Synonyms for numpy or Related words with numpy

scipy              matplotlib              ocaml              sympy              nemerle              clojure              cython              libxml              petsc              lapack              modula              sagemath              jython              precompiler              optimj              wxpython              scalapack              helloworld              freebasic              cilk              gettext              mathjax              jquery              bioperl              coffeescript              javac              bignum              autoit              winrt              jsoniq              pyomo              glibc              datamelt              haxe              biojava              printf              troff              netlib              cpan              javacc              metaprogramming              apmonitor              biopython              islisp              uclibc              openfoam              qsort              hypertalk              watcom              livescript             

Examples of "numpy"
SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy. There is an expanding set of scientific computing libraries that are being added to the NumPy stack every day. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.
In 2011, PyPy started development on an implementation of the numpy API for PyPy. It is not yet fully compatible with NumPy.
NumPy (pronounced () or sometimes ()) is an extension to the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays. The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors.
Row-major order is the default in NumPy (for Python).
The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for linear algebra, Fourier transforms and random number generation, but not with the generality of the equivalent functions in SciPy. NumPy can also be used as an efficient multi-dimensional container of data with arbitrary data-types. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. Older versions of SciPy used Numeric as an array type, which is now deprecated in favor of the newer NumPy array code.
Python can winsorize data using NumPy and SciPy libraries :
NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents. NumPy address the slowness problem partly by providing multidimensional arrays and functions and operators that operate efficiently on arrays, requiring (re)writing some code, mostly inner loops using NumPy.
Benchmarks show that ND4S runs roughly twice as fast as NumPy on large matrices.
Oliphant is the author of the textbook "Guide To NumPy" and associated manuals.
And to plot Wald distribution in Python using matplotlib and NumPy:
In early 2005, NumPy developer Travis Oliphant wanted to unify the community around a single array package and ported Numarray's features to Numeric, releasing the result as NumPy 1.0 in 2006. This new project was part of SciPy. To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in version 1.5.0.
The random numbers for formula_9 are generated using the numpy mathematics package.
Python has an equivalent package, ipfn that can be installed via pip. The package supports numpy and pandas input objects.
Python bindings of the widely used computer vision library OpenCV utilize NumPy arrays to store and operate on data.
"It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the "interpreter" but not removing the dynamic indirection."
Using NumPy in Python gives functionality comparable to MATLAB since they are both interpreted, and they both allow the user to write fast programs as long as most operations work on arrays or matrices instead of scalars. In comparison, MATLAB boasts a large number of additional toolboxes, notably Simulink, whereas NumPy is intrinsically integrated with Python, a more modern and complete programming language. Moreover, complementary Python packages are available; SciPy is a library that adds more MATLAB-like functionality and Matplotlib is a plotting package that provides MATLAB-like plotting functionality. Internally, both MATLAB and NumPy rely on BLAS and LAPACK for efficient linear algebra computations.
Similarly to the embedded R UDFs in MonetDB, the database now has support for UDFs written in Python/NumPy. The implementation uses Numpy arrays (themselves Python wrappers for C arrays), as a result there is limited overhead - providing a functional Python integration with speed matching native SQL functions. The Embedded Python functions also support mapped operations, allowing user to execute Python functions in parallel within SQL queries. The practical side of the feature gives users access to Python/NumPy/SciPy libraries, which can provide a large selection of statistical/analytical functions.
Many numerical software applications use BLAS-compatible libraries to do linear algebra computations, including Armadillo, LAPACK, LINPACK, GNU Octave, Mathematica, MATLAB, NumPy, and R.
InVesalius was developed using Python and works under Linux, Windows and Mac OS X. It also uses graphic libraries VTK, wxPython, Numpy, Scipy and GDCM.
An implementation of a matrix package was completed by Jim Fulton, then generalized by Jim Hugunin to become "Numeric", also variously called Numerical Python extensions or NumPy.