👀 Views: 71 💬 Answers: 1 📅 Created: 2025-06-12

numpy concatenation shapes array Python

I need some guidance on I've hit a wall trying to Hey everyone, I'm running into an issue that's driving me crazy. I'm trying to concatenate two 1D NumPy arrays of different shapes using `np.concatenate`, but I encounter an unexpected ValueError. I have the following code where I attempt to concatenate two arrays: ```python import numpy as np a = np.array([1, 2, 3]) # Shape (3,) b = np.array([[4, 5], [6, 7]]) # Shape (2, 2) result = np.concatenate((a, b), axis=0) ``` When I run this code, I get this behavior: ``` ValueError: all the input arrays must have same number of dimensions ``` From what I understand, `np.concatenate` should be able to handle arrays of different shapes as long as they are compatible along the specified axis. However, in this case, it seems to be rejecting the operation due to the shape disparity. I've tried reshaping `b` to be a 1D array by flattening it using `b.flatten()` and this works without scenario: ```python result_flattened = np.concatenate((a, b.flatten()), axis=0) ``` This gives me the expected output, but I want to understand why the original attempt fails. Shouldn't `np.concatenate` allow for flexibility in shape if the dimensions align correctly? Is there a better approach to handle cases where I might want to concatenate arrays of differing dimensions without flattening them? Also, is there a specific version of NumPy where this behavior might differ, as I’m using version 1.24? This issue appeared after updating to Python 3.9. Thanks, I really appreciate it! This is my first time working with Python 3.9. Cheers for any assistance! Any suggestions would be helpful.