Overview

Dataset statistics

Number of variables47
Number of observations164
Missing cells1859
Missing cells (%)24.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory64.8 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
Author종로구
URLhttps://data.seoul.go.kr/dataList/OA-19952/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
데이터갱신일자 is highly imbalanced (69.0%)Imbalance
업태구분명 is highly imbalanced (65.4%)Imbalance
위생업태명 is highly imbalanced (53.6%)Imbalance
발한실여부 is highly imbalanced (69.4%)Imbalance
건물소유구분명 is highly imbalanced (83.6%)Imbalance
세탁기수 is highly imbalanced (64.5%)Imbalance
여성종사자수 is highly imbalanced (80.3%)Imbalance
남성종사자수 is highly imbalanced (80.3%)Imbalance
회수건조수 is highly imbalanced (66.9%)Imbalance
침대수 is highly imbalanced (69.3%)Imbalance
인허가취소일자 has 164 (100.0%) missing valuesMissing
폐업일자 has 26 (15.9%) missing valuesMissing
휴업시작일자 has 164 (100.0%) missing valuesMissing
휴업종료일자 has 164 (100.0%) missing valuesMissing
재개업일자 has 164 (100.0%) missing valuesMissing
전화번호 has 4 (2.4%) missing valuesMissing
소재지우편번호 has 2 (1.2%) missing valuesMissing
지번주소 has 2 (1.2%) missing valuesMissing
도로명주소 has 119 (72.6%) missing valuesMissing
도로명우편번호 has 119 (72.6%) missing valuesMissing
좌표정보(X) has 20 (12.2%) missing valuesMissing
좌표정보(Y) has 20 (12.2%) missing valuesMissing
건물지상층수 has 72 (43.9%) missing valuesMissing
건물지하층수 has 76 (46.3%) missing valuesMissing
사용시작지상층 has 81 (49.4%) missing valuesMissing
사용끝지상층 has 134 (81.7%) missing valuesMissing
발한실여부 has 18 (11.0%) missing valuesMissing
조건부허가신고사유 has 164 (100.0%) missing valuesMissing
조건부허가시작일자 has 164 (100.0%) missing valuesMissing
조건부허가종료일자 has 164 (100.0%) missing valuesMissing
다중이용업소여부 has 18 (11.0%) 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 76 (46.3%) zerosZeros
건물지하층수 has 77 (47.0%) zerosZeros
사용시작지상층 has 72 (43.9%) zerosZeros
사용끝지상층 has 2 (1.2%) zerosZeros

Reproduction

Analysis started2024-05-11 06:34:53.223755
Analysis finished2024-05-11 06:34:54.502903
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3000000
164 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000
2nd row3000000
3rd row3000000
4th row3000000
5th row3000000

Common Values

ValueCountFrequency (%)
3000000 164
100.0%

Length

2024-05-11T15:34:54.612240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:54.788352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000 164
100.0%

관리번호
Text

UNIQUE 

Distinct164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T15:34:55.153272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3608
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

Unique164 ?
Unique (%)100.0%

Sample

1st row3000000-202-1960-00378
2nd row3000000-202-1960-00387
3rd row3000000-202-1960-00389
4th row3000000-202-1961-00380
5th row3000000-202-1961-00382
ValueCountFrequency (%)
3000000-202-1960-00378 1
 
0.6%
3000000-202-1992-00503 1
 
0.6%
3000000-202-1995-00473 1
 
0.6%
3000000-202-1993-00487 1
 
0.6%
3000000-202-1994-00468 1
 
0.6%
3000000-202-1994-00470 1
 
0.6%
3000000-202-1994-00488 1
 
0.6%
3000000-202-1994-00489 1
 
0.6%
3000000-202-1995-00471 1
 
0.6%
3000000-202-1995-00472 1
 
0.6%
Other values (154) 154
93.9%
2024-05-11T15:34:55.798666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1646
45.6%
- 492
 
13.6%
2 415
 
11.5%
3 247
 
6.8%
9 208
 
5.8%
1 192
 
5.3%
4 139
 
3.9%
8 99
 
2.7%
7 66
 
1.8%
6 55
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3116
86.4%
Dash Punctuation 492
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1646
52.8%
2 415
 
13.3%
3 247
 
7.9%
9 208
 
6.7%
1 192
 
6.2%
4 139
 
4.5%
8 99
 
3.2%
7 66
 
2.1%
6 55
 
1.8%
5 49
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1646
45.6%
- 492
 
13.6%
2 415
 
11.5%
3 247
 
6.8%
9 208
 
5.8%
1 192
 
5.3%
4 139
 
3.9%
8 99
 
2.7%
7 66
 
1.8%
6 55
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1646
45.6%
- 492
 
13.6%
2 415
 
11.5%
3 247
 
6.8%
9 208
 
5.8%
1 192
 
5.3%
4 139
 
3.9%
8 99
 
2.7%
7 66
 
1.8%
6 55
 
1.5%
Distinct156
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum1960-11-19 00:00:00
Maximum2023-12-13 00:00:00
2024-05-11T15:34:56.069622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:34:56.420764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing164
Missing (%)100.0%
Memory size1.6 KiB
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3
138 
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 (%)
3 138
84.1%
1 26
 
15.9%

Length

2024-05-11T15:34:56.753014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:56.953189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 138
84.1%
1 26
 
15.9%

영업상태명
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
폐업
138 
영업/정상
26 

Length

Max length5
Median length2
Mean length2.4756098
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 138
84.1%
영업/정상 26
 
15.9%

Length

2024-05-11T15:34:57.181542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:57.401189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 138
84.1%
영업/정상 26
 
15.9%
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2
138 
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 (%)
2 138
84.1%
1 26
 
15.9%

Length

2024-05-11T15:34:57.694258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:57.904592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 138
84.1%
1 26
 
15.9%
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
폐업
138 
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 138
84.1%
영업 26
 
15.9%

Length

2024-05-11T15:34:58.119256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:34:58.330015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 138
84.1%
영업 26
 
15.9%

폐업일자
Date

MISSING 

Distinct126
Distinct (%)91.3%
Missing26
Missing (%)15.9%
Memory size1.4 KiB
Minimum1988-12-08 00:00:00
Maximum2024-04-01 00:00:00
2024-05-11T15:34:58.556588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:34:58.936082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing164
Missing (%)100.0%
Memory size1.6 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing164
Missing (%)100.0%
Memory size1.6 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing164
Missing (%)100.0%
Memory size1.6 KiB

전화번호
Text

MISSING 

Distinct120
Distinct (%)75.0%
Missing4
Missing (%)2.4%
Memory size1.4 KiB
2024-05-11T15:34:59.397608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.15
Min length8

Characters and Unicode

Total characters1624
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

Unique113 ?
Unique (%)70.6%

Sample

1st row0207367280
2nd row02 00000
3rd row02 00000
4th row02 7654579
5th row02 7635366
ValueCountFrequency (%)
02 87
33.5%
0200000000 25
 
9.6%
00000 11
 
4.2%
7631106 3
 
1.2%
0222367757 2
 
0.8%
7417080 2
 
0.8%
7350516 2
 
0.8%
7432005 2
 
0.8%
7252059 1
 
0.4%
027659 1
 
0.4%
Other values (124) 124
47.7%
2024-05-11T15:35:00.223076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 542
33.4%
2 248
15.3%
7 154
 
9.5%
136
 
8.4%
6 100
 
6.2%
3 99
 
6.1%
1 85
 
5.2%
5 81
 
5.0%
4 79
 
4.9%
9 54
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1488
91.6%
Space Separator 136
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 542
36.4%
2 248
16.7%
7 154
 
10.3%
6 100
 
6.7%
3 99
 
6.7%
1 85
 
5.7%
5 81
 
5.4%
4 79
 
5.3%
9 54
 
3.6%
8 46
 
3.1%
Space Separator
ValueCountFrequency (%)
136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1624
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 542
33.4%
2 248
15.3%
7 154
 
9.5%
136
 
8.4%
6 100
 
6.2%
3 99
 
6.1%
1 85
 
5.2%
5 81
 
5.0%
4 79
 
4.9%
9 54
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 542
33.4%
2 248
15.3%
7 154
 
9.5%
136
 
8.4%
6 100
 
6.2%
3 99
 
6.1%
1 85
 
5.2%
5 81
 
5.0%
4 79
 
4.9%
9 54
 
3.3%
Distinct158
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T15:35:00.896819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.0060976
Min length3

Characters and Unicode

Total characters985
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

Unique156 ?
Unique (%)95.1%

Sample

1st row81.78
2nd row180.04
3rd row112.10
4th row223.07
5th row71.68
ValueCountFrequency (%)
422.19 4
 
2.4%
00 4
 
2.4%
806.10 1
 
0.6%
430.65 1
 
0.6%
81.78 1
 
0.6%
587.27 1
 
0.6%
980.09 1
 
0.6%
298.24 1
 
0.6%
326.14 1
 
0.6%
949.46 1
 
0.6%
Other values (148) 148
90.2%
2024-05-11T15:35:01.917125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 164
16.6%
1 112
11.4%
2 104
10.6%
0 100
10.2%
4 89
9.0%
3 77
7.8%
6 76
7.7%
5 70
7.1%
9 65
 
6.6%
7 61
 
6.2%
Other values (2) 67
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 813
82.5%
Other Punctuation 172
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 112
13.8%
2 104
12.8%
0 100
12.3%
4 89
10.9%
3 77
9.5%
6 76
9.3%
5 70
8.6%
9 65
8.0%
7 61
7.5%
8 59
7.3%
Other Punctuation
ValueCountFrequency (%)
. 164
95.3%
, 8
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 985
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 164
16.6%
1 112
11.4%
2 104
10.6%
0 100
10.2%
4 89
9.0%
3 77
7.8%
6 76
7.7%
5 70
7.1%
9 65
 
6.6%
7 61
 
6.2%
Other values (2) 67
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 985
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 164
16.6%
1 112
11.4%
2 104
10.6%
0 100
10.2%
4 89
9.0%
3 77
7.8%
6 76
7.7%
5 70
7.1%
9 65
 
6.6%
7 61
 
6.2%
Other values (2) 67
6.8%

소재지우편번호
Text

MISSING 

Distinct94
Distinct (%)58.0%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
2024-05-11T15:35:02.442692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0987654
Min length6

Characters and Unicode

Total characters988
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

Unique58 ?
Unique (%)35.8%

Sample

1st row110030
2nd row110450
3rd row110480
4th row110370
5th row110845
ValueCountFrequency (%)
110862 6
 
3.7%
110521 5
 
3.1%
110842 5
 
3.1%
110846 4
 
2.5%
110130 4
 
2.5%
110450 4
 
2.5%
110320 4
 
2.5%
110841 4
 
2.5%
110350 4
 
2.5%
110420 4
 
2.5%
Other values (84) 118
72.8%
2024-05-11T15:35:03.228169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 381
38.6%
0 267
27.0%
8 70
 
7.1%
2 63
 
6.4%
4 54
 
5.5%
3 41
 
4.1%
5 40
 
4.0%
6 23
 
2.3%
7 18
 
1.8%
- 16
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 972
98.4%
Dash Punctuation 16
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 381
39.2%
0 267
27.5%
8 70
 
7.2%
2 63
 
6.5%
4 54
 
5.6%
3 41
 
4.2%
5 40
 
4.1%
6 23
 
2.4%
7 18
 
1.9%
9 15
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 988
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 381
38.6%
0 267
27.0%
8 70
 
7.1%
2 63
 
6.4%
4 54
 
5.5%
3 41
 
4.1%
5 40
 
4.0%
6 23
 
2.3%
7 18
 
1.8%
- 16
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 381
38.6%
0 267
27.0%
8 70
 
7.1%
2 63
 
6.4%
4 54
 
5.5%
3 41
 
4.1%
5 40
 
4.0%
6 23
 
2.3%
7 18
 
1.8%
- 16
 
1.6%

지번주소
Text

MISSING 

Distinct151
Distinct (%)93.2%
Missing2
Missing (%)1.2%
Memory size1.4 KiB
2024-05-11T15:35:03.715489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length39.5
Mean length22.919753
Min length17

Characters and Unicode

Total characters3713
Distinct characters138
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)88.3%

Sample

1st row서울특별시 종로구 청운동 108-14번지
2nd row서울특별시 종로구 원남동 86-1번지
3rd row서울특별시 종로구 효제동 318-1번지
4th row서울특별시 종로구 묘동 141번지
5th row서울특별시 종로구 충신동 79-1번지
ValueCountFrequency (%)
서울특별시 162
23.0%
종로구 162
23.0%
창신동 16
 
2.3%
숭인동 15
 
2.1%
평창동 8
 
1.1%
낙원동 6
 
0.9%
운니동 5
 
0.7%
관수동 5
 
0.7%
명륜1가 5
 
0.7%
관철동 4
 
0.6%
Other values (242) 317
45.0%
2024-05-11T15:35:04.524426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
682
18.4%
178
 
4.8%
176
 
4.7%
165
 
4.4%
164
 
4.4%
163
 
4.4%
163
 
4.4%
162
 
4.4%
162
 
4.4%
162
 
4.4%
Other values (128) 1536
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2246
60.5%
Space Separator 682
 
18.4%
Decimal Number 639
 
17.2%
Dash Punctuation 127
 
3.4%
Other Punctuation 9
 
0.2%
Uppercase Letter 8
 
0.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
178
 
7.9%
176
 
7.8%
165
 
7.3%
164
 
7.3%
163
 
7.3%
163
 
7.3%
162
 
7.2%
162
 
7.2%
162
 
7.2%
151
 
6.7%
Other values (109) 600
26.7%
Decimal Number
ValueCountFrequency (%)
1 156
24.4%
2 104
16.3%
5 67
10.5%
0 65
10.2%
3 52
 
8.1%
4 48
 
7.5%
6 44
 
6.9%
8 35
 
5.5%
9 35
 
5.5%
7 33
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
B 6
75.0%
K 1
 
12.5%
D 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
, 8
88.9%
/ 1
 
11.1%
Space Separator
ValueCountFrequency (%)
682
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2246
60.5%
Common 1459
39.3%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
178
 
7.9%
176
 
7.8%
165
 
7.3%
164
 
7.3%
163
 
7.3%
163
 
7.3%
162
 
7.2%
162
 
7.2%
162
 
7.2%
151
 
6.7%
Other values (109) 600
26.7%
Common
ValueCountFrequency (%)
682
46.7%
1 156
 
10.7%
- 127
 
8.7%
2 104
 
7.1%
5 67
 
4.6%
0 65
 
4.5%
3 52
 
3.6%
4 48
 
3.3%
6 44
 
3.0%
8 35
 
2.4%
Other values (6) 79
 
5.4%
Latin
ValueCountFrequency (%)
B 6
75.0%
K 1
 
12.5%
D 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2246
60.5%
ASCII 1467
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
682
46.5%
1 156
 
10.6%
- 127
 
8.7%
2 104
 
7.1%
5 67
 
4.6%
0 65
 
4.4%
3 52
 
3.5%
4 48
 
3.3%
6 44
 
3.0%
8 35
 
2.4%
Other values (9) 87
 
5.9%
Hangul
ValueCountFrequency (%)
178
 
7.9%
176
 
7.8%
165
 
7.3%
164
 
7.3%
163
 
7.3%
163
 
7.3%
162
 
7.2%
162
 
7.2%
162
 
7.2%
151
 
6.7%
Other values (109) 600
26.7%

도로명주소
Text

MISSING 

Distinct44
Distinct (%)97.8%
Missing119
Missing (%)72.6%
Memory size1.4 KiB
2024-05-11T15:35:05.034980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length36
Mean length30.355556
Min length22

Characters and Unicode

Total characters1366
Distinct characters127
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)95.6%

Sample

1st row서울특별시 종로구 창신길 22 (창신동)
2nd row서울특별시 종로구 종로 313-6 (창신동)
3rd row서울특별시 종로구 계동길 84-3 (계동)
4th row서울특별시 종로구 자하문로 35-6 (통인동)
5th row서울특별시 종로구 숭인동길 46 (숭인동)
ValueCountFrequency (%)
서울특별시 45
 
17.1%
종로구 45
 
17.1%
종로 5
 
1.9%
창신동 5
 
1.9%
지하2층 5
 
1.9%
지하1층 4
 
1.5%
숭인동 4
 
1.5%
4층 3
 
1.1%
19 3
 
1.1%
96 3
 
1.1%
Other values (125) 141
53.6%
2024-05-11T15:35:05.785500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
218
 
16.0%
88
 
6.4%
59
 
4.3%
47
 
3.4%
46
 
3.4%
46
 
3.4%
) 45
 
3.3%
( 45
 
3.3%
45
 
3.3%
45
 
3.3%
Other values (117) 682
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 806
59.0%
Space Separator 218
 
16.0%
Decimal Number 194
 
14.2%
Close Punctuation 45
 
3.3%
Open Punctuation 45
 
3.3%
Other Punctuation 39
 
2.9%
Dash Punctuation 13
 
1.0%
Uppercase Letter 6
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
88
 
10.9%
59
 
7.3%
47
 
5.8%
46
 
5.7%
46
 
5.7%
45
 
5.6%
45
 
5.6%
45
 
5.6%
45
 
5.6%
25
 
3.1%
Other values (100) 315
39.1%
Decimal Number
ValueCountFrequency (%)
2 40
20.6%
1 36
18.6%
3 22
11.3%
6 20
10.3%
0 15
 
7.7%
4 15
 
7.7%
5 14
 
7.2%
7 12
 
6.2%
8 11
 
5.7%
9 9
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
B 5
83.3%
K 1
 
16.7%
Space Separator
ValueCountFrequency (%)
218
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Other Punctuation
ValueCountFrequency (%)
, 39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 806
59.0%
Common 554
40.6%
Latin 6
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
88
 
10.9%
59
 
7.3%
47
 
5.8%
46
 
5.7%
46
 
5.7%
45
 
5.6%
45
 
5.6%
45
 
5.6%
45
 
5.6%
25
 
3.1%
Other values (100) 315
39.1%
Common
ValueCountFrequency (%)
218
39.4%
) 45
 
8.1%
( 45
 
8.1%
2 40
 
7.2%
, 39
 
7.0%
1 36
 
6.5%
3 22
 
4.0%
6 20
 
3.6%
0 15
 
2.7%
4 15
 
2.7%
Other values (5) 59
 
10.6%
Latin
ValueCountFrequency (%)
B 5
83.3%
K 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 806
59.0%
ASCII 560
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
218
38.9%
) 45
 
8.0%
( 45
 
8.0%
2 40
 
7.1%
, 39
 
7.0%
1 36
 
6.4%
3 22
 
3.9%
6 20
 
3.6%
0 15
 
2.7%
4 15
 
2.7%
Other values (7) 65
 
11.6%
Hangul
ValueCountFrequency (%)
88
 
10.9%
59
 
7.3%
47
 
5.8%
46
 
5.7%
46
 
5.7%
45
 
5.6%
45
 
5.6%
45
 
5.6%
45
 
5.6%
25
 
3.1%
Other values (100) 315
39.1%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct38
Distinct (%)84.4%
Missing119
Missing (%)72.6%
Infinite0
Infinite (%)0.0%
Mean3117.8444
Minimum3009
Maximum3197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:35:06.178246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3009
5-th percentile3018.6
Q13100
median3121
Q33163
95-th percentile3192.8
Maximum3197
Range188
Interquartile range (IQR)63

Descriptive statistics

Standard deviation54.470574
Coefficient of variation (CV)0.017470587
Kurtosis-0.61286724
Mean3117.8444
Median Absolute Deviation (MAD)42
Skewness-0.48450148
Sum140303
Variance2967.0434
MonotonicityNot monotonic
2024-05-11T15:35:06.442329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3192 3
 
1.8%
3132 2
 
1.2%
3025 2
 
1.2%
3119 2
 
1.2%
3106 2
 
1.2%
3193 2
 
1.2%
3053 1
 
0.6%
3168 1
 
0.6%
3057 1
 
0.6%
3121 1
 
0.6%
Other values (28) 28
 
17.1%
(Missing) 119
72.6%
ValueCountFrequency (%)
3009 1
0.6%
3012 1
0.6%
3017 1
0.6%
3025 2
1.2%
3036 1
0.6%
3040 1
0.6%
3053 1
0.6%
3057 1
0.6%
3070 1
0.6%
3077 1
0.6%
ValueCountFrequency (%)
3197 1
 
0.6%
3193 2
1.2%
3192 3
1.8%
3175 1
 
0.6%
3173 1
 
0.6%
3170 1
 
0.6%
3168 1
 
0.6%
3165 1
 
0.6%
3163 1
 
0.6%
3157 1
 
0.6%
Distinct151
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T15:35:06.861455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length4.7682927
Min length2

Characters and Unicode

Total characters782
Distinct characters180
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

Unique138 ?
Unique (%)84.1%

Sample

1st row청운탕
2nd row신원탕
3rd row효제탕
4th row수은탕
5th row동산탕
ValueCountFrequency (%)
월드대중 2
 
1.1%
금호대중탕 2
 
1.1%
호텔 2
 
1.1%
보성탕 2
 
1.1%
인사동 2
 
1.1%
귀빈탕 2
 
1.1%
마동탕 2
 
1.1%
월드대중탕 2
 
1.1%
사우나 2
 
1.1%
보건탕 2
 
1.1%
Other values (153) 159
88.8%
2024-05-11T15:35:07.488354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
103
 
13.2%
33
 
4.2%
30
 
3.8%
30
 
3.8%
28
 
3.6%
22
 
2.8%
19
 
2.4%
17
 
2.2%
15
 
1.9%
12
 
1.5%
Other values (170) 473
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 753
96.3%
Space Separator 15
 
1.9%
Decimal Number 5
 
0.6%
Close Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%
Uppercase Letter 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
103
 
13.7%
33
 
4.4%
30
 
4.0%
30
 
4.0%
28
 
3.7%
22
 
2.9%
19
 
2.5%
17
 
2.3%
12
 
1.6%
12
 
1.6%
Other values (161) 447
59.4%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
4 1
 
20.0%
1 1
 
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
P 1
33.3%
S 1
33.3%
Space Separator
ValueCountFrequency (%)
15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 753
96.3%
Common 26
 
3.3%
Latin 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
103
 
13.7%
33
 
4.4%
30
 
4.0%
30
 
4.0%
28
 
3.7%
22
 
2.9%
19
 
2.5%
17
 
2.3%
12
 
1.6%
12
 
1.6%
Other values (161) 447
59.4%
Common
ValueCountFrequency (%)
15
57.7%
) 3
 
11.5%
2 3
 
11.5%
( 3
 
11.5%
4 1
 
3.8%
1 1
 
3.8%
Latin
ValueCountFrequency (%)
A 1
33.3%
P 1
33.3%
S 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 753
96.3%
ASCII 29
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
103
 
13.7%
33
 
4.4%
30
 
4.0%
30
 
4.0%
28
 
3.7%
22
 
2.9%
19
 
2.5%
17
 
2.3%
12
 
1.6%
12
 
1.6%
Other values (161) 447
59.4%
ASCII
ValueCountFrequency (%)
15
51.7%
) 3
 
10.3%
2 3
 
10.3%
( 3
 
10.3%
A 1
 
3.4%
P 1
 
3.4%
S 1
 
3.4%
4 1
 
3.4%
1 1
 
3.4%
Distinct94
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum1999-06-07 00:00:00
Maximum2024-04-23 11:27:52
2024-05-11T15:35:07.729744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:35:07.966264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
I
137 
U
27 

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 137
83.5%
U 27
 
16.5%

Length

2024-05-11T15:35:08.624020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:08.790889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 137
83.5%
u 27
 
16.5%

데이터갱신일자
Categorical

IMBALANCE 

Distinct25
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2018-08-31 23:59:59.0
135 
2023-12-03 22:01:00.0
 
6
2023-12-04 00:03:00.0
 
1
2022-10-30 22:09:00.0
 
1
2019-07-03 02:40:00.0
 
1
Other values (20)
20 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique23 ?
Unique (%)14.0%

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 23:59:59.0 135
82.3%
2023-12-03 22:01:00.0 6
 
3.7%
2023-12-04 00:03:00.0 1
 
0.6%
2022-10-30 22:09:00.0 1
 
0.6%
2019-07-03 02:40:00.0 1
 
0.6%
2018-09-18 23:59:59.0 1
 
0.6%
2021-08-04 02:40:00.0 1
 
0.6%
2020-09-24 02:40:00.0 1
 
0.6%
2022-10-31 23:00:00.0 1
 
0.6%
2020-10-18 02:40:00.0 1
 
0.6%
Other values (15) 15
 
9.1%

Length

2024-05-11T15:35:08.970692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
23:59:59.0 136
41.5%
2018-08-31 135
41.2%
02:40:00.0 9
 
2.7%
2023-12-03 7
 
2.1%
22:01:00.0 6
 
1.8%
23:00:00.0 2
 
0.6%
23:09:00.0 2
 
0.6%
2022-10-30 2
 
0.6%
00:03:00.0 2
 
0.6%
2021-10-27 1
 
0.3%
Other values (26) 26
 
7.9%

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
공동탕업
142 
한증막업
 
9
목욕장업 기타
 
7
공동탕업+찜질시설서비스영업
 
5
찜질시설서비스영업
 
1

Length

Max length14
Median length4
Mean length4.4634146
Min length4

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 142
86.6%
한증막업 9
 
5.5%
목욕장업 기타 7
 
4.3%
공동탕업+찜질시설서비스영업 5
 
3.0%
찜질시설서비스영업 1
 
0.6%

Length

2024-05-11T15:35:09.183313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:09.369228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 142
83.0%
한증막업 9
 
5.3%
목욕장업 7
 
4.1%
기타 7
 
4.1%
공동탕업+찜질시설서비스영업 5
 
2.9%
찜질시설서비스영업 1
 
0.6%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct124
Distinct (%)86.1%
Missing20
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean199213.11
Minimum196092.74
Maximum201948.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:35:09.581291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum196092.74
5-th percentile196585.85
Q1198145.47
median199062.86
Q3200441.23
95-th percentile201551.7
Maximum201948.56
Range5855.8165
Interquartile range (IQR)2295.7542

Descriptive statistics

Standard deviation1588.3812
Coefficient of variation (CV)0.0079732763
Kurtosis-0.94218951
Mean199213.11
Median Absolute Deviation (MAD)1302.7994
Skewness-0.12518232
Sum28686688
Variance2522954.8
MonotonicityNot monotonic
2024-05-11T15:35:09.823597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198966.466796388 5
 
3.0%
199045.043602772 3
 
1.8%
201344.918271945 3
 
1.8%
201302.883783666 3
 
1.8%
199302.818541341 2
 
1.2%
197583.394691008 2
 
1.2%
199961.039493914 2
 
1.2%
201908.102083278 2
 
1.2%
198920.853316964 2
 
1.2%
196467.975088253 2
 
1.2%
Other values (114) 118
72.0%
(Missing) 20
 
12.2%
ValueCountFrequency (%)
196092.743907822 1
0.6%
196216.586675488 1
0.6%
196318.153259189 1
0.6%
196339.659911355 1
0.6%
196377.526734114 1
0.6%
196467.975088253 2
1.2%
196578.171451494 1
0.6%
196629.359036534 1
0.6%
196649.891554192 1
0.6%
196659.343136833 1
0.6%
ValueCountFrequency (%)
201948.560407436 1
0.6%
201908.102083278 2
1.2%
201890.636280279 1
0.6%
201857.226592825 1
0.6%
201752.877100553 1
0.6%
201741.450560034 1
0.6%
201572.767098652 1
0.6%
201432.311420646 2
1.2%
201400.866708175 1
0.6%
201381.869145567 1
0.6%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct124
Distinct (%)86.1%
Missing20
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean452736.74
Minimum451811.27
Maximum456478.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:35:10.105610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum451811.27
5-th percentile451904.14
Q1452111.9
median452409.69
Q3452743.59
95-th percentile455869.2
Maximum456478.44
Range4667.1643
Interquartile range (IQR)631.68459

Descriptive statistics

Standard deviation1050.0437
Coefficient of variation (CV)0.0023193251
Kurtosis4.3142805
Mean452736.74
Median Absolute Deviation (MAD)310.31696
Skewness2.1945176
Sum65194091
Variance1102591.8
MonotonicityNot monotonic
2024-05-11T15:35:10.401819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452608.062544497 5
 
3.0%
451939.677286976 3
 
1.8%
452514.172975774 3
 
1.8%
451982.388503793 3
 
1.8%
451834.817165156 2
 
1.2%
452513.612017871 2
 
1.2%
452169.557249756 2
 
1.2%
452325.377310889 2
 
1.2%
452332.712151374 2
 
1.2%
452566.134030104 2
 
1.2%
Other values (114) 118
72.0%
(Missing) 20
 
12.2%
ValueCountFrequency (%)
451811.272474605 1
 
0.6%
451826.346589316 1
 
0.6%
451834.817165156 2
1.2%
451873.15032812 1
 
0.6%
451882.607111048 1
 
0.6%
451897.183587236 1
 
0.6%
451901.406014861 1
 
0.6%
451919.655473692 1
 
0.6%
451939.677286976 3
1.8%
451955.795979598 1
 
0.6%
ValueCountFrequency (%)
456478.436790904 1
0.6%
456251.740629682 1
0.6%
456198.595931715 1
0.6%
456108.810250198 2
1.2%
456007.528701216 1
0.6%
456003.295259389 1
0.6%
455959.269196762 1
0.6%
455358.793133704 1
0.6%
455080.991010485 1
0.6%
454934.50415762 1
0.6%

위생업태명
Categorical

IMBALANCE 

Distinct6
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
공동탕업
127 
<NA>
18 
한증막업
 
8
목욕장업 기타
 
6
공동탕업+찜질시설서비스영업
 
4

Length

Max length14
Median length4
Mean length4.3841463
Min length4

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 127
77.4%
<NA> 18
 
11.0%
한증막업 8
 
4.9%
목욕장업 기타 6
 
3.7%
공동탕업+찜질시설서비스영업 4
 
2.4%
찜질시설서비스영업 1
 
0.6%

Length

2024-05-11T15:35:10.785900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:11.010675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 127
74.7%
na 18
 
10.6%
한증막업 8
 
4.7%
목욕장업 6
 
3.5%
기타 6
 
3.5%
공동탕업+찜질시설서비스영업 4
 
2.4%
찜질시설서비스영업 1
 
0.6%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)12.0%
Missing72
Missing (%)43.9%
Infinite0
Infinite (%)0.0%
Mean1.1413043
Minimum0
Maximum16
Zeros76
Zeros (%)46.3%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:35:11.196478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.1433919
Coefficient of variation (CV)2.75421
Kurtosis10.96518
Mean1.1413043
Median Absolute Deviation (MAD)0
Skewness3.2866713
Sum105
Variance9.8809126
MonotonicityNot monotonic
2024-05-11T15:35:11.430336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 76
46.3%
5 3
 
1.8%
4 3
 
1.8%
2 2
 
1.2%
8 2
 
1.2%
16 1
 
0.6%
13 1
 
0.6%
3 1
 
0.6%
10 1
 
0.6%
15 1
 
0.6%
(Missing) 72
43.9%
ValueCountFrequency (%)
0 76
46.3%
1 1
 
0.6%
2 2
 
1.2%
3 1
 
0.6%
4 3
 
1.8%
5 3
 
1.8%
8 2
 
1.2%
10 1
 
0.6%
13 1
 
0.6%
15 1
 
0.6%
ValueCountFrequency (%)
16 1
 
0.6%
15 1
 
0.6%
13 1
 
0.6%
10 1
 
0.6%
8 2
1.2%
5 3
1.8%
4 3
1.8%
3 1
 
0.6%
2 2
1.2%
1 1
 
0.6%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)8.0%
Missing76
Missing (%)46.3%
Infinite0
Infinite (%)0.0%
Mean0.31818182
Minimum0
Maximum7
Zeros77
Zeros (%)47.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:35:11.639947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0885555
Coefficient of variation (CV)3.4211743
Kurtosis20.812657
Mean0.31818182
Median Absolute Deviation (MAD)0
Skewness4.3654745
Sum28
Variance1.184953
MonotonicityNot monotonic
2024-05-11T15:35:11.819273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 77
47.0%
1 5
 
3.0%
2 2
 
1.2%
5 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
7 1
 
0.6%
(Missing) 76
46.3%
ValueCountFrequency (%)
0 77
47.0%
1 5
 
3.0%
2 2
 
1.2%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
7 1
 
0.6%
ValueCountFrequency (%)
7 1
 
0.6%
5 1
 
0.6%
4 1
 
0.6%
3 1
 
0.6%
2 2
 
1.2%
1 5
 
3.0%
0 77
47.0%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)7.2%
Missing81
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean0.45783133
Minimum0
Maximum13
Zeros72
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:35:11.975549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.8
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6986451
Coefficient of variation (CV)3.7101985
Kurtosis37.324086
Mean0.45783133
Median Absolute Deviation (MAD)0
Skewness5.6118196
Sum38
Variance2.8853952
MonotonicityNot monotonic
2024-05-11T15:35:12.223704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 72
43.9%
1 4
 
2.4%
4 3
 
1.8%
2 2
 
1.2%
13 1
 
0.6%
5 1
 
0.6%
(Missing) 81
49.4%
ValueCountFrequency (%)
0 72
43.9%
1 4
 
2.4%
2 2
 
1.2%
4 3
 
1.8%
5 1
 
0.6%
13 1
 
0.6%
ValueCountFrequency (%)
13 1
 
0.6%
5 1
 
0.6%
4 3
 
1.8%
2 2
 
1.2%
1 4
 
2.4%
0 72
43.9%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)20.0%
Missing134
Missing (%)81.7%
Infinite0
Infinite (%)0.0%
Mean1.9666667
Minimum0
Maximum13
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T15:35:12.434666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q11
median1
Q32
95-th percentile5.1
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4562845
Coefficient of variation (CV)1.2489582
Kurtosis14.353362
Mean1.9666667
Median Absolute Deviation (MAD)0
Skewness3.496483
Sum59
Variance6.0333333
MonotonicityNot monotonic
2024-05-11T15:35:12.600392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 18
 
11.0%
2 5
 
3.0%
4 3
 
1.8%
0 2
 
1.2%
13 1
 
0.6%
6 1
 
0.6%
(Missing) 134
81.7%
ValueCountFrequency (%)
0 2
 
1.2%
1 18
11.0%
2 5
 
3.0%
4 3
 
1.8%
6 1
 
0.6%
13 1
 
0.6%
ValueCountFrequency (%)
13 1
 
0.6%
6 1
 
0.6%
4 3
 
1.8%
2 5
 
3.0%
1 18
11.0%
0 2
 
1.2%
Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
77 
0
73 
1
12 
2
 
2

Length

Max length4
Median length1
Mean length2.4085366
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 77
47.0%
0 73
44.5%
1 12
 
7.3%
2 2
 
1.2%

Length

2024-05-11T15:35:12.871306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:13.111078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 77
47.0%
0 73
44.5%
1 12
 
7.3%
2 2
 
1.2%
Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
123 
2
28 
1
 
10
0
 
3

Length

Max length4
Median length4
Mean length3.25
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> 123
75.0%
2 28
 
17.1%
1 10
 
6.1%
0 3
 
1.8%

Length

2024-05-11T15:35:13.331302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:13.512576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 123
75.0%
2 28
 
17.1%
1 10
 
6.1%
0 3
 
1.8%

한실수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
83 
0
81 

Length

Max length4
Median length4
Mean length2.5182927
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 83
50.6%
0 81
49.4%

Length

2024-05-11T15:35:13.683882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:13.852776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
50.6%
0 81
49.4%

양실수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
83 
0
81 

Length

Max length4
Median length4
Mean length2.5182927
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 83
50.6%
0 81
49.4%

Length

2024-05-11T15:35:14.095094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:14.368575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
50.6%
0 81
49.4%

욕실수
Categorical

Distinct6
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
78 
0
74 
2
4
 
2
6
 
1

Length

Max length4
Median length1
Mean length2.4268293
Min length1

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 78
47.6%
0 74
45.1%
2 8
 
4.9%
4 2
 
1.2%
6 1
 
0.6%
1 1
 
0.6%

Length

2024-05-11T15:35:14.561824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:14.766630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 78
47.6%
0 74
45.1%
2 8
 
4.9%
4 2
 
1.2%
6 1
 
0.6%
1 1
 
0.6%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)1.4%
Missing18
Missing (%)11.0%
Memory size460.0 B
False
138 
True
 
8
(Missing)
18 
ValueCountFrequency (%)
False 138
84.1%
True 8
 
4.9%
(Missing) 18
 
11.0%
2024-05-11T15:35:14.931553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
83 
0
81 

Length

Max length4
Median length4
Mean length2.5182927
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 83
50.6%
0 81
49.4%

Length

2024-05-11T15:35:15.157060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:15.369619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
50.6%
0 81
49.4%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing164
Missing (%)100.0%
Memory size1.6 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing164
Missing (%)100.0%
Memory size1.6 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing164
Missing (%)100.0%
Memory size1.6 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
158 
임대
 
4
자가
 
2

Length

Max length4
Median length4
Mean length3.9268293
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> 158
96.3%
임대 4
 
2.4%
자가 2
 
1.2%

Length

2024-05-11T15:35:15.527142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:15.704031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 158
96.3%
임대 4
 
2.4%
자가 2
 
1.2%

세탁기수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
153 
0
 
11

Length

Max length4
Median length4
Mean length3.7987805
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> 153
93.3%
0 11
 
6.7%

Length

2024-05-11T15:35:15.887469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:16.066177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 153
93.3%
0 11
 
6.7%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
159 
0
 
5

Length

Max length4
Median length4
Mean length3.9085366
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> 159
97.0%
0 5
 
3.0%

Length

2024-05-11T15:35:16.234455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:16.405096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 159
97.0%
0 5
 
3.0%

남성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
159 
0
 
5

Length

Max length4
Median length4
Mean length3.9085366
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> 159
97.0%
0 5
 
3.0%

Length

2024-05-11T15:35:16.581587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:16.757662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 159
97.0%
0 5
 
3.0%

회수건조수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
154 
0
 
10

Length

Max length4
Median length4
Mean length3.8170732
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> 154
93.9%
0 10
 
6.1%

Length

2024-05-11T15:35:16.945648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:17.075350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 154
93.9%
0 10
 
6.1%

침대수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
155 
0
 
9

Length

Max length4
Median length4
Mean length3.8353659
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
94.5%
0 9
 
5.5%

Length

2024-05-11T15:35:17.223971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:35:17.415579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 155
94.5%
0 9
 
5.5%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing18
Missing (%)11.0%
Memory size460.0 B
False
146 
(Missing)
18 
ValueCountFrequency (%)
False 146
89.0%
(Missing) 18
 
11.0%
2024-05-11T15:35:17.572031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030000003000000-202-1960-0037819601119<NA>3폐업2폐업20000415<NA><NA><NA>020736728081.78110030서울특별시 종로구 청운동 108-14번지<NA><NA>청운탕2000-05-03 00:00:00I2018-08-31 23:59:59.0공동탕업197322.999823453585.548253공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130000003000000-202-1960-0038719601230<NA>3폐업2폐업19900521<NA><NA><NA>02 00000180.04110450서울특별시 종로구 원남동 86-1번지<NA><NA>신원탕2003-02-07 00:00:00I2018-08-31 23:59:59.0공동탕업199723.996906452550.98055공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230000003000000-202-1960-0038919601231<NA>3폐업2폐업19930513<NA><NA><NA>02 00000112.10110480서울특별시 종로구 효제동 318-1번지<NA><NA>효제탕2001-11-20 00:00:00I2018-08-31 23:59:59.0공동탕업200078.609206452158.608874공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330000003000000-202-1961-0038019611230<NA>3폐업2폐업20021014<NA><NA><NA>02 7654579223.07110370서울특별시 종로구 묘동 141번지<NA><NA>수은탕2002-10-15 00:00:00I2018-08-31 23:59:59.0공동탕업199129.658326452402.028533공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430000003000000-202-1961-0038219611230<NA>3폐업2폐업19980109<NA><NA><NA>02 763536671.68110845서울특별시 종로구 충신동 79-1번지<NA><NA>동산탕2001-11-20 00:00:00I2018-08-31 23:59:59.0공동탕업<NA><NA>공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530000003000000-202-1962-0037919620518<NA>3폐업2폐업19910920<NA><NA><NA>0200000000130.40110410서울특별시 종로구 인의동 28-15번지<NA><NA>인의탕2003-02-07 00:00:00I2018-08-31 23:59:59.0공동탕업199807.217011452291.476067공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
630000003000000-202-1963-0039419630605<NA>3폐업2폐업19910701<NA><NA><NA>020000000092.40110033서울특별시 종로구 효자동 188-6번지<NA><NA>효자탕2001-09-29 00:00:00I2018-08-31 23:59:59.0공동탕업<NA><NA>공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
730000003000000-202-1963-0039919630121<NA>3폐업2폐업19960127<NA><NA><NA>02 00000114.78110522서울특별시 종로구 명륜2가 123-0번지<NA><NA>성균탕2001-09-29 00:00:00I2018-08-31 23:59:59.0공동탕업199706.73479453634.388072공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
830000003000000-202-1963-0041019630613<NA>1영업/정상1영업<NA><NA><NA><NA>02 7634343249.10110855서울특별시 종로구 창신동 578-1번지서울특별시 종로구 창신길 22 (창신동)3106현대옥사우나2017-10-30 15:17:39I2018-08-31 23:59:59.0공동탕업200904.097307452258.945718공동탕업<NA><NA><NA>1<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
930000003000000-202-1963-0041319630613<NA>3폐업2폐업19990604<NA><NA><NA>020928451336.00110827서울특별시 종로구 숭인동 1062-0번지<NA><NA>신설탕1999-06-07 00:00:00I2018-08-31 23:59:59.0공동탕업201857.226593452601.660021공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
15430000003000000-202-2014-0000120140331<NA>1영업/정상1영업<NA><NA><NA><NA>02 67301133193.31<NA><NA>서울특별시 종로구 삼일대로30길 31 (익선동, 이비스 앰배서더 인사동 호텔)3132호텔 이비스앰배서더 인사동2022-03-22 13:52:02U2022-03-24 02:40:00.0공동탕업198999.525786452484.270873공동탕업00<NA><NA>11002N0<NA><NA><NA>자가0<NA><NA>00N
15530000003000000-202-2014-0000220141231<NA>3폐업2폐업20180523<NA><NA><NA><NA>264.00110420서울특별시 종로구 관수동 20번지서울특별시 종로구 수표로 96, 4층 (관수동, 국일관)3192힐링한강스파2018-05-23 09:13:19I2018-08-31 23:59:59.0공동탕업199045.043603451939.677287공동탕업15744<NA><NA>002N0<NA><NA><NA><NA>00000N
15630000003000000-202-2015-0000120150526<NA>3폐업2폐업20200909<NA><NA><NA><NA>6.00<NA><NA>서울특별시 종로구 종로67길 3-16 (숭인동)3112토궁(자연의아침)2020-09-10 08:54:42U2020-09-12 02:40:00.0찜질시설서비스영업201741.45056452510.374475찜질시설서비스영업1011<NA><NA>000N0<NA><NA><NA><NA>00000N
15730000003000000-202-2015-000022015-12-15<NA>3폐업2폐업2023-11-01<NA><NA><NA>02 22720822193.04110-430서울특별시 종로구 장사동 227-1 지하1층서울특별시 종로구 청계천로 137, 지하1층 (장사동)3193센츄럴관광호텔 사우나2023-10-25 13:58:28U2022-10-30 22:07:00.0공동탕업199302.818541451834.817165<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
15830000003000000-202-2017-000012017-12-27<NA>1영업/정상1영업<NA><NA><NA><NA>02 742 9500219.86110-500서울특별시 종로구 이화동 25-1 종로노인종합복지관 1층서울특별시 종로구 율곡로19길 17-8, 종로노인종합복지관 1층 (이화동)3100종로노인종합복지관2023-04-17 10:58:33U2022-12-03 23:09:00.0목욕장업 기타200462.615226452715.017605<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
15930000003000000-202-2018-000012018-05-31<NA>1영업/정상1영업<NA><NA><NA><NA>02 738 6100105.52110-290서울특별시 종로구 인사동 34-2 지하2층서울특별시 종로구 인사동길 20-9, 지하2층 (인사동)3163마루2023-09-01 14:14:47U2022-12-09 00:03:00.0공동탕업198797.815433452231.193563<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16030000003000000-202-2019-0000120190822<NA>1영업/정상1영업<NA><NA><NA><NA>0263537700233.95110450서울특별시 종로구 원남동 49-17번지 4층서울특별시 종로구 율곡로 180, 4층 (원남동)3127오라카이 대학로 호텔2019-08-22 17:40:26I2019-08-24 02:22:10.0공동탕업199821.363678452603.475535공동탕업0044<NA><NA>000N0<NA><NA><NA><NA>00000N
16130000003000000-202-2023-000012023-09-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>1412.80110-121서울특별시 종로구 종로1가 24 르메이에르종로타운1서울특별시 종로구 종로 19, 르메이에르종로타운1 B2,B3층 (종로1가)3157자마이카 피트니스2023-09-18 16:00:32I2022-12-08 22:00:00.0공동탕업198150.300374452019.212643<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16230000003000000-202-2023-000022023-12-13<NA>1영업/정상1영업<NA><NA><NA><NA>02 741 7811329.20110-842서울특별시 종로구 창신동 442-3 이스턴호텔서울특별시 종로구 종로 286, 이스턴호텔 2층 (창신동)3119이스턴사우나2024-03-08 10:43:53U2023-12-02 23:00:00.0공동탕업200840.428333452076.902<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16330000003000000-202-2023-000032023-12-13<NA>1영업/정상1영업<NA><NA><NA><NA>02 627326001780.39110-847서울특별시 종로구 평창동 153-3 KB골든라이프케어평창카운티서울특별시 종로구 평창문화로 87, KB골든라이프케어평창카운티 지하2층 (평창동)3009SPA평창2024-01-26 17:22:58U2023-11-30 22:08:00.0공동탕업197349.173373456108.81025<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>