Transportation
Autobahn – Wikipedia
Bedspring Bridge was unforgettable experience | Sooke News Mirror
Electric cars aren’t seven times more expensive than gasoline-powered cars, as claimed on Facebook – Climate Feedback
Boring Roads
I have to admit driving in New York is downright boring compared to West Virginia. The roads are so flat, so straight, and so wide.
But then again, New York’s mountains have wide valleys, and it’s rare that a road has to traverse a narrow canyon or do any kind of significant climbing. Few major highways climb over mountains in New York.
County Route 47 in Catskills near Winnisook Lake at 2,680 elevation is the highest elevation all season highway in New York. There aren’t a lot of high elevation roads in New York for sure. The highest Interstate in New York is Southern Tier Expressway in Almond at 2,110 elevation
Most Popular Makes of Auto in NY State
This shows the percentage of automobiles registered by the ten most popular makes of automobile in New York State. Chevrolet is by far the most popular in the state, although foreigns are more popular downstate.
| Make | CHEVR | FORD | TOYOT | HONDA | JEEP | NISSA | SUBAR | DODGE | GMC | HYUND |
|---|---|---|---|---|---|---|---|---|---|---|
| County | ||||||||||
| ALBANY | 11.3 | 11.1 | 10.1 | 12.7 | 4.3 | 5.6 | 5.3 | 2.7 | 2.0 | 3.1 |
| ALLEGANY | 20.4 | 17.8 | 5.5 | 3.4 | 5.5 | 3.4 | 3.0 | 7.6 | 5.4 | 1.4 |
| BRONX | 5.0 | 7.1 | 17.0 | 17.9 | 4.3 | 8.4 | 2.3 | 2.4 | 1.2 | 3.9 |
| BROOME | 14.1 | 10.7 | 15.1 | 8.7 | 3.8 | 5.8 | 4.3 | 3.6 | 3.1 | 4.6 |
| CATTARAUGUS | 20.4 | 16.5 | 6.4 | 3.6 | 6.3 | 3.0 | 3.3 | 6.1 | 5.5 | 1.7 |
| CAYUGA | 21.6 | 13.1 | 7.6 | 6.9 | 5.4 | 4.9 | 4.6 | 4.3 | 3.7 | 1.9 |
| CHAUTAUQUA | 17.8 | 17.0 | 7.6 | 6.0 | 5.9 | 3.6 | 4.9 | 5.6 | 3.4 | 2.6 |
| CHEMUNG | 15.3 | 13.1 | 8.9 | 6.8 | 4.6 | 9.5 | 4.0 | 5.1 | 4.2 | 3.8 |
| CHENANGO | 18.2 | 16.7 | 7.6 | 5.3 | 5.3 | 4.0 | 6.2 | 5.3 | 4.5 | 2.6 |
| CLINTON | 13.7 | 14.9 | 9.1 | 8.3 | 4.6 | 4.2 | 5.1 | 3.8 | 4.6 | 4.7 |
| COLUMBIA | 12.1 | 12.3 | 14.0 | 7.9 | 4.4 | 3.7 | 8.0 | 3.3 | 3.7 | 2.2 |
| CORTLAND | 19.3 | 13.8 | 6.5 | 5.5 | 5.2 | 6.5 | 6.5 | 4.9 | 3.3 | 2.7 |
| DELAWARE | 15.9 | 13.0 | 8.5 | 6.8 | 5.4 | 4.4 | 7.0 | 5.2 | 4.6 | 2.6 |
| DUTCHESS | 9.4 | 9.4 | 10.2 | 13.9 | 4.8 | 5.4 | 7.7 | 2.8 | 2.3 | 4.0 |
| ERIE | 17.4 | 14.5 | 9.1 | 6.4 | 6.1 | 4.2 | 4.1 | 3.3 | 3.1 | 3.1 |
| ESSEX | 15.5 | 15.9 | 9.3 | 6.5 | 5.8 | 3.0 | 6.2 | 4.0 | 4.3 | 2.2 |
| FRANKLIN | 17.2 | 16.5 | 7.5 | 5.3 | 5.0 | 2.6 | 4.8 | 5.1 | 5.8 | 2.6 |
| FULTON | 18.1 | 14.3 | 8.5 | 6.4 | 5.9 | 6.5 | 3.0 | 5.1 | 2.8 | 2.1 |
| GENESEE | 24.5 | 13.5 | 8.9 | 4.2 | 5.3 | 2.7 | 2.6 | 4.9 | 4.4 | 1.7 |
| GREENE | 14.0 | 13.6 | 7.4 | 7.1 | 4.9 | 3.9 | 8.8 | 3.7 | 4.8 | 2.7 |
| HAMILTON | 15.6 | 13.7 | 10.9 | 6.4 | 5.4 | 3.2 | 5.3 | 3.7 | 4.5 | 1.9 |
| HERKIMER | 16.9 | 15.0 | 7.6 | 7.0 | 5.4 | 4.0 | 4.5 | 4.4 | 3.8 | 2.7 |
| JEFFERSON | 14.2 | 16.5 | 8.4 | 7.2 | 5.8 | 3.6 | 3.9 | 5.1 | 3.2 | 2.5 |
| KINGS | 4.1 | 6.5 | 16.3 | 14.2 | 3.6 | 9.1 | 3.0 | 1.8 | 1.1 | 3.6 |
| LEWIS | 16.4 | 20.9 | 5.8 | 8.6 | 5.1 | 2.2 | 2.9 | 4.8 | 4.9 | 1.5 |
| LIVINGSTON | 21.0 | 15.7 | 7.2 | 6.2 | 5.6 | 3.1 | 3.7 | 5.5 | 3.7 | 2.0 |
| MADISON | 17.9 | 13.6 | 9.3 | 5.3 | 5.7 | 3.1 | 5.3 | 4.6 | 3.9 | 2.2 |
| MONROE | 16.6 | 10.2 | 10.5 | 10.3 | 4.3 | 5.7 | 4.7 | 2.8 | 3.1 | 3.2 |
| MONTGOMERY | 17.4 | 14.4 | 6.9 | 8.9 | 5.9 | 5.6 | 3.2 | 4.8 | 3.1 | 2.5 |
| NASSAU | 6.2 | 7.6 | 11.4 | 12.3 | 6.3 | 7.7 | 3.3 | 2.1 | 1.8 | 4.2 |
| NEW YORK | 3.9 | 7.4 | 11.9 | 11.3 | 4.3 | 4.6 | 4.1 | 1.6 | 1.0 | 2.3 |
| NIAGARA | 23.7 | 13.7 | 7.0 | 5.2 | 5.8 | 3.4 | 2.2 | 4.0 | 4.3 | 2.4 |
| ONEIDA | 13.5 | 13.3 | 10.1 | 8.4 | 5.2 | 4.7 | 4.8 | 3.6 | 3.8 | 2.9 |
| ONONDAGA | 14.9 | 10.1 | 10.3 | 8.0 | 5.9 | 4.8 | 6.0 | 3.6 | 2.5 | 3.2 |
| ONTARIO | 16.2 | 14.1 | 9.7 | 7.7 | 4.7 | 4.0 | 5.5 | 3.4 | 3.7 | 3.2 |
| ORANGE | 8.5 | 10.8 | 12.1 | 12.3 | 5.3 | 6.2 | 5.2 | 3.2 | 2.0 | 4.7 |
| ORLEANS | 27.5 | 15.5 | 5.3 | 4.6 | 4.7 | 2.4 | 2.4 | 5.3 | 5.2 | 1.7 |
| OSWEGO | 21.4 | 13.9 | 5.8 | 4.3 | 6.5 | 4.3 | 3.6 | 5.2 | 3.2 | 2.0 |
| OTSEGO | 13.9 | 14.6 | 9.6 | 8.9 | 4.9 | 5.2 | 7.2 | 4.9 | 3.7 | 2.5 |
| OUT-OF-STATE | 15.7 | 17.8 | 8.7 | 2.0 | 3.8 | 10.2 | 2.3 | 3.1 | 2.1 | 4.0 |
| PUTNAM | 8.4 | 8.6 | 10.1 | 13.9 | 5.9 | 4.2 | 9.3 | 2.5 | 2.5 | 3.9 |
| QUEENS | 4.8 | 7.3 | 16.6 | 15.0 | 4.1 | 9.8 | 2.7 | 1.9 | 1.2 | 3.7 |
| RENSSELAER | 12.9 | 12.5 | 9.2 | 12.0 | 4.9 | 5.1 | 6.0 | 3.0 | 2.9 | 2.9 |
| RICHMOND | 5.5 | 8.1 | 11.5 | 12.0 | 5.7 | 9.5 | 2.6 | 2.2 | 1.8 | 5.8 |
| ROCKLAND | 5.0 | 8.8 | 16.3 | 15.9 | 4.5 | 6.3 | 5.5 | 1.7 | 1.3 | 4.2 |
| SARATOGA | 10.2 | 11.0 | 11.1 | 13.8 | 5.0 | 4.9 | 5.4 | 2.6 | 2.8 | 3.0 |
| SCHENECTADY | 10.5 | 10.4 | 9.7 | 14.8 | 4.6 | 6.2 | 4.8 | 2.9 | 2.7 | 3.5 |
| SCHOHARIE | 16.8 | 13.8 | 7.1 | 8.1 | 5.6 | 3.8 | 5.7 | 4.8 | 4.2 | 2.4 |
| SCHUYLER | 14.8 | 15.8 | 8.1 | 5.5 | 5.6 | 5.6 | 6.0 | 6.3 | 3.6 | 2.3 |
| SENECA | 18.4 | 17.9 | 7.4 | 5.4 | 4.9 | 5.2 | 3.3 | 5.2 | 3.1 | 2.8 |
| ST LAWRENCE | 20.5 | 15.4 | 8.1 | 4.8 | 5.6 | 2.2 | 3.9 | 5.9 | 4.6 | 1.5 |
| STEUBEN | 17.0 | 15.4 | 6.7 | 4.7 | 6.1 | 5.5 | 4.2 | 6.6 | 4.3 | 2.5 |
| SUFFOLK | 8.9 | 10.3 | 10.8 | 11.2 | 6.9 | 7.0 | 3.2 | 2.9 | 2.0 | 4.7 |
| SULLIVAN | 11.7 | 12.6 | 9.9 | 7.8 | 5.9 | 5.0 | 5.2 | 4.6 | 3.0 | 4.1 |
| TIOGA | 15.3 | 14.3 | 10.7 | 7.6 | 4.6 | 5.8 | 4.8 | 5.2 | 4.2 | 3.1 |
| TOMPKINS | 11.5 | 9.8 | 14.0 | 12.1 | 3.7 | 5.2 | 9.1 | 3.2 | 2.1 | 2.8 |
| ULSTER | 8.9 | 10.0 | 11.1 | 10.4 | 5.3 | 6.2 | 7.9 | 3.8 | 3.1 | 3.8 |
| WARREN | 12.5 | 12.4 | 9.8 | 10.9 | 5.4 | 4.0 | 6.2 | 3.2 | 3.2 | 4.7 |
| WASHINGTON | 15.3 | 14.9 | 8.3 | 8.8 | 5.3 | 4.0 | 5.3 | 4.6 | 3.6 | 3.7 |
| WAYNE | 21.2 | 15.1 | 6.6 | 6.2 | 5.0 | 4.1 | 3.8 | 4.5 | 4.1 | 2.3 |
| WESTCHESTER | 6.0 | 7.2 | 11.1 | 14.7 | 5.7 | 5.0 | 6.5 | 1.6 | 1.6 | 2.9 |
| WYOMING | 22.8 | 17.4 | 6.2 | 3.5 | 6.3 | 2.4 | 2.9 | 5.4 | 4.1 | 1.5 |
| YATES | 19.3 | 16.3 | 7.7 | 5.1 | 5.3 | 3.4 | 4.7 | 5.3 | 4.7 | 2.3 |
import pandas as pd
import seaborn as sns
url='/media/hd2/auto/autoreg.csv.zip'
df=pd.read_csv(url)
sf=df[((df['Record Type']=='VEH'))].groupby(['County','Make']).count()['VIN'].unstack().T
sf=(sf/sf.sum()*100).fillna(0).T
tb=sf[ sf.sum().sort_values(ascending=False).index[:10]]
tb
cm =sns.color_palette("Spectral_r", as_cmap=True)
html=tb.style.background_gradient(cmap=cm,axis=1).render()
with open('/tmp/auto.html', 'w') as f:
f.write(html)