Skip to content

Latest commit

 

History

History
32 lines (18 loc) · 2.83 KB

05_plotting_libraries.md

File metadata and controls

32 lines (18 loc) · 2.83 KB
Raw

The following list provides a few plotting libraries to get users started based on their use case, it targets beginners and tries to focus on a small number of libraries not to overwhelm users with too many options.

The foundation: Matplotlib, most used plotting library, best for two-dimensional non-interactive plots. A possible replacement is pygal, it provides similar functionality but generates vector graphics SVG output and has a more user-friendly interface.

Specific use cases:

  • Specialized statistical plots, like automatically fitting a linear regression with confidence interval or like scatter plots color-coded by category.

    • seaborn: it builds on top of Matplotlib and it can also be used as a replacement for matplotlib just for an easier way to specify color palettes and plotting aestetics

  • Grammar of graphics plotting, if you find the interface of Matplotlib too verbose, Python provides packages based on a different paradigm of plot syntax based on R's ggplot2

    • ggplot: it provides similar functionality to Matplotlib and is also based on Matplotlib but provides a different interface.
    • altair: it has a simpler interface compared to ggplot and generates Javascript based plots easily embeddable into the Jupyter Notebook or exported as PNG.

  • Interactive plots, i.e. pan, zoom that work in the Jupyter Notebooks but also can be exported as Javascript to work standalone on a webpage.

    • bokeh: maintained by Continuum Analytics, the company behind Anaconda
    • plotly: is both a library and a cloud service where you can store and share your visualizations (it has free/paid accounts)

  • Interactive map visualization

    *folium: Creates HTML pages that include the Leaflet.js javascript plotting library to display data on top of maps. *plotly: it supports color-coded country/world maps embedded in the Jupyter Notebook.

  • Realtime plots that update with streaming data, even integrated in a dashboard with user interaction.

    • bokeh plot server: it is part of Bokeh but requires to launch a separate Python process that takes care of responding to events from User Interface or from streaming data updates.

  • 3D plots are not easy to interpret, it is worth first consider if a combination of 2D plots could provide a better insight into the data

    • mplot3d: Matplotlib tookit for 3D visualization