implementing np.concatenate on Mixed Data Types Leading to Unexpected Object Array Creation
Can someone help me understand I'm writing unit tests and I'm trying to concatenate two NumPy arrays of different data types, but I'm running into issues where the resulting array is unexpectedly of type `object`. I've got the following code to combine a float array and an integer array: ```python import numpy as np float_array = np.array([1.5, 2.5, 3.5]) integer_array = np.array([1, 2, 3]) # This is where I'm working with the scenario combined_array = np.concatenate((float_array, integer_array)) print(combined_array) print(combined_array.dtype) ``` When I run this, the output is: ``` [1.5 2.5 3.5 1. 2. 3. ] object ``` I expected the resulting array to be of type `float`, but it seems to be treating the integer values as objects. I also looked into using `np.vstack`, but I want to ensure I have a single dimensional array instead of a two-dimensional one after concatenation. I've tried explicitly setting the dtype for the combined array using: ```python combined_array = np.concatenate((float_array, integer_array.astype(float))) ``` This worked, but I want to understand why the initial attempt led to an object array. Is this expected behavior when mixing types in `np.concatenate`, and how can I avoid such issues in the future? Any insights or best practices would be greatly appreciated! The project is a service built with Python. Any help would be greatly appreciated! This is for a REST API running on Windows 11. Any advice would be much appreciated. I've been using Python for about a year now. Thanks for any help you can provide!