{"id":22293,"date":"2020-11-13T18:59:51","date_gmt":"2020-11-13T13:29:51","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/scipy-tutorial\/"},"modified":"2024-04-30T18:44:55","modified_gmt":"2024-04-30T13:14:55","slug":"scipy-tutorial","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/scipy-tutorial\/","title":{"rendered":"SciPy Tutorial for Beginners"},"content":{"rendered":"\n

In this tutorial, we are going to start from scratch and see how to use SciPy, scipy in python<\/a> and introduce you to some of its most important features. Also, we are going to go through the different modules or sub-packages present in the SciPy package and see how they are used.
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What is SciPy?<\/strong><\/h2>\n\n\n\n

SciPy<\/a> is a free and open-source Python<\/a> library used for scientific computing and technical computing. It is a collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. It adds significant power to the interactive Python session by providing the user with high-level commands and classes for manipulating and visualizing data. As mentioned earlier, SciPy builds on NumPy and therefore if you import SciPy, there is no need to import NumPy<\/a>.
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Installing SciPy<\/strong><\/h2>\n\n\n\n

Before learning more about the core functionality of SciPy, it should be installed in the system.<\/p>\n\n\n\n

Install on Windows and Linux<\/strong><\/h3>\n\n\n\n

Here are a few methods that can be used to install SciPy on Windows or Linux.<\/p>\n\n\n\n

Install SciPy using pip<\/h3>\n\n\n\n

We can install the SciPy library by using the pip command. Pip is basically a recursive acronym which stands for \u2018Pip Installs Packages\u2019. It is a standard package manager which can be installed in most of the operating systems. To install, run the following command in the terminal:<\/p>\n\n\n\n

pip install scipy  <\/strong><\/pre>\n\n\n\n
Note: Use pip to install SciPy in Linux.<\/strong><\/p>\n\n\n\n
Install SciPy using Anaconda<\/h3>\n\n\n\n
We can also install SciPy packages by using Anaconda. First, we need to download the Anaconda navigator and then open the anaconda prompt type the following command:<\/p>\n\n\n\n
conda install -c anaconda scipy  <\/pre>\n\n\n\n
Install on Mac<\/strong><\/h3>\n\n\n\n
The mac doesn't have the preinstall package manager, but you can install various popular package managers. Run the following commands in the terminal it will download the SciPy as well as matplotlib, pandas, NumPy.<\/p>\n\n\n\n
sudo port install py35-numpy py35-scipy py35-matplotlib py35-ipython +notebook py35-pandas py35-sympy py35-nose  
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Also, you can use Homebrew to install these packages. But keep in mind that it has incomplete coverage of the SciPy ecosystem:
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brew install numpy scipy ipython jupyter<\/pre>\n\n\n\n
Sub packages in SciPy<\/strong><\/h2>\n\n\n\n
Many dedicated software tools are necessary for Python scientific computing, and SciPy is one such tool or library offering many Python modules that we can work with in order to perform complex operations.<\/p>\n\n\n\n
The following table shows some of the modules or sub-packages that can be used for computing:<\/p>\n\n\n\n
Sr.<\/strong><\/td>Sub-Package<\/strong><\/td>Description<\/strong><\/td><\/tr>
1.<\/strong><\/td>scipy.cluster<\/td>Cluster algorithms are used to vector quantization\/ Kmeans.<\/td><\/tr>
2.<\/strong><\/td>scipy.constants<\/td>It represents physical and mathematical constants.<\/td><\/tr>
3.<\/strong><\/td>scipy.fftpack<\/td>It is used for Fourier transform.<\/td><\/tr>
4.<\/strong><\/td>scipy.integrate<\/td>Integration routines<\/td><\/tr>
5.<\/strong><\/td>scipy.interpolation<\/td>Interpolation<\/td><\/tr>
6.<\/strong><\/td>scipy.linalg<\/td>It is used for linear algebra routine.<\/td><\/tr>
7.<\/strong><\/td>scipy.io<\/td>It is used for data input and output.<\/td><\/tr>
8.<\/strong><\/td>scipy.ndimage<\/td>It is used for the n-dimension image.<\/td><\/tr>
9.<\/strong><\/td>scipy.odr<\/td>Orthogonal distance regression.<\/td><\/tr>
10.<\/strong><\/td>scipy.optimize<\/td>It is used for optimization.<\/td><\/tr>
11.<\/strong><\/td>scipy.signal<\/td>It is used in signal processing.<\/td><\/tr>
12.<\/strong><\/td>scipy.sparse<\/td>Sparse matrices and associated routines.<\/td><\/tr>
13.<\/strong><\/td>scipy.spatial<\/td>Spatial data structures and algorithms.<\/td><\/tr>
14.<\/strong><\/td>scipy.special<\/td>Special Function.<\/td><\/tr>
15.<\/strong><\/td>scipy.stats<\/td>Statistics.<\/td><\/tr>
16.<\/strong><\/td>scipy.weaves<\/td>It is a tool for writing.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
SciPy Cluster<\/strong><\/h2>\n\n\n\n
Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. Each group which is formed from clustering is known as a cluster. There are two types of the cluster, which are:<\/p>\n\n\n\n