HoloViz provides a set of Python packages that make viz easier, more accurate, and more powerful: Panel for making apps and dashboards for your plots from any supported plotting library, hvPlot to quickly generate interactive plots from your data, HoloViews to help you make all of your data instantly visualizable, GeoViews to extend HoloViews for geographic data, Datashader for rendering even the largest datasets, Param to create declarative user-configurable objects, and Colorcet for perceptually uniform colormaps.
HoloViz tools build on the many excellent visualization tools available in the scientific python ecosystem, allowing you to access their power conveniently and efficiently. The core tools make use of Bokeh's interactive plotting, Matplotlib's publication-quality output, and Plotly's interactive 3D visualizations. Panel lets you combine any of these visualizations with output from nearly any other Python plotting library, including specific support for seaborn, altair, vega, plotnine, graphviz, ggplot2, plus anything that can generate HTML, PNG, or SVG.
HoloViz tools and examples generally work with any Python standard data types (lists, dictionaries, etc.), plus
Pandas or
Dask DataFrames and
NumPy,
Xarray, or
Dask arrays, including remote data from the
Intake data catalog library. They also use
Dask and
Numba to speed up computations along with algorithms and functions from
SciPy.
HoloViz tools are designed for general-purpose use, 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.
HoloViz tools provide extensive support for
Jupyter notebooks, as well as for standalone web servers and exporting as static files.
No comments:
Post a Comment