Overview

Dataset statistics

Number of variables11
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory98.7 B

Variable types

Categorical5
Text1
Numeric5

Dataset

Description서울특별시 강남구 연료별 자동차 등록 현황으로 시군구명, 연료종류, 용도, 승용, 승합, 화물, 특수 등록대수, 총등록대수, 관리기관, 데이터기준일자 정보입니다
Author서울특별시 강남구
URLhttps://www.data.go.kr/data/15037647/fileData.do

Alerts

시군구명 has constant value ""Constant
관리기관명 has constant value ""Constant
관리기관전화번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
승용 is highly overall correlated with 총등록대수High correlation
승합 is highly overall correlated with 화물 and 2 other fieldsHigh correlation
화물 is highly overall correlated with 승합 and 2 other fieldsHigh correlation
특수 is highly overall correlated with 승합 and 2 other fieldsHigh correlation
총등록대수 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
총등록대수 has unique valuesUnique
승용 has 2 (8.7%) zerosZeros
승합 has 13 (56.5%) zerosZeros
화물 has 12 (52.2%) zerosZeros
특수 has 18 (78.3%) zerosZeros

Reproduction

Analysis started2023-12-12 03:21:02.157070
Analysis finished2023-12-12 03:21:05.786386
Duration3.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
서울특별시 강남구
23 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 강남구
2nd row서울특별시 강남구
3rd row서울특별시 강남구
4th row서울특별시 강남구
5th row서울특별시 강남구

Common Values

ValueCountFrequency (%)
서울특별시 강남구 23
100.0%

Length

2023-12-12T12:21:05.864235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:21:05.984911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 23
50.0%
강남구 23
50.0%

연료종류
Text

Distinct12
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T12:21:06.155199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.6086957
Min length2

Characters and Unicode

Total characters129
Distinct characters28
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st rowCNG
2nd rowCNG
3rd row경유
4th row경유
5th row기타연료
ValueCountFrequency (%)
cng 2
8.7%
경유 2
8.7%
기타연료 2
8.7%
수소 2
8.7%
엘피지 2
8.7%
전기 2
8.7%
하이브리드(경유+전기 2
8.7%
하이브리드(휘발유+전기 2
8.7%
휘발유 2
8.7%
휘발유(무연 2
8.7%
Other values (2) 3
13.0%
2023-12-12T12:21:06.505333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 14
 
10.9%
( 9
 
7.0%
) 9
 
7.0%
기 9
 
7.0%
발 8
 
6.2%
휘 8
 
6.2%
전 7
 
5.4%
연 6
 
4.7%
브 5
 
3.9%
+ 5
 
3.9%
Other values (18) 49
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
75.2%
Open Punctuation 9
 
7.0%
Close Punctuation 9
 
7.0%
Uppercase Letter 9
 
7.0%
Math Symbol 5
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 14
14.4%
기 9
 
9.3%
발 8
 
8.2%
휘 8
 
8.2%
전 7
 
7.2%
연 6
 
6.2%
브 5
 
5.2%
드 5
 
5.2%
리 5
 
5.2%
하 5
 
5.2%
Other values (10) 25
25.8%
Uppercase Letter
ValueCountFrequency (%)
G 3
33.3%
C 2
22.2%
N 2
22.2%
L 1
 
11.1%
P 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97
75.2%
Common 23
 
17.8%
Latin 9
 
7.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 14
14.4%
기 9
 
9.3%
발 8
 
8.2%
휘 8
 
8.2%
전 7
 
7.2%
연 6
 
6.2%
브 5
 
5.2%
드 5
 
5.2%
리 5
 
5.2%
하 5
 
5.2%
Other values (10) 25
25.8%
Latin
ValueCountFrequency (%)
G 3
33.3%
C 2
22.2%
N 2
22.2%
L 1
 
11.1%
P 1
 
11.1%
Common
ValueCountFrequency (%)
( 9
39.1%
) 9
39.1%
+ 5
21.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97
75.2%
ASCII 32
 
24.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 14
14.4%
기 9
 
9.3%
발 8
 
8.2%
휘 8
 
8.2%
전 7
 
7.2%
연 6
 
6.2%
브 5
 
5.2%
드 5
 
5.2%
리 5
 
5.2%
하 5
 
5.2%
Other values (10) 25
25.8%
ASCII
ValueCountFrequency (%)
( 9
28.1%
) 9
28.1%
+ 5
15.6%
G 3
 
9.4%
C 2
 
6.2%
N 2
 
6.2%
L 1
 
3.1%
P 1
 
3.1%

용도
Categorical

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size316.0 B
비사업용
12 
사업용
11 

Length

Max length4
Median length4
Mean length3.5217391
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비사업용
2nd row사업용
3rd row비사업용
4th row사업용
5th row비사업용

Common Values

ValueCountFrequency (%)
비사업용 12
52.2%
사업용 11
47.8%

Length

2023-12-12T12:21:06.670165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:21:06.782411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 12
52.2%
사업용 11
47.8%

승용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9350.2174
Minimum0
Maximum65484
Zeros2
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T12:21:06.905896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q122
median346
Q34609.5
95-th percentile61710.3
Maximum65484
Range65484
Interquartile range (IQR)4587.5

Descriptive statistics

Standard deviation20160.808
Coefficient of variation (CV)2.1561861
Kurtosis4.091142
Mean9350.2174
Median Absolute Deviation (MAD)346
Skewness2.329745
Sum215055
Variance4.064582 × 108
MonotonicityNot monotonic
2023-12-12T12:21:07.047852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 2
 
8.7%
32 1
 
4.3%
220 1
 
4.3%
1 1
 
4.3%
81 1
 
4.3%
3152 1
 
4.3%
63116 1
 
4.3%
346 1
 
4.3%
65484 1
 
4.3%
1340 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
0 2
8.7%
1 1
4.3%
2 1
4.3%
4 1
4.3%
12 1
4.3%
32 1
4.3%
76 1
4.3%
81 1
4.3%
157 1
4.3%
220 1
4.3%
ValueCountFrequency (%)
65484 1
4.3%
63116 1
4.3%
49059 1
4.3%
13222 1
4.3%
6102 1
4.3%
5590 1
4.3%
3629 1
4.3%
3152 1
4.3%
1836 1
4.3%
1594 1
4.3%

승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.43478
Minimum0
Maximum3663
Zeros13
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T12:21:07.170847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q363
95-th percentile679.1
Maximum3663
Range3663
Interquartile range (IQR)63

Descriptive statistics

Standard deviation767.02126
Coefficient of variation (CV)3.3724888
Kurtosis20.605084
Mean227.43478
Median Absolute Deviation (MAD)0
Skewness4.4653253
Sum5231
Variance588321.62
MonotonicityNot monotonic
2023-12-12T12:21:07.288796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 13
56.5%
5 2
 
8.7%
195 1
 
4.3%
3663 1
 
4.3%
392 1
 
4.3%
87 1
 
4.3%
711 1
 
4.3%
74 1
 
4.3%
52 1
 
4.3%
47 1
 
4.3%
ValueCountFrequency (%)
0 13
56.5%
5 2
 
8.7%
47 1
 
4.3%
52 1
 
4.3%
74 1
 
4.3%
87 1
 
4.3%
195 1
 
4.3%
392 1
 
4.3%
711 1
 
4.3%
3663 1
 
4.3%
ValueCountFrequency (%)
3663 1
 
4.3%
711 1
 
4.3%
392 1
 
4.3%
195 1
 
4.3%
87 1
 
4.3%
74 1
 
4.3%
52 1
 
4.3%
47 1
 
4.3%
5 2
 
8.7%
0 13
56.5%

화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean657.43478
Minimum0
Maximum12022
Zeros12
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T12:21:07.424330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3132
95-th percentile1259.6
Maximum12022
Range12022
Interquartile range (IQR)132

Descriptive statistics

Standard deviation2497.4443
Coefficient of variation (CV)3.7987712
Kurtosis22.162789
Mean657.43478
Median Absolute Deviation (MAD)0
Skewness4.6765841
Sum15121
Variance6237228.3
MonotonicityNot monotonic
2023-12-12T12:21:07.554459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 12
52.2%
36 1
 
4.3%
12022 1
 
4.3%
1300 1
 
4.3%
239 1
 
4.3%
175 1
 
4.3%
896 1
 
4.3%
60 1
 
4.3%
90 1
 
4.3%
39 1
 
4.3%
Other values (2) 2
 
8.7%
ValueCountFrequency (%)
0 12
52.2%
36 1
 
4.3%
39 1
 
4.3%
60 1
 
4.3%
90 1
 
4.3%
103 1
 
4.3%
161 1
 
4.3%
175 1
 
4.3%
239 1
 
4.3%
896 1
 
4.3%
ValueCountFrequency (%)
12022 1
4.3%
1300 1
4.3%
896 1
4.3%
239 1
4.3%
175 1
4.3%
161 1
4.3%
103 1
4.3%
90 1
4.3%
60 1
4.3%
39 1
4.3%

특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.521739
Minimum0
Maximum214
Zeros18
Zeros (%)78.3%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T12:21:07.665012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile135.1
Maximum214
Range214
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52.846149
Coefficient of variation (CV)3.0160333
Kurtosis10.059594
Mean17.521739
Median Absolute Deviation (MAD)0
Skewness3.2416348
Sum403
Variance2792.7154
MonotonicityNot monotonic
2023-12-12T12:21:07.815548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
78.3%
214 1
 
4.3%
146 1
 
4.3%
37 1
 
4.3%
4 1
 
4.3%
2 1
 
4.3%
ValueCountFrequency (%)
0 18
78.3%
2 1
 
4.3%
4 1
 
4.3%
37 1
 
4.3%
146 1
 
4.3%
214 1
 
4.3%
ValueCountFrequency (%)
214 1
 
4.3%
146 1
 
4.3%
37 1
 
4.3%
4 1
 
4.3%
2 1
 
4.3%
0 18
78.3%

총등록대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10252.609
Minimum1
Maximum65701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T12:21:08.236107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q1119
median375
Q34952
95-th percentile64789
Maximum65701
Range65700
Interquartile range (IQR)4833

Descriptive statistics

Standard deviation21771.002
Coefficient of variation (CV)2.1234597
Kurtosis3.5770181
Mean10252.609
Median Absolute Deviation (MAD)374
Skewness2.2602092
Sum235810
Variance4.7397651 × 108
MonotonicityNot monotonic
2023-12-12T12:21:08.362912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
73 1
 
4.3%
195 1
 
4.3%
1 1
 
4.3%
81 1
 
4.3%
3152 1
 
4.3%
63268 1
 
4.3%
346 1
 
4.3%
65701 1
 
4.3%
1340 1
 
4.3%
13222 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
4 1
4.3%
73 1
4.3%
76 1
4.3%
81 1
4.3%
157 1
4.3%
175 1
4.3%
195 1
4.3%
220 1
4.3%
ValueCountFrequency (%)
65701 1
4.3%
64958 1
4.3%
63268 1
4.3%
13222 1
4.3%
7197 1
4.3%
6141 1
4.3%
3763 1
4.3%
3432 1
4.3%
3152 1
4.3%
1931 1
4.3%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
서울특별시 강남구청
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 강남구청
2nd row서울특별시 강남구청
3rd row서울특별시 강남구청
4th row서울특별시 강남구청
5th row서울특별시 강남구청

Common Values

ValueCountFrequency (%)
서울특별시 강남구청 23
100.0%

Length

2023-12-12T12:21:08.524446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:21:08.635271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 23
50.0%
강남구청 23
50.0%

관리기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
02-3423-6494
23 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02-3423-6494
2nd row02-3423-6494
3rd row02-3423-6494
4th row02-3423-6494
5th row02-3423-6494

Common Values

ValueCountFrequency (%)
02-3423-6494 23
100.0%

Length

2023-12-12T12:21:08.754247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:21:08.896270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
02-3423-6494 23
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2022-06-28
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06-28
2nd row2022-06-28
3rd row2022-06-28
4th row2022-06-28
5th row2022-06-28

Common Values

ValueCountFrequency (%)
2022-06-28 23
100.0%

Length

2023-12-12T12:21:09.114279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:21:09.318209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-06-28 23
100.0%

Interactions

2023-12-12T12:21:04.996696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:02.491948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:03.261010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:03.877133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.452095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:05.102776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:02.613347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:03.405430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.008883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.547572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:05.220719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:02.758982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:03.529141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.123996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.665840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:05.343356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:02.937942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:03.640698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.251772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.802648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:05.435076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:03.107574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:03.757850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.344468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:21:04.894897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:21:09.416819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료종류용도승용승합화물특수총등록대수
연료종류1.0000.0000.0000.2840.4110.3350.000
용도0.0001.0000.4000.0000.0120.0000.552
승용0.0000.4001.0000.6370.6340.8390.975
승합0.2840.0000.6371.0000.9840.7790.542
화물0.4110.0120.6340.9841.0001.0000.181
특수0.3350.0000.8390.7791.0001.0000.000
총등록대수0.0000.5520.9750.5420.1810.0001.000
2023-12-12T12:21:09.591587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용승합화물특수총등록대수용도
승용1.0000.3200.4020.3940.9040.244
승합0.3201.0000.7670.6530.5270.000
화물0.4020.7671.0000.7030.5910.000
특수0.3940.6530.7031.0000.5280.000
총등록대수0.9040.5270.5910.5281.0000.352
용도0.2440.0000.0000.0000.3521.000

Missing values

2023-12-12T12:21:05.562713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:21:05.722472image/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.

Sample

시군구명연료종류용도승용승합화물특수총등록대수관리기관명관리기관전화번호데이터기준일자
0서울특별시 강남구CNG비사업용32536073서울특별시 강남구청02-3423-64942022-06-28
1서울특별시 강남구CNG사업용019500195서울특별시 강남구청02-3423-64942022-06-28
2서울특별시 강남구경유비사업용4905936631202221464958서울특별시 강남구청02-3423-64942022-06-28
3서울특별시 강남구경유사업용159439213001463432서울특별시 강남구청02-3423-64942022-06-28
4서울특별시 강남구기타연료비사업용128723937375서울특별시 강남구청02-3423-64942022-06-28
5서울특별시 강남구기타연료사업용001750175서울특별시 강남구청02-3423-64942022-06-28
6서울특별시 강남구수소비사업용157000157서울특별시 강남구청02-3423-64942022-06-28
7서울특별시 강남구수소사업용20002서울특별시 강남구청02-3423-64942022-06-28
8서울특별시 강남구엘피지비사업용559071189607197서울특별시 강남구청02-3423-64942022-06-28
9서울특별시 강남구엘피지사업용3629746003763서울특별시 강남구청02-3423-64942022-06-28
시군구명연료종류용도승용승합화물특수총등록대수관리기관명관리기관전화번호데이터기준일자
13서울특별시 강남구하이브리드(경유+전기)비사업용220000220서울특별시 강남구청02-3423-64942022-06-28
14서울특별시 강남구하이브리드(경유+전기)사업용40004서울특별시 강남구청02-3423-64942022-06-28
15서울특별시 강남구하이브리드(휘발유+전기)비사업용1322200013222서울특별시 강남구청02-3423-64942022-06-28
16서울특별시 강남구하이브리드(휘발유+전기)사업용13400001340서울특별시 강남구청02-3423-64942022-06-28
17서울특별시 강남구휘발유비사업용6548452161465701서울특별시 강남구청02-3423-64942022-06-28
18서울특별시 강남구휘발유사업용346000346서울특별시 강남구청02-3423-64942022-06-28
19서울특별시 강남구휘발유(무연)비사업용6311647103263268서울특별시 강남구청02-3423-64942022-06-28
20서울특별시 강남구휘발유(무연)사업용31520003152서울특별시 강남구청02-3423-64942022-06-28
21서울특별시 강남구휘발유(유연)비사업용8100081서울특별시 강남구청02-3423-64942022-06-28
22서울특별시 강남구휘발유(유연)사업용10001서울특별시 강남구청02-3423-64942022-06-28