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NumPy icon

NumPy

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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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  • FreeOpen Source

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  • US flagUnited States

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  • Mac
  • Windows
  • Linux
  • BSD
  • Python
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NumPy information

  • Developed by

    US flagNumPy
  • Licensing

    Open Source and Free product.
  • Written in

  • Alternatives

    51 alternatives listed
  • Supported Languages

    • English

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GitHub repository

  •  29,658 Stars
  •  10,946 Forks
  •  2321 Open Issues
  •   Updated Jun 9, 2025 
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NumPy was added to AlternativeTo by Paul on Jun 17, 2024 and this page was last updated Jul 14, 2024. NumPy is sometimes referred to as Numerical Python.
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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.

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