Overview

Dataset statistics

Number of variables8
Number of observations88
Missing cells3
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory66.5 B

Variable types

Categorical2
DateTime1
Text4
Numeric1

Dataset

Description대구광역시 북구 관내 숙박업(유형, 신고일자, 업소명, 도로명주소, 지번주소, 전화번호, 객실수 등) 현황에 대한 정보를 제공합니다.
Author대구광역시 북구
URLhttps://www.data.go.kr/data/15006318/fileData.do

Alerts

업종명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
소재지전화 has 3 (3.4%) missing valuesMissing
영업소 주소(도로명) has unique valuesUnique
영업소 주소(지번) has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:59:44.859765
Analysis finished2023-12-12 13:59:45.850386
Duration0.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size836.0 B
숙박업(일반)
88 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
숙박업(일반) 88
100.0%

Length

2023-12-12T22:59:45.934203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:59:46.371509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박업(일반 88
100.0%

신고일자
Date

Distinct73
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
Minimum1989-08-07 00:00:00
Maximum2020-08-24 00:00:00
2023-12-12T22:59:46.540140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:59:46.718025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업소명
Text

Distinct84
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size836.0 B
2023-12-12T22:59:47.055201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length18
Mean length5.6363636
Min length1

Characters and Unicode

Total characters496
Distinct characters159
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

Unique82 ?
Unique (%)93.2%

Sample

1st row소울모텔
2nd row귀빈장
3rd row로즈모텔
4th row청오
5th row영모텔
ValueCountFrequency (%)
호텔 6
 
5.8%
스위트호텔 4
 
3.8%
럭셔리모텔 2
 
1.9%
루바토호텔 1
 
1.0%
소울모텔 1
 
1.0%
지투(g2)모텔 1
 
1.0%
동천점 1
 
1.0%
명덕여관 1
 
1.0%
벤 1
 
1.0%
글램호텔 1
 
1.0%
Other values (85) 85
81.7%
2023-12-12T22:59:47.545540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 60
 
12.1%
모 39
 
7.9%
호 24
 
4.8%
16
 
3.2%
스 14
 
2.8%
리 11
 
2.2%
여 9
 
1.8%
) 8
 
1.6%
트 8
 
1.6%
장 8
 
1.6%
Other values (149) 299
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 422
85.1%
Uppercase Letter 27
 
5.4%
Space Separator 16
 
3.2%
Close Punctuation 8
 
1.6%
Open Punctuation 8
 
1.6%
Decimal Number 7
 
1.4%
Lowercase Letter 6
 
1.2%
Other Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 60
 
14.2%
모 39
 
9.2%
호 24
 
5.7%
스 14
 
3.3%
리 11
 
2.6%
여 9
 
2.1%
트 8
 
1.9%
장 8
 
1.9%
점 7
 
1.7%
대 7
 
1.7%
Other values (119) 235
55.7%
Uppercase Letter
ValueCountFrequency (%)
O 3
11.1%
E 3
11.1%
B 2
 
7.4%
R 2
 
7.4%
G 2
 
7.4%
C 2
 
7.4%
A 2
 
7.4%
T 2
 
7.4%
H 2
 
7.4%
L 1
 
3.7%
Other values (6) 6
22.2%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
l 1
16.7%
o 1
16.7%
t 1
16.7%
h 1
16.7%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 2
28.6%
0 1
 
14.3%
4 1
 
14.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 422
85.1%
Common 41
 
8.3%
Latin 33
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 60
 
14.2%
모 39
 
9.2%
호 24
 
5.7%
스 14
 
3.3%
리 11
 
2.6%
여 9
 
2.1%
트 8
 
1.9%
장 8
 
1.9%
점 7
 
1.7%
대 7
 
1.7%
Other values (119) 235
55.7%
Latin
ValueCountFrequency (%)
O 3
 
9.1%
E 3
 
9.1%
B 2
 
6.1%
R 2
 
6.1%
G 2
 
6.1%
C 2
 
6.1%
A 2
 
6.1%
T 2
 
6.1%
H 2
 
6.1%
e 2
 
6.1%
Other values (11) 11
33.3%
Common
ValueCountFrequency (%)
16
39.0%
) 8
19.5%
( 8
19.5%
1 3
 
7.3%
2 2
 
4.9%
. 1
 
2.4%
- 1
 
2.4%
0 1
 
2.4%
4 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 422
85.1%
ASCII 74
 
14.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 60
 
14.2%
모 39
 
9.2%
호 24
 
5.7%
스 14
 
3.3%
리 11
 
2.6%
여 9
 
2.1%
트 8
 
1.9%
장 8
 
1.9%
점 7
 
1.7%
대 7
 
1.7%
Other values (119) 235
55.7%
ASCII
ValueCountFrequency (%)
16
21.6%
) 8
 
10.8%
( 8
 
10.8%
O 3
 
4.1%
E 3
 
4.1%
1 3
 
4.1%
B 2
 
2.7%
R 2
 
2.7%
G 2
 
2.7%
C 2
 
2.7%
Other values (20) 25
33.8%

영업소 주소(도로명)
Text

UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2023-12-12T22:59:47.906042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length24.840909
Min length21

Characters and Unicode

Total characters2186
Distinct characters71
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)100.0%

Sample

1st row대구광역시 북구 고성로 182 (고성동2가)
2nd row대구광역시 북구 팔달로27길 11-9 (노원동3가)
3rd row대구광역시 북구 대구체육관로 24 (산격동)
4th row대구광역시 북구 팔달로35길 113 (노원동3가)
5th row대구광역시 북구 경진로1길 17 (복현동)
ValueCountFrequency (%)
대구광역시 88
19.9%
북구 88
19.9%
복현동 14
 
3.2%
산격동 12
 
2.7%
동천동 12
 
2.7%
태전동 8
 
1.8%
대현동 8
 
1.8%
대현로 7
 
1.6%
칠성동2가 7
 
1.6%
동천로 6
 
1.4%
Other values (123) 192
43.4%
2023-12-12T22:59:48.389910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
354
16.2%
구 180
 
8.2%
대 120
 
5.5%
동 118
 
5.4%
1 94
 
4.3%
시 91
 
4.2%
북 90
 
4.1%
역 88
 
4.0%
) 88
 
4.0%
( 88
 
4.0%
Other values (61) 875
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1279
58.5%
Space Separator 354
 
16.2%
Decimal Number 341
 
15.6%
Close Punctuation 88
 
4.0%
Open Punctuation 88
 
4.0%
Dash Punctuation 34
 
1.6%
Other Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 180
14.1%
대 120
 
9.4%
동 118
 
9.2%
시 91
 
7.1%
북 90
 
7.0%
역 88
 
6.9%
로 88
 
6.9%
광 88
 
6.9%
길 49
 
3.8%
현 30
 
2.3%
Other values (45) 337
26.3%
Decimal Number
ValueCountFrequency (%)
1 94
27.6%
2 65
19.1%
3 46
13.5%
8 25
 
7.3%
0 25
 
7.3%
4 22
 
6.5%
7 19
 
5.6%
9 17
 
5.0%
6 15
 
4.4%
5 13
 
3.8%
Space Separator
ValueCountFrequency (%)
354
100.0%
Close Punctuation
ValueCountFrequency (%)
) 88
100.0%
Open Punctuation
ValueCountFrequency (%)
( 88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1279
58.5%
Common 907
41.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 180
14.1%
대 120
 
9.4%
동 118
 
9.2%
시 91
 
7.1%
북 90
 
7.0%
역 88
 
6.9%
로 88
 
6.9%
광 88
 
6.9%
길 49
 
3.8%
현 30
 
2.3%
Other values (45) 337
26.3%
Common
ValueCountFrequency (%)
354
39.0%
1 94
 
10.4%
) 88
 
9.7%
( 88
 
9.7%
2 65
 
7.2%
3 46
 
5.1%
- 34
 
3.7%
8 25
 
2.8%
0 25
 
2.8%
4 22
 
2.4%
Other values (6) 66
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1279
58.5%
ASCII 907
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
354
39.0%
1 94
 
10.4%
) 88
 
9.7%
( 88
 
9.7%
2 65
 
7.2%
3 46
 
5.1%
- 34
 
3.7%
8 25
 
2.8%
0 25
 
2.8%
4 22
 
2.4%
Other values (6) 66
 
7.3%
Hangul
ValueCountFrequency (%)
구 180
14.1%
대 120
 
9.4%
동 118
 
9.2%
시 91
 
7.1%
북 90
 
7.0%
역 88
 
6.9%
로 88
 
6.9%
광 88
 
6.9%
길 49
 
3.8%
현 30
 
2.3%
Other values (45) 337
26.3%

영업소 주소(지번)
Text

UNIQUE 

Distinct88
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size836.0 B
2023-12-12T22:59:48.716149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length19.670455
Min length17

Characters and Unicode

Total characters1731
Distinct characters38
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

Unique88 ?
Unique (%)100.0%

Sample

1st row대구광역시 북구 고성동2가 57-2
2nd row대구광역시 북구 노원동3가 744-8
3rd row대구광역시 북구 산격동 1449-9
4th row대구광역시 북구 노원동3가 1031-1
5th row대구광역시 북구 복현동 415
ValueCountFrequency (%)
대구광역시 88
25.0%
북구 88
25.0%
복현동 14
 
4.0%
산격동 12
 
3.4%
동천동 12
 
3.4%
태전동 8
 
2.3%
대현동 8
 
2.3%
칠성동2가 7
 
2.0%
노원동3가 5
 
1.4%
관음동 5
 
1.4%
Other values (94) 105
29.8%
2023-12-12T22:59:49.240915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
352
20.3%
구 176
 
10.2%
동 100
 
5.8%
대 96
 
5.5%
광 88
 
5.1%
역 88
 
5.1%
시 88
 
5.1%
북 88
 
5.1%
1 85
 
4.9%
- 76
 
4.4%
Other values (28) 494
28.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 904
52.2%
Decimal Number 399
23.1%
Space Separator 352
 
20.3%
Dash Punctuation 76
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 176
19.5%
동 100
11.1%
대 96
10.6%
광 88
9.7%
역 88
9.7%
시 88
9.7%
북 88
9.7%
가 24
 
2.7%
현 22
 
2.4%
복 14
 
1.5%
Other values (16) 120
13.3%
Decimal Number
ValueCountFrequency (%)
1 85
21.3%
2 57
14.3%
4 50
12.5%
3 40
10.0%
8 35
8.8%
7 34
 
8.5%
9 32
 
8.0%
0 27
 
6.8%
5 21
 
5.3%
6 18
 
4.5%
Space Separator
ValueCountFrequency (%)
352
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 904
52.2%
Common 827
47.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 176
19.5%
동 100
11.1%
대 96
10.6%
광 88
9.7%
역 88
9.7%
시 88
9.7%
북 88
9.7%
가 24
 
2.7%
현 22
 
2.4%
복 14
 
1.5%
Other values (16) 120
13.3%
Common
ValueCountFrequency (%)
352
42.6%
1 85
 
10.3%
- 76
 
9.2%
2 57
 
6.9%
4 50
 
6.0%
3 40
 
4.8%
8 35
 
4.2%
7 34
 
4.1%
9 32
 
3.9%
0 27
 
3.3%
Other values (2) 39
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 904
52.2%
ASCII 827
47.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
352
42.6%
1 85
 
10.3%
- 76
 
9.2%
2 57
 
6.9%
4 50
 
6.0%
3 40
 
4.8%
8 35
 
4.2%
7 34
 
4.1%
9 32
 
3.9%
0 27
 
3.3%
Other values (2) 39
 
4.7%
Hangul
ValueCountFrequency (%)
구 176
19.5%
동 100
11.1%
대 96
10.6%
광 88
9.7%
역 88
9.7%
시 88
9.7%
북 88
9.7%
가 24
 
2.7%
현 22
 
2.4%
복 14
 
1.5%
Other values (16) 120
13.3%

소재지전화
Text

MISSING 

Distinct85
Distinct (%)100.0%
Missing3
Missing (%)3.4%
Memory size836.0 B
2023-12-12T22:59:49.540331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1020
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

Unique85 ?
Unique (%)100.0%

Sample

1st row053-357-1288
2nd row053-357-6645
3rd row053-935-8855
4th row053-351-6657
5th row053-955-7862
ValueCountFrequency (%)
053-352-0306 1
 
1.2%
053-942-9778 1
 
1.2%
053-326-3054 1
 
1.2%
053-327-1210 1
 
1.2%
053-958-0540 1
 
1.2%
053-326-8853 1
 
1.2%
053-324-5656 1
 
1.2%
053-324-6655 1
 
1.2%
053-323-9431 1
 
1.2%
053-326-3901 1
 
1.2%
Other values (75) 75
88.2%
2023-12-12T22:59:49.913109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 180
17.6%
3 179
17.5%
- 170
16.7%
0 153
15.0%
2 57
 
5.6%
1 57
 
5.6%
9 52
 
5.1%
4 51
 
5.0%
6 45
 
4.4%
7 39
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 850
83.3%
Dash Punctuation 170
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 180
21.2%
3 179
21.1%
0 153
18.0%
2 57
 
6.7%
1 57
 
6.7%
9 52
 
6.1%
4 51
 
6.0%
6 45
 
5.3%
7 39
 
4.6%
8 37
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 180
17.6%
3 179
17.5%
- 170
16.7%
0 153
15.0%
2 57
 
5.6%
1 57
 
5.6%
9 52
 
5.1%
4 51
 
5.0%
6 45
 
4.4%
7 39
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 180
17.6%
3 179
17.5%
- 170
16.7%
0 153
15.0%
2 57
 
5.6%
1 57
 
5.6%
9 52
 
5.1%
4 51
 
5.0%
6 45
 
4.4%
7 39
 
3.8%

객실수
Real number (ℝ)

Distinct39
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.318182
Minimum7
Maximum303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T22:59:50.067827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9
Q117.5
median27
Q335
95-th percentile51.95
Maximum303
Range296
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation32.104193
Coefficient of variation (CV)1.0950267
Kurtosis62.004299
Mean29.318182
Median Absolute Deviation (MAD)8.5
Skewness7.2738525
Sum2580
Variance1030.6792
MonotonicityNot monotonic
2023-12-12T22:59:50.189257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
35 7
 
8.0%
10 6
 
6.8%
36 6
 
6.8%
19 6
 
6.8%
30 5
 
5.7%
18 4
 
4.5%
28 4
 
4.5%
27 4
 
4.5%
26 3
 
3.4%
13 3
 
3.4%
Other values (29) 40
45.5%
ValueCountFrequency (%)
7 2
 
2.3%
8 2
 
2.3%
9 2
 
2.3%
10 6
6.8%
11 1
 
1.1%
12 2
 
2.3%
13 3
3.4%
14 2
 
2.3%
15 1
 
1.1%
16 1
 
1.1%
ValueCountFrequency (%)
303 1
1.1%
61 1
1.1%
60 1
1.1%
56 1
1.1%
53 1
1.1%
50 1
1.1%
48 1
1.1%
47 1
1.1%
45 1
1.1%
42 1
1.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size836.0 B
2023-01-17
88 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-17
2nd row2023-01-17
3rd row2023-01-17
4th row2023-01-17
5th row2023-01-17

Common Values

ValueCountFrequency (%)
2023-01-17 88
100.0%

Length

2023-12-12T22:59:50.309056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:59:50.405584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-17 88
100.0%

Interactions

2023-12-12T22:59:45.519852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:59:50.472110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신고일자업소명영업소 주소(도로명)영업소 주소(지번)소재지전화객실수
신고일자1.0000.9161.0001.0001.0001.000
업소명0.9161.0001.0001.0001.0000.809
영업소 주소(도로명)1.0001.0001.0001.0001.0001.000
영업소 주소(지번)1.0001.0001.0001.0001.0001.000
소재지전화1.0001.0001.0001.0001.0001.000
객실수1.0000.8091.0001.0001.0001.000

Missing values

2023-12-12T22:59:45.647857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:59:45.787766image/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

업종명신고일자업소명영업소 주소(도로명)영업소 주소(지번)소재지전화객실수데이터기준일자
0숙박업(일반)1989-08-07소울모텔대구광역시 북구 고성로 182 (고성동2가)대구광역시 북구 고성동2가 57-2053-357-1288152023-01-17
1숙박업(일반)1996-12-18귀빈장대구광역시 북구 팔달로27길 11-9 (노원동3가)대구광역시 북구 노원동3가 744-8053-357-664582023-01-17
2숙박업(일반)1996-12-01로즈모텔대구광역시 북구 대구체육관로 24 (산격동)대구광역시 북구 산격동 1449-9053-935-8855182023-01-17
3숙박업(일반)1996-12-18청오대구광역시 북구 팔달로35길 113 (노원동3가)대구광역시 북구 노원동3가 1031-1053-351-6657132023-01-17
4숙박업(일반)1996-12-10영모텔대구광역시 북구 경진로1길 17 (복현동)대구광역시 북구 복현동 415053-955-7862302023-01-17
5숙박업(일반)1996-12-18린시아대구광역시 북구 경진로1길 10-12 (복현동)대구광역시 북구 복현동 412-4053-955-0644182023-01-17
6숙박업(일반)1996-12-18제일여인숙대구광역시 북구 팔달로27길 11-7 (노원동3가)대구광역시 북구 노원동3가 744-7053-357-636672023-01-17
7숙박업(일반)1996-09-18옥천여인숙대구광역시 북구 대현남로서4길 29 (대현동)대구광역시 북구 대현동 330-6053-954-6973102023-01-17
8숙박업(일반)1996-12-18대구여인숙대구광역시 북구 칠성시장로7길 39-22 (칠성동1가)대구광역시 북구 칠성동1가 17-55<NA>92023-01-17
9숙박업(일반)1996-12-18영일여인숙대구광역시 북구 칠성시장로7길 39-8 (칠성동1가)대구광역시 북구 칠성동1가 18-115053-253-9370122023-01-17
업종명신고일자업소명영업소 주소(도로명)영업소 주소(지번)소재지전화객실수데이터기준일자
78숙박업(일반)2008-12-16호텔인터불고 엑스코대구광역시 북구 유통단지로 80 (산격동)대구광역시 북구 산격동 1674053-380-01143032023-01-17
79숙박업(일반)2011-08-26루엔갤러리대구광역시 북구 유통단지로13길 13-11 (산격동)대구광역시 북구 산격동 1617053-381-0050262023-01-17
80숙박업(일반)2014-01-02청담모텔대구광역시 북구 한강로8길 22-1 (금호동)대구광역시 북구 금호동 808053-312-2060202023-01-17
81숙박업(일반)2014-01-10휴대구광역시 북구 한강로8길 22 (금호동)대구광역시 북구 금호동 809053-311-1003192023-01-17
82숙박업(일반)2014-02-12에이치에비뉴 대구동천점대구광역시 북구 동천로 128-12 (동천동)대구광역시 북구 동천동 910-2053-322-0055472023-01-17
83숙박업(일반)2014-04-22스위트호텔대구광역시 북구 한강로8길 21-2 (금호동)대구광역시 북구 금호동 813053-312-0003422023-01-17
84숙박업(일반)2014-08-25씨원(C1)대구광역시 북구 한강로8길 21-1 (금호동)대구광역시 북구 금호동 812053-314-0026402023-01-17
85숙박업(일반)2014-12-01브라운도트대구동천점대구광역시 북구 동암로12길 24-20 (동천동)대구광역시 북구 동천동 970-3053-351-8888612023-01-17
86숙박업(일반)2016-11-28호텔 더알토대구광역시 북구 칠성남로30길 34 (칠성동2가)대구광역시 북구 칠성동2가 302-111053-254-3000562023-01-17
87숙박업(일반)2020-08-24호텔 비대구광역시 북구 동천로 138-18 (동천동)대구광역시 북구 동천동 897-5<NA>362023-01-17