Overview

Dataset statistics

Number of variables9
Number of observations424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.4 KiB
Average record size in memory73.3 B

Variable types

Numeric1
Categorical6
Text1
DateTime1

Dataset

Description중소기업의 규제를 개선하기 위한 사업으로 규제특례 부여를 통해 신사업, 신기술의 안정성을 검증하기 위한 실증을 진행하고 안정성 입증시 법령개정, 임시허가 등을 지원
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15124790/fileData.do

Alerts

지역 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
특구명 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
차수 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 특구명 and 3 other fieldsHigh correlation
특구사업자등록형태 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:16:04.335570
Analysis finished2023-12-12 09:16:05.349730
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct424
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.5
Minimum1
Maximum424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2023-12-12T18:16:05.450651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22.15
Q1106.75
median212.5
Q3318.25
95-th percentile402.85
Maximum424
Range423
Interquartile range (IQR)211.5

Descriptive statistics

Standard deviation122.54251
Coefficient of variation (CV)0.57667063
Kurtosis-1.2
Mean212.5
Median Absolute Deviation (MAD)106
Skewness0
Sum90100
Variance15016.667
MonotonicityStrictly increasing
2023-12-12T18:16:05.635830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
293 1
 
0.2%
291 1
 
0.2%
290 1
 
0.2%
289 1
 
0.2%
288 1
 
0.2%
287 1
 
0.2%
286 1
 
0.2%
285 1
 
0.2%
284 1
 
0.2%
Other values (414) 414
97.6%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
424 1
0.2%
423 1
0.2%
422 1
0.2%
421 1
0.2%
420 1
0.2%
419 1
0.2%
418 1
0.2%
417 1
0.2%
416 1
0.2%
415 1
0.2%

특구명
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
산업용 헴프
37 
바이오메디컬
 
24
수소그린모빌리티
 
23
스마트 웰니스
 
22
디지털 헬스케어
 
20
Other values (25)
298 

Length

Max length18
Median length12
Mean length7.7830189
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row스마트 웰니스
2nd row스마트 웰니스
3rd row스마트 웰니스
4th row스마트 웰니스
5th row스마트 웰니스

Common Values

ValueCountFrequency (%)
산업용 헴프 37
 
8.7%
바이오메디컬 24
 
5.7%
수소그린모빌리티 23
 
5.4%
스마트 웰니스 22
 
5.2%
디지털 헬스케어 20
 
4.7%
액화수소산업 20
 
4.7%
이동식 협동로봇 17
 
4.0%
무인 저속 특장차 17
 
4.0%
정밀의료산업 16
 
3.8%
5G 차세대 스마트공장 16
 
3.8%
Other values (20) 212
50.0%

Length

2023-12-12T18:16:05.801572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산업용 37
 
5.1%
헴프 37
 
5.1%
스마트 33
 
4.5%
차세대 28
 
3.8%
바이오메디컬 24
 
3.3%
수소그린모빌리티 23
 
3.1%
웰니스 22
 
3.0%
전기차 22
 
3.0%
디지털 20
 
2.7%
헬스케어 20
 
2.7%
Other values (39) 466
63.7%

차수
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
3차
142 
2차
100 
1차
55 
4차
43 
5차
43 
Other values (2)
41 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1차
2nd row1차
3rd row1차
4th row1차
5th row1차

Common Values

ValueCountFrequency (%)
3차 142
33.5%
2차 100
23.6%
1차 55
 
13.0%
4차 43
 
10.1%
5차 43
 
10.1%
7차 29
 
6.8%
6차 12
 
2.8%

Length

2023-12-12T18:16:05.973308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:16:06.146044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3차 142
33.5%
2차 100
23.6%
1차 55
 
13.0%
4차 43
 
10.1%
5차 43
 
10.1%
7차 29
 
6.8%
6차 12
 
2.8%

지역
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
경북
60 
강원
56 
울산
51 
대구
39 
경남
37 
Other values (9)
181 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경북 60
14.2%
강원 56
13.2%
울산 51
12.0%
대구 39
9.2%
경남 37
8.7%
부산 29
6.8%
광주 28
6.6%
전북 25
5.9%
대전 24
 
5.7%
충남 21
 
5.0%
Other values (4) 54
12.7%

Length

2023-12-12T18:16:06.390969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 60
14.2%
강원 56
13.2%
울산 51
12.0%
대구 39
9.2%
경남 37
8.7%
부산 29
6.8%
광주 28
6.6%
전북 25
5.9%
대전 24
 
5.7%
충남 21
 
5.0%
Other values (4) 54
12.7%

기업명
Text

Distinct421
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-12-12T18:16:06.664845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length20
Mean length7.8136792
Min length2

Characters and Unicode

Total characters3313
Distinct characters366
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)98.6%

Sample

1st row경북대학교 산학협력단
2nd row㈜멘티스로지텍
3rd row㈜코렌텍
4th row㈜지비에스커먼웰스
5th row(재)대구경북첨단의료산업진흥재단
ValueCountFrequency (%)
주식회사 43
 
8.4%
산학협력단 7
 
1.4%
강원대학교 3
 
0.6%
재단법인 3
 
0.6%
디앨(주 2
 
0.4%
한국가스안전공사 2
 
0.4%
울산지점 2
 
0.4%
inc 2
 
0.4%
자산운용(주 2
 
0.4%
선박해양플랜트연구소 2
 
0.4%
Other values (444) 444
86.7%
2023-12-12T18:16:07.157537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 217
 
6.5%
스 133
 
4.0%
이 110
 
3.3%
주 98
 
3.0%
90
 
2.7%
( 76
 
2.3%
) 76
 
2.3%
에 66
 
2.0%
지 61
 
1.8%
원 60
 
1.8%
Other values (356) 2326
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2783
84.0%
Other Symbol 217
 
6.5%
Space Separator 90
 
2.7%
Open Punctuation 76
 
2.3%
Close Punctuation 76
 
2.3%
Uppercase Letter 51
 
1.5%
Lowercase Letter 11
 
0.3%
Other Punctuation 7
 
0.2%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 133
 
4.8%
이 110
 
4.0%
주 98
 
3.5%
에 66
 
2.4%
지 61
 
2.2%
원 60
 
2.2%
사 59
 
2.1%
크 54
 
1.9%
산 47
 
1.7%
식 45
 
1.6%
Other values (323) 2050
73.7%
Uppercase Letter
ValueCountFrequency (%)
S 7
13.7%
E 5
9.8%
K 5
9.8%
I 4
 
7.8%
T 4
 
7.8%
P 3
 
5.9%
L 3
 
5.9%
G 3
 
5.9%
N 3
 
5.9%
C 3
 
5.9%
Other values (8) 11
21.6%
Lowercase Letter
ValueCountFrequency (%)
c 4
36.4%
o 2
18.2%
n 2
18.2%
d 1
 
9.1%
t 1
 
9.1%
v 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 4
57.1%
& 1
 
14.3%
/ 1
 
14.3%
, 1
 
14.3%
Other Symbol
ValueCountFrequency (%)
㈜ 217
100.0%
Space Separator
ValueCountFrequency (%)
90
100.0%
Open Punctuation
ValueCountFrequency (%)
( 76
100.0%
Close Punctuation
ValueCountFrequency (%)
) 76
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3000
90.6%
Common 251
 
7.6%
Latin 62
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 217
 
7.2%
스 133
 
4.4%
이 110
 
3.7%
주 98
 
3.3%
에 66
 
2.2%
지 61
 
2.0%
원 60
 
2.0%
사 59
 
2.0%
크 54
 
1.8%
산 47
 
1.6%
Other values (324) 2095
69.8%
Latin
ValueCountFrequency (%)
S 7
 
11.3%
E 5
 
8.1%
K 5
 
8.1%
I 4
 
6.5%
c 4
 
6.5%
T 4
 
6.5%
P 3
 
4.8%
L 3
 
4.8%
G 3
 
4.8%
N 3
 
4.8%
Other values (14) 21
33.9%
Common
ValueCountFrequency (%)
90
35.9%
( 76
30.3%
) 76
30.3%
. 4
 
1.6%
2 2
 
0.8%
& 1
 
0.4%
/ 1
 
0.4%
, 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2783
84.0%
ASCII 313
 
9.4%
None 217
 
6.5%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 217
100.0%
Hangul
ValueCountFrequency (%)
스 133
 
4.8%
이 110
 
4.0%
주 98
 
3.5%
에 66
 
2.4%
지 61
 
2.2%
원 60
 
2.2%
사 59
 
2.1%
크 54
 
1.9%
산 47
 
1.7%
식 45
 
1.6%
Other values (323) 2050
73.7%
ASCII
ValueCountFrequency (%)
90
28.8%
( 76
24.3%
) 76
24.3%
S 7
 
2.2%
E 5
 
1.6%
K 5
 
1.6%
. 4
 
1.3%
I 4
 
1.3%
c 4
 
1.3%
T 4
 
1.3%
Other values (22) 38
12.1%

기업규모
Categorical

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
소상공인
110 
소기업
105 
중기업
87 
비영리
74 
중견기업
29 
Other values (2)
19 

Length

Max length4
Median length3
Mean length3.3301887
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row비영리
2nd row소기업
3rd row중기업
4th row소상공인
5th row비영리

Common Values

ValueCountFrequency (%)
소상공인 110
25.9%
소기업 105
24.8%
중기업 87
20.5%
비영리 74
17.5%
중견기업 29
 
6.8%
대기업 18
 
4.2%
소솽공인 1
 
0.2%

Length

2023-12-12T18:16:07.360710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:16:07.531047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소상공인 110
25.9%
소기업 105
24.8%
중기업 87
20.5%
비영리 74
17.5%
중견기업 29
 
6.8%
대기업 18
 
4.2%
소솽공인 1
 
0.2%

특구사업자등록형태
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
최초
352 
추가
72 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
최초 352
83.0%
추가 72
 
17.0%

Length

2023-12-12T18:16:07.693720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:16:07.807985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
최초 352
83.0%
추가 72
 
17.0%

특구사업자통계등록일자
Date

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Minimum2020-07-31 00:00:00
Maximum2022-10-31 00:00:00
2023-12-12T18:16:07.917824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:16:08.080674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

역외기업이전형태
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
해당사항없음
302 
지사설립
105 
본사이전
 
17

Length

Max length6
Median length6
Mean length5.4245283
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지사설립
2nd row지사설립
3rd row지사설립
4th row해당사항없음
5th row해당사항없음

Common Values

ValueCountFrequency (%)
해당사항없음 302
71.2%
지사설립 105
 
24.8%
본사이전 17
 
4.0%

Length

2023-12-12T18:16:08.277253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:16:08.417144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당사항없음 302
71.2%
지사설립 105
 
24.8%
본사이전 17
 
4.0%

Interactions

2023-12-12T18:16:05.017692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:16:08.511218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번특구명차수지역기업규모특구사업자등록형태특구사업자통계등록일자역외기업이전형태
연번1.0000.9970.9450.8560.2060.6900.8460.397
특구명0.9971.0000.9961.0000.3950.7130.9310.675
차수0.9450.9961.0000.9000.3030.2580.8870.335
지역0.8561.0000.9001.0000.3420.4750.6940.419
기업규모0.2060.3950.3030.3421.0000.0000.2120.000
특구사업자등록형태0.6900.7130.2580.4750.0001.0000.5290.000
특구사업자통계등록일자0.8460.9310.8870.6940.2120.5291.0000.338
역외기업이전형태0.3970.6750.3350.4190.0000.0000.3381.000
2023-12-12T18:16:08.673185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역특구명특구사업자등록형태역외기업이전형태기업규모차수
지역1.0000.9800.3670.2550.1320.564
특구명0.9801.0000.5600.3980.1710.955
특구사업자등록형태0.3670.5601.0000.0000.0000.275
역외기업이전형태0.2550.3980.0001.0000.0000.238
기업규모0.1320.1710.0000.0001.0000.110
차수0.5640.9550.2750.2380.1101.000
2023-12-12T18:16:08.851317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번특구명차수지역기업규모특구사업자등록형태역외기업이전형태
연번1.0000.8820.8550.5680.1050.5330.258
특구명0.8821.0000.9550.9800.1710.5600.398
차수0.8550.9551.0000.5640.1100.2750.238
지역0.5680.9800.5641.0000.1320.3670.255
기업규모0.1050.1710.1100.1321.0000.0000.000
특구사업자등록형태0.5330.5600.2750.3670.0001.0000.000
역외기업이전형태0.2580.3980.2380.2550.0000.0001.000

Missing values

2023-12-12T18:16:05.141785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:16:05.283741image/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

연번특구명차수지역기업명기업규모특구사업자등록형태특구사업자통계등록일자역외기업이전형태
01스마트 웰니스1차대구경북대학교 산학협력단비영리최초2020-07-31지사설립
12스마트 웰니스1차대구㈜멘티스로지텍소기업최초2020-07-31지사설립
23스마트 웰니스1차대구㈜코렌텍중기업최초2020-07-31지사설립
34스마트 웰니스1차대구㈜지비에스커먼웰스소상공인최초2020-07-31해당사항없음
45스마트 웰니스1차대구(재)대구경북첨단의료산업진흥재단비영리최초2020-07-31해당사항없음
56스마트 웰니스1차대구㈜엔도비전소기업최초2020-07-31해당사항없음
67스마트 웰니스1차대구㈜이노리젠소상공인최초2020-07-31해당사항없음
78스마트 웰니스1차대구경북대학교병원(칠곡)비영리최초2020-07-31해당사항없음
89스마트 웰니스1차대구㈜제윤소기업최초2020-07-31해당사항없음
910스마트 웰니스1차대구클루피㈜소기업최초2020-07-31지사설립
연번특구명차수지역기업명기업규모특구사업자등록형태특구사업자통계등록일자역외기업이전형태
414415암모니아 혼소 연료추진시스템 선박7차경남엠엔에스아이㈜중기업최초2022-06-30해당사항없음
415416암모니아 혼소 연료추진시스템 선박7차경남㈜대양산전소상공인최초2022-06-30지사설립
416417암모니아 혼소 연료추진시스템 선박7차경남삼우중공업대기업최초2022-06-30해당사항없음
417418암모니아 혼소 연료추진시스템 선박7차경남대우조선해양㈜대기업최초2022-06-30해당사항없음
418419암모니아 혼소 연료추진시스템 선박7차경남협성철광㈜소기업최초2022-06-30해당사항없음
419420암모니아 혼소 연료추진시스템 선박7차경남에스티엑스엔진㈜중견기업최초2022-06-30해당사항없음
420421암모니아 혼소 연료추진시스템 선박7차경남유니온㈜중기업최초2022-06-30해당사항없음
421422암모니아 혼소 연료추진시스템 선박7차경남에스탱크엔지니어링㈜중기업최초2022-06-30해당사항없음
422423암모니아 혼소 연료추진시스템 선박7차경남이케이중공업㈜중기업최초2022-06-30해당사항없음
423424암모니아 혼소 연료추진시스템 선박7차경남선박해양플랜트연구소비영리최초2022-06-30해당사항없음