Glyphs are the building blocks of Bokeh plot such as lines, circles, rectangles, and other shapes. It contains various methods to draw different vectorized graphical glyphs. It actually is a subclass of plot class defined in bokeh.models module.įigure class simplifies plot creation. This module contains definition of Figure class. This is a higher level interface that has functionality for composing visual glyphs. The low-level objects that comprise a Bokeh scene graph are called Models. A Bokeh plot results in an object containing visual and data aspects of a scene which is used by BokehJS library. It provides great deal of flexibility to the application developer in developing visualizations. Bokeh - Basic Conceptsīokeh package offers two interfaces using which various plotting operations can be performed. The sine wave will be displayed inside the notebook. The only change you need to make is to import output_notebook instead of output_file from otting module.įrom otting import figure, output_notebook, showĬall to output_notebook() function sets Jupyter notebook’s output cell as the destination for show() function as shown below −Įnter the code in a notebook cell and run it. Output on browser Bokeh - Jupyter Notebookĭisplaying Bokeh figure in Jupyter notebook is very similar to the above. Complete code and its output is as follows This will render the line plot in ‘sine.html’ and will be displayed in browser. P.line(x, y, legend = "sine", line_width = 2)įinally, set the output file and call show() function. The Figure object contains a line() method that adds a line glyph to the figure. P = figure(title = "sine wave example", x_axis_label = 'x', y_axis_label = 'y') To obtain a Bokeh Figure object, specify the title and x and y axis labels as below − Next, set up two numpy arrays where second array is sine value of first. The show() function displays the Bokeh figure in browser on in notebook. The output_file() function is used to specify a HTML file to store output. The figure() function creates a new figure for plotting. To begin with, import following functions from otting modules −įrom otting import figure, output_file, show To verify if Bokeh has been successfully installed, import bokeh package in Python terminal and check its version −Ĭreating a simple line plot between two numpy arrays is very simple. In addition to the above dependencies, you may require additional packages such as pandas, psutil, etc., for specific purposes. If you are using Anaconda distribution, use conda package manager as follows − Generally, above packages are installed automatically when Bokeh is installed using Python’s built-in Package manager PIP as shown below − Bokeh package has the following dependencies − Current version of Bokeh at the time of writing this tutorial is ver. Bokeh - Environment Setupīokeh can be installed on CPython versions 2.7 and 3.5+ only both with Standard distribution and Anaconda distribution. It is distributed under Berkeley Source Distribution (BSD) license. They can also be rendered inīokeh is an open source project. Plots can be embedded in output of Flask or Django enabled web applications. Powerfulīy adding custom JavaScript, it is possible to generate visualizations for specialised use-cases. You can give your audience a wide range of options and tools for inferring and looking at data from various angles so that user can perform “what if” analysis. Bokeh creates interactive plots that change when the user interacts with them. This is an important advantage of Bokeh over Matplotlib and Seaborn, both produce static plots. Productivityīokeh can easily interact with other popular Pydata tools such as Pandas and Jupyter notebook. Some of the important features of Bokeh are as follows − Flexibilityīokeh is useful for common plotting requirements as well as custom and complex use-cases. Featuresīokeh primarily converts the data source into a JSON file which is used as input for BokehJS, a JavaScript library, which in turn is written in TypeScript and renders the visualizations in modern browsers. Bokeh can easily connect with these tools and produce interactive plots, dashboards and data applications. NumFocus also supports PyData, an educational program, involved in development of other important tools such as NumPy, Pandas and more. The Bokeh project is sponsored by NumFocus. Hence, it proves to be extremely useful for developing web based dashboards. Unlike Matplotlib and Seaborn, they are also Python packages for data visualization, Bokeh renders its plots using HTML and JavaScript. Bokeh is a data visualization library for Python.
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