Getting ValueError when attempting to concatenate NumPy arrays of different shapes despite using np.newaxis
I'm experimenting with I've encountered a strange issue with I'm converting an old project and I'm a bit lost with I'm trying to concatenate two NumPy arrays with different shapes, but I'm hitting a `ValueError` that says: `ValueError: all the input arrays must have the same number of dimensions`. I'm using NumPy version 1.21.0. Here's the code snippet I'm working with: ```python import numpy as np array1 = np.array([[1, 2, 3], [4, 5, 6]]) # shape (2, 3) array2 = np.array([7, 8, 9]) # shape (3,) # Trying to add an axis to array2 array2_reshaped = array2[np.newaxis, :] # shape (1, 3) # Attempting to concatenate along axis 0 result = np.concatenate((array1, array2_reshaped), axis=0) ``` I thought that using `np.newaxis` would effectively add a new dimension to `array2`, making it `(1, 3)`, but it seems to be causing an scenario when concatenating with `array1`, which is `(2, 3)`. I expected `result` to have a shape of `(3, 3)` with the two rows from `array1` and the new row from `array2_reshaped`. Would someone be able to explain why this is happening? I've also tried using `array2.reshape(1, -1)` for reshaping with the same result. Any suggestions on how to concatenate these arrays successfully while avoiding this behavior? I recently upgraded to Python 3.11. Thanks for taking the time to read this! This is my first time working with Python LTS. I'm open to any suggestions. Thanks for taking the time to read this!