pyqtgraph
Scientific Graphics and GUI Library for Python
Decision gist · record as of 2026-08-14
Yes. PyQtGraph is a mature, actively maintained library with low install friction, no known vulnerabilities, and a permissive MIT license. It is the right choice if you need fast, interactive scientific graphics in a PyQt application and are willing to depend on Qt and numpy. If you need standalone plotting without Qt, PyQtGraph may not be necessary.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyQt5, PyQt6, or PySide6 to be installed separately; Qt 5.15 or Qt 6.8+ required.
- Low friction: pure Python wheel with only numpy and colorama as runtime dependencies.
- Active maintenance with recent releases; last commit 2026-08-11.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute PyQtGraph with minimal restrictions in commercial or open-source projects.
last release 2025-11-16 (271 days) · last repo commit 2026-08-11 · 4,398 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,058,230 downloads/mo, #4,427 on PyPI
Alternatives
Verify before relying
pip install pyqtgraph
import pyqtgraph as pg
import numpy as np
app = pg.mkQApp()
plot = pg.plot(np.random.normal(size=10))
app.exec()- Whether optional dependencies (scipy, pyopengl, h5py, etc.) are automatically installed or must be added separately for their features.
- Performance characteristics for large datasets or high-frequency updates compared to other visualization approaches.
- Specific minimum Qt patch versions required beyond 5.15 and 6.8+.
What it is and what it does
PyQtGraph is a visualization library designed for mathematics, science, and engineering applications that need fast, interactive graphics despite being written entirely in Python. It achieves performance by leveraging numpy for numerical computation, Qt's GraphicsView framework for 2D rendering, and OpenGL for 3D display. The library integrates directly with PyQt5, PyQt6, or PySide6, making it suitable for building custom scientific applications with interactive plots, real-time data visualization, and embedded graphics.
The package requires Python 3.10+ and a compatible Qt framework (Qt 5.15 or Qt 6.8+), with numpy 2.0+ as a core dependency. Additional features like 3D graphics, image processing, HDF5 export, and Jupyter support are available through optional third-party libraries. The library is actively maintained and widely used in scientific and engineering tools.
Use it for
- Build interactive real-time plotting dashboards for scientific instruments or data acquisition systems.
- Create 2D and 3D visualizations of numerical data with numpy arrays in custom PyQt applications.
- Develop image processing and analysis tools with fast rendering and interactive controls.
- Embed live data plots and graphs in laboratory or engineering software with minimal overhead.
- Visualize scientific simulations or computational results with interactive exploration capabilities.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
PyQtGraph is a mature, actively maintained library with low install friction, no known vulnerabilities, and a permissive MIT license. It is the right choice if you need fast, interactive scientific graphics in a PyQt application and are willing to depend on Qt and numpy. If you need standalone plotting without Qt, PyQtGraph may not be necessary.
Install
pyqtgraph on PyPI
Before you install
Low friction: pure Python wheel with only numpy and colorama as runtime dependencies. Active maintenance with recent releases; last commit 2026-08-11. Supports current Python versions and modern Qt versions.
Requires PyQt5, PyQt6, or PySide6 to be installed separately; Qt 5.15 or Qt 6.8+ required.
License in practice
MIT license is permissive; you can use, modify, and distribute PyQtGraph with minimal restrictions in commercial or open-source projects.
Quickstart
pip install pyqtgraph
import pyqtgraph as pg
import numpy as np
app = pg.mkQApp()
plot = pg.plot(np.random.normal(size=10))
app.exec()Verify before relying
- Whether optional dependencies (scipy, pyopengl, h5py, etc.) are automatically installed or must be added separately for their features.
- Performance characteristics for large datasets or high-frequency updates compared to other visualization approaches.
- Specific minimum Qt patch versions required beyond 5.15 and 6.8+.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpycolorama |
| Maintenance | Actively maintained 271 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,058,230 / month, #4,427 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: Other EnvironmentIntended Audience :: Science/ResearchOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: VisualizationTopic :: Software Development :: Libraries :: Python ModulesTopic :: Software Development :: User Interfaces |
Evidence: pyqtgraph-0.14.0-py3-none-any.whl
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