Sparse Matrices Operations in SciPy
Learn how to work with sparse matrices in SciPy to save memory and computational resources
Sparse data are datasets where most elements are zero or have insignificant values. This may be a common scenario in fields like machine learning, graph theory, and scientific computing. Instead of storing and processing large matrices full of zeroes, SciPy provides some tools to save memory and computational resources. The SciPy module scipy.sparse offers data structures and algorithms to store, manipulate, and perform computations on sparse matrices. This article explores these tools and give some examples.
Work with sparse data
SciPy’s sparse module provides optimized tools to store and manipulate these sparse datasets efficiently. SciPy implements several types of sparse matrix formats, each of them is optimal for different operations. The available matrix formats are:
- CSR (Compressed Sparse Row): Efficient for row slicing and matrix-vector products.
- CSC (Compressed Sparse Column): Efficient for column slicing and arithmetic operations.
- COO (Coordinate Format): Simple format used to construct sparse matrices.
