Overview

Dataset statistics

Number of variables9
Number of observations6279
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory478.4 KiB
Average record size in memory78.0 B

Variable types

Numeric6
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-22230/F/1/datasetView.do

Alerts

승용 is highly overall correlated with 계 and 3 other fieldsHigh correlation
승합 is highly overall correlated with 화물 and 4 other fieldsHigh correlation
화물 is highly overall correlated with 승합 and 5 other fieldsHigh correlation
특수 is highly overall correlated with 승합 and 2 other fieldsHigh correlation
계 is highly overall correlated with 승용 and 5 other fieldsHigh correlation
시군구별 is highly overall correlated with 승용 and 4 other fieldsHigh correlation
연료별 is highly overall correlated with 승용 and 3 other fieldsHigh correlation
용도별 is highly overall correlated with 승용 and 6 other fieldsHigh correlation
승용 is highly skewed (γ1 = 20.82741836)Skewed
승합 is highly skewed (γ1 = 21.4005318)Skewed
화물 is highly skewed (γ1 = 20.43431994)Skewed
특수 is highly skewed (γ1 = 23.28228042)Skewed
계 is highly skewed (γ1 = 20.82138405)Skewed
승용 has 684 (10.9%) zerosZeros
승합 has 2856 (45.5%) zerosZeros
화물 has 2753 (43.8%) zerosZeros
특수 has 4718 (75.1%) zerosZeros

Reproduction

Analysis started2024-01-06 04:02:21.670459
Analysis finished2024-01-06 04:02:34.316530
Duration12.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.6226
Minimum2005
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2024-01-06T04:02:34.459342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12009
median2013
Q32016
95-th percentile2019
Maximum2019
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2728584
Coefficient of variation (CV)0.0021230302
Kurtosis-1.1542744
Mean2012.6226
Median Absolute Deviation (MAD)4
Skewness-0.17066597
Sum12637257
Variance18.257319
MonotonicityIncreasing
2024-01-06T04:02:34.813978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2019 522
 
8.3%
2018 503
 
8.0%
2016 479
 
7.6%
2017 477
 
7.6%
2015 457
 
7.3%
2014 436
 
6.9%
2013 433
 
6.9%
2012 428
 
6.8%
2011 414
 
6.6%
2010 392
 
6.2%
Other values (5) 1738
27.7%
ValueCountFrequency (%)
2005 324
5.2%
2006 334
5.3%
2007 338
5.4%
2008 354
5.6%
2009 388
6.2%
2010 392
6.2%
2011 414
6.6%
2012 428
6.8%
2013 433
6.9%
2014 436
6.9%
ValueCountFrequency (%)
2019 522
8.3%
2018 503
8.0%
2017 477
7.6%
2016 479
7.6%
2015 457
7.3%
2014 436
6.9%
2013 433
6.9%
2012 428
6.8%
2011 414
6.6%
2010 392
6.2%

시군구별
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size49.2 KiB
강남구
 
286
송파구
 
268
영등포구
 
262
금천구
 
260
강동구
 
260
Other values (22)
4943 

Length

Max length4
Median length3
Mean length3.0772416
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
강남구 286
 
4.6%
송파구 268
 
4.3%
영등포구 262
 
4.2%
금천구 260
 
4.1%
강동구 260
 
4.1%
서초구 259
 
4.1%
구로구 257
 
4.1%
은평구 256
 
4.1%
강서구 256
 
4.1%
노원구 253
 
4.0%
Other values (17) 3662
58.3%

Length

2024-01-06T04:02:35.097890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 286
 
4.6%
송파구 268
 
4.3%
영등포구 262
 
4.2%
금천구 260
 
4.1%
강동구 260
 
4.1%
서초구 259
 
4.1%
구로구 257
 
4.1%
은평구 256
 
4.1%
강서구 256
 
4.1%
노원구 253
 
4.0%
Other values (16) 3662
58.3%

연료별
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size49.2 KiB
엘피지
750 
경유
750 
휘발유(무연)
747 
휘발유
743 
기타연료
736 
Other values (13)
2553 

Length

Max length13
Median length12
Mean length5.1360089
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNG
2nd rowCNG
3rd row경유
4th row경유
5th row기타연료

Common Values

ValueCountFrequency (%)
엘피지 750
11.9%
경유 750
11.9%
휘발유(무연) 747
11.9%
휘발유 743
11.8%
기타연료 736
11.7%
CNG 735
11.7%
하이브리드(휘발유+전기) 480
7.6%
전기 366
5.8%
휘발유(유연) 350
5.6%
하이브리드(LPG+전기) 336
5.4%
Other values (8) 286
 
4.6%

Length

2024-01-06T04:02:35.363473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
엘피지 750
11.9%
경유 750
11.9%
휘발유(무연 747
11.9%
휘발유 743
11.8%
기타연료 736
11.7%
cng 735
11.7%
하이브리드(휘발유+전기 480
7.6%
전기 366
5.8%
휘발유(유연 350
5.6%
하이브리드(lpg+전기 336
5.4%
Other values (8) 286
 
4.6%

용도별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.2 KiB
비사업용
3593 
사업용
2672 
계
 
14

Length

Max length4
Median length4
Mean length3.5677656
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비사업용 3593
57.2%
사업용 2672
42.6%
계 14
 
0.2%

Length

2024-01-06T04:02:35.766415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-06T04:02:36.079853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 3593
57.2%
사업용 2672
42.6%
계 14
 
0.2%

승용
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2544
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11440.183
Minimum0
Maximum2670803
Zeros684
Zeros (%)10.9%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2024-01-06T04:02:36.404798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median63
Q35066
95-th percentile34105.1
Maximum2670803
Range2670803
Interquartile range (IQR)5063

Descriptive statistics

Standard deviation117822.14
Coefficient of variation (CV)10.298974
Kurtosis437.83722
Mean11440.183
Median Absolute Deviation (MAD)63
Skewness20.827418
Sum71832906
Variance1.3882057 × 1010
MonotonicityNot monotonic
2024-01-06T04:02:36.712261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 684
 
10.9%
1 374
 
6.0%
2 322
 
5.1%
3 227
 
3.6%
4 161
 
2.6%
5 95
 
1.5%
6 82
 
1.3%
7 64
 
1.0%
10 55
 
0.9%
8 53
 
0.8%
Other values (2534) 4162
66.3%
ValueCountFrequency (%)
0 684
10.9%
1 374
6.0%
2 322
5.1%
3 227
 
3.6%
4 161
 
2.6%
5 95
 
1.5%
6 82
 
1.3%
7 64
 
1.0%
8 53
 
0.8%
9 35
 
0.6%
ValueCountFrequency (%)
2670803 1
< 0.1%
2658637 1
< 0.1%
2641190 1
< 0.1%
2598344 1
< 0.1%
2560154 1
< 0.1%
2510742 1
< 0.1%
2462515 1
< 0.1%
2447876 1
< 0.1%
2443261 1
< 0.1%
2434230 1
< 0.1%

승합
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1159
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean741.56426
Minimum0
Maximum198696
Zeros2856
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2024-01-06T04:02:37.182917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q360
95-th percentile3265.2
Maximum198696
Range198696
Interquartile range (IQR)60

Descriptive statistics

Standard deviation7664.8533
Coefficient of variation (CV)10.336061
Kurtosis475.5995
Mean741.56426
Median Absolute Deviation (MAD)1
Skewness21.400532
Sum4656282
Variance58749977
MonotonicityNot monotonic
2024-01-06T04:02:37.631009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2856
45.5%
1 294
 
4.7%
2 222
 
3.5%
3 139
 
2.2%
4 82
 
1.3%
5 61
 
1.0%
12 53
 
0.8%
9 51
 
0.8%
10 48
 
0.8%
6 47
 
0.7%
Other values (1149) 2426
38.6%
ValueCountFrequency (%)
0 2856
45.5%
1 294
 
4.7%
2 222
 
3.5%
3 139
 
2.2%
4 82
 
1.3%
5 61
 
1.0%
6 47
 
0.7%
7 37
 
0.6%
8 39
 
0.6%
9 51
 
0.8%
ValueCountFrequency (%)
198696 1
< 0.1%
195302 1
< 0.1%
191335 1
< 0.1%
185343 1
< 0.1%
176999 1
< 0.1%
169922 1
< 0.1%
162723 1
< 0.1%
156871 1
< 0.1%
149991 1
< 0.1%
141927 1
< 0.1%

화물
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1398
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1655.5573
Minimum0
Maximum388988
Zeros2753
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2024-01-06T04:02:38.059729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q3120
95-th percentile7263.2
Maximum388988
Range388988
Interquartile range (IQR)120

Descriptive statistics

Standard deviation17041.809
Coefficient of variation (CV)10.2937
Kurtosis426.72112
Mean1655.5573
Median Absolute Deviation (MAD)7
Skewness20.43432
Sum10395244
Variance2.9042327 × 108
MonotonicityNot monotonic
2024-01-06T04:02:38.549089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2753
43.8%
1 144
 
2.3%
2 68
 
1.1%
7 46
 
0.7%
4 44
 
0.7%
16 43
 
0.7%
3 43
 
0.7%
11 41
 
0.7%
29 40
 
0.6%
31 40
 
0.6%
Other values (1388) 3017
48.0%
ValueCountFrequency (%)
0 2753
43.8%
1 144
 
2.3%
2 68
 
1.1%
3 43
 
0.7%
4 44
 
0.7%
5 32
 
0.5%
6 40
 
0.6%
7 46
 
0.7%
8 39
 
0.6%
9 26
 
0.4%
ValueCountFrequency (%)
388988 1
< 0.1%
386876 1
< 0.1%
379247 1
< 0.1%
370894 1
< 0.1%
366306 1
< 0.1%
360103 1
< 0.1%
353905 1
< 0.1%
349285 1
< 0.1%
347765 1
< 0.1%
346980 1
< 0.1%

특수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct261
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.362956
Minimum0
Maximum8204
Zeros4718
Zeros (%)75.1%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2024-01-06T04:02:38.920286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile89
Maximum8204
Range8204
Interquartile range (IQR)0

Descriptive statistics

Standard deviation270.07732
Coefficient of variation (CV)11.085573
Kurtosis590.80438
Mean24.362956
Median Absolute Deviation (MAD)0
Skewness23.28228
Sum152975
Variance72941.758
MonotonicityNot monotonic
2024-01-06T04:02:39.177019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4718
75.1%
1 403
 
6.4%
2 171
 
2.7%
3 81
 
1.3%
4 50
 
0.8%
5 32
 
0.5%
27 20
 
0.3%
7 13
 
0.2%
29 12
 
0.2%
38 11
 
0.2%
Other values (251) 768
 
12.2%
ValueCountFrequency (%)
0 4718
75.1%
1 403
 
6.4%
2 171
 
2.7%
3 81
 
1.3%
4 50
 
0.8%
5 32
 
0.5%
6 8
 
0.1%
7 13
 
0.2%
8 4
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
8204 1
< 0.1%
7993 1
< 0.1%
7581 1
< 0.1%
7181 1
< 0.1%
6742 1
< 0.1%
5828 1
< 0.1%
5206 1
< 0.1%
4680 1
< 0.1%
4313 1
< 0.1%
3865 1
< 0.1%

계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2897
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13861.667
Minimum1
Maximum3124651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.3 KiB
2024-01-06T04:02:39.499199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q122
median175
Q35838
95-th percentile40115.4
Maximum3124651
Range3124650
Interquartile range (IQR)5816

Descriptive statistics

Standard deviation142313.06
Coefficient of variation (CV)10.266663
Kurtosis436.24536
Mean13861.667
Median Absolute Deviation (MAD)174
Skewness20.821384
Sum87037407
Variance2.0253008 × 1010
MonotonicityNot monotonic
2024-01-06T04:02:39.868097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 350
 
5.6%
2 226
 
3.6%
3 156
 
2.5%
4 103
 
1.6%
5 76
 
1.2%
7 72
 
1.1%
6 63
 
1.0%
8 55
 
0.9%
14 45
 
0.7%
12 44
 
0.7%
Other values (2887) 5089
81.0%
ValueCountFrequency (%)
1 350
5.6%
2 226
3.6%
3 156
2.5%
4 103
 
1.6%
5 76
 
1.2%
6 63
 
1.0%
7 72
 
1.1%
8 55
 
0.9%
9 41
 
0.7%
10 36
 
0.6%
ValueCountFrequency (%)
3124651 1
< 0.1%
3124157 1
< 0.1%
3116256 1
< 0.1%
3083007 1
< 0.1%
3056588 1
< 0.1%
3013541 1
< 0.1%
2981400 1
< 0.1%
2977599 1
< 0.1%
2973877 1
< 0.1%
2969184 1
< 0.1%

Interactions

2024-01-06T04:02:31.732777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:23.319585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:24.722826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:26.347327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:28.213942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:29.827263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:32.115590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:23.495755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:24.989805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:26.629952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:28.404104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:30.123903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:32.565286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:23.671938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:25.250195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:26.906379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:28.593114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:30.422716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:32.890761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:23.905247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:25.531751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:27.196619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:28.890127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:30.756743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:33.238653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:24.197057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:25.813371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:27.490423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:29.264768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:31.055031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:33.503772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:24.453049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:26.079244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:27.778133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:29.539468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-06T04:02:31.314903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-06T04:02:40.098751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시군구별연료별용도별승용승합화물특수계
연도1.0000.0000.2930.0000.0000.0000.0000.0500.000
시군구별0.0001.0000.6800.9140.9200.7970.9290.7811.000
연료별0.2930.6801.0000.9370.9210.7890.9210.6891.000
용도별0.0000.9140.9371.0000.9430.9410.9430.7881.000
승용0.0000.9200.9210.9431.0001.0000.9820.9301.000
승합0.0000.7970.7890.9411.0001.0001.0000.9211.000
화물0.0000.9290.9210.9430.9821.0001.0001.0001.000
특수0.0500.7810.6890.7880.9300.9211.0001.0001.000
계0.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-01-06T04:02:40.500232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료별용도별시군구별
연료별1.0000.7360.244
용도별0.7361.0000.705
시군구별0.2440.7051.000
2024-01-06T04:02:40.671139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도승용승합화물특수계시군구별연료별용도별
연도1.0000.026-0.112-0.134-0.057-0.0140.0000.1220.000
승용0.0261.0000.4630.4920.2650.8840.7150.7050.707
승합-0.1120.4631.0000.7170.6020.7000.5020.4440.707
화물-0.1340.4920.7171.0000.6900.6700.7310.7050.707
특수-0.0570.2650.6020.6901.0000.4220.4530.3750.706
계-0.0140.8840.7000.6700.4221.0000.9980.9991.000
시군구별0.0000.7150.5020.7310.4530.9981.0000.2440.705
연료별0.1220.7050.4440.7050.3750.9990.2441.0000.736
용도별0.0000.7070.7070.7070.7061.0000.7050.7361.000

Missing values

2024-01-06T04:02:33.840345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-06T04:02:34.143989image/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

연도시군구별연료별용도별승용승합화물특수계
02005종로구CNG비사업용03205
12005종로구CNG사업용0770077
22005종로구경유비사업용6938329760395816332
32005종로구경유사업용1224819390592492
42005종로구기타연료비사업용584219
52005종로구기타연료사업용0179080
62005종로구엘피지비사업용2251122957804058
72005종로구엘피지사업용262435702684
82005종로구휘발유비사업용9536284509609
92005종로구휘발유사업용36835003688
연도시군구별연료별용도별승용승합화물특수계
62692019강동구하이브리드(CNG+전기)사업용02002
62702019강동구하이브리드(LPG+전기)비사업용7300073
62712019강동구하이브리드(경유+전기)비사업용10001
62722019강동구하이브리드(휘발유+전기)비사업용36950003695
62732019강동구하이브리드(휘발유+전기)사업용3000030
62742019강동구휘발유비사업용281801955028254
62752019강동구휘발유사업용7030073
62762019강동구휘발유(무연)비사업용412761928041323
62772019강동구휘발유(무연)사업용232000232
62782019강동구휘발유(유연)비사업용5200052