implementing np.concatenate causing shape mismatch errors in multi-dimensional arrays
I'm working through a tutorial and I'm experimenting with I've been banging my head against this for hours... I'm relatively new to this, so bear with me. I'm experiencing a shape mismatch behavior when trying to concatenate multiple 3D NumPy arrays along a specific axis. The behavior message I'm working with is: `ValueError: all the input arrays must have the same number of dimensions`. I've verified the shapes of the arrays, and they seem correct, yet I'm not able to concatenate them successfully. Here’s a snippet of the code where I'm working with the scenario: ```python import numpy as np # Creating three 3D arrays of different shapes array1 = np.random.rand(2, 3, 4) # Shape (2, 3, 4) array2 = np.random.rand(2, 3, 4) # Shape (2, 3, 4) array3 = np.random.rand(2, 3, 5) # Shape (2, 3, 5) # Attempt to concatenate along the last axis result = np.concatenate((array1, array2, array3), axis=-1) ``` I expected the `result` to concatenate `array1` and `array2` along the last axis without scenario, but when adding `array3`, the dimension mismatch causes the behavior. I've tried using `np.stack` instead, but that doesn't suit my needs since it adds a new axis. I also checked whether I could modify the shapes to make them compatible, but due to how my data is structured, this isn't feasible. Any advice on how to handle this situation properly? Additionally, would there be a more appropriate way to manage arrays of varying shapes when concatenating them like this? I'm currently using NumPy version 1.24.1. I'm working on a service that needs to handle this. Any ideas what could be causing this? Thanks for taking the time to read this! This is part of a larger web app I'm building.