Over the past decade, machine learning has achieved significant advancements, with applications spanning diverse fields such as physics, medicine, economics or energy. A pressing challenge in contemporary machine learning is optimizing models for time and energy efficiency. One effective approach to enhance time efficiency is sparsification. While contemporary machine learning libraries such as PyTorch, TensorFlow, and SciPy offer decently optimized kernels for dense matrix computations, their performance for sparse matrix operations often falls short. To bridge the performance gap between dense and sparse computations in the Python world, we present pyGinkgo - Python bindings for the Ginkgo library. pyGinkgo enables Python users to leverage Ginkgo's advanced capabilities for performing sparse computations within Python, offering significant potential for improving the performance of sparse neural networks and beyond. In this poster, we share initial benchmark results, demonstrating pyGinkgo's potential to enhance performance in sparse matrix computations and hence, sparse neural networks within Python-based workflows.