Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells2624
Missing cells (%)3.7%
Duplicate rows271
Duplicate rows (%)2.7%
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 271 (2.7%) duplicate rowsDuplicates
연료 is highly imbalanced (59.4%)Imbalance
현소유자의출생년도 has 2623 (26.2%) missing valuesMissing

Reproduction

Analysis started2024-03-13 07:47:42.995961
Analysis finished2024-03-13 07:47:44.242515
Duration1.25 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
202202
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202202
2nd row202202
3rd row202202
4th row202202
5th row202202

Common Values

ValueCountFrequency (%)
202202 10000
100.0%

Length

2024-03-13T16:47:44.295654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T16:47:44.398604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202202 10000
100.0%

사용본거지시읍면동_행정동기준
Text

Distinct427
Distinct (%)4.3%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-13T16:47:44.692025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length13.893089
Min length11

Characters and Unicode

Total characters138917
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

Unique5 ?
Unique (%)0.1%

Sample

1st row서울특별시 강서구 가양1동
2nd row서울특별시 서초구 잠원동
3rd row서울특별시 강남구 수서동
4th row서울특별시 송파구 가락1동
5th row서울특별시 서초구 양재1동
ValueCountFrequency (%)
서울특별시 9999
33.3%
강남구 2052
 
6.8%
서초구 1125
 
3.8%
송파구 922
 
3.1%
마포구 635
 
2.1%
영등포구 516
 
1.7%
성동구 513
 
1.7%
대치1동 490
 
1.6%
양천구 448
 
1.5%
성북구 419
 
1.4%
Other values (442) 12878
42.9%
2024-03-13T16:47:45.130860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19998
14.4%
서 12271
 
8.8%
동 11194
 
8.1%
구 10452
 
7.5%
시 10035
 
7.2%
별 9999
 
7.2%
울 9999
 
7.2%
특 9999
 
7.2%
1 3080
 
2.2%
강 2784
 
2.0%
Other values (184) 39106
28.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111603
80.3%
Space Separator 19998
 
14.4%
Decimal Number 7170
 
5.2%
Other Punctuation 146
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 12271
 
11.0%
동 11194
 
10.0%
구 10452
 
9.4%
시 10035
 
9.0%
별 9999
 
9.0%
울 9999
 
9.0%
특 9999
 
9.0%
강 2784
 
2.5%
남 2185
 
2.0%
대 1574
 
1.4%
Other values (172) 31111
27.9%
Decimal Number
ValueCountFrequency (%)
1 3080
43.0%
2 2234
31.2%
4 780
 
10.9%
3 678
 
9.5%
5 140
 
2.0%
6 112
 
1.6%
7 109
 
1.5%
8 21
 
0.3%
0 10
 
0.1%
9 6
 
0.1%
Space Separator
ValueCountFrequency (%)
19998
100.0%
Other Punctuation
ValueCountFrequency (%)
. 146
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111603
80.3%
Common 27314
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 12271
 
11.0%
동 11194
 
10.0%
구 10452
 
9.4%
시 10035
 
9.0%
별 9999
 
9.0%
울 9999
 
9.0%
특 9999
 
9.0%
강 2784
 
2.5%
남 2185
 
2.0%
대 1574
 
1.4%
Other values (172) 31111
27.9%
Common
ValueCountFrequency (%)
19998
73.2%
1 3080
 
11.3%
2 2234
 
8.2%
4 780
 
2.9%
3 678
 
2.5%
. 146
 
0.5%
5 140
 
0.5%
6 112
 
0.4%
7 109
 
0.4%
8 21
 
0.1%
Other values (2) 16
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111603
80.3%
ASCII 27314
 
19.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19998
73.2%
1 3080
 
11.3%
2 2234
 
8.2%
4 780
 
2.9%
3 678
 
2.5%
. 146
 
0.5%
5 140
 
0.5%
6 112
 
0.4%
7 109
 
0.4%
8 21
 
0.1%
Other values (2) 16
 
0.1%
Hangul
ValueCountFrequency (%)
서 12271
 
11.0%
동 11194
 
10.0%
구 10452
 
9.4%
시 10035
 
9.0%
별 9999
 
9.0%
울 9999
 
9.0%
특 9999
 
9.0%
강 2784
 
2.5%
남 2185
 
2.0%
대 1574
 
1.4%
Other values (172) 31111
27.9%

차명
Text

Distinct275
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T16:47:45.431522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length13.6668
Min length2

Characters and Unicode

Total characters136668
Distinct characters184
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)0.5%

Sample

1st row그랜저 하이브리드(GRANDEUR H
2nd row렉서스 ES300h
3rd row일진무시동전기냉동탑차
4th row그랜저 하이브리드
5th row토요타 Prius C
ValueCountFrequency (%)
하이브리드 2912
 
11.4%
렉서스 1341
 
5.2%
그랜저 1041
 
4.1%
니로 893
 
3.5%
es300h 858
 
3.4%
토요타 689
 
2.7%
hybrid 673
 
2.6%
model 573
 
2.2%
4matic 514
 
2.0%
range 497
 
1.9%
Other values (320) 15562
60.9%
2024-03-13T16:47:45.833833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15737
 
11.5%
이 4763
 
3.5%
리 4111
 
3.0%
드 4087
 
3.0%
브 4080
 
3.0%
하 4079
 
3.0%
E 4051
 
3.0%
0 3840
 
2.8%
e 3509
 
2.6%
A 3470
 
2.5%
Other values (174) 84941
62.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43871
32.1%
Uppercase Letter 41627
30.5%
Lowercase Letter 19965
14.6%
Space Separator 15737
 
11.5%
Decimal Number 10847
 
7.9%
Open Punctuation 2687
 
2.0%
Close Punctuation 969
 
0.7%
Dash Punctuation 576
 
0.4%
Letter Number 262
 
0.2%
Other Punctuation 81
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 4763
 
10.9%
리 4111
 
9.4%
드 4087
 
9.3%
브 4080
 
9.3%
하 4079
 
9.3%
렉 1809
 
4.1%
스 1477
 
3.4%
서 1346
 
3.1%
타 1326
 
3.0%
그 1163
 
2.7%
Other values (107) 15630
35.6%
Uppercase Letter
ValueCountFrequency (%)
E 4051
 
9.7%
A 3470
 
8.3%
R 3139
 
7.5%
N 2763
 
6.6%
I 2656
 
6.4%
S 2337
 
5.6%
C 2269
 
5.5%
T 2171
 
5.2%
O 2136
 
5.1%
H 2032
 
4.9%
Other values (15) 14603
35.1%
Lowercase Letter
ValueCountFrequency (%)
e 3509
17.6%
d 1857
9.3%
r 1842
9.2%
n 1618
 
8.1%
h 1368
 
6.9%
a 1245
 
6.2%
o 1243
 
6.2%
i 1043
 
5.2%
g 913
 
4.6%
y 784
 
3.9%
Other values (14) 4543
22.8%
Decimal Number
ValueCountFrequency (%)
0 3840
35.4%
5 2183
20.1%
3 1984
18.3%
4 1282
 
11.8%
6 478
 
4.4%
2 293
 
2.7%
7 279
 
2.6%
8 236
 
2.2%
1 174
 
1.6%
9 98
 
0.9%
Letter Number
ValueCountFrequency (%)
Ⅱ 186
71.0%
Ⅲ 76
29.0%
Space Separator
ValueCountFrequency (%)
15737
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2687
100.0%
Close Punctuation
ValueCountFrequency (%)
) 969
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 576
100.0%
Other Punctuation
ValueCountFrequency (%)
. 81
100.0%
Math Symbol
ValueCountFrequency (%)
+ 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61854
45.3%
Hangul 43871
32.1%
Common 30943
22.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 4763
 
10.9%
리 4111
 
9.4%
드 4087
 
9.3%
브 4080
 
9.3%
하 4079
 
9.3%
렉 1809
 
4.1%
스 1477
 
3.4%
서 1346
 
3.1%
타 1326
 
3.0%
그 1163
 
2.7%
Other values (107) 15630
35.6%
Latin
ValueCountFrequency (%)
E 4051
 
6.5%
e 3509
 
5.7%
A 3470
 
5.6%
R 3139
 
5.1%
N 2763
 
4.5%
I 2656
 
4.3%
S 2337
 
3.8%
C 2269
 
3.7%
T 2171
 
3.5%
O 2136
 
3.5%
Other values (41) 33353
53.9%
Common
ValueCountFrequency (%)
15737
50.9%
0 3840
 
12.4%
( 2687
 
8.7%
5 2183
 
7.1%
3 1984
 
6.4%
4 1282
 
4.1%
) 969
 
3.1%
- 576
 
1.9%
6 478
 
1.5%
2 293
 
0.9%
Other values (6) 914
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92535
67.7%
Hangul 43871
32.1%
Number Forms 262
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15737
 
17.0%
E 4051
 
4.4%
0 3840
 
4.1%
e 3509
 
3.8%
A 3470
 
3.7%
R 3139
 
3.4%
N 2763
 
3.0%
( 2687
 
2.9%
I 2656
 
2.9%
S 2337
 
2.5%
Other values (55) 48346
52.2%
Hangul
ValueCountFrequency (%)
이 4763
 
10.9%
리 4111
 
9.4%
드 4087
 
9.3%
브 4080
 
9.3%
하 4079
 
9.3%
렉 1809
 
4.1%
스 1477
 
3.4%
서 1346
 
3.1%
타 1326
 
3.0%
그 1163
 
2.7%
Other values (107) 15630
35.6%
Number Forms
ValueCountFrequency (%)
Ⅱ 186
71.0%
Ⅲ 76
29.0%

연료
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
하이브리드(휘발유+전기)
7274 
전기
2378 
수소
 
146
하이브리드(경유+전기)
 
131
하이브리드(LPG+전기)
 
70

Length

Max length13
Median length13
Mean length10.2105
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
하이브리드(휘발유+전기) 7274
72.7%
전기 2378
 
23.8%
수소 146
 
1.5%
하이브리드(경유+전기) 131
 
1.3%
하이브리드(LPG+전기) 70
 
0.7%
하이브리드(CNG+전기) 1
 
< 0.1%

Length

2024-03-13T16:47:45.950042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T16:47:46.040702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
하이브리드(휘발유+전기 7274
72.7%
전기 2378
 
23.8%
수소 146
 
1.5%
하이브리드(경유+전기 131
 
1.3%
하이브리드(lpg+전기 70
 
0.7%
하이브리드(cng+전기 1
 
< 0.1%

최초등록일
Real number (ℝ)

Distinct2281
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20186597
Minimum20070103
Maximum20220228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T16:47:46.161014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20070103
5-th percentile20130219
Q120171026
median20191211
Q320210413
95-th percentile20211210
Maximum20220228
Range150125
Interquartile range (IQR)39387

Descriptive statistics

Standard deviation27231.489
Coefficient of variation (CV)0.0013489886
Kurtosis1.3065403
Mean20186597
Median Absolute Deviation (MAD)19307
Skewness-1.2700403
Sum2.0186597 × 1011
Variance7.41554 × 108
MonotonicityNot monotonic
2024-03-13T16:47:46.300769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20211130 31
 
0.3%
20211118 28
 
0.3%
20210928 27
 
0.3%
20211109 25
 
0.2%
20211102 24
 
0.2%
20211119 24
 
0.2%
20211223 24
 
0.2%
20211126 24
 
0.2%
20210825 23
 
0.2%
20211022 23
 
0.2%
Other values (2271) 9747
97.5%
ValueCountFrequency (%)
20070103 1
< 0.1%
20070228 1
< 0.1%
20070416 1
< 0.1%
20070820 1
< 0.1%
20071002 1
< 0.1%
20071024 1
< 0.1%
20071101 1
< 0.1%
20071213 1
< 0.1%
20071227 1
< 0.1%
20080122 1
< 0.1%
ValueCountFrequency (%)
20220228 15
0.1%
20220225 9
0.1%
20220224 6
 
0.1%
20220223 16
0.2%
20220222 14
0.1%
20220221 16
0.2%
20220218 6
 
0.1%
20220217 6
 
0.1%
20220216 4
 
< 0.1%
20220215 4
 
< 0.1%

현소유자의출생년도
Real number (ℝ)

MISSING 

Distinct78
Distinct (%)1.1%
Missing2623
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean1972.9286
Minimum1924
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T16:47:46.433251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1924
5-th percentile1952
Q11965
median1974
Q31982
95-th percentile1990.2
Maximum2013
Range89
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.014054
Coefficient of variation (CV)0.0060894521
Kurtosis-0.14875078
Mean1972.9286
Median Absolute Deviation (MAD)9
Skewness-0.3785704
Sum14554294
Variance144.33749
MonotonicityNot monotonic
2024-03-13T16:47:46.562335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1979 248
 
2.5%
1980 242
 
2.4%
1981 242
 
2.4%
1970 241
 
2.4%
1971 240
 
2.4%
1982 235
 
2.4%
1974 232
 
2.3%
1983 228
 
2.3%
1972 224
 
2.2%
1976 221
 
2.2%
Other values (68) 5024
50.2%
(Missing) 2623
26.2%
ValueCountFrequency (%)
1924 1
 
< 0.1%
1928 2
 
< 0.1%
1930 2
 
< 0.1%
1932 2
 
< 0.1%
1933 4
< 0.1%
1934 5
0.1%
1935 3
< 0.1%
1936 4
< 0.1%
1937 6
0.1%
1938 4
< 0.1%
ValueCountFrequency (%)
2013 1
 
< 0.1%
2011 7
0.1%
2010 1
 
< 0.1%
2004 1
 
< 0.1%
2003 1
 
< 0.1%
2001 2
 
< 0.1%
2000 4
< 0.1%
1999 3
 
< 0.1%
1998 7
0.1%
1997 8
0.1%

제원관리번호
Text

Distinct971
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T16:47:46.755915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters170000
Distinct characters22
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

Unique219 ?
Unique (%)2.2%

Sample

1st rowA0810012000501319
2nd row01020004500051317
3rd rowB8B10011000003121
4th rowA0810010801171318
5th row01020006700001118
ValueCountFrequency (%)
a0810010801171318 266
 
2.7%
01020006800001318 177
 
1.8%
07020000600021219 166
 
1.7%
a0810010800451317 106
 
1.1%
07020000700011221 104
 
1.0%
a0110006100081217 103
 
1.0%
01020004500041316 95
 
0.9%
01020004500031315 90
 
0.9%
a0810012700081221 89
 
0.9%
a0810012700031221 88
 
0.9%
Other values (961) 8716
87.2%
2024-03-13T16:47:47.073662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 70712
41.6%
1 37627
22.1%
2 18846
 
11.1%
8 8019
 
4.7%
3 7144
 
4.2%
A 5750
 
3.4%
6 5303
 
3.1%
7 5100
 
3.0%
5 4096
 
2.4%
9 3852
 
2.3%
Other values (12) 3551
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164093
96.5%
Uppercase Letter 5907
 
3.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 5750
97.3%
D 42
 
0.7%
B 36
 
0.6%
C 20
 
0.3%
R 17
 
0.3%
G 15
 
0.3%
J 13
 
0.2%
M 4
 
0.1%
S 4
 
0.1%
N 3
 
0.1%
Other values (2) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 70712
43.1%
1 37627
22.9%
2 18846
 
11.5%
8 8019
 
4.9%
3 7144
 
4.4%
6 5303
 
3.2%
7 5100
 
3.1%
5 4096
 
2.5%
9 3852
 
2.3%
4 3394
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 164093
96.5%
Latin 5907
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 5750
97.3%
D 42
 
0.7%
B 36
 
0.6%
C 20
 
0.3%
R 17
 
0.3%
G 15
 
0.3%
J 13
 
0.2%
M 4
 
0.1%
S 4
 
0.1%
N 3
 
0.1%
Other values (2) 3
 
0.1%
Common
ValueCountFrequency (%)
0 70712
43.1%
1 37627
22.9%
2 18846
 
11.5%
8 8019
 
4.9%
3 7144
 
4.4%
6 5303
 
3.2%
7 5100
 
3.1%
5 4096
 
2.5%
9 3852
 
2.3%
4 3394
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 70712
41.6%
1 37627
22.1%
2 18846
 
11.1%
8 8019
 
4.7%
3 7144
 
4.2%
A 5750
 
3.4%
6 5303
 
3.1%
7 5100
 
3.0%
5 4096
 
2.4%
9 3852
 
2.3%
Other values (12) 3551
 
2.1%

Interactions

2024-03-13T16:47:43.533489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:47:43.358626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:47:43.616745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T16:47:43.441210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T16:47:47.162363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료최초등록일현소유자의출생년도
연료1.0000.4700.076
최초등록일0.4701.0000.143
현소유자의출생년도0.0760.1431.000
2024-03-13T16:47:47.238084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최초등록일현소유자의출생년도연료
최초등록일1.0000.0840.270
현소유자의출생년도0.0841.0000.030
연료0.2700.0301.000

Missing values

2024-03-13T16:47:43.988040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T16:47:44.097021image/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.
2024-03-13T16:47:44.190542image/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
22885202202서울특별시 강서구 가양1동그랜저 하이브리드(GRANDEUR H하이브리드(휘발유+전기)20201014<NA>A0810012000501319
6777202202서울특별시 서초구 잠원동렉서스 ES300h하이브리드(휘발유+전기)20171130197801020004500051317
44006202202서울특별시 강남구 수서동일진무시동전기냉동탑차전기20211213<NA>B8B10011000003121
48288202202서울특별시 송파구 가락1동그랜저 하이브리드하이브리드(휘발유+전기)201810041985A0810010801171318
42628202202서울특별시 서초구 양재1동토요타 Prius C하이브리드(휘발유+전기)20180807198001020006700001118
20688202202서울특별시 양천구 신정3동쏘렌토 하이브리드하이브리드(휘발유+전기)202201281988A0110007001811221
52954202202서울특별시 종로구 혜화동Model S 90D전기20180328<NA>07020000100001217
25487202202서울특별시 광진구 자양1동아이오닉 일렉트릭(IONIQ ELEC전기20180309<NA>A0810010500301217
5351202202서울특별시 강남구 역삼2동TWIZY전기20210518<NA>A0410001100011419
45541202202서울특별시 서초구 내곡동EV6전기202110221986A0110007300201221
기준년월사용본거지시읍면동_행정동기준차명연료최초등록일현소유자의출생년도제원관리번호
38847202202서울특별시 중랑구 면목3.8동아이오닉 하이브리드(IONIQ HY하이브리드(휘발유+전기)201702101991A0810010500061216
992202202서울특별시 동작구 신대방1동CHEVROLET BOLT EV전기20190318198702620000700021218
34141202202서울특별시 성북구 돈암2동BMW 530e하이브리드(휘발유+전기)20210517197800120005701501220
29100202202서울특별시 마포구 성산2동넥쏘 (NEXO) 수소전기차수소202012181963A0810011300091220
10113202202서울특별시 양천구 목5동렉서스 ES300h하이브리드(휘발유+전기)20180228196801020004500061317
3184202202서울특별시 강남구 삼성1동볼보 XC40B4 AWD하이브리드(휘발유+전기)20211124199200920004100051221
39373202202서울특별시 마포구 성산1동렉서스 LS500h AWD하이브리드(휘발유+전기)20171221<NA>01020006600011317
14918202202서울특별시 동작구 대방동토요타 Camry Hybrid LE하이브리드(휘발유+전기)20200821197601020006500061319
32649202202서울특별시 송파구 잠실7동CR-V HYBRID 4WD하이브리드(휘발유+전기)20210521195702220004200011220
30868202202서울특별시 서초구 반포3동쏘나타 하이브리드(SONATA HYB하이브리드(휘발유+전기)201901031983A0810009801441218

Duplicate rows

Most frequently occurring

기준년월사용본거지시읍면동_행정동기준차명연료최초등록일현소유자의출생년도제원관리번호# duplicates
130202202서울특별시 강남구 대치4동쏘울 EV전기20171204<NA>A011000550079121712
8202202서울특별시 강남구 대치1동EV6전기20211102<NA>A011000730003122110
37202202서울특별시 강남구 대치1동Model 3 Long Range전기20211109<NA>070200006001312219
46202202서울특별시 강남구 대치1동니로 EV전기20211117<NA>A01100061005312219
107202202서울특별시 강남구 대치2동코나 일렉트릭 (KONA ELECTRIC전기20201020<NA>A08100109011312199
123202202서울특별시 강남구 대치4동Model Y Long Range전기20210602<NA>070200007000112219
12202202서울특별시 강남구 대치1동EV6전기20211115<NA>A01100073000312218
14202202서울특별시 강남구 대치1동EV6전기20211118<NA>A01100073000312218
38202202서울특별시 강남구 대치1동Model 3 Long Range전기20211110<NA>070200006001312218
41202202서울특별시 강남구 대치1동니로 EV전기20210825<NA>A01100061005312218