PyQtGraph vs matplotlib
Side-by-side — pricing, strengths, tradeoffs, and fit.
PyQtGraph
Pure-python graphics and GUI library built on PyQt4/PySide and NumPy.
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
One-liner
Pure-python graphics and GUI library built on PyQt4/PySide and NumPy.
One-liner
A Python 2D plotting library for data visualization.
Maturity
Maturity
EstablishedMaturity
EstablishedOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Developers building interactive GUIs with real-time data visualization needs
Scientific researchers requiring high-performance plotting and analysis tools
✓ 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
Projects that require cloud-based deployment without local installation capabilities
Applications needing a web-based interface rather than desktop GUI
✕ 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
High-performance data visualization for scientific applications
Seamless integration with PyQt4/PySide and NumPy
Supports real-time data analysis and display
↑ 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 documentation and examples
Performance issues with large datasets
Dependency on PyQt4/PySide can be restrictive
Active development but community size is relatively small
↓ Weaknesses
Steep learning curve for complex visualizations
Performance issues with large datasets
Limited interactivity out-of-the-box
Verbosity in code for complex plots