How to visualize data in Python
Welcome to PyViz! PyViz is a coordinated effort to make data visualization in Python easier to use, easier to learn, and more powerful.
Focusing on interactive plotting in web browsers, PyViz provides:
- High-level tools that make it easier to apply Python plotting libraries to your data.
- A comprehensive tutorial showing how to use the available tools together to do a wide range of different tasks.
- A Conda metapackage "pyviz" that makes it simple to install matching versions of libraries that work well together.
- Sample datasets to work with.
Core high-level libraries
These PyViz-maintained packages each make great starting points --
for making apps and dashboards for your plots from any supported plotting library,
to quickly generate interactive plots from your data,
to help you make all of your data instantly visualizable, and
to extend HoloViews for geographic data.
Supported viz libraries
Objects from nearly every other plotting library can be used with
, including specific support for all those listed here plus anything that
can generate HTML, PNG, or SVG. HoloViews also supports
for 3D visualizations.
Supported data/compute libraries
PyViz core tools work with any Python data types (lists, dictionaries, etc.), plus
arrays, including remote data from the
data catalog library. They also use
to speed up computations along with algorithms and functions from
Other supported tools
PyViz tools both use and support declarative user-configurable
We recommend using perceptually uniform colormaps such as those provided by
PyViz tools are general purpose, but also support some domain-specific datatypes like graphs from NetworkX and geographic data from GeoPandas and Cartopy and Iris .
Panel can be used with yt for volumetric and physics data and SymPy or LaTeX for visualizing equations.
PyViz tools provide extensive support for Jupyter notebooks, as well as for standalone web servers and exporting as static files.
The Background page explains the PyViz approach in more detail, including how these tools fit together. Or you can just skim the material in the Tutorial online, to get an idea what is covered by these tools. If what you see looks relevant to you, you can then follow the steps outlined in Installation to get the libraries, tutorial, and sample data on your own system so you can work through the tutorial yourself. You’ll then have simple-to-adapt starting points for solving your own visualization problems using Python.