![]() ![]() 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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