What? Python code…
1. Get a Linux server running on your Windows PC.
2. Start using Python in Linux Jupyter Notebooks.
3. Start writing daily journal entries in NeoVim.
Python extends English to have better expressiveness. It's the most popular programming language in the world because it's as easy for the beginner as it is powerful for the pro.
Learn Python in Familiar Web Browser
Python is not only the most popular programming language in the world and at the center of AI, but it’s also a pleasure to use (it was named after Monty Python and not the snake). Python is no longer just tech, but has become as fundamental to education and literacy as reading, writing and arithmetic.
When Did This Happen?
It started January of 1987 when the ABC programming language came out from Centrum Wiskunde & Informatica (CWI) in the Netherlands. Guido van Rossum, the creator of Python worked for these folks and absorbed what what an ideal learning languages entails, then combined it with what two kooky contingencies in tech needed: the uber-powerful C-programmers and the beleaguered system administers (sysadmins). He did it as a summer project in 1991.
JavaScript in comparison was just a knock-off of Java and kludged into Netscape as an afterthought. Python has intention and you can feel it. That’s what makes it so loved and used everywhere from WebDev to NASA to AI. It’s taken over CompSci 101 courses, and grows with the pros so that switching languages for specialized problem domains is optional. Never before has a generalized language achieved the 80/20-rule ideal so thoroughly.
What’s best is that if you’re running Python on Linux, you can use Python’s remarkably friendly features as a way of easing yourself into good parts of Linux: the command-line interface (CLI) — also known as the Linux Terminal or sometimes just shell. Simply use standard Linux paths (the easy kind with forward-slashes) in your Python code, keeping your work Cloud Compatible, and indeed world-compatible.
How Does This Help With Linux?
Python does not exist in a vacuum. There is an intimate relationship between a programming language and the system on which it operates. Learning one helps you learn the other. Python code that refers to files and folders on a Windows PC, for example, will not work on a Linux server. One reason is that Windows uses back-slashes in its file paths, while Linux uses forward-slashes.
One example of how this hurts is that a Data Scientist who is used to working on a Windows PC will not be able to use their Python code on a Linux server without first changing all the file paths in their code. There are also environment variables, package dependencies, and more. The list of differences between Windows and Linux is long and frustrating.
MyKoz.AI removes the separation between the Python code and the Linux environment, even while you’re working on Windows. You can plan your code so that it’s directly deployable into the cloud, or on other Linux production servers. This is a huge advantage for Data Scientists who are working on Windows PCs, but want their work to become regular ongoing reports.
Vendors sometimes try to solve this problem by providing a Linux VM bundled into their product, resulting in a bloated and slow experience. Or they might set up a whole job scheduler and workflow system on another server for you to upload to, which is overkill and requires yet more people to manage it. It’s much easier to just know now to make real Linux services where you sit.