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Python SciPy Tutorial

Python SciPy

SciPy 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 SciPy
  • SciPy Sub packages
  • SciPy Installation
  • SciPy Cluster
  • SciPy Constant
  • SciPy FFTpack
  • SciPy Integrate
  • SciPy Interpolation
  • SciPy I/O
  • SciPy Linear Algebra
  • SciPy Ndimage
  • SciPy Optimize
  • SciPy Stats
  • SciPy Sparse Matrix
  • SciPy Spatial
  • SciPy ODR

What is SciPy

The SciPy is an open-source 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 N-dimension array manipulation.

It provides many user-friendly 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 data-processing and system-prototyping environment as similar to MATLAB. It is easy to use and provides great flexibility to scientists and engineers.


Python 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 open-source 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. SciPy

Numpy 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.


Before learning SciPy, you should have a basic understanding of Python and Mathematics.


Our SciPy Tutorial is designed to help beginners and professionals.


We 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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