Using "cProfile" to analyze Python code performance

The video tutorial explains how to use CProfile to generate runtime performance profiles of Python programs. It covers the three-stage process of profiling: setting up the application, generating a human-readable report, and identifying bottlenecks for optimization. The tutorial also demonstrates using PStats to analyze profiling results and highlights the importance of focusing on functions that consume the most time. Additionally, it discusses optimizing Python code using Cython for improved performance.

Explain how the P Stats library complements C profile.

What are the potential pitfalls of analyzing the total number of function calls without context?

What changes were made to the fast version of the program to improve performance?