index.rst

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.

• Customizing Plots

  Controlling the look and feel of the visual representation.

• 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.

• 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:

• Installing and Configuring HoloViews

  Additional information about installation and configuration options.

• Styling Plots

  How to control common styling options including color/style cycles and colormapping.

• 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.

• Link 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.

• 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.

Annotating your Data <Annotating_Data> Composing Elements <Composing_Elements> Customizing Plots <Customizing_Plots> Dimensioned Containers <Dimensioned_Containers> Building Composite Objects <Building_Composite_Objects> Live Data <Live_Data> Tabular Datasets <Tabular_Datasets> Gridded Datasets <Gridded_Datasets> Indexing and Selecting Data <Indexing_and_Selecting_Data> Transforming Elements <Transforming_Elements> Responding to Events <Responding_to_Events> Custom Interactivity <Custom_Interactivity> Data Processing Pipelines <Data_Pipelines> Creating interactive network graphs <Network_Graphs> Working with large data <Large_Data> Working with streaming data <Streaming_Data> Creating interactive dashboards <Dashboards> Styling Plots <Styling_Plots> Colormaps <Colormaps> Plotting with Bokeh <Plotting_with_Bokeh> Deploying Bokeh Apps <Deploying_Bokeh_Apps> Plotting with matplotlib <Plotting_with_Matplotlib> Plotting with plotly <Plotting_with_Plotly> Working with Plot and Renderers <Plots_and_Renderers> Exporting and Archiving <Exporting_and_Archiving> Continuous Coordinates <Continuous_Coordinates>