Overview

Dataset statistics

Number of variables9
Number of observations26
Missing cells19
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory81.9 B

Variable types

Categorical2
Text2
Numeric5

Dataset

Description광주광역시 자치구별 화물자동차 등록현황(2023년)입니다. 차종의 형태,규격 등에 따라 각 등록대수를 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/3041772/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
동구 is highly overall correlated with 서구 and 2 other fieldsHigh correlation
서구 is highly overall correlated with 동구 and 3 other fieldsHigh correlation
남구 is highly overall correlated with 서구 and 1 other fieldsHigh correlation
북구 is highly overall correlated with 동구 and 2 other fieldsHigh correlation
광산구 is highly overall correlated with 동구 and 3 other fieldsHigh correlation
규격 has 19 (73.1%) missing valuesMissing
동구 has 5 (19.2%) zerosZeros
서구 has 7 (26.9%) zerosZeros
남구 has 13 (50.0%) zerosZeros
북구 has 6 (23.1%) zerosZeros
광산구 has 5 (19.2%) zerosZeros

Reproduction

Analysis started2024-03-14 11:21:10.708153
Analysis finished2024-03-14 11:21:17.928604
Duration7.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size336.0 B
일반
16 
개별
5 
용달
5 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 16
61.5%
개별 5
 
19.2%
용달 5
 
19.2%

Length

2024-03-14T20:21:18.036976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:21:18.208880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 16
61.5%
개별 5
 
19.2%
용달 5
 
19.2%

형태
Text

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Memory size336.0 B
2024-03-14T20:21:18.691092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length8
Mean length5.3846154
Min length2

Characters and Unicode

Total characters140
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)34.6%

Sample

1st row일반형
2nd row일반형
3rd row일반형
4th row밴형(현금수송차량 제외)
5th row덤프형
ValueCountFrequency (%)
일반형 6
20.7%
기타 3
10.3%
밴형(현금수송차량 2
 
6.9%
제외 2
 
6.9%
현금수송용차량 2
 
6.9%
청소용차량 2
 
6.9%
특수자동차 2
 
6.9%
탱크로리 1
 
3.4%
피견인차 1
 
3.4%
소방용차량 1
 
3.4%
Other values (7) 7
24.1%
2024-03-14T20:21:19.429129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
형 13
 
9.3%
차 11
 
7.9%
수 8
 
5.7%
량 8
 
5.7%
용 7
 
5.0%
일 6
 
4.3%
반 6
 
4.3%
5
 
3.6%
송 5
 
3.6%
금 4
 
2.9%
Other values (40) 67
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126
90.0%
Space Separator 5
 
3.6%
Open Punctuation 4
 
2.9%
Close Punctuation 4
 
2.9%
Other Punctuation 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
형 13
 
10.3%
차 11
 
8.7%
수 8
 
6.3%
량 8
 
6.3%
용 7
 
5.6%
일 6
 
4.8%
반 6
 
4.8%
송 5
 
4.0%
금 4
 
3.2%
현 4
 
3.2%
Other values (36) 54
42.9%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126
90.0%
Common 14
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
형 13
 
10.3%
차 11
 
8.7%
수 8
 
6.3%
량 8
 
6.3%
용 7
 
5.6%
일 6
 
4.8%
반 6
 
4.8%
송 5
 
4.0%
금 4
 
3.2%
현 4
 
3.2%
Other values (36) 54
42.9%
Common
ValueCountFrequency (%)
5
35.7%
( 4
28.6%
) 4
28.6%
, 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126
90.0%
ASCII 14
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
형 13
 
10.3%
차 11
 
8.7%
수 8
 
6.3%
량 8
 
6.3%
용 7
 
5.6%
일 6
 
4.8%
반 6
 
4.8%
송 5
 
4.0%
금 4
 
3.2%
현 4
 
3.2%
Other values (36) 54
42.9%
ASCII
ValueCountFrequency (%)
5
35.7%
( 4
28.6%
) 4
28.6%
, 1
 
7.1%

규격
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Memory size336.0 B
2024-03-14T20:21:20.063118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length7.8571429
Min length4

Characters and Unicode

Total characters55
Distinct characters21
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

Unique7 ?
Unique (%)100.0%

Sample

1st row1톤 이하
2nd row1톤 초과 5톤 미만
3rd row5톤 이상
4th row진개덤프
5th row암롤트럭
ValueCountFrequency (%)
1톤 3
16.7%
초과 3
16.7%
5톤 3
16.7%
이하 2
11.1%
미만 2
11.1%
2.5톤 2
11.1%
이상 1
 
5.6%
진개덤프 1
 
5.6%
암롤트럭 1
 
5.6%
2024-03-14T20:21:21.087141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
20.0%
톤 8
14.5%
5 5
 
9.1%
1 3
 
5.5%
이 3
 
5.5%
초 3
 
5.5%
과 3
 
5.5%
만 2
 
3.6%
2 2
 
3.6%
. 2
 
3.6%
Other values (11) 13
23.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32
58.2%
Space Separator 11
 
20.0%
Decimal Number 10
 
18.2%
Other Punctuation 2
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
톤 8
25.0%
이 3
 
9.4%
초 3
 
9.4%
과 3
 
9.4%
만 2
 
6.2%
미 2
 
6.2%
하 2
 
6.2%
진 1
 
3.1%
개 1
 
3.1%
덤 1
 
3.1%
Other values (6) 6
18.8%
Decimal Number
ValueCountFrequency (%)
5 5
50.0%
1 3
30.0%
2 2
 
20.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32
58.2%
Common 23
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
톤 8
25.0%
이 3
 
9.4%
초 3
 
9.4%
과 3
 
9.4%
만 2
 
6.2%
미 2
 
6.2%
하 2
 
6.2%
진 1
 
3.1%
개 1
 
3.1%
덤 1
 
3.1%
Other values (6) 6
18.8%
Common
ValueCountFrequency (%)
11
47.8%
5 5
21.7%
1 3
 
13.0%
2 2
 
8.7%
. 2
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32
58.2%
ASCII 23
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
47.8%
5 5
21.7%
1 3
 
13.0%
2 2
 
8.7%
. 2
 
8.7%
Hangul
ValueCountFrequency (%)
톤 8
25.0%
이 3
 
9.4%
초 3
 
9.4%
과 3
 
9.4%
만 2
 
6.2%
미 2
 
6.2%
하 2
 
6.2%
진 1
 
3.1%
개 1
 
3.1%
덤 1
 
3.1%
Other values (6) 6
18.8%

동구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.307692
Minimum0
Maximum189
Zeros5
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T20:21:21.450016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median9
Q323.75
95-th percentile108.75
Maximum189
Range189
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation43.841322
Coefficient of variation (CV)1.7323319
Kurtosis7.762834
Mean25.307692
Median Absolute Deviation (MAD)8
Skewness2.7336397
Sum658
Variance1922.0615
MonotonicityNot monotonic
2024-03-14T20:21:21.819083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 5
19.2%
4 3
 
11.5%
10 2
 
7.7%
8 2
 
7.7%
15 1
 
3.8%
102 1
 
3.8%
23 1
 
3.8%
12 1
 
3.8%
111 1
 
3.8%
13 1
 
3.8%
Other values (8) 8
30.8%
ValueCountFrequency (%)
0 5
19.2%
2 1
 
3.8%
3 1
 
3.8%
4 3
11.5%
7 1
 
3.8%
8 2
 
7.7%
10 2
 
7.7%
12 1
 
3.8%
13 1
 
3.8%
15 1
 
3.8%
ValueCountFrequency (%)
189 1
3.8%
111 1
3.8%
102 1
3.8%
48 1
3.8%
33 1
3.8%
28 1
3.8%
24 1
3.8%
23 1
3.8%
15 1
3.8%
13 1
3.8%

서구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.769231
Minimum0
Maximum592
Zeros7
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T20:21:22.077532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median14
Q373.75
95-th percentile174.5
Maximum592
Range592
Interquartile range (IQR)73.5

Descriptive statistics

Standard deviation120.46885
Coefficient of variation (CV)1.9192342
Kurtosis15.669173
Mean62.769231
Median Absolute Deviation (MAD)14
Skewness3.6725732
Sum1632
Variance14512.745
MonotonicityNot monotonic
2024-03-14T20:21:22.278492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 7
26.9%
3 2
 
7.7%
51 2
 
7.7%
78 1
 
3.8%
141 1
 
3.8%
109 1
 
3.8%
34 1
 
3.8%
592 1
 
3.8%
10 1
 
3.8%
61 1
 
3.8%
Other values (8) 8
30.8%
ValueCountFrequency (%)
0 7
26.9%
1 1
 
3.8%
2 1
 
3.8%
3 2
 
7.7%
7 1
 
3.8%
10 1
 
3.8%
18 1
 
3.8%
34 1
 
3.8%
49 1
 
3.8%
51 2
 
7.7%
ValueCountFrequency (%)
592 1
3.8%
182 1
3.8%
152 1
3.8%
141 1
3.8%
109 1
3.8%
88 1
3.8%
78 1
3.8%
61 1
3.8%
51 2
7.7%
49 1
3.8%

남구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.307692
Minimum0
Maximum331
Zeros13
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T20:21:22.477263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q383.25
95-th percentile194.75
Maximum331
Range331
Interquartile range (IQR)83.25

Descriptive statistics

Standard deviation86.057083
Coefficient of variation (CV)1.7106148
Kurtosis3.4489231
Mean50.307692
Median Absolute Deviation (MAD)0.5
Skewness1.9153347
Sum1308
Variance7405.8215
MonotonicityNot monotonic
2024-03-14T20:21:22.672627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 13
50.0%
2 2
 
7.7%
331 1
 
3.8%
177 1
 
3.8%
123 1
 
3.8%
57 1
 
3.8%
17 1
 
3.8%
1 1
 
3.8%
101 1
 
3.8%
179 1
 
3.8%
Other values (3) 3
 
11.5%
ValueCountFrequency (%)
0 13
50.0%
1 1
 
3.8%
2 2
 
7.7%
17 1
 
3.8%
26 1
 
3.8%
57 1
 
3.8%
92 1
 
3.8%
101 1
 
3.8%
123 1
 
3.8%
177 1
 
3.8%
ValueCountFrequency (%)
331 1
3.8%
200 1
3.8%
179 1
3.8%
177 1
3.8%
123 1
3.8%
101 1
3.8%
92 1
3.8%
57 1
3.8%
26 1
3.8%
17 1
3.8%

북구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.07692
Minimum0
Maximum757
Zeros6
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T20:21:23.133219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median74.5
Q3188
95-th percentile647.75
Maximum757
Range757
Interquartile range (IQR)186.75

Descriptive statistics

Standard deviation217.25247
Coefficient of variation (CV)1.410026
Kurtosis2.2202213
Mean154.07692
Median Absolute Deviation (MAD)74.5
Skewness1.7180872
Sum4006
Variance47198.634
MonotonicityNot monotonic
2024-03-14T20:21:23.340043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 6
23.1%
2 2
 
7.7%
31 1
 
3.8%
146 1
 
3.8%
433 1
 
3.8%
81 1
 
3.8%
68 1
 
3.8%
757 1
 
3.8%
82 1
 
3.8%
170 1
 
3.8%
Other values (10) 10
38.5%
ValueCountFrequency (%)
0 6
23.1%
1 1
 
3.8%
2 2
 
7.7%
8 1
 
3.8%
19 1
 
3.8%
31 1
 
3.8%
68 1
 
3.8%
81 1
 
3.8%
82 1
 
3.8%
113 1
 
3.8%
ValueCountFrequency (%)
757 1
3.8%
694 1
3.8%
509 1
3.8%
433 1
3.8%
321 1
3.8%
224 1
3.8%
194 1
3.8%
170 1
3.8%
151 1
3.8%
146 1
3.8%

광산구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.46154
Minimum0
Maximum1622
Zeros5
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T20:21:23.596020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.25
median60
Q3431.25
95-th percentile1531
Maximum1622
Range1622
Interquartile range (IQR)426

Descriptive statistics

Standard deviation519.39459
Coefficient of variation (CV)1.5078449
Kurtosis1.3283792
Mean344.46154
Median Absolute Deviation (MAD)60
Skewness1.5821254
Sum8956
Variance269770.74
MonotonicityNot monotonic
2024-03-14T20:21:23.811753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 5
19.2%
60 2
 
7.7%
320 1
 
3.8%
5 1
 
3.8%
67 1
 
3.8%
1474 1
 
3.8%
197 1
 
3.8%
780 1
 
3.8%
195 1
 
3.8%
869 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
0 5
19.2%
4 1
 
3.8%
5 1
 
3.8%
6 1
 
3.8%
17 1
 
3.8%
18 1
 
3.8%
25 1
 
3.8%
31 1
 
3.8%
60 2
 
7.7%
67 1
 
3.8%
ValueCountFrequency (%)
1622 1
3.8%
1550 1
3.8%
1474 1
3.8%
869 1
3.8%
861 1
3.8%
780 1
3.8%
465 1
3.8%
330 1
3.8%
320 1
3.8%
197 1
3.8%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size336.0 B
2023-12-31
26 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-31
2nd row2023-12-31
3rd row2023-12-31
4th row2023-12-31
5th row2023-12-31

Common Values

ValueCountFrequency (%)
2023-12-31 26
100.0%

Length

2024-03-14T20:21:24.175358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:21:24.382413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-31 26
100.0%

Interactions

2024-03-14T20:21:16.135133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:11.054607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:12.331212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:13.599945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:14.862388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:16.396516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:11.308040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:12.588306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:13.855093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:15.121346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:16.648015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:11.561748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:12.842614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:14.104073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:15.368244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:16.902318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:11.809046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:13.090012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:14.347977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:15.618616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:17.162325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:12.067926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:13.340349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:14.602872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:21:15.871098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:21:24.489828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분형태규격동구서구남구북구광산구
구분1.0000.0001.0000.3660.2130.2300.3380.531
형태0.0001.0001.0000.0000.0000.0000.0000.689
규격1.0001.0001.0001.0001.0001.0001.0001.000
동구0.3660.0001.0001.0000.9310.6850.7870.629
서구0.2130.0001.0000.9311.0000.8520.8150.637
남구0.2300.0001.0000.6850.8521.0000.7620.645
북구0.3380.0001.0000.7870.8150.7621.0000.703
광산구0.5310.6891.0000.6290.6370.6450.7031.000
2024-03-14T20:21:24.682295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동구서구남구북구광산구구분
동구1.0000.7640.3800.8230.6220.274
서구0.7641.0000.5270.7900.8490.134
남구0.3800.5271.0000.3910.6070.104
북구0.8230.7900.3911.0000.6920.197
광산구0.6220.8490.6070.6921.0000.223
구분0.2740.1340.1040.1970.2231.000

Missing values

2024-03-14T20:21:17.528126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:21:17.840181image/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

구분형태규격동구서구남구북구광산구데이터기준일자
0일반일반형1톤 이하1078331313202023-12-31
1일반일반형1톤 초과 5톤 미만815217722415502023-12-31
2일반일반형5톤 이상4818212350916222023-12-31
3일반밴형(현금수송차량 제외)<NA>770162023-12-31
4일반덤프형<NA>3320182023-12-31
5일반견인형(트랙터)<NA>331801514652023-12-31
6일반냉장냉동형<NA>244957193302023-12-31
7일반구난형(렉커)<NA>4117113602023-12-31
8일반유류,화학물질수송용 탱크로리<NA>0000252023-12-31
9일반현금수송용차량<NA>000002023-12-31
구분형태규격동구서구남구북구광산구데이터기준일자
16개별일반형1톤 초과 2.5톤 이하45101461952023-12-31
17개별일반형2.5톤 초과 5톤 미만158823217802023-12-31
18개별밴형<NA>2026202023-12-31
19개별특수자동차<NA>86101701972023-12-31
20개별기타<NA>131008202023-12-31
21용달일반형<NA>1115929275714742023-12-31
22용달밴형(현금수송차량 제외)<NA>123420068602023-12-31
23용달현금수송용차량<NA>000002023-12-31
24용달특수자동차<NA>23109081672023-12-31
25용달기타<NA>1023043302023-12-31