Overview

Dataset statistics

Number of variables6
Number of observations247
Missing cells113
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory48.5 B

Variable types

Categorical2
Text4

Dataset

Description전북특별자치도 한옥체험업 현황 데이터입니다. 시도, 시군구, 가옥명, 주소, 대표자, 웹사이트 주소 등의 데이터를 제공합니다.
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055600/fileData.do

Alerts

시도 has constant value ""Constant
시군구 is highly imbalanced (66.7%)Imbalance
웹사이트 주소 (홈페이지 블로그) has 112 (45.3%) missing valuesMissing
가옥명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 17:35:12.711390
Analysis finished2024-03-14 17:35:13.988895
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
전라북도
247 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라북도 247
100.0%

Length

2024-03-15T02:35:14.107658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:35:14.265020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 247
100.0%

시군구
Categorical

IMBALANCE 

Distinct10
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
전주시
206 
남원시
 
11
완주군
 
9
정읍시
 
6
김제시
 
5
Other values (5)
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique3 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 206
83.4%
남원시 11
 
4.5%
완주군 9
 
3.6%
정읍시 6
 
2.4%
김제시 5
 
2.0%
익산시 4
 
1.6%
부안군 3
 
1.2%
진안군 1
 
0.4%
임실군 1
 
0.4%
고창군 1
 
0.4%

Length

2024-03-15T02:35:14.441752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:35:14.758718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전주시 206
83.4%
남원시 11
 
4.5%
완주군 9
 
3.6%
정읍시 6
 
2.4%
김제시 5
 
2.0%
익산시 4
 
1.6%
부안군 3
 
1.2%
진안군 1
 
0.4%
임실군 1
 
0.4%
고창군 1
 
0.4%

가옥명
Text

UNIQUE 

Distinct247
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-15T02:35:15.916492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length4.417004
Min length1

Characters and Unicode

Total characters1091
Distinct characters257
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)100.0%

Sample

1st row문화공간 학인당
2nd row문화공간 양사재
3rd row풍남헌
4th row전주한옥생활체험관
5th row소담원
ValueCountFrequency (%)
고택 7
 
2.3%
한옥 3
 
1.0%
교동 3
 
1.0%
문화공간 2
 
0.7%
동락원 2
 
0.7%
한옥마을숙박 2
 
0.7%
가인당 2
 
0.7%
가은채 2
 
0.7%
숙박 2
 
0.7%
전주 2
 
0.7%
Other values (267) 272
91.0%
2024-03-15T02:35:17.533009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52
 
4.8%
한 38
 
3.5%
옥 35
 
3.2%
관 29
 
2.7%
당 25
 
2.3%
가 23
 
2.1%
고 21
 
1.9%
원 18
 
1.6%
이 16
 
1.5%
전 16
 
1.5%
Other values (247) 818
75.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1012
92.8%
Space Separator 52
 
4.8%
Decimal Number 8
 
0.7%
Uppercase Letter 7
 
0.6%
Close Punctuation 5
 
0.5%
Open Punctuation 5
 
0.5%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 38
 
3.8%
옥 35
 
3.5%
관 29
 
2.9%
당 25
 
2.5%
가 23
 
2.3%
고 21
 
2.1%
원 18
 
1.8%
이 16
 
1.6%
전 16
 
1.6%
헌 15
 
1.5%
Other values (232) 776
76.7%
Uppercase Letter
ValueCountFrequency (%)
G 2
28.6%
H 1
14.3%
E 1
14.3%
O 1
14.3%
T 1
14.3%
A 1
14.3%
Decimal Number
ValueCountFrequency (%)
2 3
37.5%
8 2
25.0%
6 1
 
12.5%
9 1
 
12.5%
1 1
 
12.5%
Space Separator
ValueCountFrequency (%)
52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1011
92.7%
Common 72
 
6.6%
Latin 7
 
0.6%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 38
 
3.8%
옥 35
 
3.5%
관 29
 
2.9%
당 25
 
2.5%
가 23
 
2.3%
고 21
 
2.1%
원 18
 
1.8%
이 16
 
1.6%
전 16
 
1.6%
헌 15
 
1.5%
Other values (231) 775
76.7%
Common
ValueCountFrequency (%)
52
72.2%
) 5
 
6.9%
( 5
 
6.9%
2 3
 
4.2%
. 2
 
2.8%
8 2
 
2.8%
6 1
 
1.4%
9 1
 
1.4%
1 1
 
1.4%
Latin
ValueCountFrequency (%)
G 2
28.6%
H 1
14.3%
E 1
14.3%
O 1
14.3%
T 1
14.3%
A 1
14.3%
Han
ValueCountFrequency (%)
家 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1011
92.7%
ASCII 79
 
7.2%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52
65.8%
) 5
 
6.3%
( 5
 
6.3%
2 3
 
3.8%
G 2
 
2.5%
. 2
 
2.5%
8 2
 
2.5%
H 1
 
1.3%
E 1
 
1.3%
O 1
 
1.3%
Other values (5) 5
 
6.3%
Hangul
ValueCountFrequency (%)
한 38
 
3.8%
옥 35
 
3.5%
관 29
 
2.9%
당 25
 
2.5%
가 23
 
2.3%
고 21
 
2.1%
원 18
 
1.8%
이 16
 
1.6%
전 16
 
1.6%
헌 15
 
1.5%
Other values (231) 775
76.7%
CJK
ValueCountFrequency (%)
家 1
100.0%

주소
Text

Distinct246
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-03-15T02:35:18.572421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length28.040486
Min length19

Characters and Unicode

Total characters6926
Distinct characters140
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

Unique245 ?
Unique (%)99.2%

Sample

1st row 전라북도 전주시 완산구 향교길 45 (교동)
2nd row 전라북도 전주시 완산구 오목대길 40 (교동, 양사재)
3rd row 전라북도 전주시 완산구 은행로 35 (풍남동3가, 풍남헌)
4th row 전라북도 전주시 완산구 어진길 29 (풍남동3가)
5th row 전라북도 전주시 완산구 오목대길 70 (교동, 소담원)
ValueCountFrequency (%)
전라북도 247
17.1%
전주시 206
14.2%
완산구 206
14.2%
교동 93
 
6.4%
풍남동3가 71
 
4.9%
향교길 37
 
2.6%
은행로 33
 
2.3%
최명희길 24
 
1.7%
한지길 18
 
1.2%
전주천동로 17
 
1.2%
Other values (340) 495
34.2%
2024-03-15T02:35:19.582043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1407
20.3%
전 489
 
7.1%
라 251
 
3.6%
도 251
 
3.6%
북 247
 
3.6%
동 246
 
3.6%
주 235
 
3.4%
시 232
 
3.3%
산 225
 
3.2%
완 217
 
3.1%
Other values (130) 3126
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3979
57.5%
Space Separator 1407
 
20.3%
Decimal Number 927
 
13.4%
Close Punctuation 213
 
3.1%
Open Punctuation 213
 
3.1%
Dash Punctuation 180
 
2.6%
Other Punctuation 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 489
 
12.3%
라 251
 
6.3%
도 251
 
6.3%
북 247
 
6.2%
동 246
 
6.2%
주 235
 
5.9%
시 232
 
5.8%
산 225
 
5.7%
완 217
 
5.5%
구 209
 
5.3%
Other values (115) 1377
34.6%
Decimal Number
ValueCountFrequency (%)
1 186
20.1%
3 150
16.2%
2 113
12.2%
5 109
11.8%
4 83
9.0%
6 73
 
7.9%
8 67
 
7.2%
9 52
 
5.6%
7 51
 
5.5%
0 43
 
4.6%
Space Separator
ValueCountFrequency (%)
1407
100.0%
Close Punctuation
ValueCountFrequency (%)
) 213
100.0%
Open Punctuation
ValueCountFrequency (%)
( 213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 180
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3979
57.5%
Common 2947
42.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 489
 
12.3%
라 251
 
6.3%
도 251
 
6.3%
북 247
 
6.2%
동 246
 
6.2%
주 235
 
5.9%
시 232
 
5.8%
산 225
 
5.7%
완 217
 
5.5%
구 209
 
5.3%
Other values (115) 1377
34.6%
Common
ValueCountFrequency (%)
1407
47.7%
) 213
 
7.2%
( 213
 
7.2%
1 186
 
6.3%
- 180
 
6.1%
3 150
 
5.1%
2 113
 
3.8%
5 109
 
3.7%
4 83
 
2.8%
6 73
 
2.5%
Other values (5) 220
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3979
57.5%
ASCII 2947
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1407
47.7%
) 213
 
7.2%
( 213
 
7.2%
1 186
 
6.3%
- 180
 
6.1%
3 150
 
5.1%
2 113
 
3.8%
5 109
 
3.7%
4 83
 
2.8%
6 73
 
2.5%
Other values (5) 220
 
7.5%
Hangul
ValueCountFrequency (%)
전 489
 
12.3%
라 251
 
6.3%
도 251
 
6.3%
북 247
 
6.2%
동 246
 
6.2%
주 235
 
5.9%
시 232
 
5.8%
산 225
 
5.7%
완 217
 
5.5%
구 209
 
5.3%
Other values (115) 1377
34.6%

대표자
Text

Distinct210
Distinct (%)85.4%
Missing1
Missing (%)0.4%
Memory size2.1 KiB
2024-03-15T02:35:20.705243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length3
Mean length3.2642276
Min length2

Characters and Unicode

Total characters803
Distinct characters124
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique185 ?
Unique (%)75.2%

Sample

1st row백*제
2nd row정*민
3rd row최*례
4th row김*균
5th row임*숙
ValueCountFrequency (%)
김*숙 4
 
1.6%
김*수 4
 
1.6%
이*우 4
 
1.6%
정*숙 3
 
1.2%
명 3
 
1.2%
1 3
 
1.2%
외 3
 
1.2%
김*희 3
 
1.2%
양*성 3
 
1.2%
복*산 3
 
1.2%
Other values (205) 224
87.2%
2024-03-15T02:35:22.113467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 250
31.1%
이 48
 
6.0%
김 45
 
5.6%
박 28
 
3.5%
정 18
 
2.2%
숙 16
 
2.0%
유 14
 
1.7%
서 11
 
1.4%
11
 
1.4%
최 10
 
1.2%
Other values (114) 352
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 531
66.1%
Other Punctuation 258
32.1%
Space Separator 11
 
1.4%
Decimal Number 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 48
 
9.0%
김 45
 
8.5%
박 28
 
5.3%
정 18
 
3.4%
숙 16
 
3.0%
유 14
 
2.6%
서 11
 
2.1%
최 10
 
1.9%
임 9
 
1.7%
수 9
 
1.7%
Other values (110) 323
60.8%
Other Punctuation
ValueCountFrequency (%)
* 250
96.9%
, 8
 
3.1%
Space Separator
ValueCountFrequency (%)
11
100.0%
Decimal Number
ValueCountFrequency (%)
1 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 531
66.1%
Common 272
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 48
 
9.0%
김 45
 
8.5%
박 28
 
5.3%
정 18
 
3.4%
숙 16
 
3.0%
유 14
 
2.6%
서 11
 
2.1%
최 10
 
1.9%
임 9
 
1.7%
수 9
 
1.7%
Other values (110) 323
60.8%
Common
ValueCountFrequency (%)
* 250
91.9%
11
 
4.0%
, 8
 
2.9%
1 3
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 531
66.1%
ASCII 272
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 250
91.9%
11
 
4.0%
, 8
 
2.9%
1 3
 
1.1%
Hangul
ValueCountFrequency (%)
이 48
 
9.0%
김 45
 
8.5%
박 28
 
5.3%
정 18
 
3.4%
숙 16
 
3.0%
유 14
 
2.6%
서 11
 
2.1%
최 10
 
1.9%
임 9
 
1.7%
수 9
 
1.7%
Other values (110) 323
60.8%

웹사이트 주소 (홈페이지 블로그)
Text

MISSING 

Distinct119
Distinct (%)88.1%
Missing112
Missing (%)45.3%
Memory size2.1 KiB
2024-03-15T02:35:23.008379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length25
Mean length16.925926
Min length1

Characters and Unicode

Total characters2285
Distinct characters73
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)84.4%

Sample

1st rowwww.from1908.kr
2nd rowwww.yangsajae.kr
3rd rowwww.poongnam.co.kr
4th rowwww.jjhanok.com
5th rowwww.buyongheon.com
ValueCountFrequency (%)
royalroom.co.kr 11
 
8.1%
4
 
3.0%
www.gaindang.co.kr 2
 
1.5%
www.sosohanhanok.co.kr 2
 
1.5%
www.eodang.co.kr 2
 
1.5%
starrest.co.kr 1
 
0.7%
www.gaeunchae.k 1
 
0.7%
www.leegahanok.com 1
 
0.7%
www.lovenamu.co.kr 1
 
0.7%
강령전.com 1
 
0.7%
Other values (109) 109
80.7%
2024-03-15T02:35:24.435330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 267
 
11.7%
w 264
 
11.6%
o 260
 
11.4%
a 152
 
6.7%
r 129
 
5.6%
n 119
 
5.2%
k 116
 
5.1%
m 110
 
4.8%
c 102
 
4.5%
h 83
 
3.6%
Other values (63) 683
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1867
81.7%
Other Punctuation 334
 
14.6%
Other Letter 53
 
2.3%
Decimal Number 25
 
1.1%
Dash Punctuation 5
 
0.2%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
한 5
 
9.4%
옥 5
 
9.4%
전 3
 
5.7%
모 2
 
3.8%
주 2
 
3.8%
마 2
 
3.8%
을 2
 
3.8%
숙 2
 
3.8%
박 2
 
3.8%
이 2
 
3.8%
Other values (26) 26
49.1%
Lowercase Letter
ValueCountFrequency (%)
w 264
14.1%
o 260
13.9%
a 152
 
8.1%
r 129
 
6.9%
n 119
 
6.4%
k 116
 
6.2%
m 110
 
5.9%
c 102
 
5.5%
h 83
 
4.4%
e 77
 
4.1%
Other values (14) 455
24.4%
Decimal Number
ValueCountFrequency (%)
1 6
24.0%
0 4
16.0%
2 4
16.0%
4 3
12.0%
8 3
12.0%
9 3
12.0%
7 1
 
4.0%
5 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
. 267
79.9%
/ 51
 
15.3%
: 16
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1867
81.7%
Common 365
 
16.0%
Hangul 53
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
한 5
 
9.4%
옥 5
 
9.4%
전 3
 
5.7%
모 2
 
3.8%
주 2
 
3.8%
마 2
 
3.8%
을 2
 
3.8%
숙 2
 
3.8%
박 2
 
3.8%
이 2
 
3.8%
Other values (26) 26
49.1%
Latin
ValueCountFrequency (%)
w 264
14.1%
o 260
13.9%
a 152
 
8.1%
r 129
 
6.9%
n 119
 
6.4%
k 116
 
6.2%
m 110
 
5.9%
c 102
 
5.5%
h 83
 
4.4%
e 77
 
4.1%
Other values (14) 455
24.4%
Common
ValueCountFrequency (%)
. 267
73.2%
/ 51
 
14.0%
: 16
 
4.4%
1 6
 
1.6%
- 5
 
1.4%
0 4
 
1.1%
2 4
 
1.1%
4 3
 
0.8%
8 3
 
0.8%
9 3
 
0.8%
Other values (3) 3
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2232
97.7%
Hangul 53
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 267
12.0%
w 264
11.8%
o 260
11.6%
a 152
 
6.8%
r 129
 
5.8%
n 119
 
5.3%
k 116
 
5.2%
m 110
 
4.9%
c 102
 
4.6%
h 83
 
3.7%
Other values (27) 630
28.2%
Hangul
ValueCountFrequency (%)
한 5
 
9.4%
옥 5
 
9.4%
전 3
 
5.7%
모 2
 
3.8%
주 2
 
3.8%
마 2
 
3.8%
을 2
 
3.8%
숙 2
 
3.8%
박 2
 
3.8%
이 2
 
3.8%
Other values (26) 26
49.1%

Missing values

2024-03-15T02:35:13.393998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:35:13.671185image/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.
2024-03-15T02:35:13.843157image/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

시도시군구가옥명주소대표자웹사이트 주소 (홈페이지 블로그)
0전라북도전주시문화공간 학인당전라북도 전주시 완산구 향교길 45 (교동)백*제www.from1908.kr
1전라북도전주시문화공간 양사재전라북도 전주시 완산구 오목대길 40 (교동, 양사재)정*민www.yangsajae.kr
2전라북도전주시풍남헌전라북도 전주시 완산구 은행로 35 (풍남동3가, 풍남헌)최*례www.poongnam.co.kr
3전라북도전주시전주한옥생활체험관전라북도 전주시 완산구 어진길 29 (풍남동3가)김*균www.jjhanok.com
4전라북도전주시소담원전라북도 전주시 완산구 오목대길 70 (교동, 소담원)임*숙<NA>
5전라북도전주시참다원전라북도 전주시 완산구 향교길 155-9 (교동)배*식<NA>
6전라북도전주시부용헌전라북도 전주시 완산구 향교길 147 (교동)이*재www.buyongheon.com
7전라북도전주시일락당전라북도 전주시 완산구 최명희길 17-5 (풍남동3가)윤*화ilrak.yghosting.kr/srb/
8전라북도전주시모련다원전라북도 전주시 완산구 향교길 82 (교동)최*민www.모련다원.com
9전라북도전주시산민재전라북도 전주시 완산구 향교길 153 (교동)황*배<NA>
시도시군구가옥명주소대표자웹사이트 주소 (홈페이지 블로그)
237전라북도완주군전통한지생활문화체험관(소양대승한지마을)전라북도 완주군 소양면 복은길 18-4완주군http://www.hanjivil.com
238전라북도완주군녹운재전라북도 완주군 소양면 송광수만로 472-18정*이http://nocwoonjae.alltheway.kr/
239전라북도완주군전통문화체험장전라북도 완주군 고산면 대아저수로 392완주군http://wanjutc.kr/
240전라북도완주군청풍헌전라북도 완주군 고산면 동봉길 20-6서*주http://sirangol.alltheway.kr/
241전라북도진안군괴정고택전라북도 진안군 주천면 감나무골길 31-3김*옥<NA>
242전라북도임실군임실필봉농악보존회전라북도 임실군 강진면 필봉굿길 92-3양*성http://www.pilbong.co.kr
243전라북도고창군고창읍성한옥마을전라북도 고창군 고창읍 동리로 128김*일www.고창읍성한옥마을.kr
244전라북도부안군나비의 꿈전라북도 부안군 진서면 내소사로 129박*우http://www.nabidream.net/
245전라북도부안군이갑수 고택전라북도 부안군 부안읍 선은2길 5원*연<NA>
246전라북도부안군선은동 고택전라북도 부안군 부안읍 선은2길 7-5이*훈<NA>