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Harika V
"Fresh B.Tech Grad Turned Dynamic Educator with 1 Year of Multi-Subject Expertise"
To optimize the performance of a Python application, start by profiling to identify bottlenecks using tools like cProfile or Pyflame. Next, focus on code efficiency, optimizing critical sections, using built-in functions, and avoiding unnecessary loops or recursion. Choose efficient data structures, minimize memory consumption, and select the right algorithms. Leverage well-optimized external libraries, implement concurrency with multithreading or multiprocessing, and consider JIT compilation with tools like Numba. For performance-critical sections, use C extensions such as Cython or ctypes. Implement caching and tune garbage collection to reduce its impact. Lastly, periodically re-profile your code to ensure ongoing improvements, keeping in mind that optimization should be based on actual bottlenecks rather than premature optimization.
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