Overview

Dataset statistics

Number of variables3
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory832.0 B
Average record size in memory29.7 B

Variable types

Text2
Numeric1

Dataset

Description충청남도에 있는 경제단체 28건의 현황 정보입니다. 위 데이터를 통해 경제단체 기관 및 단체명, 회원 수, 주요기능 등의 정보를 확인할수 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=24&beforeMenuCd=DOM_000000201001001000&publicdatapk=15120065

Alerts

기관명 has unique valuesUnique
회원수 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:19:40.062557
Analysis finished2024-01-09 22:19:40.388173
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-01-10T07:19:40.521922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length16.5
Mean length12.464286
Min length6

Characters and Unicode

Total characters349
Distinct characters77
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

Unique28 ?
Unique (%)100.0%

Sample

1st row대전상공회의소
2nd row충남북부상공회의소
3rd row서산상공회의소
4th row당진상공회의소
5th row대전충남경영자총협회
ValueCountFrequency (%)
대전세종충남연합회 2
 
5.3%
대전상공회의소 1
 
2.6%
한국경영혁신 1
 
2.6%
중소기업기술혁신협회 1
 
2.6%
대전세종충남지회 1
 
2.6%
충청남도중소기업연합회 1
 
2.6%
충남벤처협회 1
 
2.6%
중소기업융합 1
 
2.6%
한국여성경제인협회 1
 
2.6%
세종충남지회 1
 
2.6%
Other values (27) 27
71.1%
2024-01-10T07:19:40.811708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
 
6.9%
24
 
6.9%
23
 
6.6%
14
 
4.0%
13
 
3.7%
12
 
3.4%
11
 
3.2%
10
 
2.9%
9
 
2.6%
9
 
2.6%
Other values (67) 200
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 337
96.6%
Space Separator 10
 
2.9%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
24
 
7.1%
24
 
7.1%
23
 
6.8%
14
 
4.2%
13
 
3.9%
12
 
3.6%
11
 
3.3%
9
 
2.7%
9
 
2.7%
9
 
2.7%
Other values (64) 189
56.1%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 337
96.6%
Common 12
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
24
 
7.1%
24
 
7.1%
23
 
6.8%
14
 
4.2%
13
 
3.9%
12
 
3.6%
11
 
3.3%
9
 
2.7%
9
 
2.7%
9
 
2.7%
Other values (64) 189
56.1%
Common
ValueCountFrequency (%)
10
83.3%
( 1
 
8.3%
) 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 337
96.6%
ASCII 12
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
24
 
7.1%
24
 
7.1%
23
 
6.8%
14
 
4.2%
13
 
3.9%
12
 
3.6%
11
 
3.3%
9
 
2.7%
9
 
2.7%
9
 
2.7%
Other values (64) 189
56.1%
ASCII
ValueCountFrequency (%)
10
83.3%
( 1
 
8.3%
) 1
 
8.3%

회원수
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4352.75
Minimum15
Maximum65000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-10T07:19:40.913309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile20.9
Q1102.5
median400
Q31525
95-th percentile26205
Maximum65000
Range64985
Interquartile range (IQR)1422.5

Descriptive statistics

Standard deviation13845.946
Coefficient of variation (CV)3.1809651
Kurtosis15.287037
Mean4352.75
Median Absolute Deviation (MAD)338.5
Skewness3.9024427
Sum121877
Variance1.9171021 × 108
MonotonicityNot monotonic
2024-01-10T07:19:41.004148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1751 1
 
3.6%
360 1
 
3.6%
46 1
 
3.6%
77 1
 
3.6%
108 1
 
3.6%
16 1
 
3.6%
15 1
 
3.6%
113 1
 
3.6%
1108 1
 
3.6%
126 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
15 1
3.6%
16 1
3.6%
30 1
3.6%
37 1
3.6%
46 1
3.6%
77 1
3.6%
86 1
3.6%
108 1
3.6%
113 1
3.6%
126 1
3.6%
ValueCountFrequency (%)
65000 1
3.6%
38000 1
3.6%
4300 1
3.6%
2082 1
3.6%
1906 1
3.6%
1751 1
3.6%
1600 1
3.6%
1500 1
3.6%
1108 1
3.6%
705 1
3.6%
Distinct23
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-01-10T07:19:41.176398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length21.5
Mean length15.142857
Min length8

Characters and Unicode

Total characters424
Distinct characters105
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

Unique20 ?
Unique (%)71.4%

Sample

1st row경제활성화 및 정보교류
2nd row경제활성화 및 정보교류
3rd row경제활성화 및 정보교류
4th row경제활성화 및 정보교류
5th row노사민정, 기업경영 활성화 도모
ValueCountFrequency (%)
13
 
12.6%
중소기업 6
 
5.8%
경제활성화 4
 
3.9%
정보교류 4
 
3.9%
당사자조직 3
 
2.9%
지원 3
 
2.9%
충남 3
 
2.9%
통한 2
 
1.9%
2
 
1.9%
사회적기업 2
 
1.9%
Other values (52) 61
59.2%
2024-01-10T07:19:41.455854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75
 
17.7%
21
 
5.0%
17
 
4.0%
13
 
3.1%
11
 
2.6%
11
 
2.6%
10
 
2.4%
10
 
2.4%
9
 
2.1%
8
 
1.9%
Other values (95) 239
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
80.2%
Space Separator 75
 
17.7%
Other Punctuation 7
 
1.7%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
21
 
6.2%
17
 
5.0%
13
 
3.8%
11
 
3.2%
11
 
3.2%
10
 
2.9%
10
 
2.9%
9
 
2.6%
8
 
2.4%
8
 
2.4%
Other values (90) 222
65.3%
Other Punctuation
ValueCountFrequency (%)
, 4
57.1%
· 3
42.9%
Space Separator
ValueCountFrequency (%)
75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
80.2%
Common 84
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
21
 
6.2%
17
 
5.0%
13
 
3.8%
11
 
3.2%
11
 
3.2%
10
 
2.9%
10
 
2.9%
9
 
2.6%
8
 
2.4%
8
 
2.4%
Other values (90) 222
65.3%
Common
ValueCountFrequency (%)
75
89.3%
, 4
 
4.8%
· 3
 
3.6%
) 1
 
1.2%
( 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
80.2%
ASCII 81
 
19.1%
None 3
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
75
92.6%
, 4
 
4.9%
) 1
 
1.2%
( 1
 
1.2%
Hangul
ValueCountFrequency (%)
21
 
6.2%
17
 
5.0%
13
 
3.8%
11
 
3.2%
11
 
3.2%
10
 
2.9%
10
 
2.9%
9
 
2.6%
8
 
2.4%
8
 
2.4%
Other values (90) 222
65.3%
None
ValueCountFrequency (%)
· 3
100.0%

Interactions

2024-01-10T07:19:40.210004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:19:41.529186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명회원수주요기능
기관명1.0001.0001.000
회원수1.0001.0000.000
주요기능1.0000.0001.000

Missing values

2024-01-10T07:19:40.299909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:19:40.364079image/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

기관명회원수주요기능
0대전상공회의소1751경제활성화 및 정보교류
1충남북부상공회의소1906경제활성화 및 정보교류
2서산상공회의소440경제활성화 및 정보교류
3당진상공회의소705경제활성화 및 정보교류
4대전충남경영자총협회180노사민정, 기업경영 활성화 도모
5소비자교육중앙회충남도지부1600소비자 피해구제
6한국여성소비자연합충남도지회656소비자 보호 및 물가안정
7충청남도 소상공인연합회2082소상공인 활성화
8한국노총충남지역본부38000노동조합 및 노동자 권익보호
9민주노총충남지역본부65000노동조합 및 노동자 권익보호
기관명회원수주요기능
18충남벤처협회200벤처기업간 정보 교환
19중소기업융합 대전세종충남연합회454이업종간 상생협력 및 교류
20한국여성경제인협회 세종충남지회126여성기업인 경영활동 지원
21중소기업중앙회대전세종충남 지역본부1108중소기업 협력 및 지원
22충남신용보증재단113소기업 및 소상공인 채무보증
23청운대산학협력단(충남사회적경제지원센터)15사회적경제조직 발굴·자립까지 종합적 지원
24충남사회경제네트워크16사회적기업 권역별 통합지원기관(고용부)
25충남사회적기업협의회108충남 사회적기업 당사자조직
26충남마을기업협의회77충남 마을기업 당사자조직
27충남자활기업협회46충남 자활기업의 당사자조직