Talk Python To Me

Everyone in the Python space is familiar with Notebooks these days. One of the original notebook environments was SageMath. Created by William Stein, and collaborators, it began as an open-source, Python-based, computational environment focused on mathematicians.

Python is one of the primary languages for IoT devices. With runtimes such as CircuitPython and MicroPython, they are ideal for the really small IoT chips. Maybe you've heard of the Circuit Playground Express, BBC micro:bit, or the fancy Adafruit CLUE. They aren't too expensive (ranging from $25 to $50 each). But for large groups such as classrooms, this can be a lot of money. Moreover, getting your hands on these devices can sometimes be tricky as well.

Time is a simple thing, right? And working with it in Python is great. You just import datetime and then (somewhat oddly) use the datetime class from that module.

On this episode, we are going to weave a thread through three different areas of Python programming that at first seem unlikely to have much in common. Yet, the core will be the same throughout. I think this is a cool lesson to learn as you get deeper into programming and a great story to highlight it.

The toolchain for modern data science can be intimidating. How do you choose between all the data visualization libraries out there? How about creating interactive web apps from those analyses? On this episode, we dive into a project that attempts to bring the whole story together: HoloViz.

Are you using interactive notebooks for your data exploration or day-to-day programming? What environment do you use? Was it Jupyter and now you've made the move to JupyterLab? That's a great choice. But did you know there are more environments out there to choose from and compare? Have you heard of Callisto or Iodide? How about CoCalc or PolyNote? That's just the tip of the iceberg!

Do you write tests for your code? You probably should. And most of the time, pytest is the industry standard these days. But pytest can be much more than what you get from just installing it as a tool.

Refactoring your code is a fundamental step on the path to professional and maintainable software. We rarely have the perfect picture of what we need to build when we start writing code and attempts to over plan and overdesign software often lead to analysis paralysis rather than ideal outcomes.

The debate about whether Python is fast or slow is never-ending. It depends on what you're optimizing for: Server CPU consumption? Developer time? Maintainability? There are many factors. But if we keep our eye on pure computational speed in the Python layer, then yes, Python is slow.

Are you a web developer who uses Flask? It has become the most popular Python web framework. Even if you have used it for years, I bet we cover at least one thing that will surprise you and make your Flask code better.

As developers and technologists, it's easy to think that powerful and unique ideas will percolate to the top. If we build something amazing, enthusiastic users will find and share our creations.

Has anyone told you that you should get into data science? Have you heard it's a great career? In fact, data scientist is the best job in America according to Glassdoor's 2018 rankings.

Traditionally, when we have depended upon software to make a decision with real-world implications, that software was deterministic. It had some inputs, a few if statements, and we could point to the exact line of code where the decision was made. And the same inputs lead to the same decisions.

If you work on the data science or data visualization side of Python, you may have come to it from a scripting side of things. Writing just a little Python, using its magical libraries, with little structure or formalism to build a powerful analysis tool that runs in the terminal or maybe a jupyter notebook. What if you could take that same code, sprinkle in just a bit of a simple API, and turn it into a fast and dynamic single page application allowing your users to dive into the visualizations on the web?

Did you come to Python from the academic side of the world? Maybe got into working with code for research or lab work and found you liked coding more than your first field of study. Whatever the reason, many people make the transition from the academic world over to tech and industry.