How do I speed up my Python code?

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Dheeraj

IT Corporate Trainer with 9 years of experience in Edu tech

1. Use Built-in Functions & Libraries Python’s built-in functions (like sum(), map(), and zip()) are optimized in C and run much faster than manual loops. 2. Use List Comprehensions List comprehensions are much faster than loops for creating lists. 3. Use Generators Instead of Lists Generators...
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1. Use Built-in Functions & Libraries Python’s built-in functions (like sum(), map(), and zip()) are optimized in C and run much faster than manual loops. 2. Use List Comprehensions List comprehensions are much faster than loops for creating lists. 3. Use Generators Instead of Lists Generators don’t store all values in memory, making them more efficient. 4. Use NumPy for Numerical Operations If you're dealing with numbers, NumPy is much faster than Python’s built-in lists. 5. Avoid Unnecessary Loops Loops are slow in Python. Try using vectorized operations, built-in functions, or even multiprocessing. 6. Use multiprocessing or asyncio for Parallel Processing If your code is CPU-intensive, split tasks into multiple processes. 7. Use lru_cache for Function Memoization If a function gets called multiple times with the same arguments, caching can save time. 8. Use Cython or JIT Compilation (Numba) For performance-critical parts, you can compile Python to C using Cython or JIT (Just-In-Time) compile with Numba. 9. Profile Your Code Use Python’s built-in profiler to find bottlenecks. 10. Use PyPy Instead of CPython PyPy is an alternative Python interpreter that can make your code significantly faster in some cases. read less
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Data Science & Business Intelligence Expert with 9 Years of Experience

Speed Up Python Code: Use built-in functions Optimize loops & list comprehensions Use NumPy, Pandas, Cython, Numba Enable parallel processing (multiprocessing, asyncio) Prefer local variables over global Try PyPy for JIT compilation
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Use built-in functions Optimize loops & list comprehensions Use NumPy, Pandas, Cython, Numba Enable parallel processing (multiprocessing, asyncio) Prefer local variables over global Try PyPy for JIT compilatio
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