How can I optimize data processing in Python?
You can optimize data processing in Python by using libraries like NumPy and Pandas, leveraging vectorization, and minimizing loops.
What are some specific techniques I can use?
Some techniques include using built-in functions, applying multiprocessing, and utilizing JIT compilation with Numba.
How does using NumPy help with performance?
NumPy provides efficient array operations and reduces overhead by using contiguous memory, which speeds up computations.
Are there any best practices for handling large datasets?
Yes, consider using chunking, lazy loading, and optimizing data types to manage memory usage effectively.
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