Python SciPy TutorialSciPy tutorial provides basic and advanced concepts of SciPy. Our SciPy tutorial is designed for beginners and professionals. In this tutorial, we are going to discuss the following topics.
What is SciPyThe SciPy is an opensource scientific library of Python that is distributed under a BSD license. It is used to solve the complex scientific and mathematical problems. It is built on top of the Numpy extension, which means if we import the SciPy, there is no need to import Numpy. The Scipy is pronounced as Sigh pi, and it depends on the Numpy, including the appropriate and fast Ndimension array manipulation. It provides many userfriendly and effective numerical functions for numerical integration and optimization. The SciPy library supports integration, gradient optimization, special functions, ordinary differential equation solvers, parallel programming tools, and many more. We can say that SciPy implementation exists in every complex numerical computation. The scipy is a dataprocessing and systemprototyping environment as similar to MATLAB. It is easy to use and provides great flexibility to scientists and engineers. HistoryPython was expanded in the 1990s to include an array type for numerical computing called numeric. This numeric package was replaced by Numpy (blend of Numeric and NumArray) in 2006. There was a growing number of extension module and developers were interested to create a complete environment for scientific and technical computing. Travis Oliphant, Eric Jones, and Pearu Peterson merged code they had written and called the new package SciPy. The newly created package provided a standard collection of common numerical operation on the top of Numpy. Why use SciPy?SciPy contain significant mathematical algorithms that provide easiness to develop sophisticated and dedicated applications. Being an opensource library, it has a large community across the world to the development of its additional module, and it is much beneficial for scientific application and data scientists. Numpy vs. SciPyNumpy and SciPy both are used for mathematical and numerical analysis. Numpy is suitable for basic operations such as sorting, indexing and many more because it contains array data, whereas SciPy consists of all the numeric data. Numpy contains many functions that are used to resolve the linear algebra, Fourier transforms, etc. whereas SciPy library contains full featured version of the linear algebra module as well many other numerical algorithms. Note: Remember that if you are doing the scientific computing using Python, you should install both Numpy and SciPy. Because many features belong to SciPy rather than the Numpy.PrerequisiteBefore learning SciPy, you should have a basic understanding of Python and Mathematics. AudienceOur SciPy Tutorial is designed to help beginners and professionals. ProblemWe assure that you will not find any problem in this SciPy Tutorial. But if there is any mistake, please post the problem in contact form.
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