In order to get the best use out of the Panel user guide, it is important to have a grasp of some core concepts, ideas, and terminology.
Panel provides three main types of component:
Panel. These components are introduced and explained in the Components user guide, but briefly:
Panewraps a user supplied object of almost any type and turns it into a renderable view. When the wrapped
objector any parameter changes, a pane will update the view accordingly.
Widgetis a control component that allows users to provide input to your app or dashboard, typically by clicking or editing objects in a browser, but also controllable from within Python.
Panelis a hierarchical container to lay out multiple components (panes, widgets, or other
Panels) into an arrangement that forms an app or dashboard.
Panel is a very flexible system that supports many different usage patterns, via multiple application programming interfaces (APIs). Each API has its own advantages and disadvantages, and is suitable for different tasks and ways of working. The API user guide goes through each of the APIs in detail, comparing their pros and cons and providing recommendations on when to use each.
interact API will be familiar to ipywidgets users; it provides a very simple API to define an interactive view of the results of a Python function. This approach works by declaring functions whose arguments will be inspected to infer a set of widgets. Changing any of the resulting widgets causes the function to be re-run, updating the displayed output. This approach makes it extremely easy to get started and also easy to rearrange and reconfigure the resulting plots and widgets, but it may not be suited to more complex scenarios. See the Interact user guide for more detail.
Defining a reactive function using the
pn.depends decorator provides an explicit way to link specific inputs (such as the value of a widget) to some computation in a function, reactively updating the output of the function whenever the parameter changes. This approach is a highly convenient, intuitive, and flexible way of building interactive UIs.
Panel itself is built on the param library, which allows capturing parameters and their allowable values entirely independently of any GUI code. By using Param to declare the parameters along with methods that depend on those parameters, even very complex GUIs can be encapsulated in a tidy, well-organized, maintainable, and declarative way. Panel will automatically convert parameter definition to corresponding widgets, allowing the same codebase to support command-line, batch, server, and GUI usage. This API requires the use of the param library to express the inputs and encapsulate the computations to be performed, but once implemented this approach leads to flexible, robust, and well encapsulated code. See the Panel Param user guide for more detail.
At the lowest level, you can build interactive applications using
Panel components and connect them using explicit callbacks. Registering callbacks on components to modify other components provides full flexibility in building interactive features, but once you have defined numerous callbacks it can be very difficult to track how they all interact. This approach affords the most amount of flexibility but can easily grow in complexity, and is not recommended as a starting point for most users. That said, it is the interface that all the other APIs are built on, so it is powerful and is a good approach for building entirely new ways of working with Panel, or when you need some specific behavior not covered by the other APIs. See the Widgets user guide and Links user guide for more detail.
Display and rendering¶
Throughout this user guide we will cover a number of ways to display Panel objects, including display in a Jupyter notebook, in a standalone server, by saving and embedding, and more. For a detailed description see the Deploy and Export user guide.
All of Panel's documentation is built from Jupyter notebooks that you can explore at your own pace. Panel does not require Jupyter in any way, but it has extensive Jupyter support:
The Panel extension loads BokehJS, any custom models required, and optionally additional custom JS and CSS in Jupyter notebook environments. It also allows passing any
Given a Panel model
pn.ipywidgetwill return an ipywidget model that renders the object in the notebook. This can be useful for including an panel widget in an ipywidget layout and deploying Panel objects using Voilà.
Jupyter notebooks allow the final value of a notebook cell to display itself, using a mechanism called rich display. As long as
pn.extension() has been called in a notebook, all Panel components (widgets, panes, and panels) will display themselves when placed on the last line of a notebook cell.
.app()method present on all viewable Panel objects allows displaying a Panel server process inline in a notebook, which can be useful for debugging a standalone server interactively.
Even when working in a Python REPL that does not support rich-media output (e.g. in a text-based terminal), a panel can be still be launched in a browser tab:
.show()method is present on all viewable Panel objects and starts a server instance then opens a browser tab to point to it. To support working remotely, a specific port on which to launch the app can be supplied.
Similar to .show() on a Panel object but allows serving one or more Panel apps on a single server. Supplying a dictionary mapping from the URL slugs to the individual Panel objects being served allows launching multiple apps at once.
Panel mirrors Bokeh's command-line interface for launching and exporting apps and dashboards:
panel serve app.py¶
panel servecommand allows allows interactively displaying and deploying Panel web-server apps from the commandline.
panel serve app.ipynb¶
panel servealso supports using Jupyter notebook files, where it will serve any Panel objects that were marked
.servable()in a notebook cell. This feature allows you to maintain a notebook for exploring and analysis that provides certain elements meant for broader consumption as a standalone app.
When not working interactively, a Panel object can be exported to a static file.
.save() to PNG¶
.savemethod present on all viewable Panel objects allows saving the visual representation of a Panel object to a PNG file.
.save() to HTML¶
.saveto HTML allows sharing the full Panel object, including any static links ("jslink"s) between widgets and other components, but other features that depend on having a live running Python process will not work (as for many of the Panel webpages).
Panel objects can be serialized into a static JSON format that captures the widget state space and the corresponding plots or other viewable items for each combination of widget values, allowing fully usable Panel objects to be embedded into external HTML files or emails. For simple cases, this approach allows distributing or publishing Panel apps that no longer require a Python server in any way. Embedding can be enabled when using
.save(), using the
.embed() method or globally using Python and Environment variables on
.embed()method embeds the contents of the object it is being called on in the notebook.
Linking and callbacks¶
One of the most important aspects of a general app and dashboarding framework is the ability to link different components in flexible ways, scheduling callbacks in response to internal and external events. Panel provides convenient lower and higher-level APIs to achieve both. For more details, see the Links user guide.
.param.watchmethod allows listening to parameter changes on an object using Python callbacks. It is the lowest level API and provides the most amount of control, but higher-level APIs are more appropriate for most users and most use cases.
.link()method present on all viewable Panel objects is a convenient API to link the parameters of two objects together, uni- or bi-directionally.
.jslink()method directly links properties of the underlying Bokeh models, making it possible to define interactivity that works even without a running Python server.
State and configuration¶
Panel provides top-level objects to hold current state and control high-level configuration variables.
pn.config object allows setting various configuration variables, the config variables can also be set as environment variables or passed through the
css_files(: External CSS files to load.
js_files: External JS files to load. Dictionary should map from exported name to the URL of the JS file.
raw_css: List of raw CSS strings to add to load.
safe_embed: Whether to record all set events when embedding rather than just those that are changed
sizing_mode: Specify the default sizing mode behavior of panels.
Python and Environment variables¶
PANEL_COMMS): Whether to render output in Jupyter with the default Jupyter extension or use the
PANEL_EMBED): Whether plot data will be embedded.
PANEL_EMBED_JSON): Whether to save embedded state to json files.
PANEL_EMBED_JSON_PREFIX): Prefix for randomly generated json directories.
PANEL_EMBED_LOAD_PATH): Where to load json files for embedded state.
PANEL_EMBED_SAVE_PATH): Where to save json files for embedded state.
PANEL_INLINE): Whether to inline JS and CSS resources. If disabled, resources are loaded from CDN if one is available.
pn.state object makes various global state available and provides methods to manage that state:
cache: A global cache which can be used to share data between different processes.
cookies: HTTP request cookies for the current session.
curdoc: When running a server session this property holds the current bokeh Document.
headers: HTTP request headers for the current session.
session_args: When running a server session this return the request arguments.
webdriver: Caches the current webdriver to speed up export of bokeh models to PNGs.
kill_all_servers: Stops all running server sessions.