Overview

Dataset statistics

Number of variables6
Number of observations118
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory49.1 B

Variable types

Categorical3
Text3

Dataset

Description전통사찰지정현황161231기준
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=201891

Alerts

종 단 is highly imbalanced (55.0%)Imbalance
등록번호 has unique valuesUnique
주 소 has unique valuesUnique

Reproduction

Analysis started2024-03-14 00:29:11.363878
Analysis finished2024-03-14 00:29:11.735304
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군별
Categorical

Distinct14
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
　남원시
15 
　김제시
14 
전주시
11 
　정읍시
10 
　익산시
9 
Other values (9)
59 

Length

Max length4
Median length4
Mean length3.8050847
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
　남원시 15
12.7%
　김제시 14
11.9%
전주시 11
9.3%
　정읍시 10
8.5%
　익산시 9
7.6%
　완주군 9
7.6%
　고창군 8
 
6.8%
군산시 7
 
5.9%
　진안군 7
 
5.9%
　부안군 7
 
5.9%
Other values (4) 21
17.8%

Length

2024-03-14T09:29:11.783396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남원시 15
12.7%
김제시 14
11.9%
전주시 11
9.3%
정읍시 10
8.5%
익산시 9
7.6%
완주군 9
7.6%
고창군 8
 
6.8%
군산시 7
 
5.9%
진안군 7
 
5.9%
부안군 7
 
5.9%
Other values (4) 21
17.8%

사찰명
Text

Distinct109
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-14T09:29:12.043267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9915254
Min length2

Characters and Unicode

Total characters353
Distinct characters104
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

Unique102 ?
Unique (%)86.4%

Sample

1st row남고사
2nd row동고사
3rd row불정사
4th row서고사
5th row선린사
ValueCountFrequency (%)
실상사 3
 
2.5%
문수사 3
 
2.5%
일광사 2
 
1.7%
정혜사 2
 
1.7%
용화사 2
 
1.7%
미륵암 2
 
1.7%
백련사 2
 
1.7%
천황사 1
 
0.8%
금당사 1
 
0.8%
보흥사 1
 
0.8%
Other values (99) 99
83.9%
2024-03-14T09:29:12.411852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 94
26.6%
암 29
 
8.2%
원 9
 
2.5%
상 8
 
2.3%
선 6
 
1.7%
수 6
 
1.7%
정 6
 
1.7%
림 5
 
1.4%
성 5
 
1.4%
천 5
 
1.4%
Other values (94) 180
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 353
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 94
26.6%
암 29
 
8.2%
원 9
 
2.5%
상 8
 
2.3%
선 6
 
1.7%
수 6
 
1.7%
정 6
 
1.7%
림 5
 
1.4%
성 5
 
1.4%
천 5
 
1.4%
Other values (94) 180
51.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 353
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 94
26.6%
암 29
 
8.2%
원 9
 
2.5%
상 8
 
2.3%
선 6
 
1.7%
수 6
 
1.7%
정 6
 
1.7%
림 5
 
1.4%
성 5
 
1.4%
천 5
 
1.4%
Other values (94) 180
51.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 353
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 94
26.6%
암 29
 
8.2%
원 9
 
2.5%
상 8
 
2.3%
선 6
 
1.7%
수 6
 
1.7%
정 6
 
1.7%
림 5
 
1.4%
성 5
 
1.4%
천 5
 
1.4%
Other values (94) 180
51.0%

종 단
Categorical

IMBALANCE 

Distinct7
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
조계종
76 
태고종
36 
화엄종
 
2
보문종
 
1
심우회
 
1
Other values (2)
 
2

Length

Max length9
Median length3
Mean length3.0508475
Min length3

Unique

Unique4 ?
Unique (%)3.4%

Sample

1st row조계종
2nd row태고종
3rd row태고종
4th row조계종
5th row태고종

Common Values

ValueCountFrequency (%)
조계종 76
64.4%
태고종 36
30.5%
화엄종 2
 
1.7%
보문종 1
 
0.8%
심우회 1
 
0.8%
관음종 1
 
0.8%
진묵대사유적진흥회 1
 
0.8%

Length

2024-03-14T09:29:12.550281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:29:12.637514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조계종 76
64.4%
태고종 36
30.5%
화엄종 2
 
1.7%
보문종 1
 
0.8%
심우회 1
 
0.8%
관음종 1
 
0.8%
진묵대사유적진흥회 1
 
0.8%

등록번호
Text

UNIQUE 

Distinct118
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-14T09:29:12.895593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0932203
Min length3

Characters and Unicode

Total characters483
Distinct characters12
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

Unique118 ?
Unique (%)100.0%

Sample

1st row제1호
2nd row제88호
3rd row제84호
4th row제99호
5th row제86호
ValueCountFrequency (%)
제1호 1
 
0.8%
제92호 1
 
0.8%
제18호 1
 
0.8%
제17호 1
 
0.8%
제52호 1
 
0.8%
제16호 1
 
0.8%
제12호 1
 
0.8%
제14호 1
 
0.8%
제15호 1
 
0.8%
제70호 1
 
0.8%
Other values (108) 108
91.5%
2024-03-14T09:29:13.287245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 118
24.4%
호 118
24.4%
1 52
10.8%
5 22
 
4.6%
9 22
 
4.6%
6 22
 
4.6%
7 22
 
4.6%
4 22
 
4.6%
2 22
 
4.6%
8 21
 
4.3%
Other values (2) 42
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 247
51.1%
Other Letter 236
48.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 52
21.1%
5 22
8.9%
9 22
8.9%
6 22
8.9%
7 22
8.9%
4 22
8.9%
2 22
8.9%
8 21
8.5%
0 21
8.5%
3 21
8.5%
Other Letter
ValueCountFrequency (%)
제 118
50.0%
호 118
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 247
51.1%
Hangul 236
48.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 52
21.1%
5 22
8.9%
9 22
8.9%
6 22
8.9%
7 22
8.9%
4 22
8.9%
2 22
8.9%
8 21
8.5%
0 21
8.5%
3 21
8.5%
Hangul
ValueCountFrequency (%)
제 118
50.0%
호 118
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 247
51.1%
Hangul 236
48.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 118
50.0%
호 118
50.0%
ASCII
ValueCountFrequency (%)
1 52
21.1%
5 22
8.9%
9 22
8.9%
6 22
8.9%
7 22
8.9%
4 22
8.9%
2 22
8.9%
8 21
8.5%
0 21
8.5%
3 21
8.5%

주 소
Text

UNIQUE 

Distinct118
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-14T09:29:13.567582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length17.09322
Min length13

Characters and Unicode

Total characters2017
Distinct characters169
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

Unique118 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 남고산성1길 53-88
2nd row전주시 완산구 낙수정2길 103-100
3rd row전주시 완산구 남고산성1길 53-140
4th row전주시 덕진구 정여립로 1010-90
5th row전주시 덕진구 한배미6길 55
ValueCountFrequency (%)
남원시 15
 
3.2%
김제시 14
 
3.0%
전주시 11
 
2.3%
정읍시 10
 
2.1%
익산시 9
 
1.9%
완주군 9
 
1.9%
고창군 8
 
1.7%
군산시 7
 
1.5%
진안군 7
 
1.5%
부안군 7
 
1.5%
Other values (310) 376
79.5%
2024-03-14T09:29:13.943000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
355
 
17.6%
1 86
 
4.3%
면 80
 
4.0%
길 75
 
3.7%
2 74
 
3.7%
산 69
 
3.4%
시 68
 
3.4%
군 61
 
3.0%
- 55
 
2.7%
3 53
 
2.6%
Other values (159) 1041
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1118
55.4%
Decimal Number 463
23.0%
Space Separator 355
 
17.6%
Dash Punctuation 55
 
2.7%
Close Punctuation 13
 
0.6%
Open Punctuation 13
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 80
 
7.2%
길 75
 
6.7%
산 69
 
6.2%
시 68
 
6.1%
군 61
 
5.5%
로 41
 
3.7%
주 30
 
2.7%
원 25
 
2.2%
안 24
 
2.1%
읍 22
 
2.0%
Other values (145) 623
55.7%
Decimal Number
ValueCountFrequency (%)
1 86
18.6%
2 74
16.0%
3 53
11.4%
5 46
9.9%
4 45
9.7%
6 37
8.0%
0 33
 
7.1%
7 32
 
6.9%
8 30
 
6.5%
9 27
 
5.8%
Space Separator
ValueCountFrequency (%)
355
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1118
55.4%
Common 899
44.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 80
 
7.2%
길 75
 
6.7%
산 69
 
6.2%
시 68
 
6.1%
군 61
 
5.5%
로 41
 
3.7%
주 30
 
2.7%
원 25
 
2.2%
안 24
 
2.1%
읍 22
 
2.0%
Other values (145) 623
55.7%
Common
ValueCountFrequency (%)
355
39.5%
1 86
 
9.6%
2 74
 
8.2%
- 55
 
6.1%
3 53
 
5.9%
5 46
 
5.1%
4 45
 
5.0%
6 37
 
4.1%
0 33
 
3.7%
7 32
 
3.6%
Other values (4) 83
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1118
55.4%
ASCII 899
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
355
39.5%
1 86
 
9.6%
2 74
 
8.2%
- 55
 
6.1%
3 53
 
5.9%
5 46
 
5.1%
4 45
 
5.0%
6 37
 
4.1%
0 33
 
3.7%
7 32
 
3.6%
Other values (4) 83
 
9.2%
Hangul
ValueCountFrequency (%)
면 80
 
7.2%
길 75
 
6.7%
산 69
 
6.2%
시 68
 
6.1%
군 61
 
5.5%
로 41
 
3.7%
주 30
 
2.7%
원 25
 
2.2%
안 24
 
2.1%
읍 22
 
2.0%
Other values (145) 623
55.7%

자료출처
Categorical

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
74 
문화유산과
42 
0
 
2

Length

Max length5
Median length4
Mean length4.3050847
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 74
62.7%
문화유산과 42
35.6%
0 2
 
1.7%

Length

2024-03-14T09:29:14.065825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:29:14.189087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 74
62.7%
문화유산과 42
35.6%
0 2
 
1.7%

Correlations

2024-03-14T09:29:14.250561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별종 단자료출처
시군별1.0000.0000.345
종 단0.0001.0000.000
자료출처0.3450.0001.000
2024-03-14T09:29:14.320390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종 단자료출처시군별
종 단1.0000.0000.000
자료출처0.0001.0000.403
시군별0.0000.4031.000
2024-03-14T09:29:14.394644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별종 단자료출처
시군별1.0000.0000.403
종 단0.0001.0000.000
자료출처0.4030.0001.000

Missing values

2024-03-14T09:29:11.613683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:29:11.700977image/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

시군별사찰명종 단등록번호주 소자료출처
0전주시남고사조계종제1호전주시 완산구 남고산성1길 53-88문화유산과
1전주시동고사태고종제88호전주시 완산구 낙수정2길 103-100문화유산과
2전주시불정사태고종제84호전주시 완산구 남고산성1길 53-140문화유산과
3전주시서고사조계종제99호전주시 덕진구 정여립로 1010-90문화유산과
4전주시선린사태고종제86호전주시 덕진구 한배미6길 55문화유산과
5전주시승암사태고종제87호전주시 완산구 바람쐬는길 47-13문화유산과
6전주시실상사태고종제85호전주시 덕진구 어은로 89-11문화유산과
7전주시약수암태고종제89호전주시 덕진구 도당산로 46-10문화유산과
8전주시정혜사보문종제81호전주시 완산구 외칠봉1길 36문화유산과
9전주시학소암조계종제100호전주시 완산구 평화7길 49-67문화유산과
시군별사찰명종 단등록번호주 소자료출처
108고창군소요사태고종제31호고창군 부안면 질마재로 226-236<NA>
109고창군용화사태고종제93호고창군 대산면 연화길 88-56<NA>
110고창군참당암조계종제113호고창군 아산면 도솔길 194-77<NA>
111부안군개암사조계종제32호부안군 상서면 개암로 248<NA>
112부안군내소사조계종제33호부안군 진서면 내소사로 243<NA>
113부안군내원암조계종제110호부안군 위도면 내원암길 42<NA>
114부안군성황사조계종제34호부안군 부안읍 서림공원길 92<NA>
115부안군실상사조계종제117호부안군 변산면 실상길 122<NA>
116부안군용화사화엄종제102호부안군 행안면 미륵골길 43<NA>
117부안군월명암조계종제97호부안군 변산면 내변산로 236-180<NA>