Overview

Dataset statistics

Number of variables7
Number of observations307
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 KiB
Average record size in memory56.4 B

Variable types

Categorical4
Text2
DateTime1

Dataset

Description전북특별자치도 예비 또는 인증 사회적기업 리스트로 기업명, 지역, 대표자, 업종, 지정일자 등의 데이터를 제공하고 있습니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/3042543/fileData.do

Alerts

유형 is highly overall correlated with 분류High correlation
분류 is highly overall correlated with 유형High correlation
기업명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 13:23:10.437249
Analysis finished2024-03-14 13:23:11.725886
Duration1.29 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
인증 사회적기업
180 
예비 사회적기업
127 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row예비 사회적기업
2nd row예비 사회적기업
3rd row예비 사회적기업
4th row예비 사회적기업
5th row예비 사회적기업

Common Values

ValueCountFrequency (%)
인증 사회적기업 180
58.6%
예비 사회적기업 127
41.4%

Length

2024-03-14T22:23:11.938507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:23:12.250857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사회적기업 307
50.0%
인증 180
29.3%
예비 127
20.7%

시군
Categorical

Distinct14
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
전주시
126 
익산시
36 
군산시
35 
완주군
24 
정읍시
17 
Other values (9)
69 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row완주군
2nd row전주시
3rd row전주시
4th row완주군
5th row전주시

Common Values

ValueCountFrequency (%)
전주시 126
41.0%
익산시 36
 
11.7%
군산시 35
 
11.4%
완주군 24
 
7.8%
정읍시 17
 
5.5%
남원시 17
 
5.5%
김제시 13
 
4.2%
고창군 9
 
2.9%
순창군 8
 
2.6%
무주군 6
 
2.0%
Other values (4) 16
 
5.2%

Length

2024-03-14T22:23:12.589897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 126
41.0%
익산시 36
 
11.7%
군산시 35
 
11.4%
완주군 24
 
7.8%
정읍시 17
 
5.5%
남원시 17
 
5.5%
김제시 13
 
4.2%
고창군 9
 
2.9%
순창군 8
 
2.6%
무주군 6
 
2.0%
Other values (4) 16
 
5.2%

기업명
Text

UNIQUE 

Distinct307
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T22:23:13.559218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length21
Mean length10.641694
Min length3

Characters and Unicode

Total characters3267
Distinct characters400
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

Unique307 ?
Unique (%)100.0%

Sample

1st row(유)꽃님이숍
2nd row㈜필리그란폴스튜디오
3rd row(유)플로에듀
4th row㈜ 법정문서 (Legal services Co., Ltd.)
5th row(유)디모션아트컴퍼니
ValueCountFrequency (%)
유한회사 44
 
9.2%
주식회사 36
 
7.5%
사단법인 18
 
3.8%
농업회사법인 15
 
3.1%
협동조합 6
 
1.3%
영농조합법인 4
 
0.8%
사회적협동조합 4
 
0.8%
유 4
 
0.8%
㈜ 3
 
0.6%
사 2
 
0.4%
Other values (338) 342
71.5%
2024-03-14T22:23:14.790066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 212
 
6.5%
173
 
5.3%
회 171
 
5.2%
유 114
 
3.5%
한 91
 
2.8%
인 91
 
2.8%
주 78
 
2.4%
합 78
 
2.4%
조 74
 
2.3%
) 69
 
2.1%
Other values (390) 2116
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2893
88.6%
Space Separator 173
 
5.3%
Close Punctuation 69
 
2.1%
Open Punctuation 66
 
2.0%
Lowercase Letter 28
 
0.9%
Uppercase Letter 15
 
0.5%
Other Symbol 12
 
0.4%
Other Punctuation 8
 
0.2%
Decimal Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 212
 
7.3%
회 171
 
5.9%
유 114
 
3.9%
한 91
 
3.1%
인 91
 
3.1%
주 78
 
2.7%
합 78
 
2.7%
조 74
 
2.6%
법 66
 
2.3%
동 62
 
2.1%
Other values (355) 1856
64.2%
Lowercase Letter
ValueCountFrequency (%)
e 6
21.4%
t 4
14.3%
o 2
 
7.1%
r 2
 
7.1%
s 2
 
7.1%
a 2
 
7.1%
g 2
 
7.1%
d 2
 
7.1%
y 1
 
3.6%
n 1
 
3.6%
Other values (4) 4
14.3%
Uppercase Letter
ValueCountFrequency (%)
L 3
20.0%
C 2
13.3%
A 2
13.3%
S 1
 
6.7%
M 1
 
6.7%
T 1
 
6.7%
I 1
 
6.7%
O 1
 
6.7%
Q 1
 
6.7%
G 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 4
50.0%
& 2
25.0%
, 2
25.0%
Decimal Number
ValueCountFrequency (%)
7 1
33.3%
6 1
33.3%
0 1
33.3%
Space Separator
ValueCountFrequency (%)
173
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Open Punctuation
ValueCountFrequency (%)
( 66
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2905
88.9%
Common 319
 
9.8%
Latin 43
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 212
 
7.3%
회 171
 
5.9%
유 114
 
3.9%
한 91
 
3.1%
인 91
 
3.1%
주 78
 
2.7%
합 78
 
2.7%
조 74
 
2.5%
법 66
 
2.3%
동 62
 
2.1%
Other values (356) 1868
64.3%
Latin
ValueCountFrequency (%)
e 6
 
14.0%
t 4
 
9.3%
L 3
 
7.0%
o 2
 
4.7%
C 2
 
4.7%
r 2
 
4.7%
s 2
 
4.7%
A 2
 
4.7%
a 2
 
4.7%
g 2
 
4.7%
Other values (15) 16
37.2%
Common
ValueCountFrequency (%)
173
54.2%
) 69
 
21.6%
( 66
 
20.7%
. 4
 
1.3%
& 2
 
0.6%
, 2
 
0.6%
7 1
 
0.3%
6 1
 
0.3%
0 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2893
88.6%
ASCII 362
 
11.1%
None 12
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 212
 
7.3%
회 171
 
5.9%
유 114
 
3.9%
한 91
 
3.1%
인 91
 
3.1%
주 78
 
2.7%
합 78
 
2.7%
조 74
 
2.6%
법 66
 
2.3%
동 62
 
2.1%
Other values (355) 1856
64.2%
ASCII
ValueCountFrequency (%)
173
47.8%
) 69
 
19.1%
( 66
 
18.2%
e 6
 
1.7%
t 4
 
1.1%
. 4
 
1.1%
L 3
 
0.8%
o 2
 
0.6%
C 2
 
0.6%
r 2
 
0.6%
Other values (24) 31
 
8.6%
None
ValueCountFrequency (%)
㈜ 12
100.0%

대표자
Text

Distinct265
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2024-03-14T22:23:16.406743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.1270358
Min length3

Characters and Unicode

Total characters960
Distinct characters142
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique235 ?
Unique (%)76.5%

Sample

1st row송*라
2nd row노*향
3rd row김*정
4th row양*희
5th row홍*현
ValueCountFrequency (%)
김*희 5
 
1.6%
김*훈 4
 
1.3%
김*호 4
 
1.3%
이*희 4
 
1.3%
김*민 3
 
1.0%
이*원 3
 
1.0%
김*진 3
 
1.0%
이*영 3
 
1.0%
김*철 3
 
1.0%
김*미 2
 
0.6%
Other values (258) 279
89.1%
2024-03-14T22:23:18.445504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 318
33.1%
김 64
 
6.7%
이 37
 
3.9%
정 21
 
2.2%
박 21
 
2.2%
최 19
 
2.0%
희 16
 
1.7%
영 13
 
1.4%
원 12
 
1.2%
훈 11
 
1.1%
Other values (132) 428
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 628
65.4%
Other Punctuation 325
33.9%
Space Separator 7
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 64
 
10.2%
이 37
 
5.9%
정 21
 
3.3%
박 21
 
3.3%
최 19
 
3.0%
희 16
 
2.5%
영 13
 
2.1%
원 12
 
1.9%
훈 11
 
1.8%
유 10
 
1.6%
Other values (129) 404
64.3%
Other Punctuation
ValueCountFrequency (%)
* 318
97.8%
, 7
 
2.2%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 628
65.4%
Common 332
34.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 64
 
10.2%
이 37
 
5.9%
정 21
 
3.3%
박 21
 
3.3%
최 19
 
3.0%
희 16
 
2.5%
영 13
 
2.1%
원 12
 
1.9%
훈 11
 
1.8%
유 10
 
1.6%
Other values (129) 404
64.3%
Common
ValueCountFrequency (%)
* 318
95.8%
, 7
 
2.1%
7
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 628
65.4%
ASCII 332
34.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 318
95.8%
, 7
 
2.1%
7
 
2.1%
Hangul
ValueCountFrequency (%)
김 64
 
10.2%
이 37
 
5.9%
정 21
 
3.3%
박 21
 
3.3%
최 19
 
3.0%
희 16
 
2.5%
영 13
 
2.1%
원 12
 
1.9%
훈 11
 
1.8%
유 10
 
1.6%
Other values (129) 404
64.3%

업종
Categorical

Distinct17
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
제조
63 
교육
47 
기타
47 
문화예술
38 
농식품
36 
Other values (12)
76 

Length

Max length7
Median length2
Mean length2.7003257
Min length2

Unique

Unique4 ?
Unique (%)1.3%

Sample

1st row제조
2nd row문화예술
3rd row교육
4th row기타
5th row문화예술

Common Values

ValueCountFrequency (%)
제조 63
20.5%
교육 47
15.3%
기타 47
15.3%
문화예술 38
12.4%
농식품 36
11.7%
환경 28
9.1%
보건복지 11
 
3.6%
기타(유통) 8
 
2.6%
사회복지 7
 
2.3%
가사간병 6
 
2.0%
Other values (7) 16
 
5.2%

Length

2024-03-14T22:23:18.863795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제조 63
20.5%
교육 47
15.3%
기타 47
15.3%
문화예술 38
12.4%
농식품 37
12.1%
환경 28
9.1%
보건복지 11
 
3.6%
기타(유통 8
 
2.6%
사회복지 7
 
2.3%
가사간병 6
 
2.0%
Other values (6) 15
 
4.9%

지정일 및 인증일
Date

Distinct93
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Minimum2007-10-29 00:00:00
Maximum2021-09-15 00:00:00
2024-03-14T22:23:19.243376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:23:19.660119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
<NA>
180 
지역형
90 
부처형
29 
지역부처
 
8

Length

Max length4
Median length4
Mean length3.6123779
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 180
58.6%
지역형 90
29.3%
부처형 29
 
9.4%
지역부처 8
 
2.6%

Length

2024-03-14T22:23:20.074947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:23:20.409577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 180
58.6%
지역형 90
29.3%
부처형 29
 
9.4%
지역부처 8
 
2.6%

Correlations

2024-03-14T22:23:20.630444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류시군업종지정일 및 인증일유형
분류1.0000.1010.3981.000NaN
시군0.1011.0000.3940.6520.145
업종0.3980.3941.0000.5220.425
지정일 및 인증일1.0000.6520.5221.0000.930
유형NaN0.1450.4250.9301.000
2024-03-14T22:23:20.835633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군유형분류업종
시군1.0000.0730.0770.143
유형0.0731.0001.0000.249
분류0.0771.0001.0000.349
업종0.1430.2490.3491.000
2024-03-14T22:23:20.993046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류시군업종유형
분류1.0000.0770.3491.000
시군0.0771.0000.1430.073
업종0.3490.1431.0000.249
유형1.0000.0730.2491.000

Missing values

2024-03-14T22:23:11.189743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:23:11.576739image/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

분류시군기업명대표자업종지정일 및 인증일유형
0예비 사회적기업완주군(유)꽃님이숍송*라제조2019-12-26부처형
1예비 사회적기업전주시㈜필리그란폴스튜디오노*향문화예술2019-12-26부처형
2예비 사회적기업전주시(유)플로에듀김*정교육2019-12-26부처형
3예비 사회적기업완주군㈜ 법정문서 (Legal services Co., Ltd.)양*희기타2019-12-26부처형
4예비 사회적기업전주시(유)디모션아트컴퍼니홍*현문화예술2019-06-21부처형
5예비 사회적기업전주시유한회사 굿모닝준모닝조*모문화예술2019-06-21부처형
6예비 사회적기업완주군유한회사 전북플라워가든연구소강*정교육2020-12-28부처형
7예비 사회적기업완주군농가살림연구소 주식회사박*진교육2020-12-28부처형
8예비 사회적기업군산시주식회사 지방조*능기타2019-07-09부처형
9예비 사회적기업군산시군산공예협동조합 (구 협동조합 수공예협회)최*정문화예술2019-07-09부처형
분류시군기업명대표자업종지정일 및 인증일유형
297인증 사회적기업전주시유한회사하이하우징민*선기타2021-07-15<NA>
298인증 사회적기업전주시은혜상사 주식회사박*만,김*환기타2021-07-15<NA>
299인증 사회적기업군산시협동조합아토고*우문화예술2021-07-15<NA>
300인증 사회적기업완주군(사)전북풋볼아카데미방*철기타2021-09-08<NA>
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