Exploratory Data Analysis in Python

Abstract

Data science is extremely important because of the immense value of data. Python provides extensive library support for data science and analytics, which has functions, tools, and methods to manage and analyze data. Python Libraries are used for exploratory data analysis. Libraries in Python such as Numpy, Pandas, Matplotlib, SciPy, etc. are used for the same. Data visualization's major objective is to make it simpler to spot patterns, trends, and outliers in big data sets. One of the processes in the data science process is data visualization, which asserts that after data has been gathered, processed, and modelled, it must be represented to draw conclusions. As a result, it is crucial to have systems in place for managing and regulating the quality of corporate data, metadata, and data sources. So, this chapter focuses on the libraries used in Python, their properties, functions, how few data structures are related to them, and a detailed explanation about their purpose serving as a better foundation for learning them.