Overview

Dataset statistics

Number of variables9
Number of observations340
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.0 KiB
Average record size in memory72.4 B

Variable types

Text4
Categorical5

Dataset

Description키,분류1,분류2,분류3,검색어,명칭,행정 시,행정 구,행정 동
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12994/S/1/datasetView.do

Alerts

분류1 has constant value ""Constant
분류2 has constant value ""Constant
행정 시 has constant value ""Constant
분류3 is highly imbalanced (53.9%)Imbalance
키 has unique valuesUnique

Reproduction

Analysis started2023-12-11 04:05:25.300744
Analysis finished2023-12-11 04:05:26.260197
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키
Text

UNIQUE 

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T13:05:26.429526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters4080
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)100.0%

Sample

1st rowBE_IW18-0221
2nd rowBE_IW18-0035
3rd rowBE_IW18-0036
4th rowBE_IW18-0037
5th rowBE_IW18-0038
ValueCountFrequency (%)
be_iw18-0221 1
 
0.3%
be_iw18-0257 1
 
0.3%
be_iw18-0266 1
 
0.3%
be_iw18-0265 1
 
0.3%
be_iw18-0264 1
 
0.3%
be_iw18-0263 1
 
0.3%
be_iw18-0262 1
 
0.3%
be_iw18-0261 1
 
0.3%
be_iw18-0260 1
 
0.3%
be_iw18-0259 1
 
0.3%
Other values (330) 330
97.1%
2023-12-11T13:05:26.760133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 514
12.6%
0 512
12.5%
8 404
9.9%
B 340
8.3%
E 340
8.3%
_ 340
8.3%
I 340
8.3%
W 340
8.3%
- 340
8.3%
2 174
 
4.3%
Other values (6) 436
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2040
50.0%
Uppercase Letter 1360
33.3%
Connector Punctuation 340
 
8.3%
Dash Punctuation 340
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 514
25.2%
0 512
25.1%
8 404
19.8%
2 174
 
8.5%
3 115
 
5.6%
4 65
 
3.2%
6 64
 
3.1%
7 64
 
3.1%
9 64
 
3.1%
5 64
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 340
25.0%
E 340
25.0%
I 340
25.0%
W 340
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 340
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2720
66.7%
Latin 1360
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 514
18.9%
0 512
18.8%
8 404
14.9%
_ 340
12.5%
- 340
12.5%
2 174
 
6.4%
3 115
 
4.2%
4 65
 
2.4%
6 64
 
2.4%
7 64
 
2.4%
Other values (2) 128
 
4.7%
Latin
ValueCountFrequency (%)
B 340
25.0%
E 340
25.0%
I 340
25.0%
W 340
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 514
12.6%
0 512
12.5%
8 404
9.9%
B 340
8.3%
E 340
8.3%
_ 340
8.3%
I 340
8.3%
W 340
8.3%
- 340
8.3%
2 174
 
4.3%
Other values (6) 436
10.7%

분류1
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
관광/숙박
340 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관광/숙박
2nd row관광/숙박
3rd row관광/숙박
4th row관광/숙박
5th row관광/숙박

Common Values

ValueCountFrequency (%)
관광/숙박 340
100.0%

Length

2023-12-11T13:05:26.939797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:05:27.037836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관광/숙박 340
100.0%

분류2
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
숙박
340 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
숙박 340
100.0%

Length

2023-12-11T13:05:27.256633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:05:27.362221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
숙박 340
100.0%

분류3
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
여관-여인숙
227 
모텔
105 
팬션
 
5
콘도
 
2
민박
 
1

Length

Max length6
Median length6
Mean length4.6705882
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row여관-여인숙
2nd row모텔
3rd row모텔
4th row모텔
5th row모텔

Common Values

ValueCountFrequency (%)
여관-여인숙 227
66.8%
모텔 105
30.9%
팬션 5
 
1.5%
콘도 2
 
0.6%
민박 1
 
0.3%

Length

2023-12-11T13:05:27.509044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:05:27.639785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관-여인숙 227
66.8%
모텔 105
30.9%
팬션 5
 
1.5%
콘도 2
 
0.6%
민박 1
 
0.3%

검색어
Text

Distinct319
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T13:05:27.973981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length4.0852941
Min length2

Characters and Unicode

Total characters1389
Distinct characters263
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique303 ?
Unique (%)89.1%

Sample

1st row서울장
2nd row밀라노모텔
3rd row발렌시아모텔
4th row벨라지오호텔
5th row보보호텔
ValueCountFrequency (%)
시네마모텔 4
 
1.2%
스카이모텔 4
 
1.2%
테마모텔 3
 
0.9%
w모텔 2
 
0.6%
로데오모텔 2
 
0.6%
샐몬모텔 2
 
0.6%
삼원장 2
 
0.6%
초콜렛호텔 2
 
0.6%
청수장 2
 
0.6%
쉴모텔 2
 
0.6%
Other values (310) 316
92.7%
2023-12-11T13:05:28.580571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 187
 
13.5%
모 150
 
10.8%
장 75
 
5.4%
스 45
 
3.2%
호 42
 
3.0%
리 25
 
1.8%
라 21
 
1.5%
마 20
 
1.4%
이 20
 
1.4%
인 19
 
1.4%
Other values (253) 785
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1359
97.8%
Uppercase Letter 17
 
1.2%
Decimal Number 8
 
0.6%
Other Punctuation 4
 
0.3%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 187
 
13.8%
모 150
 
11.0%
장 75
 
5.5%
스 45
 
3.3%
호 42
 
3.1%
리 25
 
1.8%
라 21
 
1.5%
마 20
 
1.5%
이 20
 
1.5%
인 19
 
1.4%
Other values (233) 755
55.6%
Uppercase Letter
ValueCountFrequency (%)
L 2
 
11.8%
W 2
 
11.8%
G 1
 
5.9%
S 1
 
5.9%
M 1
 
5.9%
X 1
 
5.9%
P 1
 
5.9%
C 1
 
5.9%
F 1
 
5.9%
E 1
 
5.9%
Other values (5) 5
29.4%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
1 3
37.5%
5 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
/ 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1359
97.8%
Latin 17
 
1.2%
Common 13
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 187
 
13.8%
모 150
 
11.0%
장 75
 
5.5%
스 45
 
3.3%
호 42
 
3.1%
리 25
 
1.8%
라 21
 
1.5%
마 20
 
1.5%
이 20
 
1.5%
인 19
 
1.4%
Other values (233) 755
55.6%
Latin
ValueCountFrequency (%)
L 2
 
11.8%
W 2
 
11.8%
G 1
 
5.9%
S 1
 
5.9%
M 1
 
5.9%
X 1
 
5.9%
P 1
 
5.9%
C 1
 
5.9%
F 1
 
5.9%
E 1
 
5.9%
Other values (5) 5
29.4%
Common
ValueCountFrequency (%)
2 4
30.8%
/ 4
30.8%
1 3
23.1%
5 1
 
7.7%
1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1359
97.8%
ASCII 30
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 187
 
13.8%
모 150
 
11.0%
장 75
 
5.5%
스 45
 
3.3%
호 42
 
3.1%
리 25
 
1.8%
라 21
 
1.5%
마 20
 
1.5%
이 20
 
1.5%
인 19
 
1.4%
Other values (233) 755
55.6%
ASCII
ValueCountFrequency (%)
2 4
 
13.3%
/ 4
 
13.3%
1 3
 
10.0%
L 2
 
6.7%
W 2
 
6.7%
5 1
 
3.3%
G 1
 
3.3%
1
 
3.3%
S 1
 
3.3%
M 1
 
3.3%
Other values (10) 10
33.3%

명칭
Text

Distinct321
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T13:05:28.961813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.0176471
Min length1

Characters and Unicode

Total characters1366
Distinct characters261
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305 ?
Unique (%)89.7%

Sample

1st row서울장
2nd row밀라노모텔
3rd row발렌시아모텔
4th row벨라지오호텔
5th row보보호텔
ValueCountFrequency (%)
스카이모텔 4
 
1.2%
테마모텔 3
 
0.9%
첼로모텔 2
 
0.6%
쉴모텔 2
 
0.6%
로데오모텔 2
 
0.6%
보성장 2
 
0.6%
에버그린 2
 
0.6%
초콜렛호텔 2
 
0.6%
w모텔 2
 
0.6%
시네마 2
 
0.6%
Other values (311) 317
93.2%
2023-12-11T13:05:29.520792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
텔 178
 
13.0%
모 141
 
10.3%
장 75
 
5.5%
스 45
 
3.3%
호 42
 
3.1%
리 25
 
1.8%
라 21
 
1.5%
이 20
 
1.5%
마 20
 
1.5%
인 19
 
1.4%
Other values (251) 780
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1341
98.2%
Uppercase Letter 17
 
1.2%
Decimal Number 8
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
텔 178
 
13.3%
모 141
 
10.5%
장 75
 
5.6%
스 45
 
3.4%
호 42
 
3.1%
리 25
 
1.9%
라 21
 
1.6%
이 20
 
1.5%
마 20
 
1.5%
인 19
 
1.4%
Other values (233) 755
56.3%
Uppercase Letter
ValueCountFrequency (%)
L 2
 
11.8%
W 2
 
11.8%
V 1
 
5.9%
E 1
 
5.9%
O 1
 
5.9%
I 1
 
5.9%
N 1
 
5.9%
U 1
 
5.9%
M 1
 
5.9%
S 1
 
5.9%
Other values (5) 5
29.4%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
1 3
37.5%
5 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1341
98.2%
Latin 17
 
1.2%
Common 8
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
텔 178
 
13.3%
모 141
 
10.5%
장 75
 
5.6%
스 45
 
3.4%
호 42
 
3.1%
리 25
 
1.9%
라 21
 
1.6%
이 20
 
1.5%
마 20
 
1.5%
인 19
 
1.4%
Other values (233) 755
56.3%
Latin
ValueCountFrequency (%)
L 2
 
11.8%
W 2
 
11.8%
V 1
 
5.9%
E 1
 
5.9%
O 1
 
5.9%
I 1
 
5.9%
N 1
 
5.9%
U 1
 
5.9%
M 1
 
5.9%
S 1
 
5.9%
Other values (5) 5
29.4%
Common
ValueCountFrequency (%)
2 4
50.0%
1 3
37.5%
5 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1341
98.2%
ASCII 25
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
텔 178
 
13.3%
모 141
 
10.5%
장 75
 
5.6%
스 45
 
3.4%
호 42
 
3.1%
리 25
 
1.9%
라 21
 
1.6%
이 20
 
1.5%
마 20
 
1.5%
인 19
 
1.4%
Other values (233) 755
56.3%
ASCII
ValueCountFrequency (%)
2 4
16.0%
1 3
 
12.0%
L 2
 
8.0%
W 2
 
8.0%
5 1
 
4.0%
V 1
 
4.0%
E 1
 
4.0%
O 1
 
4.0%
I 1
 
4.0%
N 1
 
4.0%
Other values (8) 8
32.0%

행정 시
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
서울특별시
340 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 340
100.0%

Length

2023-12-11T13:05:29.725424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:05:29.884180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 340
100.0%

행정 구
Categorical

Distinct25
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
관악구
32 
강서구
28 
종로구
22 
송파구
 
21
영등포구
 
20
Other values (20)
217 

Length

Max length4
Median length3
Mean length3.1058824
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금천구
2nd row성동구
3rd row강북구
4th row송파구
5th row마포구

Common Values

ValueCountFrequency (%)
관악구 32
 
9.4%
강서구 28
 
8.2%
종로구 22
 
6.5%
송파구 21
 
6.2%
영등포구 20
 
5.9%
광진구 20
 
5.9%
강남구 19
 
5.6%
중랑구 19
 
5.6%
구로구 18
 
5.3%
강북구 17
 
5.0%
Other values (15) 124
36.5%

Length

2023-12-11T13:05:30.032972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
관악구 32
 
9.4%
강서구 28
 
8.2%
종로구 22
 
6.5%
송파구 21
 
6.2%
영등포구 20
 
5.9%
광진구 20
 
5.9%
강남구 19
 
5.6%
중랑구 19
 
5.6%
구로구 18
 
5.3%
강북구 17
 
5.0%
Other values (15) 124
36.5%

행정 동
Text

Distinct133
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2023-12-11T13:05:30.261826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.9441176
Min length2

Characters and Unicode

Total characters1341
Distinct characters127
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)17.1%

Sample

1st row가산동
2nd row왕십리도선동
3rd row인수동
4th row방이2동
5th row서교동
ValueCountFrequency (%)
방이2동 15
 
4.4%
화곡1동 15
 
4.4%
신림동 14
 
4.1%
신촌동 14
 
4.1%
종로1.2.3.4가동 12
 
3.5%
영등포동 9
 
2.6%
역삼1동 8
 
2.4%
청룡동 8
 
2.4%
상봉2동 7
 
2.1%
화양동 7
 
2.1%
Other values (123) 231
67.9%
2023-12-11T13:05:30.678506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 342
25.5%
1 78
 
5.8%
2 77
 
5.7%
신 42
 
3.1%
. 41
 
3.1%
화 34
 
2.5%
곡 25
 
1.9%
3 25
 
1.9%
4 24
 
1.8%
로 23
 
1.7%
Other values (117) 630
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1079
80.5%
Decimal Number 221
 
16.5%
Other Punctuation 41
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 342
31.7%
신 42
 
3.9%
화 34
 
3.2%
곡 25
 
2.3%
로 23
 
2.1%
가 23
 
2.1%
방 22
 
2.0%
림 19
 
1.8%
이 19
 
1.8%
대 19
 
1.8%
Other values (109) 511
47.4%
Decimal Number
ValueCountFrequency (%)
1 78
35.3%
2 77
34.8%
3 25
 
11.3%
4 24
 
10.9%
6 8
 
3.6%
5 5
 
2.3%
7 4
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1079
80.5%
Common 262
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 342
31.7%
신 42
 
3.9%
화 34
 
3.2%
곡 25
 
2.3%
로 23
 
2.1%
가 23
 
2.1%
방 22
 
2.0%
림 19
 
1.8%
이 19
 
1.8%
대 19
 
1.8%
Other values (109) 511
47.4%
Common
ValueCountFrequency (%)
1 78
29.8%
2 77
29.4%
. 41
15.6%
3 25
 
9.5%
4 24
 
9.2%
6 8
 
3.1%
5 5
 
1.9%
7 4
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1079
80.5%
ASCII 262
 
19.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 342
31.7%
신 42
 
3.9%
화 34
 
3.2%
곡 25
 
2.3%
로 23
 
2.1%
가 23
 
2.1%
방 22
 
2.0%
림 19
 
1.8%
이 19
 
1.8%
대 19
 
1.8%
Other values (109) 511
47.4%
ASCII
ValueCountFrequency (%)
1 78
29.8%
2 77
29.4%
. 41
15.6%
3 25
 
9.5%
4 24
 
9.2%
6 8
 
3.1%
5 5
 
1.9%
7 4
 
1.5%

Correlations

2023-12-11T13:05:30.789769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류3행정 구
분류31.0000.629
행정 구0.6291.000
2023-12-11T13:05:30.887446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류3행정 구
분류31.0000.315
행정 구0.3151.000
2023-12-11T13:05:31.007859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류3행정 구
분류31.0000.315
행정 구0.3151.000

Missing values

2023-12-11T13:05:26.060285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:05:26.216064image/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

키분류1분류2분류3검색어명칭행정 시행정 구행정 동
0BE_IW18-0221관광/숙박숙박여관-여인숙서울장서울장서울특별시금천구가산동
1BE_IW18-0035관광/숙박숙박모텔밀라노모텔밀라노모텔서울특별시성동구왕십리도선동
2BE_IW18-0036관광/숙박숙박모텔발렌시아모텔발렌시아모텔서울특별시강북구인수동
3BE_IW18-0037관광/숙박숙박모텔벨라지오호텔벨라지오호텔서울특별시송파구방이2동
4BE_IW18-0038관광/숙박숙박모텔보보호텔보보호텔서울특별시마포구서교동
5BE_IW18-0039관광/숙박숙박모텔본모텔본모텔서울특별시강서구염창동
6BE_IW18-0040관광/숙박숙박모텔사랑방모텔사랑방모텔서울특별시도봉구방학2동
7BE_IW18-0041관광/숙박숙박모텔삼오모텔삼오모텔서울특별시종로구종로1.2.3.4가동
8BE_IW18-0042관광/숙박숙박모텔삼우모텔삼우모텔서울특별시송파구삼전동
9BE_IW18-0043관광/숙박숙박모텔샐몬모텔샐몬모텔서울특별시광진구중곡1동
키분류1분류2분류3검색어명칭행정 시행정 구행정 동
330BE_IW18-0302관광/숙박숙박여관-여인숙태화장태화장서울특별시광진구능동
331BE_IW18-0303관광/숙박숙박여관-여인숙터치모텔터치모텔서울특별시서대문구신촌동
332BE_IW18-0304관광/숙박숙박여관-여인숙테마테마서울특별시광진구화양동
333BE_IW18-0305관광/숙박숙박여관-여인숙테마21테마21서울특별시구로구구로5동
334BE_IW18-0306관광/숙박숙박여관-여인숙테마25시테마25시서울특별시관악구낙성대동
335BE_IW18-0307관광/숙박숙박여관-여인숙테마모텔테마모텔서울특별시동대문구전농1동
336BE_IW18-0308관광/숙박숙박여관-여인숙테마호텔테마호텔서울특별시광진구구의3동
337BE_IW18-0309관광/숙박숙박여관-여인숙투헤븐투헤븐서울특별시종로구종로1.2.3.4가동
338BE_IW18-0310관광/숙박숙박여관-여인숙트리플알트리플알서울특별시강동구길동
339BE_IW18-0311관광/숙박숙박여관-여인숙티모텔티서울특별시강남구삼성2동