NumPy
Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source.
NumPy is the fundamental package for scientific computing with Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. NumPy is open source and widely used in the scientific computing community.
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Key Features of the Site
Multidimensional arrays
NumPy provides a powerful N-dimensional array object that is at the core of most scientific computing in Python.
Broadcasting
A powerful mechanism that allows NumPy to work with arrays of different shapes when performing arithmetic operations.
Mathematical functions
A large collection of mathematical functions to operate on these arrays.
Linear algebra
Comprehensive tools for linear algebra, Fourier transform, and random number capabilities.
Integration with C/C++ and Fortran code
NumPy arrays facilitate easy integration with code written in C, C++, and Fortran.
Memory efficiency
NumPy arrays are more memory efficient than Python lists, especially for large datasets.
Speed
Operations on NumPy arrays are significantly faster than equivalent operations on Python lists.
Interoperability
NumPy supports a wide range of hardware and computing platforms, and integrates well with distributed, GPU, and sparse array libraries.
Community support
A large and active community contributes to the development and maintenance of NumPy, ensuring it stays up-to-date with the latest scientific computing needs.
Documentation
Comprehensive documentation and user guides are available, making it easier for new users to get started with NumPy.
Similar Sites and Competitors to numpy.org
Additional information
License
NumPy is released under the BSD license, making it free for both academic and commercial use.
Contributing
NumPy welcomes contributions from the community, including code, documentation, and financial support.
Related projects
NumPy is part of a larger ecosystem of scientific computing libraries in Python, including SciPy, Matplotlib, and Pandas.
Performance
NumPy is optimized for performance, with many operations implemented in C for speed.
Education
NumPy is widely used in educational settings for teaching scientific computing and data analysis.
Industry use
NumPy is used in a wide range of industries, including finance, engineering, and data science, for its powerful array manipulation capabilities.
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