One of the design goals of NumPy was to make it buildable without a Fortran compiler, and if you don’t have LAPACK available NumPy will use its own implementation. SciPy requires a Fortran compiler to be built, and heavily depends on wrapped Fortran code.
The linalg modules in NumPy and SciPy have some common functions but with different docstrings, and scipy.linalgcontains functions not found in numpy.linalg, such as functions related to LU decomposition and the Schur decomposition, multiple ways of calculating the pseudoinverse, and matrix transcendentals like the matrix logarithm. Some functions that exist in both have augmented functionality in scipy.linalg; for example scipy.linalg.eig() can take a second matrix argument for solving generalized eigenvalue problems.
