👀 Views: 294 💬 Answers: 1 📅 Created: 2025-07-16

numpy arrays data-manipulation Python

I'm migrating some code and Quick question that's been bugging me - I'm working on a project and hit a roadblock..... I'm working with an scenario when trying to resize a NumPy array in Python while preserving the existing data. I have a 2D array that I need to expand from shape (3, 3) to (5, 5) but I want to keep the original values intact and fill the new elements with zeros. Here's what I've tried: ```python import numpy as np original_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Attempting to resize directly resized_array = np.resize(original_array, (5, 5)) print(resized_array) ``` When I run this code, the output is not what I expected: ``` [[1 2 3 4 5] [6 7 8 9 1] [2 3 4 5 6] [7 8 9 1 2] [3 4 5 6 7]] ``` It seems like `np.resize` is reusing elements from the original array rather than preserving the existing data. I then tried using `np.pad` to create a new array but encountered an scenario with dimension mismatches on initialization: ```python resized_array = np.pad(original_array, ((0, 2), (0, 2)), mode='constant') print(resized_array) ``` The output is correct, but I got confused about the padding dimensions. The original shape was (3, 3) and I padded it by 2 on both axes, which resulted in a shape of (5, 5). My confusion lies in the fact that I initially thought `np.pad` should take the total size rather than the difference in size. How can I correctly resize my NumPy array while ensuring the original values stay intact and the new values are filled with zeros? Is there a more efficient method for this operation, or am I just misusing the functions? My development environment is macOS. Am I missing something obvious? I'm working on a service that needs to handle this. I'd really appreciate any guidance on this. Is there a simpler solution I'm overlooking? This is happening in both development and production on Debian.