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-The fundamental package needed for scientific computing with Python is
-called NumPy. This package contains:
- * a powerful N-dimensional array object
- * sophisticated (broadcasting) functions
- * basic linear algebra functions
- * basic Fourier transforms
- * sophisticated random number capabilities
- * tools for integrating Fortran code.
-
-Besides its obvious scientific uses, NumPy can also be used as an
-efficient multi-dimensional container of generic data. Arbitrary
-data-types can be defined. This allows NumPy to seamlessly and
-quickly integrate with a wide-variety of databases.
+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.