👀 Views: 445 💬 Answers: 1 📅 Created: 2025-07-11

numpy np.select data-manipulation Python

I'm updating my dependencies and I'm trying to debug I'm trying to use `np.select` to assign values based on multiple conditions, but I'm running into unexpected behavior when some of my conditions overlap... For example, I want to assign different labels based on the values in an array, but it seems like `np.select` is only evaluating the first matching condition and ignoring the subsequent ones even when the conditions logically overlap. Here’s a small snippet of my code: ```python import numpy as np # Sample data values = np.array([10, 20, 30, 40, 50]) # Defining conditions and choices conditions = [ (values < 20), (values >= 20) & (values < 40), (values >= 30) # Overlapping condition ] choices = ['low', 'medium', 'high'] # Using np.select labels = np.select(conditions, choices, default='unknown') print(labels) ``` I'm expecting the output to correctly label 'low' for 10, 'medium' for 20 and 30, and 'high' for 40 and 50. However, I'm getting: ``` ['low' 'medium' 'low' 'high' 'high'] ``` It seems like 30 is being labeled as 'low' instead of 'medium'. The logical conditions should capture all ranges distinctly, but it appears that `np.select` is not evaluating the overlapping conditions the way I anticipated. I've tried rearranging the order of the conditions, but I still get the same result. Could there be something I'm missing about how `np.select` evaluates the conditions, or is this a known scenario with version 1.24.3? Any insights would be greatly appreciated! For reference, this is a production microservice. Has anyone else encountered this? Thanks for your help in advance! I recently upgraded to Python latest. Am I approaching this the right way?