic assets and tools for creating 3D models.

Course Spotlight: Using Jupyter Notebooks

In this step-by-step course, you learn how to get started with the Jupyter Notebook, an open source web application that you can use to create and share documents that contain live code, equations, visualizations, and text.

Topics:

Show Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

Are you familiar with the role data engineers play in the modern landscape of data science and Python? Data engineering is a sub-discipline that focuses on the transportation, transformation, and storage of data. 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 on data engineering, we talk about a project where researchers downloaded 10 million Jupyter notebooks from Github to gather insights about the current state of data science technology.

We also discuss an article about validating data in Python with the package Cerberus. And this led us to a conversation about a set of coding challenges from Advent of Code.

We also cover several other articles and projects from the Python community including, building my own chess engine, the visual guide to NumPy, a free and open-source alternative to SAP, a library for working with STL files and 3D objects, and is Python really a bottleneck?

Course Spotlight: Building With Django REST Framework

This course will get you ready to build with Django REST Framework. The Django REST framework (DRF) is a toolkit built on top of the Django web framework that reduces the amount of code you need to write to create REST interfaces.

Topics:

Show Links:

What Is Data Engineering and Is It Right for You? — In this article, you’ll get an overview of the discipline of data engineering. You’ll learn what is and isn’t part of a data engineer’s job, who data engineers work with, and why data engineers play a crucial role in many industries.

Building My Own Chess Engine — Writing your own chess engine is a great way to explore computational complexity and combinatorial aspects of programming. Not to mention it’s pretty fun! Follow along with this reflection on how one coder created his own Chess engine from scratch.

We Downloaded 10,000,000 Jupyter Notebooks From Github: This Is What We Learned — The JetBrains Datalore team downloaded ten million Jupyter Notebooks and analyzed them to determine things like which languages were the most popular, what kinds of content are in notebook cells, and how consistently notebooks can be reproduced. It’s a fascinating look into trends in data science technology!

Is Python Really a Bottleneck? — Python is slow. From one perspective, that is. But what are the true bottlenecks in the data engineering/data processing space, and how does Python compare to other technologies when those factors are considered?

Validating Data in Python With Cerberus — Thanks to an Advent of Code challenge, author Hector Castro was exposed to the Cerberus Python package for data validation. Get a quick introduction to Cerberus and see Hector’s solution to an Advent of Code challenge in this quick-yet-informative read.

NumPy Illustrated: The Visual Guide to NumPy — This illustrated guide to NumPy is a great way to learn NumPy or brush up on the package. Full of great visual aides, this tutorial covers all the basics and more!

Projects:

Additional Links:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas