User Guide#

The User Guide is the primary resource documenting key concepts that will help you use HoloViews in your work. For newcomers, a gentle introduction to HoloViews can be found in our Getting Started guide and an overview of some interesting HoloViews examples can be found in our Gallery. If you are looking for a specific component (or wish to view the available range of primitives), see our Reference Gallery.

Core guides#

These user guides provide detailed explanation of some of the core concepts in HoloViews:

Annotating your Data

Core concepts when annotating your data with semantic metadata.

Composing Elements

Composing your data into layouts and overlays with the + and * operators.

Applying Customizations

Using the options system to declare customizations.

Style Mapping

Mapping your data to the visual attributes of your plot.

Dimensioned Containers

Multi-dimensional containers for animating and faceting your data flexibly.

Building Composite Objects

How to build and work with nested composite objects.

Live Data

Lazily generate data on the fly and generate engaging interactive visualizations.

Tabular Datasets

Explore tabular data with NumPy, pandas and dask.

Gridded Datasets

Explore gridded data (n-dimensional arrays) with NumPy and XArray.

Geometry Data

Working with and representing geometry data such as lines, multi-lines, polygons, multi-polygons and contours.

Indexing and Selecting Data

Select and index subsets of your data with HoloViews.

Transforming Elements

Apply operations to your data that can be used to build data analysis pipelines

Responding to Events

Allow your visualizations to respond to Python events using the ‘streams’ system.

Custom Interactivity

Use Bokeh and ‘linked streams’ to directly interact with your visualizations.

Data Processing Pipelines

Chain operations to build sophisticated, interactive and lazy data analysis pipelines.

Creating interactive network graphs

Using the Graph element to display small and large networks interactively.

Working with large data

Leverage Datashader to interactively explore millions or billions of datapoints.

Working with Streaming Data

Demonstrates how to leverage the streamz library with HoloViews to work with streaming datasets.

Creating interactive dashboards

Use external widget libraries to build custom, interactive dashboards.

Supplementary guides#

These guides provide detail about specific additional features in HoloViews:

Configuring HoloViews

Information about configuration options.

Customizing Plots

How to customize plots including their titles, axis labels, ranges, ticks and more.


Overview of colormaps available, including when and how to use each type.

Plotting with Bokeh

Styling options and unique Bokeh features such as plot tools and using bokeh models directly.

Deploying Bokeh Apps

Using bokeh server using scripts and notebooks.

Linking Bokeh plots

Using Links to define custom interactions on a plot without a Python server

Plotting with matplotlib

Styling options and unique Matplotlib features such as GIF/MP4 support.

Plotting with plotly

Styling options and unique plotly features, focusing on 3D plotting.

Working with renderers and plots

Using the Renderer and Plot classes for access to the plotting machinery.

Using linked brushing to cross-filter complex datasets

Explains how to use the link_selections helper to cross-filter multiple elements.

Using Annotators to edit and label data

Explains how to use the annotate helper to edit and annotate elements with the help of drawing tools and editable tables.

Exporting and Archiving

Archive both your data and visualization in scripts and notebooks.

Continuous Coordinates

How continuous coordinates are handled, specifically focusing on Image and Raster types.

Notebook Magics

IPython magics supported in Jupyter Notebooks.