Overview

Dataset statistics

Number of variables10
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory83.4 B

Variable types

Categorical8
Text2

Dataset

Description전라북도 시군별 농촌공동체 회사 현황(시군(군산시, 익산시, 정읍시, 남원시, 김제시 등), 공동체명, 기간(2011~2017), 사업내용 등)
Author전북특별자치도
URLhttps://www.data.go.kr/data/3081336/fileData.do

Alerts

2013 is highly overall correlated with 시군 and 1 other fieldsHigh correlation
2012 is highly overall correlated with 2011High correlation
2011 is highly overall correlated with 시군 and 1 other fieldsHigh correlation
2016 is highly overall correlated with 2015 and 1 other fieldsHigh correlation
2017 is highly overall correlated with 2016High correlation
시군 is highly overall correlated with 2011 and 1 other fieldsHigh correlation
2015 is highly overall correlated with 2014 and 1 other fieldsHigh correlation
2014 is highly overall correlated with 2013 and 1 other fieldsHigh correlation
2013 is highly imbalanced (51.5%)Imbalance
공 동 체 명 has unique valuesUnique
사 업 내 용 has unique valuesUnique

Reproduction

Analysis started2024-03-14 22:41:57.959671
Analysis finished2024-03-14 22:41:59.703965
Duration1.74 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size432.0 B
완주군
9 
정읍시
8 
진안군
4 
익산시
3 
임실군
3 
Other values (7)
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)7.9%

Sample

1st row군산시
2nd row군산시
3rd row익산시
4th row익산시
5th row익산시

Common Values

ValueCountFrequency (%)
완주군 9
23.7%
정읍시 8
21.1%
진안군 4
10.5%
익산시 3
 
7.9%
임실군 3
 
7.9%
군산시 2
 
5.3%
남원시 2
 
5.3%
김제시 2
 
5.3%
부안군 2
 
5.3%
장수군 1
 
2.6%
Other values (2) 2
 
5.3%

Length

2024-03-15T07:42:00.104276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
완주군 9
23.7%
정읍시 8
21.1%
진안군 4
10.5%
익산시 3
 
7.9%
임실군 3
 
7.9%
군산시 2
 
5.3%
남원시 2
 
5.3%
김제시 2
 
5.3%
부안군 2
 
5.3%
장수군 1
 
2.6%
Other values (2) 2
 
5.3%

공 동 체 명
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-03-15T07:42:02.059618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.8421053
Min length5

Characters and Unicode

Total characters298
Distinct characters136
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

Unique38 ?
Unique (%)100.0%

Sample

1st row우리(영)
2nd row군산콩나물(영)
3rd row(유)함해국
4th row(주)산마루
5th row다송리사람들
ValueCountFrequency (%)
우리(영 1
 
2.0%
줌마뜨레 1
 
2.0%
산들바다마을(영 1
 
2.0%
생자협동조합 1
 
2.0%
이웃린 1
 
2.0%
영농조합법인 1
 
2.0%
미디어공동체완두콩(협 1
 
2.0%
에버팜협동조합 1
 
2.0%
꿈드림영농조합 1
 
2.0%
용담호에핀꽃(영 1
 
2.0%
Other values (39) 39
79.6%
2024-03-15T07:42:04.511421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 23
 
7.7%
) 23
 
7.7%
영 19
 
6.4%
11
 
3.7%
마 9
 
3.0%
을 7
 
2.3%
동 6
 
2.0%
협 6
 
2.0%
산 6
 
2.0%
조 5
 
1.7%
Other values (126) 183
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 241
80.9%
Open Punctuation 23
 
7.7%
Close Punctuation 23
 
7.7%
Space Separator 11
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 19
 
7.9%
마 9
 
3.7%
을 7
 
2.9%
동 6
 
2.5%
협 6
 
2.5%
산 6
 
2.5%
조 5
 
2.1%
합 5
 
2.1%
들 5
 
2.1%
리 4
 
1.7%
Other values (123) 169
70.1%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 241
80.9%
Common 57
 
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 19
 
7.9%
마 9
 
3.7%
을 7
 
2.9%
동 6
 
2.5%
협 6
 
2.5%
산 6
 
2.5%
조 5
 
2.1%
합 5
 
2.1%
들 5
 
2.1%
리 4
 
1.7%
Other values (123) 169
70.1%
Common
ValueCountFrequency (%)
( 23
40.4%
) 23
40.4%
11
19.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 241
80.9%
ASCII 57
 
19.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 23
40.4%
) 23
40.4%
11
19.3%
Hangul
ValueCountFrequency (%)
영 19
 
7.9%
마 9
 
3.7%
을 7
 
2.9%
동 6
 
2.5%
협 6
 
2.5%
산 6
 
2.5%
조 5
 
2.1%
합 5
 
2.1%
들 5
 
2.1%
리 4
 
1.7%
Other values (123) 169
70.1%

2011
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size432.0 B
<NA>
30 
신규
8 

Length

Max length4
Median length4
Mean length3.5789474
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 30
78.9%
신규 8
 
21.1%

Length

2024-03-15T07:42:05.051366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:42:05.464913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 30
78.9%
신규 8
 
21.1%

2012
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size432.0 B
<NA>
30 
계속
5 
신규
 
3

Length

Max length4
Median length4
Mean length3.5789474
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 30
78.9%
계속 5
 
13.2%
신규 3
 
7.9%

Length

2024-03-15T07:42:05.842880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:42:06.175602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 30
78.9%
계속 5
 
13.2%
신규 3
 
7.9%

2013
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size432.0 B
<NA>
34 
신규
4 

Length

Max length4
Median length4
Mean length3.7894737
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 34
89.5%
신규 4
 
10.5%

Length

2024-03-15T07:42:06.393780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:42:06.697947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 34
89.5%
신규 4
 
10.5%

2014
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size432.0 B
<NA>
29 
신규
6 
계속
3 

Length

Max length4
Median length4
Mean length3.5263158
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 29
76.3%
신규 6
 
15.8%
계속 3
 
7.9%

Length

2024-03-15T07:42:07.102163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:42:07.455923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 29
76.3%
신규 6
 
15.8%
계속 3
 
7.9%

2015
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size432.0 B
<NA>
27 
신규
6 
계속
5 

Length

Max length4
Median length4
Mean length3.4210526
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 27
71.1%
신규 6
 
15.8%
계속 5
 
13.2%

Length

2024-03-15T07:42:07.748094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:42:08.104617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 27
71.1%
신규 6
 
15.8%
계속 5
 
13.2%

2016
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size432.0 B
<NA>
28 
신규
7 
계속
3 

Length

Max length4
Median length4
Mean length3.4736842
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 28
73.7%
신규 7
 
18.4%
계속 3
 
7.9%

Length

2024-03-15T07:42:08.386537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:42:08.602900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
73.7%
신규 7
 
18.4%
계속 3
 
7.9%

2017
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size432.0 B
<NA>
28 
계속
7 
신규
3 

Length

Max length4
Median length4
Mean length3.4736842
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 28
73.7%
계속 7
 
18.4%
신규 3
 
7.9%

Length

2024-03-15T07:42:08.941360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T07:42:09.303121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
73.7%
계속 7
 
18.4%
신규 3
 
7.9%

사 업 내 용
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size432.0 B
2024-03-15T07:42:10.169018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length8.4736842
Min length2

Characters and Unicode

Total characters322
Distinct characters152
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row친환경쌀, 농산물꾸러미사업
2nd row콩나물
3rd row구절초사업
4th row누룽지, 누룽지피자
5th row전통장류
ValueCountFrequency (%)
등 4
 
5.4%
및 3
 
4.1%
교육 2
 
2.7%
체험프로그램 2
 
2.7%
2종 1
 
1.4%
중간지원 1
 
1.4%
마을지구 1
 
1.4%
30개 1
 
1.4%
홍삼액 1
 
1.4%
단호박떡 1
 
1.4%
Other values (57) 57
77.0%
2024-03-15T07:42:11.365849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
 
11.2%
, 15
 
4.7%
지 8
 
2.5%
초 7
 
2.2%
사 7
 
2.2%
식 6
 
1.9%
물 6
 
1.9%
공 5
 
1.6%
즙 4
 
1.2%
등 4
 
1.2%
Other values (142) 224
69.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 264
82.0%
Space Separator 36
 
11.2%
Other Punctuation 15
 
4.7%
Decimal Number 3
 
0.9%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 8
 
3.0%
초 7
 
2.7%
사 7
 
2.7%
식 6
 
2.3%
물 6
 
2.3%
공 5
 
1.9%
즙 4
 
1.5%
등 4
 
1.5%
업 4
 
1.5%
스 4
 
1.5%
Other values (135) 209
79.2%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
0 1
33.3%
3 1
33.3%
Space Separator
ValueCountFrequency (%)
36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 264
82.0%
Common 58
 
18.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 8
 
3.0%
초 7
 
2.7%
사 7
 
2.7%
식 6
 
2.3%
물 6
 
2.3%
공 5
 
1.9%
즙 4
 
1.5%
등 4
 
1.5%
업 4
 
1.5%
스 4
 
1.5%
Other values (135) 209
79.2%
Common
ValueCountFrequency (%)
36
62.1%
, 15
25.9%
( 2
 
3.4%
) 2
 
3.4%
2 1
 
1.7%
0 1
 
1.7%
3 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 264
82.0%
ASCII 58
 
18.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
62.1%
, 15
25.9%
( 2
 
3.4%
) 2
 
3.4%
2 1
 
1.7%
0 1
 
1.7%
3 1
 
1.7%
Hangul
ValueCountFrequency (%)
지 8
 
3.0%
초 7
 
2.7%
사 7
 
2.7%
식 6
 
2.3%
물 6
 
2.3%
공 5
 
1.9%
즙 4
 
1.5%
등 4
 
1.5%
업 4
 
1.5%
스 4
 
1.5%
Other values (135) 209
79.2%

Correlations

2024-03-15T07:42:11.547764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군공 동 체 명20122014201520162017사 업 내 용
시군1.0001.0000.5650.0000.0000.0000.9031.000
공 동 체 명1.0001.0001.0001.0001.0001.0001.0001.000
20120.5651.0001.000NaNNaNNaNNaN1.000
20140.0001.000NaN1.000NaNNaNNaN1.000
20150.0001.000NaNNaN1.000NaNNaN1.000
20160.0001.000NaNNaNNaN1.000NaN1.000
20170.9031.000NaNNaNNaNNaN1.0001.000
사 업 내 용1.0001.0001.0001.0001.0001.0001.0001.000
2024-03-15T07:42:11.852051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20132012201120162017시군20152014
20131.000NaNNaNNaNNaN1.000NaN1.000
2012NaN1.0001.000NaNNaN0.435NaNNaN
2011NaN1.0001.000NaNNaN1.000NaNNaN
2016NaNNaNNaN1.0001.0000.0001.000NaN
2017NaNNaNNaN1.0001.0000.482NaNNaN
시군1.0000.4351.0000.0000.4821.0000.0000.000
2015NaNNaNNaN1.000NaN0.0001.0001.000
20141.000NaNNaNNaNNaN0.0001.0001.000
2024-03-15T07:42:12.333548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군2011201220132014201520162017
시군1.0001.0000.4351.0000.0000.0000.0000.482
20111.0001.0001.0000.000NaN0.0000.0000.000
20120.4351.0001.0000.000NaN0.0000.0000.000
20131.0000.0000.0001.0001.0000.0000.0000.000
20140.000NaNNaN1.0001.0001.000NaN0.000
20150.0000.0000.0000.0001.0001.0001.000NaN
20160.0000.0000.0000.000NaN1.0001.0001.000
20170.4820.0000.0000.0000.000NaN1.0001.000

Missing values

2024-03-15T07:41:59.008291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T07:41:59.507449image/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

시군공 동 체 명2011201220132014201520162017사 업 내 용
0군산시우리(영)신규계속<NA><NA><NA><NA><NA>친환경쌀, 농산물꾸러미사업
1군산시군산콩나물(영)<NA><NA><NA><NA><NA>신규계속콩나물
2익산시(유)함해국신규<NA><NA><NA><NA><NA><NA>구절초사업
3익산시(주)산마루<NA>신규<NA><NA><NA><NA><NA>누룽지, 누룽지피자
4익산시다송리사람들<NA><NA>신규<NA><NA><NA><NA>전통장류
5정읍시햇빛즐기는마을<NA><NA>신규계속<NA><NA><NA>오디즙, 오디엑기스
6정읍시산들(영)<NA><NA>신규<NA><NA><NA><NA>산야초(효소)
7정읍시내장산복분자(영)<NA><NA>신규계속<NA><NA><NA>복분자주
8정읍시단풍만나원(영)<NA><NA><NA>신규계속<NA><NA>울외장아찌, 무장아찌
9정읍시청아(영)<NA><NA><NA><NA>신규계속<NA>애완동물용 조사료
시군공 동 체 명2011201220132014201520162017사 업 내 용
28진안군(주)진안마을<NA>신규<NA><NA><NA><NA><NA>건나물, 잡곡세트 가공판매사업
29진안군능길(유)<NA><NA><NA><NA>신규<NA><NA>도라지, 고사리 등
30임실군임실배과수(영)<NA><NA><NA>신규계속<NA><NA>배즙, 배엑기스, 체험프로그램
31임실군임실생약(영)<NA><NA><NA><NA><NA><NA>신규엉겅퀴
32임실군이플(영)<NA><NA><NA><NA><NA>신규계속요구르트
33장수군장수신농(영)<NA><NA><NA>신규계속<NA><NA>사과즙, 체험프로그램
34순창군순창친환경(영)<NA><NA><NA>신규<NA><NA><NA>학교급식 및 공공급식
35고창군황토 복분자 (영)<NA>신규<NA><NA><NA><NA><NA>복분자 및 와인 가공 판매
36부안군산들바다마을(영)<NA><NA><NA><NA><NA><NA>신규절임김치,두부류
37부안군하얀들(협)<NA><NA><NA><NA>신규<NA><NA>청초효염, 삼백초 등