Overview

Dataset statistics

Number of variables47
Number of observations170
Missing cells1861
Missing cells (%)23.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.2 KiB
Average record size in memory404.8 B

Variable types

Categorical21
Text7
DateTime3
Unsupported7
Numeric7
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author용산구
URLhttps://data.seoul.go.kr/dataList/OA-19954/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
데이터갱신일자 is highly imbalanced (71.9%)Imbalance
업태구분명 is highly imbalanced (64.5%)Imbalance
위생업태명 is highly imbalanced (54.4%)Imbalance
사용끝지하층 is highly imbalanced (62.1%)Imbalance
건물소유구분명 is highly imbalanced (76.4%)Imbalance
세탁기수 is highly imbalanced (56.9%)Imbalance
여성종사자수 is highly imbalanced (80.9%)Imbalance
남성종사자수 is highly imbalanced (86.6%)Imbalance
회수건조수 is highly imbalanced (56.9%)Imbalance
침대수 is highly imbalanced (63.2%)Imbalance
다중이용업소여부 is highly imbalanced (90.2%)Imbalance
인허가취소일자 has 170 (100.0%) missing valuesMissing
폐업일자 has 26 (15.3%) missing valuesMissing
휴업시작일자 has 170 (100.0%) missing valuesMissing
휴업종료일자 has 170 (100.0%) missing valuesMissing
재개업일자 has 170 (100.0%) missing valuesMissing
전화번호 has 7 (4.1%) missing valuesMissing
도로명주소 has 117 (68.8%) missing valuesMissing
도로명우편번호 has 118 (69.4%) missing valuesMissing
좌표정보(X) has 15 (8.8%) missing valuesMissing
좌표정보(Y) has 15 (8.8%) missing valuesMissing
건물지상층수 has 53 (31.2%) missing valuesMissing
사용시작지상층 has 70 (41.2%) missing valuesMissing
사용끝지상층 has 125 (73.5%) missing valuesMissing
욕실수 has 98 (57.6%) missing valuesMissing
발한실여부 has 13 (7.6%) missing valuesMissing
조건부허가신고사유 has 170 (100.0%) missing valuesMissing
조건부허가시작일자 has 170 (100.0%) missing valuesMissing
조건부허가종료일자 has 170 (100.0%) missing valuesMissing
다중이용업소여부 has 13 (7.6%) missing valuesMissing
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가신고사유 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 64 (37.6%) zerosZeros
사용시작지상층 has 55 (32.4%) zerosZeros
욕실수 has 61 (35.9%) zerosZeros

Reproduction

Analysis started2024-05-11 06:40:39.879348
Analysis finished2024-05-11 06:40:40.898799
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
3020000
170 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000
2nd row3020000
3rd row3020000
4th row3020000
5th row3020000

Common Values

ValueCountFrequency (%)
3020000 170
100.0%

Length

2024-05-11T15:40:41.029764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:41.256172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000 170
100.0%

관리번호
Text

UNIQUE 

Distinct170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-11T15:40:41.548801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3740
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

Unique170 ?
Unique (%)100.0%

Sample

1st row3020000-202-1900-00001
2nd row3020000-202-1900-00002
3rd row3020000-202-1900-00003
4th row3020000-202-1900-00004
5th row3020000-202-1900-00005
ValueCountFrequency (%)
3020000-202-1900-00001 1
 
0.6%
3020000-202-2001-00374 1
 
0.6%
3020000-202-1999-00277 1
 
0.6%
3020000-202-2002-00001 1
 
0.6%
3020000-202-1999-00278 1
 
0.6%
3020000-202-1999-00279 1
 
0.6%
3020000-202-2000-00372 1
 
0.6%
3020000-202-2000-00373 1
 
0.6%
3020000-202-2000-00374 1
 
0.6%
3020000-202-2000-00375 1
 
0.6%
Other values (160) 160
94.1%
2024-05-11T15:40:42.061608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1623
43.4%
2 661
17.7%
- 510
 
13.6%
3 305
 
8.2%
1 182
 
4.9%
9 168
 
4.5%
8 85
 
2.3%
7 69
 
1.8%
6 60
 
1.6%
4 39
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3230
86.4%
Dash Punctuation 510
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1623
50.2%
2 661
20.5%
3 305
 
9.4%
1 182
 
5.6%
9 168
 
5.2%
8 85
 
2.6%
7 69
 
2.1%
6 60
 
1.9%
4 39
 
1.2%
5 38
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1623
43.4%
2 661
17.7%
- 510
 
13.6%
3 305
 
8.2%
1 182
 
4.9%
9 168
 
4.5%
8 85
 
2.3%
7 69
 
1.8%
6 60
 
1.6%
4 39
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1623
43.4%
2 661
17.7%
- 510
 
13.6%
3 305
 
8.2%
1 182
 
4.9%
9 168
 
4.5%
8 85
 
2.3%
7 69
 
1.8%
6 60
 
1.6%
4 39
 
1.0%
Distinct153
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1900-01-01 00:00:00
Maximum2018-08-13 00:00:00
2024-05-11T15:40:42.300518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:40:42.539464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
3
144 
1
26 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 144
84.7%
1 26
 
15.3%

Length

2024-05-11T15:40:42.807776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:42.995369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 144
84.7%
1 26
 
15.3%

영업상태명
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
폐업
144 
영업/정상
26 

Length

Max length5
Median length2
Mean length2.4588235
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 144
84.7%
영업/정상 26
 
15.3%

Length

2024-05-11T15:40:43.201460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:43.424771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 144
84.7%
영업/정상 26
 
15.3%
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2
144 
1
26 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 144
84.7%
1 26
 
15.3%

Length

2024-05-11T15:40:43.692766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:43.867239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 144
84.7%
1 26
 
15.3%
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
폐업
144 
영업
26 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 144
84.7%
영업 26
 
15.3%

Length

2024-05-11T15:40:44.071424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:44.274997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 144
84.7%
영업 26
 
15.3%

폐업일자
Date

MISSING 

Distinct139
Distinct (%)96.5%
Missing26
Missing (%)15.3%
Memory size1.5 KiB
Minimum1900-01-01 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T15:40:44.478772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:40:44.706191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB

전화번호
Text

MISSING 

Distinct156
Distinct (%)95.7%
Missing7
Missing (%)4.1%
Memory size1.5 KiB
2024-05-11T15:40:45.109263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.6441718
Min length6

Characters and Unicode

Total characters1572
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

Unique149 ?
Unique (%)91.4%

Sample

1st row7570020
2nd row7962803
3rd row7948970
4th row7929605
5th row7190389
ValueCountFrequency (%)
02 89
33.2%
749 3
 
1.1%
7908864 2
 
0.7%
794 2
 
0.7%
790 2
 
0.7%
7040013 2
 
0.7%
7929605 2
 
0.7%
797 2
 
0.7%
7935515 2
 
0.7%
7568531 2
 
0.7%
Other values (157) 160
59.7%
2024-05-11T15:40:46.108113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 308
19.6%
7 232
14.8%
2 216
13.7%
9 143
9.1%
120
 
7.6%
1 114
 
7.3%
5 103
 
6.6%
3 88
 
5.6%
8 87
 
5.5%
4 82
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1452
92.4%
Space Separator 120
 
7.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 308
21.2%
7 232
16.0%
2 216
14.9%
9 143
9.8%
1 114
 
7.9%
5 103
 
7.1%
3 88
 
6.1%
8 87
 
6.0%
4 82
 
5.6%
6 79
 
5.4%
Space Separator
ValueCountFrequency (%)
120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1572
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 308
19.6%
7 232
14.8%
2 216
13.7%
9 143
9.1%
120
 
7.6%
1 114
 
7.3%
5 103
 
6.6%
3 88
 
5.6%
8 87
 
5.5%
4 82
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 308
19.6%
7 232
14.8%
2 216
13.7%
9 143
9.1%
120
 
7.6%
1 114
 
7.3%
5 103
 
6.6%
3 88
 
5.6%
8 87
 
5.5%
4 82
 
5.2%
Distinct122
Distinct (%)72.2%
Missing1
Missing (%)0.6%
Memory size1.5 KiB
2024-05-11T15:40:46.625146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.5029586
Min length3

Characters and Unicode

Total characters930
Distinct characters12
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

Unique112 ?
Unique (%)66.3%

Sample

1st row.00
2nd row.00
3rd row.00
4th row.00
5th row.00
ValueCountFrequency (%)
00 35
 
20.7%
198.00 4
 
2.4%
231.00 4
 
2.4%
315.05 2
 
1.2%
240.11 2
 
1.2%
297.00 2
 
1.2%
264.00 2
 
1.2%
165.00 2
 
1.2%
102.91 2
 
1.2%
330.00 2
 
1.2%
Other values (112) 112
66.3%
2024-05-11T15:40:47.396533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 235
25.3%
. 169
18.2%
1 91
 
9.8%
3 70
 
7.5%
2 63
 
6.8%
5 54
 
5.8%
4 54
 
5.8%
8 49
 
5.3%
9 46
 
4.9%
7 44
 
4.7%
Other values (2) 55
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 750
80.6%
Other Punctuation 180
 
19.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 235
31.3%
1 91
 
12.1%
3 70
 
9.3%
2 63
 
8.4%
5 54
 
7.2%
4 54
 
7.2%
8 49
 
6.5%
9 46
 
6.1%
7 44
 
5.9%
6 44
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 169
93.9%
, 11
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 235
25.3%
. 169
18.2%
1 91
 
9.8%
3 70
 
7.5%
2 63
 
6.8%
5 54
 
5.8%
4 54
 
5.8%
8 49
 
5.3%
9 46
 
4.9%
7 44
 
4.7%
Other values (2) 55
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 235
25.3%
. 169
18.2%
1 91
 
9.8%
3 70
 
7.5%
2 63
 
6.8%
5 54
 
5.8%
4 54
 
5.8%
8 49
 
5.3%
9 46
 
4.9%
7 44
 
4.7%
Other values (2) 55
 
5.9%
Distinct75
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-11T15:40:47.890446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0588235
Min length6

Characters and Unicode

Total characters1030
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

Unique35 ?
Unique (%)20.6%

Sample

1st row140821
2nd row140842
3rd row140841
4th row140807
5th row140850
ValueCountFrequency (%)
140892 11
 
6.5%
140893 10
 
5.9%
140823 7
 
4.1%
140858 6
 
3.5%
140889 6
 
3.5%
140860 5
 
2.9%
140879 4
 
2.4%
140842 4
 
2.4%
140090 4
 
2.4%
140818 4
 
2.4%
Other values (65) 109
64.1%
2024-05-11T15:40:48.804349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 235
22.8%
1 201
19.5%
4 195
18.9%
8 164
15.9%
9 62
 
6.0%
2 47
 
4.6%
3 45
 
4.4%
7 27
 
2.6%
6 23
 
2.2%
5 21
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1020
99.0%
Dash Punctuation 10
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 235
23.0%
1 201
19.7%
4 195
19.1%
8 164
16.1%
9 62
 
6.1%
2 47
 
4.6%
3 45
 
4.4%
7 27
 
2.6%
6 23
 
2.3%
5 21
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1030
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 235
22.8%
1 201
19.5%
4 195
18.9%
8 164
15.9%
9 62
 
6.0%
2 47
 
4.6%
3 45
 
4.4%
7 27
 
2.6%
6 23
 
2.2%
5 21
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 235
22.8%
1 201
19.5%
4 195
18.9%
8 164
15.9%
9 62
 
6.0%
2 47
 
4.6%
3 45
 
4.4%
7 27
 
2.6%
6 23
 
2.2%
5 21
 
2.0%
Distinct144
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-11T15:40:49.384230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length40
Mean length23.876471
Min length19

Characters and Unicode

Total characters4059
Distinct characters93
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

Unique122 ?
Unique (%)71.8%

Sample

1st row서울특별시 용산구 동자동 35-44번지
2nd row서울특별시 용산구 용산동2가 47-1번지
3rd row서울특별시 용산구 용산동2가 18-4번지
4th row서울특별시 용산구 갈월동 98-38번지
5th row서울특별시 용산구 원효로4가 87-5번지
ValueCountFrequency (%)
서울특별시 170
23.6%
용산구 170
23.6%
한남동 38
 
5.3%
이태원동 22
 
3.1%
보광동 14
 
1.9%
용산동2가 11
 
1.5%
동자동 10
 
1.4%
한강로3가 9
 
1.2%
지하1층 9
 
1.2%
후암동 8
 
1.1%
Other values (174) 260
36.1%
2024-05-11T15:40:50.210356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
706
 
17.4%
188
 
4.6%
185
 
4.6%
176
 
4.3%
174
 
4.3%
170
 
4.2%
170
 
4.2%
170
 
4.2%
170
 
4.2%
170
 
4.2%
Other values (83) 1780
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2402
59.2%
Decimal Number 765
 
18.8%
Space Separator 706
 
17.4%
Dash Punctuation 160
 
3.9%
Open Punctuation 9
 
0.2%
Close Punctuation 9
 
0.2%
Other Punctuation 7
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
188
 
7.8%
185
 
7.7%
176
 
7.3%
174
 
7.2%
170
 
7.1%
170
 
7.1%
170
 
7.1%
170
 
7.1%
170
 
7.1%
157
 
6.5%
Other values (66) 672
28.0%
Decimal Number
ValueCountFrequency (%)
1 139
18.2%
2 131
17.1%
3 107
14.0%
6 68
8.9%
4 64
8.4%
7 64
8.4%
5 53
 
6.9%
8 52
 
6.8%
0 46
 
6.0%
9 41
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 6
85.7%
. 1
 
14.3%
Space Separator
ValueCountFrequency (%)
706
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 160
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2402
59.2%
Common 1657
40.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
188
 
7.8%
185
 
7.7%
176
 
7.3%
174
 
7.2%
170
 
7.1%
170
 
7.1%
170
 
7.1%
170
 
7.1%
170
 
7.1%
157
 
6.5%
Other values (66) 672
28.0%
Common
ValueCountFrequency (%)
706
42.6%
- 160
 
9.7%
1 139
 
8.4%
2 131
 
7.9%
3 107
 
6.5%
6 68
 
4.1%
4 64
 
3.9%
7 64
 
3.9%
5 53
 
3.2%
8 52
 
3.1%
Other values (7) 113
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2402
59.2%
ASCII 1657
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
706
42.6%
- 160
 
9.7%
1 139
 
8.4%
2 131
 
7.9%
3 107
 
6.5%
6 68
 
4.1%
4 64
 
3.9%
7 64
 
3.9%
5 53
 
3.2%
8 52
 
3.1%
Other values (7) 113
 
6.8%
Hangul
ValueCountFrequency (%)
188
 
7.8%
185
 
7.7%
176
 
7.3%
174
 
7.2%
170
 
7.1%
170
 
7.1%
170
 
7.1%
170
 
7.1%
170
 
7.1%
157
 
6.5%
Other values (66) 672
28.0%

도로명주소
Text

MISSING 

Distinct51
Distinct (%)96.2%
Missing117
Missing (%)68.8%
Memory size1.5 KiB
2024-05-11T15:40:50.666550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length34
Mean length28.358491
Min length22

Characters and Unicode

Total characters1503
Distinct characters108
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

Unique49 ?
Unique (%)92.5%

Sample

1st row서울특별시 용산구 서빙고로5길 7 (한강로2가)
2nd row서울특별시 용산구 보광로 63 (보광동)
3rd row서울특별시 용산구 후암로 29 (후암동,2층)
4th row서울특별시 용산구 원효로 123-12 (원효로3가)
5th row서울특별시 용산구 이촌로18길 11-6 (이촌동)
ValueCountFrequency (%)
서울특별시 53
 
18.6%
용산구 53
 
18.6%
후암로 7
 
2.5%
한남동 5
 
1.8%
후암동 5
 
1.8%
보광동 5
 
1.8%
이태원동 4
 
1.4%
이태원로 4
 
1.4%
이촌동 3
 
1.1%
용산동2가 3
 
1.1%
Other values (110) 143
50.2%
2024-05-11T15:40:51.589007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
232
 
15.4%
61
 
4.1%
60
 
4.0%
58
 
3.9%
57
 
3.8%
) 56
 
3.7%
( 56
 
3.7%
54
 
3.6%
54
 
3.6%
53
 
3.5%
Other values (98) 762
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 927
61.7%
Space Separator 232
 
15.4%
Decimal Number 203
 
13.5%
Close Punctuation 56
 
3.7%
Open Punctuation 56
 
3.7%
Other Punctuation 24
 
1.6%
Dash Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
61
 
6.6%
60
 
6.5%
58
 
6.3%
57
 
6.1%
54
 
5.8%
54
 
5.8%
53
 
5.7%
53
 
5.7%
53
 
5.7%
50
 
5.4%
Other values (83) 374
40.3%
Decimal Number
ValueCountFrequency (%)
1 48
23.6%
2 37
18.2%
3 25
12.3%
0 17
 
8.4%
4 17
 
8.4%
9 16
 
7.9%
5 14
 
6.9%
7 12
 
5.9%
8 10
 
4.9%
6 7
 
3.4%
Space Separator
ValueCountFrequency (%)
232
100.0%
Close Punctuation
ValueCountFrequency (%)
) 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Other Punctuation
ValueCountFrequency (%)
, 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 927
61.7%
Common 576
38.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
61
 
6.6%
60
 
6.5%
58
 
6.3%
57
 
6.1%
54
 
5.8%
54
 
5.8%
53
 
5.7%
53
 
5.7%
53
 
5.7%
50
 
5.4%
Other values (83) 374
40.3%
Common
ValueCountFrequency (%)
232
40.3%
) 56
 
9.7%
( 56
 
9.7%
1 48
 
8.3%
2 37
 
6.4%
3 25
 
4.3%
, 24
 
4.2%
0 17
 
3.0%
4 17
 
3.0%
9 16
 
2.8%
Other values (5) 48
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 927
61.7%
ASCII 576
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
232
40.3%
) 56
 
9.7%
( 56
 
9.7%
1 48
 
8.3%
2 37
 
6.4%
3 25
 
4.3%
, 24
 
4.2%
0 17
 
3.0%
4 17
 
3.0%
9 16
 
2.8%
Other values (5) 48
 
8.3%
Hangul
ValueCountFrequency (%)
61
 
6.6%
60
 
6.5%
58
 
6.3%
57
 
6.1%
54
 
5.8%
54
 
5.8%
53
 
5.7%
53
 
5.7%
53
 
5.7%
50
 
5.4%
Other values (83) 374
40.3%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)80.8%
Missing118
Missing (%)69.4%
Infinite0
Infinite (%)0.0%
Mean4364.5385
Minimum4303
Maximum4428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:40:51.869906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4303
5-th percentile4313
Q14331.75
median4358
Q34399.75
95-th percentile4419.9
Maximum4428
Range125
Interquartile range (IQR)68

Descriptive statistics

Standard deviation36.914029
Coefficient of variation (CV)0.0084577166
Kurtosis-1.2878197
Mean4364.5385
Median Absolute Deviation (MAD)33
Skewness0.16365899
Sum226956
Variance1362.6456
MonotonicityNot monotonic
2024-05-11T15:40:52.198481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4323 3
 
1.8%
4325 3
 
1.8%
4349 3
 
1.8%
4415 2
 
1.2%
4313 2
 
1.2%
4410 2
 
1.2%
4406 2
 
1.2%
4303 1
 
0.6%
4426 1
 
0.6%
4328 1
 
0.6%
Other values (32) 32
 
18.8%
(Missing) 118
69.4%
ValueCountFrequency (%)
4303 1
 
0.6%
4306 1
 
0.6%
4313 2
1.2%
4320 1
 
0.6%
4323 3
1.8%
4325 3
1.8%
4328 1
 
0.6%
4331 1
 
0.6%
4332 1
 
0.6%
4337 1
 
0.6%
ValueCountFrequency (%)
4428 1
0.6%
4426 1
0.6%
4421 1
0.6%
4419 1
0.6%
4415 2
1.2%
4414 1
0.6%
4410 2
1.2%
4409 1
0.6%
4406 2
1.2%
4405 1
0.6%
Distinct152
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-05-11T15:40:52.675181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length4.6117647
Min length2

Characters and Unicode

Total characters784
Distinct characters170
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)80.0%

Sample

1st row부곡탕
2nd row용암탕
3rd row영수탕
4th row청룡사우나
5th row수정탕
ValueCountFrequency (%)
이태원랜드 3
 
1.7%
혜림 3
 
1.7%
한남 3
 
1.7%
세종한증랜드 2
 
1.1%
사우나 2
 
1.1%
현대 2
 
1.1%
신흥 2
 
1.1%
목욕탕 2
 
1.1%
원신 2
 
1.1%
도원대중사우나 2
 
1.1%
Other values (146) 154
87.0%
2024-05-11T15:40:53.671214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56
 
7.1%
38
 
4.8%
36
 
4.6%
36
 
4.6%
27
 
3.4%
25
 
3.2%
24
 
3.1%
23
 
2.9%
21
 
2.7%
16
 
2.0%
Other values (160) 482
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 742
94.6%
Close Punctuation 9
 
1.1%
Open Punctuation 9
 
1.1%
Uppercase Letter 8
 
1.0%
Space Separator 7
 
0.9%
Decimal Number 6
 
0.8%
Lowercase Letter 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
56
 
7.5%
38
 
5.1%
36
 
4.9%
36
 
4.9%
27
 
3.6%
25
 
3.4%
24
 
3.2%
23
 
3.1%
21
 
2.8%
16
 
2.2%
Other values (145) 440
59.3%
Uppercase Letter
ValueCountFrequency (%)
E 2
25.0%
P 1
12.5%
L 1
12.5%
C 1
12.5%
A 1
12.5%
T 1
12.5%
N 1
12.5%
Lowercase Letter
ValueCountFrequency (%)
o 1
33.3%
p 1
33.3%
s 1
33.3%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
4 3
50.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 742
94.6%
Common 31
 
4.0%
Latin 11
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
56
 
7.5%
38
 
5.1%
36
 
4.9%
36
 
4.9%
27
 
3.6%
25
 
3.4%
24
 
3.2%
23
 
3.1%
21
 
2.8%
16
 
2.2%
Other values (145) 440
59.3%
Latin
ValueCountFrequency (%)
E 2
18.2%
o 1
9.1%
P 1
9.1%
p 1
9.1%
s 1
9.1%
L 1
9.1%
C 1
9.1%
A 1
9.1%
T 1
9.1%
N 1
9.1%
Common
ValueCountFrequency (%)
) 9
29.0%
( 9
29.0%
7
22.6%
2 3
 
9.7%
4 3
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 742
94.6%
ASCII 42
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
56
 
7.5%
38
 
5.1%
36
 
4.9%
36
 
4.9%
27
 
3.6%
25
 
3.4%
24
 
3.2%
23
 
3.1%
21
 
2.8%
16
 
2.2%
Other values (145) 440
59.3%
ASCII
ValueCountFrequency (%)
) 9
21.4%
( 9
21.4%
7
16.7%
2 3
 
7.1%
4 3
 
7.1%
E 2
 
4.8%
o 1
 
2.4%
P 1
 
2.4%
p 1
 
2.4%
s 1
 
2.4%
Other values (5) 5
11.9%
Distinct101
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1999-01-27 00:00:00
Maximum2024-05-08 11:30:05
2024-05-11T15:40:53.987935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:40:54.290409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
I
144 
U
26 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 144
84.7%
U 26
 
15.3%

Length

2024-05-11T15:40:54.530684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:54.711830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 144
84.7%
u 26
 
15.3%

데이터갱신일자
Categorical

IMBALANCE 

Distinct27
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2018-08-31 23:59:59.0
144 
2023-11-30 23:04:00.0
 
1
2022-12-07 22:05:00.0
 
1
2023-12-05 00:08:00.0
 
1
2021-03-20 02:40:00.0
 
1
Other values (22)
22 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique26 ?
Unique (%)15.3%

Sample

1st row2018-08-31 23:59:59.0
2nd row2018-08-31 23:59:59.0
3rd row2018-08-31 23:59:59.0
4th row2018-08-31 23:59:59.0
5th row2018-08-31 23:59:59.0

Common Values

ValueCountFrequency (%)
2018-08-31 23:59:59.0 144
84.7%
2023-11-30 23:04:00.0 1
 
0.6%
2022-12-07 22:05:00.0 1
 
0.6%
2023-12-05 00:08:00.0 1
 
0.6%
2021-03-20 02:40:00.0 1
 
0.6%
2023-12-04 00:03:00.0 1
 
0.6%
2020-02-07 02:40:00.0 1
 
0.6%
2021-10-31 23:07:00.0 1
 
0.6%
2021-11-01 21:01:00.0 1
 
0.6%
2022-11-01 23:04:00.0 1
 
0.6%
Other values (17) 17
 
10.0%

Length

2024-05-11T15:40:54.927848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-31 144
42.4%
23:59:59.0 144
42.4%
02:40:00.0 12
 
3.5%
2022-12-07 3
 
0.9%
2023-11-30 2
 
0.6%
23:04:00.0 2
 
0.6%
2023-12-04 2
 
0.6%
22:07:00.0 1
 
0.3%
23:02:00.0 1
 
0.3%
2020-10-07 1
 
0.3%
Other values (28) 28
 
8.2%

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
공동탕업
145 
찜질시설서비스영업
15 
한증막업
 
5
목욕장업 기타
 
4
공동탕업+찜질시설서비스영업
 
1

Length

Max length14
Median length4
Mean length4.5705882
Min length4

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 145
85.3%
찜질시설서비스영업 15
 
8.8%
한증막업 5
 
2.9%
목욕장업 기타 4
 
2.4%
공동탕업+찜질시설서비스영업 1
 
0.6%

Length

2024-05-11T15:40:55.187342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:55.426462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 145
83.3%
찜질시설서비스영업 15
 
8.6%
한증막업 5
 
2.9%
목욕장업 4
 
2.3%
기타 4
 
2.3%
공동탕업+찜질시설서비스영업 1
 
0.6%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct109
Distinct (%)70.3%
Missing15
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean198487.87
Minimum195739.26
Maximum200757.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:40:55.706194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum195739.26
5-th percentile196118.75
Q1197238.9
median198760.02
Q3199745.38
95-th percentile200435.38
Maximum200757.11
Range5017.8546
Interquartile range (IQR)2506.48

Descriptive statistics

Standard deviation1430.9045
Coefficient of variation (CV)0.0072090273
Kurtosis-1.2839567
Mean198487.87
Median Absolute Deviation (MAD)1166.712
Skewness-0.28108011
Sum30765620
Variance2047487.6
MonotonicityNot monotonic
2024-05-11T15:40:56.168071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199974.400997613 6
 
3.5%
199990.489238997 4
 
2.4%
197593.304078325 4
 
2.4%
197882.621568784 3
 
1.8%
199690.208158387 3
 
1.8%
196118.749575977 3
 
1.8%
198555.28287218 3
 
1.8%
197668.820819879 3
 
1.8%
199337.628628981 3
 
1.8%
195739.258577095 3
 
1.8%
Other values (99) 120
70.6%
(Missing) 15
 
8.8%
ValueCountFrequency (%)
195739.258577095 3
1.8%
195981.173932443 1
 
0.6%
196005.836412851 1
 
0.6%
196042.960109242 1
 
0.6%
196087.56840435 1
 
0.6%
196118.749575977 3
1.8%
196149.859380737 1
 
0.6%
196209.832221119 1
 
0.6%
196243.289518577 1
 
0.6%
196290.526521217 1
 
0.6%
ValueCountFrequency (%)
200757.113163707 2
1.2%
200562.054329169 1
0.6%
200559.36615505 1
0.6%
200504.503946848 1
0.6%
200502.179579742 2
1.2%
200448.0782913 1
0.6%
200429.934020602 1
0.6%
200401.947859104 1
0.6%
200387.227409604 1
0.6%
200086.18456484 2
1.2%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct109
Distinct (%)70.3%
Missing15
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean448258.96
Minimum446114.16
Maximum450126.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:40:56.452579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum446114.16
5-th percentile446828.36
Q1447696.84
median448085.23
Q3448874.61
95-th percentile449985.97
Maximum450126.95
Range4012.7964
Interquartile range (IQR)1177.7689

Descriptive statistics

Standard deviation939.48492
Coefficient of variation (CV)0.0020958531
Kurtosis-0.37387031
Mean448258.96
Median Absolute Deviation (MAD)551.945
Skewness0.27406303
Sum69480139
Variance882631.91
MonotonicityNot monotonic
2024-05-11T15:40:56.720296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448243.817170275 6
 
3.5%
448264.185008539 4
 
2.4%
450126.951675139 4
 
2.4%
449867.976662872 3
 
1.8%
447982.926141057 3
 
1.8%
447760.157527455 3
 
1.8%
449141.085294225 3
 
1.8%
449985.969344304 3
 
1.8%
447413.487156381 3
 
1.8%
447887.456942178 3
 
1.8%
Other values (99) 120
70.6%
(Missing) 15
 
8.8%
ValueCountFrequency (%)
446114.155238838 1
0.6%
446233.81723078 1
0.6%
446246.110967125 1
0.6%
446494.73009764 1
0.6%
446505.474827549 1
0.6%
446590.228847578 1
0.6%
446620.932361871 2
1.2%
446917.260025951 1
0.6%
446986.908802137 2
1.2%
447043.764420482 1
0.6%
ValueCountFrequency (%)
450126.951675139 4
2.4%
450096.883470219 1
 
0.6%
450037.741397659 1
 
0.6%
449985.969344304 3
1.8%
449918.557607209 1
 
0.6%
449867.976662872 3
1.8%
449863.755347723 2
1.2%
449829.998949456 2
1.2%
449760.779873134 2
1.2%
449672.909204653 1
 
0.6%

위생업태명
Categorical

IMBALANCE 

Distinct6
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
공동탕업
133 
찜질시설서비스영업
14 
<NA>
 
13
한증막업
 
5
목욕장업 기타
 
4

Length

Max length14
Median length4
Mean length4.5411765
Min length4

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 133
78.2%
찜질시설서비스영업 14
 
8.2%
<NA> 13
 
7.6%
한증막업 5
 
2.9%
목욕장업 기타 4
 
2.4%
공동탕업+찜질시설서비스영업 1
 
0.6%

Length

2024-05-11T15:40:57.122913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:57.447366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 133
76.4%
찜질시설서비스영업 14
 
8.0%
na 13
 
7.5%
한증막업 5
 
2.9%
목욕장업 4
 
2.3%
기타 4
 
2.3%
공동탕업+찜질시설서비스영업 1
 
0.6%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)11.1%
Missing53
Missing (%)31.2%
Infinite0
Infinite (%)0.0%
Mean2.4273504
Minimum0
Maximum34
Zeros64
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:40:57.783600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile8.4
Maximum34
Range34
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3476435
Coefficient of variation (CV)1.7911067
Kurtosis24.527161
Mean2.4273504
Median Absolute Deviation (MAD)0
Skewness4.0696361
Sum284
Variance18.902004
MonotonicityNot monotonic
2024-05-11T15:40:58.206393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 64
37.6%
4 10
 
5.9%
3 9
 
5.3%
5 8
 
4.7%
2 5
 
2.9%
6 5
 
2.9%
1 5
 
2.9%
7 4
 
2.4%
10 3
 
1.8%
14 1
 
0.6%
Other values (3) 3
 
1.8%
(Missing) 53
31.2%
ValueCountFrequency (%)
0 64
37.6%
1 5
 
2.9%
2 5
 
2.9%
3 9
 
5.3%
4 10
 
5.9%
5 8
 
4.7%
6 5
 
2.9%
7 4
 
2.4%
8 1
 
0.6%
10 3
 
1.8%
ValueCountFrequency (%)
34 1
 
0.6%
18 1
 
0.6%
14 1
 
0.6%
10 3
 
1.8%
8 1
 
0.6%
7 4
 
2.4%
6 5
2.9%
5 8
4.7%
4 10
5.9%
3 9
5.3%
Distinct6
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
79 
0
65 
1
10 
2
3
 
4

Length

Max length4
Median length1
Mean length2.3941176
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 79
46.5%
0 65
38.2%
1 10
 
5.9%
2 9
 
5.3%
3 4
 
2.4%
4 3
 
1.8%

Length

2024-05-11T15:40:58.611851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:40:59.008069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 79
46.5%
0 65
38.2%
1 10
 
5.9%
2 9
 
5.3%
3 4
 
2.4%
4 3
 
1.8%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)7.0%
Missing70
Missing (%)41.2%
Infinite0
Infinite (%)0.0%
Mean0.73
Minimum0
Maximum6
Zeros55
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:40:59.414308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1268531
Coefficient of variation (CV)1.5436344
Kurtosis6.5322701
Mean0.73
Median Absolute Deviation (MAD)0
Skewness2.325206
Sum73
Variance1.269798
MonotonicityNot monotonic
2024-05-11T15:40:59.675711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 55
32.4%
1 31
18.2%
2 7
 
4.1%
3 3
 
1.8%
4 2
 
1.2%
5 1
 
0.6%
6 1
 
0.6%
(Missing) 70
41.2%
ValueCountFrequency (%)
0 55
32.4%
1 31
18.2%
2 7
 
4.1%
3 3
 
1.8%
4 2
 
1.2%
5 1
 
0.6%
6 1
 
0.6%
ValueCountFrequency (%)
6 1
 
0.6%
5 1
 
0.6%
4 2
 
1.2%
3 3
 
1.8%
2 7
 
4.1%
1 31
18.2%
0 55
32.4%

사용끝지상층
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)15.6%
Missing125
Missing (%)73.5%
Infinite0
Infinite (%)0.0%
Mean1.9111111
Minimum0
Maximum7
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:40:59.918367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2760419
Coefficient of variation (CV)0.66769632
Kurtosis4.9911781
Mean1.9111111
Median Absolute Deviation (MAD)1
Skewness1.8903423
Sum86
Variance1.6282828
MonotonicityNot monotonic
2024-05-11T15:41:00.171284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 20
 
11.8%
2 14
 
8.2%
3 6
 
3.5%
4 2
 
1.2%
7 1
 
0.6%
0 1
 
0.6%
5 1
 
0.6%
(Missing) 125
73.5%
ValueCountFrequency (%)
0 1
 
0.6%
1 20
11.8%
2 14
8.2%
3 6
 
3.5%
4 2
 
1.2%
5 1
 
0.6%
7 1
 
0.6%
ValueCountFrequency (%)
7 1
 
0.6%
5 1
 
0.6%
4 2
 
1.2%
3 6
 
3.5%
2 14
8.2%
1 20
11.8%
0 1
 
0.6%
Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
84 
0
55 
1
26 
2
 
4
3
 
1

Length

Max length4
Median length1
Mean length2.4823529
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 84
49.4%
0 55
32.4%
1 26
 
15.3%
2 4
 
2.4%
3 1
 
0.6%

Length

2024-05-11T15:41:00.528671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:00.833158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 84
49.4%
0 55
32.4%
1 26
 
15.3%
2 4
 
2.4%
3 1
 
0.6%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
140 
1
21 
2
 
7
0
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.4705882
Min length1

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 140
82.4%
1 21
 
12.4%
2 7
 
4.1%
0 1
 
0.6%
3 1
 
0.6%

Length

2024-05-11T15:41:01.131768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:01.389704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 140
82.4%
1 21
 
12.4%
2 7
 
4.1%
0 1
 
0.6%
3 1
 
0.6%

한실수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
101 
0
69 

Length

Max length4
Median length4
Mean length2.7823529
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 101
59.4%
0 69
40.6%

Length

2024-05-11T15:41:01.759731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:02.018654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
59.4%
0 69
40.6%

양실수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
101 
0
69 

Length

Max length4
Median length4
Mean length2.7823529
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 101
59.4%
0 69
40.6%

Length

2024-05-11T15:41:02.295001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:02.609354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
59.4%
0 69
40.6%

욕실수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)9.7%
Missing98
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean0.5
Minimum0
Maximum8
Zeros61
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:41:02.867306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.45
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.424138
Coefficient of variation (CV)2.848276
Kurtosis13.440281
Mean0.5
Median Absolute Deviation (MAD)0
Skewness3.5058802
Sum36
Variance2.028169
MonotonicityNot monotonic
2024-05-11T15:41:03.770083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 61
35.9%
2 5
 
2.9%
4 2
 
1.2%
3 1
 
0.6%
8 1
 
0.6%
6 1
 
0.6%
1 1
 
0.6%
(Missing) 98
57.6%
ValueCountFrequency (%)
0 61
35.9%
1 1
 
0.6%
2 5
 
2.9%
3 1
 
0.6%
4 2
 
1.2%
6 1
 
0.6%
8 1
 
0.6%
ValueCountFrequency (%)
8 1
 
0.6%
6 1
 
0.6%
4 2
 
1.2%
3 1
 
0.6%
2 5
 
2.9%
1 1
 
0.6%
0 61
35.9%

발한실여부
Boolean

MISSING 

Distinct2
Distinct (%)1.3%
Missing13
Missing (%)7.6%
Memory size472.0 B
False
105 
True
52 
(Missing)
13 
ValueCountFrequency (%)
False 105
61.8%
True 52
30.6%
(Missing) 13
 
7.6%
2024-05-11T15:41:04.070527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
101 
0
69 

Length

Max length4
Median length4
Mean length2.7823529
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 101
59.4%
0 69
40.6%

Length

2024-05-11T15:41:04.372358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:04.627938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
59.4%
0 69
40.6%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
160 
자가
 
7
임대
 
3

Length

Max length4
Median length4
Mean length3.8823529
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 160
94.1%
자가 7
 
4.1%
임대 3
 
1.8%

Length

2024-05-11T15:41:04.889428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:05.272154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 160
94.1%
자가 7
 
4.1%
임대 3
 
1.8%

세탁기수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
155 
0
 
15

Length

Max length4
Median length4
Mean length3.7352941
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 155
91.2%
0 15
 
8.8%

Length

2024-05-11T15:41:05.621735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:05.884029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 155
91.2%
0 15
 
8.8%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
165 
0
 
5

Length

Max length4
Median length4
Mean length3.9117647
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 165
97.1%
0 5
 
2.9%

Length

2024-05-11T15:41:06.147831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:06.376743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 165
97.1%
0 5
 
2.9%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
165 
0
 
4
2
 
1

Length

Max length4
Median length4
Mean length3.9117647
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 165
97.1%
0 4
 
2.4%
2 1
 
0.6%

Length

2024-05-11T15:41:06.590234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:06.798823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 165
97.1%
0 4
 
2.4%
2 1
 
0.6%

회수건조수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
155 
0
 
15

Length

Max length4
Median length4
Mean length3.7352941
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 155
91.2%
0 15
 
8.8%

Length

2024-05-11T15:41:06.989713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:07.191626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 155
91.2%
0 15
 
8.8%

침대수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
158 
0
 
12

Length

Max length4
Median length4
Mean length3.7882353
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 158
92.9%
0 12
 
7.1%

Length

2024-05-11T15:41:07.417505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:41:07.626542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 158
92.9%
0 12
 
7.1%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)1.3%
Missing13
Missing (%)7.6%
Memory size472.0 B
False
155 
True
 
2
(Missing)
 
13
ValueCountFrequency (%)
False 155
91.2%
True 2
 
1.2%
(Missing) 13
 
7.6%
2024-05-11T15:41:07.775899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030200003020000-202-1900-0000119000101<NA>3폐업2폐업19990226<NA><NA><NA>7570020.00140821서울특별시 용산구 동자동 35-44번지<NA><NA>부곡탕2003-03-20 00:00:00I2018-08-31 23:59:59.0공동탕업197668.82082449985.969344공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130200003020000-202-1900-0000219000101<NA>3폐업2폐업19831011<NA><NA><NA>7962803.00140842서울특별시 용산구 용산동2가 47-1번지<NA><NA>용암탕2003-03-20 00:00:00I2018-08-31 23:59:59.0공동탕업198880.937875448993.697585공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230200003020000-202-1900-0000319000101<NA>3폐업2폐업19710609<NA><NA><NA>7948970.00140841서울특별시 용산구 용산동2가 18-4번지<NA><NA>영수탕2003-03-20 00:00:00I2018-08-31 23:59:59.0공동탕업198704.256988449220.631107공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330200003020000-202-1900-0000419000101<NA>3폐업2폐업19000101<NA><NA><NA>7929605.00140807서울특별시 용산구 갈월동 98-38번지<NA><NA>청룡사우나2003-03-10 00:00:00I2018-08-31 23:59:59.0공동탕업197492.006217448773.77781공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430200003020000-202-1900-0000519000101<NA>3폐업2폐업20001015<NA><NA><NA>7190389.00140850서울특별시 용산구 원효로4가 87-5번지<NA><NA>수정탕2003-03-20 00:00:00I2018-08-31 23:59:59.0공동탕업195739.258577447887.456942공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530200003020000-202-1900-0000619000101<NA>3폐업2폐업20010220<NA><NA><NA><NA>.00140893서울특별시 용산구 한남동 737-28번지<NA><NA>이태원사우나2003-03-20 00:00:00I2018-08-31 23:59:59.0공동탕업199745.379091448092.921878공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
630200003020000-202-1900-0000719000101<NA>3폐업2폐업20010802<NA><NA><NA><NA>.00140906서울특별시 용산구 이촌동 301-155번지 수정상가(지하)<NA><NA>올리브2003-03-20 00:00:00I2018-08-31 23:59:59.0공동탕업197024.194985446620.932362공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
730200003020000-202-1960-0034119601230<NA>3폐업2폐업20170405<NA><NA><NA>0207932151894.30140875서울특별시 용산구 한강로2가 204번지서울특별시 용산구 서빙고로5길 7 (한강로2가)4387용산2017-04-05 16:57:10I2018-08-31 23:59:59.0공동탕업196930.571494447117.639755공동탕업4<NA>11<NA><NA><NA><NA><NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
830200003020000-202-1960-0034819601231<NA>3폐업2폐업20021107<NA><NA><NA>7930040126.18140893서울특별시 용산구 한남동 738-1번지<NA><NA>한남2002-11-20 00:00:00I2018-08-31 23:59:59.0공동탕업199923.483971448305.985454공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
930200003020000-202-1960-0036719601230<NA>3폐업2폐업20090401<NA><NA><NA>0207133672498.30140896서울특별시 용산구 효창동 5-477번지<NA><NA>효창2007-05-09 00:00:00I2018-08-31 23:59:59.0공동탕업196503.656293448785.747424공동탕업3<NA>11<NA><NA><NA><NA><NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
16030200003020000-202-2011-0000120110225<NA>1영업/정상1영업<NA><NA><NA><NA>02 795 996674.00140857서울특별시 용산구 이태원동 226-3번지 지하1층서울특별시 용산구 회나무로26길 12 (이태원동,지하1층)4345웰니스짐2017-11-21 16:19:04I2018-08-31 23:59:59.0찜질시설서비스영업199155.510675448589.384222찜질시설서비스영업00<NA><NA><NA><NA>000N0<NA><NA><NA><NA>0<NA><NA>00N
16130200003020000-202-2013-0000120130628<NA>3폐업2폐업20140821<NA><NA><NA><NA>273.05140818서울특별시 용산구 동자동 12번지 지하1층호서울특별시 용산구 후암로 107, 지하1층호 (동자동)4323스포파크2013-06-28 17:04:14I2018-08-31 23:59:59.0공동탕업197593.304078450126.951675공동탕업00<NA><NA>1<NA>004N0<NA><NA><NA><NA>0<NA><NA>00N
16230200003020000-202-2013-0000220130628<NA>3폐업2폐업20140627<NA><NA><NA>7276200325.28140818서울특별시 용산구 동자동 12번지 지하1층호서울특별시 용산구 후암로 107, 지하1층호 (동자동)4323스포(spo)2013-06-28 17:10:41I2018-08-31 23:59:59.0공동탕업197593.304078450126.951675공동탕업00<NA><NA>1<NA>004N0<NA><NA><NA><NA>0<NA><NA>00Y
16330200003020000-202-2014-0000120141017<NA>1영업/정상1영업<NA><NA><NA><NA><NA>300.00140858서울특별시 용산구 이태원동 131-36서울특별시 용산구 보광로60길 10 (이태원동)4406히즈2021-11-01 16:27:34U2021-11-03 02:40:00.0공동탕업199462.558574447958.068968공동탕업4023<NA><NA>001Y0<NA><NA><NA>임대00200N
16430200003020000-202-2014-000022014-12-05<NA>1영업/정상1영업<NA><NA><NA><NA>7275000330.85140-818서울특별시 용산구 동자동 12 게이트웨이타워서울특별시 용산구 후암로 107, 게이트웨이타워 지하1층 (동자동)4323펜타클(PENTACLE)2023-08-24 14:28:50U2022-12-07 22:06:00.0공동탕업197593.304078450126.951675<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16530200003020000-202-2016-0000120161125<NA>3폐업2폐업20190729<NA><NA><NA>02 749 41221,458.27140895서울특별시 용산구 한남동 732-20번지서울특별시 용산구 우사단로14길 34, 1층 2층 (한남동)4405이태원랜드2019-07-29 16:40:48U2019-07-31 02:40:00.0공동탕업199690.208158447982.926141공동탕업1412<NA><NA>000N0<NA><NA><NA>자가00000N
16630200003020000-202-2017-0000120170120<NA>1영업/정상1영업<NA><NA><NA><NA><NA>168.74140806서울특별시 용산구 갈월동 69-27 지하2층서울특별시 용산구 한강대로 305, 지하2층 (갈월동)4320수사우나2021-07-30 14:01:20U2021-08-01 02:40:00.0공동탕업197419.271266449254.786814공동탕업02<NA><NA><NA><NA>000N0<NA><NA><NA>자가00000N
16730200003020000-202-2017-0000220170726<NA>1영업/정상1영업<NA><NA><NA><NA>02 22237000227.80140877서울특별시 용산구 한강로3가 40-969번지서울특별시 용산구 청파로20길 95, 6층 (한강로3가, 서울드래곤시티)4372서울드래곤시티 이비스스타일 (사우나)2017-09-21 10:16:05I2018-08-31 23:59:59.0공동탕업+찜질시설서비스영업196610.66384447742.348636공동탕업+찜질시설서비스영업3446<NA><NA><NA>002Y0<NA><NA><NA>자가00000N
16830200003020000-202-2018-0000120180813<NA>3폐업2폐업20200529<NA><NA><NA>02 779 0302102.91140900서울특별시 용산구 후암동 79번지 한진여관목욕탕서울특별시 용산구 후암로 59, 한진여관목욕탕 1층 (후암동)4325한진목욕탕2020-05-29 14:23:59U2020-05-31 02:40:00.0공동탕업197882.621569449867.976663공동탕업5112<NA><NA>000Y0<NA><NA><NA>자가00000N
16930200003020000-202-2018-0000220180813<NA>3폐업2폐업20200710<NA><NA><NA><NA>102.91140900서울특별시 용산구 후암동 79 한진여관목욕탕서울특별시 용산구 후암로 59, 한진여관목욕탕 2층 (후암동)4325한진목욕탕2020-07-10 09:46:23U2020-07-12 02:40:00.0공동탕업197882.621569449867.976663공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N