How to avoid dtype issues when using np.concatenate with mixed array types?
I'm trying to configure I'm attempting to set up This might be a silly question, but Can someone help me understand I'm sure I'm missing something obvious here, but I'm working with issues when trying to concatenate NumPy arrays of different data types using `np.concatenate`... I have two arrays, one of type `float` and the other of type `int`, and when I concatenate them, I end up getting unexpected results. Here's what I've tried: ```python import numpy as np a = np.array([1, 2, 3], dtype=int) b = np.array([1.1, 2.2, 3.3], dtype=float) # Attempting to concatenate the arrays result = np.concatenate((a, b)) print(result) ``` The output is as expected, but when I check the dtype of the `result`, it defaults to `float64`, which is fine, but I was hoping for a more consistent handling of types. I would like to ensure that all items in the final array retain their intended types without unnecessary type promotion if possible. I've also tried using `np.vstack` and `np.hstack`, but I encounter similar issues. Is there a way to force `np.concatenate` to maintain a specific dtype throughout the operation? What would be the best practice to handle such cases, especially when working with large datasets where performance could be impacted by type conversions? What's the best practice here? I'm working with Python in a Docker container on Debian. Am I missing something obvious? For reference, this is a production web app. Is there a better approach? My team is using Python for this application.