Replace x.y.z with the Bokeh version you want to use. The "bokeh-mathjax" files are required to enable The "bokeh-gl" files are required to enable The "bokeh-api" files are required to use the The "bokeh-tables" files are only necessary if you are using Bokeh’s The "bokeh-widgets" files are only necessary if you are using any of the Other scripts are optional and only need to be included if you want to use Only the Bokeh core library bokeh-x.y.z.min.js is always required. org / bokeh / release / bokeh - mathjax - x. org / bokeh / release / bokeh - tables - x. org / bokeh / release / bokeh - widgets - x. Information on the data sets included in Bokeh’s sample data.Īfter installing Bokeh, you can automatically download and install the In Bokeh’s GitHub repository, you can find a number of examples. Because this sample data is rather large, it is not included in Includes a variety of freely available data tables and databases that you can Optionally, Bokeh can download and install a collection of sample data. Including Bokeh plots in Sphinx documentation. Necessary to make use of the bokeh.sphinxext Sphinx extension for Necessary for PNG and SVG export to PNG and SVG images. Necessary to enable detailed memory logging in the Bokeh server. To generate Bokeh graph renderers directly from NetworkX data. Necessary to use the from_networkx() function Necessary for Custom extensions or for definingĬustomJS implementations in TypeScript. Depending on your setup, there may be additional packages or Necessary for certain optional features: Jupyterīokeh can display content in classic Jupyter notebooks as well as in In addition to the required dependencies, some additional packages are Installing required dependencies #įor basic usage, Bokeh requires the following libraries:Īll those packages are automatically installed if you use conda or Project, please see the Setting up a development environment instructions in theĬontributor guide. If you want to install a development version of Bokeh to contribute to the Python Bokeh Plotting glyphs over a Google Map. Python for Kids - Fun Tutorial to Learn Python Programming. Once you have Bokeh installed, build your first visualization by followingĬheck the user guide for a comprehensive overview of all the things youĬan do with Bokeh. Python Bokeh Plotting Squares with Crosses on a Graph Python Bokeh Plotting Squares with Dots on a Graph. 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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