Data visualization is the graphic representation of data. It involves producing images that communicate relationships among the represented data to viewers of the images. This communication is achieved through the use of a systematic mapping between graphic marks and data values in the creation of the visualization.
Data visualization is the discipline of trying to understand data by placing it in a visual context so that patterns, trends and correlations that might not otherwise be detected can be exposed. Python offers multiple great graphing libraries that come packed with lots of different features. No matter if you want to create interactive, live or highly customized plots python has an excellent library for you.
1.Matplotlib: low level, provides lots of freedom
2.Pandas Visualization: easy to use interface, built on Matplotlib
3.Seaborn: high-level interface, great default styles
4.ggplot: based on R’s ggplot2, uses Grammar of Graphics
5.Plotly: can create interactive plots
Here, we will learn how to create basic plots using Matplotlib, Pandas visualization and Seaborn as well as how to use some specific features of each library. This article will focus on the syntax and not on interpreting the graphs.