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Data present in large amounts needs to be dealt with properly. This is why computers with large capacities are used. Scientific and technical computations of large datasets can be done with the help of a library in Python known as SciPy. SciPy is short of ‘Scientific Python’.

The Numpy library in Python is a pre-requisite to SciPy because SciPy is built on top of Numpy. Ensure that Numpy library is installed before installing SciPy library. It is an open-source software that is easily available to install and use.

It has many features of data science and machine learning that are required to successfully process and work with data. It can be used to perform operations on Numpy arrays. The computational speed is high, and it is easy to understand.

Installation of SciPy

pip install scipy

**Note** − This is the command to download it for windows operating system.

sudo port install py35-scipy py35-numpy

**Note** − This is the command to download it for mac operating system.

sudo apt-get install python-scipy python-numpy

**Note** − This is the command to download it for Linux operating system.

SciPy can also be used for various other purposes such as −

- Integration
- Interpolation
- Least squares used in Regression
- Optimization
- Signal processing
- Linear algebra

Let us understand how values can be integrated (mathematical operation)

import scipy.integrate my_fun = lambda x: 11.345*x i = scipy.integrate.quad(my_fun, 0, 3.1) print("The integrated values are : ") print(i)

The integrated values are : (54.512725, 6.052128243005939e-13)

- The required libraries are imported, and alias names are given for ease of use.
- A lambda function is defined to generate data values.
- These are the values that are integrated.
- The ‘integrate’ function present in SciPy is called.
- The output is displayed on the console.

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