Introduction 
Computer Science has always been a field of continuous evolution and regular advancements in terms of software efficiency, programming methodologies, user interface and applications. With the advent of data sciences or data analytics, it has become easier and efficient to handle big data, often called huge data.
Data science or data analytics is a process of analyzing a large set of data points to get answers to questions related to that dataset.
Python provides a powerful interface for scientific computing using its popular libraries and is being extensively used for data sciences.
NumPy and Pandas are the most extensively used libraries supported by Python. This chapter focuses on NumPy, which are a good tool and a complete library offered by Python for data analysis. Most of the statistical analysis which needs data to be stored in memory uses NumPy.

WHAT Is NumPy
The NumPy library is a popular Python library used for scientific computing applications and is an acronym for "Numerical Python". NumPy ("Numerical Python" or "Numeric Python") is an open-source module of Python that provides functions for fast mathematical computation on arrays and matrices. Arrays, in general. Refer to a named group of homogeneous (same data type) elements. NumPy provides excellent Ndarray-n-dimensional array-objects. In an 'ndarray' object, we can store multiple items of the same data type. It is the facility around the array object that makes NumPy convenient for performing math and data manipulation

Let us first revise the concept of arrays.

What is an array?
An array is a container which can hold a fixed number of items and these items should be of the same type. It is a contiguous (one after the other) memory location holding elements of the same type. An array consists of:
• Element-Each item stored in an array is called an element.
• Index-Each location of an element in an array has a numerical index, which is used to identify the element.