Strategies for Efficient Plotting with Large Data Sets in Matplotlib

Community
1

Hi everyone,

I’m currently grappling with some large data sets and am finding that my usual Matplotlib configurations are starting to struggle with performance. I’m reaching out to see if anyone has experience handling large volumes of data with Matplotlib and can share their strategies for maintaining efficiency and responsiveness.

Specifically, I’m interested in learning about:

  1. Performance Optimization: Are there particular settings or techniques you use to speed up plotting or reduce memory usage when working with large data sets?
  2. Data Downsampling: Do you use any methods for downsampling or aggregating data before plotting to make the process more manageable?
  3. Plot Rendering: Have you found any best practices for improving the rendering time of complex plots or managing large numbers of plot elements?
  4. Alternative Libraries: In cases where Matplotlib is struggling, have you found alternative libraries or tools that complement Matplotlib or offer better performance for large data sets?

I have been through these resources/articles Improving interactive plotting speed with large datasets splunk interview questions

If you have any tips, examples, or resources you’ve found helpful, I’d love to hear about them. I’m keen to optimize my workflow and ensure that I can continue producing high-quality visualizations without hitting performance bottlenecks.

Thanks in advance for your insights and advice!

Best Regards :pray:
Rileybailey

2

I think the answer depends on a lot on the shape of your data.

If you have a of scatter points, something like datashader (which ships with an mpl artist!) is the right choice, if you have huge multi-scale images then something like slippy-maps, image pyramids, or modest-image is the right thing, if you have a large number of short time series vs a small number of very long time series you might have different views on how to pre-compute (but in either case you may want to loop at a GPU leveraging library like pyqtgraph). If you have huge amounts of volume data you may want to look at something in the vtk space that does proper volume rendering. If you have big tables you may want to look at vaex.

This is one set of the problems that @ksunden 's work in GitHub - matplotlib/data-prototype is intended to address.