What are some tips to improve NumPy performance?

What are some tips to improve NumPy performance?

To enhance NumPy performance, consider using vectorization instead of loops, leveraging built-in functions, and using the right data types.

Can you explain what vectorization means?

Vectorization refers to the process of converting operations that would typically be performed in a loop into a single operation that can be applied to entire arrays at once.

Are there specific functions in NumPy that are optimized for performance?

Yes, functions like np.sum(), np.mean(), and np.dot() are optimized for performance and should be preferred over manual loops.

What about memory usage? Any tips for that?

To optimize memory usage, use smaller data types like float32 instead of float64, and consider using memory-mapped arrays for large datasets.