Overview

Dataset statistics

Number of variables6
Number of observations97
Missing cells7
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory50.4 B

Variable types

Numeric1
Text3
DateTime2

Dataset

Description충청남도 홍성군 지역내 산업단지 입주기업 현황으로 단지명으로 분류를 하여 ,회사명, 업종분류를 하여 업종별로도 사업의 분류를 할수있게 기재되어있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=438&beforeMenuCd=DOM_000000201001001000&publicdatapk=15028983

Alerts

데이터기준일자 has constant value ""Constant
연락처 has 7 (7.2%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:44:00.415129
Analysis finished2024-01-09 22:44:01.284213
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2024-01-10T07:44:01.364289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.8
Q125
median49
Q373
95-th percentile92.2
Maximum97
Range96
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.145456
Coefficient of variation (CV)0.57439705
Kurtosis-1.2
Mean49
Median Absolute Deviation (MAD)24
Skewness0
Sum4753
Variance792.16667
MonotonicityStrictly increasing
2024-01-10T07:44:01.529662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
74 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
Other values (87) 87
89.7%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%
Distinct96
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
2024-01-10T07:44:01.775172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length7.5876289
Min length4

Characters and Unicode

Total characters736
Distinct characters168
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)97.9%

Sample

1st row(주)세아산업
2nd row(주)인창테크
3rd row(주)인천금속
4th row(주)청운브레이크
5th row(주)켐트로
ValueCountFrequency (%)
주식회사 6
 
5.4%
주)동신포리마 4
 
3.6%
원강금속(주 3
 
2.7%
2공장 3
 
2.7%
주)참그로 2
 
1.8%
㈜한진오토모티브 2
 
1.8%
제2공장 2
 
1.8%
피에스텍(주 1
 
0.9%
주)내주 1
 
0.9%
주)블루월드 1
 
0.9%
Other values (86) 86
77.5%
2024-01-10T07:44:02.103131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
77
 
10.5%
( 70
 
9.5%
) 70
 
9.5%
16
 
2.2%
14
 
1.9%
14
 
1.9%
13
 
1.8%
12
 
1.6%
11
 
1.5%
11
 
1.5%
Other values (158) 428
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 570
77.4%
Open Punctuation 70
 
9.5%
Close Punctuation 70
 
9.5%
Space Separator 14
 
1.9%
Decimal Number 8
 
1.1%
Other Symbol 3
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
77
 
13.5%
16
 
2.8%
14
 
2.5%
13
 
2.3%
12
 
2.1%
11
 
1.9%
11
 
1.9%
11
 
1.9%
11
 
1.9%
8
 
1.4%
Other values (150) 386
67.7%
Decimal Number
ValueCountFrequency (%)
2 6
75.0%
4 1
 
12.5%
3 1
 
12.5%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Symbol
ValueCountFrequency (%)
3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 573
77.9%
Common 163
 
22.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
77
 
13.4%
16
 
2.8%
14
 
2.4%
13
 
2.3%
12
 
2.1%
11
 
1.9%
11
 
1.9%
11
 
1.9%
11
 
1.9%
8
 
1.4%
Other values (151) 389
67.9%
Common
ValueCountFrequency (%)
( 70
42.9%
) 70
42.9%
14
 
8.6%
2 6
 
3.7%
4 1
 
0.6%
3 1
 
0.6%
- 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 570
77.4%
ASCII 163
 
22.1%
None 3
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
77
 
13.5%
16
 
2.8%
14
 
2.5%
13
 
2.3%
12
 
2.1%
11
 
1.9%
11
 
1.9%
11
 
1.9%
11
 
1.9%
8
 
1.4%
Other values (150) 386
67.7%
ASCII
ValueCountFrequency (%)
( 70
42.9%
) 70
42.9%
14
 
8.6%
2 6
 
3.7%
4 1
 
0.6%
3 1
 
0.6%
- 1
 
0.6%
None
ValueCountFrequency (%)
3
100.0%
Distinct90
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size908.0 B
Minimum1996-02-09 00:00:00
Maximum2020-08-01 00:00:00
2024-01-10T07:44:02.222093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:44:02.341269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct77
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Memory size908.0 B
2024-01-10T07:44:02.503164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length25.412371
Min length20

Characters and Unicode

Total characters2465
Distinct characters43
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

Unique63 ?
Unique (%)64.9%

Sample

1st row충청남도 홍성군 갈산면 내포로 1607-43
2nd row충청남도 홍성군 갈산면 내포로 1607-41
3rd row충청남도 홍성군 갈산면 내포로 1607-82
4th row충청남도 홍성군 갈산면 내포로 1607-81
5th row충청남도 홍성군 갈산면 내포로 1607-66
ValueCountFrequency (%)
충청남도 97
20.0%
홍성군 97
20.0%
결성면 28
 
5.8%
산업로116번길 28
 
5.8%
광천읍 23
 
4.7%
갈산면 21
 
4.3%
내포로 16
 
3.3%
구항면 15
 
3.1%
충서로966번길 15
 
3.1%
광천로430번길 15
 
3.1%
Other values (88) 131
27.0%
2024-01-10T07:44:02.782797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
389
 
15.8%
1 185
 
7.5%
125
 
5.1%
120
 
4.9%
6 100
 
4.1%
99
 
4.0%
97
 
3.9%
97
 
3.9%
97
 
3.9%
97
 
3.9%
Other values (33) 1059
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1400
56.8%
Decimal Number 607
24.6%
Space Separator 389
 
15.8%
Dash Punctuation 69
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
125
 
8.9%
120
 
8.6%
99
 
7.1%
97
 
6.9%
97
 
6.9%
97
 
6.9%
97
 
6.9%
94
 
6.7%
72
 
5.1%
69
 
4.9%
Other values (21) 433
30.9%
Decimal Number
ValueCountFrequency (%)
1 185
30.5%
6 100
16.5%
4 74
 
12.2%
0 52
 
8.6%
9 43
 
7.1%
3 42
 
6.9%
2 34
 
5.6%
8 27
 
4.4%
7 26
 
4.3%
5 24
 
4.0%
Space Separator
ValueCountFrequency (%)
389
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1400
56.8%
Common 1065
43.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
125
 
8.9%
120
 
8.6%
99
 
7.1%
97
 
6.9%
97
 
6.9%
97
 
6.9%
97
 
6.9%
94
 
6.7%
72
 
5.1%
69
 
4.9%
Other values (21) 433
30.9%
Common
ValueCountFrequency (%)
389
36.5%
1 185
17.4%
6 100
 
9.4%
4 74
 
6.9%
- 69
 
6.5%
0 52
 
4.9%
9 43
 
4.0%
3 42
 
3.9%
2 34
 
3.2%
8 27
 
2.5%
Other values (2) 50
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1400
56.8%
ASCII 1065
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
389
36.5%
1 185
17.4%
6 100
 
9.4%
4 74
 
6.9%
- 69
 
6.5%
0 52
 
4.9%
9 43
 
4.0%
3 42
 
3.9%
2 34
 
3.2%
8 27
 
2.5%
Other values (2) 50
 
4.7%
Hangul
ValueCountFrequency (%)
125
 
8.9%
120
 
8.6%
99
 
7.1%
97
 
6.9%
97
 
6.9%
97
 
6.9%
97
 
6.9%
94
 
6.7%
72
 
5.1%
69
 
4.9%
Other values (21) 433
30.9%

연락처
Text

MISSING 

Distinct82
Distinct (%)91.1%
Missing7
Missing (%)7.2%
Memory size908.0 B
2024-01-10T07:44:03.015185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12
Min length11

Characters and Unicode

Total characters1080
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)85.6%

Sample

1st row041-630-8007
2nd row041-631-0744
3rd row032-811-8300
4th row041-631-6690
5th row041-633-5633
ValueCountFrequency (%)
041-634-6591 4
 
4.4%
041-630-8000 3
 
3.3%
041-642-0878 2
 
2.2%
041-977-8314 2
 
2.2%
041-641-0031 2
 
2.2%
032-556-0337 1
 
1.1%
041-642-7822 1
 
1.1%
041-630-5371 1
 
1.1%
041-632-4455 1
 
1.1%
041-632-2811 1
 
1.1%
Other values (72) 72
80.0%
2024-01-10T07:44:03.468840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 180
16.7%
0 169
15.6%
1 154
14.3%
4 152
14.1%
6 110
10.2%
3 87
8.1%
2 64
 
5.9%
5 49
 
4.5%
8 49
 
4.5%
7 36
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 900
83.3%
Dash Punctuation 180
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 169
18.8%
1 154
17.1%
4 152
16.9%
6 110
12.2%
3 87
9.7%
2 64
 
7.1%
5 49
 
5.4%
8 49
 
5.4%
7 36
 
4.0%
9 30
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1080
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 180
16.7%
0 169
15.6%
1 154
14.3%
4 152
14.1%
6 110
10.2%
3 87
8.1%
2 64
 
5.9%
5 49
 
4.5%
8 49
 
4.5%
7 36
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 180
16.7%
0 169
15.6%
1 154
14.3%
4 152
14.1%
6 110
10.2%
3 87
8.1%
2 64
 
5.9%
5 49
 
4.5%
8 49
 
4.5%
7 36
 
3.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
Minimum2020-08-31 00:00:00
Maximum2020-08-31 00:00:00
2024-01-10T07:44:03.613183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:44:03.733263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T07:44:01.068653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:44:03.818673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번기업명최초등록일자소재지도로명주소연락처
연번1.0000.9430.9630.9240.965
기업명0.9431.0000.9960.9971.000
최초등록일자0.9630.9961.0000.9910.859
소재지도로명주소0.9240.9970.9911.0000.919
연락처0.9651.0000.8590.9191.000

Missing values

2024-01-10T07:44:01.157803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:44:01.245320image/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

연번기업명최초등록일자소재지도로명주소연락처데이터기준일자
01(주)세아산업2011-07-28충청남도 홍성군 갈산면 내포로 1607-43041-630-80072020-08-31
12(주)인창테크2011-02-24충청남도 홍성군 갈산면 내포로 1607-41041-631-07442020-08-31
23(주)인천금속2011-02-01충청남도 홍성군 갈산면 내포로 1607-82032-811-83002020-08-31
34(주)청운브레이크2010-11-11충청남도 홍성군 갈산면 내포로 1607-81041-631-66902020-08-31
45(주)켐트로2011-07-18충청남도 홍성군 갈산면 내포로 1607-66041-633-56332020-08-31
56(주)탑티엔씨2020-01-16충청남도 홍성군 갈산면 내포로 1607-81041-631-27702020-08-31
67(주)한국소재2018-01-30충청남도 홍성군 갈산면 내포로 1607-19041-631-04162020-08-31
78(주)홍성브레이크2010-10-29충청남도 홍성군 갈산면 내포로 1607-24041-630-25002020-08-31
89(주)홍성이엔지2012-05-30충청남도 홍성군 갈산면 내포로 1607-19041-634-99092020-08-31
910강원산업2017-01-06충청남도 홍성군 갈산면 내포로 1607-56<NA>2020-08-31
연번기업명최초등록일자소재지도로명주소연락처데이터기준일자
8788경원컴포싱 주식회사2016-08-09충청남도 홍성군 은하면 은하로184번길 111-15041-406-75122020-08-31
8889드래곤모터스(주)2020-02-17충청남도 홍성군 은하면 은하로184번길 111-21041-350-51002020-08-31
8990에스더블류도로안전 주식회사2008-11-20충청남도 홍성군 은하면 은하로184번길 111-26041-642-11472020-08-31
9091중앙식품2007-01-23충청남도 홍성군 은하면 은하로184번길 111-32041-641-35702020-08-31
9192(주)로미칼2012-01-25충청남도 홍성군 은하면 천광로 856-1402-6949-41712020-08-31
9293(주)경남금속2015-09-18충청남도 홍성군 갈산면 산단로388번길 28041-631-83222020-08-31
9394(주)수천중공업2014-06-16충청남도 홍성군 갈산면 취생리 산 126-1041-635-06022020-08-31
9495(주)우심시스템2017-01-26충청남도 홍성군 갈산면 산단로388번길 60041-339-37002020-08-31
9596일진전기(주)2013-04-18충청남도 홍성군 갈산면 산단로 467041-413-31732020-08-31
9697주식회사 벽산2019-01-25충청남도 홍성군 갈산면 산단로 51602-2260-61352020-08-31