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NumPy

Return to Python, Scientific Computing

NumPy is a fundamental package for scientific computing with Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy's array object is faster and more compact than Python's built-in list structure, making it an essential tool for data-intensive computations. The library also offers tools for integrating C, C++, and Fortran code into Python applications, making it a critical component for high-performance computing projects. Its widespread adoption is due to its ability to handle complex numerical calculations with ease, serving as the backbone for other Python libraries such as SciPy, pandas, and scikit-learn.

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* https://community.chocolatey.org/packages/numpy
* https://formulae.brew.sh/formula/numpy
* https://github.com/numpy/numpy
* https://numpy.org
* https://scipy.org/install

NumPy is the fundamental Python package for scientific computing. It contains among other things:

* a powerful N-dimensional array object
* sophisticated (broadcasting) functions
* tools for integrating C / CPP | C++ and Fortran code
* useful linear algebra, Fourier transform, and random number capabilities
* Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

NumPy is licensed under the BSD license, enabling reuse with few restriction

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