How can I optimize memory usage in NumPy?

How can I optimize memory usage in NumPy?

You can optimize memory usage in NumPy by using data types that consume less memory, like 'float32' instead of 'float64', and by using views instead of copies.

What are some specific techniques to reduce memory footprint?

Some techniques include using in-place operations, leveraging memory-mapped files for large arrays, and utilizing the 'np.compress' function to filter data.

Can you explain how memory-mapped files work?

Memory-mapped files allow you to access large arrays on disk as if they were in memory, which helps in handling datasets that don't fit into RAM.

Are there any tools or libraries that can help with this?

Yes, libraries like Dask and CuPy can help manage larger-than-memory datasets and provide optimized operations for NumPy-like arrays.