Faster Python Code

This is the repository for the LinkedIn Learning course Faster Python Code. The full course is available from LinkedIn Learning.

By optimizing your Python code, you can ensure that your code uses fewer resources and runs faster than it did previously. In this advanced course, explore tips and techniques that can help you optimize your code to make it more efficient. Instructor Miki Tebeka covers general tools of the trade, including how to leverage the tools Python provides for measuring time, and how to use line_profiler to get line-by-line profiling information. Miki also shares how to pick the right data structures, how approximation algorithms can speed up your code, and how to use NumPy for fast numeric computation. He wraps up the course with a discussion of how to integrate performance in your process.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the “Using GitHub Codespaces with this course” video to learn how to get started.

Installing

1. To use these exercise files, you must have the following installed:
   • Python
   • pypy3
2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
3. Create a virtual environment and install dependencies
   • python -m venv venv
   • ./venv/bin/python -m pip install -r requirements.txt
   • Point your IDE to use the Python from ./venv/bin/python

pypy On Codespaces

pypy is installed in the codespace. If you want to run it with IPython, use the following command:

~/pypy3-env/bin/ipython