👀 Views: 0 💬 Answers: 1 📅 Created: 2025-06-18

numpy data-analysis unique Python

I'm refactoring my project and I'm stuck on something that should probably be simple. I'm trying to utilize `np.unique` with the `return_counts` parameter to count the occurrences of unique elements in a multidimensional array. However, I’m running into an unexpected behavior when I pass a 2D array which contains `NaN` values. The output seems inconsistent. Here's what I'm using: ```python import numpy as np array = np.array([[1, 2, 2], [3, 4, np.nan], [1, np.nan, np.nan]]) unique_values, counts = np.unique(array, return_counts=True) print(unique_values) print(counts) ``` I expected to see unique counts of `1, 2, 3, 4, NaN` but instead, I'm only getting `1, 2, 3, 4`. The count for `NaN` is missing. According to the documentation, `NaN` should be considered a unique value, but it seems to be skipped in the results. I've tried adding `np.nan` explicitly using `np.nan_to_num` to check if that resolves the scenario, but it doesn't seem to help. Any insights on why `NaN` is being excluded and how I might resolve this to include it in the count? What's the best practice here? The project is a desktop app built with Python.