The middle level allows the user to control the basic building blocks of each chart (for example, the dots in a scatter plot) and has the same specificity as Matplotlib. It includes methods for building common charts such as bar plots, box plots, and histograms. The topmost level is for creating charts quickly. The unique selling proposition is its ability to create interactive, web-ready plots, accessible output as JSON objects, HTML documents, or interactive web applications.īokeh has three interfaces with varying degrees of control to accommodate different types of users. It also supports streaming and real-time data. However, seasoned Matplotlib users might need time to adjust to this new mindset.īokeh, native to Python, is also based on The Grammar of Graphics like ggplot. The Grammar of Graphics has been hailed as an “intuitive” method for plotting. For example, the user can start with axes and add points, a line, a trend line, etc. ggplot operates differently compared to Matplotlib: it lets users layer components to create a complete plot. You can construct plots using high-level grammar without worrying about the implementation details. Ggplot is a Python visualization library based on R’s ggplot2 and the Grammar of Graphics. Seaborn is a higher-level library- it’s easier to generate specific plots, including heat maps, time series, and violin plots. Seaborn puts visualization at the core of understanding any data. Seaborn’s default styles and color palettes are much more sophisticated than Matplotlib.

Seaborn is a popular data visualization library built on top of Matplotlib. You can create grids, labels, legends, etc., with ease since everything is easily customizable. Matplotlib’s versatility allows for visualization types such as:

Best data visualization tools python