Introduction to Data Visualization in Python. Learn how to make graphs using Matplotlib, Pandas and Seaborn. Learn how to create basic plots using Matplotlib, Pandas visualization and Seaborn as well as how to use some specific features of each library.
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.
To get a little overview here are a few popular plotting libraries:
- Matplotlib:low level, provides lots of freedom
- Pandas Visualization:easy to use interface, built on Matplotlib
- Seaborn:high-level interface, great default styles
- ggplot:based on R’s ggplot2, uses Grammar of Graphics
- Plotly:can create interactive plots
In this article, 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, which I will cover in another blog post.
In further articles, I will go over interactive plotting tools like Plotly, which is built on D3 and can also be used with JavaScript.
#python #matplotlib #pandas #seaborn #data-science
