Description
Provides scientific computing tools for Python, including optimization, linear algebra, statistics, signal processing, integration, interpolation, and sparse matrices. It is a foundation for many research, engineering, and data-analysis workflows.
Developers, researchers, and students use it as a library. Numerical results depend on method choice, input data, and tolerances, so review assumptions when results drive scientific, financial, or engineering decisions.