Unexpected result with np.where when combining conditions on 2D arrays in NumPy 1.24.0
I'm testing a new approach and I've been banging my head against this for hours... I've searched everywhere and can't find a clear answer. I've looked through the documentation and I'm still confused about Quick question that's been bugging me - I'm trying to use `np.where` to apply multiple conditions on a 2D NumPy array, but I'm getting unexpected results..... My goal is to create a new array that assigns a value based on conditions applied to each element of another array. Here’s the code I have: ```python import numpy as np data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # I want to set values to 10 where data is less than 5, # set to 20 where data is greater than 5, # and set to 0 otherwise. result = np.where(data < 5, 10, np.where(data > 5, 20, 0)) print(result) ``` When I run this, I expect to get an output like: ``` [[10 10 10] [10 10 20] [ 0 20 20]] ``` but instead, I get: ``` [[10 10 10] [20 20 20] [20 20 20]] ``` It seems like the inner `np.where` isn't working as intended. I suspect it might be due to how the conditions are being evaluated, but I need to figure out what I'm doing wrong. I've tried breaking it down into separate steps and even verified the comparisons independently, but the result remains the same. Any insights on how I should structure this to get the desired outcome? Is there a better approach to handle multiple conditions in `np.where` for a 2D array? I’m using NumPy version 1.24.0. For context: I'm using Python on macOS. I'd really appreciate any guidance on this. Thanks in advance! For context: I'm using Python on Ubuntu. My development environment is macOS. Thanks in advance! For reference, this is a production application. I'm using Python latest in this project. Am I approaching this the right way?