What is Data Visualization?
Data visualization is the field of representing data and information in graphic form. Visualizing the data makes it easier for us to comprehend the data and hence finding patterns, trends, and correlations within large data become much easier.
Python has some excellent data visualization libraries to make interactive charts, graphs, and everything in between. But not knowing the advantages and core features of these libraries, it would be difficult for one to choose among these libraries. Hence in this article, we shall look at some essential data visualization libraries in brief to overcome this difficulty.
Matplotlib is one of the oldest and also the most widely used data visualization library in Python. It is used to create static, animated, and interactive 2D data visualizations in Python and can also be highly customized to create advanced visualizations like 3D plots. Matplotlib can be used in Python shells, Jupyter notebooks, web application, and is also supported by various Python GUI toolkits.
Matplotlib’s biggest advantage is the freedom it offers to customize almost everything and make beautiful and advanced data visualizations, but such visualizations can be easily done with other libraries.
Seaborn is built on top of Matplotlib and supports pandas and NumPy data structures. Using Seaborn, beautiful top-grade visualizations can be made with only a few lines of code. Seaborn can even take data frames and arrays that contain the entire dataset and make plots out of them while handling all the semantic mapping and statistical aggregation internally.
Seabron should be the go-to choice for the ones looking to create beautiful static diagrams. For interactive web-embedded visualizations, there are better libraries than Seaborn.
Bokeh is built on top of Bokeh.js used to create highly interactive visualizations that can be embedded in webpages. Bokeh is based on the grammar of graphics and is available in multiple languages (Python, R, Julia, Lua). Bokeh can also embed all the visualizations into an HTML file that can be run with or without a server.
For interactive data visualizations, Bokeh shall be one’s choice. There is a very narrow gap between Bokeh and Plotly. Bokeh is better at data handling and dashboard interactions, but doesn’t support 3D visualizations out of the box and has comparatively lesser graph types.
Plotly is built on top of Plotly.Js and offers similar webpage integration like Bokeh. Plotly offers excellent interactivity and over 40 different types of graphs. Plotly can be used to create beautiful 3D visualizations and also be used to create standalone HTML visualizations. Plotly required a little more effort but results in an elegant, highly interactive visualization.
Plotly shall be the choice for ones looking for creating highly interactive visualizations. But choosing between Bokeh and Plotly is more of a personal preference and one can find which suits them better by looking at their visualization galleries. (Plotly, Bokeh)
Matplotlib, Seaborn, Bokeh, and Plotly are unique in their own way. Also, there are few other libraries such as Altair, ggplot, and Pygal that are good for data visualization, but I didn’t include them in the list as the former libraries were more popular.
Choosing one among them boils down to one’s needs and personal preference. To summarize, Seaborn for static visualizations and either Plotly or Bokeh for highly interactive web embedded visualizations.
: Choosing one of many Python visualization tools by Gabriela Moreira Mafra, https://blog.magrathealabs.com/choosing-one-of-many-python-visualization-tools-7eb36fa5855f
: Top 5 Python Libraries For Data Visualization by Disha Misal, https://analyticsindiamag.com/top-5-python-libraries-for-data-visualization/
: Data Visualization Libraries Python by Mohd Sanad Zaki Rizvi, https://www.analyticsvidhya.com/blog/2020/03/6-data-visualization-python-libraries/