Overview

Dataset statistics

Number of variables8
Number of observations95
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory67.4 B

Variable types

Numeric2
Categorical4
Text1
DateTime1

Dataset

Description경상남도 밀양시 일반화물자동차운송사업자 업체 현황에 대한 자료로, 업체명, 사업자구분, 차량대수, 소재지에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15114959/fileData.do

Alerts

시군구 has constant value ""Constant
업종구분 has constant value ""Constant
데이터기준날짜 has constant value ""Constant
차량대수 is highly overall correlated with 사업자구분High correlation
사업자구분 is highly overall correlated with 차량대수 and 1 other fieldsHigh correlation
소재지 is highly overall correlated with 사업자구분High correlation
연번 has unique valuesUnique
업체명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:38:23.410786
Analysis finished2023-12-12 10:38:24.520795
Duration1.11 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48
Minimum1
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T19:38:24.627717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.7
Q124.5
median48
Q371.5
95-th percentile90.3
Maximum95
Range94
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.568098
Coefficient of variation (CV)0.57433536
Kurtosis-1.2
Mean48
Median Absolute Deviation (MAD)24
Skewness0
Sum4560
Variance760
MonotonicityStrictly increasing
2023-12-12T19:38:24.797711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
2 1
 
1.1%
71 1
 
1.1%
70 1
 
1.1%
69 1
 
1.1%
68 1
 
1.1%
67 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
Other values (85) 85
89.5%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
95 1
1.1%
94 1
1.1%
93 1
1.1%
92 1
1.1%
91 1
1.1%
90 1
1.1%
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
밀양시
95 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
밀양시 95
100.0%

Length

2023-12-12T19:38:24.936045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:38:25.035804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
밀양시 95
100.0%

업체명
Text

UNIQUE 

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size892.0 B
2023-12-12T19:38:25.350310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.7263158
Min length3

Characters and Unicode

Total characters544
Distinct characters127
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)100.0%

Sample

1st row(주)혁신통운
2nd row(주)금마로지스
3rd row호운통운(주)
4th row밀양츄레라 (김명석)
5th row정해동
ValueCountFrequency (%)
주)혁신통운 1
 
1.0%
㈜대진유조 1
 
1.0%
일등렉카 1
 
1.0%
강순호 1
 
1.0%
주)한양물류 1
 
1.0%
청룡특수렉커 1
 
1.0%
현대렉카 1
 
1.0%
최재완 1
 
1.0%
경성상운㈜ 1
 
1.0%
㈜길창운수 1
 
1.0%
Other values (87) 87
89.7%
2023-12-12T19:38:25.877636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 37
 
6.8%
) 37
 
6.8%
주 35
 
6.4%
운 31
 
5.7%
수 24
 
4.4%
㈜ 24
 
4.4%
물 15
 
2.8%
카 11
 
2.0%
스 11
 
2.0%
대 10
 
1.8%
Other values (117) 309
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 439
80.7%
Open Punctuation 37
 
6.8%
Close Punctuation 37
 
6.8%
Other Symbol 24
 
4.4%
Uppercase Letter 3
 
0.6%
Space Separator 2
 
0.4%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 35
 
8.0%
운 31
 
7.1%
수 24
 
5.5%
물 15
 
3.4%
카 11
 
2.5%
스 11
 
2.5%
대 10
 
2.3%
양 9
 
2.1%
로 9
 
2.1%
동 9
 
2.1%
Other values (108) 275
62.6%
Uppercase Letter
ValueCountFrequency (%)
L 1
33.3%
O 1
33.3%
H 1
33.3%
Decimal Number
ValueCountFrequency (%)
7 1
50.0%
9 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 24
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 463
85.1%
Common 78
 
14.3%
Latin 3
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 35
 
7.6%
운 31
 
6.7%
수 24
 
5.2%
㈜ 24
 
5.2%
물 15
 
3.2%
카 11
 
2.4%
스 11
 
2.4%
대 10
 
2.2%
양 9
 
1.9%
로 9
 
1.9%
Other values (109) 284
61.3%
Common
ValueCountFrequency (%)
( 37
47.4%
) 37
47.4%
2
 
2.6%
7 1
 
1.3%
9 1
 
1.3%
Latin
ValueCountFrequency (%)
L 1
33.3%
O 1
33.3%
H 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 439
80.7%
ASCII 81
 
14.9%
None 24
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 37
45.7%
) 37
45.7%
2
 
2.5%
7 1
 
1.2%
9 1
 
1.2%
L 1
 
1.2%
O 1
 
1.2%
H 1
 
1.2%
Hangul
ValueCountFrequency (%)
주 35
 
8.0%
운 31
 
7.1%
수 24
 
5.5%
물 15
 
3.4%
카 11
 
2.5%
스 11
 
2.5%
대 10
 
2.3%
양 9
 
2.1%
로 9
 
2.1%
동 9
 
2.1%
Other values (108) 275
62.6%
None
ValueCountFrequency (%)
㈜ 24
100.0%

업종구분
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
일반화물
95 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반화물 95
100.0%

Length

2023-12-12T19:38:26.021921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:38:26.129095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반화물 95
100.0%

사업자구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
법인
59 
개인
36 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
법인 59
62.1%
개인 36
37.9%

Length

2023-12-12T19:38:26.254090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:38:26.395967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 59
62.1%
개인 36
37.9%

차량대수
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.073684
Minimum1
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2023-12-12T19:38:26.558179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q342
95-th percentile98.3
Maximum116
Range115
Interquartile range (IQR)41

Descriptive statistics

Standard deviation33.602571
Coefficient of variation (CV)1.2887543
Kurtosis0.30039598
Mean26.073684
Median Absolute Deviation (MAD)5
Skewness1.2295847
Sum2477
Variance1129.1328
MonotonicityNot monotonic
2023-12-12T19:38:26.714901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 28
29.5%
2 13
 
13.7%
6 4
 
4.2%
73 4
 
4.2%
42 3
 
3.2%
5 2
 
2.1%
20 2
 
2.1%
13 2
 
2.1%
15 2
 
2.1%
11 2
 
2.1%
Other values (30) 33
34.7%
ValueCountFrequency (%)
1 28
29.5%
2 13
13.7%
3 1
 
1.1%
4 2
 
2.1%
5 2
 
2.1%
6 4
 
4.2%
9 1
 
1.1%
11 2
 
2.1%
13 2
 
2.1%
14 1
 
1.1%
ValueCountFrequency (%)
116 1
1.1%
115 1
1.1%
112 1
1.1%
105 1
1.1%
99 1
1.1%
98 1
1.1%
89 2
2.1%
86 1
1.1%
78 1
1.1%
76 1
1.1%

소재지
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Memory size892.0 B
경상남도 밀양시 무안면 사명로 1103
10 
경상남도 밀양시 내이동
9 
경상남도 밀양시 산내면 가인2길 81
 
6
경상남도 밀양시 북성로7길 34-2, 4동 103호 (내일동, 신화아파트)
 
5
경상남도 밀양시 교동
 
5
Other values (31)
60 

Length

Max length43
Median length40
Mean length20.442105
Min length11

Unique

Unique17 ?
Unique (%)17.9%

Sample

1st row경상남도 밀양시 북성로7길 34-2, 4동 104호 (내일동, 신화아파트)
2nd row경상남도 밀양시 밀양대로 1755-1, 제일훼미리타운 203호 (삼문동)
3rd row경상남도 밀양시 북성로7길 34-2, 4동 103호 (내일동, 신화아파트)
4th row경상남도 밀양시 내이동
5th row경상남도 밀양시 하남읍

Common Values

ValueCountFrequency (%)
경상남도 밀양시 무안면 사명로 1103 10
 
10.5%
경상남도 밀양시 내이동 9
 
9.5%
경상남도 밀양시 산내면 가인2길 81 6
 
6.3%
경상남도 밀양시 북성로7길 34-2, 4동 103호 (내일동, 신화아파트) 5
 
5.3%
경상남도 밀양시 교동 5
 
5.3%
경상남도 밀양시 가곡동 5
 
5.3%
경상남도 밀양시 삼문동 5
 
5.3%
경상남도 밀양시 부북면 4
 
4.2%
경상남도 밀양시 상동면 상동로 213-12 4
 
4.2%
경상남도 밀양시 북성로7길 34-2, 4동 104호 (내일동, 신화아파트) 3
 
3.2%
Other values (26) 39
41.1%

Length

2023-12-12T19:38:26.912422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상남도 95
21.4%
밀양시 95
21.4%
무안면 14
 
3.2%
내이동 12
 
2.7%
하남읍 11
 
2.5%
사명로 10
 
2.3%
1103 10
 
2.3%
내일동 9
 
2.0%
부북면 9
 
2.0%
34-2 8
 
1.8%
Other values (69) 170
38.4%

데이터기준날짜
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
Minimum2023-06-21 00:00:00
Maximum2023-06-21 00:00:00
2023-12-12T19:38:27.067903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:27.206645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T19:38:24.006600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:23.781190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:24.108830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:23.889495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:38:27.362342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업체명사업자구분차량대수소재지
연번1.0001.0000.6710.5900.644
업체명1.0001.0001.0001.0001.000
사업자구분0.6711.0001.0000.7701.000
차량대수0.5901.0000.7701.0000.729
소재지0.6441.0001.0000.7291.000
2023-12-12T19:38:27.575768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지사업자구분
소재지1.0000.796
사업자구분0.7961.000
2023-12-12T19:38:27.700445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번차량대수사업자구분소재지
연번1.0000.4340.4980.229
차량대수0.4341.0000.5800.289
사업자구분0.4980.5801.0000.796
소재지0.2290.2890.7961.000

Missing values

2023-12-12T19:38:24.279398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:38:24.459821image/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

연번시군구업체명업종구분사업자구분차량대수소재지데이터기준날짜
01밀양시(주)혁신통운일반화물법인2경상남도 밀양시 북성로7길 34-2, 4동 104호 (내일동, 신화아파트)2023-06-21
12밀양시(주)금마로지스일반화물법인2경상남도 밀양시 밀양대로 1755-1, 제일훼미리타운 203호 (삼문동)2023-06-21
23밀양시호운통운(주)일반화물법인14경상남도 밀양시 북성로7길 34-2, 4동 103호 (내일동, 신화아파트)2023-06-21
34밀양시밀양츄레라 (김명석)일반화물개인1경상남도 밀양시 내이동2023-06-21
45밀양시정해동일반화물개인1경상남도 밀양시 하남읍2023-06-21
56밀양시(주)동방상운일반화물법인1경상남도 밀양시 무안면 사명로 11032023-06-21
67밀양시(주)창대운수일반화물법인11경상남도 밀양시 무안면 사명로 11032023-06-21
78밀양시지성운수(주)일반화물법인5경상남도 밀양시 북성로7길 34-2, 4동 104호 (내일동, 신화아파트)2023-06-21
89밀양시(주)일신로지스일반화물법인20경상남도 밀양시 무안면 사명로 11032023-06-21
910밀양시(주)미래화물일반화물법인1경상남도 밀양시 북성로7길 34-2, 4동 103호 (내일동, 신화아파트)2023-06-21
연번시군구업체명업종구분사업자구분차량대수소재지데이터기준날짜
8586밀양시삼문렉카일반화물개인2경상남도 밀양시 삼문동2023-06-21
8687밀양시세원특수일반화물개인4경상남도 밀양시 상남면2023-06-21
8788밀양시한신상운㈜일반화물법인86경상남도 밀양시 상동면 상동로 213-122023-06-21
8889밀양시쌍마공업사일반화물개인2경상남도 밀양시 삼문동2023-06-21
8990밀양시(유)신흥특수화물일반화물법인116경상남도 밀양시 상동면 상동로 213-122023-06-21
9091밀양시(주)강호일반화물법인73경상남도 밀양시 단장면 표충로 4242023-06-21
9192밀양시덕창종합물류㈜일반화물법인112경상남도 밀양시 산내면 가인2길 812023-06-21
9293밀양시(주)태경물류일반화물법인42경상남도 밀양시 북성로7길 34-2, 4동 103호 (내일동, 신화아파트)2023-06-21
9394밀양시79렉카일반화물개인2경상남도 밀양시 가곡동2023-06-21
9495밀양시(주)초동물류일반화물법인4경상남도 밀양시 초동면 초동중앙로 404-102023-06-21