Overview

Dataset statistics

Number of variables8
Number of observations920
Missing cells453
Missing cells (%)6.2%
Duplicate rows356
Duplicate rows (%)38.7%
Total size in memory59.4 KiB
Average record size in memory66.1 B

Variable types

Categorical5
Text1
Numeric2

Dataset

Description2023년 6월 27일 기준 충청남도 서천군에 등록된 일반화물자동차 현황 데이터로 상호(소유주), 사업의 종류, 차명, 차종, 총중량, 최대적재량, 사업자구분, 법인번호를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=40&beforeMenuCd=DOM_000000201001001000&publicdatapk=15115507

Alerts

사업의종류 has constant value ""Constant
Dataset has 356 (38.7%) duplicate rowsDuplicates
상호 is highly overall correlated with 총중량 and 3 other fieldsHigh correlation
법인번호 is highly overall correlated with 상호 and 1 other fieldsHigh correlation
사업자구분 is highly overall correlated with 상호 and 1 other fieldsHigh correlation
총중량 is highly overall correlated with 최대적재량 and 2 other fieldsHigh correlation
최대적재량 is highly overall correlated with 총중량 and 1 other fieldsHigh correlation
차종 is highly overall correlated with 총중량High correlation
사업자구분 is highly imbalanced (51.7%)Imbalance
총중량 has 268 (29.1%) missing valuesMissing
최대적재량 has 181 (19.7%) missing valuesMissing

Reproduction

Analysis started2024-01-09 22:13:23.526747
Analysis finished2024-01-09 22:13:24.504306
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상호
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
로뎀운송 주식회사 서천지점
298 
주식회사 공단운수
88 
합자회사대성화물
76 
씨제이대한통운(주)
70 
(합)길흥화물운송사
70 
Other values (36)
318 

Length

Max length14
Median length12
Mean length10.180435
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row이**
2nd row천지렉카(주)
3rd row천지렉카(주)
4th row천지렉카(주)
5th row서**

Common Values

ValueCountFrequency (%)
로뎀운송 주식회사 서천지점 298
32.4%
주식회사 공단운수 88
 
9.6%
합자회사대성화물 76
 
8.3%
씨제이대한통운(주) 70
 
7.6%
(합)길흥화물운송사 70
 
7.6%
(주)한길물류 62
 
6.7%
동백상운 주식회사 40
 
4.3%
주식회사 서천운수 26
 
2.8%
주식회사서부특수운송 22
 
2.4%
공** 16
 
1.7%
Other values (31) 152
16.5%

Length

2024-01-10T07:13:24.559094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
주식회사 452
26.8%
로뎀운송 298
17.7%
서천지점 298
17.7%
공단운수 88
 
5.2%
합자회사대성화물 76
 
4.5%
씨제이대한통운(주 70
 
4.2%
합)길흥화물운송사 70
 
4.2%
주)한길물류 62
 
3.7%
동백상운 40
 
2.4%
서천운수 26
 
1.5%
Other values (35) 204
12.1%

사업의종류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
(구)일반화물
920 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(구)일반화물
2nd row(구)일반화물
3rd row(구)일반화물
4th row(구)일반화물
5th row(구)일반화물

Common Values

ValueCountFrequency (%)
(구)일반화물 920
100.0%

Length

2024-01-10T07:13:24.645541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:13:24.712394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구)일반화물 920
100.0%

차명
Text

Distinct225
Distinct (%)24.6%
Missing4
Missing (%)0.4%
Memory size7.3 KiB
2024-01-10T07:13:24.881731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length18
Mean length9.9716157
Min length2

Characters and Unicode

Total characters9134
Distinct characters234
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row메가트럭
2nd row마이티Ⅱ3.5톤(터보)
3rd row엠뱅크코란도언더리프트
4th row세인고소작업차
5th row쌍용17.5톤카고트럭
ValueCountFrequency (%)
트라고(trago 46
 
4.1%
한특40피트컨테이너샤시 44
 
3.9%
한특40피트 40
 
3.5%
한특 38
 
3.4%
컨테이너샤시 32
 
2.8%
샤시 24
 
2.1%
컨테이너 20
 
1.8%
트랙터 20
 
1.8%
fh 20
 
1.8%
캐스보러 18
 
1.6%
Other values (244) 832
73.4%
2024-01-10T07:13:25.185352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
트 714
 
7.8%
한 296
 
3.2%
럭 260
 
2.8%
톤 250
 
2.7%
4 238
 
2.6%
5 222
 
2.4%
218
 
2.4%
고 208
 
2.3%
이 206
 
2.3%
0 198
 
2.2%
Other values (224) 6324
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6586
72.1%
Decimal Number 1122
 
12.3%
Uppercase Letter 628
 
6.9%
Space Separator 218
 
2.4%
Other Punctuation 162
 
1.8%
Open Punctuation 110
 
1.2%
Close Punctuation 110
 
1.2%
Other Symbol 66
 
0.7%
Lowercase Letter 60
 
0.7%
Letter Number 52
 
0.6%
Other values (2) 20
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 714
 
10.8%
한 296
 
4.5%
럭 260
 
3.9%
톤 250
 
3.8%
고 208
 
3.2%
이 206
 
3.1%
특 186
 
2.8%
러 182
 
2.8%
대 180
 
2.7%
카 162
 
2.5%
Other values (170) 3942
59.9%
Uppercase Letter
ValueCountFrequency (%)
T 96
15.3%
R 92
14.6%
A 78
12.4%
O 72
11.5%
G 62
9.9%
E 30
 
4.8%
F 28
 
4.5%
X 24
 
3.8%
N 22
 
3.5%
H 22
 
3.5%
Other values (14) 102
16.2%
Decimal Number
ValueCountFrequency (%)
4 238
21.2%
5 222
19.8%
0 198
17.6%
2 136
12.1%
3 102
9.1%
1 86
 
7.7%
6 64
 
5.7%
7 32
 
2.9%
8 24
 
2.1%
9 20
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
x 14
23.3%
t 10
16.7%
s 8
13.3%
o 6
10.0%
r 6
10.0%
c 6
10.0%
f 4
 
6.7%
n 2
 
3.3%
e 2
 
3.3%
ℓ 2
 
3.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 42
80.8%
Ⅲ 10
 
19.2%
Dash Punctuation
ValueCountFrequency (%)
－ 6
60.0%
- 4
40.0%
Space Separator
ValueCountFrequency (%)
218
100.0%
Other Punctuation
ValueCountFrequency (%)
. 162
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Other Symbol
ValueCountFrequency (%)
㎥ 66
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6586
72.1%
Common 1810
 
19.8%
Latin 738
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 714
 
10.8%
한 296
 
4.5%
럭 260
 
3.9%
톤 250
 
3.8%
고 208
 
3.2%
이 206
 
3.1%
특 186
 
2.8%
러 182
 
2.8%
대 180
 
2.7%
카 162
 
2.5%
Other values (170) 3942
59.9%
Latin
ValueCountFrequency (%)
T 96
13.0%
R 92
12.5%
A 78
10.6%
O 72
 
9.8%
G 62
 
8.4%
Ⅱ 42
 
5.7%
E 30
 
4.1%
F 28
 
3.8%
X 24
 
3.3%
N 22
 
3.0%
Other values (25) 192
26.0%
Common
ValueCountFrequency (%)
4 238
13.1%
5 222
12.3%
218
12.0%
0 198
10.9%
. 162
9.0%
2 136
7.5%
( 110
6.1%
) 110
6.1%
3 102
5.6%
1 86
 
4.8%
Other values (9) 228
12.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6586
72.1%
ASCII 2422
 
26.5%
CJK Compat 66
 
0.7%
Number Forms 52
 
0.6%
None 6
 
0.1%
Letterlike Symbols 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
트 714
 
10.8%
한 296
 
4.5%
럭 260
 
3.9%
톤 250
 
3.8%
고 208
 
3.2%
이 206
 
3.1%
특 186
 
2.8%
러 182
 
2.8%
대 180
 
2.7%
카 162
 
2.5%
Other values (170) 3942
59.9%
ASCII
ValueCountFrequency (%)
4 238
 
9.8%
5 222
 
9.2%
218
 
9.0%
0 198
 
8.2%
. 162
 
6.7%
2 136
 
5.6%
( 110
 
4.5%
) 110
 
4.5%
3 102
 
4.2%
T 96
 
4.0%
Other values (39) 830
34.3%
CJK Compat
ValueCountFrequency (%)
㎥ 66
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 42
80.8%
Ⅲ 10
 
19.2%
None
ValueCountFrequency (%)
－ 6
100.0%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 2
100.0%

차종
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
화물일반형-카고대형
202 
화물특수용도형-특수용도형대형
168 
화물
142 
화물특수용도형-피견인대형
124 
트레일러
92 
Other values (23)
192 

Length

Max length15
Median length13
Mean length8.8891304
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화물일반형-카고대형
2nd row화물일반형-카고중형
3rd row특수구난소형
4th row특수특수작업형대형
5th row화물일반형-카고대형

Common Values

ValueCountFrequency (%)
화물일반형-카고대형 202
22.0%
화물특수용도형-특수용도형대형 168
18.3%
화물 142
15.4%
화물특수용도형-피견인대형 124
13.5%
트레일러 92
10.0%
특수견인대형 74
 
8.0%
화물일반형-카고중형 18
 
2.0%
특수구난소형 16
 
1.7%
화물일반형-카고소형 10
 
1.1%
화물특수용도형-특수용도형중형 10
 
1.1%
Other values (18) 64
 
7.0%

Length

2024-01-10T07:13:25.294331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화물일반형-카고대형 202
22.0%
화물특수용도형-특수용도형대형 168
18.3%
화물 142
15.4%
화물특수용도형-피견인대형 124
13.5%
트레일러 92
10.0%
특수견인대형 74
 
8.0%
화물일반형-카고중형 18
 
2.0%
특수구난소형 16
 
1.7%
화물일반형-카고소형 10
 
1.1%
화물특수용도형-특수용도형중형 10
 
1.1%
Other values (18) 64
 
7.0%

총중량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct194
Distinct (%)29.8%
Missing268
Missing (%)29.1%
Infinite0
Infinite (%)0.0%
Mean25809.202
Minimum2860
Maximum39980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2024-01-10T07:13:25.393773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2860
5-th percentile3465
Q124700
median30400
Q330930
95-th percentile39105
Maximum39980
Range37120
Interquartile range (IQR)6230

Descriptive statistics

Standard deviation10016.607
Coefficient of variation (CV)0.38810217
Kurtosis-0.1491932
Mean25809.202
Median Absolute Deviation (MAD)4740
Skewness-0.88547257
Sum16827600
Variance1.0033242 × 108
MonotonicityNot monotonic
2024-01-10T07:13:25.727592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30795 40
 
4.3%
30500 24
 
2.6%
30910 20
 
2.2%
38875 16
 
1.7%
25030 16
 
1.7%
30930 12
 
1.3%
30800 12
 
1.3%
31000 10
 
1.1%
30970 10
 
1.1%
30950 10
 
1.1%
Other values (184) 482
52.4%
(Missing) 268
29.1%
ValueCountFrequency (%)
2860 2
 
0.2%
2875 2
 
0.2%
2895 2
 
0.2%
2905 2
 
0.2%
2915 2
 
0.2%
2970 6
0.7%
2985 2
 
0.2%
3000 2
 
0.2%
3125 2
 
0.2%
3245 2
 
0.2%
ValueCountFrequency (%)
39980 2
 
0.2%
39920 2
 
0.2%
39840 2
 
0.2%
39760 2
 
0.2%
39630 2
 
0.2%
39410 6
0.7%
39345 2
 
0.2%
39320 2
 
0.2%
39240 4
0.4%
39230 2
 
0.2%

최대적재량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct103
Distinct (%)13.9%
Missing181
Missing (%)19.7%
Infinite0
Infinite (%)0.0%
Mean18772.677
Minimum0
Maximum27500
Zeros5
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2024-01-10T07:13:25.838916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q114000
median23000
Q325600
95-th percentile27000
Maximum27500
Range27500
Interquartile range (IQR)11600

Descriptive statistics

Standard deviation8864.9999
Coefficient of variation (CV)0.47222887
Kurtosis-0.61179911
Mean18772.677
Median Absolute Deviation (MAD)4000
Skewness-0.90403151
Sum13873008
Variance78588223
MonotonicityNot monotonic
2024-01-10T07:13:25.944152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27000 74
 
8.0%
25000 68
 
7.4%
16500 62
 
6.7%
26500 52
 
5.7%
25500 46
 
5.0%
24000 22
 
2.4%
4500 22
 
2.4%
500 20
 
2.2%
27500 14
 
1.5%
25600 12
 
1.3%
Other values (93) 347
37.7%
(Missing) 181
19.7%
ValueCountFrequency (%)
0 5
 
0.5%
500 20
2.2%
600 2
 
0.2%
650 2
 
0.2%
700 4
 
0.4%
750 2
 
0.2%
800 4
 
0.4%
1000 2
 
0.2%
1200 2
 
0.2%
1400 2
 
0.2%
ValueCountFrequency (%)
27500 14
 
1.5%
27300 2
 
0.2%
27200 2
 
0.2%
27100 8
 
0.9%
27000 74
8.0%
26900 4
 
0.4%
26500 52
5.7%
26200 2
 
0.2%
26000 8
 
0.9%
25900 8
 
0.9%

사업자구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
법인
824 
개인
96 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row법인
3rd row법인
4th row법인
5th row개인

Common Values

ValueCountFrequency (%)
법인 824
89.6%
개인 96
 
10.4%

Length

2024-01-10T07:13:26.063193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:13:26.155590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 824
89.6%
개인 96
 
10.4%

법인번호
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size7.3 KiB
135211-0012832
298 
<NA>
96 
164411-0001802
88 
164413-0001030
76 
164412-0000018
70 
Other values (17)
292 

Length

Max length14
Median length14
Mean length12.956522
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row164411-0013542
3rd row164411-0013542
4th row164411-0013542
5th row<NA>

Common Values

ValueCountFrequency (%)
135211-0012832 298
32.4%
<NA> 96
 
10.4%
164411-0001802 88
 
9.6%
164413-0001030 76
 
8.3%
164412-0000018 70
 
7.6%
110111-0006167 70
 
7.6%
164511-0025446 62
 
6.7%
164411-0006894 40
 
4.3%
164411-0004260 26
 
2.8%
164411-0001343 22
 
2.4%
Other values (12) 72
 
7.8%

Length

2024-01-10T07:13:26.253898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
135211-0012832 298
32.4%
na 96
 
10.4%
164411-0001802 88
 
9.6%
164413-0001030 76
 
8.3%
164412-0000018 70
 
7.6%
110111-0006167 70
 
7.6%
164511-0025446 62
 
6.7%
164411-0006894 40
 
4.3%
164411-0004260 26
 
2.8%
164411-0001343 22
 
2.4%
Other values (12) 72
 
7.8%

Interactions

2024-01-10T07:13:24.092705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:13:23.945725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:13:24.166256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:13:24.020924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:13:26.340335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호차종총중량최대적재량사업자구분법인번호
상호1.0000.8910.8990.8960.9951.000
차종0.8911.0000.8520.8050.4920.861
총중량0.8990.8521.0000.9230.4680.829
최대적재량0.8960.8050.9231.0000.5020.803
사업자구분0.9950.4920.4680.5021.000NaN
법인번호1.0000.8610.8290.803NaN1.000
2024-01-10T07:13:26.452439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호법인번호사업자구분차종
상호1.0001.0000.9690.400
법인번호1.0001.0001.0000.423
사업자구분0.9691.0001.0000.419
차종0.4000.4230.4191.000
2024-01-10T07:13:26.548352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총중량최대적재량상호차종사업자구분법인번호
총중량1.0000.6910.5330.5230.3590.488
최대적재량0.6911.0000.5270.4500.3840.449
상호0.5330.5271.0000.4000.9691.000
차종0.5230.4500.4001.0000.4190.423
사업자구분0.3590.3840.9690.4191.0001.000
법인번호0.4880.4491.0000.4231.0001.000

Missing values

2024-01-10T07:13:24.266762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:13:24.369828image/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.
2024-01-10T07:13:24.451776image/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

상호사업의종류차명차종총중량최대적재량사업자구분법인번호
0이**(구)일반화물메가트럭화물일반형-카고대형104155000개인<NA>
1천지렉카(주)(구)일반화물마이티Ⅱ3.5톤(터보)화물일반형-카고중형67351634법인164411-0013542
2천지렉카(주)(구)일반화물엠뱅크코란도언더리프트특수구난소형2970500법인164411-0013542
3천지렉카(주)(구)일반화물세인고소작업차특수특수작업형대형129400법인164411-0013542
4서**(구)일반화물쌍용17.5톤카고트럭화물일반형-카고대형2994516900개인<NA>
5강**(구)일반화물현대18톤초장축카고트럭화물일반형-카고대형3089017100개인<NA>
6김**(구)일반화물토미26톤카고화물일반형-카고대형3932011500개인<NA>
7김**(구)일반화물세인고소작업차특수특수작업형대형13155<NA>개인<NA>
8김**(구)일반화물뉴파워트럭화물일반형-카고대형225807800개인<NA>
9최**(구)일반화물JW450 고소작업차특수특수용도형대형16120<NA>개인<NA>
상호사업의종류차명차종총중량최대적재량사업자구분법인번호
910씨제이대한통운(주)(구)일반화물포터Ⅱ(PORTERⅡ)화물<NA><NA>법인110111-0006167
911씨제이대한통운(주)(구)일반화물현대슈퍼트럭화물<NA><NA>법인110111-0006167
912공**(구)일반화물<NA><NA><NA><NA>개인<NA>
913공**(구)일반화물대우19톤카고트럭화물일반형-카고대형<NA><NA>개인<NA>
914공**(구)일반화물대우11.5톤카고트럭화물일반형-카고대형<NA><NA>개인<NA>
915공**(구)일반화물온세입자형분말운송차화물특수용도형-특수용도형대형<NA><NA>개인<NA>
916공**(구)일반화물현대5톤트럭화물일반형-카고대형<NA><NA>개인<NA>
917공**(구)일반화물현대 11톤 카고트럭화물일반형-카고대형<NA><NA>개인<NA>
918공**(구)일반화물대우트랙터특수견인대형<NA><NA>개인<NA>
919공**(구)일반화물<NA><NA><NA><NA>개인<NA>

Duplicate rows

Most frequently occurring

상호사업의종류차명차종총중량최대적재량사업자구분법인번호# duplicates
175로뎀운송 주식회사 서천지점(구)일반화물한특40피트컨테이너샤시화물특수용도형-피견인대형3079527000법인135211-001283240
165로뎀운송 주식회사 서천지점(구)일반화물한특트레일러<NA>26500법인135211-001283220
169로뎀운송 주식회사 서천지점(구)일반화물한특40피트 컨테이너 샤시화물특수용도형-특수용도형대형3050026500법인135211-001283220
170로뎀운송 주식회사 서천지점(구)일반화물한특40피트 컨테이너샤시화물특수용도형-특수용도형대형3091025500법인135211-001283220
261주식회사 공단운수(구)일반화물트라고(TRAGO)화물<NA><NA>법인164411-000180210
109로뎀운송 주식회사 서천지점(구)일반화물대한트레일러<NA>25900법인135211-00128328
149로뎀운송 주식회사 서천지점(구)일반화물캐스보러트레일러<NA>25500법인135211-00128328
172로뎀운송 주식회사 서천지점(구)일반화물한특40피트구즈넥샤시화물특수용도형-특수용도형대형3097027000법인135211-00128328
61(합)길흥화물운송사(구)일반화물한국특장기술6.5톤트럭화물<NA><NA>법인164412-00000186
143로뎀운송 주식회사 서천지점(구)일반화물진도40피트 구즈넥 컨테이너샤시화물특수용도형-특수용도형대형3035026500법인135211-00128326