Python bokeh button example

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Python bokeh button example

While the main goal of Bokeh is to provide a path to create rich interactive visualizations in the browser, purely from python, there will always be specialized use-cases that are outside the capabilities of the core library. For this reason, Bokeh provides different ways for users to supply custom JavaScript when necessary, so that users may add custom or specialized behaviours in response to property changes and other events.

One mechanism is the ability to add entire new custom extension models, as described in Extending Bokeh. However it is also possible to supply small snippets of JavaScript as callbacks to use, e. This kind of callback can be used to add interesting interactions to Bokeh documents without the need to use a Bokeh server but can also be used in conjunction with a Bokeh server. The explicit purpose of these callbacks is to embed raw JavaScript code for a browser to execute.

If any part of the code is derived from untrusted user inputs, then you must take appropriate care to sanitize the user input prior to passing to Bokeh. To supply a snippet of JavaScript code that should be executed in the browser when some event occurs, use the CustomJS model:. Note that in addition to the code property, CustomJS also accepts an args property that maps string names to Bokeh models.

Additionally, the model that triggers the callback i.

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The full format for a property change event is, e. Additionally, some Bokeh models have additional specialized events. For example, the ColumnDataSource also supports "patch" and "stream" events, for executing CustomJS callbacks whenever the data source is patched or streamed to. Below is an example that shows how to attach a CustomJS callback to a Slider widget, so that whenever the slider value updates, the callback is executed to update some data:.

The event can be specified as a string such as 'tap' above, or an event class import from the bokeh. The following code imports bokeh.

python bokeh button example

The result is a plot that when interacted with, displays the corresponding event on the right:. A common use case for property callbacks is responsing to changes to widgets. The code below shows an example of CustomJS set on a slider Widget that changes the source of a plot when the slider is used.

Another common scenario is wanting to specify the same kind of callback to be executed whenever a selection changes. As a simple demonstration, the example below simply copies selected points on the first plot to the second. However, more sophisticated actions and computations are easily constructed in a similar way. Another more sophisticated example is shown below.

It computes the average y value of any selected points including multiple disjoint selectionsand draws a line through that value. The properties of range objects may also be connected to CustomJS callbacks in order to perform specialized work whenever a range changes:. Selection tools emit events that can drive useful callbacks. In addition to the generic mechanisms described above for adding CustomJS callbacks to Bokeh models, there are also a some Bokeh models that have a. The callbacks described below were added early to Bokeh in an ad-hoc fashion.

Many of them can be accomplished with the generic mechanism described above, and as such, may be deprecated in favor of the generic mechanism in the future.

The HoverTool has a callback which comes with two pieces of built-in data: the indexand the geometry. The index is the indices of any points that the hover tool is over.Moving beyond static plots. In the first part of this series, we walked through creating a basic histogram in Bokeha powerful Python visualization library. The final result, which shows the distribution of arrival delays of flights departing New York City in is shown below with a nice tooltip!

If we want to create more engaging visualization, we can allow users to explore the data on their own through interactions. For example, in this histogram, one valuable feature would be the ability to select specific airlines to make comparisons or the option to change the width of the bins to examine the data in finer detail. Fortunately, these are both features we can add on top of our existing plot using Bokeh.

The initial development of the histogram may have seemed involved for a simple plot, but now we get to see the payoff of using a powerful library like Bokeh! All the code for this series is available on GitHub. I encourage anyone to check it out for all the data cleaning details an uninspiring but necessary part of data science and to experiment with the code!

For interactive Bokeh plots, we can still use a Jupyter Notebook to show the results or we can write Python scripts and run a Bokeh server. For development, I usually work in a Jupyter Notebook because it is easier to rapidly iterate and change plots without having to restart the server.

I then move to a server to display the final results. You can see both a standalone script and the full notebook on GitHub. There are two classes of interactions in Bokeh: passive and active. Passive interactions, covered in Part I, are also known as inspectors because they allow users to examine a plot in more detail but do not change the information displayed. One example is a tooltip that appears when a user hovers over a data point:.

The second class of interaction is called active because it changes the actual data displayed on the plot. This can be anything from selecting a subset of the data such as specific airlines to changing the degree of a polynomial regression fit.

When I view graphs, I enjoy playing with active interactions such as those on FlowingData because they allow me to do my own exploration of the data. I find it more insightful to discover conclusions from the data on my own with some direction from the designer rather than from a completely static chart. Moreover, giving users some amount of freedom allows them to come away with slightly different interpretations that can generate beneficial discussion about the dataset.

Data Visualization with Bokeh in Python, Part III: Making a Complete Dashboard

Once we start adding active interactions, we need to move beyond single lines of code and into functions that encapsulate specific actions. For a Bokeh widget interaction, there are three main functions that to implement:.

Before we can make the plot, we need to plan out the data that will be displayed.

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For our interactive histogram, we will offer users three controllable parameters:. For the function that makes the dataset for the plot, we need to allow each of these parameters to be specified.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time.

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. The answer has been updated to reflect changes since Bokeh 0. A complete example pared from the sliders demo, that uses features from Bokeh 0.

Learn more. Running python code by clicking a button in Bokeh Ask Question. Asked 4 years, 7 months ago. Active 1 year, 5 months ago. Viewed 4k times. Does anyone have an example of how to run python code in jupyter by clicking a button in Bokeh? Chuck Chuck 95 2 2 silver badges 8 8 bronze badges. This is similar to what i am trying to accomplish but related to select items and much more involved: github. The answer here is great. You should accept it if you feel it has answered your question.

Active Oldest Votes. This was really helpful. I appreciate it. Looks like thorough documentation here bokeh.Bokeh documents and applications are capable of supporting various kinds of interactions. These are often associated with events, such as mouse or touch events, interactive downsampling mode activation, widget or tool events, and others. The classes in this module represent these different events, so that callbacks can be attached and executed when they occur.

It is possible to respond to events with CustomJS callbacks, which will function with or without a Bokeh server. Alternatively it is possible to trigger Python code to run when events happen, in the context of a Bokeh application running on a Bokeh server.

The callback should accept a single argument eventwhich will be passed the concrete event object that triggered the callback.

There is no throttling of events. Some events such as MouseMove may trigger at a very high rate. During interactive actions such as panning or zooming, Bokeh can optionally, temporarily draw a reduced set of the data, in order to maintain high interactive rates. This event fires whenever a LOD mode has just begun. This event fires whenever a LOD mode has just ended.

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The enter event is generated when the mouse leaves the entire Plot canvas, including any border padding and space for axes or legends. The leave event is generated when the mouse leaves the entire Plot canvas, including any border padding and space for axes or legends. This may change in future releases. Note that data space coordinates are relative to the default range, not any extra ranges, and the the screen space origin is at the top left of the HTML canvas.

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Announce a button click event on a plot ResetTool. Note There is no throttling of events. Note The enter event is generated when the mouse leaves the entire Plot canvas, including any border padding and space for axes or legends.

Note The leave event is generated when the mouse leaves the entire Plot canvas, including any border padding and space for axes or legends. Note This event can fire at a very high rate, potentially increasing network traffic or CPU load. Note This event is only applicable for touch-enabled devices.Widgets are interactive controls that can be added to Bokeh applications to provide a front end user interface to a visualization.

They can drive new computations, update plots, and connect to other programmatic functionality. When used with the Bokeh server, widgets can run arbitrary Python code, enabling complex applications. To use widgets, you must add them to your document and define their functionality. Widgets can be added directly to the document root or nested inside a layout. This will work in standalone HTML documents. Use bokeh serve to start the Bokeh server and set up event handlers with.

Event handlers are user-defined Python functions that can be attached to widgets. These functions are then called when certain attributes on the widget are changed. The necessary function signature of event handlers is determined by how they are attached to widgets whether they are passed through.

All widgets have an. Additionally, some widgets, including the button, dropdown, and checkbox, have an. For the Button, this handler is called without parameters.

For the other widgets with. Bokeh provides a simple default set of widgets, largely based off the Bootstrap JavaScript library. In the future, it will be possible for users to wrap and use other widget libraries, or their own custom widgets. For more information about the attributes to watch using. Widgets can be found under bokeh.

Bokeh also provides a checkbox button group, that can have multiple options selected simultaneously:.

python bokeh button example

Bokeh provides a sophisticated data table widget based on SlickGrid. Note that since the table is configured with a data source object, any plots that share this data source will automatically have selections linked between the plot and the table even in static HTML documents. The Bokeh slider can be configured with start and end values, a step size, an initial value and a title :.

The Bokeh range-slider can be configured with start and end values, a step size, an initial value and a title :. The remaining Paragraph arguments are 'width' and 'height'. For this example, those values are and respectively.Creating an interactive visualization application in Bokeh. Sometimes I learn a data science technique to solve a specific problem.

Data Visualization with Bokeh in Python, Part II: Interactions

Data science requires knowledge of many different skills and you never know where that next idea you will use will come from! In the case of Bokeh, several weeks after trying it out, I found a perfect use case in my work as a data science researcher.

My research project involves increasing the energy efficiency of commercial buildings using data science, and, for a recent conferencewe needed a way to show off the results of the many techniques we apply. By the time most people at a conference see their third slide deck, they have already stopped paying attention. Skeptical, our team prepared a back-up presentation, but after I showed them some prototypes, they gave it their full support.

The final interactive dashboard was a stand-out at the conference and will be adopted by our team for future use:. Along these lines, I started this series to share the capabilities of Bokeha powerful plotting library in Python that allows you to make interactive plots and dashboards.

This third post is a continuation of my Bokeh series, with Part I focused on building a simple graph, and Part II showing how to add interactions to a Bokeh plot. In this post, we will see how to set up a full Bokeh application and run a local Bokeh server accessible in your browser!

This article will focus on the structure of a Bokeh application rather than the plot details, but the full code for everything can be found on GitHub. We will continue to use the NYCFlights13 dataseta real collection of flight information from flights departing 3 NYC airports in There are overflights in the dataset, and for our dashboard, we will focus primarily on exploring the arrival delay information. This will set-up a local Bokeh server and open the application in your browser you can also make Bokeh plots available publicly online, but for now we will stick to local hosting.

Following is a short clip showing how we can interact with the complete dashboard:. At the top we see a number of tabs, each of which contains a different section of the application. The idea of a dashboard is that while each tab can stand on its own, we can join many of them together to enable a complete exploration of the data.

The video shows the range of charts we can make with Bokeh, from histograms and density plots, to data tables that we can sort by column, to fully interactive maps. Besides the range of figures we can create in Bokeh, another benefit of using this library is interactions. Each tab has an interactive element which lets users engage with the data and make their own discoveries.

From experience, when exploring a dataset, people like to come to insights on their own, which we can allow by letting them select and filter data through various controls. I highly recommend downloading the code for yourself to follow along!Bases: bokeh. Widgetbokeh. This is an abstract base class used to help organize the hierarchy of Bokeh model types. It is not useful to instantiate on its own. This property is useful only if this component is a child element of a layout e.

Self alignment can be overridden by the parent container e.

python bokeh button example

Note: the class names are simply added as-is, no other guarantees are provided. It is also permissible to assign from tuples, however these are adapted — the property will always contain a list. If Truethe widget will be greyed-out and not responsive to UI events.

Use exactly height pixels. Use as little vertical space as possible, not less than the minimum height if set. The starting point is the preferred height if set. The height of the component may shrink or grow depending on the parent layout, aspect management and other factors. Use as much vertical space as possible, not more than the maximum height if set. This is an experimental feature and may change in future. Use it at your own discretion. A mapping of event names to lists of CustomJS callbacks.

Typically, rather then modifying this property directly, callbacks should be added using the Model. A mapping of attribute names to lists of CustomJS callbacks, to be set up on BokehJS side when the document is created. No uniqueness guarantees or other conditions are enforced on any names that are provided, nor is the name used directly by Bokeh for any reason.

This is a high-level setting for maintaining width and height of the component.

Python Create Interactive Charts and Maps with Bokeh

Component is not responsive. It will retain its original width and height regardless of any subsequent browser window resize events. Component will responsively resize to stretch to the available width, without maintaining any aspect ratio. Component will responsively resize to stretch to the available height, without maintaining any aspect ratio. Component is completely responsive, independently in width and height, and will occupy all the available horizontal and vertical space, even if this changes the aspect ratio of the component.

Component will responsively resize to stretch to the available width, while maintaining the original or provided aspect ratio.

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Component will responsively resize to stretch to the available height, while maintaining the original or provided aspect ratio. Component will responsively resize to both the available width and height, while maintaining the original or provided aspect ratio. List of events that are subscribed to by Python callbacks. This is the set of events that will be communicated from BokehJS back to Python for this model.

Or simply a convenient way to attach any necessary metadata to a model that can be accessed by CustomJS callbacks, etc.


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