Python Visualization Libraries
There are many options for doing Data Visualization in Python. Matplotlib visualization library is among the most widely used plotting library and first choice for many data scientists and machine learning researchers and practitioners.
Matplotlib includes the Pyplot module which provides a MATLAB-like interface. Matplotlib is designed to be as usable as MATLAB, with the ability to use Python, and the advantage of being free and open-source.
Among all visualization libraries for Python, we enlist some of the most popular:
- Altair. Altair is a declarative statistical visualization library for Python, based on Vega and Vega-Lite.
- Bokeh. Bokeh is a Python library for creating interactive visualizations for modern web browsers.
- Ggplot. Ggplot is a Python implementation of the grammar of graphics ggplot2. Ggplot is not necessary a feature-by-feature equivalent of ggplot2, but does have some overlap.
- HoloViews. HoloViews is an open-source Python library designed to make data analysis and visualization seamless and simple.
- Matplotlib. Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
- Plotly. Plotly's Python graphing library makes interactive, publication-quality graphs.
- Plotnine. plotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2 used in R programming language.
- Seaborn. Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
For more information, you can visit the notes on Data Visualization with Python from our previous Workshops.
🔖 Please see the Jupyter Notebook on Data Visualization with Python.
- cartopy. Cartopy is a Python package designed for geospatial data processing to produce maps and other geospatial data analyses. Cartopy makes use of the powerful PROJ, NumPy and Shapely libraries and includes a programmatic interface built on top of Matplotlib for the creation of publication quality maps.
- geoplotlib. geoplotlib is a Python toolbox for visualizing geographical data and making maps.
- ipyleaflet. ipyleaflet creates interactive maps in a Jupyter Notebook.
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Folium. An alternative to Ipyleaflet, Folium is also a bridge to
leaflet.js. The difference is that Folium is built toward static visualizations, whereas Ipyleaflet builds interactive widgets. A useful feature of Folium is that it provides easy functionality to export an interactive map to HTML, making it a useful tool in web development. - lonboard. lonboard is a Python library for fast, interactive geospatial vector data visualization in Jupyter.
- STAC ipyleaflet. STAC ipyleaflet is a customized version of ipyleaflet built to be an in-jupyter-notebook interactive mapping library that prioritizes access to STAC catalog data.
- TorchGeo. TorchGeo is a PyTorch domain library, similar to torchvision, providing datasets, samplers, transforms, and pre-trained models specific to geospatial data.
🔖 Please see the Jupyter Notebook on Visualization of Vector and Raster files
- Scientists Guide to Plotting Data in Python. Earth Lab, CU Boulder.
- Maps in Scientific Python: Cartopy. An Introduction to Earth and Environmental Data Science. Ryan Abernathey, Kerry Key, Tim Crone, and Julius Busecke.
- Geographic Data Visualization. Introduction to Python for Geographic Data Analysis. Henrikki Tenkanen, Vuokko Heikinheimo & David Whipp.
Created: 01/29/2024; Updated: 01/30/2024.
Carlos Lizárraga.
