Unexpected NaN results when calculating the median of a masked NumPy array
I'm updating my dependencies and I'm not sure how to approach Can someone help me understand I've searched everywhere and can't find a clear answer..... I'm integrating two systems and I'm sure I'm missing something obvious here, but I tried several approaches but none seem to work... I'm working on a personal project and I'm currently working with a masked NumPy array and I noticed that when I try to calculate the median, I end up with unexpected NaN values. My array contains some missing values, which I've masked using `np.ma.masked_array()`. Here’s the code snippet I’m using: ```python import numpy as np # Creating a masked array with some values masked data = np.array([1, 2, np.nan, 4, 5]) masked_data = np.ma.masked_array(data, mask=np.isnan(data)) # Trying to compute the median of the masked array median_value = np.ma.median(masked_data) print(median_value) ``` When I run this code, I get the output `nan` instead of the expected median value of 3. This is confusing because I expected that the masked values would be ignored. I have also tried using `np.ma.median(masked_data.compressed())`, but that returns `3.0`, which is correct, yet I'd like to understand why the first approach fails. I’m using NumPy version 1.23.0. Is there a specific reason for this behavior? Am I missing something in how the masked array computes statistics? Any tips on best practices for using masked arrays in NumPy would also be appreciated. Thanks! For context: I'm using Python on Windows. How would you solve this? For context: I'm using Python on Ubuntu. I'd really appreciate any guidance on this. What am I doing wrong? I'd really appreciate any guidance on this. The stack includes Python and several other technologies. Any feedback is welcome! My development environment is Ubuntu 20.04. Is there a better approach? My team is using Python for this desktop app. Any advice would be much appreciated. This issue appeared after updating to Python 3.11. Is there a simpler solution I'm overlooking?