👀 Views: 43 💬 Answers: 1 📅 Created: 2025-08-27

numpy array masking Python

I'm testing a new approach and I'm sure I'm missing something obvious here, but I tried several approaches but none seem to work. I'm dealing with a 3D NumPy array and trying to find the indices of elements that meet a certain condition using `np.where`. However, I'm getting unexpected results, especially when masking the data. My array looks something like this: ```python import numpy as np array_3d = np.array([[[1, 2, np.nan], [4, 5, 6]], [[7, np.nan, 9], [10, 11, 12]]]) ``` I want to find indices where the values are greater than 5 and not NaN. I attempted to use: ```python indices = np.where((array_3d > 5) & ~np.isnan(array_3d)) ``` But the output I'm getting seems confusing: ```python (array([1, 1, 1]), array([1, 1, 1]), array([0, 1, 2])) ``` I expected to get only the index of the elements greater than 5, specifically the values 6, 7, 10, 11, and 12. Instead, the output seems to include irrelevant indices. I've also tried isolating the NaN values first using: ```python mask = ~np.isnan(array_3d) filtered_array = array_3d[mask] indices = np.where(filtered_array > 5) ``` But I end up losing the original structure of the 3D array, and it becomes a 1D array, which is not what I want. What steps can I take to correctly find the indices of elements greater than 5 without losing the dimensionality of the original array? Is there a better approach to achieve this? I’m using NumPy version 1.23.0. I'm working on a application that needs to handle this. I'm working on a CLI tool that needs to handle this. I'd really appreciate any guidance on this. I'm using Python stable in this project. Could this be a known issue?