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)