Understanding dtype promotion when using np.concatenate with mixed types in NumPy 1.22
I'm relatively new to this, so bear with me. I'm working with issues with dtype promotion when using `np.concatenate` on arrays of mixed types in NumPy 1.22. Specifically, I have two arrays: the first is an integer array and the second is a float array. When I concatenate them, I expected the resulting array to be of type float, but it appears to be of type object instead. Here’s the code snippet I'm using: ```python import numpy as np a = np.array([1, 2, 3], dtype=int) b = np.array([1.5, 2.5, 3.5], dtype=float) result = np.concatenate((a, b)) print(result) print(result.dtype) ``` When I run this code, I get the output: ``` [1 2 3 1.5 2.5 3.5] object ``` I was expecting the dtype to be `float64`. I’ve also tried using `np.asarray` on both arrays before concatenation, but the result remains the same. Could anyone explain why this is happening and how I can ensure that the resulting array has the correct dtype? Is there a best practice for avoiding this scenario when concatenating arrays of mixed types? My development environment is macOS. I'd really appreciate any guidance on this.