Play with NOAA's Heating Degree Day and Cooling Degree Day data.
Required packages: pandas, matplotlib, dateutil
Recommended packages: seaborn
First, build a csv, "degree_days.csv", from NOAA's data. If the csv already exists, calling the script just updates the file with the most recent data.
$ python degree_days.py
Then load, reformat, and plot the data for a region and type of season.
from degree_days import load_file, plot_region_seasons
df = load_file()
# plot a season by census region or state, highlighting the last couple years
plot_region_season(df, "USA", "HDD", ["2013", "2014"], "USA.png")
plot_region_season(df, "NY", "HDD", ["2013", "2014"], "NY.png")- The 2014 "cooling season" spans 2014-01-01 to 2014-12-31.
- The 2014 "heating season" spans 2014-07-01 to 2015-06-30.
- Larger values on the charts means a colder "heating season" (winter) or a warmer "cooling season" (summer).
- Census regions: "NEW ENGLAND", "MIDDLE ATLANTIC", "E N CENTRAL", "W N CENTRAL", "SOUTH ATLANTIC", "E S CENTRAL", "W S CENTRAL", "MOUNTAIN", "PACIFIC", "USA", or state abbreviations, like "NY".
