Unexpected dtype changes when stacking arrays of mixed types in NumPy
I'm stuck trying to I'm working on a personal project and I'm working with an scenario when trying to stack arrays of different data types using `numpy.vstack`. I have two arrays: one is an integer array and the other is a float array. When I stack them, I expect the resulting array to have a float dtype, but it seems to be defaulting to integer dtype. Here’s the code I'm using: ```python import numpy as np int_array = np.array([[1, 2], [3, 4]]) # dtype is int float_array = np.array([[1.5, 2.5], [3.5, 4.5]]) # dtype is float stacked_array = np.vstack((int_array, float_array)) print(stacked_array.dtype) print(stacked_array) ``` I expected the output dtype to be `float64`, but instead, I see `int64`. The printed stacked array looks like this: ``` [[1. 2. ] [3. 4. ] [1.5 2.5] [3.5 4.5]] ``` I’ve tried explicitly specifying the dtype when creating the first array, like this: ```python int_array = np.array([[1, 2], [3, 4]], dtype=np.float64) ``` But when I run `np.vstack` with this change, I still see `float64` in the dtype, which is what I wanted, but the performance isn't ideal as it involves unnecessary type conversion. Is there a more efficient way to stack these arrays while ensuring the correct dtype for mixed types without losing performance? Any insights on why the dtype defaults to integer in the first case would also be appreciated. I'm on Ubuntu 22.04 using the latest version of Python. Could this be a known issue? I've been using Python for about a year now. Could someone point me to the right documentation?