Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells2496
Missing cells (%)3.6%
Duplicate rows224
Duplicate rows (%)2.2%
Total size in memory654.3 KiB
Average record size in memory67.0 B

Variable types

Categorical2
Text3
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21245/F/1/datasetView.do

Alerts

기준년월 has constant value ""Constant
Dataset has 224 (2.2%) duplicate rowsDuplicates
연료 is highly imbalanced (70.0%)Imbalance
현소유자의출생년도 has 2494 (24.9%) missing valuesMissing

Reproduction

Analysis started2024-03-13 07:47:58.690928
Analysis finished2024-03-13 07:47:59.737643
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
201912
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201912
2nd row201912
3rd row201912
4th row201912
5th row201912

Common Values

ValueCountFrequency (%)
201912 10000
100.0%

Length

2024-03-13T16:47:59.789580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T16:47:59.866288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201912 10000
100.0%

사용본거지시읍면동_행정동기준
Text

Distinct421
Distinct (%)4.2%
Missing2
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-13T16:48:00.154628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length13.875875
Min length11

Characters and Unicode

Total characters138731
Distinct characters194
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row서울특별시 강남구 역삼1동
2nd row서울특별시 관악구 남현동
3rd row서울특별시 양천구 신정3동
4th row서울특별시 강서구 염창동
5th row서울특별시 강동구 고덕1동
ValueCountFrequency (%)
서울특별시 9998
33.3%
강남구 1673
 
5.6%
강서구 867
 
2.9%
송파구 756
 
2.5%
서초구 706
 
2.4%
영등포구 478
 
1.6%
역삼1동 448
 
1.5%
가양1동 412
 
1.4%
강동구 412
 
1.4%
노원구 385
 
1.3%
Other values (436) 13859
46.2%
2024-03-13T16:48:00.561089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19996
14.4%
서 12125
 
8.7%
동 11271
 
8.1%
구 10605
 
7.6%
시 10068
 
7.3%
울 9998
 
7.2%
특 9998
 
7.2%
별 9998
 
7.2%
강 3263
 
2.4%
1 3107
 
2.2%
Other values (184) 38302
27.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111414
80.3%
Space Separator 19996
 
14.4%
Decimal Number 7159
 
5.2%
Other Punctuation 162
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 12125
 
10.9%
동 11271
 
10.1%
구 10605
 
9.5%
시 10068
 
9.0%
울 9998
 
9.0%
특 9998
 
9.0%
별 9998
 
9.0%
강 3263
 
2.9%
남 1782
 
1.6%
대 1197
 
1.1%
Other values (172) 31109
27.9%
Decimal Number
ValueCountFrequency (%)
1 3107
43.4%
2 2065
28.8%
3 780
 
10.9%
4 771
 
10.8%
5 149
 
2.1%
6 118
 
1.6%
7 109
 
1.5%
8 36
 
0.5%
9 16
 
0.2%
0 8
 
0.1%
Space Separator
ValueCountFrequency (%)
19996
100.0%
Other Punctuation
ValueCountFrequency (%)
. 162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111414
80.3%
Common 27317
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 12125
 
10.9%
동 11271
 
10.1%
구 10605
 
9.5%
시 10068
 
9.0%
울 9998
 
9.0%
특 9998
 
9.0%
별 9998
 
9.0%
강 3263
 
2.9%
남 1782
 
1.6%
대 1197
 
1.1%
Other values (172) 31109
27.9%
Common
ValueCountFrequency (%)
19996
73.2%
1 3107
 
11.4%
2 2065
 
7.6%
3 780
 
2.9%
4 771
 
2.8%
. 162
 
0.6%
5 149
 
0.5%
6 118
 
0.4%
7 109
 
0.4%
8 36
 
0.1%
Other values (2) 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111414
80.3%
ASCII 27317
 
19.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19996
73.2%
1 3107
 
11.4%
2 2065
 
7.6%
3 780
 
2.9%
4 771
 
2.8%
. 162
 
0.6%
5 149
 
0.5%
6 118
 
0.4%
7 109
 
0.4%
8 36
 
0.1%
Other values (2) 24
 
0.1%
Hangul
ValueCountFrequency (%)
서 12125
 
10.9%
동 11271
 
10.1%
구 10605
 
9.5%
시 10068
 
9.0%
울 9998
 
9.0%
특 9998
 
9.0%
별 9998
 
9.0%
강 3263
 
2.9%
남 1782
 
1.6%
대 1197
 
1.1%
Other values (172) 31109
27.9%

차명
Text

Distinct143
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T16:48:00.826785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length12.3661
Min length2

Characters and Unicode

Total characters123661
Distinct characters162
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.3%

Sample

1st row렉서스 ES300h
2nd row토요타 Camry Hybrid
3rd row그랜저(GRANDEUR) 하이브리드
4th rowK7 하이브리드
5th row쏘나타(SONATA) 하이브리드
ValueCountFrequency (%)
하이브리드 4244
17.9%
렉서스 1804
 
7.6%
그랜저 1337
 
5.6%
니로 1223
 
5.2%
es300h 1136
 
4.8%
토요타 1071
 
4.5%
hybrid 808
 
3.4%
쏘나타 783
 
3.3%
아이오닉 571
 
2.4%
hyb 563
 
2.4%
Other values (180) 10195
43.0%
2024-03-13T16:48:01.222136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13949
 
11.3%
이 5805
 
4.7%
리 5219
 
4.2%
드 5210
 
4.2%
브 5196
 
4.2%
하 5196
 
4.2%
E 3768
 
3.0%
0 3708
 
3.0%
A 3546
 
2.9%
N 2907
 
2.4%
Other values (152) 69157
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52024
42.1%
Uppercase Letter 36226
29.3%
Space Separator 13949
 
11.3%
Lowercase Letter 10080
 
8.2%
Decimal Number 7860
 
6.4%
Open Punctuation 2570
 
2.1%
Close Punctuation 831
 
0.7%
Other Punctuation 98
 
0.1%
Dash Punctuation 21
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 5805
 
11.2%
리 5219
 
10.0%
드 5210
 
10.0%
브 5196
 
10.0%
하 5196
 
10.0%
렉 2380
 
4.6%
타 2121
 
4.1%
스 1834
 
3.5%
서 1807
 
3.5%
그 1555
 
3.0%
Other values (88) 15701
30.2%
Uppercase Letter
ValueCountFrequency (%)
E 3768
 
10.4%
A 3546
 
9.8%
N 2907
 
8.0%
S 2694
 
7.4%
R 2348
 
6.5%
O 2331
 
6.4%
I 2158
 
6.0%
T 1985
 
5.5%
C 1944
 
5.4%
H 1930
 
5.3%
Other values (15) 10615
29.3%
Lowercase Letter
ValueCountFrequency (%)
h 1818
18.0%
r 1304
12.9%
i 1131
11.2%
y 980
9.7%
d 841
8.3%
b 661
 
6.6%
a 624
 
6.2%
e 453
 
4.5%
m 348
 
3.5%
o 315
 
3.1%
Other values (13) 1605
15.9%
Decimal Number
ValueCountFrequency (%)
0 3708
47.2%
3 1697
21.6%
5 1063
 
13.5%
4 537
 
6.8%
7 441
 
5.6%
2 273
 
3.5%
1 51
 
0.6%
8 32
 
0.4%
9 30
 
0.4%
6 28
 
0.4%
Space Separator
ValueCountFrequency (%)
13949
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2570
100.0%
Close Punctuation
ValueCountFrequency (%)
) 831
100.0%
Other Punctuation
ValueCountFrequency (%)
. 98
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52024
42.1%
Latin 46308
37.4%
Common 25329
20.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 5805
 
11.2%
리 5219
 
10.0%
드 5210
 
10.0%
브 5196
 
10.0%
하 5196
 
10.0%
렉 2380
 
4.6%
타 2121
 
4.1%
스 1834
 
3.5%
서 1807
 
3.5%
그 1555
 
3.0%
Other values (88) 15701
30.2%
Latin
ValueCountFrequency (%)
E 3768
 
8.1%
A 3546
 
7.7%
N 2907
 
6.3%
S 2694
 
5.8%
R 2348
 
5.1%
O 2331
 
5.0%
I 2158
 
4.7%
T 1985
 
4.3%
C 1944
 
4.2%
H 1930
 
4.2%
Other values (39) 20697
44.7%
Common
ValueCountFrequency (%)
13949
55.1%
0 3708
 
14.6%
( 2570
 
10.1%
3 1697
 
6.7%
5 1063
 
4.2%
) 831
 
3.3%
4 537
 
2.1%
7 441
 
1.7%
2 273
 
1.1%
. 98
 
0.4%
Other values (5) 162
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71635
57.9%
Hangul 52024
42.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13949
19.5%
E 3768
 
5.3%
0 3708
 
5.2%
A 3546
 
5.0%
N 2907
 
4.1%
S 2694
 
3.8%
( 2570
 
3.6%
R 2348
 
3.3%
O 2331
 
3.3%
I 2158
 
3.0%
Other values (53) 31656
44.2%
Hangul
ValueCountFrequency (%)
이 5805
 
11.2%
리 5219
 
10.0%
드 5210
 
10.0%
브 5196
 
10.0%
하 5196
 
10.0%
렉 2380
 
4.6%
타 2121
 
4.1%
스 1834
 
3.5%
서 1807
 
3.5%
그 1555
 
3.0%
Other values (88) 15701
30.2%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
100.0%

연료
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
하이브리드(휘발유+전기)
8338 
전기
1417 
하이브리드(LPG+전기)
 
177
수소
 
58
하이브리드(경유+전기)
 
7

Length

Max length13
Median length13
Mean length11.3768
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row하이브리드(휘발유+전기)
2nd row하이브리드(휘발유+전기)
3rd row하이브리드(휘발유+전기)
4th row하이브리드(휘발유+전기)
5th row하이브리드(휘발유+전기)

Common Values

ValueCountFrequency (%)
하이브리드(휘발유+전기) 8338
83.4%
전기 1417
 
14.2%
하이브리드(LPG+전기) 177
 
1.8%
수소 58
 
0.6%
하이브리드(경유+전기) 7
 
0.1%
하이브리드(CNG+전기) 3
 
< 0.1%

Length

2024-03-13T16:48:01.332430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T16:48:01.417304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하이브리드(휘발유+전기 8338
83.4%
전기 1417
 
14.2%
하이브리드(lpg+전기 177
 
1.8%
수소 58
 
0.6%
하이브리드(경유+전기 7
 
0.1%
하이브리드(cng+전기 3
 
< 0.1%

최초등록일
Real number (ℝ)

Distinct2139
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20165713
Minimum20051212
Maximum20191231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T16:48:01.528969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20051212
5-th percentile20120116
Q120151023
median20170928
Q320181211
95-th percentile20191113
Maximum20191231
Range140019
Interquartile range (IQR)30188

Descriptive statistics

Standard deviation24884.203
Coefficient of variation (CV)0.0012339858
Kurtosis0.75462783
Mean20165713
Median Absolute Deviation (MAD)10816
Skewness-1.1517195
Sum2.0165713 × 1011
Variance6.1922356 × 108
MonotonicityNot monotonic
2024-03-13T16:48:01.703765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191231 31
 
0.3%
20181130 30
 
0.3%
20181031 29
 
0.3%
20170629 28
 
0.3%
20191127 28
 
0.3%
20181129 27
 
0.3%
20191205 26
 
0.3%
20170616 25
 
0.2%
20190329 23
 
0.2%
20170921 23
 
0.2%
Other values (2129) 9730
97.3%
ValueCountFrequency (%)
20051212 1
< 0.1%
20051222 1
< 0.1%
20051227 1
< 0.1%
20051228 1
< 0.1%
20060602 1
< 0.1%
20061012 1
< 0.1%
20061018 1
< 0.1%
20070118 1
< 0.1%
20070302 1
< 0.1%
20070702 1
< 0.1%
ValueCountFrequency (%)
20191231 31
0.3%
20191230 18
0.2%
20191227 14
0.1%
20191226 11
 
0.1%
20191224 19
0.2%
20191223 12
 
0.1%
20191220 16
0.2%
20191219 13
0.1%
20191218 5
 
0.1%
20191217 10
 
0.1%

현소유자의출생년도
Real number (ℝ)

MISSING 

Distinct75
Distinct (%)1.0%
Missing2494
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean1971.7597
Minimum1911
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T16:48:01.839665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1911
5-th percentile1952
Q11964
median1973
Q31981
95-th percentile1989
Maximum2011
Range100
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.590081
Coefficient of variation (CV)0.0058780393
Kurtosis-0.24918337
Mean1971.7597
Median Absolute Deviation (MAD)9
Skewness-0.40053408
Sum14800028
Variance134.32997
MonotonicityNot monotonic
2024-03-13T16:48:01.948239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 264
 
2.6%
1982 253
 
2.5%
1971 246
 
2.5%
1973 245
 
2.5%
1972 237
 
2.4%
1974 235
 
2.4%
1983 233
 
2.3%
1975 228
 
2.3%
1979 225
 
2.2%
1980 222
 
2.2%
Other values (65) 5118
51.2%
(Missing) 2494
24.9%
ValueCountFrequency (%)
1911 1
 
< 0.1%
1929 1
 
< 0.1%
1930 1
 
< 0.1%
1931 2
 
< 0.1%
1932 1
 
< 0.1%
1933 1
 
< 0.1%
1934 2
 
< 0.1%
1935 2
 
< 0.1%
1936 6
0.1%
1937 5
0.1%
ValueCountFrequency (%)
2011 2
 
< 0.1%
2002 1
 
< 0.1%
2001 1
 
< 0.1%
1999 1
 
< 0.1%
1998 1
 
< 0.1%
1997 6
 
0.1%
1996 3
 
< 0.1%
1995 4
 
< 0.1%
1994 17
0.2%
1993 32
0.3%

제원관리번호
Text

Distinct517
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T16:48:02.366785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters170000
Distinct characters29
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)0.9%

Sample

1st row01020004500051317
2nd row01020006500001317
3rd rowA0810008700611314
4th rowA0110006000601317
5th rowA0810008300901212
ValueCountFrequency (%)
a0810010801171318 532
 
5.3%
01020006800001318 317
 
3.2%
a0110006100081217 202
 
2.0%
a0810010800451317 196
 
2.0%
01020004500041316 157
 
1.6%
a0810008700801315 152
 
1.5%
01020004500061317 147
 
1.5%
a0810010900271218 141
 
1.4%
a0810010500291217 141
 
1.4%
a0810010800691317 139
 
1.4%
Other values (507) 7876
78.8%
2024-03-13T16:48:02.663979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 69759
41.0%
1 40005
23.5%
2 13065
 
7.7%
8 10352
 
6.1%
3 7284
 
4.3%
A 6203
 
3.6%
6 5966
 
3.5%
5 5211
 
3.1%
7 5185
 
3.0%
4 3722
 
2.2%
Other values (19) 3248
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 163657
96.3%
Uppercase Letter 6343
 
3.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6203
97.8%
D 35
 
0.6%
C 21
 
0.3%
R 19
 
0.3%
G 19
 
0.3%
J 14
 
0.2%
B 6
 
0.1%
M 6
 
0.1%
S 5
 
0.1%
T 3
 
< 0.1%
Other values (9) 12
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 69759
42.6%
1 40005
24.4%
2 13065
 
8.0%
8 10352
 
6.3%
3 7284
 
4.5%
6 5966
 
3.6%
5 5211
 
3.2%
7 5185
 
3.2%
4 3722
 
2.3%
9 3108
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 163657
96.3%
Latin 6343
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6203
97.8%
D 35
 
0.6%
C 21
 
0.3%
R 19
 
0.3%
G 19
 
0.3%
J 14
 
0.2%
B 6
 
0.1%
M 6
 
0.1%
S 5
 
0.1%
T 3
 
< 0.1%
Other values (9) 12
 
0.2%
Common
ValueCountFrequency (%)
0 69759
42.6%
1 40005
24.4%
2 13065
 
8.0%
8 10352
 
6.3%
3 7284
 
4.5%
6 5966
 
3.6%
5 5211
 
3.2%
7 5185
 
3.2%
4 3722
 
2.3%
9 3108
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 69759
41.0%
1 40005
23.5%
2 13065
 
7.7%
8 10352
 
6.1%
3 7284
 
4.3%
A 6203
 
3.6%
6 5966
 
3.5%
5 5211
 
3.1%
7 5185
 
3.0%
4 3722
 
2.2%
Other values (19) 3248
 
1.9%

Interactions

2024-03-13T16:47:59.226353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:47:59.055459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:47:59.312820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:47:59.133806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T16:48:02.755399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료최초등록일현소유자의출생년도
연료1.0000.4900.032
최초등록일0.4901.0000.056
현소유자의출생년도0.0320.0561.000
2024-03-13T16:48:02.843024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초등록일현소유자의출생년도연료
최초등록일1.0000.0450.284
현소유자의출생년도0.0451.0000.014
연료0.2840.0141.000

Missing values

2024-03-13T16:47:59.461095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T16:47:59.599905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-13T16:47:59.692404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준년월사용본거지시읍면동_행정동기준차명연료최초등록일현소유자의출생년도제원관리번호
33100201912서울특별시 강남구 역삼1동렉서스 ES300h하이브리드(휘발유+전기)20170822<NA>01020004500051317
16020201912서울특별시 관악구 남현동토요타 Camry Hybrid하이브리드(휘발유+전기)20180131198401020006500001317
83248201912서울특별시 양천구 신정3동그랜저(GRANDEUR) 하이브리드하이브리드(휘발유+전기)201404111970A0810008700611314
15506201912서울특별시 강서구 염창동K7 하이브리드하이브리드(휘발유+전기)201810231970A0110006000601317
17853201912서울특별시 강동구 고덕1동쏘나타(SONATA) 하이브리드하이브리드(휘발유+전기)201304221960A0810008300901212
72227201912서울특별시 광진구 자양1동쏘나타 하이브리드(SONATA HYB하이브리드(휘발유+전기)201702021970A0810009800751216
78545201912서울특별시 송파구 잠실2동그랜저(GRANDEUR) 하이브리드하이브리드(휘발유+전기)201407171960A0810008700611314
728201912서울특별시 강서구 가양1동그랜저 하이브리드하이브리드(휘발유+전기)20170511<NA>A0810010800451317
12496201912서울특별시 강남구 도곡2동렉서스 ES300h하이브리드(휘발유+전기)20150513194001020004500021314
17009201912서울특별시 광진구 중곡2동K5 하이브리드하이브리드(휘발유+전기)201806211985A0110005803231218
기준년월사용본거지시읍면동_행정동기준차명연료최초등록일현소유자의출생년도제원관리번호
52258201912서울특별시 마포구 아현동렉서스 ES300h하이브리드(휘발유+전기)20160329198601020004500031315
28055201912서울특별시 강동구 명일2동쏘나타(SONATA) 하이브리드하이브리드(휘발유+전기)201409221967A0810008301341214
62992201912서울특별시 동작구 노량진1동토요타 Camry Hybrid하이브리드(휘발유+전기)20180228197801020006500001317
41435201912서울특별시 용산구 원효로1동니로 하이브리드하이브리드(휘발유+전기)201707241982A0110006100111217
38368201912서울특별시 서대문구 연희동니로 하이브리드하이브리드(휘발유+전기)20170303<NA>A0110006100041216
4654201912서울특별시 서대문구 남가좌1동렉서스 NX300h하이브리드(휘발유+전기)20160912197801020005500011315
22947201912서울특별시 강남구 역삼1동렉서스 ES300h하이브리드(휘발유+전기)20190624<NA>01020006800001318
44686201912서울특별시 성동구 마장동니로 하이브리드하이브리드(휘발유+전기)201903291963A0110006100301219
70739201912서울특별시 강서구 화곡1동베르나 하이브리드(VERNA)하이브리드(휘발유+전기)200804181972A0810006702751107
11570201912서울특별시 강남구 대치4동코나 일렉트릭 (KONA ELECTRIC전기20181102<NA>A0810010900291218

Duplicate rows

Most frequently occurring

기준년월사용본거지시읍면동_행정동기준차명연료최초등록일현소유자의출생년도제원관리번호# duplicates
33201912서울특별시 강남구 대치4동아이오닉 일렉트릭(IONIQ ELEC전기20170616<NA>A081001050029121716
183201912서울특별시 서초구 양재2동K5 하이브리드하이브리드(휘발유+전기)20160112<NA>A011000580154121516
30201912서울특별시 강남구 대치4동쏘울 EV전기20171206<NA>A011000550079121714
29201912서울특별시 강남구 대치4동쏘울 EV전기20171205<NA>A011000550079121712
34201912서울특별시 강남구 대치4동아이오닉 일렉트릭(IONIQ ELEC전기20170629<NA>A081001050029121712
11201912서울특별시 강남구 대치2동쏘울 EV전기20180718<NA>A011000550079121711
28201912서울특별시 강남구 대치4동쏘울 EV전기20171204<NA>A011000550079121711
42201912서울특별시 강남구 대치4동아이오닉 일렉트릭(IONIQ ELEC전기20170921<NA>A081001050029121711
184201912서울특별시 서초구 양재2동K5 하이브리드하이브리드(휘발유+전기)20160113<NA>A011000580154121511
37201912서울특별시 강남구 대치4동아이오닉 일렉트릭(IONIQ ELEC전기20170718<NA>A081001050029121710