Overview

Dataset statistics

Number of variables17
Number of observations231
Missing cells15
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 KiB
Average record size in memory151.6 B

Variable types

Categorical1
Text1
Numeric15

Dataset

Description국내에 등록되어 도로를 운행 중인 자동차의 용도별, 차종별 시군구별 연간 자동차 주행거리 현황입니다. 단위(천km)
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15088482/fileData.do

Alerts

전체_계 is highly overall correlated with 전체_승용차 and 13 other fieldsHigh correlation
전체_승용차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
전체_승합차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
전체_화물차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
전체_특수차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
비사업용_계 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
비사업용_승용차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
비사업용_승합차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
비사업용_화물차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
비사업용_특수차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
사업용_계 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
사업용_승용차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
사업용_승합차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
사업용_화물차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation
사업용_특수차 is highly overall correlated with 전체_계 and 13 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 14:58:19.379685
Analysis finished2023-12-12 14:58:45.417690
Duration26.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct16
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
31 
서울특별시
25 
경상북도
23 
전라남도
22 
강원도
18 
Other values (11)
112 

Length

Max length7
Median length5
Mean length4.1385281
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 31
13.4%
서울특별시 25
10.8%
경상북도 23
10.0%
전라남도 22
9.5%
강원도 18
7.8%
경상남도 18
7.8%
부산광역시 16
6.9%
충청남도 16
6.9%
전라북도 14
 
6.1%
충청북도 12
 
5.2%
Other values (6) 36
15.6%

Length

2023-12-12T23:58:45.487395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 31
13.4%
서울특별시 25
10.8%
경상북도 23
10.0%
전라남도 22
9.5%
강원도 18
7.8%
경상남도 18
7.8%
부산광역시 16
6.9%
충청남도 16
6.9%
전라북도 14
 
6.1%
충청북도 12
 
5.2%
Other values (6) 36
15.6%
Distinct209
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-12T23:58:45.863575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.038961
Min length2

Characters and Unicode

Total characters702
Distinct characters133
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)87.4%

Sample

1st row종로구
2nd row중 구
3rd row용산구
4th row성동구
5th row광진구
ValueCountFrequency (%)
25
 
9.8%
6
 
2.3%
6
 
2.3%
5
 
2.0%
4
 
1.6%
4
 
1.6%
고성군 2
 
0.8%
강서구 2
 
0.8%
광양시 1
 
0.4%
진안군 1
 
0.4%
Other values (200) 200
78.1%
2023-12-12T23:58:46.436941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87
 
12.4%
78
 
11.1%
74
 
10.5%
25
 
3.6%
22
 
3.1%
20
 
2.8%
18
 
2.6%
18
 
2.6%
17
 
2.4%
16
 
2.3%
Other values (123) 327
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 677
96.4%
Space Separator 25
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
87
 
12.9%
78
 
11.5%
74
 
10.9%
22
 
3.2%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (122) 314
46.4%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 677
96.4%
Common 25
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
87
 
12.9%
78
 
11.5%
74
 
10.9%
22
 
3.2%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (122) 314
46.4%
Common
ValueCountFrequency (%)
25
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 677
96.4%
ASCII 25
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
87
 
12.9%
78
 
11.5%
74
 
10.9%
22
 
3.2%
20
 
3.0%
18
 
2.7%
18
 
2.7%
17
 
2.5%
16
 
2.4%
13
 
1.9%
Other values (122) 314
46.4%
ASCII
ValueCountFrequency (%)
25
100.0%

전체_계
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1463685.7
Minimum0
Maximum9377141.2
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:46.620424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200948.98
Q1411072.47
median1016272.4
Q31896432
95-th percentile4246321.6
Maximum9377141.2
Range9377141.2
Interquartile range (IQR)1485359.5

Descriptive statistics

Standard deviation1502381.6
Coefficient of variation (CV)1.0264373
Kurtosis6.3410861
Mean1463685.7
Median Absolute Deviation (MAD)659628.35
Skewness2.242898
Sum3.3664771 × 108
Variance2.2571506 × 1012
MonotonicityNot monotonic
2023-12-12T23:58:46.794618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
578443.4 1
 
0.4%
538290.3 1
 
0.4%
271211.8 1
 
0.4%
749098.7 1
 
0.4%
647364.8 1
 
0.4%
449948.8 1
 
0.4%
4298515.9 1
 
0.4%
1898145.2 1
 
0.4%
1992145.1 1
 
0.4%
819285.9 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
50560.1 1
0.4%
121778.3 1
0.4%
131506.5 1
0.4%
157826.6 1
0.4%
175024.8 1
0.4%
175348.7 1
0.4%
188457.5 1
0.4%
193725.3 1
0.4%
194253.9 1
0.4%
ValueCountFrequency (%)
9377141.2 1
0.4%
7844223.8 1
0.4%
7633544.4 1
0.4%
6766605.5 1
0.4%
6496598.9 1
0.4%
5895447.2 1
0.4%
5882444.4 1
0.4%
5832645.3 1
0.4%
5561498.2 1
0.4%
4511371.8 1
0.4%

전체_승용차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1077765.3
Minimum0
Maximum7067681.9
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:46.989194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile128401.64
Q1253620.33
median722892
Q31403499.2
95-th percentile3283621.8
Maximum7067681.9
Range7067681.9
Interquartile range (IQR)1149878.8

Descriptive statistics

Standard deviation1182682.4
Coefficient of variation (CV)1.0973469
Kurtosis7.1625758
Mean1077765.3
Median Absolute Deviation (MAD)490354.2
Skewness2.3927737
Sum2.4788601 × 108
Variance1.3987376 × 1012
MonotonicityNot monotonic
2023-12-12T23:58:47.153870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420142.2 1
 
0.4%
318393.5 1
 
0.4%
146260.8 1
 
0.4%
500468.3 1
 
0.4%
418825.3 1
 
0.4%
307152.9 1
 
0.4%
3324810.5 1
 
0.4%
1387806.4 1
 
0.4%
1396514.5 1
 
0.4%
558650.8 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
38590.3 1
0.4%
74798.7 1
0.4%
94911.9 1
0.4%
106608.4 1
0.4%
108086.7 1
0.4%
114891.8 1
0.4%
123897.2 1
0.4%
125558.0 1
0.4%
125904.6 1
0.4%
ValueCountFrequency (%)
7067681.9 1
0.4%
6674052.8 1
0.4%
6213090.0 1
0.4%
5357641.9 1
0.4%
5229516.9 1
0.4%
5090271.6 1
0.4%
4352130.2 1
0.4%
4314266.8 1
0.4%
4311216.1 1
0.4%
3588792.8 1
0.4%

전체_승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean79168.804
Minimum0
Maximum419268.7
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:47.335825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9228.84
Q120346.35
median56669.45
Q396535.175
95-th percentile254627.75
Maximum419268.7
Range419268.7
Interquartile range (IQR)76188.825

Descriptive statistics

Standard deviation81592.204
Coefficient of variation (CV)1.0306105
Kurtosis4.7003653
Mean79168.804
Median Absolute Deviation (MAD)38179.85
Skewness2.0732286
Sum18208825
Variance6.6572878 × 109
MonotonicityNot monotonic
2023-12-12T23:58:47.529885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61038.5 1
 
0.4%
63410.3 1
 
0.4%
14473.4 1
 
0.4%
46719.9 1
 
0.4%
55990.8 1
 
0.4%
22963.8 1
 
0.4%
269282.9 1
 
0.4%
86559.7 1
 
0.4%
94035.0 1
 
0.4%
39925.8 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
3696.0 1
0.4%
6439.0 1
0.4%
6957.4 1
0.4%
7993.8 1
0.4%
8183.1 1
0.4%
8238.1 1
0.4%
8333.8 1
0.4%
8505.1 1
0.4%
8594.3 1
0.4%
ValueCountFrequency (%)
419268.7 1
0.4%
404015.9 1
0.4%
399893.1 1
0.4%
380199.5 1
0.4%
371867.9 1
0.4%
343585.3 1
0.4%
339790.9 1
0.4%
322831.5 1
0.4%
299804.9 1
0.4%
292107.5 1
0.4%

전체_화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean289028.46
Minimum0
Maximum1804262.4
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:47.686472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile58635.165
Q1105573.6
median211588.35
Q3362621.4
95-th percentile796970.56
Maximum1804262.4
Range1804262.4
Interquartile range (IQR)257047.8

Descriptive statistics

Standard deviation258791.67
Coefficient of variation (CV)0.89538474
Kurtosis5.9565752
Mean289028.46
Median Absolute Deviation (MAD)114350.75
Skewness2.0503652
Sum66476547
Variance6.6973131 × 1010
MonotonicityNot monotonic
2023-12-12T23:58:47.857624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92949.4 1
 
0.4%
153783.3 1
 
0.4%
104479.2 1
 
0.4%
190793.8 1
 
0.4%
167085.6 1
 
0.4%
118541.2 1
 
0.4%
675093.6 1
 
0.4%
386835.1 1
 
0.4%
476680.7 1
 
0.4%
215376.1 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
8230.7 1
0.4%
29317.3 1
0.4%
39046.2 1
0.4%
40181.2 1
0.4%
40506.8 1
0.4%
41336.9 1
0.4%
47245.7 1
0.4%
48304.4 1
0.4%
48672.3 1
0.4%
ValueCountFrequency (%)
1804262.4 1
0.4%
1218327.2 1
0.4%
1091600.8 1
0.4%
1069256.1 1
0.4%
1035244.7 1
0.4%
1029900.5 1
0.4%
951940.7 1
0.4%
944669.6 1
0.4%
936259.7 1
0.4%
900208.5 1
0.4%

전체_특수차
Real number (ℝ)

HIGH CORRELATION 

Distinct229
Distinct (%)99.6%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean17723.175
Minimum0
Maximum211157.1
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:48.040409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile715.335
Q12696.825
median6861.65
Q321266.3
95-th percentile63398.275
Maximum211157.1
Range211157.1
Interquartile range (IQR)18569.475

Descriptive statistics

Standard deviation27645.873
Coefficient of variation (CV)1.5598713
Kurtosis16.01942
Mean17723.175
Median Absolute Deviation (MAD)5591.15
Skewness3.5131672
Sum4076330.3
Variance7.6429429 × 108
MonotonicityNot monotonic
2023-12-12T23:58:48.178602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1574.9 2
 
0.9%
4313.3 1
 
0.4%
11605.7 1
 
0.4%
7306.3 1
 
0.4%
5998.4 1
 
0.4%
11116.7 1
 
0.4%
5463.1 1
 
0.4%
1290.9 1
 
0.4%
29328.8 1
 
0.4%
36944.0 1
 
0.4%
Other values (219) 219
94.8%
ValueCountFrequency (%)
0.0 1
0.4%
43.2 1
0.4%
319.9 1
0.4%
359.3 1
0.4%
408.4 1
0.4%
409.3 1
0.4%
483.8 1
0.4%
541.9 1
0.4%
542.8 1
0.4%
591.9 1
0.4%
ValueCountFrequency (%)
211157.1 1
0.4%
168355.0 1
0.4%
124064.2 1
0.4%
112962.6 1
0.4%
111523.7 1
0.4%
108599.6 1
0.4%
107453.0 1
0.4%
105303.8 1
0.4%
92979.7 1
0.4%
79769.8 1
0.4%

비사업용_계
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1210224.6
Minimum0
Maximum8010968
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:48.319224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile189643.12
Q1350990.95
median826227.35
Q31594454.7
95-th percentile3574118.3
Maximum8010968
Range8010968
Interquartile range (IQR)1243463.7

Descriptive statistics

Standard deviation1232142.1
Coefficient of variation (CV)1.0181102
Kurtosis6.8660825
Mean1210224.6
Median Absolute Deviation (MAD)523408.3
Skewness2.2876294
Sum2.7835167 × 108
Variance1.5181741 × 1012
MonotonicityNot monotonic
2023-12-12T23:58:48.477801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
508777.4 1
 
0.4%
460795.1 1
 
0.4%
222319.1 1
 
0.4%
673534.3 1
 
0.4%
577991.8 1
 
0.4%
430876.2 1
 
0.4%
3818126.9 1
 
0.4%
1642901.6 1
 
0.4%
1742164.1 1
 
0.4%
694955.9 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
43812.1 1
0.4%
118738.6 1
0.4%
127694.2 1
0.4%
154161.0 1
0.4%
156557.7 1
0.4%
167735.4 1
0.4%
168827.9 1
0.4%
169485.9 1
0.4%
173503.0 1
0.4%
ValueCountFrequency (%)
8010968.0 1
0.4%
6561265.4 1
0.4%
6011920.8 1
0.4%
5750804.4 1
0.4%
5363408.4 1
0.4%
4988098.5 1
0.4%
4951540.5 1
0.4%
3971437.1 1
0.4%
3823430.2 1
0.4%
3818126.9 1
0.4%

비사업용_승용차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean962527.47
Minimum0
Maximum6597851.7
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:48.961735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile126382.1
Q1245550.65
median642829.8
Q31252273.1
95-th percentile2800319.2
Maximum6597851.7
Range6597851.7
Interquartile range (IQR)1006722.4

Descriptive statistics

Standard deviation1026150.2
Coefficient of variation (CV)1.0660996
Kurtosis7.1857738
Mean962527.47
Median Absolute Deviation (MAD)430273.1
Skewness2.3352864
Sum2.2138132 × 108
Variance1.0529842 × 1012
MonotonicityNot monotonic
2023-12-12T23:58:49.118214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
404924.6 1
 
0.4%
302888.6 1
 
0.4%
144673.7 1
 
0.4%
494037.8 1
 
0.4%
411588.3 1
 
0.4%
302118.8 1
 
0.4%
3174135.6 1
 
0.4%
1320346.6 1
 
0.4%
1333675.1 1
 
0.4%
484672.2 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
33812.4 1
0.4%
74597.2 1
0.4%
93408.4 1
0.4%
105859.1 1
0.4%
107892.6 1
0.4%
113870.4 1
0.4%
117845.9 1
0.4%
119803.8 1
0.4%
122981.1 1
0.4%
ValueCountFrequency (%)
6597851.7 1
0.4%
5528922.9 1
0.4%
5138643.4 1
0.4%
4972594.3 1
0.4%
4259793.1 1
0.4%
4137744.6 1
0.4%
4016227.4 1
0.4%
3406416.3 1
0.4%
3174135.6 1
0.4%
3114022.7 1
0.4%

비사업용_승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean43408.554
Minimum0
Maximum236445.7
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:49.257805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7529.335
Q113804.225
median32280.7
Q356099.1
95-th percentile131438.55
Maximum236445.7
Range236445.7
Interquartile range (IQR)42294.875

Descriptive statistics

Standard deviation40807.522
Coefficient of variation (CV)0.94008019
Kurtosis4.9479963
Mean43408.554
Median Absolute Deviation (MAD)19641.35
Skewness2.0368667
Sum9983967.4
Variance1.6652538 × 109
MonotonicityNot monotonic
2023-12-12T23:58:49.442805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38637.8 1
 
0.4%
21149.0 1
 
0.4%
8748.3 1
 
0.4%
25160.8 1
 
0.4%
25038.4 1
 
0.4%
17263.8 1
 
0.4%
116246.5 1
 
0.4%
57596.3 1
 
0.4%
68559.9 1
 
0.4%
28613.2 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
1895.9 1
0.4%
4805.5 1
0.4%
5661.7 1
0.4%
5674.8 1
0.4%
5930.8 1
0.4%
6062.6 1
0.4%
6607.8 1
0.4%
6778.2 1
0.4%
7188.2 1
0.4%
ValueCountFrequency (%)
236445.7 1
0.4%
221969.5 1
0.4%
184930.7 1
0.4%
181287.3 1
0.4%
181002.5 1
0.4%
168267.7 1
0.4%
166327.2 1
0.4%
165983.3 1
0.4%
145827.2 1
0.4%
145550.5 1
0.4%

비사업용_화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean202457.57
Minimum0
Maximum1165517.2
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:49.607991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45109.13
Q188015.6
median156047.55
Q3251738.57
95-th percentile572055.94
Maximum1165517.2
Range1165517.2
Interquartile range (IQR)163722.97

Descriptive statistics

Standard deviation174260.69
Coefficient of variation (CV)0.86072697
Kurtosis5.9212933
Mean202457.57
Median Absolute Deviation (MAD)73896.4
Skewness2.1377573
Sum46565240
Variance3.0366787 × 1010
MonotonicityNot monotonic
2023-12-12T23:58:49.742804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64747.6 1
 
0.4%
136041.7 1
 
0.4%
68217.3 1
 
0.4%
152890.5 1
 
0.4%
140268.8 1
 
0.4%
110739.4 1
 
0.4%
522067.6 1
 
0.4%
262888.1 1
 
0.4%
337000.5 1
 
0.4%
179844.6 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
8063.9 1
0.4%
27131.5 1
0.4%
28173.2 1
0.4%
28414.7 1
0.4%
30234.2 1
0.4%
38806.3 1
0.4%
39068.3 1
0.4%
40025.8 1
0.4%
41918.0 1
0.4%
ValueCountFrequency (%)
1165517.2 1
0.4%
914044.3 1
0.4%
842768.0 1
0.4%
761696.4 1
0.4%
735673.9 1
0.4%
679029.9 1
0.4%
658431.7 1
0.4%
644602.7 1
0.4%
641083.8 1
0.4%
596041.1 1
0.4%

비사업용_특수차
Real number (ℝ)

HIGH CORRELATION 

Distinct228
Distinct (%)99.1%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1831.0387
Minimum0
Maximum11661.4
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:49.885779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile270.175
Q1548.675
median1163.35
Q32485.025
95-th percentile5843.345
Maximum11661.4
Range11661.4
Interquartile range (IQR)1936.35

Descriptive statistics

Standard deviation1953.7262
Coefficient of variation (CV)1.0670043
Kurtosis7.8883602
Mean1831.0387
Median Absolute Deviation (MAD)722.95
Skewness2.513177
Sum421138.9
Variance3817046.2
MonotonicityNot monotonic
2023-12-12T23:58:50.028373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
389.5 2
 
0.9%
2058.5 2
 
0.9%
337.5 1
 
0.4%
406.7 1
 
0.4%
679.7 1
 
0.4%
1445.3 1
 
0.4%
1096.2 1
 
0.4%
754.2 1
 
0.4%
5677.3 1
 
0.4%
2070.6 1
 
0.4%
Other values (218) 218
94.4%
ValueCountFrequency (%)
0.0 1
0.4%
39.9 1
0.4%
136.3 1
0.4%
160.3 1
0.4%
188.9 1
0.4%
193.9 1
0.4%
194.6 1
0.4%
209.4 1
0.4%
227.2 1
0.4%
257.2 1
0.4%
ValueCountFrequency (%)
11661.4 1
0.4%
11525.1 1
0.4%
11153.4 1
0.4%
8572.1 1
0.4%
8283.7 1
0.4%
7579.0 1
0.4%
7289.5 1
0.4%
7148.6 1
0.4%
6705.3 1
0.4%
6653.5 1
0.4%

사업용_계
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean253461.07
Minimum0
Maximum4763883.4
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:50.186822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7759.65
Q137374.1
median157229.95
Q3312793.23
95-th percentile681909.64
Maximum4763883.4
Range4763883.4
Interquartile range (IQR)275419.13

Descriptive statistics

Standard deviation468312.06
Coefficient of variation (CV)1.8476686
Kurtosis60.280506
Mean253461.07
Median Absolute Deviation (MAD)128459.75
Skewness6.9503629
Sum58296045
Variance2.1931619 × 1011
MonotonicityNot monotonic
2023-12-12T23:58:50.328418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69666.0 1
 
0.4%
77495.2 1
 
0.4%
48892.7 1
 
0.4%
75564.4 1
 
0.4%
69373.0 1
 
0.4%
19072.6 1
 
0.4%
480388.9 1
 
0.4%
255243.7 1
 
0.4%
249981.0 1
 
0.4%
124330.0 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
3039.7 1
0.4%
3665.6 1
0.4%
3812.3 1
0.4%
6520.9 1
0.4%
6748.0 1
0.4%
6818.4 1
0.4%
6845.0 1
0.4%
7104.2 1
0.4%
7153.0 1
0.4%
ValueCountFrequency (%)
4763883.4 1
0.4%
4262610.7 1
0.4%
1607652.9 1
0.4%
1366173.1 1
0.4%
1191438.7 1
0.4%
1072279.0 1
0.4%
929224.0 1
0.4%
881104.8 1
0.4%
859264.1 1
0.4%
754684.7 1
0.4%

사업용_승용차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean115237.78
Minimum0
Maximum4300835.6
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:50.471040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1042.045
Q15429.575
median26404.8
Q393469.475
95-th percentile344128.85
Maximum4300835.6
Range4300835.6
Interquartile range (IQR)88039.9

Descriptive statistics

Standard deviation413357.24
Coefficient of variation (CV)3.5869941
Kurtosis81.868306
Mean115237.78
Median Absolute Deviation (MAD)24378.55
Skewness8.6512775
Sum26504689
Variance1.7086421 × 1011
MonotonicityNot monotonic
2023-12-12T23:58:50.618030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15217.6 1
 
0.4%
15504.9 1
 
0.4%
1587.1 1
 
0.4%
6430.4 1
 
0.4%
7236.9 1
 
0.4%
5034.1 1
 
0.4%
150674.9 1
 
0.4%
67459.9 1
 
0.4%
62839.4 1
 
0.4%
73978.6 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
194.0 1
0.4%
201.5 1
0.4%
276.3 1
0.4%
382.1 1
0.4%
486.9 1
0.4%
749.3 1
0.4%
811.4 1
0.4%
867.2 1
0.4%
916.2 1
0.4%
ValueCountFrequency (%)
4300835.6 1
0.4%
4054604.6 1
0.4%
1544524.8 1
0.4%
1008748.8 1
0.4%
777028.9 1
0.4%
684167.2 1
0.4%
543956.4 1
0.4%
492212.6 1
0.4%
477128.7 1
0.4%
469830.2 1
0.4%

사업용_승합차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean35760.253
Minimum0
Maximum303955.1
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:50.753901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1579.82
Q14894.475
median22055.25
Q344959.475
95-th percentile136140.75
Maximum303955.1
Range303955.1
Interquartile range (IQR)40065

Descriptive statistics

Standard deviation45898.78
Coefficient of variation (CV)1.2835138
Kurtosis8.7655214
Mean35760.253
Median Absolute Deviation (MAD)17898.1
Skewness2.6071104
Sum8224858.2
Variance2.106698 × 109
MonotonicityNot monotonic
2023-12-12T23:58:50.941473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22400.7 1
 
0.4%
42261.3 1
 
0.4%
5725.1 1
 
0.4%
21559.2 1
 
0.4%
30952.3 1
 
0.4%
5700.0 1
 
0.4%
153036.5 1
 
0.4%
28963.4 1
 
0.4%
25475.1 1
 
0.4%
11312.6 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
528.6 1
0.4%
726.0 1
0.4%
1245.0 1
0.4%
1295.7 1
0.4%
1316.2 1
0.4%
1316.8 1
0.4%
1404.9 1
0.4%
1412.2 1
0.4%
1511.4 1
0.4%
ValueCountFrequency (%)
303955.1 1
0.4%
265573.3 1
0.4%
213872.3 1
0.4%
197299.3 1
0.4%
163447.5 1
0.4%
162582.8 1
0.4%
154860.2 1
0.4%
153036.5 1
0.4%
146922.5 1
0.4%
141544.1 1
0.4%

사업용_화물차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean86570.897
Minimum0
Maximum638745.2
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:51.098759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3505.12
Q115071.9
median53826.85
Q3121471.22
95-th percentile266924.44
Maximum638745.2
Range638745.2
Interquartile range (IQR)106399.32

Descriptive statistics

Standard deviation98737.029
Coefficient of variation (CV)1.1405338
Kurtosis7.7789909
Mean86570.897
Median Absolute Deviation (MAD)44553.6
Skewness2.274343
Sum19911306
Variance9.749001 × 109
MonotonicityNot monotonic
2023-12-12T23:58:51.259751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28201.8 1
 
0.4%
17741.6 1
 
0.4%
36261.9 1
 
0.4%
37903.3 1
 
0.4%
26816.8 1
 
0.4%
7801.7 1
 
0.4%
153026.0 1
 
0.4%
123947.0 1
 
0.4%
139680.1 1
 
0.4%
35531.5 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
166.7 1
0.4%
902.6 1
0.4%
1113.0 1
0.4%
1311.0 1
0.4%
1700.5 1
0.4%
2155.6 1
0.4%
2742.3 1
0.4%
3202.6 1
0.4%
3260.8 1
0.4%
ValueCountFrequency (%)
638745.2 1
0.4%
621915.1 1
0.4%
449544.4 1
0.4%
396089.0 1
0.4%
329904.4 1
0.4%
307338.0 1
0.4%
304282.9 1
0.4%
299188.3 1
0.4%
298786.7 1
0.4%
297916.4 1
0.4%

사업용_특수차
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean15892.134
Minimum0
Maximum210756.7
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:58:51.408596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile195.47
Q11830.25
median5574.55
Q318312.275
95-th percentile59905.215
Maximum210756.7
Range210756.7
Interquartile range (IQR)16482.025

Descriptive statistics

Standard deviation26925.115
Coefficient of variation (CV)1.6942416
Kurtosis18.015816
Mean15892.134
Median Absolute Deviation (MAD)4851.15
Skewness3.7356068
Sum3655190.9
Variance7.2496182 × 108
MonotonicityNot monotonic
2023-12-12T23:58:51.552113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3845.9 1
 
0.4%
1987.5 1
 
0.4%
5318.6 1
 
0.4%
9671.4 1
 
0.4%
4366.9 1
 
0.4%
536.7 1
 
0.4%
23651.5 1
 
0.4%
34873.3 1
 
0.4%
21986.4 1
 
0.4%
3507.3 1
 
0.4%
Other values (220) 220
95.2%
ValueCountFrequency (%)
0.0 1
0.4%
3.3 1
0.4%
41.1 1
0.4%
91.6 1
0.4%
97.5 1
0.4%
110.5 1
0.4%
114.9 1
0.4%
116.0 1
0.4%
135.2 1
0.4%
146.3 1
0.4%
ValueCountFrequency (%)
210756.7 1
0.4%
166693.8 1
0.4%
117483.8 1
0.4%
109400.2 1
0.4%
107823.2 1
0.4%
107264.1 1
0.4%
105985.4 1
0.4%
94150.3 1
0.4%
91426.3 1
0.4%
76244.7 1
0.4%

Interactions

2023-12-12T23:58:43.779739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:20.208784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:21.853775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:23.839858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:25.241416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:26.939160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:28.495726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:30.376888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:32.095641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:33.698142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:35.309222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:37.352339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:38.855483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:40.382729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:42.066864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:43.861475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:20.370165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:21.974614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:23.936100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:25.342961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:27.040502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:28.601862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:30.477227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:32.195382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:33.802725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:58:35.415517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T23:58:43.385987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:58:51.651571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도전체_계전체_승용차전체_승합차전체_화물차전체_특수차비사업용_계비사업용_승용차비사업용_승합차비사업용_화물차비사업용_특수차사업용_계사업용_승용차사업용_승합차사업용_화물차사업용_특수차
시도1.0000.5480.5480.5250.5580.3600.5150.4860.4790.4960.3920.5990.4820.5760.5460.410
전체_계0.5481.0000.9820.9400.8890.5340.9540.9810.8640.8620.7850.7820.7430.9300.7820.500
전체_승용차0.5480.9821.0000.9260.8470.4370.9560.9960.8430.8440.7460.7900.7700.9040.7410.384
전체_승합차0.5250.9400.9261.0000.8040.3230.8290.9260.8460.7940.7300.6820.5750.9310.7680.290
전체_화물차0.5580.8890.8470.8041.0000.7300.9040.8940.8380.8950.7720.6030.3740.7650.8230.704
전체_특수차0.3600.5340.4370.3230.7301.0000.5560.5490.4140.5410.4610.3150.1320.4350.5620.997
비사업용_계0.5150.9540.9560.8290.9040.5561.0000.9850.9520.9560.9010.7050.4520.7850.9070.530
비사업용_승용차0.4860.9810.9960.9260.8940.5490.9851.0000.8720.8740.7870.6690.5610.8900.7910.523
비사업용_승합차0.4790.8640.8430.8460.8380.4140.9520.8721.0000.9360.8700.5830.3150.7640.8390.365
비사업용_화물차0.4960.8620.8440.7940.8950.5410.9560.8740.9361.0000.9230.6790.4690.7560.8940.510
비사업용_특수차0.3920.7850.7460.7300.7720.4610.9010.7870.8700.9231.0000.5970.4500.6870.8470.431
사업용_계0.5990.7820.7900.6820.6030.3150.7050.6690.5830.6790.5971.0000.8790.7940.5370.300
사업용_승용차0.4820.7430.7700.5750.3740.1320.4520.5610.3150.4690.4500.8791.0000.7370.0000.000
사업용_승합차0.5760.9300.9040.9310.7650.4350.7850.8900.7640.7560.6870.7940.7371.0000.7720.435
사업용_화물차0.5460.7820.7410.7680.8230.5620.9070.7910.8390.8940.8470.5370.0000.7721.0000.559
사업용_특수차0.4100.5000.3840.2900.7040.9970.5300.5230.3650.5100.4310.3000.0000.4350.5591.000
2023-12-12T23:58:51.823040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체_계전체_승용차전체_승합차전체_화물차전체_특수차비사업용_계비사업용_승용차비사업용_승합차비사업용_화물차비사업용_특수차사업용_계사업용_승용차사업용_승합차사업용_화물차사업용_특수차시도
전체_계1.0000.9920.9540.9430.7190.9800.9770.9660.9280.8610.9150.8480.8740.8750.6830.246
전체_승용차0.9921.0000.9420.9080.6710.9790.9800.9600.9070.8450.8890.8580.8600.8310.6360.246
전체_승합차0.9540.9421.0000.9070.6580.9430.9390.9520.8970.8170.8870.8090.9560.8390.6250.232
전체_화물차0.9430.9080.9071.0000.7610.9380.9220.9340.9660.8770.8770.7390.8210.9340.7280.223
전체_특수차0.7190.6710.6580.7611.0000.6890.6820.6580.6860.7190.7680.5860.6190.7820.9950.130
비사업용_계0.9800.9790.9430.9380.6891.0000.9980.9800.9420.8710.8410.7930.8440.8480.6530.236
비사업용_승용차0.9770.9800.9390.9220.6820.9981.0000.9760.9220.8580.8390.7980.8410.8420.6470.210
비사업용_승합차0.9660.9600.9520.9340.6580.9800.9761.0000.9290.8500.8460.7970.8400.8550.6220.216
비사업용_화물차0.9280.9070.8970.9660.6860.9420.9220.9291.0000.8910.7970.7090.8060.8260.6480.225
비사업용_특수차0.8610.8450.8170.8770.7190.8710.8580.8500.8911.0000.7470.6650.7280.7640.6660.169
사업용_계0.9150.8890.8870.8770.7680.8410.8390.8460.7970.7471.0000.8870.8590.9040.7450.331
사업용_승용차0.8480.8580.8090.7390.5860.7930.7980.7970.7090.6650.8871.0000.7680.7170.5590.263
사업용_승합차0.8740.8600.9560.8210.6190.8440.8410.8400.8060.7280.8590.7681.0000.7680.5930.264
사업용_화물차0.8750.8310.8390.9340.7820.8480.8420.8550.8260.7640.9040.7170.7681.0000.7610.256
사업용_특수차0.6830.6360.6250.7280.9950.6530.6470.6220.6480.6660.7450.5590.5930.7611.0000.151
시도0.2460.2460.2320.2230.1300.2360.2100.2160.2250.1690.3310.2630.2640.2560.1511.000

Missing values

2023-12-12T23:58:44.910650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:58:45.110994image/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.
2023-12-12T23:58:45.267770image/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

시도시군구전체_계전체_승용차전체_승합차전체_화물차전체_특수차비사업용_계비사업용_승용차비사업용_승합차비사업용_화물차비사업용_특수차사업용_계사업용_승용차사업용_승합차사업용_화물차사업용_특수차
0서울특별시종로구578443.4420142.261038.592949.44313.3508777.4404924.638637.864747.6467.469666.015217.622400.728201.83845.9
1서울특별시중 구690370.1467645.958074.5152592.012057.8524448.9390630.445252.187797.4769.0165921.277015.512822.464794.611288.8
2서울특별시용산구833429.4665372.231638.6132461.23957.5761472.1637034.227962.693533.62941.771957.428338.03676.138927.61015.8
3서울특별시성동구1454743.51073266.3135510.5242983.72983.01084382.6871838.439444.9172089.31010.0370360.9201428.096065.670894.41972.9
4서울특별시광진구1274866.8965133.463493.9241161.55078.01104670.1885432.041886.4176874.0477.6170196.879701.421607.564287.54600.4
5서울특별시동대문구1220638.7913643.655356.3248944.12694.71037490.8825041.648824.8162712.3912.0183148.088602.06531.686231.81782.7
6서울특별시중랑구1517720.01104373.696489.8310164.36692.31217651.0952288.959385.9204611.31365.0300068.9152084.737103.9105553.05327.3
7서울특별시성북구1454077.21124867.0125090.0201821.42298.81238109.31041685.254194.0141312.6917.5215967.983181.870896.060508.81381.3
8서울특별시강북구1155322.7716396.090825.6345697.82403.3797658.3634950.441358.1120479.2870.7357664.481445.549467.6225218.61532.6
9서울특별시도봉구1241415.8951197.975874.6210984.83358.41013970.1824914.946117.2142040.4897.6227445.6126283.029757.468944.42460.8
시도시군구전체_계전체_승용차전체_승합차전체_화물차전체_특수차비사업용_계비사업용_승용차비사업용_승합차비사업용_화물차비사업용_특수차사업용_계사업용_승용차사업용_승합차사업용_화물차사업용_특수차
221경상남도창녕군481021.8330842.021976.5124830.53372.7452930.2320683.717089.5114085.01072.128091.610158.34887.110745.52300.6
222경상남도고성군370222.7255171.217372.894320.43358.3347920.2246287.913356.387287.7988.322302.58883.34016.57032.82370.0
223경상남도남해군271802.1177206.921492.772238.0864.5250904.3171643.710627.868243.4389.520897.85563.210865.03994.7475.0
224경상남도하동군334505.9221039.212546.095423.45497.3318412.7217353.610703.289838.9517.116093.23685.61842.85584.54980.2
225경상남도산청군299834.0174488.412098.4103759.19488.2265599.5172166.19953.383046.5433.734234.52322.32145.120712.79054.5
226경상남도함양군262795.2166483.814727.880318.21265.5247054.5162302.38723.475626.6402.115740.74181.56004.44691.5863.3
227경상남도거창군427859.3276338.227169.5112948.311403.4392093.0272082.114090.8105113.1807.035766.44256.113078.77835.210596.3
228경상남도합천군319079.8199749.713148.5101781.74399.9298712.2197546.79321.091243.6600.920367.62203.03827.410538.13799.0
229제주특별자치도제주시7844223.86674052.8371867.9782811.215491.83080340.42373217.2106294.7596041.14787.44763883.44300835.6265573.3186770.210704.4
230제주특별자치도서귀포시1224901.9844640.357348.1317923.84989.71159526.7812659.443294.1301566.02007.265375.231980.814054.116357.82982.5