👀 Views: 42 💬 Answers: 1 📅 Created: 2025-06-09

numpy structured-array data-manipulation Python

I've encountered a strange issue with I'm trying to extract unique values from a structured NumPy array and count their occurrences using `np.unique` with the `return_counts=True` argument... However, I'm encountering some unexpected behavior when applying it to a structured array. Here’s the structured array I’m working with: ```python import numpy as np data = np.array([(1, 'apple'), (2, 'banana'), (1, 'apple'), (3, 'banana'), (2, 'banana')], dtype=[('id', 'i4'), ('fruit', 'U10')]) ``` When I run the following code: ```python unique_fruits, counts = np.unique(data['fruit'], return_counts=True) print(unique_fruits) print(counts) ``` I expect to see the unique fruits and their corresponding counts. However, the output is: ``` ['apple' 'banana'] [2 3] ``` This is correct for the counts, but I am confused because I thought the counts should reflect the number of unique 'id' entries associated with each fruit rather than just the raw occurrences of the fruit names. I’ve tried using `np.unique` with the `axis` parameter, but it seems to not apply to structured arrays the way I expected: ```python unique_ids, counts_by_id = np.unique(data['id'], return_counts=True) ``` This gives me unique IDs, but that’s not what I need in this case. I also looked into using a dictionary comprehension to tally counts manually, but I’d prefer a more concise solution. Is there a more efficient way to achieve what I am looking for, or is this just a limitation of how `np.unique` works with structured arrays? Any insights would be greatly appreciated! For context: I'm using Python on macOS. Thanks for taking the time to read this!