Overview

Dataset statistics

Number of variables12
Number of observations200
Missing cells34
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.9 KiB
Average record size in memory101.7 B

Variable types

Text5
Numeric6
Categorical1

Alerts

Y_AXIS is highly overall correlated with HOUS_ID and 1 other fieldsHigh correlation
HOUS_ID is highly overall correlated with Y_AXIS and 1 other fieldsHigh correlation
BLD_CD is highly overall correlated with Y_AXIS and 1 other fieldsHigh correlation
ROOM_CNT has 34 (17.0%) missing valuesMissing
HOTEL_CD has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:39:44.068850
Analysis finished2023-12-10 06:40:05.799402
Duration21.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

HOTEL_CD
Text

UNIQUE 

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:06.289420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1200
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)100.0%

Sample

1st rowM00078
2nd rowH00007
3rd rowG00059
4th rowH01733
5th rowG00054
ValueCountFrequency (%)
m00078 1
 
0.5%
m00305 1
 
0.5%
h01775 1
 
0.5%
m00284 1
 
0.5%
m00309 1
 
0.5%
m03666 1
 
0.5%
g00144 1
 
0.5%
m00159 1
 
0.5%
m00301 1
 
0.5%
h02722 1
 
0.5%
Other values (190) 190
95.0%
2023-12-10T15:40:07.164152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 494
41.2%
M 106
 
8.8%
1 100
 
8.3%
2 72
 
6.0%
7 68
 
5.7%
3 58
 
4.8%
H 50
 
4.2%
9 48
 
4.0%
6 46
 
3.8%
G 44
 
3.7%
Other values (3) 114
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
83.3%
Uppercase Letter 200
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 494
49.4%
1 100
 
10.0%
2 72
 
7.2%
7 68
 
6.8%
3 58
 
5.8%
9 48
 
4.8%
6 46
 
4.6%
4 44
 
4.4%
8 38
 
3.8%
5 32
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
M 106
53.0%
H 50
25.0%
G 44
22.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
83.3%
Latin 200
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 494
49.4%
1 100
 
10.0%
2 72
 
7.2%
7 68
 
6.8%
3 58
 
5.8%
9 48
 
4.8%
6 46
 
4.6%
4 44
 
4.4%
8 38
 
3.8%
5 32
 
3.2%
Latin
ValueCountFrequency (%)
M 106
53.0%
H 50
25.0%
G 44
22.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 494
41.2%
M 106
 
8.8%
1 100
 
8.3%
2 72
 
6.0%
7 68
 
5.7%
3 58
 
4.8%
H 50
 
4.2%
9 48
 
4.0%
6 46
 
3.8%
G 44
 
3.7%
Other values (3) 114
 
9.5%

HOTEL_NM
Text

Distinct197
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:07.768856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length6.695
Min length1

Characters and Unicode

Total characters1339
Distinct characters255
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)97.0%

Sample

1st row독립문호텔
2nd row호텔더디자이너스 종로
3rd row윤스테이
4th row신라스테이 광화문
5th row우 게스트하우스
ValueCountFrequency (%)
호텔 22
 
7.0%
게스트하우스 12
 
3.8%
명동 11
 
3.5%
서울 5
 
1.6%
더 3
 
1.0%
하우스 3
 
1.0%
동대문 3
 
1.0%
호스텔 3
 
1.0%
토요코인 2
 
0.6%
모텔 2
 
0.6%
Other values (240) 247
78.9%
2023-12-10T15:40:08.780523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
113
 
8.4%
스 95
 
7.1%
텔 82
 
6.1%
호 64
 
4.8%
이 47
 
3.5%
하 38
 
2.8%
동 35
 
2.6%
우 33
 
2.5%
트 32
 
2.4%
여 24
 
1.8%
Other values (245) 776
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1127
84.2%
Space Separator 113
 
8.4%
Uppercase Letter 49
 
3.7%
Decimal Number 22
 
1.6%
Lowercase Letter 20
 
1.5%
Letter Number 4
 
0.3%
Other Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 95
 
8.4%
텔 82
 
7.3%
호 64
 
5.7%
이 47
 
4.2%
하 38
 
3.4%
동 35
 
3.1%
우 33
 
2.9%
트 32
 
2.8%
여 24
 
2.1%
관 23
 
2.0%
Other values (198) 654
58.0%
Uppercase Letter
ValueCountFrequency (%)
H 7
14.3%
O 6
12.2%
L 4
 
8.2%
E 4
 
8.2%
S 4
 
8.2%
T 4
 
8.2%
P 3
 
6.1%
B 3
 
6.1%
A 2
 
4.1%
W 2
 
4.1%
Other values (7) 10
20.4%
Lowercase Letter
ValueCountFrequency (%)
s 2
 
10.0%
o 2
 
10.0%
e 2
 
10.0%
c 2
 
10.0%
a 2
 
10.0%
k 1
 
5.0%
r 1
 
5.0%
m 1
 
5.0%
u 1
 
5.0%
h 1
 
5.0%
Other values (5) 5
25.0%
Decimal Number
ValueCountFrequency (%)
2 6
27.3%
1 5
22.7%
8 3
13.6%
7 2
 
9.1%
6 2
 
9.1%
5 2
 
9.1%
3 1
 
4.5%
0 1
 
4.5%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
50.0%
Ⅰ 1
25.0%
Ⅲ 1
25.0%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
' 1
25.0%
. 1
25.0%
Space Separator
ValueCountFrequency (%)
113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1127
84.2%
Common 139
 
10.4%
Latin 73
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 95
 
8.4%
텔 82
 
7.3%
호 64
 
5.7%
이 47
 
4.2%
하 38
 
3.4%
동 35
 
3.1%
우 33
 
2.9%
트 32
 
2.8%
여 24
 
2.1%
관 23
 
2.0%
Other values (198) 654
58.0%
Latin
ValueCountFrequency (%)
H 7
 
9.6%
O 6
 
8.2%
L 4
 
5.5%
E 4
 
5.5%
S 4
 
5.5%
T 4
 
5.5%
P 3
 
4.1%
B 3
 
4.1%
s 2
 
2.7%
o 2
 
2.7%
Other values (25) 34
46.6%
Common
ValueCountFrequency (%)
113
81.3%
2 6
 
4.3%
1 5
 
3.6%
8 3
 
2.2%
7 2
 
1.4%
& 2
 
1.4%
6 2
 
1.4%
5 2
 
1.4%
' 1
 
0.7%
3 1
 
0.7%
Other values (2) 2
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1127
84.2%
ASCII 208
 
15.5%
Number Forms 4
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
54.3%
H 7
 
3.4%
2 6
 
2.9%
O 6
 
2.9%
1 5
 
2.4%
L 4
 
1.9%
E 4
 
1.9%
S 4
 
1.9%
T 4
 
1.9%
P 3
 
1.4%
Other values (34) 52
25.0%
Hangul
ValueCountFrequency (%)
스 95
 
8.4%
텔 82
 
7.3%
호 64
 
5.7%
이 47
 
4.2%
하 38
 
3.4%
동 35
 
3.1%
우 33
 
2.9%
트 32
 
2.8%
여 24
 
2.1%
관 23
 
2.0%
Other values (198) 654
58.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
50.0%
Ⅰ 1
25.0%
Ⅲ 1
25.0%

Y_AXIS
Real number (ℝ)

HIGH CORRELATION 

Distinct186
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean552461.3
Minimum550623
Maximum557024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:09.043371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum550623
5-th percentile551316.55
Q1551699
median552416
Q3552940.5
95-th percentile554069.3
Maximum557024
Range6401
Interquartile range (IQR)1241.5

Descriptive statistics

Standard deviation1023.3873
Coefficient of variation (CV)0.0018524144
Kurtosis4.1083132
Mean552461.3
Median Absolute Deviation (MAD)599.5
Skewness1.5166564
Sum1.1049226 × 108
Variance1047321.5
MonotonicityNot monotonic
2023-12-10T15:40:09.291535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551431 3
 
1.5%
551604 3
 
1.5%
552836 2
 
1.0%
552558 2
 
1.0%
552972 2
 
1.0%
551775 2
 
1.0%
554285 2
 
1.0%
552007 2
 
1.0%
551387 2
 
1.0%
551499 2
 
1.0%
Other values (176) 178
89.0%
ValueCountFrequency (%)
550623 1
0.5%
550826 1
0.5%
550947 2
1.0%
551037 1
0.5%
551165 1
0.5%
551176 1
0.5%
551191 1
0.5%
551213 1
0.5%
551232 1
0.5%
551321 1
0.5%
ValueCountFrequency (%)
557024 1
0.5%
556848 1
0.5%
556791 1
0.5%
555655 1
0.5%
554773 1
0.5%
554642 1
0.5%
554623 1
0.5%
554285 2
1.0%
554132 1
0.5%
554066 1
0.5%

BLK_CD
Real number (ℝ)

Distinct143
Distinct (%)71.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238978.23
Minimum12386
Maximum415376
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:09.527050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12386
5-th percentile34471.35
Q1206553
median207212.5
Q3339596.5
95-th percentile361646.45
Maximum415376
Range402990
Interquartile range (IQR)133043.5

Descriptive statistics

Standard deviation99454.094
Coefficient of variation (CV)0.41616382
Kurtosis-0.076346806
Mean238978.23
Median Absolute Deviation (MAD)12192
Skewness-0.35623302
Sum47795646
Variance9.8911167 × 109
MonotonicityNot monotonic
2023-12-10T15:40:09.793977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200943 8
 
4.0%
349872 7
 
3.5%
282115 4
 
2.0%
338438 4
 
2.0%
12386 4
 
2.0%
207146 3
 
1.5%
206553 3
 
1.5%
206785 3
 
1.5%
337149 3
 
1.5%
337085 3
 
1.5%
Other values (133) 158
79.0%
ValueCountFrequency (%)
12386 4
2.0%
12611 1
 
0.5%
13753 1
 
0.5%
16256 3
1.5%
29424 1
 
0.5%
34737 1
 
0.5%
34791 1
 
0.5%
34839 1
 
0.5%
35187 1
 
0.5%
36026 2
1.0%
ValueCountFrequency (%)
415376 2
1.0%
415108 1
0.5%
414703 1
0.5%
414549 2
1.0%
413561 1
0.5%
411558 1
0.5%
364139 1
0.5%
361655 1
0.5%
361646 2
1.0%
361610 1
0.5%

ADDRESS
Text

Distinct198
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:10.414186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length43
Mean length27.14
Min length19

Characters and Unicode

Total characters5428
Distinct characters181
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique196 ?
Unique (%)98.0%

Sample

1st row서울특별시 종로구 통일로 220 (교북동4543)
2nd row서울특별시 종로구 수표로 89-8 (관수동)
3rd row서울특별시 종로구 사직로 103-9 1층 (필운동)
4th row서울특별시 종로구 삼봉로 71 (수송동)
5th row서울특별시 종로구 삼청로 22 영정빌딩 4층 5층 (사간동)
ValueCountFrequency (%)
서울특별시 200
 
18.4%
종로구 103
 
9.5%
중구 97
 
8.9%
퇴계로 35
 
3.2%
숭인동 19
 
1.8%
수표로22길 14
 
1.3%
낙원동 14
 
1.3%
신당동 11
 
1.0%
수표로 9
 
0.8%
창신동 9
 
0.8%
Other values (367) 574
52.9%
2023-12-10T15:40:11.320253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
886
 
16.3%
로 313
 
5.8%
) 204
 
3.8%
( 204
 
3.8%
구 204
 
3.8%
시 203
 
3.7%
서 202
 
3.7%
울 202
 
3.7%
1 201
 
3.7%
별 200
 
3.7%
Other values (171) 2609
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3112
57.3%
Decimal Number 911
 
16.8%
Space Separator 886
 
16.3%
Close Punctuation 204
 
3.8%
Open Punctuation 204
 
3.8%
Dash Punctuation 77
 
1.4%
Math Symbol 19
 
0.4%
Uppercase Letter 10
 
0.2%
Lowercase Letter 3
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 313
 
10.1%
구 204
 
6.6%
시 203
 
6.5%
서 202
 
6.5%
울 202
 
6.5%
별 200
 
6.4%
특 200
 
6.4%
동 181
 
5.8%
종 127
 
4.1%
길 109
 
3.5%
Other values (145) 1171
37.6%
Decimal Number
ValueCountFrequency (%)
1 201
22.1%
2 164
18.0%
3 105
11.5%
4 102
11.2%
6 78
 
8.6%
5 73
 
8.0%
0 56
 
6.1%
9 51
 
5.6%
7 47
 
5.2%
8 34
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 2
20.0%
L 2
20.0%
I 2
20.0%
G 2
20.0%
A 1
10.0%
K 1
10.0%
Lowercase Letter
ValueCountFrequency (%)
h 1
33.3%
e 1
33.3%
t 1
33.3%
Other Punctuation
ValueCountFrequency (%)
# 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
886
100.0%
Close Punctuation
ValueCountFrequency (%)
) 204
100.0%
Open Punctuation
ValueCountFrequency (%)
( 204
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Math Symbol
ValueCountFrequency (%)
~ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3112
57.3%
Common 2303
42.4%
Latin 13
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 313
 
10.1%
구 204
 
6.6%
시 203
 
6.5%
서 202
 
6.5%
울 202
 
6.5%
별 200
 
6.4%
특 200
 
6.4%
동 181
 
5.8%
종 127
 
4.1%
길 109
 
3.5%
Other values (145) 1171
37.6%
Common
ValueCountFrequency (%)
886
38.5%
) 204
 
8.9%
( 204
 
8.9%
1 201
 
8.7%
2 164
 
7.1%
3 105
 
4.6%
4 102
 
4.4%
6 78
 
3.4%
- 77
 
3.3%
5 73
 
3.2%
Other values (7) 209
 
9.1%
Latin
ValueCountFrequency (%)
B 2
15.4%
L 2
15.4%
I 2
15.4%
G 2
15.4%
h 1
7.7%
e 1
7.7%
A 1
7.7%
K 1
7.7%
t 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3112
57.3%
ASCII 2316
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
886
38.3%
) 204
 
8.8%
( 204
 
8.8%
1 201
 
8.7%
2 164
 
7.1%
3 105
 
4.5%
4 102
 
4.4%
6 78
 
3.4%
- 77
 
3.3%
5 73
 
3.2%
Other values (16) 222
 
9.6%
Hangul
ValueCountFrequency (%)
로 313
 
10.1%
구 204
 
6.6%
시 203
 
6.5%
서 202
 
6.5%
울 202
 
6.5%
별 200
 
6.4%
특 200
 
6.4%
동 181
 
5.8%
종 127
 
4.1%
길 109
 
3.5%
Other values (145) 1171
37.6%

HOTEL_LEVEL
Categorical

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
여관
96 
도시민박
44 
관광호텔
33 
일반호텔
17 
여인숙
10 

Length

Max length4
Median length3
Mean length2.99
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row여관
2nd row일반호텔
3rd row도시민박
4th row관광호텔
5th row도시민박

Common Values

ValueCountFrequency (%)
여관 96
48.0%
도시민박 44
22.0%
관광호텔 33
 
16.5%
일반호텔 17
 
8.5%
여인숙 10
 
5.0%

Length

2023-12-10T15:40:12.195874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:12.429249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관 96
48.0%
도시민박 44
22.0%
관광호텔 33
 
16.5%
일반호텔 17
 
8.5%
여인숙 10
 
5.0%

ROOM_CNT
Real number (ℝ)

MISSING 

Distinct75
Distinct (%)45.2%
Missing34
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean63.325301
Minimum1
Maximum619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:12.653481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median18.5
Q349.5
95-th percentile300.75
Maximum619
Range618
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation106.46475
Coefficient of variation (CV)1.6812355
Kurtosis8.7624742
Mean63.325301
Median Absolute Deviation (MAD)9.5
Skewness2.8395158
Sum10512
Variance11334.742
MonotonicityNot monotonic
2023-12-10T15:40:12.926783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 11
 
5.5%
15 9
 
4.5%
13 8
 
4.0%
12 7
 
3.5%
17 7
 
3.5%
8 6
 
3.0%
16 6
 
3.0%
14 6
 
3.0%
11 5
 
2.5%
20 5
 
2.5%
Other values (65) 96
48.0%
(Missing) 34
 
17.0%
ValueCountFrequency (%)
1 2
 
1.0%
2 2
 
1.0%
3 3
 
1.5%
5 4
 
2.0%
6 1
 
0.5%
7 2
 
1.0%
8 6
3.0%
10 11
5.5%
11 5
2.5%
12 7
3.5%
ValueCountFrequency (%)
619 1
0.5%
576 1
0.5%
430 1
0.5%
409 1
0.5%
408 1
0.5%
339 1
0.5%
337 1
0.5%
310 1
0.5%
305 1
0.5%
288 1
0.5%

HOUS_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct191
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1124696 × 1018
Minimum1.1110101 × 1018
Maximum1.1140173 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:13.170105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 1018
5-th percentile1.1110112 × 1018
Q11.1110153 × 1018
median1.1110183 × 1018
Q31.1140145 × 1018
95-th percentile1.1140165 × 1018
Maximum1.1140173 × 1018
Range3.0072 × 1015
Interquartile range (IQR)2.999225 × 1015

Descriptive statistics

Standard deviation1.5027195 × 1015
Coefficient of variation (CV)0.001350796
Kurtosis-2.0166004
Mean1.1124696 × 1018
Median Absolute Deviation (MAD)8.0500006 × 1012
Skewness0.06047979
Sum1.1329984 × 1018
Variance2.2581659 × 1030
MonotonicityNot monotonic
2023-12-10T15:40:13.432833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114012100001990006 3
 
1.5%
1114017300002310000 2
 
1.0%
1111013700001800000 2
 
1.0%
1114016800001510000 2
 
1.0%
1114015500002910045 2
 
1.0%
1111010100000600000 2
 
1.0%
1114016200002360293 2
 
1.0%
1114013200001200003 2
 
1.0%
1111018000000010012 1
 
0.5%
1114016000000940001 1
 
0.5%
Other values (181) 181
90.5%
ValueCountFrequency (%)
1111010100000600000 2
1.0%
1111010200000200001 1
0.5%
1111010200000550000 1
0.5%
1111010200000750001 1
0.5%
1111010800001060000 1
0.5%
1111010800001470004 1
0.5%
1111010800001550000 1
0.5%
1111011000000930000 1
0.5%
1111011100000190044 1
0.5%
1111011200000480000 1
0.5%
ValueCountFrequency (%)
1114017300002310000 2
1.0%
1114017300000620097 1
0.5%
1114017100001280024 1
0.5%
1114017100000770001 1
0.5%
1114017100000660000 1
0.5%
1114016800001510000 2
1.0%
1114016500025130000 1
0.5%
1114016500007060002 1
0.5%
1114016500000480000 1
0.5%
1114016500000170000 1
0.5%

BLD_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1124696 × 1024
Minimum1.1110101 × 1024
Maximum1.1140173 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:13.718503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 1024
5-th percentile1.1110112 × 1024
Q11.1110153 × 1024
median1.1110183 × 1024
Q31.1140145 × 1024
95-th percentile1.1140165 × 1024
Maximum1.1140173 × 1024
Range3.0072 × 1021
Interquartile range (IQR)2.999225 × 1021

Descriptive statistics

Standard deviation1.5027195 × 1021
Coefficient of variation (CV)0.001350796
Kurtosis-2.0166004
Mean1.1124696 × 1024
Median Absolute Deviation (MAD)8.0500005 × 1018
Skewness0.06047979
Sum2.2249393 × 1026
Variance2.2581659 × 1042
MonotonicityNot monotonic
2023-12-10T15:40:14.019172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11101750010178e+24 4
 
2.0%
1.11401730010062e+24 3
 
1.5%
1.11401210010199e+24 3
 
1.5%
1.11101740010448e+24 3
 
1.5%
1.111017500102e+24 3
 
1.5%
1.11401250010062e+24 2
 
1.0%
1.11401240010024e+24 2
 
1.0%
1.11101740010449e+24 2
 
1.0%
1.11401210010194e+24 2
 
1.0%
1.1140132001012e+24 2
 
1.0%
Other values (163) 174
87.0%
ValueCountFrequency (%)
1.1110101001006e+24 2
1.0%
1.1110102001002e+24 1
0.5%
1.11101020010055e+24 1
0.5%
1.11101020010075e+24 1
0.5%
1.11101080010106e+24 1
0.5%
1.11101080010147e+24 1
0.5%
1.11101080010155e+24 1
0.5%
1.11101100010093e+24 1
0.5%
1.11101110010019e+24 1
0.5%
1.11101120010048e+24 1
0.5%
ValueCountFrequency (%)
1.11401730010062e+24 3
1.5%
1.11401710010128e+24 1
 
0.5%
1.11401710010077e+24 1
 
0.5%
1.11401710010066e+24 1
 
0.5%
1.11401680010195e+24 2
1.0%
1.11401650012513e+24 1
 
0.5%
1.11401650010714e+24 1
 
0.5%
1.11401650010048e+24 1
 
0.5%
1.11401650010018e+24 1
 
0.5%
1.11401620010773e+24 1
 
0.5%

HOUS_ADDR
Text

Distinct191
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:14.669860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length20.37
Min length17

Characters and Unicode

Total characters4074
Distinct characters96
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

Unique183 ?
Unique (%)91.5%

Sample

1st row서울특별시 종로구 교북동 1-12번지
2nd row서울특별시 종로구 관수동 14-1번지
3rd row서울특별시 종로구 필운동 210-1번지
4th row서울특별시 종로구 수송동 156번지
5th row서울특별시 종로구 사간동 66번지
ValueCountFrequency (%)
서울특별시 200
25.0%
종로구 103
 
12.9%
중구 97
 
12.1%
숭인동 19
 
2.4%
낙원동 16
 
2.0%
신당동 11
 
1.4%
창신동 9
 
1.1%
광희동2가 6
 
0.8%
관훈동 6
 
0.8%
충무로3가 5
 
0.6%
Other values (246) 328
41.0%
2023-12-10T15:40:15.516798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
600
 
14.7%
1 207
 
5.1%
지 207
 
5.1%
구 201
 
4.9%
서 200
 
4.9%
울 200
 
4.9%
시 200
 
4.9%
별 200
 
4.9%
특 200
 
4.9%
번 200
 
4.9%
Other values (86) 1659
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2552
62.6%
Decimal Number 783
 
19.2%
Space Separator 600
 
14.7%
Dash Punctuation 139
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 207
 
8.1%
구 201
 
7.9%
서 200
 
7.8%
울 200
 
7.8%
시 200
 
7.8%
별 200
 
7.8%
특 200
 
7.8%
번 200
 
7.8%
동 161
 
6.3%
로 135
 
5.3%
Other values (74) 648
25.4%
Decimal Number
ValueCountFrequency (%)
1 207
26.4%
2 112
14.3%
3 76
 
9.7%
4 72
 
9.2%
5 69
 
8.8%
6 62
 
7.9%
9 52
 
6.6%
0 49
 
6.3%
7 44
 
5.6%
8 40
 
5.1%
Space Separator
ValueCountFrequency (%)
600
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2552
62.6%
Common 1522
37.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 207
 
8.1%
구 201
 
7.9%
서 200
 
7.8%
울 200
 
7.8%
시 200
 
7.8%
별 200
 
7.8%
특 200
 
7.8%
번 200
 
7.8%
동 161
 
6.3%
로 135
 
5.3%
Other values (74) 648
25.4%
Common
ValueCountFrequency (%)
600
39.4%
1 207
 
13.6%
- 139
 
9.1%
2 112
 
7.4%
3 76
 
5.0%
4 72
 
4.7%
5 69
 
4.5%
6 62
 
4.1%
9 52
 
3.4%
0 49
 
3.2%
Other values (2) 84
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2552
62.6%
ASCII 1522
37.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
600
39.4%
1 207
 
13.6%
- 139
 
9.1%
2 112
 
7.4%
3 76
 
5.0%
4 72
 
4.7%
5 69
 
4.5%
6 62
 
4.1%
9 52
 
3.4%
0 49
 
3.2%
Other values (2) 84
 
5.5%
Hangul
ValueCountFrequency (%)
지 207
 
8.1%
구 201
 
7.9%
서 200
 
7.8%
울 200
 
7.8%
시 200
 
7.8%
별 200
 
7.8%
특 200
 
7.8%
번 200
 
7.8%
동 161
 
6.3%
로 135
 
5.3%
Other values (74) 648
25.4%

ROAD_ADDR
Text

Distinct191
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:40:16.105640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.16
Min length14

Characters and Unicode

Total characters3632
Distinct characters88
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

Unique183 ?
Unique (%)91.5%

Sample

1st row서울특별시 종로구 통일로 220
2nd row서울특별시 종로구 수표로 89-8
3rd row서울특별시 종로구 사직로 103-9
4th row서울특별시 종로구 삼봉로 71
5th row서울특별시 종로구 삼청로 22
ValueCountFrequency (%)
서울특별시 200
25.0%
종로구 103
 
12.9%
중구 97
 
12.1%
퇴계로 35
 
4.4%
수표로22길 14
 
1.8%
수표로 9
 
1.1%
난계로29길 8
 
1.0%
충무로 7
 
0.9%
6 7
 
0.9%
9 6
 
0.8%
Other values (220) 314
39.2%
2023-12-10T15:40:16.915561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
600
16.5%
로 282
 
7.8%
시 203
 
5.6%
구 202
 
5.6%
울 200
 
5.5%
서 200
 
5.5%
별 200
 
5.5%
특 200
 
5.5%
1 143
 
3.9%
종 125
 
3.4%
Other values (78) 1277
35.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2263
62.3%
Decimal Number 693
 
19.1%
Space Separator 600
 
16.5%
Dash Punctuation 76
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 282
12.5%
시 203
9.0%
구 202
8.9%
울 200
8.8%
서 200
8.8%
별 200
8.8%
특 200
8.8%
종 125
 
5.5%
길 109
 
4.8%
중 97
 
4.3%
Other values (66) 445
19.7%
Decimal Number
ValueCountFrequency (%)
1 143
20.6%
2 120
17.3%
3 78
11.3%
6 73
10.5%
4 71
10.2%
5 54
 
7.8%
9 46
 
6.6%
7 39
 
5.6%
0 38
 
5.5%
8 31
 
4.5%
Space Separator
ValueCountFrequency (%)
600
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2263
62.3%
Common 1369
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 282
12.5%
시 203
9.0%
구 202
8.9%
울 200
8.8%
서 200
8.8%
별 200
8.8%
특 200
8.8%
종 125
 
5.5%
길 109
 
4.8%
중 97
 
4.3%
Other values (66) 445
19.7%
Common
ValueCountFrequency (%)
600
43.8%
1 143
 
10.4%
2 120
 
8.8%
3 78
 
5.7%
- 76
 
5.6%
6 73
 
5.3%
4 71
 
5.2%
5 54
 
3.9%
9 46
 
3.4%
7 39
 
2.8%
Other values (2) 69
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2263
62.3%
ASCII 1369
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
600
43.8%
1 143
 
10.4%
2 120
 
8.8%
3 78
 
5.7%
- 76
 
5.6%
6 73
 
5.3%
4 71
 
5.2%
5 54
 
3.9%
9 46
 
3.4%
7 39
 
2.8%
Other values (2) 69
 
5.0%
Hangul
ValueCountFrequency (%)
로 282
12.5%
시 203
9.0%
구 202
8.9%
울 200
8.8%
서 200
8.8%
별 200
8.8%
특 200
8.8%
종 125
 
5.5%
길 109
 
4.8%
중 97
 
4.3%
Other values (66) 445
19.7%

X_AXIS
Real number (ℝ)

Distinct184
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311270.68
Minimum308019
Maximum313851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:40:17.157515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum308019
5-th percentile309093.7
Q1310257.75
median311030
Q3312545.5
95-th percentile313674.35
Maximum313851
Range5832
Interquartile range (IQR)2287.75

Descriptive statistics

Standard deviation1446.5045
Coefficient of variation (CV)0.0046470951
Kurtosis-0.83953074
Mean311270.68
Median Absolute Deviation (MAD)1001
Skewness0.12254412
Sum62254137
Variance2092375.2
MonotonicityNot monotonic
2023-12-10T15:40:17.394301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310029 3
 
1.5%
311489 2
 
1.0%
311040 2
 
1.0%
310943 2
 
1.0%
311205 2
 
1.0%
310967 2
 
1.0%
312854 2
 
1.0%
313828 2
 
1.0%
311008 2
 
1.0%
311247 2
 
1.0%
Other values (174) 179
89.5%
ValueCountFrequency (%)
308019 1
0.5%
308295 1
0.5%
308345 1
0.5%
308905 1
0.5%
308924 2
1.0%
308993 1
0.5%
309066 1
0.5%
309077 1
0.5%
309088 1
0.5%
309094 1
0.5%
ValueCountFrequency (%)
313851 1
0.5%
313832 1
0.5%
313829 1
0.5%
313828 2
1.0%
313725 1
0.5%
313694 1
0.5%
313691 1
0.5%
313690 1
0.5%
313681 1
0.5%
313674 1
0.5%

Interactions

2023-12-10T15:40:02.954440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:44.952672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:47.984558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:50.798014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:52.905061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:55.224611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:03.107612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:45.091664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:48.148805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:50.952165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:53.084108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:56.819363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:03.275608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:45.275896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:48.322089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:51.101945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:53.280416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:57.794372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:03.442086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:45.419578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:48.468302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:51.254396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:53.464440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:58.612005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:03.630584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:45.604465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:48.983931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:51.404362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:53.703093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:59.624576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:05.125872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:47.827158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:50.631630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:52.742119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:39:55.089837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:01.937570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:40:17.555336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Y_AXISBLK_CDHOTEL_LEVELROOM_CNTHOUS_IDBLD_CDX_AXIS
Y_AXIS1.0000.5850.5260.2470.8800.8800.705
BLK_CD0.5851.0000.4020.0000.9550.9550.497
HOTEL_LEVEL0.5260.4021.0000.5690.3220.3220.617
ROOM_CNT0.2470.0000.5691.0000.5070.5070.174
HOUS_ID0.8800.9550.3220.5071.0001.0000.344
BLD_CD0.8800.9550.3220.5071.0001.0000.344
X_AXIS0.7050.4970.6170.1740.3440.3441.000
2023-12-10T15:40:17.737596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Y_AXISBLK_CDROOM_CNTHOUS_IDBLD_CDX_AXISHOTEL_LEVEL
Y_AXIS1.0000.289-0.379-0.747-0.7470.0380.334
BLK_CD0.2891.000-0.192-0.207-0.207-0.0730.252
ROOM_CNT-0.379-0.1921.0000.1390.139-0.2400.391
HOUS_ID-0.747-0.2070.1391.0001.0000.2970.395
BLD_CD-0.747-0.2070.1391.0001.0000.2970.141
X_AXIS0.038-0.073-0.2400.2970.2971.0000.298
HOTEL_LEVEL0.3340.2520.3910.3950.1410.2981.000

Missing values

2023-12-10T15:40:05.336836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:40:05.668133image/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

HOTEL_CDHOTEL_NMY_AXISBLK_CDADDRESSHOTEL_LEVELROOM_CNTHOUS_IDBLD_CDHOUS_ADDRROAD_ADDRX_AXIS
0M00078독립문호텔552781253589서울특별시 종로구 통일로 220 (교북동4543)여관3711110180000000100121111018000100010053019463서울특별시 종로구 교북동 1-12번지서울특별시 종로구 통일로 220308345
1H00007호텔더디자이너스 종로552381350549서울특별시 종로구 수표로 89-8 (관수동)일반호텔8111110155000001400011111015500100140001015064서울특별시 종로구 관수동 14-1번지서울특별시 종로구 수표로 89-8310888
2G00059윤스테이553185355837서울특별시 종로구 사직로 103-9 1층 (필운동)도시민박<NA>11110113000021000011111011300102100001028633서울특별시 종로구 필운동 210-1번지서울특별시 종로구 사직로 103-9309224
3H01733신라스테이 광화문552755219506서울특별시 종로구 삼봉로 71 (수송동)관광호텔33911110124000015600001111012400100510008015226서울특별시 종로구 수송동 156번지서울특별시 종로구 삼봉로 71310193
4G00054우 게스트하우스553339413561서울특별시 종로구 삼청로 22 영정빌딩 4층 5층 (사간동)도시민박511110144000006600001111014400100690000026531서울특별시 종로구 사간동 66번지서울특별시 종로구 삼청로 22310089
5G0005288 게스트하우스553840360777서울특별시 종로구 삼청로 75-9 (팔판동)도시민박511110139000009100001111013900100910000026175서울특별시 종로구 팔판동 91번지서울특별시 종로구 삼청로 75-9310214
6H01736아벤트리호텔종로점552801207932서울특별시 종로구 우정국로 46 (견지동)관광호텔15511110129000006500011111012900100650001015195서울특별시 종로구 견지동 65-1번지서울특별시 종로구 우정국로 46310390
7M00073아로마553235360742서울특별시 종로구 자하문로 9 (체부동)여관2311110112000004800001111011200100480000028853서울특별시 종로구 체부동 48번지서울특별시 종로구 자하문로 9309405
8G00083아뜰리에웨스트사이드553485352965서울특별시 종로구 자하문로 35-6 3층 (통인동 인왕사우나)도시민박<NA>11110108000014700041111010800101470004030543서울특별시 종로구 통인동 147-4번지서울특별시 종로구 자하문로 35-6309300
9G00069TONGIN 155553430156094서울특별시 종로구 자하문로 31-5 2층 (통인동)도시민박311110108000015500001111010800101550000030512서울특별시 종로구 통인동 155번지서울특별시 종로구 자하문로 31-5309314
HOTEL_CDHOTEL_NMY_AXISBLK_CDADDRESSHOTEL_LEVELROOM_CNTHOUS_IDBLD_CDHOUS_ADDRROAD_ADDRX_AXIS
190M00293S모텔551539208123서울특별시 중구 다산로42나길 53-1 (신당동)여관1111140162000028100341114016200102810034003634서울특별시 중구 신당동 281-34번지서울특별시 중구 다산로42나길 53-1313400
191M00281수정여관551862208039서울특별시 중구 다산로44길 30-2 (신당동)여관711140162000012100151114016200101210015004709서울특별시 중구 신당동 121-15번지서울특별시 중구 다산로44길 30-2313501
192M00303유니온장여관551893208031서울특별시 중구 다산로44길 3-5 (신당동)여관1711140162000011000051114016200101100005004575서울특별시 중구 신당동 110-5번지서울특별시 중구 다산로44길 3-5313380
193M00163화신여관551917207122서울특별시 중구 동호로34길 15 (광희동1가)여관1511140145000000100041114014500100010004014233서울특별시 중구 광희동1가 1-4번지서울특별시 중구 동호로34길 15312156
194G00139코지스테이552439336979서울특별시 중구 마장로9길 49-19 45층 (황학동)도시민박<NA>11140165000001700001114016500100180000001824서울특별시 중구 황학동 17번지서울특별시 중구 마장로9길 49-19313484
195M00210중앙여관552386208166서울특별시 중구 마장로9길 33-32 (황학동)여관1311140165000004800001114016500100480000001908서울특별시 중구 황학동 48번지서울특별시 중구 마장로9길 33-32313493
196G00198하하홈스테이550947415376서울특별시 중구 만리재로33길 21 101동 801호 (만리동1가 LIG서울역리가)도시민박<NA>11140173000023100001114017300100620003000001서울특별시 중구 만리동1가 231번지서울특별시 중구 만리재로33길 21308924
197G00182서울역리가 스카이 홈스테이550947415376서울특별시 중구 만리재로33길 21 103동 1304호 (만리동1가 LIG서울역리가)도시민박<NA>11140173000023100001114017300100620003000001서울특별시 중구 만리동1가 231번지서울특별시 중구 만리재로33길 21308924
198G00192Jin's cozy house550826415108서울특별시 중구 만리재로33길 11-1 (만리동1가 구립만리어린이집)도시민박511140173000006200971114017300100620097000847서울특별시 중구 만리동1가 62-97번지서울특별시 중구 만리재로33길 11-1308905
199H00025아트래블551592206272서울특별시 중구 명동4길 31 4층 (명동2가)일반호텔711140127000005300111114012700100530011019806서울특별시 중구 명동2가 53-11번지서울특별시 중구 명동4길 31310443