👀 Views: 100 💬 Answers: 1 📅 Created: 2025-06-14

numpy data-cleaning structured-arrays Python

I've been struggling with this for a few days now and could really use some help. I'm working with an scenario while trying to filter a NumPy array that contains mixed data types and NaN values. Specifically, I have a structured array where one field is of type float, and I'm attempting to filter out rows where this field contains NaN values. However, I am getting unexpected results and occasional errors. Here’s a simplified version of my code: ```python import numpy as np # Create a structured array with mixed types arr = np.array([(1, 2.0), (2, np.nan), (3, 4.5), (4, np.nan)], dtype=[('id', 'i4'), ('value', 'f4')]) # Attempt to filter out rows where 'value' is NaN filtered_arr = arr[~np.isnan(arr['value'])] print(filtered_arr) ``` When I run this code, I expect to get: ``` [(1, 2.0) (3, 4.5)] ``` but instead, I'm seeing the original array printed out. I’ve tried using `np.isfinite()` as well, but it returns the same results. Also, I noticed that if I try to use `np.delete()` in conjunction with the boolean mask, it raises an behavior related to the mismatch of sizes. What am I doing wrong here? Is there a better way to handle filtering for structured arrays, especially when dealing with NaN values? I'm using NumPy version 1.21.0, and I would appreciate any advice on best practices for this sort of operation.