The toolbar contains the following tools: Observe the toolbar displayed on the right side of the plot (see Figure 2). When you run the cell containing the above code, you'll see the scatter plot containing the various markers as shown in Figure 1 in a new tab page on your Web browser.įigure 1: Basic plots created using Bokeh
#RV100 BOKEH EXAMPLES CODE#
In the above, you also specified the filename of the page as my_first_graph.html, using the output_file() function if you don't specify the output filename, a random file will be generated every time you run the code (and hence a new tab is created on your browser). The circle(), line(), and triangle() glyph methods creates scatter plots with various marker shapes. The figure() function returns a plot object, which allows you to create various types of charts using the various glyphs methods. Output_file('my_first_graph.html') # name the output file P.triangle(x, y, size=10, color='gold', legend_label='triangle') P.line (x, y, width=2, color='blue', legend_label='line') P.circle (x, y, size=30, color='red', legend_label='circle')
# using various glyph methods to create scatter In Jupyter Notebook, type the following code in a new cell: from otting import figure, output_file, show In Bokeh, glyphs are the geometrical shapes (lines, circles, rectangles, etc.) in a chart. The simplest way to get started is to create a simple chart using the various glyphs methods. Glyphs are the basic visual building blocks of Bokeh plots. In Bokeh, a plot is a container that holds all the various objects (such as renderers, glyphs, or annotations) of a visualization.
#RV100 BOKEH EXAMPLES INSTALL#
To install the Bokeh library, simply use the pip command at the Anaconda Prompt/Terminal: $ pip install bokeh Once Anaconda is installed, the next step is to install the Bokeh library. Installing Bokehįor this article, I'll be using Anaconda for my Python installation. What's more, Bokeh powers your dashboards on Web browsers using JavaScript, all without you needing to write any JavaScript code.ĭashboards provide all your important information in a single page and are usually used for presenting information such as KPIs and sales results. Using Bokeh, you can create dashboards - a visual display of all your key data. So let the fun begin! What Is Bokeh?īokeh is a Python library for creating interactive visualizations for Web browsers.
#RV100 BOKEH EXAMPLES HOW TO#
In this article, I'll walk you through the basics of Bokeh: how to install it, how to create basic charts, how to deploy them on Web servers, and more. However, what if you want to generate all these charts and graphics and let your users view them on Web browsers? Also, it would be useful if the users can interact with your charts dynamically and drill down into the details they want to see. These libraries are very useful for doing data exploration, as well as visualizing and generating graphics for reports. Most data analysts and scientists using Python are familiar with plotting libraries such as matplotlib, Seaborn, and Plotly.
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