Overview

Dataset statistics

Number of variables47
Number of observations132
Missing cells1351
Missing cells (%)21.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.2 KiB
Average record size in memory405.0 B

Variable types

Categorical22
Text7
DateTime3
Unsupported7
Numeric6
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
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-19965/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
데이터갱신일자 is highly imbalanced (56.6%)Imbalance
업태구분명 is highly imbalanced (62.7%)Imbalance
위생업태명 is highly imbalanced (52.0%)Imbalance
사용끝지하층 is highly imbalanced (52.3%)Imbalance
발한실여부 is highly imbalanced (59.9%)Imbalance
건물소유구분명 is highly imbalanced (71.4%)Imbalance
여성종사자수 is highly imbalanced (61.3%)Imbalance
남성종사자수 is highly imbalanced (61.3%)Imbalance
인허가취소일자 has 132 (100.0%) missing valuesMissing
폐업일자 has 20 (15.2%) missing valuesMissing
휴업시작일자 has 132 (100.0%) missing valuesMissing
휴업종료일자 has 132 (100.0%) missing valuesMissing
재개업일자 has 132 (100.0%) missing valuesMissing
전화번호 has 3 (2.3%) missing valuesMissing
도로명주소 has 83 (62.9%) missing valuesMissing
도로명우편번호 has 84 (63.6%) missing valuesMissing
좌표정보(X) has 16 (12.1%) missing valuesMissing
좌표정보(Y) has 16 (12.1%) missing valuesMissing
건물지상층수 has 26 (19.7%) missing valuesMissing
사용시작지상층 has 78 (59.1%) missing valuesMissing
사용끝지상층 has 63 (47.7%) missing valuesMissing
발한실여부 has 19 (14.4%) missing valuesMissing
조건부허가신고사유 has 132 (100.0%) missing valuesMissing
조건부허가시작일자 has 132 (100.0%) missing valuesMissing
조건부허가종료일자 has 132 (100.0%) missing valuesMissing
다중이용업소여부 has 19 (14.4%) 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 5 (3.8%) zerosZeros
사용시작지상층 has 12 (9.1%) zerosZeros
사용끝지상층 has 7 (5.3%) zerosZeros

Reproduction

Analysis started2024-05-11 08:42:38.213628
Analysis finished2024-05-11 08:42:38.964610
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
3130000
132 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3130000
2nd row3130000
3rd row3130000
4th row3130000
5th row3130000

Common Values

ValueCountFrequency (%)
3130000 132
100.0%

Length

2024-05-11T17:42:39.013220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:39.092653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3130000 132
100.0%

관리번호
Text

UNIQUE 

Distinct132
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T17:42:39.236297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2904
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

Unique132 ?
Unique (%)100.0%

Sample

1st row3130000-202-1963-00001
2nd row3130000-202-1963-00002
3rd row3130000-202-1963-00003
4th row3130000-202-1963-00004
5th row3130000-202-1965-00001
ValueCountFrequency (%)
3130000-202-1963-00001 1
 
0.8%
3130000-202-1995-00003 1
 
0.8%
3130000-202-2000-00002 1
 
0.8%
3130000-202-2000-00001 1
 
0.8%
3130000-202-1999-00161 1
 
0.8%
3130000-202-1999-00159 1
 
0.8%
3130000-202-1998-00164 1
 
0.8%
3130000-202-1998-00163 1
 
0.8%
3130000-202-1998-00162 1
 
0.8%
3130000-202-1998-00160 1
 
0.8%
Other values (122) 122
92.4%
2024-05-11T17:42:39.523726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1250
43.0%
- 396
 
13.6%
2 346
 
11.9%
3 317
 
10.9%
1 302
 
10.4%
9 137
 
4.7%
8 47
 
1.6%
6 35
 
1.2%
7 32
 
1.1%
4 24
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2508
86.4%
Dash Punctuation 396
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1250
49.8%
2 346
 
13.8%
3 317
 
12.6%
1 302
 
12.0%
9 137
 
5.5%
8 47
 
1.9%
6 35
 
1.4%
7 32
 
1.3%
4 24
 
1.0%
5 18
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 396
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2904
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1250
43.0%
- 396
 
13.6%
2 346
 
11.9%
3 317
 
10.9%
1 302
 
10.4%
9 137
 
4.7%
8 47
 
1.6%
6 35
 
1.2%
7 32
 
1.1%
4 24
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1250
43.0%
- 396
 
13.6%
2 346
 
11.9%
3 317
 
10.9%
1 302
 
10.4%
9 137
 
4.7%
8 47
 
1.6%
6 35
 
1.2%
7 32
 
1.1%
4 24
 
0.8%
Distinct128
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum1963-06-19 00:00:00
Maximum2023-10-18 00:00:00
2024-05-11T17:42:39.644223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:42:39.768203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing132
Missing (%)100.0%
Memory size1.3 KiB
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
3
112 
1
20 

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 (%)
3 112
84.8%
1 20
 
15.2%

Length

2024-05-11T17:42:39.908385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:39.988549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 112
84.8%
1 20
 
15.2%

영업상태명
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
폐업
112 
영업/정상
20 

Length

Max length5
Median length2
Mean length2.4545455
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 112
84.8%
영업/정상 20
 
15.2%

Length

2024-05-11T17:42:40.079153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:40.167858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 112
84.8%
영업/정상 20
 
15.2%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2
112 
1
20 

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 (%)
2 112
84.8%
1 20
 
15.2%

Length

2024-05-11T17:42:40.256045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:40.348311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 112
84.8%
1 20
 
15.2%
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
폐업
112 
영업
20 

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 112
84.8%
영업 20
 
15.2%

Length

2024-05-11T17:42:40.432416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:40.518840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 112
84.8%
영업 20
 
15.2%

폐업일자
Date

MISSING 

Distinct90
Distinct (%)80.4%
Missing20
Missing (%)15.2%
Memory size1.2 KiB
Minimum2000-08-04 00:00:00
Maximum2023-11-22 00:00:00
2024-05-11T17:42:40.626921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:42:40.764881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing132
Missing (%)100.0%
Memory size1.3 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing132
Missing (%)100.0%
Memory size1.3 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing132
Missing (%)100.0%
Memory size1.3 KiB

전화번호
Text

MISSING 

Distinct127
Distinct (%)98.4%
Missing3
Missing (%)2.3%
Memory size1.2 KiB
2024-05-11T17:42:40.977826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.062016
Min length2

Characters and Unicode

Total characters1298
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

Unique126 ?
Unique (%)97.7%

Sample

1st row02 3628209
2nd row0207134192
3rd row0207172117
4th row02712 4431
5th row0207137365
ValueCountFrequency (%)
02 51
27.0%
7185700 3
 
1.6%
702 1
 
0.5%
3380034 1
 
0.5%
0231434330 1
 
0.5%
0231410875 1
 
0.5%
3628209 1
 
0.5%
7172067 1
 
0.5%
0232740911 1
 
0.5%
7126685 1
 
0.5%
Other values (127) 127
67.2%
2024-05-11T17:42:41.309194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 299
23.0%
2 226
17.4%
3 190
14.6%
7 125
9.6%
1 101
 
7.8%
70
 
5.4%
4 64
 
4.9%
6 63
 
4.9%
8 58
 
4.5%
5 54
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1228
94.6%
Space Separator 70
 
5.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 299
24.3%
2 226
18.4%
3 190
15.5%
7 125
10.2%
1 101
 
8.2%
4 64
 
5.2%
6 63
 
5.1%
8 58
 
4.7%
5 54
 
4.4%
9 48
 
3.9%
Space Separator
ValueCountFrequency (%)
70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1298
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 299
23.0%
2 226
17.4%
3 190
14.6%
7 125
9.6%
1 101
 
7.8%
70
 
5.4%
4 64
 
4.9%
6 63
 
4.9%
8 58
 
4.5%
5 54
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 299
23.0%
2 226
17.4%
3 190
14.6%
7 125
9.6%
1 101
 
7.8%
70
 
5.4%
4 64
 
4.9%
6 63
 
4.9%
8 58
 
4.5%
5 54
 
4.2%
Distinct124
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T17:42:41.598949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.1742424
Min length5

Characters and Unicode

Total characters815
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

Unique117 ?
Unique (%)88.6%

Sample

1st row150.58
2nd row140.11
3rd row188.00
4th row119.00
5th row137.00
ValueCountFrequency (%)
243.00 3
 
2.3%
306.00 2
 
1.5%
254.00 2
 
1.5%
222.00 2
 
1.5%
381.00 2
 
1.5%
258.00 2
 
1.5%
214.00 2
 
1.5%
880.00 1
 
0.8%
391.00 1
 
0.8%
332.00 1
 
0.8%
Other values (114) 114
86.4%
2024-05-11T17:42:41.994954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 199
24.4%
. 132
16.2%
2 87
10.7%
1 80
9.8%
3 63
 
7.7%
6 45
 
5.5%
4 43
 
5.3%
5 43
 
5.3%
9 38
 
4.7%
8 37
 
4.5%
Other values (2) 48
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 671
82.3%
Other Punctuation 144
 
17.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 199
29.7%
2 87
13.0%
1 80
11.9%
3 63
 
9.4%
6 45
 
6.7%
4 43
 
6.4%
5 43
 
6.4%
9 38
 
5.7%
8 37
 
5.5%
7 36
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 132
91.7%
, 12
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 815
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 199
24.4%
. 132
16.2%
2 87
10.7%
1 80
9.8%
3 63
 
7.7%
6 45
 
5.5%
4 43
 
5.3%
5 43
 
5.3%
9 38
 
4.7%
8 37
 
4.5%
Other values (2) 48
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 199
24.4%
. 132
16.2%
2 87
10.7%
1 80
9.8%
3 63
 
7.7%
6 45
 
5.5%
4 43
 
5.3%
5 43
 
5.3%
9 38
 
4.7%
8 37
 
4.5%
Other values (2) 48
 
5.9%
Distinct80
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T17:42:42.251832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0833333
Min length6

Characters and Unicode

Total characters803
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

Unique51 ?
Unique (%)38.6%

Sample

1st row121862
2nd row121801
3rd row121800
4th row121801
5th row121870
ValueCountFrequency (%)
121854 6
 
4.5%
121812 5
 
3.8%
121838 5
 
3.8%
121893 4
 
3.0%
121859 4
 
3.0%
121845 4
 
3.0%
121805 3
 
2.3%
121800 3
 
2.3%
121809 3
 
2.3%
121807 3
 
2.3%
Other values (70) 92
69.7%
2024-05-11T17:42:42.614258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 293
36.5%
2 160
19.9%
8 150
18.7%
0 40
 
5.0%
5 32
 
4.0%
4 27
 
3.4%
7 25
 
3.1%
3 23
 
2.9%
9 23
 
2.9%
6 19
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
98.6%
Dash Punctuation 11
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 293
37.0%
2 160
20.2%
8 150
18.9%
0 40
 
5.1%
5 32
 
4.0%
4 27
 
3.4%
7 25
 
3.2%
3 23
 
2.9%
9 23
 
2.9%
6 19
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 293
36.5%
2 160
19.9%
8 150
18.7%
0 40
 
5.0%
5 32
 
4.0%
4 27
 
3.4%
7 25
 
3.1%
3 23
 
2.9%
9 23
 
2.9%
6 19
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 293
36.5%
2 160
19.9%
8 150
18.7%
0 40
 
5.0%
5 32
 
4.0%
4 27
 
3.4%
7 25
 
3.1%
3 23
 
2.9%
9 23
 
2.9%
6 19
 
2.4%
Distinct131
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T17:42:42.829870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length37
Mean length23.992424
Min length18

Characters and Unicode

Total characters3167
Distinct characters125
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique130 ?
Unique (%)98.5%

Sample

1st row서울특별시 마포구 아현동 613-11번지
2nd row서울특별시 마포구 공덕동 105-228번지
3rd row서울특별시 마포구 공덕동 74-2번지
4th row서울특별시 마포구 공덕동 117-1번지
5th row서울특별시 마포구 염리동 9-122번지
ValueCountFrequency (%)
서울특별시 132
22.3%
마포구 132
22.3%
서교동 17
 
2.9%
성산동 16
 
2.7%
아현동 12
 
2.0%
망원동 12
 
2.0%
공덕동 10
 
1.7%
지하1층 10
 
1.7%
도화동 10
 
1.7%
신수동 7
 
1.2%
Other values (184) 235
39.6%
2024-05-11T17:42:43.162169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
572
18.1%
150
 
4.7%
139
 
4.4%
134
 
4.2%
133
 
4.2%
133
 
4.2%
133
 
4.2%
132
 
4.2%
132
 
4.2%
132
 
4.2%
Other values (115) 1377
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1872
59.1%
Decimal Number 591
 
18.7%
Space Separator 572
 
18.1%
Dash Punctuation 111
 
3.5%
Other Punctuation 7
 
0.2%
Uppercase Letter 7
 
0.2%
Math Symbol 3
 
0.1%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
150
 
8.0%
139
 
7.4%
134
 
7.2%
133
 
7.1%
133
 
7.1%
133
 
7.1%
132
 
7.1%
132
 
7.1%
132
 
7.1%
122
 
6.5%
Other values (92) 532
28.4%
Decimal Number
ValueCountFrequency (%)
1 130
22.0%
3 82
13.9%
4 66
11.2%
2 64
10.8%
5 55
9.3%
0 43
 
7.3%
7 42
 
7.1%
6 41
 
6.9%
9 39
 
6.6%
8 29
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
K 1
14.3%
S 1
14.3%
V 1
14.3%
I 1
14.3%
P 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 4
57.1%
. 3
42.9%
Space Separator
ValueCountFrequency (%)
572
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 111
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1872
59.1%
Common 1288
40.7%
Latin 7
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
150
 
8.0%
139
 
7.4%
134
 
7.2%
133
 
7.1%
133
 
7.1%
133
 
7.1%
132
 
7.1%
132
 
7.1%
132
 
7.1%
122
 
6.5%
Other values (92) 532
28.4%
Common
ValueCountFrequency (%)
572
44.4%
1 130
 
10.1%
- 111
 
8.6%
3 82
 
6.4%
4 66
 
5.1%
2 64
 
5.0%
5 55
 
4.3%
0 43
 
3.3%
7 42
 
3.3%
6 41
 
3.2%
Other values (7) 82
 
6.4%
Latin
ValueCountFrequency (%)
B 2
28.6%
K 1
14.3%
S 1
14.3%
V 1
14.3%
I 1
14.3%
P 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1872
59.1%
ASCII 1295
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
572
44.2%
1 130
 
10.0%
- 111
 
8.6%
3 82
 
6.3%
4 66
 
5.1%
2 64
 
4.9%
5 55
 
4.2%
0 43
 
3.3%
7 42
 
3.2%
6 41
 
3.2%
Other values (13) 89
 
6.9%
Hangul
ValueCountFrequency (%)
150
 
8.0%
139
 
7.4%
134
 
7.2%
133
 
7.1%
133
 
7.1%
133
 
7.1%
132
 
7.1%
132
 
7.1%
132
 
7.1%
122
 
6.5%
Other values (92) 532
28.4%

도로명주소
Text

MISSING 

Distinct49
Distinct (%)100.0%
Missing83
Missing (%)62.9%
Memory size1.2 KiB
2024-05-11T17:42:43.391860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length41
Mean length29.877551
Min length22

Characters and Unicode

Total characters1464
Distinct characters129
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row서울특별시 마포구 만리재옛길 75 (공덕동)
2nd row서울특별시 마포구 환일1길 3 (아현동)
3rd row서울특별시 마포구 성미산로 3 (성산동)
4th row서울특별시 마포구 포은로 145, 성신목욕탕 (망원동)
5th row서울특별시 마포구 숭문길 156 (염리동)
ValueCountFrequency (%)
서울특별시 49
 
17.1%
마포구 49
 
17.1%
지하1층 9
 
3.1%
성산동 7
 
2.4%
서교동 6
 
2.1%
마포대로 4
 
1.4%
월드컵로 4
 
1.4%
망원동 4
 
1.4%
상가동 4
 
1.4%
도화동 4
 
1.4%
Other values (115) 147
51.2%
2024-05-11T17:42:43.785739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
238
 
16.3%
58
 
4.0%
57
 
3.9%
54
 
3.7%
53
 
3.6%
1 51
 
3.5%
51
 
3.5%
) 50
 
3.4%
( 50
 
3.4%
49
 
3.3%
Other values (119) 753
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 901
61.5%
Space Separator 238
 
16.3%
Decimal Number 184
 
12.6%
Close Punctuation 50
 
3.4%
Open Punctuation 50
 
3.4%
Other Punctuation 31
 
2.1%
Uppercase Letter 4
 
0.3%
Dash Punctuation 3
 
0.2%
Math Symbol 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
58
 
6.4%
57
 
6.3%
54
 
6.0%
53
 
5.9%
51
 
5.7%
49
 
5.4%
49
 
5.4%
49
 
5.4%
49
 
5.4%
40
 
4.4%
Other values (99) 392
43.5%
Decimal Number
ValueCountFrequency (%)
1 51
27.7%
2 32
17.4%
3 21
11.4%
4 17
 
9.2%
5 16
 
8.7%
0 15
 
8.2%
7 10
 
5.4%
8 9
 
4.9%
9 8
 
4.3%
6 5
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
S 1
25.0%
K 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 30
96.8%
. 1
 
3.2%
Space Separator
ValueCountFrequency (%)
238
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 901
61.5%
Common 559
38.2%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
58
 
6.4%
57
 
6.3%
54
 
6.0%
53
 
5.9%
51
 
5.7%
49
 
5.4%
49
 
5.4%
49
 
5.4%
49
 
5.4%
40
 
4.4%
Other values (99) 392
43.5%
Common
ValueCountFrequency (%)
238
42.6%
1 51
 
9.1%
) 50
 
8.9%
( 50
 
8.9%
2 32
 
5.7%
, 30
 
5.4%
3 21
 
3.8%
4 17
 
3.0%
5 16
 
2.9%
0 15
 
2.7%
Other values (7) 39
 
7.0%
Latin
ValueCountFrequency (%)
B 2
50.0%
S 1
25.0%
K 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 901
61.5%
ASCII 563
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
238
42.3%
1 51
 
9.1%
) 50
 
8.9%
( 50
 
8.9%
2 32
 
5.7%
, 30
 
5.3%
3 21
 
3.7%
4 17
 
3.0%
5 16
 
2.8%
0 15
 
2.7%
Other values (10) 43
 
7.6%
Hangul
ValueCountFrequency (%)
58
 
6.4%
57
 
6.3%
54
 
6.0%
53
 
5.9%
51
 
5.7%
49
 
5.4%
49
 
5.4%
49
 
5.4%
49
 
5.4%
40
 
4.4%
Other values (99) 392
43.5%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct43
Distinct (%)89.6%
Missing84
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean4068.0417
Minimum3922
Maximum4206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T17:42:43.904315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3922
5-th percentile3933.4
Q13980.25
median4064.5
Q34156.25
95-th percentile4202.9
Maximum4206
Range284
Interquartile range (IQR)176

Descriptive statistics

Standard deviation92.931674
Coefficient of variation (CV)0.022844327
Kurtosis-1.4529783
Mean4068.0417
Median Absolute Deviation (MAD)91
Skewness-0.041256516
Sum195266
Variance8636.2961
MonotonicityNot monotonic
2024-05-11T17:42:44.032733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4038 2
 
1.5%
4163 2
 
1.5%
4180 2
 
1.5%
4205 2
 
1.5%
4094 2
 
1.5%
4206 1
 
0.8%
4081 1
 
0.8%
4018 1
 
0.8%
4065 1
 
0.8%
3932 1
 
0.8%
Other values (33) 33
 
25.0%
(Missing) 84
63.6%
ValueCountFrequency (%)
3922 1
0.8%
3930 1
0.8%
3932 1
0.8%
3936 1
0.8%
3940 1
0.8%
3948 1
0.8%
3953 1
0.8%
3958 1
0.8%
3959 1
0.8%
3964 1
0.8%
ValueCountFrequency (%)
4206 1
0.8%
4205 2
1.5%
4199 1
0.8%
4185 1
0.8%
4180 2
1.5%
4170 1
0.8%
4168 1
0.8%
4167 1
0.8%
4163 2
1.5%
4154 1
0.8%
Distinct119
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T17:42:44.306140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length3
Mean length4.9772727
Min length3

Characters and Unicode

Total characters657
Distinct characters164
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)81.8%

Sample

1st row행화탕
2nd row삼용탕
3rd row공덕탕
4th row마포한증원
5th row서울탕
ValueCountFrequency (%)
대호탕 3
 
2.2%
수정탕 3
 
2.2%
중앙탕 2
 
1.5%
오복탕 2
 
1.5%
옥천탕 2
 
1.5%
토정24시사우나찜질방 2
 
1.5%
신촌불한증막 2
 
1.5%
새한탕 2
 
1.5%
공덕탕 2
 
1.5%
성산탕 2
 
1.5%
Other values (114) 115
83.9%
2024-05-11T17:42:44.707887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
93
 
14.2%
23
 
3.5%
22
 
3.3%
21
 
3.2%
21
 
3.2%
16
 
2.4%
16
 
2.4%
16
 
2.4%
14
 
2.1%
13
 
2.0%
Other values (154) 402
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 632
96.2%
Decimal Number 10
 
1.5%
Space Separator 5
 
0.8%
Close Punctuation 4
 
0.6%
Open Punctuation 4
 
0.6%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
93
 
14.7%
23
 
3.6%
22
 
3.5%
21
 
3.3%
21
 
3.3%
16
 
2.5%
16
 
2.5%
16
 
2.5%
14
 
2.2%
13
 
2.1%
Other values (147) 377
59.7%
Decimal Number
ValueCountFrequency (%)
2 5
50.0%
4 5
50.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 632
96.2%
Common 23
 
3.5%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
93
 
14.7%
23
 
3.6%
22
 
3.5%
21
 
3.3%
21
 
3.3%
16
 
2.5%
16
 
2.5%
16
 
2.5%
14
 
2.2%
13
 
2.1%
Other values (147) 377
59.7%
Common
ValueCountFrequency (%)
2 5
21.7%
4 5
21.7%
5
21.7%
) 4
17.4%
( 4
17.4%
Latin
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 632
96.2%
ASCII 25
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
93
 
14.7%
23
 
3.6%
22
 
3.5%
21
 
3.3%
21
 
3.3%
16
 
2.5%
16
 
2.5%
16
 
2.5%
14
 
2.2%
13
 
2.1%
Other values (147) 377
59.7%
ASCII
ValueCountFrequency (%)
2 5
20.0%
4 5
20.0%
5
20.0%
) 4
16.0%
( 4
16.0%
K 1
 
4.0%
S 1
 
4.0%
Distinct92
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum2002-01-11 00:00:00
Maximum2023-12-27 12:15:48
2024-05-11T17:42:44.822573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T17:42:44.946499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
I
97 
U
35 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 97
73.5%
U 35
 
26.5%

Length

2024-05-11T17:42:45.065904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:45.164606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 97
73.5%
u 35
 
26.5%

데이터갱신일자
Categorical

IMBALANCE 

Distinct35
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2018-08-31 23:59:59.0
96 
2022-12-08 23:05:00.0
 
2
2021-11-26 02:40:00.0
 
2
2021-12-08 22:08:00.0
 
1
2021-02-28 02:40:00.0
 
1
Other values (30)
30 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique32 ?
Unique (%)24.2%

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 row2019-01-30 02:40:00.0
5th row2018-08-31 23:59:59.0

Common Values

ValueCountFrequency (%)
2018-08-31 23:59:59.0 96
72.7%
2022-12-08 23:05:00.0 2
 
1.5%
2021-11-26 02:40:00.0 2
 
1.5%
2021-12-08 22:08:00.0 1
 
0.8%
2021-02-28 02:40:00.0 1
 
0.8%
2019-11-17 02:40:00.0 1
 
0.8%
2018-11-02 02:37:45.0 1
 
0.8%
2021-06-23 02:40:00.0 1
 
0.8%
2021-12-06 23:02:00.0 1
 
0.8%
2021-12-08 02:40:00.0 1
 
0.8%
Other values (25) 25
 
18.9%

Length

2024-05-11T17:42:45.251076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-31 96
36.4%
23:59:59.0 96
36.4%
02:40:00.0 16
 
6.1%
2021-12-08 3
 
1.1%
22:05:00.0 3
 
1.1%
23:02:00.0 3
 
1.1%
2022-10-31 3
 
1.1%
23:05:00.0 3
 
1.1%
2022-10-30 2
 
0.8%
2021-10-31 2
 
0.8%
Other values (33) 37
 
14.0%

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
공동탕업
107 
공동탕업+찜질시설서비스영업
21 
찜질시설서비스영업
 
2
목욕장업 기타
 
1
한증막업
 
1

Length

Max length14
Median length4
Mean length5.6893939
Min length4

Unique

Unique2 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 107
81.1%
공동탕업+찜질시설서비스영업 21
 
15.9%
찜질시설서비스영업 2
 
1.5%
목욕장업 기타 1
 
0.8%
한증막업 1
 
0.8%

Length

2024-05-11T17:42:45.365004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:45.461310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 107
80.5%
공동탕업+찜질시설서비스영업 21
 
15.8%
찜질시설서비스영업 2
 
1.5%
목욕장업 1
 
0.8%
기타 1
 
0.8%
한증막업 1
 
0.8%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct107
Distinct (%)92.2%
Missing16
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean193657.09
Minimum189919.55
Maximum196459.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T17:42:45.566352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189919.55
5-th percentile191209.36
Q1192179.63
median193546.83
Q3195210.04
95-th percentile196226.77
Maximum196459.67
Range6540.1215
Interquartile range (IQR)3030.4154

Descriptive statistics

Standard deviation1689.8925
Coefficient of variation (CV)0.0087262103
Kurtosis-1.2239325
Mean193657.09
Median Absolute Deviation (MAD)1566.2015
Skewness0.025219319
Sum22464222
Variance2855736.6
MonotonicityNot monotonic
2024-05-11T17:42:45.710153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194145.610839311 3
 
2.3%
194742.383947684 2
 
1.5%
191197.357926149 2
 
1.5%
195703.425296812 2
 
1.5%
192839.116937246 2
 
1.5%
195669.217619713 2
 
1.5%
193824.338955745 2
 
1.5%
196296.081869598 2
 
1.5%
190690.39505232 1
 
0.8%
191836.687761374 1
 
0.8%
Other values (97) 97
73.5%
(Missing) 16
 
12.1%
ValueCountFrequency (%)
189919.550051873 1
0.8%
190690.39505232 1
0.8%
191010.381048042 1
0.8%
191105.380400665 1
0.8%
191197.357926149 2
1.5%
191213.365899415 1
0.8%
191304.309048494 1
0.8%
191308.167010847 1
0.8%
191320.755477769 1
0.8%
191545.21578314 1
0.8%
ValueCountFrequency (%)
196459.671564584 1
0.8%
196447.498637694 1
0.8%
196353.529036801 1
0.8%
196296.081869598 2
1.5%
196252.091092294 1
0.8%
196218.332952408 1
0.8%
196163.808718137 1
0.8%
196132.264207886 1
0.8%
196125.985603111 1
0.8%
196087.923812924 1
0.8%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct107
Distinct (%)92.2%
Missing16
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean450134.94
Minimum448236.66
Maximum453342.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T17:42:45.832507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum448236.66
5-th percentile448650.21
Q1449571.52
median450119.56
Q3450541.27
95-th percentile451722.64
Maximum453342.22
Range5105.5667
Interquartile range (IQR)969.75678

Descriptive statistics

Standard deviation923.8947
Coefficient of variation (CV)0.0020524839
Kurtosis0.84743933
Mean450134.94
Median Absolute Deviation (MAD)525.0549
Skewness0.50545779
Sum52215653
Variance853581.41
MonotonicityNot monotonic
2024-05-11T17:42:46.236135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449985.629547365 3
 
2.3%
448757.883381233 2
 
1.5%
451340.139244336 2
 
1.5%
449330.800717721 2
 
1.5%
450119.061832193 2
 
1.5%
448393.658663222 2
 
1.5%
449880.653018992 2
 
1.5%
449733.196378806 2
 
1.5%
452720.313408595 1
 
0.8%
451679.587355137 1
 
0.8%
Other values (97) 97
73.5%
(Missing) 16
 
12.1%
ValueCountFrequency (%)
448236.655548283 1
0.8%
448350.940035676 1
0.8%
448393.658663222 2
1.5%
448589.76644586 1
0.8%
448644.311298762 1
0.8%
448652.176751796 1
0.8%
448716.823596557 1
0.8%
448757.883381233 2
1.5%
448762.652741445 1
0.8%
448885.430093289 1
0.8%
ValueCountFrequency (%)
453342.222208599 1
0.8%
452720.313408595 1
0.8%
451972.154018501 1
0.8%
451940.242232911 1
0.8%
451856.908089054 1
0.8%
451740.493764456 1
0.8%
451716.686023381 1
0.8%
451710.692569697 1
0.8%
451679.587355137 1
0.8%
451557.886706368 1
0.8%

위생업태명
Categorical

IMBALANCE 

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
공동탕업
96 
<NA>
19 
공동탕업+찜질시설서비스영업
14 
목욕장업 기타
 
1
한증막업
 
1

Length

Max length14
Median length4
Mean length5.1212121
Min length4

Unique

Unique3 ?
Unique (%)2.3%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 96
72.7%
<NA> 19
 
14.4%
공동탕업+찜질시설서비스영업 14
 
10.6%
목욕장업 기타 1
 
0.8%
한증막업 1
 
0.8%
찜질시설서비스영업 1
 
0.8%

Length

2024-05-11T17:42:46.358599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:46.448828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 96
72.2%
na 19
 
14.3%
공동탕업+찜질시설서비스영업 14
 
10.5%
목욕장업 1
 
0.8%
기타 1
 
0.8%
한증막업 1
 
0.8%
찜질시설서비스영업 1
 
0.8%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)15.1%
Missing26
Missing (%)19.7%
Infinite0
Infinite (%)0.0%
Mean4.3018868
Minimum0
Maximum18
Zeros5
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T17:42:46.548670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile9.75
Maximum18
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8790535
Coefficient of variation (CV)0.66925365
Kurtosis6.4044077
Mean4.3018868
Median Absolute Deviation (MAD)1
Skewness2.0780564
Sum456
Variance8.2889488
MonotonicityNot monotonic
2024-05-11T17:42:46.652112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 28
21.2%
4 24
18.2%
5 16
12.1%
2 11
 
8.3%
0 5
 
3.8%
6 5
 
3.8%
7 4
 
3.0%
1 3
 
2.3%
9 2
 
1.5%
8 2
 
1.5%
Other values (6) 6
 
4.5%
(Missing) 26
19.7%
ValueCountFrequency (%)
0 5
 
3.8%
1 3
 
2.3%
2 11
 
8.3%
3 28
21.2%
4 24
18.2%
5 16
12.1%
6 5
 
3.8%
7 4
 
3.0%
8 2
 
1.5%
9 2
 
1.5%
ValueCountFrequency (%)
18 1
 
0.8%
15 1
 
0.8%
13 1
 
0.8%
12 1
 
0.8%
11 1
 
0.8%
10 1
 
0.8%
9 2
 
1.5%
8 2
 
1.5%
7 4
3.0%
6 5
3.8%
Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
57 
1
46 
0
16 
2
3
 
5

Length

Max length4
Median length1
Mean length2.2954545
Min length1

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 57
43.2%
1 46
34.8%
0 16
 
12.1%
2 7
 
5.3%
3 5
 
3.8%
4 1
 
0.8%

Length

2024-05-11T17:42:46.770585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:46.876940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 57
43.2%
1 46
34.8%
0 16
 
12.1%
2 7
 
5.3%
3 5
 
3.8%
4 1
 
0.8%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)11.1%
Missing78
Missing (%)59.1%
Infinite0
Infinite (%)0.0%
Mean1.537037
Minimum0
Maximum5
Zeros12
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T17:42:46.966680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile3.35
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2086631
Coefficient of variation (CV)0.78635914
Kurtosis0.94507126
Mean1.537037
Median Absolute Deviation (MAD)1
Skewness0.77560566
Sum83
Variance1.4608665
MonotonicityNot monotonic
2024-05-11T17:42:47.053706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 20
 
15.2%
1 14
 
10.6%
0 12
 
9.1%
3 5
 
3.8%
5 2
 
1.5%
4 1
 
0.8%
(Missing) 78
59.1%
ValueCountFrequency (%)
0 12
9.1%
1 14
10.6%
2 20
15.2%
3 5
 
3.8%
4 1
 
0.8%
5 2
 
1.5%
ValueCountFrequency (%)
5 2
 
1.5%
4 1
 
0.8%
3 5
 
3.8%
2 20
15.2%
1 14
10.6%
0 12
9.1%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)10.1%
Missing63
Missing (%)47.7%
Infinite0
Infinite (%)0.0%
Mean1.8405797
Minimum0
Maximum6
Zeros7
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T17:42:47.136641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2440823
Coefficient of variation (CV)0.67591874
Kurtosis1.274472
Mean1.8405797
Median Absolute Deviation (MAD)1
Skewness0.92508138
Sum127
Variance1.5477408
MonotonicityNot monotonic
2024-05-11T17:42:47.223212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 24
 
18.2%
2 20
 
15.2%
3 13
 
9.8%
0 7
 
5.3%
4 2
 
1.5%
5 2
 
1.5%
6 1
 
0.8%
(Missing) 63
47.7%
ValueCountFrequency (%)
0 7
 
5.3%
1 24
18.2%
2 20
15.2%
3 13
9.8%
4 2
 
1.5%
5 2
 
1.5%
6 1
 
0.8%
ValueCountFrequency (%)
6 1
 
0.8%
5 2
 
1.5%
4 2
 
1.5%
3 13
9.8%
2 20
15.2%
1 24
18.2%
0 7
 
5.3%
Distinct4
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
85 
0
29 
1
15 
2
 
3

Length

Max length4
Median length4
Mean length2.9318182
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 85
64.4%
0 29
 
22.0%
1 15
 
11.4%
2 3
 
2.3%

Length

2024-05-11T17:42:47.328487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:47.413942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 85
64.4%
0 29
 
22.0%
1 15
 
11.4%
2 3
 
2.3%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
103 
1
16 
0
 
6
2
 
6
3
 
1

Length

Max length4
Median length4
Mean length3.3409091
Min length1

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 103
78.0%
1 16
 
12.1%
0 6
 
4.5%
2 6
 
4.5%
3 1
 
0.8%

Length

2024-05-11T17:42:47.517216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:47.607771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 103
78.0%
1 16
 
12.1%
0 6
 
4.5%
2 6
 
4.5%
3 1
 
0.8%

한실수
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
89 
0
43 

Length

Max length4
Median length4
Mean length3.0227273
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 89
67.4%
0 43
32.6%

Length

2024-05-11T17:42:47.719229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:47.805131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 89
67.4%
0 43
32.6%

양실수
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
89 
0
43 

Length

Max length4
Median length4
Mean length3.0227273
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 89
67.4%
0 43
32.6%

Length

2024-05-11T17:42:47.896374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:47.996141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 89
67.4%
0 43
32.6%

욕실수
Categorical

Distinct4
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
83 
0
32 
2
13 
1
 
4

Length

Max length4
Median length4
Mean length2.8863636
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 83
62.9%
0 32
 
24.2%
2 13
 
9.8%
1 4
 
3.0%

Length

2024-05-11T17:42:48.090617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:48.192038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
62.9%
0 32
 
24.2%
2 13
 
9.8%
1 4
 
3.0%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)1.8%
Missing19
Missing (%)14.4%
Memory size396.0 B
False
104 
True
 
9
(Missing)
19 
ValueCountFrequency (%)
False 104
78.8%
True 9
 
6.8%
(Missing) 19
 
14.4%
2024-05-11T17:42:48.284387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
89 
0
43 

Length

Max length4
Median length4
Mean length3.0227273
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 89
67.4%
0 43
32.6%

Length

2024-05-11T17:42:48.385478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:48.469498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 89
67.4%
0 43
32.6%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing132
Missing (%)100.0%
Memory size1.3 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing132
Missing (%)100.0%
Memory size1.3 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing132
Missing (%)100.0%
Memory size1.3 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
122 
임대
 
7
자가
 
3

Length

Max length4
Median length4
Mean length3.8484848
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> 122
92.4%
임대 7
 
5.3%
자가 3
 
2.3%

Length

2024-05-11T17:42:48.570611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:48.669027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 122
92.4%
임대 7
 
5.3%
자가 3
 
2.3%

세탁기수
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
115 
0
17 

Length

Max length4
Median length4
Mean length3.6136364
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> 115
87.1%
0 17
 
12.9%

Length

2024-05-11T17:42:48.764278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:48.849476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 115
87.1%
0 17
 
12.9%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
122 
0
 
10

Length

Max length4
Median length4
Mean length3.7727273
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> 122
92.4%
0 10
 
7.6%

Length

2024-05-11T17:42:48.938723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:49.024513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 122
92.4%
0 10
 
7.6%

남성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
122 
0
 
10

Length

Max length4
Median length4
Mean length3.7727273
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> 122
92.4%
0 10
 
7.6%

Length

2024-05-11T17:42:49.113377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:49.194762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 122
92.4%
0 10
 
7.6%

회수건조수
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
117 
0
15 

Length

Max length4
Median length4
Mean length3.6590909
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> 117
88.6%
0 15
 
11.4%

Length

2024-05-11T17:42:49.291903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:49.377604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 117
88.6%
0 15
 
11.4%

침대수
Categorical

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
117 
0
15 

Length

Max length4
Median length4
Mean length3.6590909
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> 117
88.6%
0 15
 
11.4%

Length

2024-05-11T17:42:49.481712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T17:42:49.596484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 117
88.6%
0 15
 
11.4%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.9%
Missing19
Missing (%)14.4%
Memory size396.0 B
False
113 
(Missing)
19 
ValueCountFrequency (%)
False 113
85.6%
(Missing) 19
 
14.4%
2024-05-11T17:42:49.673495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031300003130000-202-1963-0000119630619<NA>3폐업2폐업20110210<NA><NA><NA>02 3628209150.58121862서울특별시 마포구 아현동 613-11번지<NA><NA>행화탕2011-02-11 14:29:29I2018-08-31 23:59:59.0공동탕업195998.179771450048.944586공동탕업101<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131300003130000-202-1963-0000219630925<NA>3폐업2폐업20050408<NA><NA><NA>0207134192140.11121801서울특별시 마포구 공덕동 105-228번지<NA><NA>삼용탕2002-01-11 00:00:00I2018-08-31 23:59:59.0공동탕업196132.264208449580.978639공동탕업402<NA>0<NA>002N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231300003130000-202-1963-0000319630619<NA>3폐업2폐업20140121<NA><NA><NA>0207172117188.00121800서울특별시 마포구 공덕동 74-2번지서울특별시 마포구 만리재옛길 75 (공덕동)4205공덕탕2012-11-28 15:01:30I2018-08-31 23:59:59.0공동탕업196296.08187449733.196379공동탕업3<NA><NA>2<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331300003130000-202-1963-0000419630619<NA>3폐업2폐업20060503<NA><NA><NA>02712 4431119.00121801서울특별시 마포구 공덕동 117-1번지<NA><NA>마포한증원2019-01-28 15:25:47U2019-01-30 02:40:00.0공동탕업196163.808718449536.964272공동탕업1<NA>11<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431300003130000-202-1965-0000119651224<NA>3폐업2폐업20000804<NA><NA><NA>0207137365137.00121870서울특별시 마포구 염리동 9-122번지<NA><NA>서울탕2002-01-11 00:00:00I2018-08-31 23:59:59.0공동탕업195236.422714450442.260798공동탕업302<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531300003130000-202-1966-0000119660509<NA>3폐업2폐업20140515<NA><NA><NA>02 7159038210.19121876서울특별시 마포구 용강동 492번지<NA><NA>신석탕2014-09-03 15:34:30I2018-08-31 23:59:59.0공동탕업<NA><NA>공동탕업2<NA><NA>1<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631300003130000-202-1967-0000119671219<NA>3폐업2폐업20090128<NA><NA><NA>0203934817126.00121859서울특별시 마포구 아현동 336-5번지<NA><NA>고려왕비탕2006-09-12 00:00:00I2018-08-31 23:59:59.0공동탕업<NA><NA>공동탕업41<NA>1<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731300003130000-202-1967-0000219671028<NA>3폐업2폐업20050607<NA><NA><NA>0203348217250.52121807서울특별시 마포구 노고산동 54-3 90번지<NA><NA>은혜탕2003-06-19 00:00:00I2018-08-31 23:59:59.0공동탕업<NA><NA>공동탕업4<NA><NA>1<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831300003130000-202-1967-0000319671214<NA>3폐업2폐업20040930<NA><NA><NA>0203246473256.56121882서울특별시 마포구 창전동 389-4번지<NA><NA>창신탕2004-09-30 00:00:00I2018-08-31 23:59:59.0공동탕업193590.743216449405.273408공동탕업3<NA><NA>2<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931300003130000-202-1968-0000119681017<NA>3폐업2폐업20121004<NA><NA><NA>0207150664367.00121856서울특별시 마포구 신수동 449-2번지<NA><NA>덕윤탕2007-05-10 00:00:00I2018-08-31 23:59:59.0공동탕업<NA><NA>공동탕업4<NA>12<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
12231300003130000-202-2009-000012009-10-21<NA>3폐업2폐업2023-06-08<NA><NA><NA>02 3076212299.04121-850서울특별시 마포구 성산동 595 도시개발공사성산아파트서울특별시 마포구 월드컵로 205, 도시개발공사성산아파트 상가동 지하1층 24호 (성산동)3953마포복지목욕탕2023-06-13 10:10:14U2022-12-05 23:05:00.0공동탕업191197.357926451340.139244<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12331300003130000-202-2010-000012010-01-14<NA>1영업/정상1영업<NA><NA><NA><NA>02 3740400794.04121-830서울특별시 마포구 상암동 17-2 지하1층서울특별시 마포구 월드컵북로44길 40 (상암동, 지하1층)3930서울특별시마포구시설관리공단2023-12-26 11:28:12U2022-11-01 22:08:00.0공동탕업190690.395052452720.313409<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12431300003130000-202-2010-0000220100402<NA>3폐업2폐업20110711<NA><NA><NA>000260091190193.00121805서울특별시 마포구 공덕동 467번지 롯데마포시티호텔3층<NA><NA>(주)엠에스에스티케이스파2010-04-02 16:45:46I2018-08-31 23:59:59.0공동탕업195564.375758449186.933273공동탕업0033<NA><NA>002N0<NA><NA><NA><NA>0<NA><NA>00N
12531300003130000-202-2011-000012011-12-02<NA>1영업/정상1영업<NA><NA><NA><NA>0260160001624.63121-835서울특별시 마포구 상암동 1587서울특별시 마포구 월드컵북로58길 15 (상암동)3922스탠포트호텔서울2023-10-25 15:31:57U2022-10-30 22:07:00.0공동탕업189919.550052453342.222209<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12631300003130000-202-2013-0000120131125<NA>3폐업2폐업20160830<NA><NA><NA>02 3721193135.28121845서울특별시 마포구 성산동 181-4번지 지하1층서울특별시 마포구 모래내로5길 9 (성산동, 지하1층)3948도심속의 휠링2014-09-03 15:59:09I2018-08-31 23:59:59.0찜질시설서비스영업191971.673012451557.886706찜질시설서비스영업00<NA><NA>11000N0<NA><NA><NA><NA>0<NA><NA>00N
12731300003130000-202-2013-000022013-12-18<NA>3폐업2폐업2023-11-22<NA><NA><NA>02 706 7277183.09121-874서울특별시 마포구 염리동 161-7 지2층서울특별시 마포구 독막로 291, 지2층 (염리동)4151마포염리동점스포애니2023-11-22 15:09:28U2022-10-31 22:04:00.0공동탕업195114.98131449171.288831<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12831300003130000-202-2015-0000120150227<NA>3폐업2폐업20200608<NA><NA><NA>02 716 2117220.32121800서울특별시 마포구 공덕동 74-2번지서울특별시 마포구 만리재옛길 75, 1.2층 (공덕동)4205공덕탕2020-06-08 11:06:46U2020-06-10 02:40:00.0공동탕업196296.08187449733.196379공동탕업0012<NA><NA>002N0<NA><NA><NA><NA>00000N
12931300003130000-202-2023-000012023-08-31<NA>1영업/정상1영업<NA><NA><NA><NA><NA>133.07121-876서울특별시 마포구 용강동 122-16 지하층 우측호서울특별시 마포구 토정로32길 4, 지하층 우측호 (용강동)4163토정24시사우나찜질방2023-12-27 12:15:48U2022-11-01 22:09:00.0찜질시설서비스영업194742.383948448757.883381<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
13031300003130000-202-2023-000022023-09-05<NA>1영업/정상1영업<NA><NA><NA><NA><NA>50.00121-814서울특별시 마포구 도화동 353 현대홈타운 상가동 지하 101호서울특별시 마포구 새창로8길 72, 상가동 지하층 101호 (도화동, 현대홈타운)4180단풍나무짐2023-11-23 10:55:18U2022-10-31 22:05:00.0공동탕업+찜질시설서비스영업195669.21762448393.658663<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
13131300003130000-202-2023-000032023-10-18<NA>1영업/정상1영업<NA><NA><NA><NA>02 702 0112319.91121-838서울특별시 마포구 서교동 355-15 무광빌딩 지하1층 101호서울특별시 마포구 양화로16길 15, 무광빌딩 지하1층 101호 (서교동)403924시남성사우나2023-10-18 10:27:22I2022-10-30 22:00:00.0공동탕업192919.641105450117.167734<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>