PyQtGraph vs Plotly
Side-by-side — pricing, strengths, tradeoffs, and fit.
PyQtGraph
Pure-python graphics and GUI library built on PyQt4/PySide and NumPy.
Plotly
Collaborative web plotting for Python and matplotlib.
At a glance
Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
· Not enough data·From Freemium
Overview
One-liner
One-liner
Pure-python graphics and GUI library built on PyQt4/PySide and NumPy.
One-liner
Collaborative web plotting for Python and matplotlib.
Maturity
Maturity
EstablishedMaturity
Not enough dataOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableStarts at
Starts at
$0
Starts at
Freemium
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
Teams needing to create and share interactive data visualizations in Python
Projects requiring high-quality, web-based graphing capabilities
Collaborative environments where multiple users need to interact with the same plots
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
Real-time streaming applications (Plotly is not designed for real-time updates)
Applications that require extremely lightweight plotting solutions
Strengths
↑ Strengths
High-performance data visualization for scientific applications
Seamless integration with PyQt4/PySide and NumPy
Supports real-time data analysis and display
↑ Strengths
Interactive charts and graphs
Collaborative web-based platform
Support for over 20 languages
High-quality data visualization capabilities
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
Limited offline support and documentation for advanced features
Performance issues with large datasets