- Rules of optimization
- Measuring time
- Using line_profiler
- Picking the right data structure
- Using the bisect module
- Memory allocation in Python
- Caching, cheating, and parallel computing
- NumPy, Numba, and Cython
- Design and code reviews
Skill Level Advanced
- [Miki] Hi there. I'm Miki Tebeka, a developer, mentor, instructor, and author. I love technology and write code daily, most of the time in Python. Optimized Python code will make your site or application more responsive, which means customers will stay longer and be more likely to return. This course is a compilation of optimization tips and tricks I've learned in the last 20 years. In this course, we'll cover how to find performance bottlenecks by profiling CPU and memory, picking the right data structures and algorithms, assorted tips and tricks to make your code faster, caching techniques, how to cheat the factory, parallel computation, using tools and languages such as C, Cython, Lambda, and others, and how to integrate performance into a process.
1. Tools of the Trade
2. Picking the Right Data Structure
3. Tricks of the Trade
Cheating example4m 21s
6. Parallel Computing
7. Beyond Python
8. Adding Optimization to Your Process
Next steps1m 5s
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