advanced patterns with np.concatenate when combining arrays of different shapes in NumPy 1.23
I'm trying to implement I'm dealing with Quick question that's been bugging me - I'm following best practices but I've looked through the documentation and I'm still confused about I've been banging my head against this for hours... I'm working with an scenario while trying to use `np.concatenate` to combine two arrays of different shapes. I expected NumPy to broadcast them, but instead, I received an behavior. Here’s the code snippet that’s causing the question: ```python import numpy as np a = np.array([[1, 2, 3], [4, 5, 6]]) # Shape (2, 3) b = np.array([[7], [8]]) # Shape (2, 1) result = np.concatenate((a, b), axis=1) ``` When I run this, I get the following behavior: ``` ValueError: all the input arrays must have the same number of dimensions ``` I thought that since both arrays have the same number of rows (2), they would concatenate without issues on axis 1. However, I realize now that their shape differences are causing the question. I tried using `np.reshape` to change the shape of array `b` to `(2, 3)`: ```python b_reshaped = np.reshape(b, (2, 3)) # Attempt to reshape result = np.concatenate((a, b_reshaped), axis=1) ``` But reshaping `b` like this doesn’t make sense logically as it alters the data I want to keep. Is there a way to effectively concatenate these arrays without losing information? Should I pad `b` with additional values? If so, what's the best practice for padding arrays for concatenation? Any suggestions would be greatly appreciated! For context: I'm using Python on Ubuntu. Thanks in advance! Is there a better approach? This is my first time working with Python 3.11. Am I approaching this the right way? Has anyone dealt with something similar?