10 patterns for faster Python code

Deep learning engineer Dido Grigorov presents 10 performance optimization techniques that can significantly accelerate Python code execution without sacrificing readability. Key strategies include using sets instead of lists for membership testing (750x faster), leveraging math module functions over operators, pre-allocating memory for known data sizes, and utilizing specialized modules like itertools and bisect for complex operations. These empirically tested methods demonstrate how small code adjustments can yield substantial performance gains.