Bokeh vs matplotlib

Side-by-side — pricing, strengths, tradeoffs, and fit.

Bokeh

Interactive Web Plotting for Python.

matplotlib

A Python 2D plotting library for data visualization.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Interactive Web Plotting for Python.

One-liner

A Python 2D plotting library for data visualization.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Projects requiring high-performance interactivity over large datasets
Web applications that need to visualize streaming data
Developers who want to create interactive plots and charts for web apps without a backend service

✓ Best for

Developers who need to quickly create publication-quality figures in Python.
Data scientists working with large datasets and requiring detailed customization of plots.
Educators teaching data visualization techniques using Python.

Not ideal for

✕ Not for

Teams needing real-time streaming (Bokeh is more suited for batch processing)
Projects with strict performance constraints where JavaScript libraries might be lighter-weight options

✕ Not for

Projects that require real-time rendering or interactive web graphics beyond basic interactivity.
Applications needing high-performance GPU-accelerated plotting capabilities.

Strengths

↑ Strengths

Interactive plots and charts for web applications
High-performance interactivity over large datasets
Supports streaming data visualization
Customizable visualizations with extensive styling options

↑ Strengths

Wide range of plot types including line, bar, scatter, and histogram plots.
Highly customizable with extensive control over every aspect of a figure.
Support for both object-oriented and pyplot interfaces.
Integration with other Python libraries like NumPy and Pandas.

Weaknesses

↓ Weaknesses

Limited third-party integration support
Performance issues with very large datasets

↓ Weaknesses

Steep learning curve for complex visualizations
Performance issues with large datasets
Limited interactivity out-of-the-box
Verbosity in code for complex plots
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