“The first thing to know about the Numerical Python package is
that it doesn’t let you do anything you cannot already do with
standard Python. It does let you do many of the same things a heck
of a lot faster. Actually, that’s not quite all; there are a number
of operations on arrays that are much more elegant to express in
Numeric or Numarray than they are with standard Python data types
and syntax. But mostly it is the impressive speed that draws users
to Numerical Python.“In essence, Numerical Python just gives you a new data type,
the array. In contrast to lists, tuples, and dictionaries, which
may contain heterogeneous elements, Numarray arrays contain only
elements of a homogeneous data type. The other useful thing about
Numarray arrays is that they may be multi-dimensional — but the
dimensionality of arrays is a bit different from the simple
nestability of lists. Numerical Python draws on the experience of
programmers–particularly those from scientific backgrounds–who
have abstracted the best features of arrays in languages such as
APL, FORTRAN, MATLAB, and S–and created arrays whose shape and
dimensionality are easily changeable. Back to this soon.“Operations on arrays in Numerical Python are performed
elementwise. Even though two-dimensional arrays are similar to
matrices from linear algebra, operations (such as multiply) have
nothing to do with the operations in linear algebra (such as matrix
multiplication)…”