The five-highest ranked repositories on GitHub related to learning how to code in Python.
Use of the Python programming language has been growing at a prodigious rate for several years.
Much of that growth has been fueled by the use of Python in burgeoning field of data science, which in 2018 became the most common use for Python, alongside web development and DevOps.
Demand for Python across these various fields remains strong, with a 2018 survey by HackerRank placing it as the language third-most sought after by employers and it also claimed third place in the list of most-loved programming languages in last year's Stack Overflow Developer Survey.
Luckily there's a plethora of good quality free resources out there for learning Python, as you can see from this extensive TechRepublic round-up.
However, if you're looking for educational materials with a heavier focus on code examples, the online code repository GitHub is a good place to start.
Here are the five-highest ranked repositories on GitHub related to learning how to code in Python.
1. Awesome Python
A curated list of 'awesome' Python frameworks, libraries, software and resources, with code covering just about everything you might use Python for.
2. TensorFlow Examples
A useful reference for those getting started with Google's TensorFlow machine-learning software framework, offering a long list of code examples demonstrating everything from basic TensorFlow operations to building neural networks.
3. The algorithms
Examples of sorting and other common computer science algorithms implemented in Python, including links to visualizations of the algorithms in action. Useful for those swatting up for a technical interview.
4. 100 days of ML coding
This repo is an eclectic mix of worksheets that walk users through the basics of getting started with machine learning, with links to the associated code samples and datasets, as well as to useful videos explaining key mathematical concepts.
5. Python Patterns
A collection of design patterns and idioms in Python, with code examples and written explanations of what the code is doing and when each pattern is appropriate to use.
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