implementing np.unique when handling structured arrays with multiple fields in NumPy 1.24
I'm wondering if anyone has experience with I'm working on a personal project and I'm migrating some code and I need some guidance on I'm working on a project and hit a roadblock. I'm working with a question with `np.unique` when trying to extract unique rows from a structured array with multiple fields. My structured array looks like this: ```python import numpy as np data = np.array([(1, 'apple', 3.5), (2, 'banana', 2.1), (1, 'apple', 3.5), (3, 'cherry', 5.0)], dtype=[('id', 'i4'), ('fruit', 'U10'), ('price', 'f4')]) ``` When I attempt to get unique rows based on all fields, I expect to get only the unique entries. However, when I run the following code: ```python unique_data = np.unique(data) print(unique_data) ``` I'm working with unexpected results. The output I'm getting is: ``` [(1, 'apple', 3.5) (1, 'apple', 3.5) (2, 'banana', 2.1) (3, 'cherry', 5. )] ``` It seems that `np.unique` is not filtering out the duplicate entries correctly. I’ve also tried using the `return_index` and `return_inverse` options to see if they would help clarify the duplicates, but I end up with similar results. Here’s what I tried next: ```python unique_data, indices = np.unique(data, return_index=True) print(unique_data) print(indices) ``` But this still yields duplicates in the output. Is there something I'm missing or should I be using a different approach to handle uniqueness in structured arrays? Any guidance on this would be much appreciated. Any ideas what could be causing this? I'm working in a Linux environment. Is there a simpler solution I'm overlooking? For context: I'm using Python on Linux. This is happening in both development and production on Windows 10. Any suggestions would be helpful. I'm developing on Debian with Python. Thanks for any help you can provide!