Space Science with Python - Near-Earth Objects #5: Orbital Elements

Last time, we converted a single parameter, namely the Absolute Magnitude, to the NEOs' corresponding, physical diameters. But NEOs are not only defined by the physical appearance and chemical composition. Dynamical properties are important to identify and understand e.g., observational biases.

This time we will use the library Seaborn that allows us to generate several plots, histograms and Kernel-Density Estimators with only a few lines of code; enabling us to dive deeply into the multi-dimensional world of orbital elements.

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Content
0:00 Introduction
1:30 Cell #1 - #4: Setting things up
2:50 Cell #5: Seaborn
5:10 Cell #6: Absolute Magnitude distribution
6:22 Cell #7: Absolute Magnitude by NEO types
9:50 Cell #8: Semi-Major Axis KDE plot
12:30 Cell #9: Multi-Grid Plot (orbital elements)
18:54 Summary & Outlook
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There is a lot to do and to learn and I hope you will join the journey. Meanwhile, if you have questions or ideas, reach out to me via:

Or drop a comment!

Talk to you later,
Thomas

[WilliamDye-willdye]

I didn't know about seaborn. Interesting. Now I'm dreaming of a good 3D animation library specifically for orbital dynamics. I've seen some packages that do part of the job, but I haven't looked around much so maybe a good library is already out there.

[josebenitez]

Very interesting use of seaborn for space science. I like a lot your code examples. At the same time, I miss a more detailed explanation of the science behind the plots. For example, the pairgrid plot is amazing and you provide some comments about what is behind the comparison of some pair of plots but more extended explanations would be great