Afterwards, we practiced using pandas and our analytic tools by writing a script to list the classrooms that have, on average, CO2 levels of at least 1,000 PPM during school hours (8:00 AM - 3:30 PM). We used the analytic tools to create a CSV with the data we need (2018 - AHS CO2 (0800-1530).csv), and then I wrote the following script, HighCO2.py, to analyze the data and output the results.
Finally, we started out blogs to document our work on this project. What you're reading right now is mine.
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About the AuthorA member of a small, select group of high school students tasked with writing software to save our town energy. Archives |