Data Analytics and Data Visualization with Python
Dimitrios Xanthidis, Han‐I Wang, Christos Manolas · 2022
Python is one of the most popular modern programming languages for data analytics, data visualization, and data science in general. Its power in addressing data analytics problems comes from its numerous built-in libraries, including Pandas, Numpy, Matplotlib, Scipy, and Seaborn, that provide functionality to read data from a variety of sources, clean data, and perform descriptive and inferential statistics operations. In addition, the libraries provide data visualization facilities, supporting the generation of all types of charts. A possible description could be that the term refers to the efficient analysis of data from various sources to produce meaningful results that aid the process of decision-making. The five steps of data analytics and data visualization that are described in detail in this chapter are data acquisition, cleaning data, exploratory analysis, modeling and validation, and visualizing results.