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: Represent and visualize path and polygon geometries with support for multi-geometries and value dimensions.
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.
Working with large data: Leverage Datashader to interactively explore millions or billions of datapoints.
Interactive Hover for Big Data: Use the selector with Datashader to enable fast, interactive hover tooltips that reveal individual data points without sacrificing aggregation.
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.
Colormaps: 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.
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.
Interactive Hover for Big Data: Explains how to use interactive hover tools with large datasets.
Notebook Magics: IPython magics supported in Jupyter Notebooks.