np.concatenate causing unexpected shape mismatch when combining arrays with different dimensions
I'm attempting to set up I'm getting frustrated with I'm a bit lost with This might be a silly question, but I've been struggling with this for a few days now and could really use some help........... I'm trying to concatenate two NumPy arrays using `np.concatenate`, but I'm running into a shape mismatch scenario. I have a 2D array of shape `(3, 2)` and a 1D array of shape `(2,)`. My intention is to combine them along the first axis, but I'm getting a `ValueError`. Here's the code I'm using: ```python import numpy as np array_2d = np.array([[1, 2], [3, 4], [5, 6]]) # shape (3, 2) array_1d = np.array([7, 8]) # shape (2,) # Attempting to concatenate result = np.concatenate((array_2d, array_1d), axis=0) ``` When I run this, I receive the following behavior: ``` ValueError: all the input arrays must have same number of dimensions ``` I've checked the shapes of both arrays and confirmed that they are indeed different dimensions. I thought a 1D array could be treated as a 2D array with one row, but that doesn't seem to be the case here. I've tried reshaping the 1D array with `array_1d.reshape(1, -1)` to make it `(1, 2)`, and that works for the concatenation: ```python reshaped_1d = array_1d.reshape(1, -1) # shape (1, 2) result = np.concatenate((array_2d, reshaped_1d), axis=0) ``` This gives the expected output, but I feel like there should be a more straightforward way to combine these arrays. Can someone explain why the original attempt failed and whether there's a cleaner approach for such scenarios in NumPy? For context: I'm using Python on macOS. Thanks in advance! My team is using Python for this microservice. Thanks, I really appreciate it! Thanks for your help in advance! I'd be grateful for any help. My development environment is Windows 10. Thanks for taking the time to read this!