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Investigating Quasar Data With Polars & Interactive marimo Notebooks (Summary)

In this course, you’ve learned to visualize quasar redshift data by building an interactive marimo dashboard using Polars, pandas, and Matplotlib. You’ve practiced retrieving, cleaning, and displaying data in your notebook. You also built interactive UI components that live-update visualizations inside the notebook.

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00:00 In the previous lesson, I showed you how to add interactive components to your marimo notebook. In this lesson, I’ll summarize the course and point you at some other content you might be interested in.

00:11 This course was all about building an interactive marimo dashboard that showed the spectral emissions of quasars. In the course, you used two DataFrame libraries, Polars and pandas.

00:22 Most of the work was done in Polars, where you cleaned the data and converted it to CSV files for use in the dashboard. Some of that CSV stuff was quasar spectral data, which you used Matplotlib to graph within the marimo notebook.

00:36 Although most of the data manipulation was done in Polars, pandas has a handy .from_h2l() method, which you used to plunder the web for its data goodness. The final version of the marimo notebook included UI components that allowed the user to change values interactively modifying the graph as a result. There’s always more content at Real Python for you.

00:59 If you want to do a deeper dive on marimo notebooks, try this course or tutorial. Or to get to know my favorite bear a little better, how about this course or tutorial?

01:10 My astronomer friend and I have written other Python space courses as well. This one is on AstroPy, a third-party astronomy library, and this one has three different astrophysics problems that you use pandas and Matplotlib to explore. Thanks, that’s all for now. If you find yourself in space and happen to get eaten by a black hole, at least you can take solace in making beautiful emission lines.

01:36 I hope you enjoyed the course.

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