Overview

Dataset statistics

Number of variables47
Number of observations258
Missing cells2937
Missing cells (%)24.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.9 KiB
Average record size in memory404.5 B

Variable types

Categorical20
Text7
DateTime4
Unsupported7
Numeric7
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-19291/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
업태구분명 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
사용끝지하층 is highly imbalanced (51.7%)Imbalance
여성종사자수 is highly imbalanced (65.8%)Imbalance
인허가취소일자 has 258 (100.0%) missing valuesMissing
폐업일자 has 87 (33.7%) missing valuesMissing
휴업시작일자 has 258 (100.0%) missing valuesMissing
휴업종료일자 has 258 (100.0%) missing valuesMissing
재개업일자 has 258 (100.0%) missing valuesMissing
전화번호 has 65 (25.2%) missing valuesMissing
도로명주소 has 88 (34.1%) missing valuesMissing
도로명우편번호 has 89 (34.5%) missing valuesMissing
좌표정보(X) has 4 (1.6%) missing valuesMissing
좌표정보(Y) has 4 (1.6%) missing valuesMissing
건물지상층수 has 124 (48.1%) missing valuesMissing
사용시작지상층 has 149 (57.8%) missing valuesMissing
사용끝지상층 has 171 (66.3%) missing valuesMissing
발한실여부 has 70 (27.1%) missing valuesMissing
조건부허가신고사유 has 258 (100.0%) missing valuesMissing
조건부허가시작일자 has 258 (100.0%) missing valuesMissing
조건부허가종료일자 has 258 (100.0%) missing valuesMissing
남성종사자수 has 213 (82.6%) missing valuesMissing
다중이용업소여부 has 67 (26.0%) missing valuesMissing
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가신고사유 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 98 (38.0%) zerosZeros
사용시작지상층 has 23 (8.9%) zerosZeros
사용끝지상층 has 17 (6.6%) zerosZeros
남성종사자수 has 24 (9.3%) zerosZeros

Reproduction

Analysis started2024-05-11 06:38:02.937702
Analysis finished2024-05-11 06:38:04.365573
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
3140000
258 

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 258
100.0%

Length

2024-05-11T15:38:04.500624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:04.676689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 258
100.0%

관리번호
Text

UNIQUE 

Distinct258
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T15:38:04.937796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters5676
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

Unique258 ?
Unique (%)100.0%

Sample

1st row3140000-206-1991-02113
2nd row3140000-206-1992-02116
3rd row3140000-206-1992-02117
4th row3140000-206-1993-02115
5th row3140000-206-1993-02118
ValueCountFrequency (%)
3140000-206-1991-02113 1
 
0.4%
3140000-206-2016-00008 1
 
0.4%
3140000-206-2013-00013 1
 
0.4%
3140000-206-2015-00003 1
 
0.4%
3140000-206-2014-00001 1
 
0.4%
3140000-206-2014-00002 1
 
0.4%
3140000-206-2014-00003 1
 
0.4%
3140000-206-2014-00004 1
 
0.4%
3140000-206-2014-00005 1
 
0.4%
3140000-206-2014-00006 1
 
0.4%
Other values (248) 248
96.1%
2024-05-11T15:38:05.568593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2557
45.0%
- 774
 
13.6%
2 625
 
11.0%
1 535
 
9.4%
3 331
 
5.8%
4 313
 
5.5%
6 306
 
5.4%
9 83
 
1.5%
7 57
 
1.0%
5 53
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4902
86.4%
Dash Punctuation 774
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2557
52.2%
2 625
 
12.7%
1 535
 
10.9%
3 331
 
6.8%
4 313
 
6.4%
6 306
 
6.2%
9 83
 
1.7%
7 57
 
1.2%
5 53
 
1.1%
8 42
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 774
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5676
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2557
45.0%
- 774
 
13.6%
2 625
 
11.0%
1 535
 
9.4%
3 331
 
5.8%
4 313
 
5.5%
6 306
 
5.4%
9 83
 
1.5%
7 57
 
1.0%
5 53
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2557
45.0%
- 774
 
13.6%
2 625
 
11.0%
1 535
 
9.4%
3 331
 
5.8%
4 313
 
5.5%
6 306
 
5.4%
9 83
 
1.5%
7 57
 
1.0%
5 53
 
0.9%
Distinct241
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum1991-11-20 00:00:00
Maximum2024-03-27 00:00:00
2024-05-11T15:38:05.882043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:38:06.158390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size2.4 KiB
Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
3
171 
1
87 

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 (%)
3 171
66.3%
1 87
33.7%

Length

2024-05-11T15:38:06.379415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:06.546680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 171
66.3%
1 87
33.7%

영업상태명
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
폐업
171 
영업/정상
87 

Length

Max length5
Median length2
Mean length3.0116279
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 171
66.3%
영업/정상 87
33.7%

Length

2024-05-11T15:38:06.752003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:06.939165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 171
66.3%
영업/정상 87
33.7%
Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2
171 
1
87 

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 (%)
2 171
66.3%
1 87
33.7%

Length

2024-05-11T15:38:07.118793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:07.298164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 171
66.3%
1 87
33.7%
Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
폐업
171 
영업
87 

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 171
66.3%
영업 87
33.7%

Length

2024-05-11T15:38:07.551203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:07.777935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 171
66.3%
영업 87
33.7%

폐업일자
Date

MISSING 

Distinct154
Distinct (%)90.1%
Missing87
Missing (%)33.7%
Memory size2.1 KiB
Minimum1997-02-22 00:00:00
Maximum2024-04-30 00:00:00
2024-05-11T15:38:07.997986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:38:08.237762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size2.4 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size2.4 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size2.4 KiB

전화번호
Text

MISSING 

Distinct188
Distinct (%)97.4%
Missing65
Missing (%)25.2%
Memory size2.1 KiB
2024-05-11T15:38:08.600866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.409326
Min length7

Characters and Unicode

Total characters2009
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

Unique183 ?
Unique (%)94.8%

Sample

1st row02 6483070
2nd row02 6034312
3rd row02 6908096
4th row02 6928336
5th row02 6548712
ValueCountFrequency (%)
02 62
 
22.7%
070 7
 
2.6%
0226089226 2
 
0.7%
0264047039 2
 
0.7%
0226483490 2
 
0.7%
032 2
 
0.7%
26073030 2
 
0.7%
26056089 2
 
0.7%
26027401 1
 
0.4%
15884195 1
 
0.4%
Other values (190) 190
69.6%
2024-05-11T15:38:09.140081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 394
19.6%
0 389
19.4%
6 254
12.6%
4 137
 
6.8%
9 133
 
6.6%
5 132
 
6.6%
1 126
 
6.3%
115
 
5.7%
7 115
 
5.7%
3 109
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1894
94.3%
Space Separator 115
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 394
20.8%
0 389
20.5%
6 254
13.4%
4 137
 
7.2%
9 133
 
7.0%
5 132
 
7.0%
1 126
 
6.7%
7 115
 
6.1%
3 109
 
5.8%
8 105
 
5.5%
Space Separator
ValueCountFrequency (%)
115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 394
19.6%
0 389
19.4%
6 254
12.6%
4 137
 
6.8%
9 133
 
6.6%
5 132
 
6.6%
1 126
 
6.3%
115
 
5.7%
7 115
 
5.7%
3 109
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 394
19.6%
0 389
19.4%
6 254
12.6%
4 137
 
6.8%
9 133
 
6.6%
5 132
 
6.6%
1 126
 
6.3%
115
 
5.7%
7 115
 
5.7%
3 109
 
5.4%
Distinct181
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T15:38:09.640549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0193798
Min length3

Characters and Unicode

Total characters1295
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

Unique149 ?
Unique (%)57.8%

Sample

1st row672.22
2nd row485.00
3rd row989.40
4th row.00
5th row.00
ValueCountFrequency (%)
00 19
 
7.4%
33.00 9
 
3.5%
49.50 7
 
2.7%
20.00 7
 
2.7%
82.50 4
 
1.6%
10.00 4
 
1.6%
66.00 3
 
1.2%
50.00 3
 
1.2%
4.00 3
 
1.2%
9.90 3
 
1.2%
Other values (171) 196
76.0%
2024-05-11T15:38:10.508473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 350
27.0%
. 258
19.9%
1 95
 
7.3%
2 93
 
7.2%
6 89
 
6.9%
3 86
 
6.6%
5 77
 
5.9%
9 72
 
5.6%
4 68
 
5.3%
8 54
 
4.2%
Other values (2) 53
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1035
79.9%
Other Punctuation 260
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 350
33.8%
1 95
 
9.2%
2 93
 
9.0%
6 89
 
8.6%
3 86
 
8.3%
5 77
 
7.4%
9 72
 
7.0%
4 68
 
6.6%
8 54
 
5.2%
7 51
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 258
99.2%
, 2
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1295
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 350
27.0%
. 258
19.9%
1 95
 
7.3%
2 93
 
7.2%
6 89
 
6.9%
3 86
 
6.6%
5 77
 
5.9%
9 72
 
5.6%
4 68
 
5.3%
8 54
 
4.2%
Other values (2) 53
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 350
27.0%
. 258
19.9%
1 95
 
7.3%
2 93
 
7.2%
6 89
 
6.9%
3 86
 
6.6%
5 77
 
5.9%
9 72
 
5.6%
4 68
 
5.3%
8 54
 
4.2%
Other values (2) 53
 
4.1%
Distinct92
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T15:38:10.966879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1744186
Min length6

Characters and Unicode

Total characters1593
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

Unique45 ?
Unique (%)17.4%

Sample

1st row158852
2nd row158838
3rd row158825
4th row158843
5th row158811
ValueCountFrequency (%)
158050 19
 
7.4%
158860 19
 
7.4%
158840 9
 
3.5%
158857 8
 
3.1%
158838 8
 
3.1%
158847 7
 
2.7%
158830 7
 
2.7%
158806 7
 
2.7%
158825 7
 
2.7%
158846 6
 
2.3%
Other values (82) 161
62.4%
2024-05-11T15:38:11.637889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 519
32.6%
5 329
20.7%
1 305
19.1%
0 112
 
7.0%
4 62
 
3.9%
6 61
 
3.8%
7 46
 
2.9%
- 45
 
2.8%
2 44
 
2.8%
3 38
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1548
97.2%
Dash Punctuation 45
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 519
33.5%
5 329
21.3%
1 305
19.7%
0 112
 
7.2%
4 62
 
4.0%
6 61
 
3.9%
7 46
 
3.0%
2 44
 
2.8%
3 38
 
2.5%
9 32
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1593
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 519
32.6%
5 329
20.7%
1 305
19.1%
0 112
 
7.0%
4 62
 
3.9%
6 61
 
3.8%
7 46
 
2.9%
- 45
 
2.8%
2 44
 
2.8%
3 38
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1593
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 519
32.6%
5 329
20.7%
1 305
19.1%
0 112
 
7.0%
4 62
 
3.9%
6 61
 
3.8%
7 46
 
2.9%
- 45
 
2.8%
2 44
 
2.8%
3 38
 
2.4%
Distinct252
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T15:38:12.174041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length36
Mean length25.825581
Min length18

Characters and Unicode

Total characters6663
Distinct characters164
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

Unique246 ?
Unique (%)95.3%

Sample

1st row서울특별시 양천구 신정동 294-5
2nd row서울특별시 양천구 신월동 505-7
3rd row서울특별시 양천구 신월동 81-16
4th row서울특별시 양천구 신월동 606-1
5th row서울특별시 양천구 목동 610-4
ValueCountFrequency (%)
서울특별시 258
19.1%
양천구 258
19.1%
신월동 102
 
7.5%
신정동 84
 
6.2%
목동 73
 
5.4%
2층 27
 
2.0%
3층 19
 
1.4%
1층 18
 
1.3%
4층 10
 
0.7%
301호 8
 
0.6%
Other values (367) 497
36.7%
2024-05-11T15:38:12.922348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1250
18.8%
1 355
 
5.3%
280
 
4.2%
260
 
3.9%
259
 
3.9%
259
 
3.9%
258
 
3.9%
258
 
3.9%
258
 
3.9%
258
 
3.9%
Other values (154) 2968
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3548
53.2%
Decimal Number 1575
23.6%
Space Separator 1250
 
18.8%
Dash Punctuation 244
 
3.7%
Open Punctuation 18
 
0.3%
Close Punctuation 18
 
0.3%
Uppercase Letter 7
 
0.1%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
280
 
7.9%
260
 
7.3%
259
 
7.3%
259
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
199
 
5.6%
Other values (132) 1001
28.2%
Decimal Number
ValueCountFrequency (%)
1 355
22.5%
2 204
13.0%
0 194
12.3%
9 159
10.1%
3 146
9.3%
4 127
 
8.1%
5 124
 
7.9%
7 96
 
6.1%
8 85
 
5.4%
6 85
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
B 3
42.9%
D 1
 
14.3%
L 1
 
14.3%
K 1
 
14.3%
T 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 17
94.4%
[ 1
 
5.6%
Close Punctuation
ValueCountFrequency (%)
) 17
94.4%
] 1
 
5.6%
Space Separator
ValueCountFrequency (%)
1250
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 244
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3548
53.2%
Common 3108
46.6%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
280
 
7.9%
260
 
7.3%
259
 
7.3%
259
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
199
 
5.6%
Other values (132) 1001
28.2%
Common
ValueCountFrequency (%)
1250
40.2%
1 355
 
11.4%
- 244
 
7.9%
2 204
 
6.6%
0 194
 
6.2%
9 159
 
5.1%
3 146
 
4.7%
4 127
 
4.1%
5 124
 
4.0%
7 96
 
3.1%
Other values (7) 209
 
6.7%
Latin
ValueCountFrequency (%)
B 3
42.9%
D 1
 
14.3%
L 1
 
14.3%
K 1
 
14.3%
T 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3548
53.2%
ASCII 3115
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1250
40.1%
1 355
 
11.4%
- 244
 
7.8%
2 204
 
6.5%
0 194
 
6.2%
9 159
 
5.1%
3 146
 
4.7%
4 127
 
4.1%
5 124
 
4.0%
7 96
 
3.1%
Other values (12) 216
 
6.9%
Hangul
ValueCountFrequency (%)
280
 
7.9%
260
 
7.3%
259
 
7.3%
259
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
258
 
7.3%
199
 
5.6%
Other values (132) 1001
28.2%

도로명주소
Text

MISSING 

Distinct166
Distinct (%)97.6%
Missing88
Missing (%)34.1%
Memory size2.1 KiB
2024-05-11T15:38:13.377262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length43
Mean length33.758824
Min length23

Characters and Unicode

Total characters5739
Distinct characters160
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

Unique162 ?
Unique (%)95.3%

Sample

1st row서울특별시 양천구 등촌로 76, 지상2층 (목동)
2nd row서울특별시 양천구 목동서로 159-1 (목동)
3rd row서울특별시 양천구 신월로27길 6 (신월동,2층)
4th row서울특별시 양천구 신월로15길 12 (신월동)
5th row서울특별시 양천구 남부순환로60길 16, 1층 (신월동)
ValueCountFrequency (%)
서울특별시 170
 
14.8%
양천구 170
 
14.8%
신정동 56
 
4.9%
신월동 52
 
4.5%
목동 50
 
4.3%
1층 28
 
2.4%
2층 24
 
2.1%
3층 23
 
2.0%
4층 15
 
1.3%
목동동로 13
 
1.1%
Other values (309) 551
47.8%
2024-05-11T15:38:14.063068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
983
 
17.1%
248
 
4.3%
1 226
 
3.9%
, 205
 
3.6%
180
 
3.1%
178
 
3.1%
177
 
3.1%
( 174
 
3.0%
) 174
 
3.0%
172
 
3.0%
Other values (150) 3022
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3195
55.7%
Space Separator 983
 
17.1%
Decimal Number 971
 
16.9%
Other Punctuation 205
 
3.6%
Open Punctuation 175
 
3.0%
Close Punctuation 175
 
3.0%
Dash Punctuation 28
 
0.5%
Uppercase Letter 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
248
 
7.8%
180
 
5.6%
178
 
5.6%
177
 
5.5%
172
 
5.4%
171
 
5.4%
170
 
5.3%
170
 
5.3%
170
 
5.3%
170
 
5.3%
Other values (130) 1389
43.5%
Decimal Number
ValueCountFrequency (%)
1 226
23.3%
2 153
15.8%
3 131
13.5%
0 106
10.9%
4 84
 
8.7%
5 79
 
8.1%
6 68
 
7.0%
7 58
 
6.0%
9 39
 
4.0%
8 27
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
B 5
71.4%
L 1
 
14.3%
D 1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 174
99.4%
[ 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 174
99.4%
] 1
 
0.6%
Space Separator
ValueCountFrequency (%)
983
100.0%
Other Punctuation
ValueCountFrequency (%)
, 205
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3195
55.7%
Common 2537
44.2%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
248
 
7.8%
180
 
5.6%
178
 
5.6%
177
 
5.5%
172
 
5.4%
171
 
5.4%
170
 
5.3%
170
 
5.3%
170
 
5.3%
170
 
5.3%
Other values (130) 1389
43.5%
Common
ValueCountFrequency (%)
983
38.7%
1 226
 
8.9%
, 205
 
8.1%
( 174
 
6.9%
) 174
 
6.9%
2 153
 
6.0%
3 131
 
5.2%
0 106
 
4.2%
4 84
 
3.3%
5 79
 
3.1%
Other values (7) 222
 
8.8%
Latin
ValueCountFrequency (%)
B 5
71.4%
L 1
 
14.3%
D 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3195
55.7%
ASCII 2544
44.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
983
38.6%
1 226
 
8.9%
, 205
 
8.1%
( 174
 
6.8%
) 174
 
6.8%
2 153
 
6.0%
3 131
 
5.1%
0 106
 
4.2%
4 84
 
3.3%
5 79
 
3.1%
Other values (10) 229
 
9.0%
Hangul
ValueCountFrequency (%)
248
 
7.8%
180
 
5.6%
178
 
5.6%
177
 
5.5%
172
 
5.4%
171
 
5.4%
170
 
5.3%
170
 
5.3%
170
 
5.3%
170
 
5.3%
Other values (130) 1389
43.5%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct82
Distinct (%)48.5%
Missing89
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean7987.5325
Minimum7900
Maximum8107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T15:38:14.718486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7900
5-th percentile7910.8
Q17944
median7995
Q38027
95-th percentile8065
Maximum8107
Range207
Interquartile range (IQR)83

Descriptive statistics

Standard deviation49.627426
Coefficient of variation (CV)0.0062131109
Kurtosis-0.92724693
Mean7987.5325
Median Absolute Deviation (MAD)37
Skewness0.036841745
Sum1349893
Variance2462.8814
MonotonicityNot monotonic
2024-05-11T15:38:14.939080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7995 11
 
4.3%
8027 7
 
2.7%
7944 7
 
2.7%
7997 6
 
2.3%
8021 6
 
2.3%
7942 5
 
1.9%
7906 5
 
1.9%
8028 5
 
1.9%
8023 5
 
1.9%
8026 4
 
1.6%
Other values (72) 108
41.9%
(Missing) 89
34.5%
ValueCountFrequency (%)
7900 1
 
0.4%
7905 2
 
0.8%
7906 5
1.9%
7910 1
 
0.4%
7912 2
 
0.8%
7914 1
 
0.4%
7915 2
 
0.8%
7916 1
 
0.4%
7917 2
 
0.8%
7918 1
 
0.4%
ValueCountFrequency (%)
8107 1
 
0.4%
8101 2
0.8%
8086 1
 
0.4%
8073 3
1.2%
8071 1
 
0.4%
8067 1
 
0.4%
8062 1
 
0.4%
8060 1
 
0.4%
8053 1
 
0.4%
8047 1
 
0.4%
Distinct251
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2024-05-11T15:38:15.286744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.6860465
Min length2

Characters and Unicode

Total characters1983
Distinct characters275
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

Unique244 ?
Unique (%)94.6%

Sample

1st row영보환경
2nd row대성공무(주)
3rd row선양건업(주)
4th row신양용역개발(주)
5th row명성기업
ValueCountFrequency (%)
주식회사 27
 
9.1%
3
 
1.0%
청소짱 2
 
0.7%
주)피알 2
 
0.7%
태현종합관리 2
 
0.7%
주)이엠피서비스 2
 
0.7%
클린스쿨서울 2
 
0.7%
주)세계보안관리시스템 2
 
0.7%
피엔에스환경 2
 
0.7%
주)미환개발 2
 
0.7%
Other values (250) 251
84.5%
2024-05-11T15:38:15.923369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
171
 
8.6%
) 137
 
6.9%
( 134
 
6.8%
87
 
4.4%
48
 
2.4%
47
 
2.4%
46
 
2.3%
40
 
2.0%
39
 
2.0%
36
 
1.8%
Other values (265) 1198
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1646
83.0%
Close Punctuation 137
 
6.9%
Open Punctuation 134
 
6.8%
Space Separator 39
 
2.0%
Uppercase Letter 15
 
0.8%
Lowercase Letter 7
 
0.4%
Decimal Number 3
 
0.2%
Dash Punctuation 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
171
 
10.4%
87
 
5.3%
48
 
2.9%
47
 
2.9%
46
 
2.8%
40
 
2.4%
36
 
2.2%
35
 
2.1%
26
 
1.6%
25
 
1.5%
Other values (243) 1085
65.9%
Uppercase Letter
ValueCountFrequency (%)
C 3
20.0%
N 3
20.0%
M 2
13.3%
H 2
13.3%
A 1
 
6.7%
J 1
 
6.7%
S 1
 
6.7%
G 1
 
6.7%
E 1
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
e 2
28.6%
s 2
28.6%
y 1
14.3%
t 1
14.3%
m 1
14.3%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
7 1
33.3%
1 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 137
100.0%
Open Punctuation
ValueCountFrequency (%)
( 134
100.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1646
83.0%
Common 315
 
15.9%
Latin 22
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
171
 
10.4%
87
 
5.3%
48
 
2.9%
47
 
2.9%
46
 
2.8%
40
 
2.4%
36
 
2.2%
35
 
2.1%
26
 
1.6%
25
 
1.5%
Other values (243) 1085
65.9%
Latin
ValueCountFrequency (%)
C 3
13.6%
N 3
13.6%
e 2
9.1%
s 2
9.1%
M 2
9.1%
H 2
9.1%
y 1
 
4.5%
t 1
 
4.5%
A 1
 
4.5%
J 1
 
4.5%
Other values (4) 4
18.2%
Common
ValueCountFrequency (%)
) 137
43.5%
( 134
42.5%
39
 
12.4%
2 1
 
0.3%
- 1
 
0.3%
7 1
 
0.3%
1 1
 
0.3%
& 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1646
83.0%
ASCII 337
 
17.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
171
 
10.4%
87
 
5.3%
48
 
2.9%
47
 
2.9%
46
 
2.8%
40
 
2.4%
36
 
2.2%
35
 
2.1%
26
 
1.6%
25
 
1.5%
Other values (243) 1085
65.9%
ASCII
ValueCountFrequency (%)
) 137
40.7%
( 134
39.8%
39
 
11.6%
C 3
 
0.9%
N 3
 
0.9%
e 2
 
0.6%
s 2
 
0.6%
M 2
 
0.6%
H 2
 
0.6%
2 1
 
0.3%
Other values (12) 12
 
3.6%
Distinct241
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum1999-01-21 00:00:00
Maximum2024-05-08 11:28:35
2024-05-11T15:38:16.169392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:38:16.459804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
I
151 
U
107 

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 151
58.5%
U 107
41.5%

Length

2024-05-11T15:38:16.720538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:16.997896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 151
58.5%
u 107
41.5%
Distinct103
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:02:00
2024-05-11T15:38:17.261955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:38:17.580157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
건물위생관리업
258 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 258
100.0%

Length

2024-05-11T15:38:17.908700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:18.128143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 258
100.0%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct194
Distinct (%)76.4%
Missing4
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean186992.72
Minimum184443.47
Maximum189743.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T15:38:18.428959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184443.47
5-th percentile184829.31
Q1185748.6
median187165.64
Q3188089.3
95-th percentile188956.01
Maximum189743.46
Range5299.9945
Interquartile range (IQR)2340.7028

Descriptive statistics

Standard deviation1378.4296
Coefficient of variation (CV)0.0073715683
Kurtosis-1.2131018
Mean186992.72
Median Absolute Deviation (MAD)1277.4854
Skewness-0.12228748
Sum47496150
Variance1900068.1
MonotonicityNot monotonic
2024-05-11T15:38:18.734564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188584.345447275 7
 
2.7%
188953.066831076 5
 
1.9%
187300.949737034 5
 
1.9%
187725.033812852 4
 
1.6%
186852.153851552 4
 
1.6%
186990.07041522 3
 
1.2%
185578.8443236 3
 
1.2%
188493.762740612 3
 
1.2%
185222.73244302 3
 
1.2%
187338.380750621 3
 
1.2%
Other values (184) 214
82.9%
(Missing) 4
 
1.6%
ValueCountFrequency (%)
184443.469766368 1
0.4%
184480.430654989 1
0.4%
184597.4496425 1
0.4%
184616.61722304 1
0.4%
184623.971611347 1
0.4%
184664.500684897 1
0.4%
184679.867647534 1
0.4%
184691.221778064 1
0.4%
184748.532781957 2
0.8%
184758.024074152 1
0.4%
ValueCountFrequency (%)
189743.464254868 2
0.8%
189348.372526225 1
0.4%
189086.853406906 1
0.4%
189078.224808079 1
0.4%
189056.077484585 1
0.4%
189042.496526196 1
0.4%
189038.037681199 1
0.4%
189021.797866206 1
0.4%
188992.794245115 1
0.4%
188977.171050288 1
0.4%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct194
Distinct (%)76.4%
Missing4
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean447228.21
Minimum445218.76
Maximum449789.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T15:38:19.049666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum445218.76
5-th percentile446100.4
Q1446594.5
median447005.31
Q3447725.22
95-th percentile449338.96
Maximum449789.61
Range4570.8534
Interquartile range (IQR)1130.7224

Descriptive statistics

Standard deviation945.82756
Coefficient of variation (CV)0.0021148656
Kurtosis0.19631587
Mean447228.21
Median Absolute Deviation (MAD)488.18916
Skewness0.81716355
Sum1.1359597 × 108
Variance894589.77
MonotonicityNot monotonic
2024-05-11T15:38:19.405659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447255.070457495 7
 
2.7%
447333.569187997 5
 
1.9%
447022.387226156 5
 
1.9%
446738.160841698 4
 
1.6%
446714.974104065 4
 
1.6%
446176.810389121 3
 
1.2%
446417.164914271 3
 
1.2%
447213.539278579 3
 
1.2%
447665.545654592 3
 
1.2%
447200.019809863 3
 
1.2%
Other values (184) 214
82.9%
(Missing) 4
 
1.6%
ValueCountFrequency (%)
445218.759951293 2
0.8%
445459.440713374 1
0.4%
445524.71062635 1
0.4%
445569.639465968 1
0.4%
445827.441199077 2
0.8%
445897.968147716 1
0.4%
445919.573406457 1
0.4%
446032.687979646 1
0.4%
446040.521974021 1
0.4%
446063.625114255 1
0.4%
ValueCountFrequency (%)
449789.613381329 1
0.4%
449727.47955699 1
0.4%
449641.506957249 1
0.4%
449602.163833876 1
0.4%
449476.396868849 1
0.4%
449450.025953076 1
0.4%
449418.483631523 2
0.8%
449416.904257735 1
0.4%
449399.489126654 1
0.4%
449386.014108408 1
0.4%

위생업태명
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
건물위생관리업
191 
<NA>
67 

Length

Max length7
Median length7
Mean length6.2209302
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 191
74.0%
<NA> 67
 
26.0%

Length

2024-05-11T15:38:19.685755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:19.905260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 191
74.0%
na 67
 
26.0%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)9.7%
Missing124
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean1.8432836
Minimum0
Maximum33
Zeros98
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T15:38:20.122672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile10
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.6885791
Coefficient of variation (CV)2.5436016
Kurtosis19.354504
Mean1.8432836
Median Absolute Deviation (MAD)0
Skewness4.009503
Sum247
Variance21.982774
MonotonicityNot monotonic
2024-05-11T15:38:20.364047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 98
38.0%
4 10
 
3.9%
5 5
 
1.9%
1 4
 
1.6%
2 3
 
1.2%
3 3
 
1.2%
8 3
 
1.2%
15 2
 
0.8%
10 2
 
0.8%
33 1
 
0.4%
Other values (3) 3
 
1.2%
(Missing) 124
48.1%
ValueCountFrequency (%)
0 98
38.0%
1 4
 
1.6%
2 3
 
1.2%
3 3
 
1.2%
4 10
 
3.9%
5 5
 
1.9%
8 3
 
1.2%
10 2
 
0.8%
12 1
 
0.4%
15 2
 
0.8%
ValueCountFrequency (%)
33 1
 
0.4%
24 1
 
0.4%
20 1
 
0.4%
15 2
 
0.8%
12 1
 
0.4%
10 2
 
0.8%
8 3
 
1.2%
5 5
1.9%
4 10
3.9%
3 3
 
1.2%
Distinct5
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
132 
0
113 
1
 
11
4
 
1
2
 
1

Length

Max length4
Median length4
Mean length2.5348837
Min length1

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 132
51.2%
0 113
43.8%
1 11
 
4.3%
4 1
 
0.4%
2 1
 
0.4%

Length

2024-05-11T15:38:20.631083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:20.857923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 132
51.2%
0 113
43.8%
1 11
 
4.3%
4 1
 
0.4%
2 1
 
0.4%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)13.8%
Missing149
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean2.9082569
Minimum0
Maximum21
Zeros23
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T15:38:21.209981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4333368
Coefficient of variation (CV)1.180548
Kurtosis7.5494272
Mean2.9082569
Median Absolute Deviation (MAD)1
Skewness2.337713
Sum317
Variance11.787802
MonotonicityNot monotonic
2024-05-11T15:38:21.556988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 25
 
9.7%
0 23
 
8.9%
3 16
 
6.2%
2 16
 
6.2%
4 7
 
2.7%
5 5
 
1.9%
7 4
 
1.6%
6 3
 
1.2%
9 3
 
1.2%
8 2
 
0.8%
Other values (5) 5
 
1.9%
(Missing) 149
57.8%
ValueCountFrequency (%)
0 23
8.9%
1 25
9.7%
2 16
6.2%
3 16
6.2%
4 7
 
2.7%
5 5
 
1.9%
6 3
 
1.2%
7 4
 
1.6%
8 2
 
0.8%
9 3
 
1.2%
ValueCountFrequency (%)
21 1
 
0.4%
14 1
 
0.4%
13 1
 
0.4%
12 1
 
0.4%
10 1
 
0.4%
9 3
1.2%
8 2
 
0.8%
7 4
1.6%
6 3
1.2%
5 5
1.9%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)17.2%
Missing171
Missing (%)66.3%
Infinite0
Infinite (%)0.0%
Mean3.0574713
Minimum0
Maximum21
Zeros17
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T15:38:21.853887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile9.7
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.6387971
Coefficient of variation (CV)1.1901329
Kurtosis7.0611686
Mean3.0574713
Median Absolute Deviation (MAD)1
Skewness2.3152128
Sum266
Variance13.240845
MonotonicityNot monotonic
2024-05-11T15:38:22.075329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 21
 
8.1%
0 17
 
6.6%
2 13
 
5.0%
3 11
 
4.3%
4 6
 
2.3%
5 5
 
1.9%
7 3
 
1.2%
6 2
 
0.8%
9 2
 
0.8%
8 2
 
0.8%
Other values (5) 5
 
1.9%
(Missing) 171
66.3%
ValueCountFrequency (%)
0 17
6.6%
1 21
8.1%
2 13
5.0%
3 11
4.3%
4 6
 
2.3%
5 5
 
1.9%
6 2
 
0.8%
7 3
 
1.2%
8 2
 
0.8%
9 2
 
0.8%
ValueCountFrequency (%)
21 1
 
0.4%
14 1
 
0.4%
13 1
 
0.4%
12 1
 
0.4%
10 1
 
0.4%
9 2
 
0.8%
8 2
 
0.8%
7 3
1.2%
6 2
 
0.8%
5 5
1.9%
Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
202 
0
46 
1
 
10

Length

Max length4
Median length4
Mean length3.3488372
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 202
78.3%
0 46
 
17.8%
1 10
 
3.9%

Length

2024-05-11T15:38:22.346877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:22.539636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 202
78.3%
0 46
 
17.8%
1 10
 
3.9%

사용끝지하층
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
215 
0
35 
1
 
8

Length

Max length4
Median length4
Mean length3.5
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> 215
83.3%
0 35
 
13.6%
1 8
 
3.1%

Length

2024-05-11T15:38:22.917109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:23.216995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 215
83.3%
0 35
 
13.6%
1 8
 
3.1%

한실수
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
131 
<NA>
127 

Length

Max length4
Median length1
Mean length2.4767442
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 131
50.8%
<NA> 127
49.2%

Length

2024-05-11T15:38:23.564811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:23.835210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 131
50.8%
na 127
49.2%

양실수
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
131 
<NA>
127 

Length

Max length4
Median length1
Mean length2.4767442
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 131
50.8%
<NA> 127
49.2%

Length

2024-05-11T15:38:24.104865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:24.382987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 131
50.8%
na 127
49.2%

욕실수
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
131 
<NA>
127 

Length

Max length4
Median length1
Mean length2.4767442
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 131
50.8%
<NA> 127
49.2%

Length

2024-05-11T15:38:24.593645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:24.793300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 131
50.8%
na 127
49.2%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing70
Missing (%)27.1%
Memory size648.0 B
False
188 
(Missing)
70 
ValueCountFrequency (%)
False 188
72.9%
(Missing) 70
 
27.1%
2024-05-11T15:38:24.964305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
0
131 
<NA>
127 

Length

Max length4
Median length1
Mean length2.4767442
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 131
50.8%
<NA> 127
49.2%

Length

2024-05-11T15:38:25.225530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:25.415595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 131
50.8%
na 127
49.2%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size2.4 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size2.4 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing258
Missing (%)100.0%
Memory size2.4 KiB
Distinct3
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
200 
임대
57 
자가
 
1

Length

Max length4
Median length4
Mean length3.5503876
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 200
77.5%
임대 57
 
22.1%
자가 1
 
0.4%

Length

2024-05-11T15:38:25.619651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:25.825118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 200
77.5%
임대 57
 
22.1%
자가 1
 
0.4%

세탁기수
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
145 
0
113 

Length

Max length4
Median length4
Mean length2.6860465
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> 145
56.2%
0 113
43.8%

Length

2024-05-11T15:38:26.046339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:26.243966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 145
56.2%
0 113
43.8%

여성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
217 
0
39 
10
 
1
1
 
1

Length

Max length4
Median length4
Mean length3.5271318
Min length1

Unique

Unique2 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 217
84.1%
0 39
 
15.1%
10 1
 
0.4%
1 1
 
0.4%

Length

2024-05-11T15:38:26.550575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:26.763451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 217
84.1%
0 39
 
15.1%
10 1
 
0.4%
1 1
 
0.4%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)26.7%
Missing213
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean4.3111111
Minimum0
Maximum43
Zeros24
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-11T15:38:27.019826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile24.4
Maximum43
Range43
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.0448936
Coefficient of variation (CV)2.0980423
Kurtosis10.228772
Mean4.3111111
Median Absolute Deviation (MAD)0
Skewness3.172313
Sum194
Variance81.810101
MonotonicityNot monotonic
2024-05-11T15:38:27.278473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 24
 
9.3%
4 4
 
1.6%
10 3
 
1.2%
6 2
 
0.8%
5 2
 
0.8%
1 2
 
0.8%
2 2
 
0.8%
3 2
 
0.8%
43 1
 
0.4%
35 1
 
0.4%
Other values (2) 2
 
0.8%
(Missing) 213
82.6%
ValueCountFrequency (%)
0 24
9.3%
1 2
 
0.8%
2 2
 
0.8%
3 2
 
0.8%
4 4
 
1.6%
5 2
 
0.8%
6 2
 
0.8%
8 1
 
0.4%
10 3
 
1.2%
28 1
 
0.4%
ValueCountFrequency (%)
43 1
 
0.4%
35 1
 
0.4%
28 1
 
0.4%
10 3
1.2%
8 1
 
0.4%
6 2
0.8%
5 2
0.8%
4 4
1.6%
3 2
0.8%
2 2
0.8%

회수건조수
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
155 
0
103 

Length

Max length4
Median length4
Mean length2.8023256
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 155
60.1%
0 103
39.9%

Length

2024-05-11T15:38:27.529907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:27.713499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 155
60.1%
0 103
39.9%

침대수
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
<NA>
157 
0
101 

Length

Max length4
Median length4
Mean length2.8255814
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> 157
60.9%
0 101
39.1%

Length

2024-05-11T15:38:27.897376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:38:28.067157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 157
60.9%
0 101
39.1%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing67
Missing (%)26.0%
Memory size648.0 B
False
191 
(Missing)
67 
ValueCountFrequency (%)
False 191
74.0%
(Missing) 67
 
26.0%
2024-05-11T15:38:28.201809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031400003140000-206-1991-0211319911120<NA>3폐업2폐업20020201<NA><NA><NA>02 6483070672.22158852서울특별시 양천구 신정동 294-5<NA><NA>영보환경2002-11-29 00:00:00I2018-08-31 23:59:59.0건물위생관리업188655.224736446191.180104건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131400003140000-206-1992-0211619920317<NA>3폐업2폐업20020527<NA><NA><NA>02 6034312485.00158838서울특별시 양천구 신월동 505-7<NA><NA>대성공무(주)2002-05-27 00:00:00I2018-08-31 23:59:59.0건물위생관리업186642.122858446831.281432건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231400003140000-206-1992-0211719920722<NA>3폐업2폐업20020527<NA><NA><NA>02 6908096989.40158825서울특별시 양천구 신월동 81-16<NA><NA>선양건업(주)2002-05-27 00:00:00I2018-08-31 23:59:59.0건물위생관리업184914.062633448256.94542건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331400003140000-206-1993-0211519930623<NA>3폐업2폐업19970522<NA><NA><NA>02 6928336.00158843서울특별시 양천구 신월동 606-1<NA><NA>신양용역개발(주)1999-01-21 00:00:00I2018-08-31 23:59:59.0건물위생관리업186328.239529446559.442002건물위생관리업<NA><NA><NA><NA><NA><NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431400003140000-206-1993-0211819930428<NA>3폐업2폐업19970222<NA><NA><NA>02 6548712.00158811서울특별시 양천구 목동 610-4<NA><NA>명성기업1999-01-21 00:00:00I2018-08-31 23:59:59.0건물위생관리업188090.500543449641.506957건물위생관리업<NA><NA><NA><NA><NA><NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531400003140000-206-1993-0211919930802<NA>3폐업2폐업20020527<NA><NA><NA>02 603376056.93158824서울특별시 양천구 신월동 60-29<NA><NA>진일용역(주)2002-05-27 00:00:00I2018-08-31 23:59:59.0건물위생관리업185103.62412448546.889552건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631400003140000-206-1993-0212019931217<NA>3폐업2폐업20021018<NA><NA><NA>02 6461117.00158800서울특별시 양천구 목동 41-10<NA><NA>(주)에녹멀티프렌트엔지니어링2002-11-12 00:00:00I2018-08-31 23:59:59.0건물위생관리업188636.616858448898.368089건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731400003140000-206-1994-0211419940107<NA>1영업/정상1영업<NA><NA><NA><NA>022654139460.00158815서울특별시 양천구 목동 731-1 지상2층서울특별시 양천구 등촌로 76, 지상2층 (목동)7959(주)서광안전시스템2022-04-06 13:40:28U2021-12-04 00:08:00.0건물위생관리업187887.974779448285.092771<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
831400003140000-206-1994-0212119941227<NA>3폐업2폐업19981226<NA><NA><NA>02 6520237723.96158859서울특별시 양천구 신정동 955-7<NA><NA>한개실업(주)1999-01-21 00:00:00I2018-08-31 23:59:59.0건물위생관리업187471.34542446912.188224건물위생관리업<NA><NA><NA><NA><NA><NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931400003140000-206-1995-0212219950308<NA>1영업/정상1영업<NA><NA><NA><NA>0226507551165.00158050서울특별시 양천구 목동 917-1서울특별시 양천구 목동서로 159-1 (목동)7997주식회사 씨비에스라이프2021-07-19 15:25:13U2021-07-21 02:40:00.0건물위생관리업188871.512838447348.132133건물위생관리업000000000N0<NA><NA><NA><NA>0<NA><NA>00N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
24831400003140000-206-2023-0000120230127<NA>1영업/정상1영업<NA><NA><NA><NA><NA>30.00158809서울특별시 양천구 목동 531-5 삼성맨션서울특별시 양천구 목동중앙본로30가길 15, 1층 101-1호 (목동, 삼성맨션)7973주식회사 유림씨앤씨2023-01-27 17:46:28I2022-11-30 22:09:00.0건물위생관리업188560.456294449188.767078<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
24931400003140000-206-2023-000022023-03-17<NA>1영업/정상1영업<NA><NA><NA><NA><NA>46.00158-845서울특별시 양천구 신월동 921-2서울특별시 양천구 지양로 86, 1층 104호 (신월동)8034가오토탈서비스2023-03-17 11:36:43I2022-12-02 23:09:00.0건물위생관리업185146.099048446766.882004<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25031400003140000-206-2023-000032023-07-20<NA>1영업/정상1영업<NA><NA><NA><NA><NA>40.26158-838서울특별시 양천구 신월동 503-23 202호서울특별시 양천구 중앙로49길 11-27, 202호 (신월동)8028(주)청명에이엔씨2023-07-24 09:17:54U2022-12-06 22:06:00.0건물위생관리업186692.09349446745.683511<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25131400003140000-206-2023-000042023-07-28<NA>1영업/정상1영업<NA><NA><NA><NA><NA>26.00158-879서울특별시 양천구 목동 909-4 향도드림네스트 316호서울특별시 양천구 목동동로 437, 향도드림네스트 316호 (목동)7984우림에어텍2024-03-07 11:01:40U2023-12-03 00:09:00.0건물위생관리업189743.464255448386.459467<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25231400003140000-206-2023-000052023-10-26<NA>1영업/정상1영업<NA><NA><NA><NA><NA>4.00158-859서울특별시 양천구 신정동 971-20 명성빌딩서울특별시 양천구 중앙로 294, 명성빌딩 6층 6-52호 (신정동)8026현욱산업안전 주식회사2023-10-26 15:18:42I2022-10-30 22:08:00.0건물위생관리업186852.153852446714.974104<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25331400003140000-206-2023-000062023-10-26<NA>1영업/정상1영업<NA><NA><NA><NA><NA>4.00158-859서울특별시 양천구 신정동 971-20 명성빌딩 6층동 638호서울특별시 양천구 중앙로 294, 명성빌딩 6층 638호 (신정동)8026토레스엔 주식회사2023-10-26 15:22:05I2022-10-30 22:08:00.0건물위생관리업186852.153852446714.974104<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25431400003140000-206-2023-000072023-12-28<NA>1영업/정상1영업<NA><NA><NA><NA>0226086955208.96158-856서울특별시 양천구 신정동 872-2 2층 전체서울특별시 양천구 국회대로 162, 2층 (신정동)7939(주)아이피엘에스2024-01-08 09:14:12U2023-11-30 23:00:00.0건물위생관리업186928.816839447322.848126<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25531400003140000-206-2024-000012024-01-16<NA>1영업/정상1영업<NA><NA><NA><NA><NA>10.00158-857서울특별시 양천구 신정동 897-5 1층 105호서울특별시 양천구 국회대로 252, 1층 105호 (신정동)7937미래와신뢰2024-01-16 11:59:14I2023-11-30 23:08:00.0건물위생관리업187823.500716447457.457884<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25631400003140000-206-2024-000022024-01-18<NA>1영업/정상1영업<NA><NA><NA><NA>0216600378106.78158-856서울특별시 양천구 신정동 887-1 2층 전체서울특별시 양천구 신정중앙로19길 2, 2층 전체호 (신정동)7938스쿨잡스2024-01-18 10:18:58I2023-11-30 22:00:00.0건물위생관리업187709.326277447195.89019<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25731400003140000-206-2024-000032024-03-27<NA>3폐업2폐업2024-03-27<NA><NA><NA><NA>3.00158-857서울특별시 양천구 신정동 905-2 해풍빌딩 403동 181호서울특별시 양천구 신정중앙로 68, 4층 403-181호 (신정동, 해풍빌딩)7945학교대장2024-03-27 15:15:19I2023-12-02 22:09:00.0건물위생관리업187496.753603447163.623643<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>