Overview

Dataset statistics

Number of variables5
Number of observations70
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory43.9 B

Variable types

Numeric2
Text3

Dataset

Description전라남도에 등록된 국제물류주선업체 현황에 대해 정보를 제공합니다.( 연번, 상호, 주소, 번호, 자본금(억원) 등에 대해서도 확인할 수 있습니다.)
Author전라남도
URLhttps://www.data.go.kr/data/15101260/fileData.do

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:59:03.799706
Analysis finished2023-12-12 18:59:05.102285
Duration1.3 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct70
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.5
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-13T03:59:05.203011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.45
Q118.25
median35.5
Q352.75
95-th percentile66.55
Maximum70
Range69
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation20.351085
Coefficient of variation (CV)0.57327
Kurtosis-1.2
Mean35.5
Median Absolute Deviation (MAD)17.5
Skewness0
Sum2485
Variance414.16667
MonotonicityStrictly increasing
2023-12-13T03:59:05.446578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
46 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
47 1
 
1.4%
45 1
 
1.4%
54 1
 
1.4%
Other values (60) 60
85.7%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%
64 1
1.4%
63 1
1.4%
62 1
1.4%
61 1
1.4%

상호
Text

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size692.0 B
2023-12-13T03:59:05.839884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.5142857
Min length3

Characters and Unicode

Total characters526
Distinct characters127
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)97.1%

Sample

1st row포스코터미널㈜
2nd row㈜태성로지스틱스
3rd row여수국제항만(주)
4th row(유)한성로지스
5th row(유)성화로직스
ValueCountFrequency (%)
주식회사 3
 
3.9%
유)제일특수화물 2
 
2.6%
유)글로벌특수 1
 
1.3%
조우로지스(주 1
 
1.3%
로하글로벌 1
 
1.3%
유)여수상운 1
 
1.3%
유)엘제이물류 1
 
1.3%
㈜삼양냉장 1
 
1.3%
유)재화로지스 1
 
1.3%
㈜제이엔물류 1
 
1.3%
Other values (63) 63
82.9%
2023-12-13T03:59:06.397244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 36
 
6.8%
㈜ 33
 
6.3%
( 30
 
5.7%
) 30
 
5.7%
로 29
 
5.5%
지 25
 
4.8%
유 19
 
3.6%
주 19
 
3.6%
이 16
 
3.0%
물 12
 
2.3%
Other values (117) 277
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 427
81.2%
Other Symbol 33
 
6.3%
Open Punctuation 30
 
5.7%
Close Punctuation 30
 
5.7%
Space Separator 6
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 36
 
8.4%
로 29
 
6.8%
지 25
 
5.9%
유 19
 
4.4%
주 19
 
4.4%
이 16
 
3.7%
물 12
 
2.8%
운 10
 
2.3%
류 10
 
2.3%
제 10
 
2.3%
Other values (113) 241
56.4%
Other Symbol
ValueCountFrequency (%)
㈜ 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 460
87.5%
Common 66
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 36
 
7.8%
㈜ 33
 
7.2%
로 29
 
6.3%
지 25
 
5.4%
유 19
 
4.1%
주 19
 
4.1%
이 16
 
3.5%
물 12
 
2.6%
운 10
 
2.2%
류 10
 
2.2%
Other values (114) 251
54.6%
Common
ValueCountFrequency (%)
( 30
45.5%
) 30
45.5%
6
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 427
81.2%
ASCII 66
 
12.5%
None 33
 
6.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 36
 
8.4%
로 29
 
6.8%
지 25
 
5.9%
유 19
 
4.4%
주 19
 
4.4%
이 16
 
3.7%
물 12
 
2.8%
운 10
 
2.3%
류 10
 
2.3%
제 10
 
2.3%
Other values (113) 241
56.4%
None
ValueCountFrequency (%)
㈜ 33
100.0%
ASCII
ValueCountFrequency (%)
( 30
45.5%
) 30
45.5%
6
 
9.1%

주소
Text

Distinct60
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size692.0 B
2023-12-13T03:59:06.826905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19.5
Mean length13.657143
Min length10

Characters and Unicode

Total characters956
Distinct characters87
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)77.1%

Sample

1st row광양시 제철로 1800
2nd row광양시 항만8로 18-35
3rd row여수시 여수산단로 1469-80
4th row광양시 남산길 31
5th row광양시 항만8로 18-35
ValueCountFrequency (%)
광양시 47
 
19.9%
항만8로 8
 
3.4%
항만7로 8
 
3.4%
여수시 8
 
3.4%
중마로 7
 
3.0%
순천시 6
 
2.5%
18-35 6
 
2.5%
15 4
 
1.7%
목포시 4
 
1.7%
2층 3
 
1.3%
Other values (113) 135
57.2%
2023-12-13T03:59:07.511450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
166
17.4%
시 67
 
7.0%
1 62
 
6.5%
로 56
 
5.9%
양 52
 
5.4%
광 49
 
5.1%
2 39
 
4.1%
5 32
 
3.3%
항 24
 
2.5%
8 23
 
2.4%
Other values (77) 386
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 488
51.0%
Decimal Number 272
28.5%
Space Separator 166
 
17.4%
Dash Punctuation 18
 
1.9%
Other Punctuation 10
 
1.0%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 67
13.7%
로 56
 
11.5%
양 52
 
10.7%
광 49
 
10.0%
항 24
 
4.9%
만 22
 
4.5%
길 18
 
3.7%
여 13
 
2.7%
수 12
 
2.5%
중 9
 
1.8%
Other values (62) 166
34.0%
Decimal Number
ValueCountFrequency (%)
1 62
22.8%
2 39
14.3%
5 32
11.8%
8 23
 
8.5%
4 22
 
8.1%
3 21
 
7.7%
7 21
 
7.7%
0 20
 
7.4%
6 18
 
6.6%
9 14
 
5.1%
Space Separator
ValueCountFrequency (%)
166
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 488
51.0%
Common 468
49.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 67
13.7%
로 56
 
11.5%
양 52
 
10.7%
광 49
 
10.0%
항 24
 
4.9%
만 22
 
4.5%
길 18
 
3.7%
여 13
 
2.7%
수 12
 
2.5%
중 9
 
1.8%
Other values (62) 166
34.0%
Common
ValueCountFrequency (%)
166
35.5%
1 62
 
13.2%
2 39
 
8.3%
5 32
 
6.8%
8 23
 
4.9%
4 22
 
4.7%
3 21
 
4.5%
7 21
 
4.5%
0 20
 
4.3%
6 18
 
3.8%
Other values (5) 44
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 488
51.0%
ASCII 468
49.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
166
35.5%
1 62
 
13.2%
2 39
 
8.3%
5 32
 
6.8%
8 23
 
4.9%
4 22
 
4.7%
3 21
 
4.5%
7 21
 
4.5%
0 20
 
4.3%
6 18
 
3.8%
Other values (5) 44
 
9.4%
Hangul
ValueCountFrequency (%)
시 67
13.7%
로 56
 
11.5%
양 52
 
10.7%
광 49
 
10.0%
항 24
 
4.9%
만 22
 
4.5%
길 18
 
3.7%
여 13
 
2.7%
수 12
 
2.5%
중 9
 
1.8%
Other values (62) 166
34.0%

번호
Text

Distinct67
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size692.0 B
2023-12-13T03:59:07.899039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.4428571
Min length3

Characters and Unicode

Total characters311
Distinct characters12
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

Unique64 ?
Unique (%)91.4%

Sample

1st row제5호
2nd row제37호
3rd row제86호
4th row제87호
5th row제89호
ValueCountFrequency (%)
제66호 2
 
2.9%
제74호 2
 
2.9%
제75호 2
 
2.9%
제122호 1
 
1.4%
제85호 1
 
1.4%
제123호 1
 
1.4%
제93호 1
 
1.4%
제67호 1
 
1.4%
제72호 1
 
1.4%
제73호 1
 
1.4%
Other values (57) 57
81.4%
2023-12-13T03:59:08.633248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 70
22.5%
호 70
22.5%
1 46
14.8%
7 18
 
5.8%
0 18
 
5.8%
3 15
 
4.8%
2 15
 
4.8%
8 13
 
4.2%
6 12
 
3.9%
5 12
 
3.9%
Other values (2) 22
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 171
55.0%
Other Letter 140
45.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 46
26.9%
7 18
 
10.5%
0 18
 
10.5%
3 15
 
8.8%
2 15
 
8.8%
8 13
 
7.6%
6 12
 
7.0%
5 12
 
7.0%
4 11
 
6.4%
9 11
 
6.4%
Other Letter
ValueCountFrequency (%)
제 70
50.0%
호 70
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171
55.0%
Hangul 140
45.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 46
26.9%
7 18
 
10.5%
0 18
 
10.5%
3 15
 
8.8%
2 15
 
8.8%
8 13
 
7.6%
6 12
 
7.0%
5 12
 
7.0%
4 11
 
6.4%
9 11
 
6.4%
Hangul
ValueCountFrequency (%)
제 70
50.0%
호 70
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171
55.0%
Hangul 140
45.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 70
50.0%
호 70
50.0%
ASCII
ValueCountFrequency (%)
1 46
26.9%
7 18
 
10.5%
0 18
 
10.5%
3 15
 
8.8%
2 15
 
8.8%
8 13
 
7.6%
6 12
 
7.0%
5 12
 
7.0%
4 11
 
6.4%
9 11
 
6.4%

자본금(억원)
Real number (ℝ)

Distinct10
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.914286
Minimum3
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-13T03:59:08.849930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median3
Q33
95-th percentile69.7
Maximum250
Range247
Interquartile range (IQR)0

Descriptive statistics

Standard deviation37.186464
Coefficient of variation (CV)2.6725385
Kurtosis25.609147
Mean13.914286
Median Absolute Deviation (MAD)0
Skewness4.7506489
Sum974
Variance1382.8331
MonotonicityNot monotonic
2023-12-13T03:59:09.060600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 57
81.4%
10 3
 
4.3%
13 2
 
2.9%
50 2
 
2.9%
250 1
 
1.4%
145 1
 
1.4%
51 1
 
1.4%
95 1
 
1.4%
85 1
 
1.4%
21 1
 
1.4%
ValueCountFrequency (%)
3 57
81.4%
10 3
 
4.3%
13 2
 
2.9%
21 1
 
1.4%
50 2
 
2.9%
51 1
 
1.4%
85 1
 
1.4%
95 1
 
1.4%
145 1
 
1.4%
250 1
 
1.4%
ValueCountFrequency (%)
250 1
 
1.4%
145 1
 
1.4%
95 1
 
1.4%
85 1
 
1.4%
51 1
 
1.4%
50 2
 
2.9%
21 1
 
1.4%
13 2
 
2.9%
10 3
 
4.3%
3 57
81.4%

Interactions

2023-12-13T03:59:04.602185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:04.363986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:04.721237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:59:04.487285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:59:09.231462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호주소번호자본금(억원)
연번1.0000.9380.7780.7940.000
상호0.9381.0001.0001.0001.000
주소0.7781.0001.0000.9981.000
번호0.7941.0000.9981.0001.000
자본금(억원)0.0001.0001.0001.0001.000
2023-12-13T03:59:09.442649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번자본금(억원)
연번1.000-0.132
자본금(억원)-0.1321.000

Missing values

2023-12-13T03:59:04.900621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:59:05.050685image/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

연번상호주소번호자본금(억원)
01포스코터미널㈜광양시 제철로 1800제5호250
12㈜태성로지스틱스광양시 항만8로 18-35제37호3
23여수국제항만(주)여수시 여수산단로 1469-80제86호145
34(유)한성로지스광양시 남산길 31제87호3
45(유)성화로직스광양시 항만8로 18-35제89호3
56㈜씨스테인웨그 로직스광양시 항만 10로 23제90호51
67㈜엠제이로지스틱스순천시 봉화1길 121제97호3
78㈜제이더블유광양시 항만7로 6제98호3
89㈜마린글로리여수시 여수산단2로 11제25호10
910(유)백아상운광양시 중마로 55제40호3
연번상호주소번호자본금(억원)
6061㈜서진로지텍광양시 항만로 114제130호21
6162하나한종합물류㈜광양시 행정1길 36, 2층제131호3
6263㈜케이알로지스광양시 항만 11로 32, 3층 2호제129호3
6364㈜지티이노페이션광양시 항만대로 755제128호3
6465㈜선경로지스광양시 항만7로 71-70, 2층제127호3
6566목포신항만운영 주식회사목포시 신항로 294번길 45제74호10
6667태웅지엘에스 주식회사장성군 장성읍 영천로 155-1제122호3
6768(유)제일특수화물담양군 봉산면 제월길 226제75호3
6869주식회사 에이스물류함평군 함평읍 함장로 1202-22제66호3
6970럭키로지스주식회사목포시 통일대로119번길 17, 8호(옥암동)제126호3