Handling dtype conflicts when merging NumPy structured arrays
I'm stuck on something that should probably be simple... I am trying to merge two NumPy structured arrays with different dtypes, but I keep running into issues with dtype conflicts. Specifically, I have two arrays, `array1` and `array2`, defined as follows: ```python import numpy as np array1 = np.array([(1, 'Alice'), (2, 'Bob')], dtype=[('id', 'i4'), ('name', 'U10')]) array2 = np.array([(1, 25), (3, 30)], dtype=[('id', 'i4'), ('age', 'i4')]) ``` I want to merge these arrays based on the `id` field, but when I try to use `np.concatenate` or `np.lib.recfunctions.join_by`, I get a `ValueError` related to the conflicting dtypes. Here’s what I tried: ```python from numpy.lib import recfunctions as rfn merged = rfn.join_by('id', array1, array2) ``` This raises the behavior: `ValueError: fields of the same name must have the same shape`, which I believe is due to the fact that the `name` field in `array1` is a string and the `age` field in `array2` is an integer. I’ve read through the documentation but still don’t understand how to resolve this dtype conflict or if there’s a better way to merge these structured arrays. Any suggestions on how to proceed? Is there a way to ensure the merged array maintains all fields without dtype issues? I'm working on a service that needs to handle this. Any help would be greatly appreciated!