Overview

Dataset statistics

Number of variables7
Number of observations24
Missing cells66
Missing cells (%)39.3%
Duplicate rows1
Duplicate rows (%)4.2%
Total size in memory1.4 KiB
Average record size in memory61.5 B

Variable types

Text4
Unsupported2
Categorical1

Dataset

Description생태마을지정및지원현황
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202233

Alerts

Dataset has 1 (4.2%) duplicate rowsDuplicates
생태마을 지정현황 및 지원내역 has 20 (83.3%) missing valuesMissing
Unnamed: 1 has 9 (37.5%) missing valuesMissing
Unnamed: 2 has 10 (41.7%) missing valuesMissing
Unnamed: 3 has 8 (33.3%) missing valuesMissing
Unnamed: 4 has 10 (41.7%) missing valuesMissing
Unnamed: 6 has 9 (37.5%) missing valuesMissing
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 01:15:29.212793
Analysis finished2024-03-14 01:15:29.708731
Duration0.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

생태마을 지정현황 및 지원내역
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing20
Missing (%)83.3%
Memory size324.0 B
2024-03-14T10:15:29.787586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.25
Min length1

Characters and Unicode

Total characters21
Distinct characters13
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

Unique4 ?
Unique (%)100.0%

Sample

1st row구분
2nd row계
3rd row자연생태우수마을
4th row자연생태복원우수마을
ValueCountFrequency (%)
구분 1
25.0%
계 1
25.0%
자연생태우수마을 1
25.0%
자연생태복원우수마을 1
25.0%
2024-03-14T10:15:30.012335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
자 2
9.5%
연 2
9.5%
생 2
9.5%
태 2
9.5%
우 2
9.5%
수 2
9.5%
마 2
9.5%
을 2
9.5%
구 1
 
4.8%
분 1
 
4.8%
Other values (3) 3
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 2
9.5%
연 2
9.5%
생 2
9.5%
태 2
9.5%
우 2
9.5%
수 2
9.5%
마 2
9.5%
을 2
9.5%
구 1
 
4.8%
분 1
 
4.8%
Other values (3) 3
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 2
9.5%
연 2
9.5%
생 2
9.5%
태 2
9.5%
우 2
9.5%
수 2
9.5%
마 2
9.5%
을 2
9.5%
구 1
 
4.8%
분 1
 
4.8%
Other values (3) 3
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
자 2
9.5%
연 2
9.5%
생 2
9.5%
태 2
9.5%
우 2
9.5%
수 2
9.5%
마 2
9.5%
을 2
9.5%
구 1
 
4.8%
분 1
 
4.8%
Other values (3) 3
14.3%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9
Missing (%)37.5%
Memory size324.0 B

Unnamed: 2
Text

MISSING 

Distinct10
Distinct (%)71.4%
Missing10
Missing (%)41.7%
Memory size324.0 B
2024-03-14T10:15:30.149111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters28
Distinct characters18
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

Unique7 ?
Unique (%)50.0%

Sample

1st row시군
2nd row남원
3rd row진안
4th row장수
5th row정읍
ValueCountFrequency (%)
임실 3
21.4%
남원 2
14.3%
진안 2
14.3%
시군 1
 
7.1%
장수 1
 
7.1%
정읍 1
 
7.1%
완주 1
 
7.1%
군산 1
 
7.1%
고창 1
 
7.1%
부안 1
 
7.1%
2024-03-14T10:15:30.394083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
임 3
 
10.7%
안 3
 
10.7%
실 3
 
10.7%
남 2
 
7.1%
원 2
 
7.1%
진 2
 
7.1%
군 2
 
7.1%
완 1
 
3.6%
창 1
 
3.6%
고 1
 
3.6%
Other values (8) 8
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
임 3
 
10.7%
안 3
 
10.7%
실 3
 
10.7%
남 2
 
7.1%
원 2
 
7.1%
진 2
 
7.1%
군 2
 
7.1%
완 1
 
3.6%
창 1
 
3.6%
고 1
 
3.6%
Other values (8) 8
28.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
임 3
 
10.7%
안 3
 
10.7%
실 3
 
10.7%
남 2
 
7.1%
원 2
 
7.1%
진 2
 
7.1%
군 2
 
7.1%
완 1
 
3.6%
창 1
 
3.6%
고 1
 
3.6%
Other values (8) 8
28.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
임 3
 
10.7%
안 3
 
10.7%
실 3
 
10.7%
남 2
 
7.1%
원 2
 
7.1%
진 2
 
7.1%
군 2
 
7.1%
완 1
 
3.6%
창 1
 
3.6%
고 1
 
3.6%
Other values (8) 8
28.6%

Unnamed: 3
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing8
Missing (%)33.3%
Memory size324.0 B
2024-03-14T10:15:30.546326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.4375
Min length2

Characters and Unicode

Total characters39
Distinct characters37
Distinct categories2 ?
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 row마을명
2nd row13개
3rd row삼산
4th row능길
5th row수분
ValueCountFrequency (%)
학동 1
 
6.2%
13개 1
 
6.2%
삼산 1
 
6.2%
능길 1
 
6.2%
수분 1
 
6.2%
원촌 1
 
6.2%
와운 1
 
6.2%
세심 1
 
6.2%
마을명 1
 
6.2%
방축도 1
 
6.2%
Other values (6) 6
37.5%
2024-03-14T10:15:30.821978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 2
 
5.1%
원 2
 
5.1%
학 1
 
2.6%
안 1
 
2.6%
도 1
 
2.6%
구 1
 
2.6%
담 1
 
2.6%
진 1
 
2.6%
부 1
 
2.6%
자 1
 
2.6%
Other values (27) 27
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37
94.9%
Decimal Number 2
 
5.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 2
 
5.4%
원 2
 
5.4%
학 1
 
2.7%
안 1
 
2.7%
도 1
 
2.7%
구 1
 
2.7%
담 1
 
2.7%
진 1
 
2.7%
부 1
 
2.7%
자 1
 
2.7%
Other values (25) 25
67.6%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
3 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37
94.9%
Common 2
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 2
 
5.4%
원 2
 
5.4%
학 1
 
2.7%
안 1
 
2.7%
도 1
 
2.7%
구 1
 
2.7%
담 1
 
2.7%
진 1
 
2.7%
부 1
 
2.7%
자 1
 
2.7%
Other values (25) 25
67.6%
Common
ValueCountFrequency (%)
1 1
50.0%
3 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37
94.9%
ASCII 2
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 2
 
5.4%
원 2
 
5.4%
학 1
 
2.7%
안 1
 
2.7%
도 1
 
2.7%
구 1
 
2.7%
담 1
 
2.7%
진 1
 
2.7%
부 1
 
2.7%
자 1
 
2.7%
Other values (25) 25
67.6%
ASCII
ValueCountFrequency (%)
1 1
50.0%
3 1
50.0%

Unnamed: 4
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing10
Missing (%)41.7%
Memory size324.0 B
2024-03-14T10:15:30.984117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.5
Min length3

Characters and Unicode

Total characters147
Distinct characters57
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)100.0%

Sample

1st row소재지
2nd row남원시 운봉읍 산덕리
3rd row진안군 동향면 능금리
4th row장수군 장수읍 수분리
5th row정읍시 칠보면 무성리
ValueCountFrequency (%)
임실군 3
 
7.5%
남원시 2
 
5.0%
진안군 2
 
5.0%
천담리 1
 
2.5%
삼계면 1
 
2.5%
세심리 1
 
2.5%
군산시 1
 
2.5%
옥도면 1
 
2.5%
방축도리 1
 
2.5%
덕치면 1
 
2.5%
Other values (26) 26
65.0%
2024-03-14T10:15:31.263254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
17.7%
리 13
 
8.8%
면 11
 
7.5%
군 10
 
6.8%
수 6
 
4.1%
안 4
 
2.7%
부 4
 
2.7%
시 4
 
2.7%
운 3
 
2.0%
실 3
 
2.0%
Other values (47) 63
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
82.3%
Space Separator 26
 
17.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 13
 
10.7%
면 11
 
9.1%
군 10
 
8.3%
수 6
 
5.0%
안 4
 
3.3%
부 4
 
3.3%
시 4
 
3.3%
운 3
 
2.5%
실 3
 
2.5%
임 3
 
2.5%
Other values (46) 60
49.6%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121
82.3%
Common 26
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 13
 
10.7%
면 11
 
9.1%
군 10
 
8.3%
수 6
 
5.0%
안 4
 
3.3%
부 4
 
3.3%
시 4
 
3.3%
운 3
 
2.5%
실 3
 
2.5%
임 3
 
2.5%
Other values (46) 60
49.6%
Common
ValueCountFrequency (%)
26
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121
82.3%
ASCII 26
 
17.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26
100.0%
Hangul
ValueCountFrequency (%)
리 13
 
10.7%
면 11
 
9.1%
군 10
 
8.3%
수 6
 
5.0%
안 4
 
3.3%
부 4
 
3.3%
시 4
 
3.3%
운 3
 
2.5%
실 3
 
2.5%
임 3
 
2.5%
Other values (46) 60
49.6%

Unnamed: 5
Categorical

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
9 
‘11~’13
6 
‘10~’12
4 
‘12~’14
3 
지정기간
1 

Length

Max length7
Median length7
Mean length5.6666667
Min length4

Unique

Unique2 ?
Unique (%)8.3%

Sample

1st row지정기간
2nd row(재지정)
3rd row<NA>
4th row<NA>
5th row‘11~’13

Common Values

ValueCountFrequency (%)
<NA> 9
37.5%
‘11~’13 6
25.0%
‘10~’12 4
16.7%
‘12~’14 3
 
12.5%
지정기간 1
 
4.2%
(재지정) 1
 
4.2%

Length

2024-03-14T10:15:31.368912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:15:31.451621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9
37.5%
‘11~’13 6
25.0%
‘10~’12 4
16.7%
‘12~’14 3
 
12.5%
지정기간 1
 
4.2%
재지정 1
 
4.2%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9
Missing (%)37.5%
Memory size324.0 B

Correlations

2024-03-14T10:15:31.513874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
생태마을 지정현황 및 지원내역Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5
생태마을 지정현황 및 지원내역1.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.0000.708
Unnamed: 31.0001.0001.0001.0001.000
Unnamed: 41.0001.0001.0001.0001.000
Unnamed: 51.0000.7081.0001.0001.000

Missing values

2024-03-14T10:15:29.404043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:15:29.509444image/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.
2024-03-14T10:15:29.638674image/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

생태마을 지정현황 및 지원내역Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
0구분최 초시군마을명소재지지정기간지원액
1<NA>지정년도<NA><NA><NA>(재지정)(백만원)
2계NaN<NA>13개<NA><NA>120
3<NA>NaN<NA><NA><NA><NA>(국90, 군30)
4자연생태우수마을2001남원삼산남원시 운봉읍 산덕리‘11~’1330
5<NA>NaN<NA><NA><NA><NA>(’11년)
6<NA>2003진안능길진안군 동향면 능금리‘10~’1210
7<NA>NaN<NA><NA><NA><NA>(’08년)
8<NA>2005장수수분장수군 장수읍 수분리‘12~’1410
9<NA>NaN<NA><NA><NA><NA>(’09년)
생태마을 지정현황 및 지원내역Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6
14<NA>2008진안하곡진안군 부귀면 수항리‘12~’14NaN
15<NA>2008임실세심임실군 삼계면 세심리‘12~’1460
16<NA>NaN<NA><NA><NA><NA>(’10년)
17<NA>NaN<NA><NA><NA><NA>※군비 30
18<NA>2009군산방축도군산시 옥도면 방축도리‘10~’12NaN
19<NA>2009임실구담임실군 덕치면 천담리‘10~’12NaN
20<NA>2009고창진마고창군 부안면 선운리‘10~’12NaN
21자연생태복원우수마을2004부안부안자연부안군 줄포면 줄포리‘11~’13NaN
22<NA>NaN<NA>생태공원<NA><NA>NaN
23<NA>2007임실대정임실군 오수면 대정리‘11~’13NaN

Duplicate rows

Most frequently occurring

생태마을 지정현황 및 지원내역Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5# duplicates
0<NA><NA><NA><NA><NA>7