👀 Views: 48 💬 Answers: 1 📅 Created: 2025-07-14

python numpy scipy sparse-matrices Python

I've been banging my head against this for hours... I keep running into I'm trying to debug I'm getting frustrated with I'm currently working with sparse matrices using Scipy's `csr_matrix` to efficiently handle large datasets... However, when I try to convert a dense NumPy array into a sparse format and then back again, I end up with unexpected zero entries in the output. Here's what I have tried: I start with a dense NumPy array: ```python import numpy as np dense_array = np.array([[1, 0, 0], [0, 0, 3], [4, 0, 0]]) print('Original Dense Array:\n', dense_array) ``` Next, I convert this array to a sparse format: ```python from scipy.sparse import csr_matrix sparse_array = csr_matrix(dense_array) print('Sparse Matrix:\n', sparse_array) ``` Then I attempt to convert it back to a dense format: ```python dense_back = sparse_array.toarray() print('Dense Array from Sparse:\n', dense_back) ``` To my surprise, the output for `dense_back` often contains additional zeros, which I don't expect based on the original data. For instance, I get: ``` Dense Array from Sparse: [[1 0 0] [0 0 3] [0 0 0]] ``` This behavior occurs sporadically, particularly when I initialize larger dense arrays with mixed zero and non-zero values. I'm using Scipy version 1.8.0 and NumPy version 1.21.0. I suspect it might be related to how I'm creating the sparse matrix or the internal representation, but I need to pinpoint the scenario. Has anyone else encountered this, or does anyone have suggestions on how to debug this further? Any insights would be greatly appreciated. Any help would be greatly appreciated! I'm coming from a different tech stack and learning Python. This is part of a larger service I'm building. Has anyone else encountered this?