advanced patterns when performing in-place operations on NumPy structured arrays
I just started working with I've searched everywhere and can't find a clear answer... I'm working with an scenario when trying to perform in-place operations on a structured NumPy array. I have a structured array with fields of different data types, and when I attempt to modify one of the fields, I'm seeing unexpected results. Here's a simplified version of my code: ```python import numpy as np # Creating a structured array with fields 'name' (str) and 'age' (int) arr = np.array([(b'Alice', 25), (b'Bob', 30), (b'Charlie', 35)], dtype=[('name', 'S10'), ('age', 'i4')]) # Trying to increment the age of each individual by 1 in-place for person in arr: person['age'] += 1 print(arr) ``` After running this code, I expect the ages to increment by one, resulting in ages 26, 31, and 36. However, the output is: ``` [(b'Alice', 25) (b'Bob', 30) (b'Charlie', 35)] ``` It seems like the in-place modification is not having the intended effect. I also tried using direct indexing: ```python arr['age'] += 1 ``` But that threw a warning about element-wise assignment. I’ve also checked the version of NumPy I’m using, which is 1.21.0. Is there a specific way to handle in-place modifications for structured arrays, or am I missing something fundamental in how structured arrays work? Any advice would be greatly appreciated! Any help would be greatly appreciated! My team is using Python for this desktop app.