Overview

Dataset statistics

Number of variables47
Number of observations546
Missing cells5394
Missing cells (%)21.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory215.5 KiB
Average record size in memory404.2 B

Variable types

Categorical22
Text7
DateTime4
Unsupported7
Numeric5
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
Author동작구
URLhttps://data.seoul.go.kr/dataList/OA-17971/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
업태구분명 is highly imbalanced (88.6%)Imbalance
위생업태명 is highly imbalanced (66.7%)Imbalance
사용끝지하층 is highly imbalanced (52.0%)Imbalance
건물소유구분명 is highly imbalanced (63.6%)Imbalance
여성종사자수 is highly imbalanced (79.5%)Imbalance
남성종사자수 is highly imbalanced (79.5%)Imbalance
회수건조수 is highly imbalanced (58.9%)Imbalance
다중이용업소여부 is highly imbalanced (97.8%)Imbalance
인허가취소일자 has 546 (100.0%) missing valuesMissing
폐업일자 has 125 (22.9%) missing valuesMissing
휴업시작일자 has 546 (100.0%) missing valuesMissing
휴업종료일자 has 546 (100.0%) missing valuesMissing
재개업일자 has 546 (100.0%) missing valuesMissing
전화번호 has 30 (5.5%) missing valuesMissing
도로명주소 has 243 (44.5%) missing valuesMissing
도로명우편번호 has 257 (47.1%) missing valuesMissing
좌표정보(X) has 51 (9.3%) missing valuesMissing
좌표정보(Y) has 51 (9.3%) missing valuesMissing
건물지상층수 has 282 (51.6%) missing valuesMissing
발한실여부 has 79 (14.5%) missing valuesMissing
조건부허가신고사유 has 546 (100.0%) missing valuesMissing
조건부허가시작일자 has 546 (100.0%) missing valuesMissing
조건부허가종료일자 has 546 (100.0%) missing valuesMissing
세탁기수 has 380 (69.6%) missing valuesMissing
다중이용업소여부 has 74 (13.6%) 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 185 (33.9%) zerosZeros
세탁기수 has 15 (2.7%) zerosZeros

Reproduction

Analysis started2024-05-11 01:54:54.546333
Analysis finished2024-05-11 01:54:56.452338
Duration1.91 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
3190000
546 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3190000
2nd row3190000
3rd row3190000
4th row3190000
5th row3190000

Common Values

ValueCountFrequency (%)
3190000 546
100.0%

Length

2024-05-11T01:54:56.758228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:54:57.222890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3190000 546
100.0%

관리번호
Text

UNIQUE 

Distinct546
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-05-11T01:54:57.733427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12012
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

Unique546 ?
Unique (%)100.0%

Sample

1st row3190000-205-1987-01675
2nd row3190000-205-1987-01676
3rd row3190000-205-1987-01677
4th row3190000-205-1987-01678
5th row3190000-205-1987-01679
ValueCountFrequency (%)
3190000-205-1987-01675 1
 
0.2%
3190000-205-2001-00007 1
 
0.2%
3190000-205-2002-00006 1
 
0.2%
3190000-205-2002-00005 1
 
0.2%
3190000-205-2002-00004 1
 
0.2%
3190000-205-2002-00003 1
 
0.2%
3190000-205-2002-00002 1
 
0.2%
3190000-205-2002-00001 1
 
0.2%
3190000-205-2004-00011 1
 
0.2%
3190000-205-2004-00010 1
 
0.2%
Other values (536) 536
98.2%
2024-05-11T01:54:59.014463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4242
35.3%
- 1638
 
13.6%
1 1499
 
12.5%
9 1278
 
10.6%
2 894
 
7.4%
3 694
 
5.8%
5 683
 
5.7%
8 412
 
3.4%
7 298
 
2.5%
6 223
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10374
86.4%
Dash Punctuation 1638
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4242
40.9%
1 1499
 
14.4%
9 1278
 
12.3%
2 894
 
8.6%
3 694
 
6.7%
5 683
 
6.6%
8 412
 
4.0%
7 298
 
2.9%
6 223
 
2.1%
4 151
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 1638
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4242
35.3%
- 1638
 
13.6%
1 1499
 
12.5%
9 1278
 
10.6%
2 894
 
7.4%
3 694
 
5.8%
5 683
 
5.7%
8 412
 
3.4%
7 298
 
2.5%
6 223
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4242
35.3%
- 1638
 
13.6%
1 1499
 
12.5%
9 1278
 
10.6%
2 894
 
7.4%
3 694
 
5.8%
5 683
 
5.7%
8 412
 
3.4%
7 298
 
2.5%
6 223
 
1.9%
Distinct402
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1987-06-01 00:00:00
Maximum2023-09-12 00:00:00
2024-05-11T01:54:59.539542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:55:00.015599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing546
Missing (%)100.0%
Memory size4.9 KiB
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
3
421 
1
125 

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 (%)
3 421
77.1%
1 125
 
22.9%

Length

2024-05-11T01:55:00.544691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:00.807019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 421
77.1%
1 125
 
22.9%

영업상태명
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
폐업
421 
영업/정상
125 

Length

Max length5
Median length2
Mean length2.6868132
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 421
77.1%
영업/정상 125
 
22.9%

Length

2024-05-11T01:55:01.100041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:01.506283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 421
77.1%
영업/정상 125
 
22.9%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2
421 
1
125 

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 (%)
2 421
77.1%
1 125
 
22.9%

Length

2024-05-11T01:55:01.838219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:02.137731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 421
77.1%
1 125
 
22.9%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
폐업
421 
영업
125 

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 421
77.1%
영업 125
 
22.9%

Length

2024-05-11T01:55:02.481126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:02.797803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 421
77.1%
영업 125
 
22.9%

폐업일자
Date

MISSING 

Distinct355
Distinct (%)84.3%
Missing125
Missing (%)22.9%
Memory size4.4 KiB
Minimum1994-12-30 00:00:00
Maximum2024-05-02 00:00:00
2024-05-11T01:55:03.037825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:55:03.318558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing546
Missing (%)100.0%
Memory size4.9 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing546
Missing (%)100.0%
Memory size4.9 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing546
Missing (%)100.0%
Memory size4.9 KiB

전화번호
Text

MISSING 

Distinct483
Distinct (%)93.6%
Missing30
Missing (%)5.5%
Memory size4.4 KiB
2024-05-11T01:55:03.971167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.054264
Min length2

Characters and Unicode

Total characters5188
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

Unique456 ?
Unique (%)88.4%

Sample

1st row0205323649
2nd row0205340498
3rd row02 5938918
4th row02 8133718
5th row02 8127050
ValueCountFrequency (%)
02 243
30.3%
0200000000 5
 
0.6%
5938918 3
 
0.4%
815 3
 
0.4%
0234739644 3
 
0.4%
591 3
 
0.4%
8341868 3
 
0.4%
8133718 3
 
0.4%
8230335 3
 
0.4%
813 3
 
0.4%
Other values (499) 530
66.1%
2024-05-11T01:55:05.116209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 955
18.4%
2 943
18.2%
8 635
12.2%
5 456
8.8%
1 419
8.1%
3 374
 
7.2%
336
 
6.5%
4 314
 
6.1%
6 262
 
5.1%
7 252
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4852
93.5%
Space Separator 336
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 955
19.7%
2 943
19.4%
8 635
13.1%
5 456
9.4%
1 419
8.6%
3 374
 
7.7%
4 314
 
6.5%
6 262
 
5.4%
7 252
 
5.2%
9 242
 
5.0%
Space Separator
ValueCountFrequency (%)
336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5188
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 955
18.4%
2 943
18.2%
8 635
12.2%
5 456
8.8%
1 419
8.1%
3 374
 
7.2%
336
 
6.5%
4 314
 
6.1%
6 262
 
5.1%
7 252
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 955
18.4%
2 943
18.2%
8 635
12.2%
5 456
8.8%
1 419
8.1%
3 374
 
7.2%
336
 
6.5%
4 314
 
6.1%
6 262
 
5.1%
7 252
 
4.9%
Distinct376
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-05-11T01:55:05.875307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.8663004
Min length3

Characters and Unicode

Total characters2657
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

Unique316 ?
Unique (%)57.9%

Sample

1st row19.00
2nd row35.69
3rd row.00
4th row494.80
5th row.00
ValueCountFrequency (%)
00 66
 
12.1%
33.00 11
 
2.0%
23.10 8
 
1.5%
26.40 7
 
1.3%
16.70 5
 
0.9%
23.50 5
 
0.9%
29.70 5
 
0.9%
24.00 5
 
0.9%
26.50 4
 
0.7%
18.00 4
 
0.7%
Other values (366) 426
78.0%
2024-05-11T01:55:06.855770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 546
20.5%
0 528
19.9%
2 290
10.9%
3 238
9.0%
1 226
8.5%
4 165
 
6.2%
6 163
 
6.1%
5 159
 
6.0%
9 123
 
4.6%
7 109
 
4.1%
Other values (2) 110
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2108
79.3%
Other Punctuation 549
 
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 528
25.0%
2 290
13.8%
3 238
11.3%
1 226
10.7%
4 165
 
7.8%
6 163
 
7.7%
5 159
 
7.5%
9 123
 
5.8%
7 109
 
5.2%
8 107
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 546
99.5%
, 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2657
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 546
20.5%
0 528
19.9%
2 290
10.9%
3 238
9.0%
1 226
8.5%
4 165
 
6.2%
6 163
 
6.1%
5 159
 
6.0%
9 123
 
4.6%
7 109
 
4.1%
Other values (2) 110
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2657
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 546
20.5%
0 528
19.9%
2 290
10.9%
3 238
9.0%
1 226
8.5%
4 165
 
6.2%
6 163
 
6.1%
5 159
 
6.0%
9 123
 
4.6%
7 109
 
4.1%
Other values (2) 110
 
4.1%
Distinct103
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-05-11T01:55:07.348271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0549451
Min length6

Characters and Unicode

Total characters3306
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

Unique27 ?
Unique (%)4.9%

Sample

1st row156816
2nd row156824
3rd row156090
4th row156834
5th row156858
ValueCountFrequency (%)
156030 26
 
4.8%
156090 23
 
4.2%
156846 15
 
2.7%
156815 14
 
2.6%
156825 13
 
2.4%
156827 13
 
2.4%
156060 13
 
2.4%
156821 13
 
2.4%
156855 12
 
2.2%
156811 12
 
2.2%
Other values (93) 392
71.8%
2024-05-11T01:55:07.991267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 708
21.4%
5 657
19.9%
6 619
18.7%
8 494
14.9%
0 279
 
8.4%
4 129
 
3.9%
2 127
 
3.8%
3 121
 
3.7%
7 85
 
2.6%
9 57
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3276
99.1%
Dash Punctuation 30
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 708
21.6%
5 657
20.1%
6 619
18.9%
8 494
15.1%
0 279
 
8.5%
4 129
 
3.9%
2 127
 
3.9%
3 121
 
3.7%
7 85
 
2.6%
9 57
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3306
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 708
21.4%
5 657
19.9%
6 619
18.7%
8 494
14.9%
0 279
 
8.4%
4 129
 
3.9%
2 127
 
3.8%
3 121
 
3.7%
7 85
 
2.6%
9 57
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3306
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 708
21.4%
5 657
19.9%
6 619
18.7%
8 494
14.9%
0 279
 
8.4%
4 129
 
3.9%
2 127
 
3.8%
3 121
 
3.7%
7 85
 
2.6%
9 57
 
1.7%
Distinct502
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-05-11T01:55:08.570180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length41
Mean length24.619048
Min length16

Characters and Unicode

Total characters13442
Distinct characters147
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

Unique465 ?
Unique (%)85.2%

Sample

1st row서울특별시 동작구 사당동 141-156
2nd row서울특별시 동작구 사당동 708-457번지
3rd row서울특별시 동작구 사당동 1131-0번지 영아A 상가 203호
4th row서울특별시 동작구 상도1동 326-14번지
5th row서울특별시 동작구 흑석동 79-93번지
ValueCountFrequency (%)
서울특별시 546
22.3%
동작구 545
22.3%
사당동 169
 
6.9%
상도동 117
 
4.8%
신대방동 68
 
2.8%
흑석동 51
 
2.1%
노량진동 48
 
2.0%
대방동 43
 
1.8%
상도1동 26
 
1.1%
1층 18
 
0.7%
Other values (626) 817
33.4%
2024-05-11T01:55:09.321963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2356
17.5%
1150
 
8.6%
1 603
 
4.5%
554
 
4.1%
546
 
4.1%
546
 
4.1%
546
 
4.1%
546
 
4.1%
546
 
4.1%
546
 
4.1%
Other values (137) 5503
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7821
58.2%
Decimal Number 2747
 
20.4%
Space Separator 2356
 
17.5%
Dash Punctuation 459
 
3.4%
Close Punctuation 19
 
0.1%
Open Punctuation 19
 
0.1%
Uppercase Letter 11
 
0.1%
Other Punctuation 8
 
0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1150
14.7%
554
 
7.1%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
446
 
5.7%
429
 
5.5%
Other values (114) 1966
25.1%
Decimal Number
ValueCountFrequency (%)
1 603
22.0%
2 393
14.3%
3 322
11.7%
0 301
11.0%
4 241
 
8.8%
5 211
 
7.7%
6 201
 
7.3%
7 174
 
6.3%
9 157
 
5.7%
8 144
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
A 6
54.5%
B 3
27.3%
P 1
 
9.1%
T 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 5
62.5%
. 2
 
25.0%
? 1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
a 1
50.0%
Space Separator
ValueCountFrequency (%)
2356
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 459
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7821
58.2%
Common 5608
41.7%
Latin 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1150
14.7%
554
 
7.1%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
446
 
5.7%
429
 
5.5%
Other values (114) 1966
25.1%
Common
ValueCountFrequency (%)
2356
42.0%
1 603
 
10.8%
- 459
 
8.2%
2 393
 
7.0%
3 322
 
5.7%
0 301
 
5.4%
4 241
 
4.3%
5 211
 
3.8%
6 201
 
3.6%
7 174
 
3.1%
Other values (7) 347
 
6.2%
Latin
ValueCountFrequency (%)
A 6
46.2%
B 3
23.1%
e 1
 
7.7%
P 1
 
7.7%
T 1
 
7.7%
a 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7821
58.2%
ASCII 5621
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2356
41.9%
1 603
 
10.7%
- 459
 
8.2%
2 393
 
7.0%
3 322
 
5.7%
0 301
 
5.4%
4 241
 
4.3%
5 211
 
3.8%
6 201
 
3.6%
7 174
 
3.1%
Other values (13) 360
 
6.4%
Hangul
ValueCountFrequency (%)
1150
14.7%
554
 
7.1%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
546
 
7.0%
446
 
5.7%
429
 
5.5%
Other values (114) 1966
25.1%

도로명주소
Text

MISSING 

Distinct302
Distinct (%)99.7%
Missing243
Missing (%)44.5%
Memory size4.4 KiB
2024-05-11T01:55:09.820531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length49
Mean length30.283828
Min length22

Characters and Unicode

Total characters9176
Distinct characters158
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

Unique301 ?
Unique (%)99.3%

Sample

1st row서울특별시 동작구 사당로27길 24 (사당동)
2nd row서울특별시 동작구 동작대로27다길 41 (사당동)
3rd row서울특별시 동작구 사당로16길 57 (사당동)
4th row서울특별시 동작구 사당로23길 126 (사당동)
5th row서울특별시 동작구 서달로10라길 16 (흑석동)
ValueCountFrequency (%)
서울특별시 303
 
17.4%
동작구 302
 
17.4%
사당동 85
 
4.9%
상도동 55
 
3.2%
1층 39
 
2.2%
신대방동 33
 
1.9%
흑석동 27
 
1.6%
노량진동 24
 
1.4%
대방동 22
 
1.3%
상가동 16
 
0.9%
Other values (448) 833
47.9%
2024-05-11T01:55:10.654619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1436
 
15.6%
700
 
7.6%
1 348
 
3.8%
339
 
3.7%
318
 
3.5%
) 318
 
3.5%
( 318
 
3.5%
303
 
3.3%
303
 
3.3%
303
 
3.3%
Other values (148) 4490
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5524
60.2%
Space Separator 1436
 
15.6%
Decimal Number 1388
 
15.1%
Close Punctuation 318
 
3.5%
Open Punctuation 318
 
3.5%
Other Punctuation 156
 
1.7%
Dash Punctuation 23
 
0.3%
Uppercase Letter 11
 
0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
700
 
12.7%
339
 
6.1%
318
 
5.8%
303
 
5.5%
303
 
5.5%
303
 
5.5%
303
 
5.5%
303
 
5.5%
266
 
4.8%
255
 
4.6%
Other values (126) 2131
38.6%
Decimal Number
ValueCountFrequency (%)
1 348
25.1%
2 243
17.5%
3 151
10.9%
0 120
 
8.6%
4 110
 
7.9%
6 98
 
7.1%
5 94
 
6.8%
7 78
 
5.6%
9 76
 
5.5%
8 70
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
A 5
45.5%
B 4
36.4%
L 1
 
9.1%
G 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 155
99.4%
? 1
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
a 1
50.0%
e 1
50.0%
Space Separator
ValueCountFrequency (%)
1436
100.0%
Close Punctuation
ValueCountFrequency (%)
) 318
100.0%
Open Punctuation
ValueCountFrequency (%)
( 318
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5524
60.2%
Common 3639
39.7%
Latin 13
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
700
 
12.7%
339
 
6.1%
318
 
5.8%
303
 
5.5%
303
 
5.5%
303
 
5.5%
303
 
5.5%
303
 
5.5%
266
 
4.8%
255
 
4.6%
Other values (126) 2131
38.6%
Common
ValueCountFrequency (%)
1436
39.5%
1 348
 
9.6%
) 318
 
8.7%
( 318
 
8.7%
2 243
 
6.7%
, 155
 
4.3%
3 151
 
4.1%
0 120
 
3.3%
4 110
 
3.0%
6 98
 
2.7%
Other values (6) 342
 
9.4%
Latin
ValueCountFrequency (%)
A 5
38.5%
B 4
30.8%
a 1
 
7.7%
L 1
 
7.7%
G 1
 
7.7%
e 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5524
60.2%
ASCII 3652
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1436
39.3%
1 348
 
9.5%
) 318
 
8.7%
( 318
 
8.7%
2 243
 
6.7%
, 155
 
4.2%
3 151
 
4.1%
0 120
 
3.3%
4 110
 
3.0%
6 98
 
2.7%
Other values (12) 355
 
9.7%
Hangul
ValueCountFrequency (%)
700
 
12.7%
339
 
6.1%
318
 
5.8%
303
 
5.5%
303
 
5.5%
303
 
5.5%
303
 
5.5%
303
 
5.5%
266
 
4.8%
255
 
4.6%
Other values (126) 2131
38.6%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct133
Distinct (%)46.0%
Missing257
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean6994.0969
Minimum6902
Maximum7073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-05-11T01:55:10.960348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6902
5-th percentile6911.4
Q16952
median6998
Q37037
95-th percentile7069
Maximum7073
Range171
Interquartile range (IQR)85

Descriptive statistics

Standard deviation49.962948
Coefficient of variation (CV)0.0071435881
Kurtosis-1.1335887
Mean6994.0969
Median Absolute Deviation (MAD)41
Skewness-0.13782194
Sum2021294
Variance2496.2961
MonotonicityNot monotonic
2024-05-11T01:55:11.240836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7071 6
 
1.1%
7051 6
 
1.1%
6980 5
 
0.9%
6990 5
 
0.9%
6970 5
 
0.9%
7072 4
 
0.7%
7064 4
 
0.7%
6945 4
 
0.7%
7008 4
 
0.7%
7016 4
 
0.7%
Other values (123) 242
44.3%
(Missing) 257
47.1%
ValueCountFrequency (%)
6902 2
0.4%
6904 2
0.4%
6906 1
 
0.2%
6907 1
 
0.2%
6908 3
0.5%
6909 3
0.5%
6910 1
 
0.2%
6911 2
0.4%
6912 1
 
0.2%
6913 1
 
0.2%
ValueCountFrequency (%)
7073 1
 
0.2%
7072 4
0.7%
7071 6
1.1%
7070 3
0.5%
7069 3
0.5%
7068 3
0.5%
7067 3
0.5%
7065 2
 
0.4%
7064 4
0.7%
7063 2
 
0.4%
Distinct399
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-05-11T01:55:11.741378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length4.6355311
Min length2

Characters and Unicode

Total characters2531
Distinct characters282
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

Unique317 ?
Unique (%)58.1%

Sample

1st row소망세탁
2nd row상미세탁소
3rd row백양사
4th row대림사
5th row백미사
ValueCountFrequency (%)
세탁소 11
 
1.9%
백양사 10
 
1.7%
현대사 8
 
1.4%
백조사 8
 
1.4%
경일사 7
 
1.2%
백양세탁소 6
 
1.0%
우리세탁소 5
 
0.9%
제일사 5
 
0.9%
백영사 4
 
0.7%
매직크리닝 4
 
0.7%
Other values (405) 509
88.2%
2024-05-11T01:55:12.472336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
233
 
9.2%
230
 
9.1%
192
 
7.6%
136
 
5.4%
76
 
3.0%
74
 
2.9%
67
 
2.6%
67
 
2.6%
55
 
2.2%
43
 
1.7%
Other values (272) 1358
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2453
96.9%
Space Separator 32
 
1.3%
Decimal Number 23
 
0.9%
Lowercase Letter 9
 
0.4%
Uppercase Letter 8
 
0.3%
Other Punctuation 3
 
0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
233
 
9.5%
230
 
9.4%
192
 
7.8%
136
 
5.5%
76
 
3.1%
74
 
3.0%
67
 
2.7%
67
 
2.7%
55
 
2.2%
43
 
1.8%
Other values (243) 1280
52.2%
Lowercase Letter
ValueCountFrequency (%)
k 1
11.1%
o 1
11.1%
l 1
11.1%
r 1
11.1%
u 1
11.1%
n 1
11.1%
a 1
11.1%
e 1
11.1%
p 1
11.1%
Decimal Number
ValueCountFrequency (%)
1 9
39.1%
4 5
21.7%
2 4
17.4%
9 2
 
8.7%
5 1
 
4.3%
3 1
 
4.3%
8 1
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
L 2
25.0%
G 2
25.0%
K 1
12.5%
C 1
12.5%
O 1
12.5%
M 1
12.5%
Other Punctuation
ValueCountFrequency (%)
& 1
33.3%
# 1
33.3%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2453
96.9%
Common 61
 
2.4%
Latin 17
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
233
 
9.5%
230
 
9.4%
192
 
7.8%
136
 
5.5%
76
 
3.1%
74
 
3.0%
67
 
2.7%
67
 
2.7%
55
 
2.2%
43
 
1.8%
Other values (243) 1280
52.2%
Latin
ValueCountFrequency (%)
L 2
 
11.8%
G 2
 
11.8%
K 1
 
5.9%
C 1
 
5.9%
k 1
 
5.9%
o 1
 
5.9%
O 1
 
5.9%
l 1
 
5.9%
r 1
 
5.9%
M 1
 
5.9%
Other values (5) 5
29.4%
Common
ValueCountFrequency (%)
32
52.5%
1 9
 
14.8%
4 5
 
8.2%
2 4
 
6.6%
9 2
 
3.3%
& 1
 
1.6%
( 1
 
1.6%
) 1
 
1.6%
+ 1
 
1.6%
# 1
 
1.6%
Other values (4) 4
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2453
96.9%
ASCII 78
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
233
 
9.5%
230
 
9.4%
192
 
7.8%
136
 
5.5%
76
 
3.1%
74
 
3.0%
67
 
2.7%
67
 
2.7%
55
 
2.2%
43
 
1.8%
Other values (243) 1280
52.2%
ASCII
ValueCountFrequency (%)
32
41.0%
1 9
 
11.5%
4 5
 
6.4%
2 4
 
5.1%
9 2
 
2.6%
L 2
 
2.6%
G 2
 
2.6%
K 1
 
1.3%
C 1
 
1.3%
k 1
 
1.3%
Other values (19) 19
24.4%
Distinct401
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1998-12-24 00:00:00
Maximum2024-05-02 10:07:33
2024-05-11T01:55:12.859307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:55:13.299606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
I
361 
U
185 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 361
66.1%
U 185
33.9%

Length

2024-05-11T01:55:13.841085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:14.174920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 361
66.1%
u 185
33.9%
Distinct124
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:04:00
2024-05-11T01:55:14.501854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T01:55:14.928077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
일반세탁업
530 
운동화전문세탁업
 
8
빨래방업
 
7
세탁업 기타
 
1

Length

Max length8
Median length5
Mean length5.032967
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반세탁업 530
97.1%
운동화전문세탁업 8
 
1.5%
빨래방업 7
 
1.3%
세탁업 기타 1
 
0.2%

Length

2024-05-11T01:55:15.376860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:15.718861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 530
96.9%
운동화전문세탁업 8
 
1.5%
빨래방업 7
 
1.3%
세탁업 1
 
0.2%
기타 1
 
0.2%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct379
Distinct (%)76.6%
Missing51
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean195512.22
Minimum191691.68
Maximum198382.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-05-11T01:55:16.073411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191691.68
5-th percentile192113.2
Q1194006.89
median195489.42
Q3197405.95
95-th percentile198106.6
Maximum198382.63
Range6690.9555
Interquartile range (IQR)3399.0578

Descriptive statistics

Standard deviation1896.0977
Coefficient of variation (CV)0.0096981034
Kurtosis-1.1472458
Mean195512.22
Median Absolute Deviation (MAD)1683.7389
Skewness-0.17435334
Sum96778549
Variance3595186.6
MonotonicityNot monotonic
2024-05-11T01:55:16.572825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197802.054854046 9
 
1.6%
191691.678396263 6
 
1.1%
192113.200600983 6
 
1.1%
193204.782207917 6
 
1.1%
193311.831569946 4
 
0.7%
193279.599095483 4
 
0.7%
197545.985381694 4
 
0.7%
193872.183173262 4
 
0.7%
195312.318811562 3
 
0.5%
193980.815977264 3
 
0.5%
Other values (369) 446
81.7%
(Missing) 51
 
9.3%
ValueCountFrequency (%)
191691.678396263 6
1.1%
191765.714787143 1
 
0.2%
191775.04452045 1
 
0.2%
191831.580904689 2
 
0.4%
191855.96589943 2
 
0.4%
191934.916129397 1
 
0.2%
191946.135275517 2
 
0.4%
191948.400369861 1
 
0.2%
191949.703160289 1
 
0.2%
191952.875072419 1
 
0.2%
ValueCountFrequency (%)
198382.633852093 1
0.2%
198372.084206789 1
0.2%
198309.48428538 1
0.2%
198309.408216847 1
0.2%
198286.160000404 1
0.2%
198258.848088523 1
0.2%
198249.192413517 1
0.2%
198240.488879447 1
0.2%
198236.514850357 1
0.2%
198235.697626812 1
0.2%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct379
Distinct (%)76.6%
Missing51
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean443766.58
Minimum441543.54
Maximum445772.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-05-11T01:55:17.005280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum441543.54
5-th percentile441888.75
Q1442884.53
median443829.81
Q3444741.52
95-th percentile445521.27
Maximum445772.63
Range4229.0892
Interquartile range (IQR)1856.9852

Descriptive statistics

Standard deviation1139.365
Coefficient of variation (CV)0.0025674872
Kurtosis-1.0892812
Mean443766.58
Median Absolute Deviation (MAD)934.83355
Skewness-0.1026758
Sum2.1966446 × 108
Variance1298152.6
MonotonicityNot monotonic
2024-05-11T01:55:17.404992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
443049.47147487 9
 
1.6%
442818.113681285 6
 
1.1%
443344.373525343 6
 
1.1%
443168.51356024 6
 
1.1%
443352.67250352 4
 
0.7%
443230.418234155 4
 
0.7%
442685.886949892 4
 
0.7%
443472.205196671 4
 
0.7%
444456.494334166 3
 
0.5%
443592.370102492 3
 
0.5%
Other values (369) 446
81.7%
(Missing) 51
 
9.3%
ValueCountFrequency (%)
441543.537610587 1
0.2%
441581.838470649 1
0.2%
441621.408468674 2
0.4%
441632.166495668 1
0.2%
441634.011539027 1
0.2%
441668.907887159 1
0.2%
441673.940740741 1
0.2%
441679.709963705 2
0.4%
441692.742121577 1
0.2%
441731.180248315 1
0.2%
ValueCountFrequency (%)
445772.626840932 2
0.4%
445764.45259219 2
0.4%
445631.450029526 1
0.2%
445610.160717686 2
0.4%
445602.132378665 2
0.4%
445574.672326745 1
0.2%
445569.92313256 1
0.2%
445555.611616414 1
0.2%
445555.297104702 1
0.2%
445552.694482149 2
0.4%

위생업태명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
일반세탁업
458 
<NA>
74 
운동화전문세탁업
 
7
빨래방업
 
6
세탁업 기타
 
1

Length

Max length8
Median length5
Mean length4.8937729
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반세탁업 458
83.9%
<NA> 74
 
13.6%
운동화전문세탁업 7
 
1.3%
빨래방업 6
 
1.1%
세탁업 기타 1
 
0.2%

Length

2024-05-11T01:55:17.788727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:18.298187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 458
83.7%
na 74
 
13.5%
운동화전문세탁업 7
 
1.3%
빨래방업 6
 
1.1%
세탁업 1
 
0.2%
기타 1
 
0.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)3.4%
Missing282
Missing (%)51.6%
Infinite0
Infinite (%)0.0%
Mean0.95833333
Minimum0
Maximum25
Zeros185
Zeros (%)33.9%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-05-11T01:55:18.589087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum25
Range25
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1329763
Coefficient of variation (CV)2.2257144
Kurtosis60.971618
Mean0.95833333
Median Absolute Deviation (MAD)0
Skewness6.0432022
Sum253
Variance4.5495881
MonotonicityNot monotonic
2024-05-11T01:55:18.951482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 185
33.9%
3 29
 
5.3%
2 14
 
2.6%
1 14
 
2.6%
4 11
 
2.0%
5 6
 
1.1%
6 3
 
0.5%
25 1
 
0.2%
7 1
 
0.2%
(Missing) 282
51.6%
ValueCountFrequency (%)
0 185
33.9%
1 14
 
2.6%
2 14
 
2.6%
3 29
 
5.3%
4 11
 
2.0%
5 6
 
1.1%
6 3
 
0.5%
7 1
 
0.2%
25 1
 
0.2%
ValueCountFrequency (%)
25 1
 
0.2%
7 1
 
0.2%
6 3
 
0.5%
5 6
 
1.1%
4 11
 
2.0%
3 29
 
5.3%
2 14
 
2.6%
1 14
 
2.6%
0 185
33.9%
Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
292 
0
205 
1
45 
4
 
2
3
 
1

Length

Max length4
Median length4
Mean length2.6043956
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 292
53.5%
0 205
37.5%
1 45
 
8.2%
4 2
 
0.4%
3 1
 
0.2%
2 1
 
0.2%

Length

2024-05-11T01:55:19.348686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:19.728019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 292
53.5%
0 205
37.5%
1 45
 
8.2%
4 2
 
0.4%
3 1
 
0.2%
2 1
 
0.2%
Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
283 
0
143 
1
101 
2
 
11
3
 
5

Length

Max length4
Median length4
Mean length2.5549451
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row1
3rd row<NA>
4th row0
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 283
51.8%
0 143
26.2%
1 101
 
18.5%
2 11
 
2.0%
3 5
 
0.9%
4 3
 
0.5%

Length

2024-05-11T01:55:20.118569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:20.424832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 283
51.8%
0 143
26.2%
1 101
 
18.5%
2 11
 
2.0%
3 5
 
0.9%
4 3
 
0.5%
Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
310 
0
145 
1
75 
2
 
10
4
 
3

Length

Max length4
Median length4
Mean length2.7032967
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row1
3rd row<NA>
4th row0
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 310
56.8%
0 145
26.6%
1 75
 
13.7%
2 10
 
1.8%
4 3
 
0.5%
3 3
 
0.5%

Length

2024-05-11T01:55:20.765133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:21.138762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 310
56.8%
0 145
26.6%
1 75
 
13.7%
2 10
 
1.8%
4 3
 
0.5%
3 3
 
0.5%
Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
385 
0
149 
1
 
9
3
 
3

Length

Max length4
Median length4
Mean length3.1153846
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 385
70.5%
0 149
 
27.3%
1 9
 
1.6%
3 3
 
0.5%

Length

2024-05-11T01:55:21.470037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:21.757994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 385
70.5%
0 149
 
27.3%
1 9
 
1.6%
3 3
 
0.5%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
388 
0
150 
1
 
7
3
 
1

Length

Max length4
Median length4
Mean length3.1318681
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 388
71.1%
0 150
 
27.5%
1 7
 
1.3%
3 1
 
0.2%

Length

2024-05-11T01:55:22.120060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:22.526288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 388
71.1%
0 150
 
27.5%
1 7
 
1.3%
3 1
 
0.2%

한실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
334 
0
212 

Length

Max length4
Median length4
Mean length2.8351648
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 334
61.2%
0 212
38.8%

Length

2024-05-11T01:55:23.073358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:23.602301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 334
61.2%
0 212
38.8%

양실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
334 
0
212 

Length

Max length4
Median length4
Mean length2.8351648
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 334
61.2%
0 212
38.8%

Length

2024-05-11T01:55:24.291698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:24.745112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 334
61.2%
0 212
38.8%

욕실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
334 
0
212 

Length

Max length4
Median length4
Mean length2.8351648
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 334
61.2%
0 212
38.8%

Length

2024-05-11T01:55:25.373423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:25.873279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 334
61.2%
0 212
38.8%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing79
Missing (%)14.5%
Memory size1.2 KiB
False
467 
(Missing)
79 
ValueCountFrequency (%)
False 467
85.5%
(Missing) 79
 
14.5%
2024-05-11T01:55:26.566185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
332 
0
212 
2
 
2

Length

Max length4
Median length4
Mean length2.8241758
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 332
60.8%
0 212
38.8%
2 2
 
0.4%

Length

2024-05-11T01:55:26.915418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:27.306366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 332
60.8%
0 212
38.8%
2 2
 
0.4%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing546
Missing (%)100.0%
Memory size4.9 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing546
Missing (%)100.0%
Memory size4.9 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing546
Missing (%)100.0%
Memory size4.9 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
476 
임대
68 
자가
 
2

Length

Max length4
Median length4
Mean length3.7435897
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 476
87.2%
임대 68
 
12.5%
자가 2
 
0.4%

Length

2024-05-11T01:55:27.765200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:28.082184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 476
87.2%
임대 68
 
12.5%
자가 2
 
0.4%

세탁기수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)4.2%
Missing380
Missing (%)69.6%
Infinite0
Infinite (%)0.0%
Mean1.4879518
Minimum0
Maximum31
Zeros15
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2024-05-11T01:55:28.359417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum31
Range31
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.468561
Coefficient of variation (CV)1.6590329
Kurtosis125.42171
Mean1.4879518
Median Absolute Deviation (MAD)0
Skewness10.526106
Sum247
Variance6.0937934
MonotonicityNot monotonic
2024-05-11T01:55:28.704038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 107
 
19.6%
2 28
 
5.1%
0 15
 
2.7%
3 8
 
1.5%
4 6
 
1.1%
31 1
 
0.2%
5 1
 
0.2%
(Missing) 380
69.6%
ValueCountFrequency (%)
0 15
 
2.7%
1 107
19.6%
2 28
 
5.1%
3 8
 
1.5%
4 6
 
1.1%
5 1
 
0.2%
31 1
 
0.2%
ValueCountFrequency (%)
31 1
 
0.2%
5 1
 
0.2%
4 6
 
1.1%
3 8
 
1.5%
2 28
 
5.1%
1 107
19.6%
0 15
 
2.7%

여성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
517 
0
 
26
1
 
3

Length

Max length4
Median length4
Mean length3.8406593
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> 517
94.7%
0 26
 
4.8%
1 3
 
0.5%

Length

2024-05-11T01:55:29.186403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:29.598544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 517
94.7%
0 26
 
4.8%
1 3
 
0.5%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
517 
0
 
26
1
 
3

Length

Max length4
Median length4
Mean length3.8406593
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> 517
94.7%
0 26
 
4.8%
1 3
 
0.5%

Length

2024-05-11T01:55:29.955894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:30.320514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 517
94.7%
0 26
 
4.8%
1 3
 
0.5%

회수건조수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
433 
1
80 
0
 
29
2
 
3
3
 
1

Length

Max length4
Median length4
Mean length3.3791209
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 433
79.3%
1 80
 
14.7%
0 29
 
5.3%
2 3
 
0.5%
3 1
 
0.2%

Length

2024-05-11T01:55:30.827839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:31.287241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 433
79.3%
1 80
 
14.7%
0 29
 
5.3%
2 3
 
0.5%
3 1
 
0.2%

침대수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
480 
0
66 

Length

Max length4
Median length4
Mean length3.6373626
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> 480
87.9%
0 66
 
12.1%

Length

2024-05-11T01:55:32.004621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T01:55:32.325089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 480
87.9%
0 66
 
12.1%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.4%
Missing74
Missing (%)13.6%
Memory size1.2 KiB
False
471 
True
 
1
(Missing)
74 
ValueCountFrequency (%)
False 471
86.3%
True 1
 
0.2%
(Missing) 74
 
13.6%
2024-05-11T01:55:32.558999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031900003190000-205-1987-0167519870601<NA>3폐업2폐업20220614<NA><NA><NA>020532364919.00156816서울특별시 동작구 사당동 141-156서울특별시 동작구 사당로27길 24 (사당동)7007소망세탁2022-06-14 17:21:48U2021-12-05 23:06:00.0일반세탁업197991.306131442558.104806<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
131900003190000-205-1987-0167619870601<NA>3폐업2폐업20141016<NA><NA><NA>020534049835.69156824서울특별시 동작구 사당동 708-457번지서울특별시 동작구 동작대로27다길 41 (사당동)7008상미세탁소2004-10-21 00:00:00I2018-08-31 23:59:59.0일반세탁업198196.672532442623.107414일반세탁업5<NA>11<NA><NA><NA><NA><NA>N<NA><NA><NA><NA>임대<NA><NA><NA><NA><NA>N
231900003190000-205-1987-0167719870601<NA>3폐업2폐업20030605<NA><NA><NA>02 5938918.00156090서울특별시 동작구 사당동 1131-0번지 영아A 상가 203호<NA><NA>백양사2003-06-05 00:00:00I2018-08-31 23:59:59.0일반세탁업197390.143153442578.04727일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331900003190000-205-1987-0167819870619<NA>3폐업2폐업19970710<NA><NA><NA>02 8133718494.80156834서울특별시 동작구 상도1동 326-14번지<NA><NA>대림사2001-09-29 00:00:00I2018-08-31 23:59:59.0일반세탁업195684.973374444497.816736일반세탁업000000000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431900003190000-205-1987-0167919870619<NA>3폐업2폐업20070604<NA><NA><NA>02 8127050.00156858서울특별시 동작구 흑석동 79-93번지<NA><NA>백미사2004-12-06 00:00:00I2018-08-31 23:59:59.0일반세탁업196447.200931444462.633885일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531900003190000-205-1987-0168019870901<NA>3폐업2폐업20041101<NA><NA><NA>02 8155917.00156030서울특별시 동작구 상도동 67-6번지<NA><NA>국제크리닝2003-07-12 00:00:00I2018-08-31 23:59:59.0일반세탁업195729.867668444060.595683일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631900003190000-205-1987-0169519870601<NA>3폐업2폐업20191203<NA><NA><NA>020583648119.90156821서울특별시 동작구 사당동 312-7번지서울특별시 동작구 사당로16길 57 (사당동)7011상신세탁소2019-12-03 13:30:48U2019-12-05 02:40:00.0일반세탁업197470.684253442103.255844일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA>1<NA><NA>1<NA>N
731900003190000-205-1987-0169619870601<NA>3폐업2폐업20150901<NA><NA><NA>020593826032.64156824서울특별시 동작구 사당동 708-690번지서울특별시 동작구 사당로23길 126 (사당동)<NA>문성사2004-10-21 00:00:00I2018-08-31 23:59:59.0일반세탁업197530.519485442896.492187일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831900003190000-205-1987-0169719870601<NA>3폐업2폐업20000527<NA><NA><NA>0205905259.00156818서울특별시 동작구 사당동 171-1번지<NA><NA>삼성크리닝2000-06-03 00:00:00I2018-08-31 23:59:59.0일반세탁업<NA><NA>일반세탁업000000000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931900003190000-205-1987-0169819870601<NA>3폐업2폐업20020501<NA><NA><NA>020593341370.26156818서울특별시 동작구 사당동 170-25번지<NA><NA>장미사2002-08-16 00:00:00I2018-08-31 23:59:59.0일반세탁업197435.087507443040.28284일반세탁업000000000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
53631900003190000-205-2019-000042019-09-11<NA>1영업/정상1영업<NA><NA><NA><NA>02 534 585565.16156-090서울특별시 동작구 사당동 1157 이수역리가아파트서울특별시 동작구 사당로27길 130, 주상가동 B202호 (사당동, 이수역리가아파트)7000리가세탁2024-04-05 16:29:14U2023-12-04 00:07:00.0일반세탁업197617.874056443032.713811<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
53731900003190000-205-2019-0000520191025<NA>1영업/정상1영업<NA><NA><NA><NA>02 824676622.50156718서울특별시 동작구 상도동 414 건영아파트서울특별시 동작구 만양로 26, 상가동 2층 205호 (상도동, 건영아파트)6918브니엘 세탁소2022-05-27 15:49:00U2021-12-04 22:09:00.0일반세탁업195489.423431444995.675592<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
53831900003190000-205-2020-0000120200122<NA>1영업/정상1영업<NA><NA><NA><NA><NA>56.41156710서울특별시 동작구 신대방동 395-68번지 보라매나산스위트서울특별시 동작구 보라매로5가길 24, B3-1호 (신대방동, 보라매나산스위트)7071크린하우스2020-01-23 15:11:50I2020-01-24 00:23:24.0일반세탁업193204.782208443168.51356일반세탁업00<NA><NA><NA><NA>000N0<NA><NA><NA><NA>40000N
53931900003190000-205-2020-0000220201022<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.47156843서울특별시 동작구 상도동 262-2서울특별시 동작구 성대로12길 8, 1층 102호 (상도동)7045오크린2020-10-26 15:12:16U2020-10-28 02:40:00.0일반세탁업194210.802823443896.756156일반세탁업00<NA><NA><NA><NA>000N0<NA><NA><NA><NA>21120N
54031900003190000-205-2021-0000120211119<NA>3폐업2폐업20221207<NA><NA><NA><NA>33.00156811서울특별시 동작구 대방동 411-3 대양빌딩서울특별시 동작구 여의대방로26길 52, 대양빌딩 지층 (대방동)7055다원인프라2022-12-07 14:14:40U2021-11-02 00:09:00.0일반세탁업193141.269269444114.55685<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54131900003190000-205-2022-0000120220622<NA>1영업/정상1영업<NA><NA><NA><NA>02 591528313.99156815서울특별시 동작구 사당동 105 신동아아파트서울특별시 동작구 동작대로29길 110, 상가동 2층 204호 (사당동, 신동아아파트)6998백합명품세탁소2022-08-31 14:56:59U2021-12-09 00:02:00.0일반세탁업197802.054854443049.471475<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54231900003190000-205-2022-0000220220713<NA>1영업/정상1영업<NA><NA><NA><NA>02 537491832.36156815서울특별시 동작구 사당동 105 사당우성아파트서울특별시 동작구 동작대로29길 115, 상가(3단지)동 3층 305호 (사당동, 사당우성아파트)6990우성세탁소2022-07-13 13:06:11I2021-12-06 23:05:00.0일반세탁업197802.054854443049.471475<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54331900003190000-205-2023-000012023-04-10<NA>3폐업2폐업2024-01-31<NA><NA><NA><NA>24.00156-823서울특별시 동작구 사당동 316-31서울특별시 동작구 사당로22나길 69, 1층 102호 (사당동)7017새날세탁소2024-01-31 10:47:40U2023-12-02 00:02:00.0일반세탁업197571.141945442040.005369<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54431900003190000-205-2023-000022023-08-09<NA>1영업/정상1영업<NA><NA><NA><NA>02 848160819.80156-722서울특별시 동작구 신대방동 713 보라매롯데낙천대아파트서울특별시 동작구 여의대방로10길 38, 상가동 지하5호 (신대방동, 보라매롯데낙천대아파트)7064롯데세탁소2023-08-09 14:06:42I2022-12-07 23:01:00.0일반세탁업192295.136304443573.337118<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54531900003190000-205-2023-000032023-09-12<NA>3폐업2폐업2024-02-14<NA><NA><NA><NA>72.12156-861서울특별시 동작구 흑석동 205-10서울특별시 동작구 흑석로7길 5, 1층 (흑석동)6911유니더스빨래방2024-02-15 08:59:49U2023-12-01 23:07:00.0일반세탁업196229.261566444985.548721<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>