![]() ![]() 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 P.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. # Add a line renderer with legend and line thickness P = figure(title="Simple Line Plot in Bokeh", x_axis_label='x', y_axis_label='y') # Create a new plot with a title and axis labels # Make Bokeh Push push output to Jupyter Notebook.įrom bokeh.io import push_notebook, show, output_notebook Here is a simple example of how to use Bokeh in Jupyter Notebook: import numpy as np If you already have a version of Python then you can run the following in cmd.exe on Windows or terminal on Mac: pip install bokehīe sure to check out the Bokeh quick start guide for several examples. Once you have anaconda installed onto your machine then you can simply run the following in cmd.exe on Windows or terminal on Mac: conda install bokeh Which you can download and install for free. Īll of those come with the Anaconda Python Distribution. If you plan on installing with Python 2.7 you will also need future. NumPy, Jinja2, Six, Requests, Tornado >= 4.0, PyYaml, DateUtil Installing Bokeh Bokeh's Docs on Installationīokeh runs on Python it has the following dependencies The -show parameter tells bokeh to open a browser window and show document defined in hello_world.py. To launch it you need to execute bokeh on the command line and use the serve command to launch the server: $ bokeh serve -show hello_world.py Plot.line('x', 'y', source=data_source, line_width=3, line_alpha=0.6) Tools="crosshair,pan,reset,save,wheel_zoom",) """Add a plotted function to the document.ĭoc: A bokeh document to which elements can be added.ĭata_source = ColumnDataSource(data=dict(x=x_values, y=y_values)) We will use this example script ( hello_world.py ): from bokeh.models import ColumnDataSource To use bokeh you need to launch a bokeh server and connect to it using a browser. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, and to extend this capability with high-performance interactivity over very large or streaming datasets.īokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. Bokeh is a Python interactive visualization library that targets modern web browsers for presentation.
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