np.concatenate scenarios with ValueError when merging arrays of different shapes in NumPy 1.24
I'm learning this framework and I'm performance testing and I've searched everywhere and can't find a clear answer. I'm trying to merge multiple NumPy arrays using `np.concatenate`, but I'm working with a `ValueError` due to mismatched shapes. I have three arrays: ```python import numpy as np a = np.array([[1, 2], [3, 4]]) # shape (2, 2) b = np.array([[5, 6, 7]]) # shape (1, 3) c = np.array([[8], [9]]) # shape (2, 1) ``` When I try to concatenate these arrays along axis 0, I expect to get a single array with the combined data: ```python result = np.concatenate((a, b, c), axis=0) ``` However, I get the following behavior: ``` ValueError: all the input arrays must have the same number of dimensions ``` I've tried reshaping the arrays using `reshape`, but that didn’t help. For instance, I attempted: ```python b_reshaped = b.reshape(1, 3) c_reshaped = c.reshape(2, 1) result = np.concatenate((a, b_reshaped, c_reshaped), axis=0) ``` This still leads to the same behavior. My ultimate goal is to combine these arrays in a way that maintains their data without losing any information. Any suggestions on how to properly concatenate arrays of different shapes in this context? Is there a recommended way to align dimensions for concatenation without losing data? Thanks in advance! I recently upgraded to Python 3.9. The project is a CLI tool built with Python. Any ideas how to fix this? I'm on Ubuntu 20.04 using the latest version of Python. Any advice would be much appreciated. For reference, this is a production desktop app. Any help would be greatly appreciated!