Unexpected results with np.add when using an object array with mixed types
I've been working on this all day and I'm experimenting with I've been researching this but I've been working on this all day and I've looked through the documentation and I'm still confused about I'm working with a strange scenario when trying to use `np.add` on an object array that contains mixed data types, specifically integers and strings... I expect that when I try to sum these elements, NumPy would handle them gracefully or throw an behavior. Instead, I'm seeing unexpected results that appear to concatenate the strings rather than perform a mathematical addition. Here's a minimal example of the code I'm working with: ```python import numpy as np # Create an object array with mixed types arr = np.array([1, 2, '3', 4], dtype=object) # Try to use np.add result = np.add(arr, 1) print(result) ``` When I run this code, the output I get is: ``` [2 3 '31'] ``` I expected the result to be an array like `[2, 3, 4, 5]`, but instead I see that the third element, which is a string, is being treated differently. It seems to be concatenating rather than adding. I've also tried using `arr.astype(int)` followed by the `np.add`, but that raises a `ValueError` because of the string type. Is there a way to handle this effectively in NumPy without having to manually check and convert types? Is this a known limitation or a behavior that I should be aware of? Any suggestions on how to correctly sum these elements would be greatly appreciated! This is part of a larger application I'm building. I'm working on a service that needs to handle this. What's the best practice here? This is for a web app running on Ubuntu 20.04. The stack includes Python and several other technologies.