Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://www.geeksforgeeks.org/introduction-to-bokeh-in-python/ below:

Introduction to Bokeh in Python

Last Updated : 24 Jul, 2025

Bokeh is a Python interactive data visualization. Unlike Matplotlib and Seaborn, Bokeh renders its plots using HTML and JavaScript. It targets modern web browsers for presentation providing elegant, concise construction of novel graphics with high-performance interactivity.
 

Some of the important features of Bokeh are given below:

• Flexibility: Bokeh can be used for common plotting requirements and for custom and complex use-cases.
• Productivity: Its interaction with other popular Pydata tools (such as Pandas and Jupyter notebook) is very easy.
• Interactivity: It creates interactive plots that changes with the user interaction.
• Powerful: Generation of visualizations for specialised use-cases can be done by adding JavaScript.
• Shareable: Visual data are shareable. They can also be rendered in Jupyter notebooks.
• Open source: Bokeh is an open source project.

Bokeh is a powerful Python library used to create interactive and beautiful visualizations for the web. It provides two main interfaces depending on your needs:

This is the most commonly used and beginner-friendly interface. It's designed to help you quickly create standard plots using simple methods.

• Key Tool: figure()
• Add visual elements (called glyphs) using .line(), .circle(), .bar(), etc.
• Perfect for: Line charts, bar charts, scatter plots, etc.

Example:

from bokeh.plotting import figure, show, output_file
output_file("line_plot.html")

p = figure(title="Line Plot", x_axis_label='X', y_axis_label='Y')
p.line([1, 2, 3], [4, 6, 2], line_width=2)
show(p)

Output

Explanation:

• output_file("line_plot.html") sets the output file where the plot will be saved and opened in your browser.
• figure() sets up the plot with a title and axis labels and p.line(...) draws a line using given x and y values.
• show(p) opens the saved HTML file to display the interactive line chart.

• This interface is for advanced users who need full control over the visualization.
• You can customize everything: layout, interactions, callbacks, data sources and more.

Bokeh makes it easy to build interactive apps using built-in UI components such as Slider, Button, Select (dropdown), TextInput and CheckboxGroup. These widgets allow users to dynamically interact with your plots, enabling real-time updates and a more engaging data exploration experience.

Example : Interactive Line Plot

from bokeh.layouts import column
from bokeh.models import Slider
from bokeh.plotting import figure
from bokeh.io import curdoc

p = figure(title="Interactive Line")
line = p.line([1, 2, 3], [2, 4, 6])

slider = Slider(start=1, end=5, value=1, step=0.1, title="Scale")

def update(attr, old, new):
    factor = slider.value
    line.data_source.data = {'x': [1, 2, 3], 'y': [i * factor for i in [2, 4, 6]]}

slider.on_change('value', update)
curdoc().add_root(column(slider, p))

Output

Explanation:

• figure() creates the plot and draws a line through (1,2), (2,4), (3,6) using p.line().
• Slider() adds a "Scale" slider (1 to 5) to adjust the Y-values.
• update() changes the line when the slider moves, scaling Y-values.
• curdoc().add_root() combines the plot and slider for display in a Bokeh app (bokeh serve).

Related articles:

• Matplotlib
• Seaborn

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4