Ed Blayney's Python for Data Final Project- Code Louisville Spring 2018
Is there more property damage due to fires in colder months than warmer ones?
- Create a jupyter notebook to communicate my analysis
- Pulled property damage due to fire data from the Louisville Fire Department on data.louisvilleky.gov.
- Saved the csv to my GitHub folder and proceeded to load the data into a sqlite3 database.
- Used a SQL query to aggregate the property loss due to fire by month of the year eliminating any fires that didn’t result in property damage. Also, I divided the total loss for each month by 1000 to reduce clutter on the visualization.
- Visualized the data using plt.bar to visualize total property loss by month of the year.
- Then I created a CSV from intellicast.com of the historic monthly average highs and lows for each month of the year, and dropped it into my jupyter notebook next to my plot.
It appears there is a significant jump in property loss due to fires in colder months, particularly when the average low temperature drops below 40 degrees fahrenheit.
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