Overview

Dataset statistics

Number of variables4
Number of observations43
Missing cells93
Missing cells (%)54.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory35.1 B

Variable types

Text3
Unsupported1

Dataset

Description산촌생태마을조성현황2019
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202855

Alerts

산촌생태마을 조성 현황 has 30 (69.8%) missing valuesMissing
Unnamed: 1 has 30 (69.8%) missing valuesMissing
Unnamed: 2 has 6 (14.0%) missing valuesMissing
Unnamed: 3 has 27 (62.8%) missing valuesMissing
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 02:38:18.820703
Analysis finished2024-03-14 02:38:19.183435
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산촌생태마을 조성 현황
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing30
Missing (%)69.8%
Memory size476.0 B
2024-03-14T11:38:19.308428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8461538
Min length1

Characters and Unicode

Total characters37
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row시군별
2nd row계
3rd row정읍시
4th row남원시
5th row김제시
ValueCountFrequency (%)
시군별 1
 
7.7%
계 1
 
7.7%
정읍시 1
 
7.7%
남원시 1
 
7.7%
김제시 1
 
7.7%
완주군 1
 
7.7%
진안군 1
 
7.7%
무주군 1
 
7.7%
장수군 1
 
7.7%
임실군 1
 
7.7%
Other values (3) 3
23.1%
2024-03-14T11:38:19.609489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 9
24.3%
시 4
 
10.8%
창 2
 
5.4%
안 2
 
5.4%
주 2
 
5.4%
읍 1
 
2.7%
남 1
 
2.7%
고 1
 
2.7%
별 1
 
2.7%
순 1
 
2.7%
Other values (13) 13
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 9
24.3%
시 4
 
10.8%
창 2
 
5.4%
안 2
 
5.4%
주 2
 
5.4%
읍 1
 
2.7%
남 1
 
2.7%
고 1
 
2.7%
별 1
 
2.7%
순 1
 
2.7%
Other values (13) 13
35.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 9
24.3%
시 4
 
10.8%
창 2
 
5.4%
안 2
 
5.4%
주 2
 
5.4%
읍 1
 
2.7%
남 1
 
2.7%
고 1
 
2.7%
별 1
 
2.7%
순 1
 
2.7%
Other values (13) 13
35.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 9
24.3%
시 4
 
10.8%
창 2
 
5.4%
안 2
 
5.4%
주 2
 
5.4%
읍 1
 
2.7%
남 1
 
2.7%
고 1
 
2.7%
별 1
 
2.7%
순 1
 
2.7%
Other values (13) 13
35.1%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing30
Missing (%)69.8%
Memory size476.0 B

Unnamed: 2
Text

MISSING 

Distinct37
Distinct (%)100.0%
Missing6
Missing (%)14.0%
Memory size476.0 B
2024-03-14T11:38:19.854880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length9.0810811
Min length6

Characters and Unicode

Total characters336
Distinct characters105
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)100.0%

Sample

1st row조성 완료(52개 마을)
2nd row산촌(95～2010년)
3rd row34개 마을
4th row산외 종산 원바실
5th row칠보 백암 원백암
ValueCountFrequency (%)
성수 3
 
2.8%
천천 3
 
2.8%
주천 2
 
1.9%
용산 2
 
1.9%
금산 2
 
1.9%
안정 2
 
1.9%
백암 2
 
1.9%
산내 2
 
1.9%
마을 2
 
1.9%
수철․지암 1
 
0.9%
Other values (87) 87
80.6%
2024-03-14T11:38:20.232594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
21.1%
산 14
 
4.2%
천 11
 
3.3%
상 10
 
3.0%
암 9
 
2.7%
․ 9
 
2.7%
동 7
 
2.1%
신 7
 
2.1%
원 6
 
1.8%
수 6
 
1.8%
Other values (95) 186
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 241
71.7%
Space Separator 71
 
21.1%
Decimal Number 10
 
3.0%
Other Punctuation 9
 
2.7%
Close Punctuation 2
 
0.6%
Open Punctuation 2
 
0.6%
Math Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 14
 
5.8%
천 11
 
4.6%
상 10
 
4.1%
암 9
 
3.7%
동 7
 
2.9%
신 7
 
2.9%
원 6
 
2.5%
수 6
 
2.5%
성 6
 
2.5%
안 6
 
2.5%
Other values (83) 159
66.0%
Decimal Number
ValueCountFrequency (%)
0 2
20.0%
2 2
20.0%
5 2
20.0%
3 1
10.0%
4 1
10.0%
9 1
10.0%
1 1
10.0%
Space Separator
ValueCountFrequency (%)
71
100.0%
Other Punctuation
ValueCountFrequency (%)
․ 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Math Symbol
ValueCountFrequency (%)
～ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 241
71.7%
Common 95
 
28.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 14
 
5.8%
천 11
 
4.6%
상 10
 
4.1%
암 9
 
3.7%
동 7
 
2.9%
신 7
 
2.9%
원 6
 
2.5%
수 6
 
2.5%
성 6
 
2.5%
안 6
 
2.5%
Other values (83) 159
66.0%
Common
ValueCountFrequency (%)
71
74.7%
․ 9
 
9.5%
0 2
 
2.1%
) 2
 
2.1%
2 2
 
2.1%
5 2
 
2.1%
( 2
 
2.1%
3 1
 
1.1%
4 1
 
1.1%
9 1
 
1.1%
Other values (2) 2
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 241
71.7%
ASCII 85
 
25.3%
Punctuation 9
 
2.7%
None 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
83.5%
0 2
 
2.4%
) 2
 
2.4%
2 2
 
2.4%
5 2
 
2.4%
( 2
 
2.4%
3 1
 
1.2%
4 1
 
1.2%
9 1
 
1.2%
1 1
 
1.2%
Hangul
ValueCountFrequency (%)
산 14
 
5.8%
천 11
 
4.6%
상 10
 
4.1%
암 9
 
3.7%
동 7
 
2.9%
신 7
 
2.9%
원 6
 
2.5%
수 6
 
2.5%
성 6
 
2.5%
안 6
 
2.5%
Other values (83) 159
66.0%
Punctuation
ValueCountFrequency (%)
․ 9
100.0%
None
ValueCountFrequency (%)
～ 1
100.0%

Unnamed: 3
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing27
Missing (%)62.8%
Memory size476.0 B
2024-03-14T11:38:20.434429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length8
Mean length10.9375
Min length6

Characters and Unicode

Total characters175
Distinct characters80
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row권역단위종합정비 (2012년 이후)
2nd row14개 마을
3rd row부귀 세동 신덕
4th row정천 갈용 무거
5th row주천 운봉 양명
ValueCountFrequency (%)
안성 3
 
6.0%
월현 2
 
4.0%
죽천 2
 
4.0%
금평 2
 
4.0%
기곡 2
 
4.0%
계북 2
 
4.0%
설천 2
 
4.0%
학정 2
 
4.0%
문성 1
 
2.0%
어전 1
 
2.0%
Other values (31) 31
62.0%
2024-03-14T11:38:20.715697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30
 
17.1%
천 8
 
4.6%
( 5
 
2.9%
안 5
 
2.9%
) 5
 
2.9%
정 5
 
2.9%
권 4
 
2.3%
역 4
 
2.3%
성 4
 
2.3%
, 4
 
2.3%
Other values (70) 101
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
69.1%
Space Separator 30
 
17.1%
Decimal Number 6
 
3.4%
Open Punctuation 5
 
2.9%
Close Punctuation 5
 
2.9%
Other Punctuation 4
 
2.3%
Control 4
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 8
 
6.6%
안 5
 
4.1%
정 5
 
4.1%
권 4
 
3.3%
역 4
 
3.3%
성 4
 
3.3%
북 4
 
3.3%
곡 3
 
2.5%
계 3
 
2.5%
금 3
 
2.5%
Other values (61) 78
64.5%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
2 2
33.3%
0 1
16.7%
4 1
16.7%
Space Separator
ValueCountFrequency (%)
30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Control
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121
69.1%
Common 54
30.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 8
 
6.6%
안 5
 
4.1%
정 5
 
4.1%
권 4
 
3.3%
역 4
 
3.3%
성 4
 
3.3%
북 4
 
3.3%
곡 3
 
2.5%
계 3
 
2.5%
금 3
 
2.5%
Other values (61) 78
64.5%
Common
ValueCountFrequency (%)
30
55.6%
( 5
 
9.3%
) 5
 
9.3%
, 4
 
7.4%
4
 
7.4%
1 2
 
3.7%
2 2
 
3.7%
0 1
 
1.9%
4 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121
69.1%
ASCII 54
30.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30
55.6%
( 5
 
9.3%
) 5
 
9.3%
, 4
 
7.4%
4
 
7.4%
1 2
 
3.7%
2 2
 
3.7%
0 1
 
1.9%
4 1
 
1.9%
Hangul
ValueCountFrequency (%)
천 8
 
6.6%
안 5
 
4.1%
정 5
 
4.1%
권 4
 
3.3%
역 4
 
3.3%
성 4
 
3.3%
북 4
 
3.3%
곡 3
 
2.5%
계 3
 
2.5%
금 3
 
2.5%
Other values (61) 78
64.5%

Correlations

2024-03-14T11:38:20.785004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산촌생태마을 조성 현황Unnamed: 2Unnamed: 3
산촌생태마을 조성 현황1.0001.0001.000
Unnamed: 21.0001.0001.000
Unnamed: 31.0001.0001.000

Missing values

2024-03-14T11:38:19.000664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:38:19.065629image/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.
2024-03-14T11:38:19.137621image/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

산촌생태마을 조성 현황Unnamed: 1Unnamed: 2Unnamed: 3
0시군별마을조성 완료(52개 마을)<NA>
1<NA>NaN산촌(95～2010년)권역단위종합정비 (2012년 이후)
2<NA>NaN<NA><NA>
3계4834개 마을14개 마을
4정읍시3산외 종산 원바실<NA>
5<NA>NaN칠보 백암 원백암<NA>
6<NA>NaN산내 종성 황토<NA>
7남원시3산동 대상 상신<NA>
8<NA>NaN산내 장항 원천<NA>
9<NA>NaN주천 고기 내기․고촌<NA>
산촌생태마을 조성 현황Unnamed: 1Unnamed: 2Unnamed: 3
33<NA>NaN천천 연평 구신<NA>
34임실군3성수 성수 수철․지암삼계 학정 학정
35<NA>NaN관촌 상월 상월․월은<NA>
36순창군3구림 안정 안정<NA>
37<NA>NaN쌍치 학선 부정<NA>
38<NA>NaN복흥 대방 갈원<NA>
39고창군3부안 용산 용산<NA>
40<NA>NaN고수 은사 은사․신기<NA>
41<NA>NaN아산 반암 호암․선동<NA>
42부안군2변산면 대항리 합구진서 석포 원암