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)
