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NumPy icon

NumPy

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including...

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Cost / License

  • Free
  • Open Source

Platforms

  • Mac
  • Windows
  • Linux
  • BSD
  • Python
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    finbarr added NumPy as alternative to RunMat
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NumPy information

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Development, Education & Reference

GitHub repository

  •  32,975 Stars
  •  12,879 Forks
  •  2253 Open Issues
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NumPy was added to AlternativeTo by Paul on and this page was last updated . NumPy is sometimes referred to as Numerical Python. 

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Install from the command line

  • brew install numpy

Be careful what you paste into a terminal. Check what a command installs before running it.

What is NumPy?

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

Powerful N-dimensional arrays Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.

Numerical computing tools NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.

Open source Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.

Interoperable NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.

Performant The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.

Easy to use NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.