Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells2535
Missing cells (%)3.6%
Duplicate rows257
Duplicate rows (%)2.6%
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 257 (2.6%) duplicate rowsDuplicates
연료 is highly imbalanced (73.6%)Imbalance
현소유자의출생년도 has 2534 (25.3%) missing valuesMissing

Reproduction

Analysis started2024-03-13 07:48:04.021606
Analysis finished2024-03-13 07:48:05.019738
Duration1 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
201812
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201812
2nd row201812
3rd row201812
4th row201812
5th row201812

Common Values

ValueCountFrequency (%)
201812 10000
100.0%

Length

2024-03-13T16:48:05.071826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T16:48:05.143004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201812 10000
100.0%

사용본거지시읍면동_행정동기준
Text

Distinct424
Distinct (%)4.2%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-13T16:48:05.444811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length13.878888
Min length11

Characters and Unicode

Total characters138775
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row서울특별시 강남구 일원2동
2nd row서울특별시 서대문구 남가좌1동
3rd row서울특별시 송파구 문정2동
4th row서울특별시 영등포구 영등포본동
5th row서울특별시 영등포구 당산2동
ValueCountFrequency (%)
서울특별시 9999
33.3%
강남구 1647
 
5.5%
서초구 839
 
2.8%
강서구 817
 
2.7%
송파구 715
 
2.4%
역삼1동 477
 
1.6%
영등포구 477
 
1.6%
마포구 388
 
1.3%
양천구 382
 
1.3%
노원구 377
 
1.3%
Other values (439) 13879
46.3%
2024-03-13T16:48:05.868392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19998
14.4%
서 12188
 
8.8%
동 11243
 
8.1%
구 10616
 
7.6%
시 10061
 
7.2%
울 9999
 
7.2%
별 9999
 
7.2%
특 9999
 
7.2%
1 3115
 
2.2%
강 3108
 
2.2%
Other values (184) 38449
27.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111439
80.3%
Space Separator 19998
 
14.4%
Decimal Number 7205
 
5.2%
Other Punctuation 133
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 12188
 
10.9%
동 11243
 
10.1%
구 10616
 
9.5%
시 10061
 
9.0%
울 9999
 
9.0%
별 9999
 
9.0%
특 9999
 
9.0%
강 3108
 
2.8%
남 1754
 
1.6%
양 1200
 
1.1%
Other values (172) 31272
28.1%
Decimal Number
ValueCountFrequency (%)
1 3115
43.2%
2 2057
28.5%
3 788
 
10.9%
4 774
 
10.7%
5 147
 
2.0%
6 122
 
1.7%
7 122
 
1.7%
8 62
 
0.9%
9 9
 
0.1%
0 9
 
0.1%
Space Separator
ValueCountFrequency (%)
19998
100.0%
Other Punctuation
ValueCountFrequency (%)
. 133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111439
80.3%
Common 27336
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 12188
 
10.9%
동 11243
 
10.1%
구 10616
 
9.5%
시 10061
 
9.0%
울 9999
 
9.0%
별 9999
 
9.0%
특 9999
 
9.0%
강 3108
 
2.8%
남 1754
 
1.6%
양 1200
 
1.1%
Other values (172) 31272
28.1%
Common
ValueCountFrequency (%)
19998
73.2%
1 3115
 
11.4%
2 2057
 
7.5%
3 788
 
2.9%
4 774
 
2.8%
5 147
 
0.5%
. 133
 
0.5%
6 122
 
0.4%
7 122
 
0.4%
8 62
 
0.2%
Other values (2) 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111439
80.3%
ASCII 27336
 
19.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19998
73.2%
1 3115
 
11.4%
2 2057
 
7.5%
3 788
 
2.9%
4 774
 
2.8%
5 147
 
0.5%
. 133
 
0.5%
6 122
 
0.4%
7 122
 
0.4%
8 62
 
0.2%
Other values (2) 18
 
0.1%
Hangul
ValueCountFrequency (%)
서 12188
 
10.9%
동 11243
 
10.1%
구 10616
 
9.5%
시 10061
 
9.0%
울 9999
 
9.0%
별 9999
 
9.0%
특 9999
 
9.0%
강 3108
 
2.8%
남 1754
 
1.6%
양 1200
 
1.1%
Other values (172) 31272
28.1%

차명
Text

Distinct102
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T16:48:06.119977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length21
Mean length12.5194
Min length2

Characters and Unicode

Total characters125194
Distinct characters139
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.2%

Sample

1st row토요타 RAV4 Hybrid
2nd row렉서스 NX300h
3rd rowINFINITI Q50S Hybrid
4th row렉서스 ES300h
5th row아이오닉 일렉트릭(IONIQ ELEC
ValueCountFrequency (%)
하이브리드 4284
18.3%
렉서스 1924
 
8.2%
es300h 1242
 
5.3%
토요타 1174
 
5.0%
니로 1132
 
4.8%
그랜저 1068
 
4.6%
쏘나타 988
 
4.2%
hyb 805
 
3.4%
hybrid 768
 
3.3%
k5 728
 
3.1%
Other values (135) 9342
39.8%
2024-03-13T16:48:06.491299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13755
 
11.0%
이 6042
 
4.8%
리 5432
 
4.3%
드 5423
 
4.3%
하 5417
 
4.3%
브 5417
 
4.3%
A 4044
 
3.2%
0 3883
 
3.1%
E 3786
 
3.0%
N 3225
 
2.6%
Other values (129) 68770
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53139
42.4%
Uppercase Letter 37700
30.1%
Space Separator 13755
 
11.0%
Lowercase Letter 8797
 
7.0%
Decimal Number 7850
 
6.3%
Open Punctuation 2867
 
2.3%
Close Punctuation 973
 
0.8%
Other Punctuation 101
 
0.1%
Dash Punctuation 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 6042
11.4%
리 5432
 
10.2%
드 5423
 
10.2%
하 5417
 
10.2%
브 5417
 
10.2%
타 2476
 
4.7%
렉 2435
 
4.6%
스 1945
 
3.7%
서 1936
 
3.6%
쏘 1552
 
2.9%
Other values (69) 15064
28.3%
Uppercase Letter
ValueCountFrequency (%)
A 4044
 
10.7%
E 3786
 
10.0%
N 3225
 
8.6%
S 3113
 
8.3%
R 2466
 
6.5%
O 2407
 
6.4%
T 2162
 
5.7%
I 2155
 
5.7%
H 1988
 
5.3%
C 1759
 
4.7%
Other values (15) 10595
28.1%
Lowercase Letter
ValueCountFrequency (%)
h 1947
22.1%
r 1305
14.8%
i 1118
12.7%
y 1006
11.4%
d 718
 
8.2%
b 665
 
7.6%
a 446
 
5.1%
m 352
 
4.0%
u 281
 
3.2%
s 279
 
3.2%
Other values (10) 680
 
7.7%
Decimal Number
ValueCountFrequency (%)
0 3883
49.5%
3 1633
20.8%
5 1136
 
14.5%
4 487
 
6.2%
7 374
 
4.8%
2 191
 
2.4%
9 55
 
0.7%
6 45
 
0.6%
8 27
 
0.3%
1 19
 
0.2%
Space Separator
ValueCountFrequency (%)
13755
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2867
100.0%
Close Punctuation
ValueCountFrequency (%)
) 973
100.0%
Other Punctuation
ValueCountFrequency (%)
. 101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53139
42.4%
Latin 46497
37.1%
Common 25558
20.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 6042
11.4%
리 5432
 
10.2%
드 5423
 
10.2%
하 5417
 
10.2%
브 5417
 
10.2%
타 2476
 
4.7%
렉 2435
 
4.6%
스 1945
 
3.7%
서 1936
 
3.6%
쏘 1552
 
2.9%
Other values (69) 15064
28.3%
Latin
ValueCountFrequency (%)
A 4044
 
8.7%
E 3786
 
8.1%
N 3225
 
6.9%
S 3113
 
6.7%
R 2466
 
5.3%
O 2407
 
5.2%
T 2162
 
4.6%
I 2155
 
4.6%
H 1988
 
4.3%
h 1947
 
4.2%
Other values (35) 19204
41.3%
Common
ValueCountFrequency (%)
13755
53.8%
0 3883
 
15.2%
( 2867
 
11.2%
3 1633
 
6.4%
5 1136
 
4.4%
) 973
 
3.8%
4 487
 
1.9%
7 374
 
1.5%
2 191
 
0.7%
. 101
 
0.4%
Other values (5) 158
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72055
57.6%
Hangul 53139
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13755
19.1%
A 4044
 
5.6%
0 3883
 
5.4%
E 3786
 
5.3%
N 3225
 
4.5%
S 3113
 
4.3%
( 2867
 
4.0%
R 2466
 
3.4%
O 2407
 
3.3%
T 2162
 
3.0%
Other values (50) 30347
42.1%
Hangul
ValueCountFrequency (%)
이 6042
11.4%
리 5432
 
10.2%
드 5423
 
10.2%
하 5417
 
10.2%
브 5417
 
10.2%
타 2476
 
4.7%
렉 2435
 
4.6%
스 1945
 
3.7%
서 1936
 
3.6%
쏘 1552
 
2.9%
Other values (69) 15064
28.3%

연료
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
하이브리드(휘발유+전기)
8639 
전기
1109 
하이브리드(LPG+전기)
 
228
수소
 
13
하이브리드(CNG+전기)
 
8

Length

Max length13
Median length13
Mean length11.7655
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 row전기

Common Values

ValueCountFrequency (%)
하이브리드(휘발유+전기) 8639
86.4%
전기 1109
 
11.1%
하이브리드(LPG+전기) 228
 
2.3%
수소 13
 
0.1%
하이브리드(CNG+전기) 8
 
0.1%
하이브리드(경유+전기) 3
 
< 0.1%

Length

2024-03-13T16:48:06.629629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T16:48:06.718667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하이브리드(휘발유+전기 8639
86.4%
전기 1109
 
11.1%
하이브리드(lpg+전기 228
 
2.3%
수소 13
 
0.1%
하이브리드(cng+전기 8
 
0.1%
하이브리드(경유+전기 3
 
< 0.1%

최초등록일
Real number (ℝ)

Distinct2032
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20157553
Minimum20060713
Maximum20181231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T16:48:06.826914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060713
5-th percentile20110824
Q120141114
median20161220
Q320180129
95-th percentile20181116
Maximum20181231
Range120518
Interquartile range (IQR)39015.25

Descriptive statistics

Standard deviation23612.425
Coefficient of variation (CV)0.0011713934
Kurtosis0.4758872
Mean20157553
Median Absolute Deviation (MAD)19082
Skewness-1.0715529
Sum2.0157553 × 1011
Variance5.575466 × 108
MonotonicityNot monotonic
2024-03-13T16:48:06.945803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20181130 41
 
0.4%
20170102 35
 
0.4%
20181213 31
 
0.3%
20170928 29
 
0.3%
20170915 28
 
0.3%
20161031 27
 
0.3%
20170818 27
 
0.3%
20181031 27
 
0.3%
20180619 26
 
0.3%
20170630 26
 
0.3%
Other values (2022) 9703
97.0%
ValueCountFrequency (%)
20060713 1
< 0.1%
20060831 1
< 0.1%
20060922 1
< 0.1%
20060927 1
< 0.1%
20061018 1
< 0.1%
20061020 1
< 0.1%
20061121 1
< 0.1%
20061226 2
< 0.1%
20070416 1
< 0.1%
20070427 1
< 0.1%
ValueCountFrequency (%)
20181231 22
0.2%
20181228 19
0.2%
20181227 16
0.2%
20181226 8
 
0.1%
20181224 20
0.2%
20181221 23
0.2%
20181220 21
0.2%
20181219 15
0.1%
20181218 14
0.1%
20181217 9
 
0.1%

현소유자의출생년도
Real number (ℝ)

MISSING 

Distinct75
Distinct (%)1.0%
Missing2534
Missing (%)25.3%
Infinite0
Infinite (%)0.0%
Mean1971.4354
Minimum1911
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T16:48:07.062931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1911
5-th percentile1951
Q11963
median1973
Q31981
95-th percentile1988
Maximum2012
Range101
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.609137
Coefficient of variation (CV)0.005888672
Kurtosis-0.24893487
Mean1971.4354
Median Absolute Deviation (MAD)9
Skewness-0.42297546
Sum14718737
Variance134.77205
MonotonicityNot monotonic
2024-03-13T16:48:07.184683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1980 266
 
2.7%
1982 259
 
2.6%
1983 244
 
2.4%
1973 242
 
2.4%
1977 240
 
2.4%
1971 240
 
2.4%
1970 239
 
2.4%
1974 236
 
2.4%
1981 235
 
2.4%
1975 229
 
2.3%
Other values (65) 5036
50.4%
(Missing) 2534
25.3%
ValueCountFrequency (%)
1911 1
 
< 0.1%
1924 1
 
< 0.1%
1930 1
 
< 0.1%
1932 1
 
< 0.1%
1933 1
 
< 0.1%
1934 2
 
< 0.1%
1935 2
 
< 0.1%
1936 1
 
< 0.1%
1937 5
0.1%
1938 8
0.1%
ValueCountFrequency (%)
2012 1
 
< 0.1%
2011 1
 
< 0.1%
2007 3
 
< 0.1%
2005 1
 
< 0.1%
2000 1
 
< 0.1%
1999 1
 
< 0.1%
1998 1
 
< 0.1%
1996 3
 
< 0.1%
1995 4
 
< 0.1%
1994 15
0.1%

제원관리번호
Text

Distinct400
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T16:48:07.358107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters170000
Distinct characters21
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

Unique68 ?
Unique (%)0.7%

Sample

1st row01020005000041316
2nd row01020005500011315
3rd row02820003100041317
4th row01020004500041316
5th rowA0810010500311217
ValueCountFrequency (%)
a0110006100081217 280
 
2.8%
a0810010801171318 259
 
2.6%
a0810008700801315 236
 
2.4%
a0810010800451317 235
 
2.4%
01020004500041316 206
 
2.1%
01020004500061317 199
 
2.0%
a0110005500791217 183
 
1.8%
01020004500031315 175
 
1.8%
a0810008300361211 166
 
1.7%
a0810008700611314 157
 
1.6%
Other values (390) 7904
79.0%
2024-03-13T16:48:07.630886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 70108
41.2%
1 39071
23.0%
2 13052
 
7.7%
8 9709
 
5.7%
3 7514
 
4.4%
A 6273
 
3.7%
5 6113
 
3.6%
6 5824
 
3.4%
7 5599
 
3.3%
4 4420
 
2.6%
Other values (11) 2317
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 163691
96.3%
Uppercase Letter 6309
 
3.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6273
99.4%
G 7
 
0.1%
S 6
 
0.1%
B 5
 
0.1%
C 4
 
0.1%
D 4
 
0.1%
M 3
 
< 0.1%
N 3
 
< 0.1%
P 2
 
< 0.1%
W 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 70108
42.8%
1 39071
23.9%
2 13052
 
8.0%
8 9709
 
5.9%
3 7514
 
4.6%
5 6113
 
3.7%
6 5824
 
3.6%
7 5599
 
3.4%
4 4420
 
2.7%
9 2281
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 163691
96.3%
Latin 6309
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6273
99.4%
G 7
 
0.1%
S 6
 
0.1%
B 5
 
0.1%
C 4
 
0.1%
D 4
 
0.1%
M 3
 
< 0.1%
N 3
 
< 0.1%
P 2
 
< 0.1%
W 1
 
< 0.1%
Common
ValueCountFrequency (%)
0 70108
42.8%
1 39071
23.9%
2 13052
 
8.0%
8 9709
 
5.9%
3 7514
 
4.6%
5 6113
 
3.7%
6 5824
 
3.6%
7 5599
 
3.4%
4 4420
 
2.7%
9 2281
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70108
41.2%
1 39071
23.0%
2 13052
 
7.7%
8 9709
 
5.7%
3 7514
 
4.4%
A 6273
 
3.7%
5 6113
 
3.6%
6 5824
 
3.4%
7 5599
 
3.3%
4 4420
 
2.6%
Other values (11) 2317
 
1.4%

Interactions

2024-03-13T16:48:04.584144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:48:04.421191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:48:04.666690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:48:04.505237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T16:48:07.713745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료최초등록일현소유자의출생년도
연료1.0000.5130.014
최초등록일0.5131.0000.078
현소유자의출생년도0.0140.0781.000
2024-03-13T16:48:07.787970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초등록일현소유자의출생년도연료
최초등록일1.0000.0600.301
현소유자의출생년도0.0601.0000.000
연료0.3010.0001.000

Missing values

2024-03-13T16:48:04.760667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T16:48:04.866544image/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.
2024-03-13T16:48:04.975077image/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
16159201812서울특별시 강남구 일원2동토요타 RAV4 Hybrid하이브리드(휘발유+전기)20160830195601020005000041316
64287201812서울특별시 서대문구 남가좌1동렉서스 NX300h하이브리드(휘발유+전기)20160811199101020005500011315
13644201812서울특별시 송파구 문정2동INFINITI Q50S Hybrid하이브리드(휘발유+전기)20171016197802820003100041317
20068201812서울특별시 영등포구 영등포본동렉서스 ES300h하이브리드(휘발유+전기)20170324197401020004500041316
12977201812서울특별시 영등포구 당산2동아이오닉 일렉트릭(IONIQ ELEC전기201709111985A0810010500311217
23653201812서울특별시 서초구 잠원동렉서스 NX300h하이브리드(휘발유+전기)20180920198101020005500051318
70988201812서울특별시 송파구 풍납2동쏘나타 (SONATA) 하이브리드하이브리드(휘발유+전기)201202101959A0810008300351211
18025201812서울특별시 강북구 삼각산동쏘나타(SONATA) 하이브리드하이브리드(휘발유+전기)201402211960A0810008301131213
29319201812서울특별시 노원구 공릉1동쏘나타(SONATA) 하이브리드하이브리드(휘발유+전기)201307011966A0810008300891212
70323201812서울특별시 구로구 구로5동토요타 Camry Hybrid하이브리드(휘발유+전기)20180928197101020006500001317
기준년월사용본거지시읍면동_행정동기준차명연료최초등록일현소유자의출생년도제원관리번호
71985201812서울특별시 노원구 공릉1동토요타 PRIUS하이브리드(휘발유+전기)20101122<NA>01020002500001209
29993201812서울특별시 송파구 잠실2동렉서스 ES300h하이브리드(휘발유+전기)20150925197801020004500031315
56387201812서울특별시 마포구 서교동렉서스 NX300h하이브리드(휘발유+전기)20171120196301020005500041317
60538201812서울특별시 은평구 신사1동니로 하이브리드하이브리드(휘발유+전기)201705241960A0110006100071217
18817201812서울특별시 관악구 신림동K5 하이브리드하이브리드(휘발유+전기)201710171982A0110005802851217
17861201812서울특별시 영등포구 여의동렉서스 ES300h하이브리드(휘발유+전기)20140425<NA>01020004500011313
62834201812서울특별시 용산구 이촌1동렉서스 ES300h하이브리드(휘발유+전기)20170913195601020004500051317
28350201812서울특별시 강남구 대치4동CHEVROLET BOLT EV전기20170511<NA>02620000700001216
35272201812서울특별시 강남구 대치2동쏘울 EV전기20180619<NA>A0110005500791217
13355201812서울특별시 관악구 난곡동아이오닉 하이브리드(IONIQ HY하이브리드(휘발유+전기)201704261983A0810010500171216

Duplicate rows

Most frequently occurring

기준년월사용본거지시읍면동_행정동기준차명연료최초등록일현소유자의출생년도제원관리번호# duplicates
222201812서울특별시 서초구 양재2동K5 하이브리드하이브리드(휘발유+전기)20160112<NA>A011000580154121521
223201812서울특별시 서초구 양재2동K5 하이브리드하이브리드(휘발유+전기)20160113<NA>A011000580154121519
29201812서울특별시 강남구 대치4동쏘울 EV전기20171204<NA>A011000550079121717
24201812서울특별시 강남구 대치4동쏘울 EV전기20170915<NA>A011000550079121716
28201812서울특별시 강남구 대치4동쏘울 EV전기20171122<NA>A011000550079121714
207201812서울특별시 서초구 양재1동쏘나타 하이브리드(SONATA HYB하이브리드(휘발유+전기)20141224<NA>A081000980018121413
15201812서울특별시 강남구 대치2동쏘울 EV전기20180718<NA>A011000550079121712
27201812서울특별시 강남구 대치4동쏘울 EV전기20171121<NA>A011000550079121712
41201812서울특별시 강남구 대치4동아이오닉 일렉트릭(IONIQ ELEC전기20170818<NA>A081001050029121712
208201812서울특별시 서초구 양재1동쏘나타 하이브리드(SONATA HYB하이브리드(휘발유+전기)20141226<NA>A081000980018121412