When should I use Bokeh instead of PyQtGraph?

Interactive browser-based visualizations and real-time streaming dashboards with Python backends. Custom interactive widgets and linked plots for data exploration applications. Python-driven web apps with interactive charts without writing any JavaScript

When should I use PyQtGraph instead of Bokeh?

High-performance real-time scientific plotting in desktop PyQt or PySide applications. Fast 2D and 3D data visualization with low-latency rendering for live sensor or instrument data. Engineering and scientific GUI applications that need interactive chart widgets

What are the main weaknesses of Bokeh?

Steeper learning curve than Plotly for standard chart types and common use cases. Large output file sizes for complex visualizations with many glyphs or data points. Less development momentum than Plotly in recent years; smaller community

What are the main weaknesses of PyQtGraph?

Desktop-only — not suitable for web, notebook, or server-side visualization use cases. Smaller community and fewer chart types than matplotlib or Plotly. Requires a PyQt5 or PySide2 desktop environment which adds a heavy GUI dependency

Bokeh vs PyQtGraph: Key Differences for Python Data Engineering

Data Visualization

Bokeh

Interactive Web Visualizations

★ 4.6

BSD-3-Clause

pip install bokeh

PyQtGraph

Scientific Graphics Library

★ 4.1

MIT

pip install pyqtgraph

Side-by-Side Comparison

Bokeh

PyQtGraph

Bokeh

PyQtGraph

Best For

✓Interactive browser-based visualizations and real-time streaming dashboards with Python backends
✓Custom interactive widgets and linked plots for data exploration applications
✓Python-driven web apps with interactive charts without writing any JavaScript

✓High-performance real-time scientific plotting in desktop PyQt or PySide applications
✓Fast 2D and 3D data visualization with low-latency rendering for live sensor or instrument data
✓Engineering and scientific GUI applications that need interactive chart widgets

Best For

✓Interactive browser-based visualizations and real-time streaming dashboards with Python backends
✓Custom interactive widgets and linked plots for data exploration applications
✓Python-driven web apps with interactive charts without writing any JavaScript

✓High-performance real-time scientific plotting in desktop PyQt or PySide applications
✓Fast 2D and 3D data visualization with low-latency rendering for live sensor or instrument data
✓Engineering and scientific GUI applications that need interactive chart widgets

Weaknesses

•Steeper learning curve than Plotly for standard chart types and common use cases
•Large output file sizes for complex visualizations with many glyphs or data points
•Less development momentum than Plotly in recent years; smaller community

•Desktop-only — not suitable for web, notebook, or server-side visualization use cases
•Smaller community and fewer chart types than matplotlib or Plotly
•Requires a PyQt5 or PySide2 desktop environment which adds a heavy GUI dependency

Weaknesses

•Steeper learning curve than Plotly for standard chart types and common use cases
•Large output file sizes for complex visualizations with many glyphs or data points
•Less development momentum than Plotly in recent years; smaller community

•Desktop-only — not suitable for web, notebook, or server-side visualization use cases
•Smaller community and fewer chart types than matplotlib or Plotly
•Requires a PyQt5 or PySide2 desktop environment which adds a heavy GUI dependency

License

BSD-3-Clause

MIT

License

BSD-3-Clause

MIT

Install

pip install bokeh

pip install pyqtgraph

Install

pip install bokeh

pip install pyqtgraph

Rating

★ 4.6

★ 4.1

Rating

★ 4.6

★ 4.1

Key Features

Bokeh

1Interactive HTML charts rendered in the browser with pan, zoom, and hover
2Bokeh Server enables live streaming data dashboards with push updates
3Wide range of chart types: line, bar, scatter, heatmap, network graphs
4Widgets (sliders, dropdowns, buttons) for interactive filtering
5Embeds seamlessly into Jupyter notebooks and web applications

PyQtGraph

1Fast scientific plotting library built on PyQt/PySide and NumPy
2Real-time plot updates at high frame rates via OpenGL acceleration
32D and 3D plotting: line, scatter, image, histogram, and mesh plots
4Interactive widgets for panning, zooming, and region of interest selection
5Embeds in PyQt desktop applications without a browser

How Python Data Engineers Use These Tools

Bokeh

Data engineers use Bokeh to build interactive monitoring dashboards for pipeline metrics and data quality KPIs. A Bokeh Server app connects directly to a database, refreshes charts on a timer, and serves a browser-based dashboard — giving stakeholders a live view of pipeline health without requiring a separate BI tool.

PyQtGraph

Python data engineers use PyQtGraph to build desktop monitoring tools for data pipeline metrics — displaying real-time throughput charts, latency histograms, and queue depth gauges in a native Python desktop application. Its high-frequency update capability makes it suitable for visualizing streaming pipeline performance data at update rates up to 100Hz.

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Individual Tool Pages

View Bokeh details →View PyQtGraph details →

Side-by-Side Comparison

Bokeh

PyQtGraph

Bokeh

PyQtGraph

Best For

✓Interactive browser-based visualizations and real-time streaming dashboards with Python backends
✓Custom interactive widgets and linked plots for data exploration applications
✓Python-driven web apps with interactive charts without writing any JavaScript

✓High-performance real-time scientific plotting in desktop PyQt or PySide applications
✓Fast 2D and 3D data visualization with low-latency rendering for live sensor or instrument data
✓Engineering and scientific GUI applications that need interactive chart widgets

Best For

✓Interactive browser-based visualizations and real-time streaming dashboards with Python backends
✓Custom interactive widgets and linked plots for data exploration applications
✓Python-driven web apps with interactive charts without writing any JavaScript

✓High-performance real-time scientific plotting in desktop PyQt or PySide applications
✓Fast 2D and 3D data visualization with low-latency rendering for live sensor or instrument data
✓Engineering and scientific GUI applications that need interactive chart widgets

Weaknesses

•Steeper learning curve than Plotly for standard chart types and common use cases
•Large output file sizes for complex visualizations with many glyphs or data points
•Less development momentum than Plotly in recent years; smaller community

•Desktop-only — not suitable for web, notebook, or server-side visualization use cases
•Smaller community and fewer chart types than matplotlib or Plotly
•Requires a PyQt5 or PySide2 desktop environment which adds a heavy GUI dependency

Weaknesses

•Steeper learning curve than Plotly for standard chart types and common use cases
•Large output file sizes for complex visualizations with many glyphs or data points
•Less development momentum than Plotly in recent years; smaller community

•Desktop-only — not suitable for web, notebook, or server-side visualization use cases
•Smaller community and fewer chart types than matplotlib or Plotly
•Requires a PyQt5 or PySide2 desktop environment which adds a heavy GUI dependency

License

BSD-3-Clause

MIT

License

BSD-3-Clause

MIT

Install

pip install bokeh

pip install pyqtgraph

Install

pip install bokeh

pip install pyqtgraph

Rating

★ 4.6

★ 4.1

Rating

★ 4.6

★ 4.1

Key Features

Bokeh

1Interactive HTML charts rendered in the browser with pan, zoom, and hover
2Bokeh Server enables live streaming data dashboards with push updates
3Wide range of chart types: line, bar, scatter, heatmap, network graphs
4Widgets (sliders, dropdowns, buttons) for interactive filtering
5Embeds seamlessly into Jupyter notebooks and web applications

PyQtGraph

1Fast scientific plotting library built on PyQt/PySide and NumPy
2Real-time plot updates at high frame rates via OpenGL acceleration
32D and 3D plotting: line, scatter, image, histogram, and mesh plots
4Interactive widgets for panning, zooming, and region of interest selection
5Embeds in PyQt desktop applications without a browser

How Python Data Engineers Use These Tools