Python Data Visualization Libraries performance comparison
Our team conducted various performance comparison tests, in 2D and 3D charting
- Real-time streaming tests
- Static data sets loading and zooming responsivity tests
When making industry-leading R&D work, or releasing commercial applications with Python, the data precision and resolutions is crucial. When the amount of data is high, most data visualization libraries struggle.
It's important to get the charts updating quickly. Interactivity including Zooming and panning, browsing data back and forth, must be there instantly when the user needs it. Real-time visualization needs to be smooth.
There are older several data viz libraries available for Python, but for demanding solutions, there are two popular ones. The "default" MatPlotLib, and "high-performance" add-on Plotly. Our tests reveal, Plotly is indeed faster in almost all tests, than MatPlotLib.
LightningChart has been pioneering the GPU-accelerated data visualization domain with .NET C# systems since 2009, and JavaScript charts lately. LightningChart® technology is now available for Python data scientists and software developers. And what a difference it makes when the amounts of data get high:
- In real-time streaming data tests, LightningChart Python is around 7000 x faster than MatplotLib, and 188 x faster than Plotly.
- In static data tests, LightningChart Python was around 44000 x faster than MatplotLib, and 96 x faster than Plotly
Some highlights
- LightningChart can visualize 2.5 M data points / sec in streaming line chart case, with 180 FPS refresh rate, where as Plotly can do 65 k points, and MatPlotLib struggles with 5000 data points with low FPS.
- LightningChart can visualize 16 M data points / sec at 180 FPS, Plotly 40 k, and MatPlotLib 40 k with low FPS.
From the performance results, we can observe: Plotly is magnitudes faster than MatPlotLib. LightningChart is yet magnitudes faster than Plotly.
What are the actual benefits?
The new high-performance charting technology enables creating advanced scientific applications, which include:
- Medical / biosignal montoring and analytics applications
- Seismic activity monitoring with long data history
- Audio and digital signal processing applications
- Situational awareness with spectral visualizations and sensor data monitoring
- Industrial monitoring applications
LightningChart Python enables advanced finance & trading applications to be created too, with capacity to visualize masses of real-time data, see more: LightningChart Python Trader
Licenses
Whereas MatplotLib is free, and Plotly partially free, LightningChart Python is free of cost for students and universities. When making advanced commercial R&D, or publishing distributable software applications, LightningChart requires a commercial license, which costs a small fraction of data scientist's monthly cost to the company.
See more info
Quite impressive! What’s your secret sauce, if you don’t mind sharing?