implementing np.select for complex conditional logic in NumPy 1.24
Hey everyone, I'm running into an issue that's driving me crazy. I'm working on a project and hit a roadblock. I'm trying to implement complex conditional logic using `np.select` in NumPy 1.24, but it seems like the output is not as expected. I have several conditions and corresponding choices that I want to evaluate on a 1D array. Here's a simplified version of my code: ```python import numpy as np # Sample data values = np.array([10, 20, 30, 40, 50]) # Define conditions and choices conditions = [values < 20, values < 40, values < 60] choices = ['low', 'medium', 'high'] # Applying np.select result = np.select(conditions, choices) print(result) ``` I expected to get an array with 'low' for values less than 20, 'medium' for those less than 40, and 'high' for anything less than 60. However, the output is: ``` ['low' 'medium' 'medium' 'medium' 'high'] ``` As you can see, the second and third elements are both marked as 'medium', but I wanted the categorization to be mutually exclusive based on the first condition that is satisfied. I've also tried using `np.where` instead, but it leads to similar overlapping categories, as the conditions overlap. Is there a way to ensure that only the first matching condition is chosen, or should I be structuring my conditions differently? Any insights would be greatly appreciated! Has anyone else encountered this? This is part of a larger service I'm building. Thanks in advance! The stack includes Python and several other technologies.