Unexpected dtype promotion when using np.add with mixed types in NumPy 1.25
I'm confused about This might be a silly question, but I've been struggling with this for a few days now and could really use some help... I've tried everything I can think of but Hey everyone, I'm running into an issue that's driving me crazy. I'm experiencing unexpected behavior when using `np.add` to perform element-wise addition on arrays of mixed data types. I have two arrays, one is an integer array and the other is a float array, but the resulting array seems to promote the dtype in a way I didn't anticipate. Here's a simplified version of my code: ```python import numpy as np a = np.array([1, 2, 3]) # Integer array b = np.array([0.5, 1.5, 2.5]) # Float array result = np.add(a, b) print(result) print(result.dtype) ``` I expected the `result` to be of dtype `float64`, which it is. However, when I try to add a third array of strings, it gives me a `TypeError`. For example: ```python c = np.array(['4', '5', '6']) # String array try: result = np.add(result, c) except TypeError as e: print(f'behavior: {e}') ``` This raises an behavior: ``` behavior: ufunc 'add' did not contain a loop with signature matching types (dtype('float64'), dtype('<U1')) -> None ``` How can I handle situations where I might be adding arrays of different types without running into type errors? Should I be converting my arrays to a common type beforehand? Is there a way to handle these mixed types gracefully in NumPy? I'm working on a web app that needs to handle this. Thanks, I really appreciate it! Thanks in advance! I'm working on a application that needs to handle this. I'd be grateful for any help. Thanks in advance!