Overview

Dataset statistics

Number of variables11
Number of observations117
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 KiB
Average record size in memory91.1 B

Variable types

Numeric2
Categorical6
Text3

Alerts

업종명 has constant value ""Constant
자료출처 has constant value ""Constant
공개여부 has constant value ""Constant
작성일 has constant value ""Constant
갱신주기 has constant value ""Constant
순번 is highly overall correlated with 시군구High correlation
시군구 is highly overall correlated with 순번High correlation
순번 has unique valuesUnique
업소명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 02:10:41.074376
Analysis finished2024-03-14 02:10:42.092332
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct117
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59
Minimum1
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T11:10:42.153868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.8
Q130
median59
Q388
95-th percentile111.2
Maximum117
Range116
Interquartile range (IQR)58

Descriptive statistics

Standard deviation33.919021
Coefficient of variation (CV)0.57489866
Kurtosis-1.2
Mean59
Median Absolute Deviation (MAD)29
Skewness0
Sum6903
Variance1150.5
MonotonicityStrictly increasing
2024-03-14T11:10:42.266178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
75 1
 
0.9%
87 1
 
0.9%
86 1
 
0.9%
85 1
 
0.9%
84 1
 
0.9%
83 1
 
0.9%
82 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
Other values (107) 107
91.5%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
117 1
0.9%
116 1
0.9%
115 1
0.9%
114 1
0.9%
113 1
0.9%
112 1
0.9%
111 1
0.9%
110 1
0.9%
109 1
0.9%
108 1
0.9%

시군구
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
전주시
101 
군산시
16 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 101
86.3%
군산시 16
 
13.7%

Length

2024-03-14T11:10:42.403868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:10:42.501643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전주시 101
86.3%
군산시 16
 
13.7%

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
외국인관광도시민박업
117 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
외국인관광도시민박업 117
100.0%

Length

2024-03-14T11:10:42.600852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:10:42.695679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
외국인관광도시민박업 117
100.0%

업소명
Text

UNIQUE 

Distinct117
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-14T11:10:42.992987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length12
Mean length5.3076923
Min length1

Characters and Unicode

Total characters621
Distinct characters204
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)100.0%

Sample

1st row전주게스트하우스
2nd row해달별
3rd row천년마루
4th row마르타숙소
5th row60-6 게스트하우스
ValueCountFrequency (%)
게스트하우스 16
 
10.7%
전주게스트하우스 2
 
1.3%
별헤는집 1
 
0.7%
pine 1
 
0.7%
guest 1
 
0.7%
house 1
 
0.7%
쉼 1
 
0.7%
전주 1
 
0.7%
어린왕자 1
 
0.7%
아침정원 1
 
0.7%
Other values (123) 123
82.6%
2024-03-14T11:10:43.649205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 64
 
10.3%
하 38
 
6.1%
우 32
 
5.2%
32
 
5.2%
트 30
 
4.8%
게 28
 
4.5%
전 10
 
1.6%
이 9
 
1.4%
주 8
 
1.3%
마 8
 
1.3%
Other values (194) 362
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 554
89.2%
Space Separator 32
 
5.2%
Lowercase Letter 15
 
2.4%
Uppercase Letter 8
 
1.3%
Decimal Number 7
 
1.1%
Other Punctuation 2
 
0.3%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 64
 
11.6%
하 38
 
6.9%
우 32
 
5.8%
트 30
 
5.4%
게 28
 
5.1%
전 10
 
1.8%
이 9
 
1.6%
주 8
 
1.4%
마 8
 
1.4%
나 7
 
1.3%
Other values (169) 320
57.8%
Lowercase Letter
ValueCountFrequency (%)
e 3
20.0%
u 2
13.3%
s 2
13.3%
n 2
13.3%
a 1
 
6.7%
r 1
 
6.7%
d 1
 
6.7%
i 1
 
6.7%
o 1
 
6.7%
t 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
P 2
25.0%
G 2
25.0%
J 2
25.0%
H 1
12.5%
D 1
12.5%
Decimal Number
ValueCountFrequency (%)
3 3
42.9%
6 2
28.6%
2 1
 
14.3%
0 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 554
89.2%
Common 44
 
7.1%
Latin 23
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 64
 
11.6%
하 38
 
6.9%
우 32
 
5.8%
트 30
 
5.4%
게 28
 
5.1%
전 10
 
1.8%
이 9
 
1.6%
주 8
 
1.4%
마 8
 
1.4%
나 7
 
1.3%
Other values (169) 320
57.8%
Latin
ValueCountFrequency (%)
e 3
13.0%
u 2
 
8.7%
s 2
 
8.7%
n 2
 
8.7%
P 2
 
8.7%
G 2
 
8.7%
J 2
 
8.7%
a 1
 
4.3%
r 1
 
4.3%
d 1
 
4.3%
Other values (5) 5
21.7%
Common
ValueCountFrequency (%)
32
72.7%
3 3
 
6.8%
6 2
 
4.5%
( 1
 
2.3%
2 1
 
2.3%
) 1
 
2.3%
& 1
 
2.3%
, 1
 
2.3%
- 1
 
2.3%
0 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 554
89.2%
ASCII 67
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 64
 
11.6%
하 38
 
6.9%
우 32
 
5.8%
트 30
 
5.4%
게 28
 
5.1%
전 10
 
1.8%
이 9
 
1.6%
주 8
 
1.4%
마 8
 
1.4%
나 7
 
1.3%
Other values (169) 320
57.8%
ASCII
ValueCountFrequency (%)
32
47.8%
e 3
 
4.5%
3 3
 
4.5%
u 2
 
3.0%
s 2
 
3.0%
n 2
 
3.0%
P 2
 
3.0%
G 2
 
3.0%
J 2
 
3.0%
6 2
 
3.0%
Other values (15) 15
22.4%

도로명주소
Text

Distinct116
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-14T11:10:43.938250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length15.74359
Min length10

Characters and Unicode

Total characters1842
Distinct characters85
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

Unique115 ?
Unique (%)98.3%

Sample

1st row전주시 완산구 경기전길 44
2nd row전주시 완산구 어진길 33-9
3rd row전주시 완산구 경기전길 186
4th row전주시 완산구 오목대길 52
5th row전주시 완산구 오목대길 49-1
ValueCountFrequency (%)
전주시 101
22.3%
완산구 97
21.5%
군산시 16
 
3.5%
향교길 10
 
2.2%
어진길 7
 
1.5%
오목대길 7
 
1.5%
전동성당길 6
 
1.3%
전주천동로 6
 
1.3%
경기전길 6
 
1.3%
팔달로 5
 
1.1%
Other values (153) 191
42.3%
2024-03-14T11:10:44.318460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
335
18.2%
전 126
 
6.8%
시 117
 
6.4%
산 114
 
6.2%
구 112
 
6.1%
주 109
 
5.9%
완 97
 
5.3%
길 85
 
4.6%
1 78
 
4.2%
- 68
 
3.7%
Other values (75) 601
32.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1070
58.1%
Decimal Number 369
 
20.0%
Space Separator 335
 
18.2%
Dash Punctuation 68
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 126
11.8%
시 117
10.9%
산 114
10.7%
구 112
10.5%
주 109
10.2%
완 97
 
9.1%
길 85
 
7.9%
로 31
 
2.9%
동 20
 
1.9%
군 16
 
1.5%
Other values (63) 243
22.7%
Decimal Number
ValueCountFrequency (%)
1 78
21.1%
3 51
13.8%
2 51
13.8%
5 38
10.3%
6 30
 
8.1%
4 27
 
7.3%
9 26
 
7.0%
8 26
 
7.0%
0 22
 
6.0%
7 20
 
5.4%
Space Separator
ValueCountFrequency (%)
335
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1070
58.1%
Common 772
41.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 126
11.8%
시 117
10.9%
산 114
10.7%
구 112
10.5%
주 109
10.2%
완 97
 
9.1%
길 85
 
7.9%
로 31
 
2.9%
동 20
 
1.9%
군 16
 
1.5%
Other values (63) 243
22.7%
Common
ValueCountFrequency (%)
335
43.4%
1 78
 
10.1%
- 68
 
8.8%
3 51
 
6.6%
2 51
 
6.6%
5 38
 
4.9%
6 30
 
3.9%
4 27
 
3.5%
9 26
 
3.4%
8 26
 
3.4%
Other values (2) 42
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1070
58.1%
ASCII 772
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
335
43.4%
1 78
 
10.1%
- 68
 
8.8%
3 51
 
6.6%
2 51
 
6.6%
5 38
 
4.9%
6 30
 
3.9%
4 27
 
3.5%
9 26
 
3.4%
8 26
 
3.4%
Other values (2) 42
 
5.4%
Hangul
ValueCountFrequency (%)
전 126
11.8%
시 117
10.9%
산 114
10.7%
구 112
10.5%
주 109
10.2%
완 97
 
9.1%
길 85
 
7.9%
로 31
 
2.9%
동 20
 
1.9%
군 16
 
1.5%
Other values (63) 243
22.7%

객실보유수
Real number (ℝ)

Distinct9
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2307692
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-14T11:10:44.420247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8447718
Coefficient of variation (CV)0.43603697
Kurtosis-0.37512737
Mean4.2307692
Median Absolute Deviation (MAD)1
Skewness0.42505726
Sum495
Variance3.403183
MonotonicityNot monotonic
2024-03-14T11:10:44.513184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 27
23.1%
2 23
19.7%
6 17
14.5%
3 17
14.5%
5 16
13.7%
7 10
 
8.5%
9 3
 
2.6%
1 3
 
2.6%
8 1
 
0.9%
ValueCountFrequency (%)
1 3
 
2.6%
2 23
19.7%
3 17
14.5%
4 27
23.1%
5 16
13.7%
6 17
14.5%
7 10
 
8.5%
8 1
 
0.9%
9 3
 
2.6%
ValueCountFrequency (%)
9 3
 
2.6%
8 1
 
0.9%
7 10
 
8.5%
6 17
14.5%
5 16
13.7%
4 27
23.1%
3 17
14.5%
2 23
19.7%
1 3
 
2.6%

전화번호
Text

Distinct116
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-14T11:10:44.707703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.837607
Min length12

Characters and Unicode

Total characters1502
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

Unique115 ?
Unique (%)98.3%

Sample

1st row063-286-8886
2nd row063-288-4860
3rd row010-4147-3223
4th row010-7392-6987
5th row010-6521-4123
ValueCountFrequency (%)
010-7178-9392 2
 
1.7%
010-6295-8151 1
 
0.9%
010-5052-2593 1
 
0.9%
010-8648-6308 1
 
0.9%
010-5688-2020 1
 
0.9%
010-5129-0065 1
 
0.9%
010-7298-5660 1
 
0.9%
010-5678-3453 1
 
0.9%
010-4434-4231 1
 
0.9%
063-231-5757 1
 
0.9%
Other values (106) 106
90.6%
2024-03-14T11:10:45.006311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 288
19.2%
- 234
15.6%
1 185
12.3%
6 127
8.5%
5 114
 
7.6%
3 109
 
7.3%
2 103
 
6.9%
4 96
 
6.4%
8 92
 
6.1%
7 85
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1268
84.4%
Dash Punctuation 234
 
15.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 288
22.7%
1 185
14.6%
6 127
10.0%
5 114
 
9.0%
3 109
 
8.6%
2 103
 
8.1%
4 96
 
7.6%
8 92
 
7.3%
7 85
 
6.7%
9 69
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1502
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 288
19.2%
- 234
15.6%
1 185
12.3%
6 127
8.5%
5 114
 
7.6%
3 109
 
7.3%
2 103
 
6.9%
4 96
 
6.4%
8 92
 
6.1%
7 85
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 288
19.2%
- 234
15.6%
1 185
12.3%
6 127
8.5%
5 114
 
7.6%
3 109
 
7.3%
2 103
 
6.9%
4 96
 
6.4%
8 92
 
6.1%
7 85
 
5.7%

자료출처
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
관광총괄과
117 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관광총괄과 117
100.0%

Length

2024-03-14T11:10:45.138692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:10:45.211481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광총괄과 117
100.0%

공개여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
공개
117 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공개 117
100.0%

Length

2024-03-14T11:10:45.283792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:10:45.363690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공개 117
100.0%

작성일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2016년 8월
117 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016년 8월
2nd row2016년 8월
3rd row2016년 8월
4th row2016년 8월
5th row2016년 8월

Common Values

ValueCountFrequency (%)
2016년 8월 117
100.0%

Length

2024-03-14T11:10:45.444916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:10:45.533443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016년 117
50.0%
8월 117
50.0%

갱신주기
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
1년
117 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1년
2nd row1년
3rd row1년
4th row1년
5th row1년

Common Values

ValueCountFrequency (%)
1년 117
100.0%

Length

2024-03-14T11:10:45.625515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:10:45.771898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1년 117
100.0%

Interactions

2024-03-14T11:10:41.547013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:10:41.404516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:10:41.614358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:10:41.474809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:10:45.846714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군구객실보유수
순번1.0000.9840.092
시군구0.9841.0000.000
객실보유수0.0920.0001.000
2024-03-14T11:10:45.947652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번객실보유수시군구
순번1.0000.0210.858
객실보유수0.0211.0000.000
시군구0.8580.0001.000

Missing values

2024-03-14T11:10:41.931408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:10:42.045716image/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

순번시군구업종명업소명도로명주소객실보유수전화번호자료출처공개여부작성일갱신주기
01전주시외국인관광도시민박업전주게스트하우스전주시 완산구 경기전길 446063-286-8886관광총괄과공개2016년 8월1년
12전주시외국인관광도시민박업해달별전주시 완산구 어진길 33-93063-288-4860관광총괄과공개2016년 8월1년
23전주시외국인관광도시민박업천년마루전주시 완산구 경기전길 1864010-4147-3223관광총괄과공개2016년 8월1년
34전주시외국인관광도시민박업마르타숙소전주시 완산구 오목대길 523010-7392-6987관광총괄과공개2016년 8월1년
45전주시외국인관광도시민박업60-6 게스트하우스전주시 완산구 오목대길 49-14010-6521-4123관광총괄과공개2016년 8월1년
56전주시외국인관광도시민박업해밀전주시 완산구 향교길 19-236010-6256-2266관광총괄과공개2016년 8월1년
67전주시외국인관광도시민박업별빛향전주시 완산구 오목대길 27-213010-7640-4350관광총괄과공개2016년 8월1년
78전주시외국인관광도시민박업초정전주시 완산구 향교길 883010-3043-5953관광총괄과공개2016년 8월1년
89전주시외국인관광도시민박업향기나무전주시 완산구 향교길 685063-284-3317관광총괄과공개2016년 8월1년
910전주시외국인관광도시민박업꽃담전주시 완산구 어진길 38-43010-2981-6763관광총괄과공개2016년 8월1년
순번시군구업종명업소명도로명주소객실보유수전화번호자료출처공개여부작성일갱신주기
107108군산시외국인관광도시민박업사이사이군산시 구영6길 642010-5401-1959관광총괄과공개2016년 8월1년
108109군산시외국인관광도시민박업쿨쿨달몽군산시 구영1길 65010-4650-9166관광총괄과공개2016년 8월1년
109110군산시외국인관광도시민박업게스트하우스 이웃군산시 구영1길 11-26010-6337-0512관광총괄과공개2016년 8월1년
110111군산시외국인관광도시민박업하늘군산시 구영3길 724070-7778-5624관광총괄과공개2016년 8월1년
111112군산시외국인관광도시민박업산들 게스트하우스군산시 동국사길 7-36063-442-1514관광총괄과공개2016년 8월1년
112113군산시외국인관광도시민박업게스트하우스 소설여행군산시 월명로 516-12010-4503-0497관광총괄과공개2016년 8월1년
113114군산시외국인관광도시민박업다락군산시 절골길 12-44010-4660-1420관광총괄과공개2016년 8월1년
114115군산시외국인관광도시민박업다호군산시 구영7길 1019010-5803-2520관광총괄과공개2016년 8월1년
115116군산시외국인관광도시민박업달군산시 구영3길 21-25010-8456-3382관광총괄과공개2016년 8월1년
116117군산시외국인관광도시민박업나무게스트하우스군산시 월명3길 94010-5065-7787관광총괄과공개2016년 8월1년