SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems.

Here is a step-by-step guide to setting up a project to use SciPy, with uv, a Python package manager. Install uv following, the instructions in the uv documentation.

In SciPy there are modules whose names don’t start with an underscore, but that should be considered private. To clarify which modules these are, we define below what the public API is …

For global optimization, other choices of objective function, and other advanced features, consider using SciPy’s Global optimization tools or the LMFIT package.

It switches between two implementations depending on the problem definition. It is the most versatile constrained minimization algorithm implemented in SciPy and the most appropriate …

Typically, global minimizers efficiently search the parameter space, while using a local minimizer (e.g., minimize) under the hood. SciPy contains a number of good global optimizers. Here, …

Integration (scipy.integrate) # The scipy.integrate sub-package provides several integration techniques including an ordinary differential equation integrator. An overview of the module is …

SciPy has many functions for performing hypothesis tests that return a test statistic and a p-value, and several of them return confidence intervals and/or other related information.

It uses the iterative procedure scipy.sparse.linalg.lsmr for finding a solution of a linear least-squares problem and only requires matrix-vector product evaluations.

If True (default), then scipy.optimize.minimize with the L-BFGS-B method is used to polish the best population member at the end, which can improve the minimization slightly.