Overview

Dataset statistics

Number of variables47
Number of observations161
Missing cells1680
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.6 KiB
Average record size in memory404.8 B

Variable types

Categorical20
Text7
DateTime3
Unsupported7
Numeric8
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
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-19972/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
데이터갱신일자 is highly imbalanced (63.5%)Imbalance
업태구분명 is highly imbalanced (57.3%)Imbalance
여성종사자수 is highly imbalanced (66.4%)Imbalance
남성종사자수 is highly imbalanced (66.4%)Imbalance
인허가취소일자 has 161 (100.0%) missing valuesMissing
폐업일자 has 24 (14.9%) missing valuesMissing
휴업시작일자 has 161 (100.0%) missing valuesMissing
휴업종료일자 has 161 (100.0%) missing valuesMissing
재개업일자 has 161 (100.0%) missing valuesMissing
전화번호 has 5 (3.1%) missing valuesMissing
도로명주소 has 108 (67.1%) missing valuesMissing
도로명우편번호 has 109 (67.7%) missing valuesMissing
좌표정보(X) has 6 (3.7%) missing valuesMissing
좌표정보(Y) has 6 (3.7%) missing valuesMissing
건물지상층수 has 35 (21.7%) missing valuesMissing
건물지하층수 has 38 (23.6%) missing valuesMissing
사용시작지상층 has 43 (26.7%) missing valuesMissing
사용끝지상층 has 92 (57.1%) missing valuesMissing
욕실수 has 37 (23.0%) missing valuesMissing
발한실여부 has 25 (15.5%) missing valuesMissing
조건부허가신고사유 has 161 (100.0%) missing valuesMissing
조건부허가시작일자 has 161 (100.0%) missing valuesMissing
조건부허가종료일자 has 161 (100.0%) missing valuesMissing
다중이용업소여부 has 25 (15.5%) 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 50 (31.1%) zerosZeros
건물지하층수 has 51 (31.7%) zerosZeros
사용시작지상층 has 57 (35.4%) zerosZeros
사용끝지상층 has 8 (5.0%) zerosZeros
욕실수 has 52 (32.3%) zerosZeros

Reproduction

Analysis started2024-05-11 03:42:12.039156
Analysis finished2024-05-11 03:42:13.242532
Duration1.2 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
3200000
161 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3200000
2nd row3200000
3rd row3200000
4th row3200000
5th row3200000

Common Values

ValueCountFrequency (%)
3200000 161
100.0%

Length

2024-05-11T03:42:13.433956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:13.768082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 161
100.0%

관리번호
Text

UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:42:14.212539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3542
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

Unique161 ?
Unique (%)100.0%

Sample

1st row3200000-202-1968-00453
2nd row3200000-202-1969-00446
3rd row3200000-202-1969-00460
4th row3200000-202-1970-00451
5th row3200000-202-1970-00462
ValueCountFrequency (%)
3200000-202-1968-00453 1
 
0.6%
3200000-202-1992-00469 1
 
0.6%
3200000-202-2000-00517 1
 
0.6%
3200000-202-2000-00478 1
 
0.6%
3200000-202-2000-00479 1
 
0.6%
3200000-202-2000-00483 1
 
0.6%
3200000-202-2000-00499 1
 
0.6%
3200000-202-2000-00505 1
 
0.6%
3200000-202-2000-00515 1
 
0.6%
3200000-202-2000-00516 1
 
0.6%
Other values (151) 151
93.8%
2024-05-11T03:42:15.332171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1523
43.0%
2 610
17.2%
- 483
 
13.6%
3 208
 
5.9%
1 189
 
5.3%
9 159
 
4.5%
4 130
 
3.7%
8 84
 
2.4%
5 63
 
1.8%
7 55
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3059
86.4%
Dash Punctuation 483
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1523
49.8%
2 610
19.9%
3 208
 
6.8%
1 189
 
6.2%
9 159
 
5.2%
4 130
 
4.2%
8 84
 
2.7%
5 63
 
2.1%
7 55
 
1.8%
6 38
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 483
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1523
43.0%
2 610
17.2%
- 483
 
13.6%
3 208
 
5.9%
1 189
 
5.3%
9 159
 
4.5%
4 130
 
3.7%
8 84
 
2.4%
5 63
 
1.8%
7 55
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1523
43.0%
2 610
17.2%
- 483
 
13.6%
3 208
 
5.9%
1 189
 
5.3%
9 159
 
4.5%
4 130
 
3.7%
8 84
 
2.4%
5 63
 
1.8%
7 55
 
1.6%
Distinct159
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum1968-02-02 00:00:00
Maximum2023-09-04 00:00:00
2024-05-11T03:42:15.839100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:42:16.448799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing161
Missing (%)100.0%
Memory size1.5 KiB
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3
137 
1
24 

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 (%)
3 137
85.1%
1 24
 
14.9%

Length

2024-05-11T03:42:16.936219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:17.328029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 137
85.1%
1 24
 
14.9%

영업상태명
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
폐업
137 
영업/정상
24 

Length

Max length5
Median length2
Mean length2.447205
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 137
85.1%
영업/정상 24
 
14.9%

Length

2024-05-11T03:42:17.910042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:18.371530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 137
85.1%
영업/정상 24
 
14.9%
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2
137 
1
24 

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 (%)
2 137
85.1%
1 24
 
14.9%

Length

2024-05-11T03:42:18.886487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:19.524106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 137
85.1%
1 24
 
14.9%
Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
폐업
137 
영업
24 

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 137
85.1%
영업 24
 
14.9%

Length

2024-05-11T03:42:20.131833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:20.526388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 137
85.1%
영업 24
 
14.9%

폐업일자
Date

MISSING 

Distinct127
Distinct (%)92.7%
Missing24
Missing (%)14.9%
Memory size1.4 KiB
Minimum1990-08-28 00:00:00
Maximum2023-06-13 00:00:00
2024-05-11T03:42:21.083262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:42:21.742384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing161
Missing (%)100.0%
Memory size1.5 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing161
Missing (%)100.0%
Memory size1.5 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing161
Missing (%)100.0%
Memory size1.5 KiB

전화번호
Text

MISSING 

Distinct154
Distinct (%)98.7%
Missing5
Missing (%)3.1%
Memory size1.4 KiB
2024-05-11T03:42:22.440678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.121795
Min length10

Characters and Unicode

Total characters1579
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

Unique152 ?
Unique (%)97.4%

Sample

1st row02 8515434
2nd row02 8838406
3rd row02 8772677
4th row0208779706
5th row02 8863836
ValueCountFrequency (%)
02 124
43.2%
07088703333 2
 
0.7%
8772677 2
 
0.7%
8629696 1
 
0.3%
8624154 1
 
0.3%
8563296 1
 
0.3%
8379921 1
 
0.3%
5846332 1
 
0.3%
8874558 1
 
0.3%
8884685 1
 
0.3%
Other values (152) 152
53.0%
2024-05-11T03:42:23.517568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 292
18.5%
0 269
17.0%
2 237
15.0%
7 148
9.4%
143
9.1%
3 106
 
6.7%
5 91
 
5.8%
6 79
 
5.0%
1 77
 
4.9%
9 77
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1436
90.9%
Space Separator 143
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 292
20.3%
0 269
18.7%
2 237
16.5%
7 148
10.3%
3 106
 
7.4%
5 91
 
6.3%
6 79
 
5.5%
1 77
 
5.4%
9 77
 
5.4%
4 60
 
4.2%
Space Separator
ValueCountFrequency (%)
143
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1579
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 292
18.5%
0 269
17.0%
2 237
15.0%
7 148
9.4%
143
9.1%
3 106
 
6.7%
5 91
 
5.8%
6 79
 
5.0%
1 77
 
4.9%
9 77
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 292
18.5%
0 269
17.0%
2 237
15.0%
7 148
9.4%
143
9.1%
3 106
 
6.7%
5 91
 
5.8%
6 79
 
5.0%
1 77
 
4.9%
9 77
 
4.9%
Distinct159
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:42:24.451881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.1428571
Min length3

Characters and Unicode

Total characters989
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

Unique157 ?
Unique (%)97.5%

Sample

1st row259.10
2nd row140.32
3rd row95.82
4th row147.50
5th row201.30
ValueCountFrequency (%)
00 2
 
1.2%
411.70 2
 
1.2%
190.00 1
 
0.6%
354.20 1
 
0.6%
4,963.42 1
 
0.6%
579.89 1
 
0.6%
259.10 1
 
0.6%
623.23 1
 
0.6%
509.76 1
 
0.6%
735.11 1
 
0.6%
Other values (149) 149
92.5%
2024-05-11T03:42:26.094488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 161
16.3%
0 124
12.5%
2 116
11.7%
1 91
9.2%
3 88
8.9%
9 75
7.6%
4 69
7.0%
7 67
6.8%
8 63
 
6.4%
5 62
 
6.3%
Other values (2) 73
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 812
82.1%
Other Punctuation 177
 
17.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124
15.3%
2 116
14.3%
1 91
11.2%
3 88
10.8%
9 75
9.2%
4 69
8.5%
7 67
8.3%
8 63
7.8%
5 62
7.6%
6 57
7.0%
Other Punctuation
ValueCountFrequency (%)
. 161
91.0%
, 16
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 161
16.3%
0 124
12.5%
2 116
11.7%
1 91
9.2%
3 88
8.9%
9 75
7.6%
4 69
7.0%
7 67
6.8%
8 63
 
6.4%
5 62
 
6.3%
Other values (2) 73
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 161
16.3%
0 124
12.5%
2 116
11.7%
1 91
9.2%
3 88
8.9%
9 75
7.6%
4 69
7.0%
7 67
6.8%
8 63
 
6.4%
5 62
 
6.3%
Other values (2) 73
7.4%
Distinct83
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:42:26.827900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0372671
Min length6

Characters and Unicode

Total characters972
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

Unique39 ?
Unique (%)24.2%

Sample

1st row151900
2nd row151840
3rd row151808
4th row151861
5th row151843
ValueCountFrequency (%)
151836 8
 
5.0%
151803 5
 
3.1%
151050 5
 
3.1%
151809 4
 
2.5%
151903 4
 
2.5%
151843 4
 
2.5%
151015 4
 
2.5%
151844 4
 
2.5%
151841 3
 
1.9%
151840 3
 
1.9%
Other values (73) 117
72.7%
2024-05-11T03:42:28.168627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 345
35.5%
5 197
20.3%
8 156
16.0%
0 71
 
7.3%
9 42
 
4.3%
3 41
 
4.2%
4 34
 
3.5%
6 29
 
3.0%
2 27
 
2.8%
7 24
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 966
99.4%
Dash Punctuation 6
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 345
35.7%
5 197
20.4%
8 156
16.1%
0 71
 
7.3%
9 42
 
4.3%
3 41
 
4.2%
4 34
 
3.5%
6 29
 
3.0%
2 27
 
2.8%
7 24
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 972
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 345
35.5%
5 197
20.3%
8 156
16.0%
0 71
 
7.3%
9 42
 
4.3%
3 41
 
4.2%
4 34
 
3.5%
6 29
 
3.0%
2 27
 
2.8%
7 24
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 972
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 345
35.5%
5 197
20.3%
8 156
16.0%
0 71
 
7.3%
9 42
 
4.3%
3 41
 
4.2%
4 34
 
3.5%
6 29
 
3.0%
2 27
 
2.8%
7 24
 
2.5%
Distinct155
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:42:28.832556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length48
Mean length23.993789
Min length18

Characters and Unicode

Total characters3863
Distinct characters87
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

Unique149 ?
Unique (%)92.5%

Sample

1st row서울특별시 관악구 신림동 1603-3번지
2nd row서울특별시 관악구 봉천동 899-12번지
3rd row서울특별시 관악구 봉천동 17-12번지
4th row서울특별시 관악구 신림동 299-3번지
5th row서울특별시 관악구 봉천동 902-4번지
ValueCountFrequency (%)
서울특별시 161
23.2%
관악구 161
23.2%
신림동 80
11.5%
봉천동 76
 
11.0%
남현동 5
 
0.7%
3
 
0.4%
3층 3
 
0.4%
지하1층 3
 
0.4%
엘리젠트오피스텔 2
 
0.3%
3-30번지 2
 
0.3%
Other values (188) 198
28.5%
2024-05-11T03:42:30.218678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
668
 
17.3%
1 172
 
4.5%
169
 
4.4%
164
 
4.2%
163
 
4.2%
163
 
4.2%
162
 
4.2%
162
 
4.2%
161
 
4.2%
161
 
4.2%
Other values (77) 1718
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2218
57.4%
Decimal Number 807
 
20.9%
Space Separator 668
 
17.3%
Dash Punctuation 154
 
4.0%
Uppercase Letter 6
 
0.2%
Other Punctuation 4
 
0.1%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
169
 
7.6%
164
 
7.4%
163
 
7.3%
163
 
7.3%
162
 
7.3%
162
 
7.3%
161
 
7.3%
161
 
7.3%
161
 
7.3%
143
 
6.4%
Other values (60) 609
27.5%
Decimal Number
ValueCountFrequency (%)
1 172
21.3%
2 89
11.0%
3 85
10.5%
6 76
9.4%
0 71
8.8%
5 71
8.8%
4 68
 
8.4%
9 60
 
7.4%
7 59
 
7.3%
8 56
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
B 3
50.0%
A 3
50.0%
Space Separator
ValueCountFrequency (%)
668
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 154
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2218
57.4%
Common 1639
42.4%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
169
 
7.6%
164
 
7.4%
163
 
7.3%
163
 
7.3%
162
 
7.3%
162
 
7.3%
161
 
7.3%
161
 
7.3%
161
 
7.3%
143
 
6.4%
Other values (60) 609
27.5%
Common
ValueCountFrequency (%)
668
40.8%
1 172
 
10.5%
- 154
 
9.4%
2 89
 
5.4%
3 85
 
5.2%
6 76
 
4.6%
0 71
 
4.3%
5 71
 
4.3%
4 68
 
4.1%
9 60
 
3.7%
Other values (5) 125
 
7.6%
Latin
ValueCountFrequency (%)
B 3
50.0%
A 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2218
57.4%
ASCII 1645
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
668
40.6%
1 172
 
10.5%
- 154
 
9.4%
2 89
 
5.4%
3 85
 
5.2%
6 76
 
4.6%
0 71
 
4.3%
5 71
 
4.3%
4 68
 
4.1%
9 60
 
3.6%
Other values (7) 131
 
8.0%
Hangul
ValueCountFrequency (%)
169
 
7.6%
164
 
7.4%
163
 
7.3%
163
 
7.3%
162
 
7.3%
162
 
7.3%
161
 
7.3%
161
 
7.3%
161
 
7.3%
143
 
6.4%
Other values (60) 609
27.5%

도로명주소
Text

MISSING 

Distinct52
Distinct (%)98.1%
Missing108
Missing (%)67.1%
Memory size1.4 KiB
2024-05-11T03:42:30.813513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length42
Mean length28.679245
Min length21

Characters and Unicode

Total characters1520
Distinct characters104
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

Unique51 ?
Unique (%)96.2%

Sample

1st row서울특별시 관악구 청림3길 22-6 (봉천동)
2nd row서울특별시 관악구 난우2길 27 (신림동)
3rd row서울특별시 관악구 쑥고개로 76-1 (봉천동)
4th row서울특별시 관악구 난곡로24가길 12 (신림동)
5th row서울특별시 관악구 조원로11길 9 (신림동)
ValueCountFrequency (%)
서울특별시 53
17.8%
관악구 53
17.8%
신림동 24
 
8.1%
봉천동 21
 
7.0%
지하1층 5
 
1.7%
봉천로 4
 
1.3%
쑥고개로 4
 
1.3%
남현동 3
 
1.0%
9 3
 
1.0%
14 3
 
1.0%
Other values (111) 125
41.9%
2024-05-11T03:42:32.048074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
245
 
16.1%
59
 
3.9%
58
 
3.8%
58
 
3.8%
56
 
3.7%
( 54
 
3.6%
) 54
 
3.6%
54
 
3.6%
53
 
3.5%
53
 
3.5%
Other values (94) 776
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 927
61.0%
Space Separator 245
 
16.1%
Decimal Number 204
 
13.4%
Open Punctuation 54
 
3.6%
Close Punctuation 54
 
3.6%
Other Punctuation 24
 
1.6%
Dash Punctuation 7
 
0.5%
Uppercase Letter 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
59
 
6.4%
58
 
6.3%
58
 
6.3%
56
 
6.0%
54
 
5.8%
53
 
5.7%
53
 
5.7%
53
 
5.7%
53
 
5.7%
38
 
4.1%
Other values (77) 392
42.3%
Decimal Number
ValueCountFrequency (%)
1 52
25.5%
2 37
18.1%
5 19
 
9.3%
3 18
 
8.8%
4 15
 
7.4%
0 15
 
7.4%
7 15
 
7.4%
9 12
 
5.9%
6 11
 
5.4%
8 10
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
B 4
80.0%
A 1
 
20.0%
Space Separator
ValueCountFrequency (%)
245
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Other Punctuation
ValueCountFrequency (%)
, 24
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 927
61.0%
Common 588
38.7%
Latin 5
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
59
 
6.4%
58
 
6.3%
58
 
6.3%
56
 
6.0%
54
 
5.8%
53
 
5.7%
53
 
5.7%
53
 
5.7%
53
 
5.7%
38
 
4.1%
Other values (77) 392
42.3%
Common
ValueCountFrequency (%)
245
41.7%
( 54
 
9.2%
) 54
 
9.2%
1 52
 
8.8%
2 37
 
6.3%
, 24
 
4.1%
5 19
 
3.2%
3 18
 
3.1%
4 15
 
2.6%
0 15
 
2.6%
Other values (5) 55
 
9.4%
Latin
ValueCountFrequency (%)
B 4
80.0%
A 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 927
61.0%
ASCII 593
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
245
41.3%
( 54
 
9.1%
) 54
 
9.1%
1 52
 
8.8%
2 37
 
6.2%
, 24
 
4.0%
5 19
 
3.2%
3 18
 
3.0%
4 15
 
2.5%
0 15
 
2.5%
Other values (7) 60
 
10.1%
Hangul
ValueCountFrequency (%)
59
 
6.4%
58
 
6.3%
58
 
6.3%
56
 
6.0%
54
 
5.8%
53
 
5.7%
53
 
5.7%
53
 
5.7%
53
 
5.7%
38
 
4.1%
Other values (77) 392
42.3%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)80.8%
Missing109
Missing (%)67.7%
Infinite0
Infinite (%)0.0%
Mean8773.4038
Minimum8700
Maximum8856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T03:42:32.436918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8700
5-th percentile8701.1
Q18736.25
median8776
Q38798.5
95-th percentile8849.8
Maximum8856
Range156
Interquartile range (IQR)62.25

Descriptive statistics

Standard deviation45.473655
Coefficient of variation (CV)0.0051831257
Kurtosis-0.80239977
Mean8773.4038
Median Absolute Deviation (MAD)30.5
Skewness0.13508123
Sum456217
Variance2067.8533
MonotonicityNot monotonic
2024-05-11T03:42:33.342565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
8700 3
 
1.9%
8754 2
 
1.2%
8749 2
 
1.2%
8787 2
 
1.2%
8806 2
 
1.2%
8786 2
 
1.2%
8783 2
 
1.2%
8776 2
 
1.2%
8734 2
 
1.2%
8711 1
 
0.6%
Other values (32) 32
 
19.9%
(Missing) 109
67.7%
ValueCountFrequency (%)
8700 3
1.9%
8702 1
 
0.6%
8704 1
 
0.6%
8708 1
 
0.6%
8711 1
 
0.6%
8724 1
 
0.6%
8726 1
 
0.6%
8729 1
 
0.6%
8731 1
 
0.6%
8734 2
1.2%
ValueCountFrequency (%)
8856 1
0.6%
8854 1
0.6%
8852 1
0.6%
8848 1
0.6%
8846 1
0.6%
8845 1
0.6%
8843 1
0.6%
8832 1
0.6%
8831 1
0.6%
8814 1
0.6%
Distinct151
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:42:33.930968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length4.1863354
Min length2

Characters and Unicode

Total characters674
Distinct characters169
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)88.8%

Sample

1st row신림
2nd row현대
3rd row쌍용
4th row양지마을
5th row서울
ValueCountFrequency (%)
현대 4
 
2.3%
사우나 3
 
1.7%
청수탕 2
 
1.2%
수정 2
 
1.2%
쑥고개사우나 2
 
1.2%
우정탕 2
 
1.2%
관악 2
 
1.2%
신림 2
 
1.2%
금호 2
 
1.2%
강남목욕탕 1
 
0.6%
Other values (150) 150
87.2%
2024-05-11T03:42:35.159058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
7.3%
46
 
6.8%
36
 
5.3%
35
 
5.2%
26
 
3.9%
18
 
2.7%
16
 
2.4%
16
 
2.4%
13
 
1.9%
13
 
1.9%
Other values (159) 406
60.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 649
96.3%
Space Separator 11
 
1.6%
Decimal Number 6
 
0.9%
Uppercase Letter 5
 
0.7%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
49
 
7.6%
46
 
7.1%
36
 
5.5%
35
 
5.4%
26
 
4.0%
18
 
2.8%
16
 
2.5%
16
 
2.5%
13
 
2.0%
13
 
2.0%
Other values (147) 381
58.7%
Uppercase Letter
ValueCountFrequency (%)
W 1
20.0%
F 1
20.0%
M 1
20.0%
D 1
20.0%
C 1
20.0%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
4 2
33.3%
1 1
 
16.7%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 649
96.3%
Common 20
 
3.0%
Latin 5
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
49
 
7.6%
46
 
7.1%
36
 
5.5%
35
 
5.4%
26
 
4.0%
18
 
2.8%
16
 
2.5%
16
 
2.5%
13
 
2.0%
13
 
2.0%
Other values (147) 381
58.7%
Common
ValueCountFrequency (%)
11
55.0%
2 3
 
15.0%
4 2
 
10.0%
) 1
 
5.0%
( 1
 
5.0%
1 1
 
5.0%
& 1
 
5.0%
Latin
ValueCountFrequency (%)
W 1
20.0%
F 1
20.0%
M 1
20.0%
D 1
20.0%
C 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 649
96.3%
ASCII 25
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
49
 
7.6%
46
 
7.1%
36
 
5.5%
35
 
5.4%
26
 
4.0%
18
 
2.8%
16
 
2.5%
16
 
2.5%
13
 
2.0%
13
 
2.0%
Other values (147) 381
58.7%
ASCII
ValueCountFrequency (%)
11
44.0%
2 3
 
12.0%
4 2
 
8.0%
W 1
 
4.0%
F 1
 
4.0%
M 1
 
4.0%
D 1
 
4.0%
) 1
 
4.0%
( 1
 
4.0%
C 1
 
4.0%
Other values (2) 2
 
8.0%
Distinct108
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Minimum1999-08-30 00:00:00
Maximum2024-04-05 14:12:32
2024-05-11T03:42:35.691782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:42:36.301488image/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
122 
U
39 

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 122
75.8%
U 39
 
24.2%

Length

2024-05-11T03:42:36.880130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:37.370861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 122
75.8%
u 39
 
24.2%

데이터갱신일자
Categorical

IMBALANCE 

Distinct23
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2018-08-31 23:59:59.0
121 
2022-11-30 23:01:00.0
18 
2020-03-04 02:40:00.0
 
2
2020-07-03 02:40:00.0
 
1
2019-06-29 02:40:00.0
 
1
Other values (18)
18 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique20 ?
Unique (%)12.4%

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 23:59:59.0 121
75.2%
2022-11-30 23:01:00.0 18
 
11.2%
2020-03-04 02:40:00.0 2
 
1.2%
2020-07-03 02:40:00.0 1
 
0.6%
2019-06-29 02:40:00.0 1
 
0.6%
2022-12-05 23:05:00.0 1
 
0.6%
2022-12-05 22:02:00.0 1
 
0.6%
2023-11-30 23:08:00.0 1
 
0.6%
2020-12-31 02:40:00.0 1
 
0.6%
2022-02-20 02:40:00.0 1
 
0.6%
Other values (13) 13
 
8.1%

Length

2024-05-11T03:42:37.732730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-31 121
37.6%
23:59:59.0 121
37.6%
2022-11-30 18
 
5.6%
23:01:00.0 18
 
5.6%
02:40:00.0 15
 
4.7%
2020-03-04 2
 
0.6%
2022-12-05 2
 
0.6%
2020-07-24 1
 
0.3%
2021-04-17 1
 
0.3%
2020-09-27 1
 
0.3%
Other values (22) 22
 
6.8%

업태구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
공동탕업
135 
찜질시설서비스영업
15 
공동탕업+찜질시설서비스영업
 
8
목욕장업 기타
 
3

Length

Max length14
Median length4
Mean length5.0186335
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 135
83.9%
찜질시설서비스영업 15
 
9.3%
공동탕업+찜질시설서비스영업 8
 
5.0%
목욕장업 기타 3
 
1.9%

Length

2024-05-11T03:42:38.250468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:38.746809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 135
82.3%
찜질시설서비스영업 15
 
9.1%
공동탕업+찜질시설서비스영업 8
 
4.9%
목욕장업 3
 
1.8%
기타 3
 
1.8%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct142
Distinct (%)91.6%
Missing6
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean194459.76
Minimum191223.83
Maximum198284.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T03:42:39.153387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191223.83
5-th percentile192039.12
Q1193498.11
median194457.49
Q3195553.38
95-th percentile196930.26
Maximum198284.08
Range7060.2514
Interquartile range (IQR)2055.2642

Descriptive statistics

Standard deviation1548.7397
Coefficient of variation (CV)0.0079643198
Kurtosis-0.46452217
Mean194459.76
Median Absolute Deviation (MAD)1039.022
Skewness0.096912893
Sum30141263
Variance2398594.7
MonotonicityNot monotonic
2024-05-11T03:42:39.834599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194458.49670882 3
 
1.9%
196996.226327062 2
 
1.2%
196384.544308046 2
 
1.2%
195254.363061755 2
 
1.2%
191376.602183978 2
 
1.2%
193671.349359142 2
 
1.2%
196359.604834606 2
 
1.2%
194869.198431832 2
 
1.2%
194421.970091108 2
 
1.2%
195572.774825165 2
 
1.2%
Other values (132) 134
83.2%
(Missing) 6
 
3.7%
ValueCountFrequency (%)
191223.827173065 1
0.6%
191376.602183978 2
1.2%
191494.217231529 1
0.6%
191555.027049753 1
0.6%
191640.4521751 1
0.6%
191683.472178088 1
0.6%
191810.955680588 1
0.6%
192136.906645169 1
0.6%
192195.130794027 1
0.6%
192250.63689136 1
0.6%
ValueCountFrequency (%)
198284.078546351 1
0.6%
198097.13526738 1
0.6%
197974.59786506 1
0.6%
197865.79666705 1
0.6%
197797.049831193 1
0.6%
196996.226327062 2
1.2%
196937.086446207 1
0.6%
196927.332458179 1
0.6%
196883.206421828 1
0.6%
196501.159765837 1
0.6%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct142
Distinct (%)91.6%
Missing6
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean441993.03
Minimum439809.67
Maximum443179.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T03:42:40.452979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439809.67
5-th percentile440485.47
Q1441448.75
median442139.54
Q3442608.06
95-th percentile443038.16
Maximum443179.97
Range3370.2956
Interquartile range (IQR)1159.3103

Descriptive statistics

Standard deviation765.61589
Coefficient of variation (CV)0.00173219
Kurtosis-0.2069801
Mean441993.03
Median Absolute Deviation (MAD)525.79156
Skewness-0.66236423
Sum68508920
Variance586167.7
MonotonicityNot monotonic
2024-05-11T03:42:41.051836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
441712.987497817 3
 
1.9%
441132.560218269 2
 
1.2%
442822.980265735 2
 
1.2%
443179.96522589 2
 
1.2%
442456.748277117 2
 
1.2%
442039.985895904 2
 
1.2%
443176.735306808 2
 
1.2%
442174.684804439 2
 
1.2%
440334.091212577 2
 
1.2%
441942.462369482 2
 
1.2%
Other values (132) 134
83.2%
(Missing) 6
 
3.7%
ValueCountFrequency (%)
439809.669640911 1
0.6%
440048.493191723 1
0.6%
440207.545761671 1
0.6%
440293.901638173 1
0.6%
440317.168618377 1
0.6%
440334.091212577 2
1.2%
440358.873851653 1
0.6%
440539.727551719 1
0.6%
440545.946420334 1
0.6%
440779.226060347 1
0.6%
ValueCountFrequency (%)
443179.96522589 2
1.2%
443176.735306808 2
1.2%
443158.567330275 1
0.6%
443157.363464064 1
0.6%
443067.134957717 1
0.6%
443044.700996218 1
0.6%
443035.350947417 1
0.6%
443013.429722508 1
0.6%
443007.002666667 1
0.6%
442973.721503978 1
0.6%

위생업태명
Categorical

Distinct5
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
공동탕업
121 
<NA>
25 
찜질시설서비스영업
 
10
공동탕업+찜질시설서비스영업
 
3
목욕장업 기타
 
2

Length

Max length14
Median length4
Mean length4.5341615
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 121
75.2%
<NA> 25
 
15.5%
찜질시설서비스영업 10
 
6.2%
공동탕업+찜질시설서비스영업 3
 
1.9%
목욕장업 기타 2
 
1.2%

Length

2024-05-11T03:42:41.603707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:42.135082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 121
74.2%
na 25
 
15.3%
찜질시설서비스영업 10
 
6.1%
공동탕업+찜질시설서비스영업 3
 
1.8%
목욕장업 2
 
1.2%
기타 2
 
1.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)11.9%
Missing35
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean3.1507937
Minimum0
Maximum20
Zeros50
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T03:42:42.717310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q34
95-th percentile8.75
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8658866
Coefficient of variation (CV)1.2269565
Kurtosis6.6603416
Mean3.1507937
Median Absolute Deviation (MAD)3
Skewness2.2322568
Sum397
Variance14.945079
MonotonicityNot monotonic
2024-05-11T03:42:43.279414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 50
31.1%
4 25
15.5%
3 12
 
7.5%
5 12
 
7.5%
2 8
 
5.0%
6 8
 
5.0%
7 3
 
1.9%
20 1
 
0.6%
17 1
 
0.6%
15 1
 
0.6%
Other values (5) 5
 
3.1%
(Missing) 35
21.7%
ValueCountFrequency (%)
0 50
31.1%
2 8
 
5.0%
3 12
 
7.5%
4 25
15.5%
5 12
 
7.5%
6 8
 
5.0%
7 3
 
1.9%
8 1
 
0.6%
9 1
 
0.6%
10 1
 
0.6%
ValueCountFrequency (%)
20 1
 
0.6%
19 1
 
0.6%
18 1
 
0.6%
17 1
 
0.6%
15 1
 
0.6%
10 1
 
0.6%
9 1
 
0.6%
8 1
 
0.6%
7 3
 
1.9%
6 8
5.0%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)4.9%
Missing38
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean0.77235772
Minimum0
Maximum5
Zeros51
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T03:42:44.025706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87602875
Coefficient of variation (CV)1.1342267
Kurtosis5.1546941
Mean0.77235772
Median Absolute Deviation (MAD)1
Skewness1.8019448
Sum95
Variance0.76742636
MonotonicityNot monotonic
2024-05-11T03:42:44.359831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 58
36.0%
0 51
31.7%
2 8
 
5.0%
3 4
 
2.5%
4 1
 
0.6%
5 1
 
0.6%
(Missing) 38
23.6%
ValueCountFrequency (%)
0 51
31.7%
1 58
36.0%
2 8
 
5.0%
3 4
 
2.5%
4 1
 
0.6%
5 1
 
0.6%
ValueCountFrequency (%)
5 1
 
0.6%
4 1
 
0.6%
3 4
 
2.5%
2 8
 
5.0%
1 58
36.0%
0 51
31.7%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)5.1%
Missing43
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean0.87288136
Minimum0
Maximum5
Zeros57
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T03:42:44.860676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1059019
Coefficient of variation (CV)1.2669556
Kurtosis2.5514646
Mean0.87288136
Median Absolute Deviation (MAD)1
Skewness1.5286985
Sum103
Variance1.223019
MonotonicityNot monotonic
2024-05-11T03:42:45.592454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 57
35.4%
1 34
21.1%
2 18
 
11.2%
3 5
 
3.1%
4 2
 
1.2%
5 2
 
1.2%
(Missing) 43
26.7%
ValueCountFrequency (%)
0 57
35.4%
1 34
21.1%
2 18
 
11.2%
3 5
 
3.1%
4 2
 
1.2%
5 2
 
1.2%
ValueCountFrequency (%)
5 2
 
1.2%
4 2
 
1.2%
3 5
 
3.1%
2 18
 
11.2%
1 34
21.1%
0 57
35.4%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)10.1%
Missing92
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean2.3913043
Minimum0
Maximum6
Zeros8
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T03:42:46.013957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile4.6
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3085397
Coefficient of variation (CV)0.54720752
Kurtosis0.32948645
Mean2.3913043
Median Absolute Deviation (MAD)1
Skewness0.041782721
Sum165
Variance1.7122762
MonotonicityNot monotonic
2024-05-11T03:42:46.424569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 23
 
14.3%
3 23
 
14.3%
0 8
 
5.0%
4 6
 
3.7%
1 5
 
3.1%
5 3
 
1.9%
6 1
 
0.6%
(Missing) 92
57.1%
ValueCountFrequency (%)
0 8
 
5.0%
1 5
 
3.1%
2 23
14.3%
3 23
14.3%
4 6
 
3.7%
5 3
 
1.9%
6 1
 
0.6%
ValueCountFrequency (%)
6 1
 
0.6%
5 3
 
1.9%
4 6
 
3.7%
3 23
14.3%
2 23
14.3%
1 5
 
3.1%
0 8
 
5.0%
Distinct4
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
67 
0
57 
1
36 
3
 
1

Length

Max length4
Median length1
Mean length2.2484472
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 67
41.6%
0 57
35.4%
1 36
22.4%
3 1
 
0.6%

Length

2024-05-11T03:42:47.007668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:47.384303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 67
41.6%
0 57
35.4%
1 36
22.4%
3 1
 
0.6%
Distinct5
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
115 
1
33 
0
 
9
2
 
3
4
 
1

Length

Max length4
Median length4
Mean length3.1428571
Min length1

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 115
71.4%
1 33
 
20.5%
0 9
 
5.6%
2 3
 
1.9%
4 1
 
0.6%

Length

2024-05-11T03:42:47.888715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:48.384129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 115
71.4%
1 33
 
20.5%
0 9
 
5.6%
2 3
 
1.9%
4 1
 
0.6%

한실수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
83 
0
78 

Length

Max length4
Median length4
Mean length2.5465839
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 83
51.6%
0 78
48.4%

Length

2024-05-11T03:42:48.943315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:49.383344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
51.6%
0 78
48.4%

양실수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
83 
0
78 

Length

Max length4
Median length4
Mean length2.5465839
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 83
51.6%
0 78
48.4%

Length

2024-05-11T03:42:50.075047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:50.573687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
51.6%
0 78
48.4%

욕실수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)7.3%
Missing37
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean1.483871
Minimum0
Maximum12
Zeros52
Zeros (%)32.3%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-05-11T03:42:51.086167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile4.85
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8500005
Coefficient of variation (CV)1.2467395
Kurtosis9.7651922
Mean1.483871
Median Absolute Deviation (MAD)1.5
Skewness2.5121327
Sum184
Variance3.422502
MonotonicityNot monotonic
2024-05-11T03:42:51.659323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 57
35.4%
0 52
32.3%
1 4
 
2.5%
6 3
 
1.9%
4 3
 
1.9%
8 2
 
1.2%
5 1
 
0.6%
12 1
 
0.6%
3 1
 
0.6%
(Missing) 37
23.0%
ValueCountFrequency (%)
0 52
32.3%
1 4
 
2.5%
2 57
35.4%
3 1
 
0.6%
4 3
 
1.9%
5 1
 
0.6%
6 3
 
1.9%
8 2
 
1.2%
12 1
 
0.6%
ValueCountFrequency (%)
12 1
 
0.6%
8 2
 
1.2%
6 3
 
1.9%
5 1
 
0.6%
4 3
 
1.9%
3 1
 
0.6%
2 57
35.4%
1 4
 
2.5%
0 52
32.3%

발한실여부
Boolean

MISSING 

Distinct2
Distinct (%)1.5%
Missing25
Missing (%)15.5%
Memory size454.0 B
False
71 
True
65 
(Missing)
25 
ValueCountFrequency (%)
False 71
44.1%
True 65
40.4%
(Missing) 25
 
15.5%
2024-05-11T03:42:52.098892image/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
78 

Length

Max length4
Median length4
Mean length2.5465839
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 83
51.6%
0 78
48.4%

Length

2024-05-11T03:42:52.599163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:53.076863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
51.6%
0 78
48.4%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing161
Missing (%)100.0%
Memory size1.5 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing161
Missing (%)100.0%
Memory size1.5 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing161
Missing (%)100.0%
Memory size1.5 KiB
Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
123 
임대
29 
자가
 
9

Length

Max length4
Median length4
Mean length3.5279503
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> 123
76.4%
임대 29
 
18.0%
자가 9
 
5.6%

Length

2024-05-11T03:42:53.664307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:54.087051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 123
76.4%
임대 29
 
18.0%
자가 9
 
5.6%

세탁기수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
132 
0
29 

Length

Max length4
Median length4
Mean length3.4596273
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> 132
82.0%
0 29
 
18.0%

Length

2024-05-11T03:42:54.439234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:54.905475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 132
82.0%
0 29
 
18.0%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
151 
0
 
10

Length

Max length4
Median length4
Mean length3.8136646
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> 151
93.8%
0 10
 
6.2%

Length

2024-05-11T03:42:55.465792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:55.834552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 151
93.8%
0 10
 
6.2%

남성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
151 
0
 
10

Length

Max length4
Median length4
Mean length3.8136646
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> 151
93.8%
0 10
 
6.2%

Length

2024-05-11T03:42:56.212121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:56.551787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 151
93.8%
0 10
 
6.2%

회수건조수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
133 
0
28 

Length

Max length4
Median length4
Mean length3.4782609
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> 133
82.6%
0 28
 
17.4%

Length

2024-05-11T03:42:56.923238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:57.228086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 133
82.6%
0 28
 
17.4%

침대수
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
134 
0
27 

Length

Max length4
Median length4
Mean length3.4968944
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> 134
83.2%
0 27
 
16.8%

Length

2024-05-11T03:42:57.551248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:42:57.883147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 134
83.2%
0 27
 
16.8%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.7%
Missing25
Missing (%)15.5%
Memory size454.0 B
False
136 
(Missing)
25 
ValueCountFrequency (%)
False 136
84.5%
(Missing) 25
 
15.5%
2024-05-11T03:42:58.136365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
032000003200000-202-1968-0045319680202<NA>3폐업2폐업20011110<NA><NA><NA>02 8515434259.10151900서울특별시 관악구 신림동 1603-3번지<NA><NA>신림2003-03-24 00:00:00I2018-08-31 23:59:59.0공동탕업193544.955681442118.221638공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
132000003200000-202-1969-0044619691224<NA>3폐업2폐업19960808<NA><NA><NA>02 8838406140.32151840서울특별시 관악구 봉천동 899-12번지<NA><NA>현대2001-09-29 00:00:00I2018-08-31 23:59:59.0공동탕업195201.229345442597.341856공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
232000003200000-202-1969-0046019690415<NA>3폐업2폐업19960419<NA><NA><NA>02 877267795.82151808서울특별시 관악구 봉천동 17-12번지<NA><NA>쌍용2001-09-29 00:00:00I2018-08-31 23:59:59.0공동탕업196376.66199442724.090606공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
332000003200000-202-1970-0045119700825<NA>3폐업2폐업20000729<NA><NA><NA>0208779706147.50151861서울특별시 관악구 신림동 299-3번지<NA><NA>양지마을2001-12-05 00:00:00I2018-08-31 23:59:59.0공동탕업193837.360726440539.727552공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
432000003200000-202-1970-0046219700203<NA>3폐업2폐업20030326<NA><NA><NA>02 8863836201.30151843서울특별시 관악구 봉천동 902-4번지<NA><NA>서울2003-03-26 00:00:00I2018-08-31 23:59:59.0공동탕업194935.765269442665.333044공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
532000003200000-202-1970-0046519701002<NA>3폐업2폐업20030924<NA><NA><NA>02 8781093146.78151830서울특별시 관악구 봉천동 696-2번지<NA><NA>신안2003-09-25 00:00:00I2018-08-31 23:59:59.0공동탕업193690.209733443035.350947공동탕업2<NA>11<NA><NA><NA><NA>2Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
632000003200000-202-1970-0046619701222<NA>3폐업2폐업20030430<NA><NA><NA>02 8571023.00151881서울특별시 관악구 신림동 704-13번지<NA><NA>유림2003-04-24 00:00:00I2018-08-31 23:59:59.0공동탕업192805.58529441017.414873공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
732000003200000-202-1972-0042519721002<NA>3폐업2폐업19900828<NA><NA><NA>02 8882919204.31151865서울특별시 관악구 신림동 394-2번지<NA><NA>유성2001-09-29 00:00:00I2018-08-31 23:59:59.0공동탕업193940.203153441192.793579공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
832000003200000-202-1972-0042719720918<NA>3폐업2폐업19900828<NA><NA><NA>02 8785989220.54151853서울특별시 관악구 신림동 84-22번지<NA><NA>대화2001-09-29 00:00:00I2018-08-31 23:59:59.0공동탕업193783.655326442214.973862공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
932000003200000-202-1972-0043519720107<NA>3폐업2폐업20010426<NA><NA><NA>0208524717280.32151886서울특별시 관악구 신림동 678-4번지<NA><NA>한성2001-06-07 00:00:00I2018-08-31 23:59:59.0공동탕업192691.690564440207.545762공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
15132000003200000-202-2015-0000220150624<NA>3폐업2폐업20171211<NA><NA><NA>02 882 397593.56151830서울특별시 관악구 봉천동 970-22번지 3층서울특별시 관악구 봉천로 245, 3층 (봉천동)8711광솔트힐링스파2017-12-11 16:36:34I2018-08-31 23:59:59.0찜질시설서비스영업193654.447995443013.429723찜질시설서비스영업5133<NA><NA>000Y0<NA><NA><NA>임대00000N
15232000003200000-202-2015-0000320151029<NA>3폐업2폐업20190227<NA><NA><NA>02 888 7757371.34151827서울특별시 관악구 봉천동 957-6번지서울특별시 관악구 은천로 14 (봉천동)8749신통방통 원적외선 찜질방2019-02-27 13:45:29U2019-03-01 02:40:00.0찜질시설서비스영업194337.714627442659.268578찜질시설서비스영업9312<NA><NA>000N0<NA><NA><NA>자가00000N
15332000003200000-202-2016-0000120160909<NA>3폐업2폐업20190614<NA><NA><NA>02 8851108883.49151050서울특별시 관악구 봉천동 1717-3번지서울특별시 관악구 청림6길 3, 지하1층 (봉천동)8734푸른샘사우나2019-06-14 10:32:21U2019-06-16 02:40:00.0공동탕업+찜질시설서비스영업196384.544308442822.980266공동탕업+찜질시설서비스영업41<NA><NA>11002N0<NA><NA><NA>임대00000N
15432000003200000-202-2016-0000220161018<NA>3폐업2폐업20210415<NA><NA><NA><NA>211.00151903서울특별시 관악구 신림동 1655-21서울특별시 관악구 시흥대로 지하 554, 1층 (신림동)<NA>힐링남성전용사우나2021-04-15 15:03:44U2021-04-17 02:40:00.0공동탕업191223.827173442252.225728공동탕업41<NA><NA>11001N0<NA><NA><NA>임대00000N
15532000003200000-202-2017-0000120170714<NA>3폐업2폐업20200925<NA><NA><NA>02 882508899.00151848서울특별시 관악구 봉천동 1663-3서울특별시 관악구 봉천로 535, 3층 (봉천동)8789연승2020-09-25 11:20:17U2020-09-27 02:40:00.0찜질시설서비스영업196170.099883441860.174642찜질시설서비스영업6033<NA><NA>000N0<NA><NA><NA>임대00000N
15632000003200000-202-2017-0000220171123<NA>3폐업2폐업20201210<NA><NA><NA><NA>196.00151892서울특별시 관악구 신림동 1432-79서울특별시 관악구 신림로 349, 지하1층 (신림동)8760WD사우나2020-12-11 08:54:22U2020-12-13 02:40:00.0공동탕업193672.292012442595.201522공동탕업183<NA><NA>11001N0<NA><NA><NA>임대00000N
15732000003200000-202-2018-0000120180531<NA>1영업/정상1영업<NA><NA><NA><NA>02 8629696190.00151894서울특별시 관악구 신림동 1474-23 상원빌딩서울특별시 관악구 남부순환로 1474, 상원빌딩 501호 (신림동)8771MF사우나2020-10-06 15:49:32U2020-10-08 02:40:00.0목욕장업 기타192342.667414442140.925842목욕장업 기타5155<NA><NA>002Y0<NA><NA><NA>임대00000N
15832000003200000-202-2018-0000220180813<NA>1영업/정상1영업<NA><NA><NA><NA>02 8760008483.40151841서울특별시 관악구 봉천동 895-18 관악중앙새마을금고서울특별시 관악구 청룡1길 2, 관악중앙새마을금고 (봉천동)8786짐박스피트니스 사우나2020-07-22 17:35:08U2020-07-24 02:40:00.0목욕장업 기타195192.467516442103.196121목욕장업 기타735500002Y0<NA><NA><NA>자가00000N
15932000003200000-202-2021-000012021-05-26<NA>1영업/정상1영업<NA><NA><NA><NA>0260125234390.19151-830서울특별시 관악구 봉천동 702-49서울특별시 관악구 보라매로 13, B101호 (봉천동)8708샤워 인 더 하우스2023-03-08 13:55:08U2022-12-02 23:00:00.0공동탕업193481.099048443157.363464<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
16032000003200000-202-2023-000012023-09-04<NA>1영업/정상1영업<NA><NA><NA><NA><NA>107.22151-822서울특별시 관악구 봉천동 874-12서울특별시 관악구 봉천로 446, 2층 (봉천동)8758데일리앤스파2023-09-04 14:36:48I2022-12-09 00:06:00.0공동탕업195451.151206442231.163377<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>