👀 Views: 1 💬 Answers: 1 📅 Created: 2025-06-11

numpy array stacking padding Python

I need help solving I've searched everywhere and can't find a clear answer... I'm experimenting with Quick question that's been bugging me - Hey everyone, I'm running into an issue that's driving me crazy... I'm trying to stack multiple 1D NumPy arrays into a 2D array using `np.stack`, but I'm working with an scenario with incompatible dimensions. I have the following arrays: ```python import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5]) c = np.array([6, 7, 8, 9]) ``` When I attempt to stack them together like this: ```python result = np.stack((a, b, c)) ``` I expect to get a 2D array, but instead, I receive the following behavior: ``` ValueError: all input arrays must have the same shape ``` I realize that the arrays have different lengths, which is causing the behavior. I've tried padding the shorter arrays with zeros to make them the same length: ```python max_length = max(len(a), len(b), len(c)) a_padded = np.pad(a, (0, max_length - len(a)), 'constant') b_padded = np.pad(b, (0, max_length - len(b)), 'constant') c_padded = np.pad(c, (0, max_length - len(c)), 'constant') result = np.stack((a_padded, b_padded, c_padded)) ``` This works, but I'm concerned about the performance impact of using `np.pad` on larger datasets. Is there a more efficient way to handle stacking arrays of different lengths? Maybe a different function or approach that doesn't involve padding? I'm using NumPy version 1.24.0. Any suggestions would be greatly appreciated! I'm working on a application that needs to handle this. What's the best practice here? I'm using Python latest in this project. Any help would be greatly appreciated! This is my first time working with Python 3.11. Thanks in advance! I'm using Python latest in this project. I'm open to any suggestions.