Pandas provides easy and simple syntax to visualize data by creating the most widely used plots, but it seems the power of Pandas’s data visualization is neglected by most people.
Pandas is a popular open source data analysis and manipulation library for the Python programming language, due to its fast, powerful, flexible and easy to use features.
Besides data analysis, Pandas also has built-in support for data visualization with matplotlib as its default backend. There are also a number of other pandas-compatible libraries, which can be loaded as native Pandas plotting backends, such as Bokeh, Plotly, holoViews, hvPlot, etc.
However, it seems that the power of Pandas’s data visualization is neglected by most people. In this article, we will use specific examples to illustrate how easily to use Pandas to create the most widely used basic plots.
We will use the GDP data of the top 6 Economies in the world, including the USA, China, Japan, Germany, United Kingdom, India, which was collected from Kaggle.