👀 Views: 83 💬 Answers: 1 📅 Created: 2025-06-23

numpy data-analysis statistics Python

I'm learning this framework and I'm maintaining legacy code that I'm deploying to production and I'm using NumPy version 1.24.3, and I've encountered an scenario when trying to compute the median of a multidimensional array that contains NaN values..... I expected `np.median` to ignore the NaN values and compute the median of the remaining numbers. However, it seems to return incorrect results. Here’s a simplified version of my code: ```python import numpy as np data = np.array([[1, 2, np.nan], [4, np.nan, 6], [np.nan, 8, 9]]) median_value = np.median(data) print(median_value) ``` The output I get is `nan`, which is unexpected. I've tried using `np.nanmedian` instead, but I want to understand why `np.median` behaves this way. Here’s how I called `np.nanmedian`: ```python median_value_nan = np.nanmedian(data) print(median_value_nan) ``` This correctly gives `5.0`, which is what I expected. I would prefer to know why `np.median` doesn’t handle NaNs as I thought it would. Is there a specific design decision behind this behavior in the current version? Are there any best practices I should follow when working with NaN values in NumPy, particularly with median calculations? Any insights would be appreciated! Am I missing something obvious? What am I doing wrong? This is happening in both development and production on macOS. Thanks for any help you can provide! I'm working on a CLI tool that needs to handle this. Am I approaching this the right way? This is for a desktop app running on Ubuntu 20.04.