# Lession - #323 Scipy-special_package

The capacities accessible in the exceptional bundle are widespread capacities, which follow broadcasting and programmed cluster circling.

Allow us to take a gander at probably the most often utilized exceptional capacities −

Cubic Root Function
Outstanding Function
Relative Error Exponential Function
Log Sum Exponential Function
Lambert Function
Changes and Combinations Function
Gamma Function

Interpolation is defined as finding a value between two points on a line or a curve. The first part of the word is "inter" as meaning "enter", which indicates us to look inside the data. In the other words, "The estimation of intermediate value between the precise data points is called as interpolation". Interpolation is very useful in statistics, science, and business or when there is a need to predict the value that exists within two existing data points.

Definite integrals are the extension after indefinite integrals, definite integrals have limits [a, b]. It gives the area of a curve bounded between given limits.

\int_{a}^b F(x>dx


It denotes the area of curve F(x>
bounded between a and b, where a is the lower limit and b is the upper limit.

In this article, we will discuss how we can solve definite integrals in python, and would also visualize the area between them using matplotlib. We would also use the NumPy module for defining the range of the variable we are integrating.

SciPy is an open-source scientific library. The installation of the SciPy package can be done through a variety of methods.

Methods differ in simple use, coverage, maintenance of old versions, system-wide versus local environment use, and control.

The most user-friendly method for beginners is with the use of Anaconda. With the use of pip along with Anaconda, we can also manage the version of SciPy. We can alternatively use the package managers for installation.