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Bokeh is an interactive visualization library for modern web browsers. Itprovides elegant, concise construction of versatile graphics, and affordshigh-performance interactivity over large or streaming datasets. Bokeh can helpanyone who would like to quickly and easily make interactive plots, dashboards,and data applications.

To offer both simplicity and the powerful and flexible features needed foradvanced customizations, Bokeh exposes two interface levels to users: Adobe photoshop install for mac.

A low-level interface that provides the most flexibility toapplication developers.

A higher-level interface centered around composing visual glyphs.

This Quickstart focuses on the bokeh.plotting interface.

Installation¶

There are multiple ways to install Bokeh, and we recommend the easiest one,which is to use the Anaconda Python distribution and enter this command ata Bash or Windows command prompt:

This installs all the dependencies that Bokeh needs. Anaconda minimizesinstallation effort on all platforms and configurations, including Windows,and also installs the examples into the examples/ subdirectory of yourAnaconda or Miniconda installation directory.

If you are confident that you have installed all needed dependencies, such asNumPy, you may instead use pip at the command line:

Note

The pip method does not install the examples. Clone the Git repositoryand look in the examples/ directory of the checkout to see examples.

Getting Started¶

Microsoft access for mac free download. Bokeh is a large library that exposes many capabilities, so this section isonly a quick tour of some common Bokeh use cases and workflows. For moredetailed information please consult the full User Guide.

Let’s begin with some examples.

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Plotting data in basic Python lists as a line plot, including zoom,pan, save, and other tools, is simple and straightforward:

When you execute this script, you will see that a new output file'lines.html' is created and that a browser automatically opens a new tabto display it (for presentation purposes we have included the plot outputdirectly inline in this document).

The basic steps to creating plots with the bokeh.plotting interface are:

In this case, plain Python lists, but NumPy arrays or Pandas series alsowork.

In this case, using output_file(), with the filename 'lines.html'.Another option is output_notebook() for use in Jupyter notebooks.

This creates a plot with typical default options and easy customizationof title, tools, and axes labels.

In this case, we use line() for our data, specifying visualcustomizations like colors, legends, and widths.

These functions save the plot to an HTML file and optionally display it ina browser.

Steps three and four can be repeated to create more than one plot, as shown insome of the examples below.

The bokeh.plotting interface is also quite handy if we need to customizethe output a bit more by adding more data series, glyphs, logarithmic axis,and so on. It’s easy to combine multiple glyphs together on one plot, as shownbelow:

Jupyter Notebooks¶

At this point we should mention Jupyter notebooks.

Jupyter notebooks are a common tool for exploratory data analysis, widelyused across the PyData community. Bokeh integrates seamlessly with Jupyternotebooks. To view the above examples in a notebook, you would onlychange output_file() to output_notebook() instead.

You can interact directly with live tutorial notebooks hosted online byMyBinder. Static versions of these may be viewed at theBokeh NBViewer Gallery.

The Bokeh GitHub repository also has a number of example notebooks in theexamples/howto directory. After cloning the repository, navigate there and run:

You can open and interact with any of the notebooks listed on the index pagethat automatically opens up. In particular, you might check out these examples,which show how Bokeh can be used together with Jupyter interactive widgets:

A basic example that combines Bokeh’s interactive capabilities withJupyter’s dropdowns and sliders.

A more advanced example that uses Numba to efficiently perform imageprocessing interactively based on Jupyter widget controls.

Note

Bokeh plots will not display inline in GitHub notebook previews. Bokehplots use JavaScript code to render, but GitHub scrubs all JavaScript frompreviewed content.

Sample Data¶

Some of the examples included in the Bokeh source make use of sample data filesthat are distributed separately. To download this data, execute the followingcommands at a Bash or Windows command prompt:

For more information see the bokeh.sampledata reference.

Concepts¶

Let’s consider the plots above, and use them to help define some core concepts.

Plot¶

Plots are a central concept in Bokeh. They are containers that hold all thevarious objects (renderers, guides, data, and tools) that comprise the finalvisualization that is presented to users. The bokeh.plotting interfaceprovides a figure() function to help with assembling all the necessary objects.

Glyphs¶

Glyphs are the basic visual marks that Bokeh can display. At the lowest level,there are glyph objects, such as Line. If you are using the low-levelbokeh.models interface, it is your responsibility to create and coordinateall the various Bokeh objects, including glyph objects and their data sources.To make life easier, the bokeh.plotting interface exposes higher-levelglyph methods such as the line() method used in the first example.The second example also adds in calls to circle() to display circleand line glyphs together on the same plot. Besides lines and circles, Bokehmakes many additional glyphs and markers available.

The visual appearance of a glyph is tied directly to the data values that areassociated with the glyph’s various attributes. In the example above we seethat positional attributes like x and y can be set to vectors of data.But glyphs also have some combination of Line Properties, Fill Properties, andHatch Properties to control their appearance. All of these attributes can be setwith “vectorized” values as well. We will show examples of this below.

Guides and Annotations¶

Bokeh plots can also have other visual components that aid presentation orhelp the user make comparisons. These fall into two categories. Guidesare visual aids that help users judge distances, angles, etc. These includegrid lines or bands, axes (such as linear, log, or datetime) that may haveticks and tick labels as well. Annotations are visual aids that label orname parts of the plot. These include titles, legends, etc.

Ranges¶

Ranges describe the to the output_file() function.

More Examples¶

Here are a few more examples to demonstrate other common tasks and use caseswith the bokeh.plotting interface.

Vectorized Colors and Sizes¶

This example shows how it is possible to provide sequences of data values forglyph attributes like fill_color and radius. Other things to look outfor in this example:

• supplying an explicit list of tool names to figure()

• fetching BokehJS resources from CDN using the mode argument

• setting the x_range and y_range explicitly

• turning a line off (by setting its value to None)

• using NumPy arrays for supplying data

Linked Panning and Brushing¶

Linking together various aspects of different plots can be a useful techniquefor data visualization. In Bokeh, such linkages are typically accomplished bysharing some plot component between plots. Below is an example thatdemonstrates linked panning (where changing the range of one plot causesothers to update) by sharing range objects between the plots. Some otherthings to look out for in this example:

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• calling figure() multiple times to create multiple plots

• using gridplot() to arrange several plots in an array

• showing new glyphs using new glyph methods triangle() andsquare()

• hiding the toolbar by setting toolbar_location to None

• setting convenience arguments color (sets both line_color andfill_color) and alpha (sets both line_alpha andfill_alpha)

Although the toolbar is hidden, the pan tool is still present and active. Clickand drag the above plots to pan them, and see how their ranges are linkedtogether.

Another linkage that is often useful is linked brushing (where a selectionon one plot causes a selection to update on other plots). Below is an examplethat demonstrates linked brushing by sharing a ColumnDataSource between twoplots:

Choose the box or lasso select tool, and click and drag to make aselection on one plot, which will update the selection on the otherplot.

Datetime Axes¶

Dealing with date and time series is another common task. Bokeh has asophisticated DatetimeAxis that can change the displayed ticks basedon the current scale of the plot. There are some inputs for which Bokehwill automatically default to DatetimeAxis, but you can alwaysexplicitly ask for one by passing the value 'datetime' to thex_axis_type or y_axis_type parameters to figure(). A few thingsof interest to look out for in this example:

• setting the plot_width and plot_height arguments to figure()

• customizing plots and other objects by assigning values to their attributes

• accessing guides and annotations with convenience Figure attributes:legend, grid, xgrid, ygrid, axis, xaxis, yaxis

Bokeh Applications¶

Bokeh also comes with an optional server component, the Bokeh Server. It ispossible to create many interesting and interactive visualizations withoutusing the Bokeh server, as we have seen above. However, the Bokeh serveraffords many novel and powerful capabilities, including:

• UI widgets and plot selections driving computations and plot updates.

• Intelligent server-side downsampling of large datasets.

• Streaming data automatically updating plots.

• Sophisticated glyph re-writing and transformations for “Big Data”.

• Plot and dashboard publishing for wider audiences.

Details of Bokeh server usage require more space than a Quickstart allows,but you can see (and interact with) a simple Bokeh server app below:

More examples of hosted Bokeh applications can be found in theServer App Examples section of the Gallery. Forinformation about how to use the server and write Bokeh server plotsand apps, consult the Running a Bokeh Server section of theUser Guide.

What’s Next?¶

This Quickstart barely scratches the surface of Bokeh’s capabilities.

For more information about the different plotting APIs Bokeh offers,using the Bokeh server, and how to embed Bokeh plots in your own apps anddocuments, check out the User Guide. For detailed information aboutall modules, classes, models, and objects, consult the Reference.If you are interested in learning how to build and develop Bokeh, or forinformation about how to create a new language binding, see theDevelopers Guide.

To see ready-made examples of how you might use Bokeh with your own data,check out the Gallery. To see detailed examples and walkthroughs aswell as find exercises for learning Bokeh by doing, work through thelive tutorial notebooks.

For questions and technical assistance, come join the Bokeh Discourse.

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Visit the Bokeh GitHub repository and try the examples.

Be sure to follow us on Twitter @bokeh!

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