Matplotlib vs Bokeh: Key Differences and When to Use Each

Matplotlib is a powerful Python library mainly for static, publication-quality plots, while Bokeh specializes in interactive, web-ready visualizations. Use Matplotlib for detailed static charts and Bokeh when you need interactive plots that users can explore in a browser.

Quick Comparison

Here is a quick side-by-side comparison of Matplotlib and Bokeh based on key factors.

Factor	Matplotlib	Bokeh
Primary Use	Static plots for reports and papers	Interactive plots for web apps
Interactivity	Limited (zoom, pan in some backends)	Rich (hover, zoom, widgets)
Output Format	Images (PNG, PDF, SVG)	HTML, JavaScript for browsers
Learning Curve	Easy for basic plots	Moderate due to web concepts
Integration	Works well with Jupyter and scripts	Best for web dashboards and apps
Customization	Highly customizable with detailed control	Customizable with interactive tools

Key Differences

Matplotlib is designed for creating static, high-quality plots that are ideal for print and academic papers. It offers detailed control over every element of the plot, making it great for precise visualizations. However, its interactivity is limited to basic zoom and pan features in some environments.

Bokeh, on the other hand, focuses on interactive visualizations that run in web browsers. It uses HTML and JavaScript under the hood, allowing users to hover, zoom, and interact with plots dynamically. This makes it perfect for dashboards and data exploration tools but requires understanding some web concepts.

While Matplotlib outputs static images like PNG or PDF, Bokeh generates HTML and JavaScript files that can be embedded in web pages. This difference affects how you share and deploy your visualizations.

Code Comparison

Here is how to create a simple line plot with Matplotlib.

python

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

plt.plot(x, y, marker='o')
plt.title('Matplotlib Line Plot')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.grid(True)
plt.show()

Output

A static line plot with points marked, titled 'Matplotlib Line Plot', showing x vs y with grid lines.

Bokeh Equivalent

Here is how to create the same line plot with Bokeh, which will be interactive in a browser.

python

from bokeh.plotting import figure, show
from bokeh.io import output_notebook

output_notebook()

x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

p = figure(title='Bokeh Line Plot', x_axis_label='X axis', y_axis_label='Y axis')
p.line(x, y, line_width=2)
p.circle(x, y, size=8, fill_color='white')

show(p)

Output

An interactive line plot with points, titled 'Bokeh Line Plot', allowing zoom, pan, and hover in a Jupyter notebook or browser.

When to Use Which

Choose Matplotlib when you need static, publication-quality plots for reports, papers, or quick data exploration in scripts and Jupyter notebooks. It is best for detailed customization and when interactivity is not required.

Choose Bokeh when you want interactive visualizations that users can explore in a web browser, such as dashboards or data apps. It is ideal for sharing data insights with others who need to interact with the plots.

Key Takeaways

Matplotlib excels at static, detailed plots for print and reports.

Bokeh provides rich interactivity for web-based visualizations.

Use Matplotlib for quick, static charts and Bokeh for interactive dashboards.

Matplotlib outputs images; Bokeh outputs HTML/JavaScript for browsers.

Learning Bokeh requires some understanding of web technologies.