T
Travis E. Oliphant
We are pleased to announce the release of NumPy 1.0.2
NumPy is a Python extension that provides a general-purpose
multi-dimensional array that can act as a container of arbitrary
raw-data formats (including bools, integers, floats, complex, Python
objects, string, unicode, and general C-structures). NumPy also
provides fast array processing and mathematics on these
multi-dimensional arrays. In addition, NumPy provides tools that allows
these arrays to form the foundation for basic image and signal
processing, linear algebra and Fourier transforms, and random number
generation. NumPy also includes easy integration with ctypes and
Fortran allowing seamless use of NumPy arrays with extensions written in
either C/C++ or Fortran.
The new support in NumPy for completely general (including nested
structure) data-types allows it to be used as a container object for any
number of applications where memory efficiency and speed are critical.
For example, the ability to handle general data-formats means that
NumPy could be used as an interface to general databases (see PyTables
for one example).
Get the latest version of NumPy by clicking on the Download link at
http://numpy.scipy.org
Enjoy!!
-NumPy Developers
NumPy is a Python extension that provides a general-purpose
multi-dimensional array that can act as a container of arbitrary
raw-data formats (including bools, integers, floats, complex, Python
objects, string, unicode, and general C-structures). NumPy also
provides fast array processing and mathematics on these
multi-dimensional arrays. In addition, NumPy provides tools that allows
these arrays to form the foundation for basic image and signal
processing, linear algebra and Fourier transforms, and random number
generation. NumPy also includes easy integration with ctypes and
Fortran allowing seamless use of NumPy arrays with extensions written in
either C/C++ or Fortran.
The new support in NumPy for completely general (including nested
structure) data-types allows it to be used as a container object for any
number of applications where memory efficiency and speed are critical.
For example, the ability to handle general data-formats means that
NumPy could be used as an interface to general databases (see PyTables
for one example).
Get the latest version of NumPy by clicking on the Download link at
http://numpy.scipy.org
Enjoy!!
-NumPy Developers