Overview

Dataset statistics

Number of variables16
Number of observations314
Missing cells1045
Missing cells (%)20.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.5 KiB
Average record size in memory135.4 B

Variable types

Categorical6
Text5
Numeric5

Dataset

Description지역주민이 각종 지역자원을 활용한 수익사업을 통해 공동의 지역문제를 해결하고 소득 및 일자리를 창출하는 마을단위의 기업
Author전라남도
URLhttps://www.data.go.kr/data/15102940/fileData.do

Alerts

구분 is highly overall correlated with 우수 마을기업 and 2 other fieldsHigh correlation
시군 is highly overall correlated with 모두애 마을기업High correlation
모두애 마을기업 is highly overall correlated with 지정연도_예비 and 6 other fieldsHigh correlation
지정연도_예비 is highly overall correlated with 지정년도_1차년 and 2 other fieldsHigh correlation
지정년도_1차년 is highly overall correlated with 지정연도_예비 and 2 other fieldsHigh correlation
지정년도_2차년 is highly overall correlated with 지정연도_예비 and 3 other fieldsHigh correlation
우수 마을기업 is highly overall correlated with 지정년도_1차년 and 3 other fieldsHigh correlation
비고_전남형 예비 지정연도 is highly overall correlated with 구분High correlation
마을기업 유형 is highly overall correlated with 모두애 마을기업High correlation
업종 is highly overall correlated with 모두애 마을기업High correlation
지정년도_3차년 is highly imbalanced (69.6%)Imbalance
모두애 마을기업 is highly imbalanced (93.6%)Imbalance
지정연도_예비 has 187 (59.6%) missing valuesMissing
지정년도_1차년 has 56 (17.8%) missing valuesMissing
지정년도_2차년 has 170 (54.1%) missing valuesMissing
우수 마을기업 has 300 (95.5%) missing valuesMissing
비고_전남형 예비 지정연도 has 210 (66.9%) missing valuesMissing
연락처 has 122 (38.9%) missing valuesMissing
마을기업명 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:36:37.294192
Analysis finished2023-12-12 02:36:42.793266
Duration5.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
행안부형
210 
전남형 예비
104 

Length

Max length6
Median length4
Mean length4.6624204
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
행안부형 210
66.9%
전남형 예비 104
33.1%

Length

2023-12-12T11:36:42.955302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:36:43.251603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
행안부형 210
50.2%
전남형 104
24.9%
예비 104
24.9%

시군
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
순천시
37 
여수시
21 
나주시
20 
해남군
20 
광양시
 
18
Other values (17)
198 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
순천시 37
 
11.8%
여수시 21
 
6.7%
나주시 20
 
6.4%
해남군 20
 
6.4%
광양시 18
 
5.7%
보성군 18
 
5.7%
담양군 15
 
4.8%
곡성군 15
 
4.8%
목포시 13
 
4.1%
완도군 13
 
4.1%
Other values (12) 124
39.5%

Length

2023-12-12T11:36:43.452216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
순천시 37
 
11.8%
여수시 21
 
6.7%
나주시 20
 
6.4%
해남군 20
 
6.4%
광양시 18
 
5.7%
보성군 18
 
5.7%
담양군 15
 
4.8%
곡성군 15
 
4.8%
목포시 13
 
4.1%
완도군 13
 
4.1%
Other values (12) 124
39.5%

마을기업명
Text

UNIQUE 

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T11:36:43.764053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length18
Mean length12.089172
Min length3

Characters and Unicode

Total characters3796
Distinct characters363
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)100.0%

Sample

1st row주식회사 골목길
2nd row유한회사 삼향골
3rd row문화예술 협동조합 나무숲
4th row주식회사 만인계마을기업
5th row주식회사 목포마중물마을기업
ValueCountFrequency (%)
영농조합법인 119
 
18.2%
주식회사 72
 
11.0%
농업회사법인 54
 
8.3%
협동조합 44
 
6.7%
영어조합법인 15
 
2.3%
유한회사 13
 
2.0%
사회적협동조합 4
 
0.6%
영어영농조합법인 2
 
0.3%
어업회사법인 2
 
0.3%
순천향동마을관리협동조합 1
 
0.2%
Other values (327) 327
50.1%
2023-12-12T11:36:44.262624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
340
 
9.0%
인 213
 
5.6%
합 206
 
5.4%
조 206
 
5.4%
법 204
 
5.4%
농 198
 
5.2%
사 177
 
4.7%
회 173
 
4.6%
영 153
 
4.0%
식 88
 
2.3%
Other values (353) 1838
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3437
90.5%
Space Separator 340
 
9.0%
Decimal Number 10
 
0.3%
Other Symbol 3
 
0.1%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 213
 
6.2%
합 206
 
6.0%
조 206
 
6.0%
법 204
 
5.9%
농 198
 
5.8%
사 177
 
5.1%
회 173
 
5.0%
영 153
 
4.5%
식 88
 
2.6%
마 87
 
2.5%
Other values (342) 1732
50.4%
Decimal Number
ValueCountFrequency (%)
1 3
30.0%
0 2
20.0%
8 1
 
10.0%
9 1
 
10.0%
4 1
 
10.0%
7 1
 
10.0%
2 1
 
10.0%
Space Separator
ValueCountFrequency (%)
340
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3440
90.6%
Common 356
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 213
 
6.2%
합 206
 
6.0%
조 206
 
6.0%
법 204
 
5.9%
농 198
 
5.8%
사 177
 
5.1%
회 173
 
5.0%
영 153
 
4.4%
식 88
 
2.6%
마 87
 
2.5%
Other values (343) 1735
50.4%
Common
ValueCountFrequency (%)
340
95.5%
) 3
 
0.8%
1 3
 
0.8%
( 3
 
0.8%
0 2
 
0.6%
8 1
 
0.3%
9 1
 
0.3%
4 1
 
0.3%
7 1
 
0.3%
2 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3437
90.5%
ASCII 356
 
9.4%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
340
95.5%
) 3
 
0.8%
1 3
 
0.8%
( 3
 
0.8%
0 2
 
0.6%
8 1
 
0.3%
9 1
 
0.3%
4 1
 
0.3%
7 1
 
0.3%
2 1
 
0.3%
Hangul
ValueCountFrequency (%)
인 213
 
6.2%
합 206
 
6.0%
조 206
 
6.0%
법 204
 
5.9%
농 198
 
5.8%
사 177
 
5.1%
회 173
 
5.0%
영 153
 
4.5%
식 88
 
2.6%
마 87
 
2.5%
Other values (342) 1732
50.4%
None
ValueCountFrequency (%)
㈜ 3
100.0%

마을기업 유형
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
일반식품
194 
전통식품
36 
관광체험
34 
기타
28 
문화예술
 
6
Other values (6)
 
16

Length

Max length4
Median length4
Mean length3.7802548
Min length2

Unique

Unique3 ?
Unique (%)1.0%

Sample

1st row관광체험
2nd row전통식품
3rd row문화예술
4th row관광체험
5th row기타

Common Values

ValueCountFrequency (%)
일반식품 194
61.8%
전통식품 36
 
11.5%
관광체험 34
 
10.8%
기타 28
 
8.9%
문화예술 6
 
1.9%
공예품 6
 
1.9%
유통기업 4
 
1.3%
교육 3
 
1.0%
사회복지 1
 
0.3%
의류신발 1
 
0.3%

Length

2023-12-12T11:36:44.434135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반식품 194
61.8%
전통식품 36
 
11.5%
관광체험 34
 
10.8%
기타 28
 
8.9%
문화예술 6
 
1.9%
공예품 6
 
1.9%
유통기업 4
 
1.3%
교육 3
 
1.0%
사회복지 1
 
0.3%
의류신발 1
 
0.3%

업종
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
제조업
96 
도소매업
74 
농업
39 
서비스
32 
농산물생산가공판매
10 
Other values (27)
63 

Length

Max length12
Median length9
Mean length3.4904459
Min length2

Unique

Unique17 ?
Unique (%)5.4%

Sample

1st row서비스
2nd row제조업
3rd row서비스
4th row서비스
5th row서비스

Common Values

ValueCountFrequency (%)
제조업 96
30.6%
도소매업 74
23.6%
농업 39
12.4%
서비스 32
 
10.2%
농산물생산가공판매 10
 
3.2%
소매업 8
 
2.5%
식품제조가공 8
 
2.5%
서비스업 7
 
2.2%
음식 5
 
1.6%
농산물 5
 
1.6%
Other values (22) 30
 
9.6%

Length

2023-12-12T11:36:44.632276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제조업 96
30.3%
도소매업 74
23.3%
농업 39
12.3%
서비스 32
 
10.1%
농산물생산가공판매 10
 
3.2%
소매업 8
 
2.5%
식품제조가공 8
 
2.5%
서비스업 7
 
2.2%
음식 5
 
1.6%
농산물 5
 
1.6%
Other values (25) 33
 
10.4%

주생산품 또는 서비스명
Text

Distinct280
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T11:36:45.006548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length22
Mean length9.3312102
Min length1

Characters and Unicode

Total characters2930
Distinct characters323
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

Unique264 ?
Unique (%)84.1%

Sample

1st row 축제, 문화예술 기획
2nd row 두부식품
3rd row 문화예술, 공연, 전시기획
4th row 전시 및 행사 대행, 유사음식점
5th row 사업지원 및 기획
ValueCountFrequency (%)
및 28
 
4.5%
농산물 16
 
2.6%
판매 16
 
2.6%
등 14
 
2.3%
체험 13
 
2.1%
가공 10
 
1.6%
절임배추 9
 
1.5%
두부 8
 
1.3%
가공판매 7
 
1.1%
지역특산물 6
 
1.0%
Other values (384) 490
79.4%
2023-12-12T11:36:45.541635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
790
27.0%
, 147
 
5.0%
물 66
 
2.3%
산 64
 
2.2%
공 43
 
1.5%
매 42
 
1.4%
농 37
 
1.3%
가 36
 
1.2%
체 33
 
1.1%
장 33
 
1.1%
Other values (313) 1639
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1954
66.7%
Space Separator 790
27.0%
Other Punctuation 152
 
5.2%
Close Punctuation 17
 
0.6%
Open Punctuation 17
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
물 66
 
3.4%
산 64
 
3.3%
공 43
 
2.2%
매 42
 
2.1%
농 37
 
1.9%
가 36
 
1.8%
체 33
 
1.7%
장 33
 
1.7%
험 33
 
1.7%
판 29
 
1.5%
Other values (307) 1538
78.7%
Other Punctuation
ValueCountFrequency (%)
, 147
96.7%
. 3
 
2.0%
· 2
 
1.3%
Space Separator
ValueCountFrequency (%)
790
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1954
66.7%
Common 976
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
물 66
 
3.4%
산 64
 
3.3%
공 43
 
2.2%
매 42
 
2.1%
농 37
 
1.9%
가 36
 
1.8%
체 33
 
1.7%
장 33
 
1.7%
험 33
 
1.7%
판 29
 
1.5%
Other values (307) 1538
78.7%
Common
ValueCountFrequency (%)
790
80.9%
, 147
 
15.1%
) 17
 
1.7%
( 17
 
1.7%
. 3
 
0.3%
· 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1954
66.7%
ASCII 974
33.2%
None 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
790
81.1%
, 147
 
15.1%
) 17
 
1.7%
( 17
 
1.7%
. 3
 
0.3%
Hangul
ValueCountFrequency (%)
물 66
 
3.4%
산 64
 
3.3%
공 43
 
2.2%
매 42
 
2.1%
농 37
 
1.9%
가 36
 
1.8%
체 33
 
1.7%
장 33
 
1.7%
험 33
 
1.7%
판 29
 
1.5%
Other values (307) 1538
78.7%
None
ValueCountFrequency (%)
· 2
100.0%

사업내용
Text

Distinct311
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T11:36:45.934410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length121
Median length36
Mean length18.875796
Min length2

Characters and Unicode

Total characters5927
Distinct characters430
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

Unique308 ?
Unique (%)98.1%

Sample

1st row게스트하우스, 카페/음식점, 문화여행상품, 가이드투어, 굿즈 제작판매
2nd row두부, 청국장, 콩물 등 생산 판매
3rd row문화예술 상품개발 및 판매, 체험프로그램 운영
4th row마을카페, 마을축제
5th row교육, 이벤트, 용역
ValueCountFrequency (%)
판매 132
 
8.8%
및 132
 
8.8%
가공 49
 
3.3%
운영 48
 
3.2%
제조 40
 
2.7%
등 32
 
2.1%
생산 32
 
2.1%
농산물 24
 
1.6%
체험 20
 
1.3%
사업 17
 
1.1%
Other values (688) 973
64.9%
2023-12-12T11:36:46.539464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1230
 
20.8%
, 267
 
4.5%
매 193
 
3.3%
판 181
 
3.1%
산 135
 
2.3%
및 134
 
2.3%
공 107
 
1.8%
물 100
 
1.7%
가 98
 
1.7%
제 88
 
1.5%
Other values (420) 3394
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4351
73.4%
Space Separator 1230
 
20.8%
Other Punctuation 275
 
4.6%
Close Punctuation 33
 
0.6%
Open Punctuation 33
 
0.6%
Decimal Number 3
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
매 193
 
4.4%
판 181
 
4.2%
산 135
 
3.1%
및 134
 
3.1%
공 107
 
2.5%
물 100
 
2.3%
가 98
 
2.3%
제 88
 
2.0%
조 85
 
2.0%
장 76
 
1.7%
Other values (409) 3154
72.5%
Other Punctuation
ValueCountFrequency (%)
, 267
97.1%
. 5
 
1.8%
/ 2
 
0.7%
· 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
1 1
33.3%
Math Symbol
ValueCountFrequency (%)
> 1
50.0%
~ 1
50.0%
Space Separator
ValueCountFrequency (%)
1230
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4351
73.4%
Common 1576
 
26.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
매 193
 
4.4%
판 181
 
4.2%
산 135
 
3.1%
및 134
 
3.1%
공 107
 
2.5%
물 100
 
2.3%
가 98
 
2.3%
제 88
 
2.0%
조 85
 
2.0%
장 76
 
1.7%
Other values (409) 3154
72.5%
Common
ValueCountFrequency (%)
1230
78.0%
, 267
 
16.9%
) 33
 
2.1%
( 33
 
2.1%
. 5
 
0.3%
0 2
 
0.1%
/ 2
 
0.1%
> 1
 
0.1%
· 1
 
0.1%
~ 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4351
73.4%
ASCII 1575
 
26.6%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1230
78.1%
, 267
 
17.0%
) 33
 
2.1%
( 33
 
2.1%
. 5
 
0.3%
0 2
 
0.1%
/ 2
 
0.1%
> 1
 
0.1%
~ 1
 
0.1%
1 1
 
0.1%
Hangul
ValueCountFrequency (%)
매 193
 
4.4%
판 181
 
4.2%
산 135
 
3.1%
및 134
 
3.1%
공 107
 
2.5%
물 100
 
2.3%
가 98
 
2.3%
제 88
 
2.0%
조 85
 
2.0%
장 76
 
1.7%
Other values (409) 3154
72.5%
None
ValueCountFrequency (%)
· 1
100.0%

지정연도_예비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)8.7%
Missing187
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean2016.622
Minimum2012
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T11:36:46.701887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2013
Q12014
median2016
Q32019
95-th percentile2021
Maximum2022
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8755376
Coefficient of variation (CV)0.001425918
Kurtosis-1.1778298
Mean2016.622
Median Absolute Deviation (MAD)3
Skewness0.22185072
Sum256111
Variance8.2687164
MonotonicityNot monotonic
2023-12-12T11:36:47.160982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2013 26
 
8.3%
2019 18
 
5.7%
2015 13
 
4.1%
2017 13
 
4.1%
2014 12
 
3.8%
2016 12
 
3.8%
2021 9
 
2.9%
2018 9
 
2.9%
2020 8
 
2.5%
2022 6
 
1.9%
(Missing) 187
59.6%
ValueCountFrequency (%)
2012 1
 
0.3%
2013 26
8.3%
2014 12
3.8%
2015 13
4.1%
2016 12
3.8%
2017 13
4.1%
2018 9
 
2.9%
2019 18
5.7%
2020 8
 
2.5%
2021 9
 
2.9%
ValueCountFrequency (%)
2022 6
 
1.9%
2021 9
 
2.9%
2020 8
 
2.5%
2019 18
5.7%
2018 9
 
2.9%
2017 13
4.1%
2016 12
3.8%
2015 13
4.1%
2014 12
3.8%
2013 26
8.3%

지정년도_1차년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)5.4%
Missing56
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean2015.7829
Minimum2010
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T11:36:47.276425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12013
median2015
Q32019.75
95-th percentile2022
Maximum2023
Range13
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation3.9243057
Coefficient of variation (CV)0.0019467898
Kurtosis-1.221255
Mean2015.7829
Median Absolute Deviation (MAD)3
Skewness0.28795839
Sum520072
Variance15.400175
MonotonicityNot monotonic
2023-12-12T11:36:47.413644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2013 36
11.5%
2012 27
8.6%
2021 23
7.3%
2011 21
 
6.7%
2014 19
 
6.1%
2020 18
 
5.7%
2016 18
 
5.7%
2015 17
 
5.4%
2010 16
 
5.1%
2017 15
 
4.8%
Other values (4) 48
15.3%
(Missing) 56
17.8%
ValueCountFrequency (%)
2010 16
5.1%
2011 21
6.7%
2012 27
8.6%
2013 36
11.5%
2014 19
6.1%
2015 17
5.4%
2016 18
5.7%
2017 15
4.8%
2018 11
 
3.5%
2019 13
 
4.1%
ValueCountFrequency (%)
2023 9
 
2.9%
2022 15
4.8%
2021 23
7.3%
2020 18
5.7%
2019 13
4.1%
2018 11
3.5%
2017 15
4.8%
2016 18
5.7%
2015 17
5.4%
2014 19
6.1%

지정년도_2차년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)9.0%
Missing170
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean2017.1042
Minimum2011
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T11:36:47.542381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2012
Q12014
median2017
Q32021
95-th percentile2022.85
Maximum2023
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6846283
Coefficient of variation (CV)0.0018266921
Kurtosis-1.3827321
Mean2017.1042
Median Absolute Deviation (MAD)3
Skewness0.082808043
Sum290463
Variance13.576486
MonotonicityNot monotonic
2023-12-12T11:36:47.685924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2014 24
 
7.6%
2022 17
 
5.4%
2013 16
 
5.1%
2018 14
 
4.5%
2020 13
 
4.1%
2021 12
 
3.8%
2017 11
 
3.5%
2015 9
 
2.9%
2023 8
 
2.5%
2012 8
 
2.5%
Other values (3) 12
 
3.8%
(Missing) 170
54.1%
ValueCountFrequency (%)
2011 5
 
1.6%
2012 8
 
2.5%
2013 16
5.1%
2014 24
7.6%
2015 9
 
2.9%
2016 3
 
1.0%
2017 11
3.5%
2018 14
4.5%
2019 4
 
1.3%
2020 13
4.1%
ValueCountFrequency (%)
2023 8
 
2.5%
2022 17
5.4%
2021 12
3.8%
2020 13
4.1%
2019 4
 
1.3%
2018 14
4.5%
2017 11
3.5%
2016 3
 
1.0%
2015 9
 
2.9%
2014 24
7.6%

지정년도_3차년
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
276 
2022
 
15
2021
 
9
2023
 
7
2019
 
4

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row2020
3rd row2022
4th row2023
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 276
87.9%
2022 15
 
4.8%
2021 9
 
2.9%
2023 7
 
2.2%
2019 4
 
1.3%
2020 3
 
1.0%

Length

2023-12-12T11:36:47.845484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:36:47.989520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 276
87.9%
2022 15
 
4.8%
2021 9
 
2.9%
2023 7
 
2.2%
2019 4
 
1.3%
2020 3
 
1.0%

우수 마을기업
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)85.7%
Missing300
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean2018
Minimum2011
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T11:36:48.107967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011.65
Q12015.25
median2018.5
Q32021.75
95-th percentile2023
Maximum2023
Range12
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.1137668
Coefficient of variation (CV)0.0020385365
Kurtosis-1.1574005
Mean2018
Median Absolute Deviation (MAD)3.5
Skewness-0.39446263
Sum28252
Variance16.923077
MonotonicityNot monotonic
2023-12-12T11:36:48.233024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2023 2
 
0.6%
2022 2
 
0.6%
2019 1
 
0.3%
2013 1
 
0.3%
2021 1
 
0.3%
2017 1
 
0.3%
2016 1
 
0.3%
2015 1
 
0.3%
2018 1
 
0.3%
2011 1
 
0.3%
Other values (2) 2
 
0.6%
(Missing) 300
95.5%
ValueCountFrequency (%)
2011 1
0.3%
2012 1
0.3%
2013 1
0.3%
2015 1
0.3%
2016 1
0.3%
2017 1
0.3%
2018 1
0.3%
2019 1
0.3%
2020 1
0.3%
2021 1
0.3%
ValueCountFrequency (%)
2023 2
0.6%
2022 2
0.6%
2021 1
0.3%
2020 1
0.3%
2019 1
0.3%
2018 1
0.3%
2017 1
0.3%
2016 1
0.3%
2015 1
0.3%
2013 1
0.3%

모두애 마을기업
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
<NA>
309 
2023
 
2
2020
 
1
2021
 
1
2022
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique3 ?
Unique (%)1.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 309
98.4%
2023 2
 
0.6%
2020 1
 
0.3%
2021 1
 
0.3%
2022 1
 
0.3%

Length

2023-12-12T11:36:48.382651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:36:48.511464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 309
98.4%
2023 2
 
0.6%
2020 1
 
0.3%
2021 1
 
0.3%
2022 1
 
0.3%

비고_전남형 예비 지정연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)10.6%
Missing210
Missing (%)66.9%
Infinite0
Infinite (%)0.0%
Mean2018.3846
Minimum2013
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-12T11:36:48.614201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12015
median2019
Q32022
95-th percentile2023
Maximum2023
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5644079
Coefficient of variation (CV)0.0017659706
Kurtosis-1.4179
Mean2018.3846
Median Absolute Deviation (MAD)3
Skewness-0.25309819
Sum209912
Variance12.705004
MonotonicityNot monotonic
2023-12-12T11:36:48.755404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2013 16
 
5.1%
2023 14
 
4.5%
2021 13
 
4.1%
2022 13
 
4.1%
2020 10
 
3.2%
2015 8
 
2.5%
2019 7
 
2.2%
2016 7
 
2.2%
2017 6
 
1.9%
2014 6
 
1.9%
(Missing) 210
66.9%
ValueCountFrequency (%)
2013 16
5.1%
2014 6
 
1.9%
2015 8
2.5%
2016 7
2.2%
2017 6
 
1.9%
2018 4
 
1.3%
2019 7
2.2%
2020 10
3.2%
2021 13
4.1%
2022 13
4.1%
ValueCountFrequency (%)
2023 14
4.5%
2022 13
4.1%
2021 13
4.1%
2020 10
3.2%
2019 7
2.2%
2018 4
 
1.3%
2017 6
1.9%
2016 7
2.2%
2015 8
2.5%
2014 6
1.9%

소재지
Text

UNIQUE 

Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-12T11:36:49.130340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length21
Mean length12.840764
Min length9

Characters and Unicode

Total characters4032
Distinct characters228
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique314 ?
Unique (%)100.0%

Sample

1st row목포시 호남로64번길 12
2nd row목포시 녹색로 1
3rd row목포시 수문로 67, 2층
4th row목포시 마인계터로 31, 1층
5th row목포시 마인계터로 40번길 10, 1층
ValueCountFrequency (%)
순천시 37
 
3.8%
여수시 21
 
2.1%
해남군 20
 
2.0%
나주시 19
 
1.9%
보성군 18
 
1.8%
광양시 17
 
1.7%
곡성군 15
 
1.5%
담양군 15
 
1.5%
완도군 13
 
1.3%
고흥군 13
 
1.3%
Other values (606) 796
80.9%
2023-12-12T11:36:49.678908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
687
 
17.0%
1 227
 
5.6%
길 222
 
5.5%
군 208
 
5.2%
2 154
 
3.8%
- 140
 
3.5%
3 115
 
2.9%
시 113
 
2.8%
로 103
 
2.6%
4 100
 
2.5%
Other values (218) 1963
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2154
53.4%
Decimal Number 1018
25.2%
Space Separator 687
 
17.0%
Dash Punctuation 140
 
3.5%
Other Punctuation 18
 
0.4%
Close Punctuation 7
 
0.2%
Open Punctuation 7
 
0.2%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 222
 
10.3%
군 208
 
9.7%
시 113
 
5.2%
로 103
 
4.8%
성 54
 
2.5%
천 52
 
2.4%
순 50
 
2.3%
안 41
 
1.9%
산 41
 
1.9%
양 37
 
1.7%
Other values (200) 1233
57.2%
Decimal Number
ValueCountFrequency (%)
1 227
22.3%
2 154
15.1%
3 115
11.3%
4 100
9.8%
5 91
8.9%
6 79
 
7.8%
8 71
 
7.0%
9 67
 
6.6%
0 57
 
5.6%
7 57
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 16
88.9%
: 1
 
5.6%
. 1
 
5.6%
Space Separator
ValueCountFrequency (%)
687
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2154
53.4%
Common 1877
46.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 222
 
10.3%
군 208
 
9.7%
시 113
 
5.2%
로 103
 
4.8%
성 54
 
2.5%
천 52
 
2.4%
순 50
 
2.3%
안 41
 
1.9%
산 41
 
1.9%
양 37
 
1.7%
Other values (200) 1233
57.2%
Common
ValueCountFrequency (%)
687
36.6%
1 227
 
12.1%
2 154
 
8.2%
- 140
 
7.5%
3 115
 
6.1%
4 100
 
5.3%
5 91
 
4.8%
6 79
 
4.2%
8 71
 
3.8%
9 67
 
3.6%
Other values (7) 146
 
7.8%
Latin
ValueCountFrequency (%)
X 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2154
53.4%
ASCII 1878
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
687
36.6%
1 227
 
12.1%
2 154
 
8.2%
- 140
 
7.5%
3 115
 
6.1%
4 100
 
5.3%
5 91
 
4.8%
6 79
 
4.2%
8 71
 
3.8%
9 67
 
3.6%
Other values (8) 147
 
7.8%
Hangul
ValueCountFrequency (%)
길 222
 
10.3%
군 208
 
9.7%
시 113
 
5.2%
로 103
 
4.8%
성 54
 
2.5%
천 52
 
2.4%
순 50
 
2.3%
안 41
 
1.9%
산 41
 
1.9%
양 37
 
1.7%
Other values (200) 1233
57.2%

연락처
Text

MISSING 

Distinct190
Distinct (%)99.0%
Missing122
Missing (%)38.9%
Memory size2.6 KiB
2023-12-12T11:36:50.003825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length12
Mean length12.192708
Min length9

Characters and Unicode

Total characters2341
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)97.9%

Sample

1st row061-284-5798
2nd row061-242-1128
3rd row061-244-1002
4th row061-981-1515
5th row061-244-6668
ValueCountFrequency (%)
061-834-0413 2
 
1.0%
061-782-0822 2
 
1.0%
061-271-7793 1
 
0.5%
061-552-1599 1
 
0.5%
061-863-3201 1
 
0.5%
070-4159-9140 1
 
0.5%
061-324-2331 1
 
0.5%
061-353-0045 1
 
0.5%
061-352-4051 1
 
0.5%
061-351-0878 1
 
0.5%
Other values (181) 181
93.8%
2023-12-12T11:36:50.445984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 384
16.4%
0 325
13.9%
6 299
12.8%
1 299
12.8%
3 172
7.3%
5 171
7.3%
4 165
7.0%
7 148
 
6.3%
2 145
 
6.2%
8 129
 
5.5%
Other values (3) 104
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1932
82.5%
Dash Punctuation 384
 
16.4%
Space Separator 24
 
1.0%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 325
16.8%
6 299
15.5%
1 299
15.5%
3 172
8.9%
5 171
8.9%
4 165
8.5%
7 148
7.7%
2 145
7.5%
8 129
 
6.7%
9 79
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 384
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2341
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 384
16.4%
0 325
13.9%
6 299
12.8%
1 299
12.8%
3 172
7.3%
5 171
7.3%
4 165
7.0%
7 148
 
6.3%
2 145
 
6.2%
8 129
 
5.5%
Other values (3) 104
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 384
16.4%
0 325
13.9%
6 299
12.8%
1 299
12.8%
3 172
7.3%
5 171
7.3%
4 165
7.0%
7 148
 
6.3%
2 145
 
6.2%
8 129
 
5.5%
Other values (3) 104
 
4.4%

Interactions

2023-12-12T11:36:40.847408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:38.582911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:39.104312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:39.689139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:40.236453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:40.979030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:38.673983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:39.225598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:39.799147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:40.366136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:41.125012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:38.774302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:39.355532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:39.910379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:40.488253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:41.263302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:38.883739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:39.480683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:40.020300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:40.616219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:41.408613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:38.987424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:39.587803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:40.124990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:36:40.734673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:36:50.565483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분시군마을기업 유형업종지정연도_예비지정년도_1차년지정년도_2차년지정년도_3차년우수 마을기업모두애 마을기업비고_전남형 예비 지정연도
구분1.0000.0000.0340.1060.0000.0000.0000.170NaNNaNNaN
시군0.0001.0000.2910.6540.0000.0000.0000.5580.0001.0000.416
마을기업 유형0.0340.2911.0000.7130.2630.3080.3930.0000.0001.0000.120
업종0.1060.6540.7131.0000.4160.0000.0450.0000.0001.0000.401
지정연도_예비0.0000.0000.2630.4161.0000.8880.7900.5380.6270.0000.234
지정년도_1차년0.0000.0000.3080.0000.8881.0000.9750.5980.4850.7710.148
지정년도_2차년0.0000.0000.3930.0450.7900.9751.0000.7170.7641.0000.894
지정년도_3차년0.1700.5580.0000.0000.5380.5980.7171.0000.688NaN1.000
우수 마을기업NaN0.0000.0000.0000.6270.4850.7640.6881.0001.000NaN
모두애 마을기업NaN1.0001.0001.0000.0000.7711.000NaN1.0001.000NaN
비고_전남형 예비 지정연도NaN0.4160.1200.4010.2340.1480.8941.000NaNNaN1.000
2023-12-12T11:36:50.728272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종마을기업 유형구분지정년도_3차년시군모두애 마을기업
업종1.0000.3210.0840.0000.2130.816
마을기업 유형0.3211.0000.0300.0000.0890.577
구분0.0840.0301.0000.1920.0001.000
지정년도_3차년0.0000.0000.1921.0000.266NaN
시군0.2130.0890.0000.2661.0001.000
모두애 마을기업0.8160.5771.000NaN1.0001.000
2023-12-12T11:36:50.920667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정연도_예비지정년도_1차년지정년도_2차년우수 마을기업비고_전남형 예비 지정연도구분시군마을기업 유형업종지정년도_3차년모두애 마을기업
지정연도_예비1.0000.8540.7310.000-0.2210.0000.0000.1280.1370.3271.000
지정년도_1차년0.8541.0000.8730.628-0.2140.0000.0000.1160.0000.4130.000
지정년도_2차년0.7310.8731.0000.858-0.0980.0000.0000.1740.0000.4821.000
우수 마을기업0.0000.6280.8581.000NaN1.0000.0000.0000.0000.0001.000
비고_전남형 예비 지정연도-0.221-0.214-0.098NaN1.0001.0000.1290.1060.1840.4470.000
구분0.0000.0000.0001.0001.0001.0000.0000.0300.0840.1921.000
시군0.0000.0000.0000.0000.1290.0001.0000.0890.2130.2661.000
마을기업 유형0.1280.1160.1740.0000.1060.0300.0891.0000.3210.0000.577
업종0.1370.0000.0000.0000.1840.0840.2130.3211.0000.0000.816
지정년도_3차년0.3270.4130.4820.0000.4470.1920.2660.0000.0001.000NaN
모두애 마을기업1.0000.0001.0001.0000.0001.0001.0000.5770.816NaN1.000

Missing values

2023-12-12T11:36:41.646379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:36:42.231544image/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.
2023-12-12T11:36:42.558298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분시군마을기업명마을기업 유형업종주생산품 또는 서비스명사업내용지정연도_예비지정년도_1차년지정년도_2차년지정년도_3차년우수 마을기업모두애 마을기업비고_전남형 예비 지정연도소재지연락처
0행안부형목포시주식회사 골목길관광체험서비스축제, 문화예술 기획게스트하우스, 카페/음식점, 문화여행상품, 가이드투어, 굿즈 제작판매201420152016<NA><NA><NA><NA>목포시 호남로64번길 12<NA>
1행안부형목포시유한회사 삼향골전통식품제조업두부식품두부, 청국장, 콩물 등 생산 판매2014201520192020<NA><NA><NA>목포시 녹색로 1061-284-5798
2행안부형목포시문화예술 협동조합 나무숲문화예술서비스문화예술, 공연, 전시기획문화예술 상품개발 및 판매, 체험프로그램 운영2016201720182022<NA><NA><NA>목포시 수문로 67, 2층061-242-1128
3행안부형목포시주식회사 만인계마을기업관광체험서비스전시 및 행사 대행, 유사음식점마을카페, 마을축제2019202020212023<NA><NA><NA>목포시 마인계터로 31, 1층061-244-1002
4행안부형목포시주식회사 목포마중물마을기업기타서비스사업지원 및 기획교육, 이벤트, 용역<NA>2019<NA><NA><NA><NA><NA>목포시 마인계터로 40번길 10, 1층<NA>
5행안부형목포시꿈바다 협동조합기타서비스공공관계서비스업도시민박 통합 서비스 플랫폼 구축202020212022<NA><NA><NA><NA>목포시 노적봉길 6, 1층061-981-1515
6행안부형목포시협동조합 시네마엠엠기타서비스동립영화, 광고독립영화, 영상 콘텐츠 제작, 온라인방송중계, 독립서점 운영202020212022<NA><NA><NA><NA>목포시 백년대로 394 2층<NA>
7행안부형목포시건맥1897 협동조합일반식품음식호프, 건어물수제맥주, 어묵, 숙박, 펍 운영 등<NA>202120222023<NA><NA><NA>목포시 해안로237번길 10-1<NA>
8행안부형목포시낭만항구협동조합기타음식건정, 부각 등건정, 부각 제조 및 소포장판매, 건정 활용 식당 운영202120222023<NA><NA><NA><NA>목포시 만호로 11-1061-244-6668
9행안부형목포시전라남도 청년예술가 협동조합문화예술서비스전시, 전시기획벽화, 조형물, 액자제작 및 전시, 산업디자인 교육20212023<NA><NA><NA><NA><NA>목포시 수문로 20번길 9<NA>
구분시군마을기업명마을기업 유형업종주생산품 또는 서비스명사업내용지정연도_예비지정년도_1차년지정년도_2차년지정년도_3차년우수 마을기업모두애 마을기업비고_전남형 예비 지정연도소재지연락처
304전남형 예비진도군관매도하늘다리 영어조합법인관광체험전자상거래해조류(미역, 김)특산물 판매 및 민박사업<NA><NA><NA><NA><NA><NA>2016진도군 관매도관호길 68-1061-544-7729
305전남형 예비진도군농업회사법인 유한회사 월가리청년회일반식품도소매업미니밤호박,배추,파미니밤호박,배추,파 등 가공 판매<NA><NA><NA><NA><NA><NA>2020진도군 월강로 189061-542-9647
306전남형 예비진도군농업회사법인 주식회사 포서마을공동체일반식품농업구기자친환경 구기자 가공 판매<NA><NA><NA><NA><NA><NA>2021진도군 포서길 12061-544-4443
307전남형 예비진도군천지인일반식품도소매업옥수수옥수수 가공식품 개발 및 판매<NA><NA><NA><NA><NA><NA>2023진도군 의신면 초사로 25061-544-0939
308전남형 예비신안군반월 영어조합법인일반식품제조업농수산물가공 판매농수산물 가공 판매<NA><NA><NA><NA><NA><NA>2016신안군 반월도길 220<NA>
309전남형 예비신안군천사의다리섬마을 협동조합일반식품제조업농수산물 가공 판매농수산물 가공 판매<NA><NA><NA><NA><NA><NA>2016신안군 박지도길 198<NA>
310전남형 예비신안군임자하늘꽃 협동조합일반식품도소매업스마트팜을 활용한 튤립체험장 조성튤립체험장 조성 및 건강채소꾸러미 상품화<NA><NA><NA><NA><NA><NA>2019신안군 임자로 98<NA>
311전남형 예비신안군증도방축어촌계 백합마을 협동조합일반식품도소매업백합,가무락,치패백합, 가무락, 치패 양식 및 판매<NA><NA><NA><NA><NA><NA>2020신안군 보물섬길 398-1061-271-7793
312전남형 예비신안군홍도유람선협업 주식회사관광체험운수업유람선 운영홍도 유람선 해상관광 프로그램<NA><NA><NA><NA><NA><NA>2022신안군 홍도리 94-6061-246-2244
313전남형 예비신안군우리콩협동조합일반식품제조업두류가공품 제조 판매농산물 판매 및 가공<NA><NA><NA><NA><NA><NA>2023신안군 임자면 신명동길 24-2061-274-1476