Overview

Dataset statistics

Number of variables47
Number of observations124
Missing cells1384
Missing cells (%)23.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.1 KiB
Average record size in memory405.1 B

Variable types

Categorical21
Text7
DateTime4
Unsupported7
Numeric6
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-19966/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (54.8%)Imbalance
사용끝지상층 is highly imbalanced (59.8%)Imbalance
사용끝지하층 is highly imbalanced (55.1%)Imbalance
여성종사자수 is highly imbalanced (88.1%)Imbalance
남성종사자수 is highly imbalanced (88.1%)Imbalance
인허가취소일자 has 124 (100.0%) missing valuesMissing
폐업일자 has 24 (19.4%) missing valuesMissing
휴업시작일자 has 124 (100.0%) missing valuesMissing
휴업종료일자 has 124 (100.0%) missing valuesMissing
재개업일자 has 124 (100.0%) missing valuesMissing
전화번호 has 2 (1.6%) missing valuesMissing
도로명주소 has 78 (62.9%) missing valuesMissing
도로명우편번호 has 78 (62.9%) missing valuesMissing
좌표정보(X) has 9 (7.3%) missing valuesMissing
좌표정보(Y) has 9 (7.3%) missing valuesMissing
건물지상층수 has 88 (71.0%) missing valuesMissing
건물지하층수 has 87 (70.2%) missing valuesMissing
사용시작지상층 has 87 (70.2%) missing valuesMissing
발한실여부 has 27 (21.8%) missing valuesMissing
조건부허가신고사유 has 124 (100.0%) missing valuesMissing
조건부허가시작일자 has 124 (100.0%) missing valuesMissing
조건부허가종료일자 has 124 (100.0%) missing valuesMissing
다중이용업소여부 has 27 (21.8%) 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 20 (16.1%) zerosZeros
건물지하층수 has 22 (17.7%) zerosZeros
사용시작지상층 has 24 (19.4%) zerosZeros

Reproduction

Analysis started2024-05-11 05:50:30.899590
Analysis finished2024-05-11 05:50:31.809278
Duration0.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3140000
124 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3140000
2nd row3140000
3rd row3140000
4th row3140000
5th row3140000

Common Values

ValueCountFrequency (%)
3140000 124
100.0%

Length

2024-05-11T14:50:31.912993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:32.120489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 124
100.0%

관리번호
Text

UNIQUE 

Distinct124
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T14:50:32.534470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2728
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

Unique124 ?
Unique (%)100.0%

Sample

1st row3140000-202-1975-00086
2nd row3140000-202-1975-00095
3rd row3140000-202-1976-00069
4th row3140000-202-1978-00052
5th row3140000-202-1978-00061
ValueCountFrequency (%)
3140000-202-1975-00086 1
 
0.8%
3140000-202-1996-00124 1
 
0.8%
3140000-202-2000-00001 1
 
0.8%
3140000-202-1999-00138 1
 
0.8%
3140000-202-1999-00137 1
 
0.8%
3140000-202-1999-00136 1
 
0.8%
3140000-202-1999-00135 1
 
0.8%
3140000-202-1998-00132 1
 
0.8%
3140000-202-1998-00131 1
 
0.8%
3140000-202-1998-00130 1
 
0.8%
Other values (114) 114
91.9%
2024-05-11T14:50:33.047499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1071
39.3%
- 372
 
13.6%
2 315
 
11.5%
1 303
 
11.1%
3 173
 
6.3%
4 154
 
5.6%
9 146
 
5.4%
8 94
 
3.4%
7 40
 
1.5%
5 31
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2356
86.4%
Dash Punctuation 372
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1071
45.5%
2 315
 
13.4%
1 303
 
12.9%
3 173
 
7.3%
4 154
 
6.5%
9 146
 
6.2%
8 94
 
4.0%
7 40
 
1.7%
5 31
 
1.3%
6 29
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2728
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1071
39.3%
- 372
 
13.6%
2 315
 
11.5%
1 303
 
11.1%
3 173
 
6.3%
4 154
 
5.6%
9 146
 
5.4%
8 94
 
3.4%
7 40
 
1.5%
5 31
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1071
39.3%
- 372
 
13.6%
2 315
 
11.5%
1 303
 
11.1%
3 173
 
6.3%
4 154
 
5.6%
9 146
 
5.4%
8 94
 
3.4%
7 40
 
1.5%
5 31
 
1.1%
Distinct118
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum1975-09-16 00:00:00
Maximum2016-08-22 00:00:00
2024-05-11T14:50:33.279146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:50:33.509222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3
100 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 100
80.6%
1 24
 
19.4%

Length

2024-05-11T14:50:33.751987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:33.919055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 100
80.6%
1 24
 
19.4%

영업상태명
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
폐업
100 
영업/정상
24 

Length

Max length5
Median length2
Mean length2.5806452
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row폐업
2nd row폐업
3rd row영업/정상
4th row폐업
5th row폐업

Common Values

ValueCountFrequency (%)
폐업 100
80.6%
영업/정상 24
 
19.4%

Length

2024-05-11T14:50:34.107679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:34.260762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 100
80.6%
영업/정상 24
 
19.4%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
100 
1
24 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 100
80.6%
1 24
 
19.4%

Length

2024-05-11T14:50:34.414860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:34.568256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 100
80.6%
1 24
 
19.4%
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
폐업
100 
영업
24 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 100
80.6%
영업 24
 
19.4%

Length

2024-05-11T14:50:34.759952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:34.926350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 100
80.6%
영업 24
 
19.4%

폐업일자
Date

MISSING 

Distinct87
Distinct (%)87.0%
Missing24
Missing (%)19.4%
Memory size1.1 KiB
Minimum1994-07-27 00:00:00
Maximum2024-03-05 00:00:00
2024-05-11T14:50:35.121289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:50:35.345217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

전화번호
Text

MISSING 

Distinct114
Distinct (%)93.4%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2024-05-11T14:50:35.673100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.07377
Min length10

Characters and Unicode

Total characters1229
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

Unique110 ?
Unique (%)90.2%

Sample

1st row02 00000
2nd row0226038684
3rd row0226422761
4th row02 6024629
5th row0206041018
ValueCountFrequency (%)
02 33
 
21.3%
0200000000 4
 
2.6%
00000 4
 
2.6%
0226029796 2
 
1.3%
0226043222 2
 
1.3%
0226439850 1
 
0.6%
0226072142 1
 
0.6%
0206527205 1
 
0.6%
0226075717 1
 
0.6%
07042005718 1
 
0.6%
Other values (105) 105
67.7%
2024-05-11T14:50:36.376995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 286
23.3%
2 260
21.2%
6 181
14.7%
4 90
 
7.3%
9 82
 
6.7%
1 66
 
5.4%
8 61
 
5.0%
3 56
 
4.6%
5 52
 
4.2%
7 50
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1184
96.3%
Space Separator 45
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 286
24.2%
2 260
22.0%
6 181
15.3%
4 90
 
7.6%
9 82
 
6.9%
1 66
 
5.6%
8 61
 
5.2%
3 56
 
4.7%
5 52
 
4.4%
7 50
 
4.2%
Space Separator
ValueCountFrequency (%)
45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 286
23.3%
2 260
21.2%
6 181
14.7%
4 90
 
7.3%
9 82
 
6.7%
1 66
 
5.4%
8 61
 
5.0%
3 56
 
4.6%
5 52
 
4.2%
7 50
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 286
23.3%
2 260
21.2%
6 181
14.7%
4 90
 
7.3%
9 82
 
6.7%
1 66
 
5.4%
8 61
 
5.0%
3 56
 
4.6%
5 52
 
4.2%
7 50
 
4.1%
Distinct121
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T14:50:36.839548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.2580645
Min length5

Characters and Unicode

Total characters776
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

Unique118 ?
Unique (%)95.2%

Sample

1st row147.90
2nd row151.53
3rd row402.78
4th row184.06
5th row187.41
ValueCountFrequency (%)
1,601.99 2
 
1.6%
343.34 2
 
1.6%
279.52 2
 
1.6%
1,454.70 1
 
0.8%
377.68 1
 
0.8%
536.87 1
 
0.8%
950.64 1
 
0.8%
681.13 1
 
0.8%
197.62 1
 
0.8%
438.58 1
 
0.8%
Other values (111) 111
89.5%
2024-05-11T14:50:37.485112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 124
16.0%
0 89
11.5%
2 85
11.0%
1 79
10.2%
3 63
8.1%
7 62
8.0%
4 57
7.3%
5 53
6.8%
6 52
6.7%
9 49
 
6.3%
Other values (2) 63
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 638
82.2%
Other Punctuation 138
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 89
13.9%
2 85
13.3%
1 79
12.4%
3 63
9.9%
7 62
9.7%
4 57
8.9%
5 53
8.3%
6 52
8.2%
9 49
7.7%
8 49
7.7%
Other Punctuation
ValueCountFrequency (%)
. 124
89.9%
, 14
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common 776
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 124
16.0%
0 89
11.5%
2 85
11.0%
1 79
10.2%
3 63
8.1%
7 62
8.0%
4 57
7.3%
5 53
6.8%
6 52
6.7%
9 49
 
6.3%
Other values (2) 63
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 776
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 124
16.0%
0 89
11.5%
2 85
11.0%
1 79
10.2%
3 63
8.1%
7 62
8.0%
4 57
7.3%
5 53
6.8%
6 52
6.7%
9 49
 
6.3%
Other values (2) 63
8.1%
Distinct67
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T14:50:37.840253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0806452
Min length6

Characters and Unicode

Total characters754
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)28.2%

Sample

1st row158824
2nd row158859
3rd row158-813
4th row158826
5th row158831
ValueCountFrequency (%)
158859 7
 
5.6%
158861 5
 
4.0%
158806 5
 
4.0%
158824 4
 
3.2%
158864 4
 
3.2%
158819 4
 
3.2%
158837 4
 
3.2%
158843 3
 
2.4%
158842 3
 
2.4%
158827 3
 
2.4%
Other values (57) 82
66.1%
2024-05-11T14:50:38.303196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 252
33.4%
1 155
20.6%
5 151
20.0%
0 30
 
4.0%
2 30
 
4.0%
6 29
 
3.8%
4 29
 
3.8%
9 23
 
3.1%
3 23
 
3.1%
7 22
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 744
98.7%
Dash Punctuation 10
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 252
33.9%
1 155
20.8%
5 151
20.3%
0 30
 
4.0%
2 30
 
4.0%
6 29
 
3.9%
4 29
 
3.9%
9 23
 
3.1%
3 23
 
3.1%
7 22
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 754
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 252
33.4%
1 155
20.6%
5 151
20.0%
0 30
 
4.0%
2 30
 
4.0%
6 29
 
3.8%
4 29
 
3.8%
9 23
 
3.1%
3 23
 
3.1%
7 22
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 252
33.4%
1 155
20.6%
5 151
20.0%
0 30
 
4.0%
2 30
 
4.0%
6 29
 
3.8%
4 29
 
3.8%
9 23
 
3.1%
3 23
 
3.1%
7 22
 
2.9%
Distinct120
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T14:50:38.681733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length42
Mean length25.233871
Min length18

Characters and Unicode

Total characters3129
Distinct characters92
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)93.5%

Sample

1st row서울특별시 양천구 신월동 50-4번지
2nd row서울특별시 양천구 신정동 944-7번지
3rd row서울특별시 양천구 목동 718-12 1층
4th row서울특별시 양천구 신월동 90-7번지
5th row서울특별시 양천구 신월동 225-6번지
ValueCountFrequency (%)
서울특별시 124
21.3%
양천구 124
21.3%
신월동 48
 
8.2%
신정동 41
 
7.0%
목동 35
 
6.0%
지하1층 6
 
1.0%
지하 5
 
0.9%
1층 4
 
0.7%
3
 
0.5%
지상1,2층 3
 
0.5%
Other values (179) 189
32.5%
2024-05-11T14:50:39.442173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
551
 
17.6%
1 158
 
5.0%
136
 
4.3%
126
 
4.0%
125
 
4.0%
125
 
4.0%
124
 
4.0%
124
 
4.0%
124
 
4.0%
124
 
4.0%
Other values (82) 1412
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1771
56.6%
Decimal Number 652
 
20.8%
Space Separator 551
 
17.6%
Dash Punctuation 115
 
3.7%
Other Punctuation 18
 
0.6%
Open Punctuation 9
 
0.3%
Close Punctuation 9
 
0.3%
Uppercase Letter 3
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
136
 
7.7%
126
 
7.1%
125
 
7.1%
125
 
7.1%
124
 
7.0%
124
 
7.0%
124
 
7.0%
124
 
7.0%
124
 
7.0%
115
 
6.5%
Other values (65) 524
29.6%
Decimal Number
ValueCountFrequency (%)
1 158
24.2%
2 94
14.4%
0 63
 
9.7%
5 58
 
8.9%
9 54
 
8.3%
3 52
 
8.0%
4 47
 
7.2%
7 46
 
7.1%
8 41
 
6.3%
6 39
 
6.0%
Space Separator
ValueCountFrequency (%)
551
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 115
100.0%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1771
56.6%
Common 1355
43.3%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
136
 
7.7%
126
 
7.1%
125
 
7.1%
125
 
7.1%
124
 
7.0%
124
 
7.0%
124
 
7.0%
124
 
7.0%
124
 
7.0%
115
 
6.5%
Other values (65) 524
29.6%
Common
ValueCountFrequency (%)
551
40.7%
1 158
 
11.7%
- 115
 
8.5%
2 94
 
6.9%
0 63
 
4.6%
5 58
 
4.3%
9 54
 
4.0%
3 52
 
3.8%
4 47
 
3.5%
7 46
 
3.4%
Other values (6) 117
 
8.6%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1771
56.6%
ASCII 1358
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
551
40.6%
1 158
 
11.6%
- 115
 
8.5%
2 94
 
6.9%
0 63
 
4.6%
5 58
 
4.3%
9 54
 
4.0%
3 52
 
3.8%
4 47
 
3.5%
7 46
 
3.4%
Other values (7) 120
 
8.8%
Hangul
ValueCountFrequency (%)
136
 
7.7%
126
 
7.1%
125
 
7.1%
125
 
7.1%
124
 
7.0%
124
 
7.0%
124
 
7.0%
124
 
7.0%
124
 
7.0%
115
 
6.5%
Other values (65) 524
29.6%

도로명주소
Text

MISSING 

Distinct46
Distinct (%)100.0%
Missing78
Missing (%)62.9%
Memory size1.1 KiB
2024-05-11T14:50:40.107400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length45
Mean length36.326087
Min length23

Characters and Unicode

Total characters1671
Distinct characters109
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)100.0%

Sample

1st row서울특별시 양천구 목동중앙본로7길 42, 1층 (목동)
2nd row서울특별시 양천구 오목로3길 16-1, 1~2층 (신월동)
3rd row서울특별시 양천구 가로공원로60가길 10-7, 1층 (신월동)
4th row서울특별시 양천구 중앙로 233, 지상1층 (신정동)
5th row서울특별시 양천구 목동중앙북로14길 6 (목동)
ValueCountFrequency (%)
서울특별시 46
 
14.0%
양천구 46
 
14.0%
목동 16
 
4.9%
지하 15
 
4.6%
신월동 14
 
4.3%
신정동 13
 
4.0%
1층 11
 
3.3%
지하1층 5
 
1.5%
오목로 4
 
1.2%
2층 4
 
1.2%
Other values (130) 155
47.1%
2024-05-11T14:50:40.597522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
283
 
16.9%
74
 
4.4%
, 74
 
4.4%
1 73
 
4.4%
50
 
3.0%
49
 
2.9%
( 49
 
2.9%
) 49
 
2.9%
49
 
2.9%
48
 
2.9%
Other values (99) 873
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 947
56.7%
Space Separator 283
 
16.9%
Decimal Number 255
 
15.3%
Other Punctuation 74
 
4.4%
Open Punctuation 49
 
2.9%
Close Punctuation 49
 
2.9%
Dash Punctuation 7
 
0.4%
Math Symbol 4
 
0.2%
Uppercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
74
 
7.8%
50
 
5.3%
49
 
5.2%
49
 
5.2%
48
 
5.1%
47
 
5.0%
46
 
4.9%
46
 
4.9%
46
 
4.9%
46
 
4.9%
Other values (82) 446
47.1%
Decimal Number
ValueCountFrequency (%)
1 73
28.6%
2 42
16.5%
0 32
12.5%
3 28
 
11.0%
4 19
 
7.5%
5 16
 
6.3%
6 16
 
6.3%
7 12
 
4.7%
8 9
 
3.5%
9 8
 
3.1%
Space Separator
ValueCountFrequency (%)
283
100.0%
Other Punctuation
ValueCountFrequency (%)
, 74
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 947
56.7%
Common 721
43.1%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
74
 
7.8%
50
 
5.3%
49
 
5.2%
49
 
5.2%
48
 
5.1%
47
 
5.0%
46
 
4.9%
46
 
4.9%
46
 
4.9%
46
 
4.9%
Other values (82) 446
47.1%
Common
ValueCountFrequency (%)
283
39.3%
, 74
 
10.3%
1 73
 
10.1%
( 49
 
6.8%
) 49
 
6.8%
2 42
 
5.8%
0 32
 
4.4%
3 28
 
3.9%
4 19
 
2.6%
5 16
 
2.2%
Other values (6) 56
 
7.8%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 947
56.7%
ASCII 724
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
283
39.1%
, 74
 
10.2%
1 73
 
10.1%
( 49
 
6.8%
) 49
 
6.8%
2 42
 
5.8%
0 32
 
4.4%
3 28
 
3.9%
4 19
 
2.6%
5 16
 
2.2%
Other values (7) 59
 
8.1%
Hangul
ValueCountFrequency (%)
74
 
7.8%
50
 
5.3%
49
 
5.2%
49
 
5.2%
48
 
5.1%
47
 
5.0%
46
 
4.9%
46
 
4.9%
46
 
4.9%
46
 
4.9%
Other values (82) 446
47.1%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)91.3%
Missing78
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean8000.1957
Minimum7904
Maximum8106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T14:50:40.768908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7904
5-th percentile7914
Q17955.25
median7995
Q38045.75
95-th percentile8098.75
Maximum8106
Range202
Interquartile range (IQR)90.5

Descriptive statistics

Standard deviation60.382713
Coefficient of variation (CV)0.0075476545
Kurtosis-1.1111426
Mean8000.1957
Median Absolute Deviation (MAD)43.5
Skewness0.24349638
Sum368009
Variance3646.072
MonotonicityNot monotonic
2024-05-11T14:50:41.301978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7968 2
 
1.6%
7965 2
 
1.6%
8101 2
 
1.6%
7955 2
 
1.6%
7962 1
 
0.8%
7936 1
 
0.8%
8033 1
 
0.8%
7921 1
 
0.8%
8029 1
 
0.8%
7997 1
 
0.8%
Other values (32) 32
25.8%
(Missing) 78
62.9%
ValueCountFrequency (%)
7904 1
0.8%
7911 1
0.8%
7912 1
0.8%
7920 1
0.8%
7921 1
0.8%
7923 1
0.8%
7930 1
0.8%
7936 1
0.8%
7938 1
0.8%
7946 1
0.8%
ValueCountFrequency (%)
8106 1
0.8%
8101 2
1.6%
8092 1
0.8%
8091 1
0.8%
8087 1
0.8%
8086 1
0.8%
8077 1
0.8%
8072 1
0.8%
8065 1
0.8%
8062 1
0.8%
Distinct120
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-11T14:50:41.867802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length13
Mean length4.4677419
Min length2

Characters and Unicode

Total characters554
Distinct characters159
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)93.5%

Sample

1st row삼온탕
2nd row은행
3rd row목동탕
4th row황금
5th row신흥
ValueCountFrequency (%)
양천탕 2
 
1.5%
목동탕 2
 
1.5%
태양 2
 
1.5%
약수탕 2
 
1.5%
대림대중탕 1
 
0.8%
금성대중목욕탕 1
 
0.8%
태형 1
 
0.8%
신성탕 1
 
0.8%
천용탕 1
 
0.8%
장수사우나 1
 
0.8%
Other values (117) 117
89.3%
2024-05-11T14:50:42.576450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52
 
9.4%
28
 
5.1%
27
 
4.9%
27
 
4.9%
19
 
3.4%
15
 
2.7%
15
 
2.7%
15
 
2.7%
13
 
2.3%
13
 
2.3%
Other values (149) 330
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 535
96.6%
Space Separator 7
 
1.3%
Decimal Number 6
 
1.1%
Close Punctuation 3
 
0.5%
Open Punctuation 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
52
 
9.7%
28
 
5.2%
27
 
5.0%
27
 
5.0%
19
 
3.6%
15
 
2.8%
15
 
2.8%
15
 
2.8%
13
 
2.4%
13
 
2.4%
Other values (143) 311
58.1%
Decimal Number
ValueCountFrequency (%)
4 3
50.0%
2 3
50.0%
Space Separator
ValueCountFrequency (%)
7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 535
96.6%
Common 19
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
52
 
9.7%
28
 
5.2%
27
 
5.0%
27
 
5.0%
19
 
3.6%
15
 
2.8%
15
 
2.8%
15
 
2.8%
13
 
2.4%
13
 
2.4%
Other values (143) 311
58.1%
Common
ValueCountFrequency (%)
7
36.8%
4 3
15.8%
2 3
15.8%
) 3
15.8%
( 2
 
10.5%
& 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 535
96.6%
ASCII 19
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
52
 
9.7%
28
 
5.2%
27
 
5.0%
27
 
5.0%
19
 
3.6%
15
 
2.8%
15
 
2.8%
15
 
2.8%
13
 
2.4%
13
 
2.4%
Other values (143) 311
58.1%
ASCII
ValueCountFrequency (%)
7
36.8%
4 3
15.8%
2 3
15.8%
) 3
15.8%
( 2
 
10.5%
& 1
 
5.3%
Distinct93
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum1999-01-21 00:00:00
Maximum2024-05-09 10:17:18
2024-05-11T14:50:42.828277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:50:43.142814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
I
83 
U
41 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 83
66.9%
U 41
33.1%

Length

2024-05-11T14:50:43.429874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:43.609678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 83
66.9%
u 41
33.1%
Distinct24
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 23:01:00
2024-05-11T14:50:43.790398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:50:44.033579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

업태구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
공동탕업
96 
공동탕업+찜질시설서비스영업
25 
찜질시설서비스영업
 
2
한증막업
 
1

Length

Max length14
Median length4
Mean length6.0967742
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 96
77.4%
공동탕업+찜질시설서비스영업 25
 
20.2%
찜질시설서비스영업 2
 
1.6%
한증막업 1
 
0.8%

Length

2024-05-11T14:50:44.304279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:44.503508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 96
77.4%
공동탕업+찜질시설서비스영업 25
 
20.2%
찜질시설서비스영업 2
 
1.6%
한증막업 1
 
0.8%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct103
Distinct (%)89.6%
Missing9
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean187053.89
Minimum184560.69
Maximum189376.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T14:50:44.728095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184560.69
5-th percentile184799.86
Q1185915.35
median187133.11
Q3188093.23
95-th percentile189014.58
Maximum189376.87
Range4816.185
Interquartile range (IQR)2177.8829

Descriptive statistics

Standard deviation1361.8511
Coefficient of variation (CV)0.007280528
Kurtosis-1.1647862
Mean187053.89
Median Absolute Deviation (MAD)1112.6038
Skewness-0.16431561
Sum21511197
Variance1854638.3
MonotonicityNot monotonic
2024-05-11T14:50:44.926839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188904.571764159 4
 
3.2%
189021.797866206 2
 
1.6%
186022.845067592 2
 
1.6%
187158.467033065 2
 
1.6%
186020.509761348 2
 
1.6%
186990.07041522 2
 
1.6%
187520.306766633 2
 
1.6%
188075.386279704 2
 
1.6%
187923.919592336 2
 
1.6%
185469.109541377 2
 
1.6%
Other values (93) 93
75.0%
(Missing) 9
 
7.3%
ValueCountFrequency (%)
184560.687279761 1
0.8%
184569.486821632 1
0.8%
184641.715758667 1
0.8%
184674.374638261 1
0.8%
184705.799035858 1
0.8%
184783.397041118 1
0.8%
184806.911534822 1
0.8%
185060.818687282 1
0.8%
185063.829655046 1
0.8%
185077.119343918 1
0.8%
ValueCountFrequency (%)
189376.872323525 1
 
0.8%
189280.689807363 1
 
0.8%
189151.208015925 1
 
0.8%
189042.496526196 1
 
0.8%
189021.797866206 2
1.6%
189011.488403267 1
 
0.8%
188990.004976184 1
 
0.8%
188904.571764159 4
3.2%
188895.844080632 1
 
0.8%
188793.497546726 1
 
0.8%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct103
Distinct (%)89.6%
Missing9
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean447189.86
Minimum445155.08
Maximum449683.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T14:50:45.122406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum445155.08
5-th percentile445963.79
Q1446457.04
median446899.77
Q3447850.89
95-th percentile449239.82
Maximum449683.22
Range4528.138
Interquartile range (IQR)1393.8476

Descriptive statistics

Standard deviation1021.7405
Coefficient of variation (CV)0.0022848024
Kurtosis-0.2462377
Mean447189.86
Median Absolute Deviation (MAD)645.85766
Skewness0.58434564
Sum51426834
Variance1043953.6
MonotonicityNot monotonic
2024-05-11T14:50:45.374989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446861.525362878 4
 
3.2%
446740.576354849 2
 
1.6%
446241.819507065 2
 
1.6%
445678.076325463 2
 
1.6%
446940.326860903 2
 
1.6%
446176.810389121 2
 
1.6%
446450.347274241 2
 
1.6%
445229.76179636 2
 
1.6%
447850.891096367 2
 
1.6%
447552.415105958 2
 
1.6%
Other values (93) 93
75.0%
(Missing) 9
 
7.3%
ValueCountFrequency (%)
445155.081213627 1
0.8%
445229.76179636 2
1.6%
445678.076325463 2
1.6%
445939.679152444 1
0.8%
445974.128928464 1
0.8%
445983.252020472 1
0.8%
446011.525304939 1
0.8%
446017.623604481 1
0.8%
446035.819636 1
0.8%
446116.629313272 1
0.8%
ValueCountFrequency (%)
449683.219205227 1
0.8%
449584.740750439 1
0.8%
449426.206993027 1
0.8%
449398.438608253 1
0.8%
449390.943275 1
0.8%
449317.766034821 1
0.8%
449206.41623963 1
0.8%
449168.905530081 1
0.8%
448953.063207171 1
0.8%
448813.925045749 1
0.8%

위생업태명
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
공동탕업
81 
<NA>
27 
공동탕업+찜질시설서비스영업
13 
찜질시설서비스영업
 
2
한증막업
 
1

Length

Max length14
Median length4
Mean length5.1290323
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
공동탕업 81
65.3%
<NA> 27
 
21.8%
공동탕업+찜질시설서비스영업 13
 
10.5%
찜질시설서비스영업 2
 
1.6%
한증막업 1
 
0.8%

Length

2024-05-11T14:50:45.590428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:45.779079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동탕업 81
65.3%
na 27
 
21.8%
공동탕업+찜질시설서비스영업 13
 
10.5%
찜질시설서비스영업 2
 
1.6%
한증막업 1
 
0.8%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)19.4%
Missing88
Missing (%)71.0%
Infinite0
Infinite (%)0.0%
Mean3
Minimum0
Maximum24
Zeros20
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T14:50:45.963563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.5
95-th percentile8.75
Maximum24
Range24
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation4.822566
Coefficient of variation (CV)1.607522
Kurtosis9.4509101
Mean3
Median Absolute Deviation (MAD)0
Skewness2.6476194
Sum108
Variance23.257143
MonotonicityNot monotonic
2024-05-11T14:50:46.168048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 20
 
16.1%
3 5
 
4.0%
8 4
 
3.2%
6 3
 
2.4%
4 2
 
1.6%
24 1
 
0.8%
11 1
 
0.8%
(Missing) 88
71.0%
ValueCountFrequency (%)
0 20
16.1%
3 5
 
4.0%
4 2
 
1.6%
6 3
 
2.4%
8 4
 
3.2%
11 1
 
0.8%
24 1
 
0.8%
ValueCountFrequency (%)
24 1
 
0.8%
11 1
 
0.8%
8 4
 
3.2%
6 3
 
2.4%
4 2
 
1.6%
3 5
 
4.0%
0 20
16.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)16.2%
Missing87
Missing (%)70.2%
Infinite0
Infinite (%)0.0%
Mean0.81081081
Minimum0
Maximum6
Zeros22
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T14:50:46.334296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3.2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3506755
Coefficient of variation (CV)1.6658331
Kurtosis5.5093126
Mean0.81081081
Median Absolute Deviation (MAD)0
Skewness2.2228867
Sum30
Variance1.8243243
MonotonicityNot monotonic
2024-05-11T14:50:46.598402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 22
 
17.7%
1 8
 
6.5%
2 3
 
2.4%
3 2
 
1.6%
4 1
 
0.8%
6 1
 
0.8%
(Missing) 87
70.2%
ValueCountFrequency (%)
0 22
17.7%
1 8
 
6.5%
2 3
 
2.4%
3 2
 
1.6%
4 1
 
0.8%
6 1
 
0.8%
ValueCountFrequency (%)
6 1
 
0.8%
4 1
 
0.8%
3 2
 
1.6%
2 3
 
2.4%
1 8
 
6.5%
0 22
17.7%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)18.9%
Missing87
Missing (%)70.2%
Infinite0
Infinite (%)0.0%
Mean0.89189189
Minimum0
Maximum6
Zeros24
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-11T14:50:46.786687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5.2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6961359
Coefficient of variation (CV)1.9017281
Kurtosis3.77642
Mean0.89189189
Median Absolute Deviation (MAD)0
Skewness2.1642763
Sum33
Variance2.8768769
MonotonicityNot monotonic
2024-05-11T14:50:46.935501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 24
 
19.4%
1 7
 
5.6%
6 2
 
1.6%
3 1
 
0.8%
2 1
 
0.8%
5 1
 
0.8%
4 1
 
0.8%
(Missing) 87
70.2%
ValueCountFrequency (%)
0 24
19.4%
1 7
 
5.6%
2 1
 
0.8%
3 1
 
0.8%
4 1
 
0.8%
5 1
 
0.8%
6 2
 
1.6%
ValueCountFrequency (%)
6 2
 
1.6%
5 1
 
0.8%
4 1
 
0.8%
3 1
 
0.8%
2 1
 
0.8%
1 7
 
5.6%
0 24
19.4%

사용끝지상층
Categorical

IMBALANCE 

Distinct6
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
101 
0
12 
2
 
4
4
 
4
7
 
2

Length

Max length4
Median length4
Mean length3.4435484
Min length1

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 101
81.5%
0 12
 
9.7%
2 4
 
3.2%
4 4
 
3.2%
7 2
 
1.6%
1 1
 
0.8%

Length

2024-05-11T14:50:47.129365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:47.297948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
81.5%
0 12
 
9.7%
2 4
 
3.2%
4 4
 
3.2%
7 2
 
1.6%
1 1
 
0.8%
Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
87 
0
25 
1
10 
2
 
2

Length

Max length4
Median length4
Mean length3.1048387
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> 87
70.2%
0 25
 
20.2%
1 10
 
8.1%
2 2
 
1.6%

Length

2024-05-11T14:50:47.471180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:47.651630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 87
70.2%
0 25
 
20.2%
1 10
 
8.1%
2 2
 
1.6%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
100 
0
12 
1
 
6
2
 
4
3
 
2

Length

Max length4
Median length4
Mean length3.4193548
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> 100
80.6%
0 12
 
9.7%
1 6
 
4.8%
2 4
 
3.2%
3 2
 
1.6%

Length

2024-05-11T14:50:47.841133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:48.001159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 100
80.6%
0 12
 
9.7%
1 6
 
4.8%
2 4
 
3.2%
3 2
 
1.6%

한실수
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
66 
<NA>
58 

Length

Max length4
Median length1
Mean length2.4032258
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 66
53.2%
<NA> 58
46.8%

Length

2024-05-11T14:50:48.181371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:48.399523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66
53.2%
na 58
46.8%

양실수
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
66 
<NA>
58 

Length

Max length4
Median length1
Mean length2.4032258
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 66
53.2%
<NA> 58
46.8%

Length

2024-05-11T14:50:48.573257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:48.753336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66
53.2%
na 58
46.8%

욕실수
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
55 
0
47 
2
20 
1
 
1
8
 
1

Length

Max length4
Median length1
Mean length2.3306452
Min length1

Unique

Unique2 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 55
44.4%
0 47
37.9%
2 20
 
16.1%
1 1
 
0.8%
8 1
 
0.8%

Length

2024-05-11T14:50:48.977364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:49.150790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 55
44.4%
0 47
37.9%
2 20
 
16.1%
1 1
 
0.8%
8 1
 
0.8%

발한실여부
Boolean

MISSING 

Distinct2
Distinct (%)2.1%
Missing27
Missing (%)21.8%
Memory size380.0 B
False
73 
True
24 
(Missing)
27 
ValueCountFrequency (%)
False 73
58.9%
True 24
 
19.4%
(Missing) 27
 
21.8%
2024-05-11T14:50:49.310589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
0
66 
<NA>
58 

Length

Max length4
Median length1
Mean length2.4032258
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 66
53.2%
<NA> 58
46.8%

Length

2024-05-11T14:50:49.516999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:49.698994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66
53.2%
na 58
46.8%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB
Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
104 
임대
11 
자가
 
9

Length

Max length4
Median length4
Mean length3.6774194
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> 104
83.9%
임대 11
 
8.9%
자가 9
 
7.3%

Length

2024-05-11T14:50:49.869122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:50.056761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 104
83.9%
임대 11
 
8.9%
자가 9
 
7.3%

세탁기수
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
101 
0
23 

Length

Max length4
Median length4
Mean length3.4435484
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 101
81.5%
0 23
 
18.5%

Length

2024-05-11T14:50:50.237781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:50.392469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 101
81.5%
0 23
 
18.5%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
122 
0
 
2

Length

Max length4
Median length4
Mean length3.9516129
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 122
98.4%
0 2
 
1.6%

Length

2024-05-11T14:50:50.548223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:50.702687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 122
98.4%
0 2
 
1.6%

남성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
122 
0
 
2

Length

Max length4
Median length4
Mean length3.9516129
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 122
98.4%
0 2
 
1.6%

Length

2024-05-11T14:50:50.889673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:51.065521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 122
98.4%
0 2
 
1.6%

회수건조수
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
102 
0
22 

Length

Max length4
Median length4
Mean length3.4677419
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> 102
82.3%
0 22
 
17.7%

Length

2024-05-11T14:50:51.255094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:51.402032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 102
82.3%
0 22
 
17.7%

침대수
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
102 
0
22 

Length

Max length4
Median length4
Mean length3.4677419
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> 102
82.3%
0 22
 
17.7%

Length

2024-05-11T14:50:51.582642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:50:51.755205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 102
82.3%
0 22
 
17.7%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)1.0%
Missing27
Missing (%)21.8%
Memory size380.0 B
False
97 
(Missing)
27 
ValueCountFrequency (%)
False 97
78.2%
(Missing) 27
 
21.8%
2024-05-11T14:50:51.895999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031400003140000-202-1975-0008619750916<NA>3폐업2폐업19970602<NA><NA><NA>02 00000147.90158824서울특별시 양천구 신월동 50-4번지<NA><NA>삼온탕1999-01-21 00:00:00I2018-08-31 23:59:59.0공동탕업184783.397041448230.469938공동탕업<NA><NA><NA><NA><NA><NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131400003140000-202-1975-0009519751122<NA>3폐업2폐업20051117<NA><NA><NA>0226038684151.53158859서울특별시 양천구 신정동 944-7번지<NA><NA>은행2005-01-24 00:00:00I2018-08-31 23:59:59.0공동탕업186860.400809446828.518281공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231400003140000-202-1976-000691976-09-06<NA>1영업/정상1영업<NA><NA><NA><NA>0226422761402.78158-813서울특별시 양천구 목동 718-12 1층서울특별시 양천구 목동중앙본로7길 42, 1층 (목동)7955목동탕2024-05-09 10:17:18U2023-12-04 23:01:00.0공동탕업188111.081854448607.421311<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
331400003140000-202-1978-0005219781229<NA>3폐업2폐업20030225<NA><NA><NA>02 6024629184.06158826서울특별시 양천구 신월동 90-7번지<NA><NA>황금2003-03-26 00:00:00I2018-08-31 23:59:59.0공동탕업185294.680514448215.242017공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431400003140000-202-1978-0006119780912<NA>3폐업2폐업20021223<NA><NA><NA>0206041018187.41158831서울특별시 양천구 신월동 225-6번지<NA><NA>신흥2003-06-13 00:00:00I2018-08-31 23:59:59.0공동탕업185307.728038447455.002869공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531400003140000-202-1978-0006519780117<NA>3폐업2폐업20080429<NA><NA><NA>0226942373323.98158857서울특별시 양천구 신정동 909-14번지<NA><NA>태양탕2007-06-07 00:00:00I2018-08-31 23:59:59.0공동탕업187449.841428447122.523897공동탕업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631400003140000-202-1978-0006819781229<NA>3폐업2폐업19960402<NA><NA><NA>02 6056825205.19158806서울특별시 양천구 목동 405-16번지<NA><NA>정수1999-01-21 00:00:00I2018-08-31 23:59:59.0공동탕업188772.839317446886.095777공동탕업<NA><NA><NA><NA><NA><NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731400003140000-202-1978-0007919780725<NA>3폐업2폐업20000925<NA><NA><NA>02 6027024163.89158862서울특별시 양천구 신정동 1050-6번지<NA><NA>대동2000-09-25 00:00:00I2018-08-31 23:59:59.0공동탕업187113.803002446351.761491공동탕업<NA><NA><NA><NA><NA><NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831400003140000-202-1978-0013319780110<NA>3폐업2폐업20070613<NA><NA><NA>0206478677231.00158801서울특별시 양천구 목동 128-50번지<NA><NA>염창한증막2005-01-24 00:00:00I2018-08-31 23:59:59.0한증막업189376.872324449168.90553한증막업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931400003140000-202-1979-0006319791201<NA>3폐업2폐업20020426<NA><NA><NA>02 6920662213.78158860서울특별시 양천구 신정동 997-5번지<NA><NA>추정탕2002-04-26 00:00:00I2018-08-31 23:59:59.0공동탕업187887.639702446924.819119공동탕업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
11431400003140000-202-2010-000012010-09-07<NA>1영업/정상1영업<NA><NA><NA><NA>02269899721424.19158-070서울특별시 양천구 신정동 1267 뉴프라자 101호서울특별시 양천구 신정로 312, 뉴프라자 지하 1층 101호 (신정동)8106토성사우나2024-04-01 14:19:31U2023-12-04 00:03:00.0공동탕업+찜질시설서비스영업187158.467033445678.076325<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
11531400003140000-202-2013-0000120130103<NA>1영업/정상1영업<NA><NA><NA><NA>0220610323710.72158811서울특별시 양천구 목동 613-2서울특별시 양천구 등촌로 220, 지하 1층 01호 (목동)7946등촌사우나2022-06-09 10:43:38U2021-12-05 23:01:00.0공동탕업187916.254333449683.219205<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
11631400003140000-202-2013-0000220130411<NA>3폐업2폐업20211001<NA><NA><NA>0226466200663.07158861서울특별시 양천구 신정동 1023-12 지하2층 1호서울특별시 양천구 은행정로 5, 지하2층 1호 (신정동)8086주)퓨처박스짐2021-10-01 11:17:59U2021-10-03 02:40:00.0공동탕업187520.306767446450.347274공동탕업000022002Y0<NA><NA><NA>임대00000N
11731400003140000-202-2013-0000320131010<NA>3폐업2폐업20171115<NA><NA><NA><NA>30.39158819서울특별시 양천구 목동 791-11번지 원빌딩6층서울특별시 양천구 목동중앙서로 7-1, 6층 (목동, 원빌딩)7965미네랄솔트찜질방2017-11-15 13:45:02I2018-08-31 23:59:59.0찜질시설서비스영업187971.937904447832.903003찜질시설서비스영업806<NA><NA><NA>000Y0<NA><NA><NA>임대0<NA><NA>00N
11831400003140000-202-2013-0000420131119<NA>3폐업2폐업20190529<NA><NA><NA>02 206284121,165.10158728서울특별시 양천구 목동 905-22번지 목동트윈빌 401호서울특별시 양천구 목동동로 339 (목동, 목동트윈빌 401호)7988(주)스포츠앤스파코리아2019-05-29 15:13:55U2019-05-31 02:40:00.0공동탕업+찜질시설서비스영업189042.496526447777.83576공동탕업+찜질시설서비스영업004400008Y0<NA><NA><NA>임대0<NA><NA>00N
11931400003140000-202-2013-0000520131213<NA>3폐업2폐업20151209<NA><NA><NA>02 26453900291.37158849서울특별시 양천구 신정동 128-113번지서울특별시 양천구 신목로 34 (신정동, 현승빌딩6층)8015닥터스톤힐링센터2014-12-23 16:57:37I2018-08-31 23:59:59.0찜질시설서비스영업188793.497547446277.050684찜질시설서비스영업606000000Y0<NA><NA><NA>임대0<NA><NA>00N
12031400003140000-202-2014-0000120140812<NA>1영업/정상1영업<NA><NA><NA><NA>02628903651,601.99158820서울특별시 양천구 목동 933 목동삼익아파트 (지하 1층) 상가동 7호서울특별시 양천구 목동동로12길 45, 상가동 지하 1층 7호 (목동, 목동삼익아파트)8007해동참숯가마사우나2022-06-13 09:29:00U2021-12-05 23:05:00.0공동탕업+찜질시설서비스영업189021.797866446740.576355<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12131400003140000-202-2016-0000120160317<NA>1영업/정상1영업<NA><NA><NA><NA>0226465001499.96158851서울특별시 양천구 신정동 210-16 3층, 4층서울특별시 양천구 목동남로 58-4 (3,4)층 (신정동)8101신성대중사우나2022-06-09 10:44:43U2021-12-05 23:01:00.0공동탕업188075.38628445229.761796<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12231400003140000-202-2016-0000220160624<NA>1영업/정상1영업<NA><NA><NA><NA>0226539691295.00158722서울특별시 양천구 목동 405-25 청학빌딩 지하1층 일부호서울특별시 양천구 오목로 344, 지하1층 일부호 (목동, 청학빌딩)8006스포사우나2022-06-09 10:45:47U2021-12-05 23:01:00.0공동탕업188904.571764446861.525363<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12331400003140000-202-2016-0000320160822<NA>1영업/정상1영업<NA><NA><NA><NA>0226475670780.77158050서울특별시 양천구 목동 962 목동트라팰리스 이스튼에비뉴동 201호서울특별시 양천구 오목로 299, 목동트라팰리스 이스튼에비뉴동 지하 2층 201호 (목동)8001(주)스포짐네트웍스2022-06-09 10:46:05U2021-12-05 23:01:00.0공동탕업+찜질시설서비스영업188472.759197447091.855964<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>