Unexpected 'ValueError' when attempting to concatenate arrays with different data types in NumPy
I'm working on a project and hit a roadblock... I’m running into a `ValueError` when trying to concatenate two NumPy arrays of different data types. I have an array of integers and another of floats, and I was expecting NumPy to handle the type conversion automatically. Instead, I receive the following behavior: ``` ValueError: could not broadcast input array from shape (3,) into shape (4,) ``` Here’s the code I am using: ```python import numpy as np # Array of integers int_array = np.array([1, 2, 3, 4]) # Array of floats float_array = np.array([1.5, 2.5, 3.5]) # Attempting to concatenate result = np.concatenate((int_array, float_array)) ``` I’ve tried using `astype()` to explicitly convert the integer array to float before concatenation, but that hasn’t worked either: ```python int_array_float = int_array.astype(float) result = np.concatenate((int_array_float, float_array)) ``` This gives me the same `ValueError`. I’m using NumPy version 1.21.0. Is there a specific way I need to handle the arrays to prevent this behavior? Any insights or best practices would be greatly appreciated! I'm working on a API that needs to handle this. For reference, this is a production microservice.