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)