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# SciPy Tutorial !(#) SciPy is an open-source Python algorithms library and mathematical tools package. Scipy is a scientific computing library based on Numpy, used in mathematics, science, engineering fields, and many high-level abstractions and physical models need to use Scipy. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal processing and image processing, ordinary differential equation solving, and other calculations commonly used in science and engineering. * * * ## Prerequisites for Learning This Tutorial Before starting the SciPy tutorial, you need to have a basic understanding of Python. If you are not familiar with Python, you can read our tutorials: * * * ## SciPy Applications Scipy is a commonly used software package in the fields of mathematics, science, and engineering. It can handle optimization, linear algebra, integration, interpolation, fitting, special functions, fast Fourier transform, signal processing, image processing, ordinary differential equation solvers, and more. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal processing and image processing, ordinary differential equation solving, and other calculations commonly used in science and engineering. The collaborative work of NumPy and SciPy can efficiently solve many problems, and has been widely applied in multiple disciplines such as astronomy, biology, meteorology and climate science, as well as materials science. * * * ## Related Links * NumPy Official Website [http://www.numpy.org/](http://www.numpy.org/) * NumPy Source Code：[https://github.com/numpy/numpy](https://github.com/numpy/numpy) * SciPy Official Website：[https://www.scipy.org/](https://www.scipy.org/) * SciPy Source Code：[https://github.com/scipy/scipy](https://github.com/scipy/scipy) * Matplotlib Official Website：[https://matplotlib.org/](https