Python is a high level, open source, interpreted language that offers a fantastic approach to object-oriented programming. It is one of the best technologies for a wide range of tasks and applications, according to data scientists. Dealing with mathematical, statistical, and scientific functions is a pleasure with Python. It provides the finest libraries for work with data science applications.

Python is one of the most popular programming languages in the fields of science and research because of its simplicity and use. This means that even those with engineering skills might quickly learn how to utilize it. It also performed best for fast prototyping.

The provides sufficient features of the Python language are listed:

• Because it makes use of elegant syntax, the programmes are simpler to read.
• It is simple and clear to get the application to run because language is simple to understand.
• the extensive standard library and public support.
• Programming testing is simple thanks to Python's interactive environment.
• It is equally simple to enhance the Python code by adding new modules that are written in those other compiled languages like C++ or C.
• Python is a powerful language that can be used to customize the user interface of other applications.
• Allows developers to run the code everywhere on Windows, Mac OS X, Linux, and UNIX.

The most popular libraries for data science are:

Python library Numpy offers mathematical functions to manage big size arrays. It offers numerous Array, Metrics, and linear algebra methods and functions.

Numerical Python is referred to as NumPy.For n-array and matrix operations in Python, it offers a plethora of useful capabilities. The library's NumPy collection type performs better and executes more fast compared to the vectorization of arithmetic computations. The manipulation of large dimensional matrices and arrays is made simple with NumPy.

One of the most widely used Python libraries for data manipulation and analysis is called Pandas: Pandas offer practical tools for working with vast amounts of structured data. Pandas offer the simplest way to conduct analysis. In addition to permitting the manipulation of time series data and numerical tables, it offers rich data structures. Pandas is the ideal tool for handling data. Pandas is made to make data manipulation, aggregation, and visualization rapid and simple. In Pandas, there are two data structures.

Scipy: Scipy is a well-liked Python library for scientific computing and data science. Scipy offers excellent capability for computer programming and scientific mathematics. SciPy has sub-modules for common tasks in science and engineering such optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and Statmodel.