scenarios when using np.concatenate on arrays with different dimensions - unexpected axis alignment
After trying multiple solutions online, I still can't figure this out. I've looked through the documentation and I'm still confused about I've hit a wall trying to I've spent hours debugging this and I'm trying to concatenate two NumPy arrays along a specific axis, but I'm running into an scenario where the dimensions don't align as expected... I have two arrays, `arr1` with shape (2, 3) and `arr2` with shape (2, 2), and I intend to concatenate them along axis 1. However, when I run the following code: ```python import numpy as np arr1 = np.array([[1, 2, 3], [4, 5, 6]]) arr2 = np.array([[7, 8], [9, 10]]) result = np.concatenate((arr1, arr2), axis=1) ``` I get the following behavior: ``` ValueError: all the input arrays must have the same number of dimensions ``` I thought that since I'm concatenating along axis 1, the first dimension should match, and it does. It seems that NumPy expects both arrays to have the same shape in all dimensions except the one I'm concatenating along. I tried reshaping `arr2` to (2, 2, 1) and then concatenating, but that didn't work either. How can I concatenate these two arrays correctly? Any insights on what I'm missing would be greatly appreciated! I'm using NumPy version 1.21.0. I appreciate any insights! The project is a web app built with Python. I'd really appreciate any guidance on this. I'm open to any suggestions. This is part of a larger service I'm building.