NumPy is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays. NumPy is built on the Numeric code base and adds features introduced by numarray as well as an extended C-API and the ability to create arrays of arbitrary type which also makes NumPy suitable for interfacing with general-purpose data-base applications. There are also basic facilities for discrete fourier transform, basic linear algebra and random number generation. If you need to build numpy for debugging, set DEBUG=y. If you use software which is having problems with numpy's new relaxed strides checking, set NPY_RSC=0. It is highly recommended to install libraries implementing BLAS and LAPACK before installing numpy. You may choose between: a) BLAS and LAPACK (reference but unoptimized and thus slow) b) OpenBLAS (optimized, provides LAPACK too) c) ATLAS and LAPACK (optimized), good to read README.ATLAS All these are available on SlackBuilds.org. If you want to use the UMFPACK library instead of SuperLU to solve unsymmetric sparse linear systems, then run this Slackbuild with NO_UMFPACK set to "no" and then install scikit-umfpack on top of scipy. In this context, UMFPACK is an optional dependency for numpy. IMPORTANT: The version installed by this SlackBuild does NOT include the oldnumeric and numarray compatibility modules since starting with version 1.9.0 these modules got removed by the numpy developers. If you need these compatibility modules please consider the numpy-legacy SlackBuild which is available for python2 only and does not conflict with this installation of numpy. This python3-numpy SlackBuild creates bindings for python3 and can be installed without conflict alongside the python2-numpy SlackBuild.