Why Is Python so Popular? GitHub Knows What’s up
The Python programming language is over 30 years old and still experiencing a 22% growth in popularity year over year, according to GitHub’s annual “Octoverse” top programming languages survey. So why is that?
Github recently published a blog post that explores Python’s enduring popularity, written by GitHub Developer and Open Source Advocate Rizel Scarlett. The post draws on insights from GitHub’s own users.
In short, Python is strongly suited for data heavy operations and it’s straightforward to learn.
The second part of why Python is on the rise is because data is on the rise and Python is great with data.
Getting Started with Python
These syntax design choices help even readers unfamiliar with code writing to more easily identify what the code is doing simply by looking at it, Scarlett writes.
Python Heavy Careers
Think Python and data. So that could be data analytics, financial data, machine learning, or artificial intelligence. Python works well with data because it excels in automating manual, repetitive tasks. Python has automated built-in modules available for commonly used automation.
Here are a few possible career choices, according to GitHub:
Machine Learning and Artificial Intelligence (AI)
Speaking of data-intensive… large-scale ML models can take up to billions of parameters of training data and they don’t look to be shrinking. Because of the high amounts of data, automatic scripting and algorithms are important. Python’s data visualization capabilities convert large datasets for AI or ML into comprehensible graphs. OpenAI uses the Python framework Pytorch as its standard framework for deep learning and as a result, ChatGPT is written in Python.
Scarlett asserted that “Python is the top preferred language for data science and research.” This is partly because it’s understandable by people who don’t have a developer background and also because it’s great with large datasets. Collecting and parsing data are time consuming tasks making the use of machine learning fairly common in data science.
Python libraries such as NumPy, Pandas, and Matplotlib are used to automate functions like cleaning, data transformation, and visualization.
python.org has 50 jobs listed which also provides a better understanding of what a career with Python will look like.
Scarlett also offered a bevy of links to GitHub resources:
- GitHub Codespaces gives 60 free hours of free dev environment in the cloud from any device at any speed.
- GitHub’s Copilot is an AI pair programmer made to help with the first lines of Python.
- These pre-built Python algorithms include networking flows to physical and neural networks.
- Here is GitHub’s step-by-step guide to learning Python in 30 days and a Python Cheatsheet.
Here are some Non-GitHub Python Resources:
- Amazon Web Services’ Deep Racer tool is more suited for learning how to train machine learning models but is also a good tool for building on or strengthening a Python foundation.
- Udemy offers a course on getting started with Python for someone looking to just get started.
- Career Foundry lists some boot camps that specifically teach Python for anyone looking for a specialized learning environment.
- And of course…. YouTube!