Performance implementing np.where in large arrays - unexpected slow execution in NumPy 1.24.2
Quick question that's been bugging me - I'm working with important performance optimization when using `np.where` on large arrays. Specifically, I have a large NumPy array (shape `(1000000,)`) and I'm trying to filter it based on a condition. Here’s a snippet of my code: ```python import numpy as np # Create a large array data = np.random.rand(1000000) # Use np.where for filtering data_filtered = np.where(data > 0.5) ``` The expected behavior is that it returns the indices of the elements greater than 0.5. However, the performance is unacceptably slow, taking several seconds for this operation. I have tried different approaches, including using list comprehensions and boolean indexing, but they seem to yield similar performance, if not worse. For example: ```python # Using boolean indexing mask = data > 0.5 data_filtered_bool = data[mask] ``` I also checked my NumPy version, which is 1.24.2, and I’ve ensured that my environment is set up correctly without any conflicting libraries. Is there a recommended approach or optimization for using `np.where` with large datasets? Are there any known performance pitfalls that I might be hitting? I’m looking for ways to improve the execution time without resorting to parallel processing or other complex solutions. My development environment is Windows. Any help would be greatly appreciated! Could someone point me to the right documentation?