Overview

Dataset statistics

Number of variables7
Number of observations217
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.0 KiB
Average record size in memory56.6 B

Variable types

Categorical4
Text3

Dataset

Description전북특별자치도 보훈 현충시설(시군,탑,동상 등)의 명칭, 형태, 소재지, 건립연도, 지정일자, 관리기관 정보를 제공합니다.단, 건립일이 미상인 경우 "YYYY-00-00"표기 하였습니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/3080865/fileData.do

Alerts

지역 is highly overall correlated with 관리기관High correlation
관리기관 is highly overall correlated with 지역 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 관리기관High correlation
명칭 has unique valuesUnique

Reproduction

Analysis started2024-03-15 01:44:52.208752
Analysis finished2024-03-15 01:44:53.518261
Duration1.31 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
정읍시
21 
진안군
21 
고창군
20 
익산시
19 
김제시
19 
Other values (9)
117 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정읍시 21
9.7%
진안군 21
9.7%
고창군 20
9.2%
익산시 19
8.8%
김제시 19
8.8%
장수군 19
8.8%
남원시 19
8.8%
군산시 17
7.8%
전주시 16
7.4%
무주군 15
6.9%
Other values (4) 31
14.3%

Length

2024-03-15T10:44:53.727978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
정읍시 21
9.7%
진안군 21
9.7%
고창군 20
9.2%
익산시 19
8.8%
김제시 19
8.8%
장수군 19
8.8%
남원시 19
8.8%
군산시 17
7.8%
전주시 16
7.4%
무주군 15
6.9%
Other values (4) 31
14.3%

명칭
Text

UNIQUE 

Distinct217
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-03-15T10:44:54.630694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length20
Mean length11.391705
Min length3

Characters and Unicode

Total characters2472
Distinct characters228
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique217 ?
Unique (%)100.0%

Sample

1st row이종주 선생 기념비
2nd row군산 3ㆍ1운동 100주년 기념관
3rd row애국지사 박준승 기념관
4th row가람문학관
5th row송와 박영관 선생 생가
ValueCountFrequency (%)
기념비 24
 
4.4%
참전 20
 
3.7%
6·25 15
 
2.8%
추모비 14
 
2.6%
애국지사 14
 
2.6%
충혼 9
 
1.7%
참전용사 7
 
1.3%
기적비 7
 
1.3%
3·1운동 7
 
1.3%
호국 6
 
1.1%
Other values (337) 417
77.2%
2024-03-15T10:44:55.978192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
323
 
13.1%
비 111
 
4.5%
기 72
 
2.9%
) 70
 
2.8%
( 70
 
2.8%
사 59
 
2.4%
전 58
 
2.3%
념 57
 
2.3%
충 55
 
2.2%
탑 53
 
2.1%
Other values (218) 1544
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1861
75.3%
Space Separator 323
 
13.1%
Decimal Number 106
 
4.3%
Close Punctuation 70
 
2.8%
Open Punctuation 70
 
2.8%
Other Punctuation 41
 
1.7%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 111
 
6.0%
기 72
 
3.9%
사 59
 
3.2%
전 58
 
3.1%
념 57
 
3.1%
충 55
 
3.0%
탑 53
 
2.8%
혼 47
 
2.5%
국 44
 
2.4%
공 37
 
2.0%
Other values (204) 1268
68.1%
Decimal Number
ValueCountFrequency (%)
2 24
22.6%
6 24
22.6%
5 24
22.6%
3 16
15.1%
1 15
14.2%
0 2
 
1.9%
9 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
· 30
73.2%
. 10
 
24.4%
/ 1
 
2.4%
Space Separator
ValueCountFrequency (%)
323
100.0%
Close Punctuation
ValueCountFrequency (%)
) 70
100.0%
Open Punctuation
ValueCountFrequency (%)
( 70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1860
75.2%
Common 611
 
24.7%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 111
 
6.0%
기 72
 
3.9%
사 59
 
3.2%
전 58
 
3.1%
념 57
 
3.1%
충 55
 
3.0%
탑 53
 
2.8%
혼 47
 
2.5%
국 44
 
2.4%
공 37
 
2.0%
Other values (203) 1267
68.1%
Common
ValueCountFrequency (%)
323
52.9%
) 70
 
11.5%
( 70
 
11.5%
· 30
 
4.9%
2 24
 
3.9%
6 24
 
3.9%
5 24
 
3.9%
3 16
 
2.6%
1 15
 
2.5%
. 10
 
1.6%
Other values (4) 5
 
0.8%
Han
ValueCountFrequency (%)
故 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1859
75.2%
ASCII 581
 
23.5%
None 30
 
1.2%
CJK 1
 
< 0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
323
55.6%
) 70
 
12.0%
( 70
 
12.0%
2 24
 
4.1%
6 24
 
4.1%
5 24
 
4.1%
3 16
 
2.8%
1 15
 
2.6%
. 10
 
1.7%
0 2
 
0.3%
Other values (3) 3
 
0.5%
Hangul
ValueCountFrequency (%)
비 111
 
6.0%
기 72
 
3.9%
사 59
 
3.2%
전 58
 
3.1%
념 57
 
3.1%
충 55
 
3.0%
탑 53
 
2.9%
혼 47
 
2.5%
국 44
 
2.4%
공 37
 
2.0%
Other values (202) 1266
68.1%
None
ValueCountFrequency (%)
· 30
100.0%
CJK
ValueCountFrequency (%)
故 1
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

형태
Categorical

Distinct11
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
비석
111 
탑
55 
동상
13 
사당
 
10
기념관
 
8
Other values (6)
20 

Length

Max length3
Median length2
Mean length1.797235
Min length1

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row비석
2nd row기념관
3rd row기념관
4th row기념관
5th row생가

Common Values

ValueCountFrequency (%)
비석 111
51.2%
탑 55
25.3%
동상 13
 
6.0%
사당 10
 
4.6%
기념관 8
 
3.7%
생가 7
 
3.2%
기타 5
 
2.3%
장소 4
 
1.8%
조형물 2
 
0.9%
공원 1
 
0.5%

Length

2024-03-15T10:44:56.244721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
비석 111
51.2%
탑 55
25.3%
동상 13
 
6.0%
사당 10
 
4.6%
기념관 8
 
3.7%
생가 7
 
3.2%
기타 5
 
2.3%
장소 4
 
1.8%
조형물 2
 
0.9%
공원 2
 
0.9%

소재지
Text

Distinct201
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-03-15T10:44:57.427799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length27
Mean length18.046083
Min length8

Characters and Unicode

Total characters3916
Distinct characters209
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

Unique189 ?
Unique (%)87.1%

Sample

1st row전북 고창군 성내면 한정길 49, 전북 고창군 성내면 월산리 508-9
2nd row전북 군산시 영명길 29
3rd row전북 정읍시 산외면 운전1길 27-3
4th row전북 익산시 여산면 가람1길 76
5th row전북 고창군 무장면 동서대로 585-11
ValueCountFrequency (%)
전북 70
 
7.7%
전라북도 31
 
3.4%
고창군 22
 
2.4%
정읍시 21
 
2.3%
김제시 19
 
2.1%
익산시 19
 
2.1%
군산시 17
 
1.9%
내 15
 
1.7%
산 10
 
1.1%
완산구 9
 
1.0%
Other values (482) 671
74.2%
2024-03-15T10:44:59.119339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
687
 
17.5%
1 183
 
4.7%
산 135
 
3.4%
면 128
 
3.3%
- 120
 
3.1%
2 111
 
2.8%
전 109
 
2.8%
북 105
 
2.7%
3 100
 
2.6%
리 96
 
2.5%
Other values (199) 2142
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2234
57.0%
Decimal Number 775
 
19.8%
Space Separator 687
 
17.5%
Dash Punctuation 120
 
3.1%
Close Punctuation 47
 
1.2%
Open Punctuation 45
 
1.1%
Other Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 135
 
6.0%
면 128
 
5.7%
전 109
 
4.9%
북 105
 
4.7%
리 96
 
4.3%
시 79
 
3.5%
길 58
 
2.6%
군 55
 
2.5%
읍 54
 
2.4%
로 51
 
2.3%
Other values (184) 1364
61.1%
Decimal Number
ValueCountFrequency (%)
1 183
23.6%
2 111
14.3%
3 100
12.9%
5 66
 
8.5%
4 63
 
8.1%
7 55
 
7.1%
9 54
 
7.0%
6 52
 
6.7%
8 47
 
6.1%
0 44
 
5.7%
Space Separator
ValueCountFrequency (%)
687
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 120
100.0%
Close Punctuation
ValueCountFrequency (%)
) 47
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2234
57.0%
Common 1682
43.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 135
 
6.0%
면 128
 
5.7%
전 109
 
4.9%
북 105
 
4.7%
리 96
 
4.3%
시 79
 
3.5%
길 58
 
2.6%
군 55
 
2.5%
읍 54
 
2.4%
로 51
 
2.3%
Other values (184) 1364
61.1%
Common
ValueCountFrequency (%)
687
40.8%
1 183
 
10.9%
- 120
 
7.1%
2 111
 
6.6%
3 100
 
5.9%
5 66
 
3.9%
4 63
 
3.7%
7 55
 
3.3%
9 54
 
3.2%
6 52
 
3.1%
Other values (5) 191
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2234
57.0%
ASCII 1682
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
687
40.8%
1 183
 
10.9%
- 120
 
7.1%
2 111
 
6.6%
3 100
 
5.9%
5 66
 
3.9%
4 63
 
3.7%
7 55
 
3.3%
9 54
 
3.2%
6 52
 
3.1%
Other values (5) 191
 
11.4%
Hangul
ValueCountFrequency (%)
산 135
 
6.0%
면 128
 
5.7%
전 109
 
4.9%
북 105
 
4.7%
리 96
 
4.3%
시 79
 
3.5%
길 58
 
2.6%
군 55
 
2.5%
읍 54
 
2.4%
로 51
 
2.3%
Other values (184) 1364
61.1%

건립연도
Text

Distinct152
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2024-03-15T10:45:00.215755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2170
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)57.1%

Sample

1st row1987-04-01
2nd row2018-06-19
3rd row2019-07-02
4th row2017-10-14
5th row1849-01-01
ValueCountFrequency (%)
2002-00-00 12
 
5.5%
2001-00-00 7
 
3.2%
1998-00-00 7
 
3.2%
2000-00-00 5
 
2.3%
2005-00-00 4
 
1.8%
1957-00-00 4
 
1.8%
1995-00-00 4
 
1.8%
2007-00-00 3
 
1.4%
1983-00-00 3
 
1.4%
1954-00-00 3
 
1.4%
Other values (142) 165
76.0%
2024-03-15T10:45:01.701640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 765
35.3%
- 434
20.0%
1 298
 
13.7%
9 201
 
9.3%
2 165
 
7.6%
8 69
 
3.2%
5 65
 
3.0%
6 51
 
2.4%
7 50
 
2.3%
4 41
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1736
80.0%
Dash Punctuation 434
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 765
44.1%
1 298
 
17.2%
9 201
 
11.6%
2 165
 
9.5%
8 69
 
4.0%
5 65
 
3.7%
6 51
 
2.9%
7 50
 
2.9%
4 41
 
2.4%
3 31
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2170
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 765
35.3%
- 434
20.0%
1 298
 
13.7%
9 201
 
9.3%
2 165
 
7.6%
8 69
 
3.2%
5 65
 
3.0%
6 51
 
2.4%
7 50
 
2.3%
4 41
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2170
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 765
35.3%
- 434
20.0%
1 298
 
13.7%
9 201
 
9.3%
2 165
 
7.6%
8 69
 
3.2%
5 65
 
3.0%
6 51
 
2.4%
7 50
 
2.3%
4 41
 
1.9%

지정일자
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2003-09-15
45 
2003-05-31
23 
2003-05-30
17 
2002-11-01
13 
2004-12-28
13 
Other values (42)
106 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique18 ?
Unique (%)8.3%

Sample

1st row2020-05-08
2nd row2020-05-08
3rd row2020-05-08
4th row2019-04-26
5th row2018-12-12

Common Values

ValueCountFrequency (%)
2003-09-15 45
20.7%
2003-05-31 23
 
10.6%
2003-05-30 17
 
7.8%
2002-11-01 13
 
6.0%
2004-12-28 13
 
6.0%
2003-12-08 12
 
5.5%
2015-05-14 7
 
3.2%
2004-12-09 7
 
3.2%
2003-05-01 5
 
2.3%
2006-04-24 5
 
2.3%
Other values (37) 70
32.3%

Length

2024-03-15T10:45:02.116098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2003-09-15 45
20.7%
2003-05-31 23
 
10.6%
2003-05-30 17
 
7.8%
2002-11-01 13
 
6.0%
2004-12-28 13
 
6.0%
2003-12-08 12
 
5.5%
2015-05-14 7
 
3.2%
2004-12-09 7
 
3.2%
2003-05-01 5
 
2.3%
2006-04-24 5
 
2.3%
Other values (37) 70
32.3%

관리기관
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
전북동부보훈지청
115 
전북서부보훈지청
102 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북동부보훈지청 115
53.0%
전북서부보훈지청 102
47.0%

Length

2024-03-15T10:45:02.474535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:45:02.800151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북동부보훈지청 115
53.0%
전북서부보훈지청 102
47.0%

Correlations

2024-03-15T10:45:03.004590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역형태지정일자관리기관
지역1.0000.1920.8211.000
형태0.1921.0000.7760.120
지정일자0.8210.7761.0000.877
관리기관1.0000.1200.8771.000
2024-03-15T10:45:03.183039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역지정일자관리기관형태
지역1.0000.3610.9720.074
지정일자0.3611.0000.6930.344
관리기관0.9720.6931.0000.112
형태0.0740.3440.1121.000
2024-03-15T10:45:03.348833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역형태지정일자관리기관
지역1.0000.0740.3610.972
형태0.0741.0000.3440.112
지정일자0.3610.3441.0000.693
관리기관0.9720.1120.6931.000

Missing values

2024-03-15T10:44:52.975573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:44:53.364962image/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

지역명칭형태소재지건립연도지정일자관리기관
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216임실군3·1운동 기념비(임실군)비석임실읍 이도리 8511977-00-002002-11-01전북동부보훈지청