How can I handle large boolean masks in NumPy without running into memory issues?
Could someone explain I'm currently working on a large dataset, specifically a 10 million x 10 array, and I'm trying to apply a boolean mask to filter rows based on certain conditions. I've created a boolean mask using NumPy but I'm running into memory issues when executing the operation. Here's the code snippet I'm using: ```python import numpy as np # Creating a large array large_array = np.random.rand(10000000, 10) # Creating a boolean mask for filtering where the sum of rows is greater than 5 boolean_mask = np.sum(large_array, axis=1) > 5 # Applying the mask filtered_array = large_array[boolean_mask] ``` When I run this, I get a `MemoryError` because the boolean mask seems to take up too much memory. I've tried reducing the size of the array but I still encounter the same scenario. I've also looked into using `numpy.memmap` for memory mapping, but Iām not sure how to integrate that with my current solution. Is there an efficient way to handle large boolean masks or alternatives to filtering without consuming so much memory? Are there any best practices to optimize this kind of operation in NumPy? This is part of a larger service I'm building. I'm working on a microservice that needs to handle this. Has anyone dealt with something similar?