Old user guide headings

User Guide

Core guides

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

• Annotating your Data How to wrap your data in Element and annotate it with additional metadata to explore and visualize it effectively.
• Composing Elements Composing your wrapped data into Overlay and Layout collections with the + and * operators.
• Customizing Plots Applying plot, style and normalization options to control the look and feel of the plotting.
• Dimensioned Containers Declaring multi-dimensional containers to animate and facet your data flexibly. Learn about HoloMap, NdOverlay, GridSpace and NdLayout types and how to use them effectively with the corresponding .layout, .overlay and .grid method.
• Building Composite Objects How to build and work with complex composite objects.
• Live Data Introducing DynamicMap to lazily declare data and generate complex interactive visualizations.
• Tabular Datasets Loading and wrapping tabular datasets in HoloViews using NumPy, pandas and dask and flexibly exploring the dataset using selection, grouping and aggregation.
• Gridded Datasets Loading and wrapping gridded dataset in HoloViews using NumPy and XArray to flexibly explore and visualize labelled n-dimensional arrays.
• Indexing and Selecting Data Effectively indexing and selecting subsets of the data on the different HoloViews datastructures.
• Transforming Elements Applying and declaring Operations that transform your allowing you to define the building blocks of a data analysis pipeline and quickly explore and visualize the effect of different parameters on your data.
• Responding to Events Effectively using Streams to dynamically control and drive your visualizations by responding to user defined events such as custom widgets or from the commandline or notebook.
• Custom Interactivity Using linked Streams to respond to events generated by interacting with a bokeh plot, e.g. by responding to mouse position, mouse taps, selections or the current axis range.
• Data Processing Pipelines Chaining different operations to build complex and lazy data analysis pipelines, which can drive interactive plots in a notebook or in a deployed dashboard.
• Working with large data Leveraging datashader support in HoloViews to effectively and interactively explore and visualize millions or even billions of datapoints.

Supplementary guides

These guides provide detail about specific additional features in HoloViews:

• Plotting with Bokeh The basics of plotting with bokeh including details about plot tools and backend specific styling options and working with bokeh models more directly.
• Deploying Bokeh Apps Instructions on how to declare and deploy bokeh apps using HoloViews in various scenarios, e.g. from scripts, from the commandline and within the notebook.
• Plotting with matplotlib The basics of plotting with matplotlib highlighting core differences in styling and controlling the layout of matplotlib figures.
• Plotting with plotly The basics of plotting with plotly focusing on 3D plotting, one of the main strengths of plotly.
• Working with renderers and plots Using HoloViews Renderer and Plot classes directly to access and manipulate your visualizations directly.
• Exporting and Archiving Using HoloViews to archive both your data and visualization from the notebook.
• Continuous Coordinates Details on how continuous coordinates are handled in HoloViews specifically focusing on the difference between Image and other Raster types.

.. toctree::
    :titlesonly:
    :hidden:
    :maxdepth: 2

    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>
    Working with large data <Large_Data>
    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>