This was my project idea in my senior year data science capstone. My group and I ended up not going with it, so I decided to go through with it on my own time to get some more personal experience. I ended up working in Pandas, SQL, and Vega Lite. In all honesty, there were no real takeaways from the data, so it was a good thing it didn't end up being the capstone project, but it was still good experience.
Note: the order in which these files were used is fire-dataset-pruning.ipynb, weather-averages.sql (used for imputing in the weather dataset), weather-dataset-pruning.ipynb, dataset-creation and dataset-importing and dataset-joining.sql, and then final-data-tables.sql. Then the tables generated from that last sql file were used in an Observable notebook, where I used vega-lite (with an API introduced by my data visualization professor) to plot data and trend lines.
Link to the Observable notebook with final graphs: https://observablehq.com/d/bee23d1ab70dbe87