Overview

Dataset statistics

Number of variables8
Number of observations23
Missing cells70
Missing cells (%)38.0%
Duplicate rows2
Duplicate rows (%)8.7%
Total size in memory1.6 KiB
Average record size in memory69.7 B

Variable types

Unsupported2
Text4
Categorical2

Alerts

Dataset has 2 (8.7%) duplicate rowsDuplicates
Unnamed: 5 is highly overall correlated with Unnamed: 7High correlation
Unnamed: 7 is highly overall correlated with Unnamed: 5High correlation
자연생태우수마을 지정현황(10개소) has 11 (47.8%) missing valuesMissing
Unnamed: 1 has 20 (87.0%) missing valuesMissing
Unnamed: 2 has 11 (47.8%) missing valuesMissing
Unnamed: 3 has 10 (43.5%) missing valuesMissing
Unnamed: 4 has 12 (52.2%) missing valuesMissing
Unnamed: 6 has 6 (26.1%) missing valuesMissing
자연생태우수마을 지정현황(10개소) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 01:21:06.613333
Analysis finished2024-03-14 01:21:07.142193
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자연생태우수마을 지정현황(10개소)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing11
Missing (%)47.8%
Memory size316.0 B

Unnamed: 1
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing20
Missing (%)87.0%
Memory size316.0 B
2024-03-14T10:21:07.205636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length7
Min length3

Characters and Unicode

Total characters21
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row구 분
2nd row자연생태 우수마을
3rd row생태복원 우수마을
ValueCountFrequency (%)
우수마을 2
33.3%
구 1
16.7%
분 1
16.7%
자연생태 1
16.7%
생태복원 1
16.7%
2024-03-14T10:21:07.440474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
생 2
9.5%
태 2
9.5%
2
9.5%
우 2
9.5%
수 2
9.5%
마 2
9.5%
을 2
9.5%
구 1
 
4.8%
1
 
4.8%
분 1
 
4.8%
Other values (4) 4
19.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18
85.7%
Control 2
 
9.5%
Space Separator 1
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
생 2
11.1%
태 2
11.1%
우 2
11.1%
수 2
11.1%
마 2
11.1%
을 2
11.1%
구 1
5.6%
분 1
5.6%
자 1
5.6%
연 1
5.6%
Other values (2) 2
11.1%
Control
ValueCountFrequency (%)
2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18
85.7%
Common 3
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
생 2
11.1%
태 2
11.1%
우 2
11.1%
수 2
11.1%
마 2
11.1%
을 2
11.1%
구 1
5.6%
분 1
5.6%
자 1
5.6%
연 1
5.6%
Other values (2) 2
11.1%
Common
ValueCountFrequency (%)
2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18
85.7%
ASCII 3
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
생 2
11.1%
태 2
11.1%
우 2
11.1%
수 2
11.1%
마 2
11.1%
을 2
11.1%
구 1
5.6%
분 1
5.6%
자 1
5.6%
연 1
5.6%
Other values (2) 2
11.1%
ASCII
ValueCountFrequency (%)
2
66.7%
1
33.3%

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing11
Missing (%)47.8%
Memory size316.0 B

Unnamed: 3
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing10
Missing (%)43.5%
Memory size316.0 B
2024-03-14T10:21:07.612495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2307692
Min length2

Characters and Unicode

Total characters29
Distinct characters28
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 row마을명
2nd row삼산
3rd row수분
4th row원촌
5th row와운
ValueCountFrequency (%)
마을명 1
 
7.7%
삼산 1
 
7.7%
수분 1
 
7.7%
원촌 1
 
7.7%
와운 1
 
7.7%
학동 1
 
7.7%
하곡 1
 
7.7%
세심 1
 
7.7%
용계 1
 
7.7%
부안 1
 
7.7%
Other values (3) 3
23.1%
2024-03-14T10:21:07.871251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 2
 
6.9%
마 1
 
3.4%
세 1
 
3.4%
대 1
 
3.4%
공 1
 
3.4%
태 1
 
3.4%
생 1
 
3.4%
연 1
 
3.4%
자 1
 
3.4%
안 1
 
3.4%
Other values (18) 18
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 2
 
6.9%
마 1
 
3.4%
세 1
 
3.4%
대 1
 
3.4%
공 1
 
3.4%
태 1
 
3.4%
생 1
 
3.4%
연 1
 
3.4%
자 1
 
3.4%
안 1
 
3.4%
Other values (18) 18
62.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 2
 
6.9%
마 1
 
3.4%
세 1
 
3.4%
대 1
 
3.4%
공 1
 
3.4%
태 1
 
3.4%
생 1
 
3.4%
연 1
 
3.4%
자 1
 
3.4%
안 1
 
3.4%
Other values (18) 18
62.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 2
 
6.9%
마 1
 
3.4%
세 1
 
3.4%
대 1
 
3.4%
공 1
 
3.4%
태 1
 
3.4%
생 1
 
3.4%
연 1
 
3.4%
자 1
 
3.4%
안 1
 
3.4%
Other values (18) 18
62.1%

Unnamed: 4
Text

MISSING 

Distinct11
Distinct (%)100.0%
Missing12
Missing (%)52.2%
Memory size316.0 B
2024-03-14T10:21:08.043824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.454545
Min length5

Characters and Unicode

Total characters115
Distinct characters48
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

Unique11 ?
Unique (%)100.0%

Sample

1st row소 재 지
2nd row남원시 운봉읍 산덕리
3rd row장수군 장수읍 수분리
4th row정읍시 칠보면 무성리
5th row남원시 산내면 부운리
ValueCountFrequency (%)
남원시 2
 
6.1%
임실군 2
 
6.1%
소 1
 
3.0%
수만리 1
 
3.0%
오수면 1
 
3.0%
줄포리 1
 
3.0%
줄포면 1
 
3.0%
부안군 1
 
3.0%
용계리 1
 
3.0%
아산면 1
 
3.0%
Other values (21) 21
63.6%
2024-03-14T10:21:08.360182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
19.1%
리 10
 
8.7%
면 8
 
7.0%
군 7
 
6.1%
수 6
 
5.2%
읍 3
 
2.6%
산 3
 
2.6%
부 3
 
2.6%
시 3
 
2.6%
줄 2
 
1.7%
Other values (38) 48
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
80.9%
Space Separator 22
 
19.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 10
 
10.8%
면 8
 
8.6%
군 7
 
7.5%
수 6
 
6.5%
읍 3
 
3.2%
산 3
 
3.2%
부 3
 
3.2%
시 3
 
3.2%
줄 2
 
2.2%
계 2
 
2.2%
Other values (37) 46
49.5%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
80.9%
Common 22
 
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 10
 
10.8%
면 8
 
8.6%
군 7
 
7.5%
수 6
 
6.5%
읍 3
 
3.2%
산 3
 
3.2%
부 3
 
3.2%
시 3
 
3.2%
줄 2
 
2.2%
계 2
 
2.2%
Other values (37) 46
49.5%
Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
80.9%
ASCII 22
 
19.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
100.0%
Hangul
ValueCountFrequency (%)
리 10
 
10.8%
면 8
 
8.6%
군 7
 
7.5%
수 6
 
6.5%
읍 3
 
3.2%
산 3
 
3.2%
부 3
 
3.2%
시 3
 
3.2%
줄 2
 
2.2%
계 2
 
2.2%
Other values (37) 46
49.5%

Unnamed: 5
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
12 
14.1.1~16.12.31
7 
15.1.1~17.12.31
3 
(재)지정기간
 
1

Length

Max length15
Median length4
Mean length8.9130435
Min length4

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row<NA>
2nd row(재)지정기간
3rd row<NA>
4th row14.1.1~16.12.31
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 12
52.2%
14.1.1~16.12.31 7
30.4%
15.1.1~17.12.31 3
 
13.0%
(재)지정기간 1
 
4.3%

Length

2024-03-14T10:21:08.510048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:21:08.613577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 12
52.2%
14.1.1~16.12.31 7
30.4%
15.1.1~17.12.31 3
 
13.0%
재)지정기간 1
 
4.3%

Unnamed: 6
Text

MISSING 

Distinct10
Distinct (%)58.8%
Missing6
Missing (%)26.1%
Memory size316.0 B
2024-03-14T10:21:08.716901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length5.9411765
Min length4

Characters and Unicode

Total characters101
Distinct characters17
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

Unique7 ?
Unique (%)41.2%

Sample

1st row국비지원
2nd row30백만원
3rd row(2011년)
4th row10백만원
5th row(2009년)
ValueCountFrequency (%)
30백만원 6
35.3%
10백만원 2
 
11.8%
2009년 2
 
11.8%
2015년 2
 
11.8%
국비지원 1
 
5.9%
2011년 1
 
5.9%
2014년 1
 
5.9%
2012년 1
 
5.9%
2010년 1
 
5.9%
2024-03-14T10:21:08.945652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19
18.8%
원 9
8.9%
1 9
8.9%
2 9
8.9%
백 8
7.9%
만 8
7.9%
( 8
7.9%
년 8
7.9%
) 8
7.9%
3 6
 
5.9%
Other values (7) 9
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48
47.5%
Other Letter 36
35.6%
Open Punctuation 8
 
7.9%
Close Punctuation 8
 
7.9%
Space Separator 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19
39.6%
1 9
18.8%
2 9
18.8%
3 6
 
12.5%
5 2
 
4.2%
9 2
 
4.2%
4 1
 
2.1%
Other Letter
ValueCountFrequency (%)
원 9
25.0%
백 8
22.2%
만 8
22.2%
년 8
22.2%
비 1
 
2.8%
지 1
 
2.8%
국 1
 
2.8%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
　 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65
64.4%
Hangul 36
35.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19
29.2%
1 9
13.8%
2 9
13.8%
( 8
12.3%
) 8
12.3%
3 6
 
9.2%
5 2
 
3.1%
9 2
 
3.1%
4 1
 
1.5%
　 1
 
1.5%
Hangul
ValueCountFrequency (%)
원 9
25.0%
백 8
22.2%
만 8
22.2%
년 8
22.2%
비 1
 
2.8%
지 1
 
2.8%
국 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64
63.4%
Hangul 36
35.6%
None 1
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19
29.7%
1 9
14.1%
2 9
14.1%
( 8
12.5%
) 8
12.5%
3 6
 
9.4%
5 2
 
3.1%
9 2
 
3.1%
4 1
 
1.6%
Hangul
ValueCountFrequency (%)
원 9
25.0%
백 8
22.2%
만 8
22.2%
년 8
22.2%
비 1
 
2.8%
지 1
 
2.8%
국 1
 
2.8%
None
ValueCountFrequency (%)
　 1
100.0%

Unnamed: 7
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
<NA>
18 
 
4 
비고
 
1

Length

Max length4
Median length4
Mean length3.5652174
Min length2

Unique

Unique1 ?
Unique (%)4.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 18
78.3%
  4
 
17.4%
비고 1
 
4.3%

Length

2024-03-14T10:21:09.050803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T10:21:09.132176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 18
94.7%
비고 1
 
5.3%

Correlations

2024-03-14T10:21:09.186031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
Unnamed: 11.0001.0001.0001.0000.000NaN
Unnamed: 31.0001.0001.0001.0001.0001.000
Unnamed: 41.0001.0001.0001.0001.0001.000
Unnamed: 51.0001.0001.0001.0000.8971.000
Unnamed: 60.0001.0001.0000.8971.0000.000
Unnamed: 7NaN1.0001.0001.0000.0001.000
2024-03-14T10:21:09.307096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 7Unnamed: 5
Unnamed: 71.0000.816
Unnamed: 50.8161.000
2024-03-14T10:21:09.396981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 5Unnamed: 7
Unnamed: 51.0000.816
Unnamed: 70.8161.000

Missing values

2024-03-14T10:21:06.852926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:21:06.953052image/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.
2024-03-14T10:21:07.054577image/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

자연생태우수마을 지정현황(10개소)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
02016.3.31현재<NA>NaN<NA><NA><NA><NA><NA>
1연번구 분최초마을명소 재 지(재)지정기간국비지원비고
2NaN<NA>지정<NA><NA><NA><NA><NA>
31자연생태 우수마을2001삼산남원시 운봉읍 산덕리14.1.1~16.12.3130백만원<NA>
4NaN<NA>NaN<NA><NA><NA>(2011년)<NA>
52<NA>2005수분장수군 장수읍 수분리15.1.1~17.12.3110백만원<NA>
6NaN<NA>NaN<NA><NA><NA>(2009년)<NA>
73<NA>2007원촌정읍시 칠보면 무성리14.1.1~16.12.3110백만원<NA>
8NaN<NA>NaN<NA><NA><NA>(2009년)<NA>
94<NA>2007와운남원시 산내면 부운리14.1.1~16.12.3130백만원<NA>
자연생태우수마을 지정현황(10개소)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
136<NA>2008하곡진안군 부귀면 수항리15.1.1~17.12.3130백만원
14NaN<NA>NaN<NA><NA><NA>(2012년)<NA>
157<NA>2008세심임실군 삼계면 세심리15.1.1~17.12.3130백만원
16NaN<NA>NaN<NA><NA><NA>(2010년)<NA>
178<NA>2014용계고창군 아산면 용계리14.1.1~16.12.3130백만원
18NaN<NA>NaN<NA><NA><NA>(2015년)<NA>
199생태복원 우수마을2004부안부안군 줄포면 줄포리14.1.1~16.12.31<NA><NA>
20NaN<NA>NaN자연생태<NA><NA><NA><NA>
21NaN<NA>NaN공원<NA><NA><NA><NA>
2210<NA>2007대정임실군 오수면 대정리14.1.1~16.12.31<NA><NA>

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7# duplicates
0<NA><NA><NA><NA>(2009년)<NA>2
1<NA><NA><NA><NA><NA><NA>2