Overview

Dataset statistics

Number of variables47
Number of observations562
Missing cells4920
Missing cells (%)18.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory221.9 KiB
Average record size in memory404.2 B

Variable types

Categorical23
Text7
DateTime4
Unsupported7
Numeric4
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
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-17970/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (91.6%)Imbalance
위생업태명 is highly imbalanced (78.2%)Imbalance
건물지하층수 is highly imbalanced (70.9%)Imbalance
사용시작지상층 is highly imbalanced (52.2%)Imbalance
사용끝지상층 is highly imbalanced (56.7%)Imbalance
사용시작지하층 is highly imbalanced (69.3%)Imbalance
사용끝지하층 is highly imbalanced (70.6%)Imbalance
한실수 is highly imbalanced (63.0%)Imbalance
양실수 is highly imbalanced (63.0%)Imbalance
욕실수 is highly imbalanced (63.0%)Imbalance
좌석수 is highly imbalanced (74.5%)Imbalance
건물소유구분명 is highly imbalanced (71.5%)Imbalance
세탁기수 is highly imbalanced (50.6%)Imbalance
여성종사자수 is highly imbalanced (63.0%)Imbalance
남성종사자수 is highly imbalanced (74.5%)Imbalance
침대수 is highly imbalanced (63.0%)Imbalance
인허가취소일자 has 562 (100.0%) missing valuesMissing
폐업일자 has 134 (23.8%) missing valuesMissing
휴업시작일자 has 562 (100.0%) missing valuesMissing
휴업종료일자 has 562 (100.0%) missing valuesMissing
재개업일자 has 562 (100.0%) missing valuesMissing
전화번호 has 34 (6.0%) missing valuesMissing
도로명주소 has 303 (53.9%) missing valuesMissing
도로명우편번호 has 312 (55.5%) missing valuesMissing
좌표정보(X) has 38 (6.8%) missing valuesMissing
좌표정보(Y) has 38 (6.8%) missing valuesMissing
건물지상층수 has 40 (7.1%) missing valuesMissing
발한실여부 has 44 (7.8%) missing valuesMissing
조건부허가신고사유 has 562 (100.0%) missing valuesMissing
조건부허가시작일자 has 562 (100.0%) missing valuesMissing
조건부허가종료일자 has 562 (100.0%) missing valuesMissing
다중이용업소여부 has 40 (7.1%) 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 478 (85.1%) zerosZeros

Reproduction

Analysis started2024-05-11 06:26:41.089702
Analysis finished2024-05-11 06:26:42.757451
Duration1.67 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
3180000
562 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3180000
2nd row3180000
3rd row3180000
4th row3180000
5th row3180000

Common Values

ValueCountFrequency (%)
3180000 562
100.0%

Length

2024-05-11T15:26:42.902557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:26:43.169795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 562
100.0%

관리번호
Text

UNIQUE 

Distinct562
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-05-11T15:26:43.477141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12364
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

Unique562 ?
Unique (%)100.0%

Sample

1st row3180000-205-1957-02062
2nd row3180000-205-1984-02042
3rd row3180000-205-1987-02039
4th row3180000-205-1987-02041
5th row3180000-205-1987-02043
ValueCountFrequency (%)
3180000-205-1957-02062 1
 
0.2%
3180000-205-2003-00014 1
 
0.2%
3180000-205-2003-00005 1
 
0.2%
3180000-205-2003-00019 1
 
0.2%
3180000-205-2003-00018 1
 
0.2%
3180000-205-2003-00017 1
 
0.2%
3180000-205-2003-00016 1
 
0.2%
3180000-205-2003-00015 1
 
0.2%
3180000-205-2003-00003 1
 
0.2%
3180000-205-2003-00058 1
 
0.2%
Other values (552) 552
98.2%
2024-05-11T15:26:44.615641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4508
36.5%
- 1686
 
13.6%
2 1389
 
11.2%
1 1184
 
9.6%
8 881
 
7.1%
3 824
 
6.7%
5 703
 
5.7%
9 621
 
5.0%
4 224
 
1.8%
7 223
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10678
86.4%
Dash Punctuation 1686
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4508
42.2%
2 1389
 
13.0%
1 1184
 
11.1%
8 881
 
8.3%
3 824
 
7.7%
5 703
 
6.6%
9 621
 
5.8%
4 224
 
2.1%
7 223
 
2.1%
6 121
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4508
36.5%
- 1686
 
13.6%
2 1389
 
11.2%
1 1184
 
9.6%
8 881
 
7.1%
3 824
 
6.7%
5 703
 
5.7%
9 621
 
5.0%
4 224
 
1.8%
7 223
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4508
36.5%
- 1686
 
13.6%
2 1389
 
11.2%
1 1184
 
9.6%
8 881
 
7.1%
3 824
 
6.7%
5 703
 
5.7%
9 621
 
5.0%
4 224
 
1.8%
7 223
 
1.8%
Distinct389
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum1957-05-28 00:00:00
Maximum2023-06-01 00:00:00
2024-05-11T15:26:44.919243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:26:45.188495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing562
Missing (%)100.0%
Memory size5.1 KiB
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
3
428 
1
134 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 428
76.2%
1 134
 
23.8%

Length

2024-05-11T15:26:45.495184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:26:45.701237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 428
76.2%
1 134
 
23.8%

영업상태명
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
폐업
428 
영업/정상
134 

Length

Max length5
Median length2
Mean length2.7153025
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 428
76.2%
영업/정상 134
 
23.8%

Length

2024-05-11T15:26:45.952170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:26:46.366181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 428
76.2%
영업/정상 134
 
23.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2
428 
1
134 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 428
76.2%
1 134
 
23.8%

Length

2024-05-11T15:26:46.660578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:26:46.882031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 428
76.2%
1 134
 
23.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
폐업
428 
영업
134 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 428
76.2%
영업 134
 
23.8%

Length

2024-05-11T15:26:47.113087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:26:47.309174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 428
76.2%
영업 134
 
23.8%

폐업일자
Date

MISSING 

Distinct341
Distinct (%)79.7%
Missing134
Missing (%)23.8%
Memory size4.5 KiB
Minimum1989-06-20 00:00:00
Maximum2024-02-29 00:00:00
2024-05-11T15:26:47.533623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:26:47.785753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing562
Missing (%)100.0%
Memory size5.1 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing562
Missing (%)100.0%
Memory size5.1 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing562
Missing (%)100.0%
Memory size5.1 KiB

전화번호
Text

MISSING 

Distinct457
Distinct (%)86.6%
Missing34
Missing (%)6.0%
Memory size4.5 KiB
2024-05-11T15:26:48.371850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.8068182
Min length2

Characters and Unicode

Total characters5178
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

Unique437 ?
Unique (%)82.8%

Sample

1st row0208438758
2nd row0208423544
3rd row02 7834955
4th row0207830281
5th row0208469221
ValueCountFrequency (%)
02 255
31.9%
0200000000 37
 
4.6%
845 4
 
0.5%
844 4
 
0.5%
846 3
 
0.4%
832 3
 
0.4%
0226759100 2
 
0.2%
4413 2
 
0.2%
7853312 2
 
0.2%
843 2
 
0.2%
Other values (464) 486
60.8%
2024-05-11T15:26:49.167452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1142
22.1%
2 904
17.5%
8 553
10.7%
4 448
 
8.7%
3 442
 
8.5%
6 375
 
7.2%
7 334
 
6.5%
328
 
6.3%
5 247
 
4.8%
1 215
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4850
93.7%
Space Separator 328
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1142
23.5%
2 904
18.6%
8 553
11.4%
4 448
 
9.2%
3 442
 
9.1%
6 375
 
7.7%
7 334
 
6.9%
5 247
 
5.1%
1 215
 
4.4%
9 190
 
3.9%
Space Separator
ValueCountFrequency (%)
328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1142
22.1%
2 904
17.5%
8 553
10.7%
4 448
 
8.7%
3 442
 
8.5%
6 375
 
7.2%
7 334
 
6.5%
328
 
6.3%
5 247
 
4.8%
1 215
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1142
22.1%
2 904
17.5%
8 553
10.7%
4 448
 
8.7%
3 442
 
8.5%
6 375
 
7.2%
7 334
 
6.5%
328
 
6.3%
5 247
 
4.8%
1 215
 
4.2%
Distinct339
Distinct (%)60.4%
Missing1
Missing (%)0.2%
Memory size4.5 KiB
2024-05-11T15:26:49.929055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0249554
Min length3

Characters and Unicode

Total characters2819
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

Unique275 ?
Unique (%)49.0%

Sample

1st row300.00
2nd row157.79
3rd row38.85
4th row46.20
5th row71.64
ValueCountFrequency (%)
24.00 27
 
4.8%
00 17
 
3.0%
33.00 16
 
2.9%
30.00 15
 
2.7%
18.00 11
 
2.0%
15.00 11
 
2.0%
20.00 10
 
1.8%
27.00 9
 
1.6%
21.00 8
 
1.4%
22.00 6
 
1.1%
Other values (329) 431
76.8%
2024-05-11T15:26:50.963757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 699
24.8%
. 561
19.9%
2 323
11.5%
1 229
 
8.1%
4 195
 
6.9%
3 182
 
6.5%
5 169
 
6.0%
6 133
 
4.7%
8 121
 
4.3%
7 103
 
3.7%
Other values (2) 104
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2257
80.1%
Other Punctuation 562
 
19.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 699
31.0%
2 323
14.3%
1 229
 
10.1%
4 195
 
8.6%
3 182
 
8.1%
5 169
 
7.5%
6 133
 
5.9%
8 121
 
5.4%
7 103
 
4.6%
9 103
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 561
99.8%
, 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2819
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 699
24.8%
. 561
19.9%
2 323
11.5%
1 229
 
8.1%
4 195
 
6.9%
3 182
 
6.5%
5 169
 
6.0%
6 133
 
4.7%
8 121
 
4.3%
7 103
 
3.7%
Other values (2) 104
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 699
24.8%
. 561
19.9%
2 323
11.5%
1 229
 
8.1%
4 195
 
6.9%
3 182
 
6.5%
5 169
 
6.0%
6 133
 
4.7%
8 121
 
4.3%
7 103
 
3.7%
Other values (2) 104
 
3.7%
Distinct118
Distinct (%)21.0%
Missing1
Missing (%)0.2%
Memory size4.5 KiB
2024-05-11T15:26:51.575634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0338681
Min length6

Characters and Unicode

Total characters3385
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)7.0%

Sample

1st row150837
2nd row150837
3rd row150895
4th row150886
5th row150840
ValueCountFrequency (%)
150841 55
 
9.8%
150840 31
 
5.5%
150818 15
 
2.7%
150832 14
 
2.5%
150899 14
 
2.5%
150070 14
 
2.5%
150858 12
 
2.1%
150867 12
 
2.1%
150837 12
 
2.1%
150800 10
 
1.8%
Other values (108) 372
66.3%
2024-05-11T15:26:52.529969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 807
23.8%
1 725
21.4%
5 671
19.8%
8 500
14.8%
4 164
 
4.8%
3 155
 
4.6%
9 101
 
3.0%
2 98
 
2.9%
7 73
 
2.2%
6 72
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3366
99.4%
Dash Punctuation 19
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 807
24.0%
1 725
21.5%
5 671
19.9%
8 500
14.9%
4 164
 
4.9%
3 155
 
4.6%
9 101
 
3.0%
2 98
 
2.9%
7 73
 
2.2%
6 72
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3385
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 807
23.8%
1 725
21.4%
5 671
19.8%
8 500
14.8%
4 164
 
4.8%
3 155
 
4.6%
9 101
 
3.0%
2 98
 
2.9%
7 73
 
2.2%
6 72
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 807
23.8%
1 725
21.4%
5 671
19.8%
8 500
14.8%
4 164
 
4.8%
3 155
 
4.6%
9 101
 
3.0%
2 98
 
2.9%
7 73
 
2.2%
6 72
 
2.1%
Distinct535
Distinct (%)95.4%
Missing1
Missing (%)0.2%
Memory size4.5 KiB
2024-05-11T15:26:53.012688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length46
Mean length26.445633
Min length19

Characters and Unicode

Total characters14836
Distinct characters168
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique509 ?
Unique (%)90.7%

Sample

1st row서울특별시 영등포구 신길동 92-3번지
2nd row서울특별시 영등포구 신길동 61-69번지
3rd row서울특별시 영등포구 여의도동 54-2번지
4th row서울특별시 영등포구 여의도동 36번지
5th row서울특별시 영등포구 신길동 190-160번지
ValueCountFrequency (%)
서울특별시 561
21.9%
영등포구 561
21.9%
신길동 202
 
7.9%
대림동 107
 
4.2%
도림동 41
 
1.6%
1층 35
 
1.4%
여의도동 32
 
1.2%
영등포동 17
 
0.7%
양평동4가 15
 
0.6%
당산동4가 15
 
0.6%
Other values (721) 976
38.1%
2024-05-11T15:26:53.812118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2484
 
16.7%
1 650
 
4.4%
617
 
4.2%
616
 
4.2%
615
 
4.1%
591
 
4.0%
569
 
3.8%
562
 
3.8%
562
 
3.8%
561
 
3.8%
Other values (158) 7009
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8896
60.0%
Decimal Number 2926
 
19.7%
Space Separator 2484
 
16.7%
Dash Punctuation 483
 
3.3%
Uppercase Letter 21
 
0.1%
Other Punctuation 9
 
0.1%
Close Punctuation 7
 
< 0.1%
Open Punctuation 7
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
617
 
6.9%
616
 
6.9%
615
 
6.9%
591
 
6.6%
569
 
6.4%
562
 
6.3%
562
 
6.3%
561
 
6.3%
561
 
6.3%
561
 
6.3%
Other values (133) 3081
34.6%
Decimal Number
ValueCountFrequency (%)
1 650
22.2%
2 374
12.8%
3 316
10.8%
0 312
10.7%
4 296
10.1%
6 225
 
7.7%
5 201
 
6.9%
7 195
 
6.7%
8 188
 
6.4%
9 169
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
B 7
33.3%
A 6
28.6%
C 2
 
9.5%
P 2
 
9.5%
T 2
 
9.5%
L 1
 
4.8%
G 1
 
4.8%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
g 1
50.0%
Space Separator
ValueCountFrequency (%)
2484
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 483
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8896
60.0%
Common 5917
39.9%
Latin 23
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
617
 
6.9%
616
 
6.9%
615
 
6.9%
591
 
6.6%
569
 
6.4%
562
 
6.3%
562
 
6.3%
561
 
6.3%
561
 
6.3%
561
 
6.3%
Other values (133) 3081
34.6%
Common
ValueCountFrequency (%)
2484
42.0%
1 650
 
11.0%
- 483
 
8.2%
2 374
 
6.3%
3 316
 
5.3%
0 312
 
5.3%
4 296
 
5.0%
6 225
 
3.8%
5 201
 
3.4%
7 195
 
3.3%
Other values (6) 381
 
6.4%
Latin
ValueCountFrequency (%)
B 7
30.4%
A 6
26.1%
C 2
 
8.7%
P 2
 
8.7%
T 2
 
8.7%
e 1
 
4.3%
L 1
 
4.3%
G 1
 
4.3%
g 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8896
60.0%
ASCII 5940
40.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2484
41.8%
1 650
 
10.9%
- 483
 
8.1%
2 374
 
6.3%
3 316
 
5.3%
0 312
 
5.3%
4 296
 
5.0%
6 225
 
3.8%
5 201
 
3.4%
7 195
 
3.3%
Other values (15) 404
 
6.8%
Hangul
ValueCountFrequency (%)
617
 
6.9%
616
 
6.9%
615
 
6.9%
591
 
6.6%
569
 
6.4%
562
 
6.3%
562
 
6.3%
561
 
6.3%
561
 
6.3%
561
 
6.3%
Other values (133) 3081
34.6%

도로명주소
Text

MISSING 

Distinct256
Distinct (%)98.8%
Missing303
Missing (%)53.9%
Memory size4.5 KiB
2024-05-11T15:26:54.305686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length48
Mean length31.733591
Min length23

Characters and Unicode

Total characters8219
Distinct characters162
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

Unique253 ?
Unique (%)97.7%

Sample

1st row서울특별시 영등포구 여의대방로 386 (여의도동)
2nd row서울특별시 영등포구 의사당대로 127 (여의도동)
3rd row서울특별시 영등포구 신길로56길 8-1 (신길동)
4th row서울특별시 영등포구 여의대방로61길 36-1 (신길동)
5th row서울특별시 영등포구 도신로56길 25, 1층 103호 (신길동)
ValueCountFrequency (%)
서울특별시 259
 
17.6%
영등포구 259
 
17.6%
신길동 84
 
5.7%
1층 36
 
2.4%
대림동 32
 
2.2%
도림동 20
 
1.4%
여의도동 13
 
0.9%
9 12
 
0.8%
당산동4가 10
 
0.7%
8 10
 
0.7%
Other values (446) 740
50.2%
2024-05-11T15:26:55.057400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1216
 
14.8%
1 360
 
4.4%
328
 
4.0%
310
 
3.8%
301
 
3.7%
300
 
3.7%
281
 
3.4%
271
 
3.3%
266
 
3.2%
) 262
 
3.2%
Other values (152) 4324
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4970
60.5%
Decimal Number 1290
 
15.7%
Space Separator 1216
 
14.8%
Close Punctuation 262
 
3.2%
Open Punctuation 262
 
3.2%
Other Punctuation 146
 
1.8%
Dash Punctuation 62
 
0.8%
Uppercase Letter 10
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
328
 
6.6%
310
 
6.2%
301
 
6.1%
300
 
6.0%
281
 
5.7%
271
 
5.5%
266
 
5.4%
262
 
5.3%
260
 
5.2%
259
 
5.2%
Other values (132) 2132
42.9%
Decimal Number
ValueCountFrequency (%)
1 360
27.9%
2 173
13.4%
3 131
 
10.2%
4 126
 
9.8%
5 99
 
7.7%
0 95
 
7.4%
6 94
 
7.3%
8 79
 
6.1%
7 77
 
6.0%
9 56
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
B 5
50.0%
A 3
30.0%
T 1
 
10.0%
P 1
 
10.0%
Space Separator
ValueCountFrequency (%)
1216
100.0%
Close Punctuation
ValueCountFrequency (%)
) 262
100.0%
Open Punctuation
ValueCountFrequency (%)
( 262
100.0%
Other Punctuation
ValueCountFrequency (%)
, 146
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 62
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4970
60.5%
Common 3238
39.4%
Latin 11
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
328
 
6.6%
310
 
6.2%
301
 
6.1%
300
 
6.0%
281
 
5.7%
271
 
5.5%
266
 
5.4%
262
 
5.3%
260
 
5.2%
259
 
5.2%
Other values (132) 2132
42.9%
Common
ValueCountFrequency (%)
1216
37.6%
1 360
 
11.1%
) 262
 
8.1%
( 262
 
8.1%
2 173
 
5.3%
, 146
 
4.5%
3 131
 
4.0%
4 126
 
3.9%
5 99
 
3.1%
0 95
 
2.9%
Other values (5) 368
 
11.4%
Latin
ValueCountFrequency (%)
B 5
45.5%
A 3
27.3%
T 1
 
9.1%
e 1
 
9.1%
P 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4970
60.5%
ASCII 3249
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1216
37.4%
1 360
 
11.1%
) 262
 
8.1%
( 262
 
8.1%
2 173
 
5.3%
, 146
 
4.5%
3 131
 
4.0%
4 126
 
3.9%
5 99
 
3.0%
0 95
 
2.9%
Other values (10) 379
 
11.7%
Hangul
ValueCountFrequency (%)
328
 
6.6%
310
 
6.2%
301
 
6.1%
300
 
6.0%
281
 
5.7%
271
 
5.5%
266
 
5.4%
262
 
5.3%
260
 
5.2%
259
 
5.2%
Other values (132) 2132
42.9%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct143
Distinct (%)57.2%
Missing312
Missing (%)55.5%
Infinite0
Infinite (%)0.0%
Mean7332.952
Minimum7201
Maximum7447
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-05-11T15:26:55.404277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7201
5-th percentile7211.35
Q17273.25
median7349
Q37384.5
95-th percentile7435
Maximum7447
Range246
Interquartile range (IQR)111.25

Descriptive statistics

Standard deviation70.392458
Coefficient of variation (CV)0.0095994707
Kurtosis-0.97430041
Mean7332.952
Median Absolute Deviation (MAD)46.5
Skewness-0.36281669
Sum1833238
Variance4955.0981
MonotonicityNot monotonic
2024-05-11T15:26:55.662696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7366 5
 
0.9%
7206 5
 
0.9%
7383 5
 
0.9%
7401 5
 
0.9%
7348 4
 
0.7%
7374 4
 
0.7%
7220 4
 
0.7%
7362 4
 
0.7%
7369 4
 
0.7%
7364 4
 
0.7%
Other values (133) 206
36.7%
(Missing) 312
55.5%
ValueCountFrequency (%)
7201 1
 
0.2%
7202 2
 
0.4%
7204 2
 
0.4%
7206 5
0.9%
7208 1
 
0.2%
7209 1
 
0.2%
7210 1
 
0.2%
7213 2
 
0.4%
7214 3
0.5%
7216 1
 
0.2%
ValueCountFrequency (%)
7447 2
0.4%
7446 3
0.5%
7445 1
 
0.2%
7442 2
0.4%
7440 1
 
0.2%
7439 1
 
0.2%
7437 1
 
0.2%
7436 1
 
0.2%
7435 2
0.4%
7433 1
 
0.2%
Distinct412
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
2024-05-11T15:26:56.232112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length4.4341637
Min length2

Characters and Unicode

Total characters2492
Distinct characters263
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

Unique342 ?
Unique (%)60.9%

Sample

1st row경북사
2nd row그린사
3rd row유미사
4th row여의명품세탁
5th row제일사
ValueCountFrequency (%)
세탁소 18
 
3.0%
백양사 14
 
2.3%
제일사 8
 
1.3%
대성사 7
 
1.2%
현대사 7
 
1.2%
삼성사 6
 
1.0%
백성사 6
 
1.0%
믿음사 6
 
1.0%
백합사 5
 
0.8%
명품세탁 5
 
0.8%
Other values (411) 520
86.4%
2024-05-11T15:26:56.960920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
238
 
9.6%
229
 
9.2%
226
 
9.1%
134
 
5.4%
60
 
2.4%
59
 
2.4%
52
 
2.1%
51
 
2.0%
47
 
1.9%
44
 
1.8%
Other values (253) 1352
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2410
96.7%
Space Separator 41
 
1.6%
Decimal Number 12
 
0.5%
Uppercase Letter 10
 
0.4%
Lowercase Letter 8
 
0.3%
Close Punctuation 5
 
0.2%
Open Punctuation 5
 
0.2%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
238
 
9.9%
229
 
9.5%
226
 
9.4%
134
 
5.6%
60
 
2.5%
59
 
2.4%
52
 
2.2%
51
 
2.1%
47
 
2.0%
44
 
1.8%
Other values (228) 1270
52.7%
Uppercase Letter
ValueCountFrequency (%)
C 2
20.0%
P 1
10.0%
O 1
10.0%
T 1
10.0%
S 1
10.0%
D 1
10.0%
L 1
10.0%
G 1
10.0%
E 1
10.0%
Decimal Number
ValueCountFrequency (%)
2 4
33.3%
1 4
33.3%
3 1
 
8.3%
6 1
 
8.3%
4 1
 
8.3%
5 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
e 3
37.5%
s 1
 
12.5%
r 1
 
12.5%
n 1
 
12.5%
a 1
 
12.5%
l 1
 
12.5%
Space Separator
ValueCountFrequency (%)
41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2410
96.7%
Common 64
 
2.6%
Latin 18
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
238
 
9.9%
229
 
9.5%
226
 
9.4%
134
 
5.6%
60
 
2.5%
59
 
2.4%
52
 
2.2%
51
 
2.1%
47
 
2.0%
44
 
1.8%
Other values (228) 1270
52.7%
Latin
ValueCountFrequency (%)
e 3
16.7%
C 2
 
11.1%
P 1
 
5.6%
O 1
 
5.6%
T 1
 
5.6%
s 1
 
5.6%
r 1
 
5.6%
n 1
 
5.6%
a 1
 
5.6%
l 1
 
5.6%
Other values (5) 5
27.8%
Common
ValueCountFrequency (%)
41
64.1%
) 5
 
7.8%
( 5
 
7.8%
2 4
 
6.2%
1 4
 
6.2%
3 1
 
1.6%
6 1
 
1.6%
4 1
 
1.6%
5 1
 
1.6%
& 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2410
96.7%
ASCII 82
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
238
 
9.9%
229
 
9.5%
226
 
9.4%
134
 
5.6%
60
 
2.5%
59
 
2.4%
52
 
2.2%
51
 
2.1%
47
 
2.0%
44
 
1.8%
Other values (228) 1270
52.7%
ASCII
ValueCountFrequency (%)
41
50.0%
) 5
 
6.1%
( 5
 
6.1%
2 4
 
4.9%
1 4
 
4.9%
e 3
 
3.7%
C 2
 
2.4%
3 1
 
1.2%
6 1
 
1.2%
P 1
 
1.2%
Other values (15) 15
 
18.3%
Distinct352
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum1999-03-10 00:00:00
Maximum2024-02-29 14:02:24
2024-05-11T15:26:57.228740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:26:57.489763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
I
450 
U
112 

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 450
80.1%
U 112
 
19.9%

Length

2024-05-11T15:26:57.758571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:26:57.912861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 450
80.1%
u 112
 
19.9%
Distinct112
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-03 00:02:00
2024-05-11T15:26:58.101988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:26:58.325252image/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 size4.5 KiB
일반세탁업
551 
빨래방업
 
6
운동화전문세탁업
 
3
세탁업 기타
 
2

Length

Max length8
Median length5
Mean length5.0088968
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반세탁업 551
98.0%
빨래방업 6
 
1.1%
운동화전문세탁업 3
 
0.5%
세탁업 기타 2
 
0.4%

Length

2024-05-11T15:26:58.573709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:26:58.809948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 551
97.7%
빨래방업 6
 
1.1%
운동화전문세탁업 3
 
0.5%
세탁업 2
 
0.4%
기타 2
 
0.4%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct445
Distinct (%)84.9%
Missing38
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean191643.71
Minimum189549.85
Maximum194592.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-05-11T15:26:59.018146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189549.85
5-th percentile190265.82
Q1190964.73
median191523.13
Q3192196.04
95-th percentile193463.51
Maximum194592.28
Range5042.4294
Interquartile range (IQR)1231.3062

Descriptive statistics

Standard deviation970.91259
Coefficient of variation (CV)0.0050662376
Kurtosis0.44821275
Mean191643.71
Median Absolute Deviation (MAD)597.42892
Skewness0.61656981
Sum1.0042131 × 108
Variance942671.26
MonotonicityNot monotonic
2024-05-11T15:26:59.275989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194370.32715363 7
 
1.2%
191362.376780507 4
 
0.7%
190804.715433424 3
 
0.5%
191163.404247512 3
 
0.5%
192169.349485278 3
 
0.5%
191811.464132786 3
 
0.5%
191300.329508822 3
 
0.5%
193809.01252005 3
 
0.5%
194015.180457719 3
 
0.5%
191499.917603867 3
 
0.5%
Other values (435) 489
87.0%
(Missing) 38
 
6.8%
ValueCountFrequency (%)
189549.847307536 1
0.2%
189574.962072527 1
0.2%
189586.236800721 2
0.4%
189600.906705305 1
0.2%
189734.544016734 1
0.2%
189744.683016677 1
0.2%
189775.62671374 1
0.2%
189789.2720811 1
0.2%
189808.723560647 1
0.2%
189835.868160807 1
0.2%
ValueCountFrequency (%)
194592.276750438 1
 
0.2%
194530.535390096 2
 
0.4%
194370.32715363 7
1.2%
194056.681860495 1
 
0.2%
194015.180457719 3
0.5%
193943.347970118 1
 
0.2%
193915.13139016 1
 
0.2%
193896.282065175 2
 
0.4%
193847.088620039 2
 
0.4%
193844.169062846 1
 
0.2%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct445
Distinct (%)84.9%
Missing38
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean445407.04
Minimum442710.66
Maximum448960.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-05-11T15:26:59.950183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum442710.66
5-th percentile443154.56
Q1444370.81
median445294.38
Q3446497.52
95-th percentile447887.6
Maximum448960.63
Range6249.9672
Interquartile range (IQR)2126.7118

Descriptive statistics

Standard deviation1408.5622
Coefficient of variation (CV)0.0031624157
Kurtosis-0.54713307
Mean445407.04
Median Absolute Deviation (MAD)1012.411
Skewness0.26438509
Sum2.3339329 × 108
Variance1984047.5
MonotonicityNot monotonic
2024-05-11T15:27:00.255525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446497.523930335 7
 
1.2%
447750.192654125 4
 
0.7%
445995.756931331 3
 
0.5%
443264.26977569 3
 
0.5%
444850.004967613 3
 
0.5%
445392.465435487 3
 
0.5%
445483.553355174 3
 
0.5%
447090.147928667 3
 
0.5%
446777.454528971 3
 
0.5%
444955.364519891 3
 
0.5%
Other values (435) 489
87.0%
(Missing) 38
 
6.8%
ValueCountFrequency (%)
442710.662421803 1
0.2%
442873.39895581 2
0.4%
442892.914010425 1
0.2%
442904.235091033 1
0.2%
442922.794000018 1
0.2%
442932.467591503 1
0.2%
442935.524868169 1
0.2%
442938.422926307 1
0.2%
442983.990632924 1
0.2%
442988.625887746 2
0.4%
ValueCountFrequency (%)
448960.629575752 1
 
0.2%
448937.07552692 1
 
0.2%
448936.418388404 1
 
0.2%
448895.406203835 1
 
0.2%
448656.726986041 3
0.5%
448496.44955288 1
 
0.2%
448467.736792029 1
 
0.2%
448446.88137154 1
 
0.2%
448421.881545422 1
 
0.2%
448406.046269711 1
 
0.2%

위생업태명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
일반세탁업
513 
<NA>
 
40
빨래방업
 
6
운동화전문세탁업
 
2
세탁업 기타
 
1

Length

Max length8
Median length5
Mean length4.930605
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반세탁업 513
91.3%
<NA> 40
 
7.1%
빨래방업 6
 
1.1%
운동화전문세탁업 2
 
0.4%
세탁업 기타 1
 
0.2%

Length

2024-05-11T15:27:00.544810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:00.727089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 513
91.1%
na 40
 
7.1%
빨래방업 6
 
1.1%
운동화전문세탁업 2
 
0.4%
세탁업 1
 
0.2%
기타 1
 
0.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)1.5%
Missing40
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean0.22413793
Minimum0
Maximum13
Zeros478
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2024-05-11T15:27:00.954284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.96858115
Coefficient of variation (CV)4.3213621
Kurtosis68.009057
Mean0.22413793
Median Absolute Deviation (MAD)0
Skewness6.9686729
Sum117
Variance0.93814945
MonotonicityNot monotonic
2024-05-11T15:27:01.240875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 478
85.1%
1 16
 
2.8%
2 10
 
1.8%
4 7
 
1.2%
3 6
 
1.1%
5 3
 
0.5%
13 1
 
0.2%
7 1
 
0.2%
(Missing) 40
 
7.1%
ValueCountFrequency (%)
0 478
85.1%
1 16
 
2.8%
2 10
 
1.8%
3 6
 
1.1%
4 7
 
1.2%
5 3
 
0.5%
7 1
 
0.2%
13 1
 
0.2%
ValueCountFrequency (%)
13 1
 
0.2%
7 1
 
0.2%
5 3
 
0.5%
4 7
 
1.2%
3 6
 
1.1%
2 10
 
1.8%
1 16
 
2.8%
0 478
85.1%

건물지하층수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
504 
<NA>
 
40
1
 
17
2
 
1

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 504
89.7%
<NA> 40
 
7.1%
1 17
 
3.0%
2 1
 
0.2%

Length

2024-05-11T15:27:01.464635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:01.646040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 504
89.7%
na 40
 
7.1%
1 17
 
3.0%
2 1
 
0.2%

사용시작지상층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
422 
1
91 
<NA>
 
40
2
 
8
3
 
1

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 422
75.1%
1 91
 
16.2%
<NA> 40
 
7.1%
2 8
 
1.4%
3 1
 
0.2%

Length

2024-05-11T15:27:01.888958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:02.107400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 422
75.1%
1 91
 
16.2%
na 40
 
7.1%
2 8
 
1.4%
3 1
 
0.2%

사용끝지상층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
445 
1
69 
<NA>
 
40
2
 
7
3
 
1

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 445
79.2%
1 69
 
12.3%
<NA> 40
 
7.1%
2 7
 
1.2%
3 1
 
0.2%

Length

2024-05-11T15:27:02.331645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:02.568673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 445
79.2%
1 69
 
12.3%
na 40
 
7.1%
2 7
 
1.2%
3 1
 
0.2%

사용시작지하층
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
513 
<NA>
 
40
1
 
9

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 513
91.3%
<NA> 40
 
7.1%
1 9
 
1.6%

Length

2024-05-11T15:27:02.774524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:02.959329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 513
91.3%
na 40
 
7.1%
1 9
 
1.6%

사용끝지하층
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
515 
<NA>
 
40
1
 
7

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 515
91.6%
<NA> 40
 
7.1%
1 7
 
1.2%

Length

2024-05-11T15:27:03.148390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:03.325197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 515
91.6%
na 40
 
7.1%
1 7
 
1.2%

한실수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
522 
<NA>
 
40

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 522
92.9%
<NA> 40
 
7.1%

Length

2024-05-11T15:27:03.585259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:03.784750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 522
92.9%
na 40
 
7.1%

양실수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
522 
<NA>
 
40

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 522
92.9%
<NA> 40
 
7.1%

Length

2024-05-11T15:27:04.016088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:04.323734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 522
92.9%
na 40
 
7.1%

욕실수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
522 
<NA>
 
40

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 522
92.9%
<NA> 40
 
7.1%

Length

2024-05-11T15:27:04.670313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:04.974033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 522
92.9%
na 40
 
7.1%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing44
Missing (%)7.8%
Memory size1.2 KiB
False
518 
(Missing)
 
44
ValueCountFrequency (%)
False 518
92.2%
(Missing) 44
 
7.8%
2024-05-11T15:27:05.118760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
520 
<NA>
 
40
3
 
2

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 520
92.5%
<NA> 40
 
7.1%
3 2
 
0.4%

Length

2024-05-11T15:27:05.319854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:05.476366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 520
92.5%
na 40
 
7.1%
3 2
 
0.4%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing562
Missing (%)100.0%
Memory size5.1 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing562
Missing (%)100.0%
Memory size5.1 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing562
Missing (%)100.0%
Memory size5.1 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
<NA>
511 
임대
 
50
자가
 
1

Length

Max length4
Median length4
Mean length3.8185053
Min length2

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> 511
90.9%
임대 50
 
8.9%
자가 1
 
0.2%

Length

2024-05-11T15:27:05.709033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:05.927226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 511
90.9%
임대 50
 
8.9%
자가 1
 
0.2%

세탁기수
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
419 
2
69 
<NA>
 
40
1
 
22
3
 
9

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 419
74.6%
2 69
 
12.3%
<NA> 40
 
7.1%
1 22
 
3.9%
3 9
 
1.6%
4 3
 
0.5%

Length

2024-05-11T15:27:06.118659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:06.291087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 419
74.6%
2 69
 
12.3%
na 40
 
7.1%
1 22
 
3.9%
3 9
 
1.6%
4 3
 
0.5%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
522 
<NA>
 
40

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 522
92.9%
<NA> 40
 
7.1%

Length

2024-05-11T15:27:06.519733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:06.685899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 522
92.9%
na 40
 
7.1%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
520 
<NA>
 
40
1
 
2

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 520
92.5%
<NA> 40
 
7.1%
1 2
 
0.4%

Length

2024-05-11T15:27:06.898142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:07.078184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 520
92.5%
na 40
 
7.1%
1 2
 
0.4%

회수건조수
Categorical

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
386 
1
130 
<NA>
40 
2
 
4
4
 
2

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 386
68.7%
1 130
 
23.1%
<NA> 40
 
7.1%
2 4
 
0.7%
4 2
 
0.4%

Length

2024-05-11T15:27:07.288222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:07.523406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 386
68.7%
1 130
 
23.1%
na 40
 
7.1%
2 4
 
0.7%
4 2
 
0.4%

침대수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.5 KiB
0
522 
<NA>
 
40

Length

Max length4
Median length1
Mean length1.2135231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 522
92.9%
<NA> 40
 
7.1%

Length

2024-05-11T15:27:07.734363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:27:07.933942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 522
92.9%
na 40
 
7.1%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing40
Missing (%)7.1%
Memory size1.2 KiB
False
522 
(Missing)
 
40
ValueCountFrequency (%)
False 522
92.9%
(Missing) 40
 
7.1%
2024-05-11T15:27:08.057906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031800003180000-205-1957-0206219570528<NA>3폐업2폐업20021231<NA><NA><NA>0208438758300.00150837서울특별시 영등포구 신길동 92-3번지<NA><NA>경북사2003-03-31 00:00:00I2018-08-31 23:59:59.0일반세탁업192807.033804445477.76872일반세탁업000000000N0<NA><NA><NA><NA>00000N
131800003180000-205-1984-0204219840528<NA>3폐업2폐업20021231<NA><NA><NA>0208423544157.79150837서울특별시 영등포구 신길동 61-69번지<NA><NA>그린사2003-03-31 00:00:00I2018-08-31 23:59:59.0일반세탁업192756.345445872.380333일반세탁업000000000N0<NA><NA><NA><NA>00000N
231800003180000-205-1987-0203919870510<NA>1영업/정상1영업<NA><NA><NA><NA>02 783495538.85150895서울특별시 영등포구 여의도동 54-2번지서울특별시 영등포구 여의대방로 386 (여의도동)7343유미사2012-11-06 13:37:10I2018-08-31 23:59:59.0일반세탁업193915.13139446295.859135일반세탁업000000000N0<NA><NA><NA><NA>00000N
331800003180000-205-1987-0204119870527<NA>1영업/정상1영업<NA><NA><NA><NA>020783028146.20150886서울특별시 영등포구 여의도동 36번지서울특별시 영등포구 의사당대로 127 (여의도동)7331여의명품세탁2010-09-17 10:31:57I2018-08-31 23:59:59.0일반세탁업193421.628256446448.157557일반세탁업000000000N0<NA><NA><NA><NA>00010N
431800003180000-205-1987-0204319870528<NA>3폐업2폐업20120814<NA><NA><NA>020846922171.64150840서울특별시 영등포구 신길동 190-160번지서울특별시 영등포구 신길로56길 8-1 (신길동)7313제일사2011-03-22 15:19:54I2018-08-31 23:59:59.0일반세탁업192080.444413445562.937275일반세탁업000000000N0<NA><NA><NA><NA>00000N
531800003180000-205-1987-0204419870510<NA>3폐업2폐업19950605<NA><NA><NA>020843520410.51150852서울특별시 영등포구 신길동 449-25번지<NA><NA>대우사2001-08-02 00:00:00I2018-08-31 23:59:59.0일반세탁업193062.485153445628.171938일반세탁업000000000N0<NA><NA><NA><NA>00000N
631800003180000-205-1987-0204519870528<NA>3폐업2폐업20160226<NA><NA><NA>020842238619.80150837서울특별시 영등포구 신길동 9-130번지서울특별시 영등포구 여의대방로61길 36-1 (신길동)7319백성사2016-02-26 16:30:46I2018-08-31 23:59:59.0일반세탁업193083.22886445927.864023일반세탁업000000000N0<NA><NA><NA><NA>00000N
731800003180000-205-1987-0204619870528<NA>3폐업2폐업20211118<NA><NA><NA>02 841352029.88150840서울특별시 영등포구 신길동 149-5 1층 103호서울특별시 영등포구 도신로56길 25, 1층 103호 (신길동)7349시대 세탁소2021-11-18 10:20:53U2021-11-20 02:40:00.0일반세탁업192502.455698445263.151068일반세탁업000000000N0<NA><NA><NA><NA>00000N
831800003180000-205-1987-0204719870520<NA>3폐업2폐업20021231<NA><NA><NA>0208440825118.65150854서울특별시 영등포구 신길동 704-0번지<NA><NA>런던사2003-03-31 00:00:00I2018-08-31 23:59:59.0일반세탁업192952.815464444574.068357일반세탁업000000000N0<NA><NA><NA><NA>00000N
931800003180000-205-1987-0204819870527<NA>3폐업2폐업20200925<NA><NA><NA>020834258819.80150840서울특별시 영등포구 신길동 146-31서울특별시 영등포구 도신로58길 26 (신길동)7349대한사2020-09-26 15:28:00U2020-09-29 02:40:00.0일반세탁업192575.06977445284.699894일반세탁업200000000N0<NA><NA><NA><NA>00010N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
55231800003180000-205-2019-0000520191210<NA>3폐업2폐업20220805<NA><NA><NA><NA>30.00150841서울특별시 영등포구 신길동 311-4서울특별시 영등포구 가마산로61길 14, 1층 101호 (신길동)7383명품세탁소2022-08-05 11:12:17U2021-12-08 00:07:00.0일반세탁업191520.304591444915.582247<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
55331800003180000-205-2020-0000120200131<NA>1영업/정상1영업<NA><NA><NA><NA>0002780778357.08150889서울특별시 영등포구 여의도동 43-4번지 롯데캐슬 아이비 B동 108호서울특별시 영등포구 국제금융로 86, 지층 B108호 (여의도동, 롯데캐슬 아이비)7333롯데명품세탁2020-01-31 14:05:31I2020-02-02 00:23:23.0일반세탁업193896.282065446442.571421일반세탁업000000000N0<NA><NA><NA><NA>20010N
55431800003180000-205-2020-0000220201116<NA>1영업/정상1영업<NA><NA><NA><NA>02 2677709434.51150901서울특별시 영등포구 영등포동2가 94-212 명화오피스텔 101호서울특별시 영등포구 국회대로56길 46, 1층 101호 (영등포동2가, 명화오피스텔)7253코리아 세탁소2020-11-16 13:39:03I2020-11-18 00:23:08.0일반세탁업192078.928296446561.226672일반세탁업000000000N0<NA><NA><NA>임대20010N
55531800003180000-205-2021-000012021-05-31<NA>1영업/정상1영업<NA><NA><NA><NA>0226367030143.64150-805서울특별시 영등포구 당산동4가 32-49 한석빌딩서울특별시 영등포구 당산로 174, 한석빌딩 1층 (당산동4가)7220노블레스 명품세탁2023-01-31 09:18:32U2022-12-02 00:02:00.0일반세탁업190998.030501447507.866644<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
55631800003180000-205-2021-0000220210908<NA>1영업/정상1영업<NA><NA><NA><NA><NA>10.53150840서울특별시 영등포구 신길동 192-78서울특별시 영등포구 도신로47길 1, 1층 (신길동)7313승화세탁소2021-09-08 13:51:59I2021-09-10 00:22:49.0일반세탁업192189.953122445474.950978일반세탁업001000000N0<NA><NA><NA>임대20000N
55731800003180000-205-2022-0000120220428<NA>3폐업2폐업20221102<NA><NA><NA><NA>19.80150867서울특별시 영등포구 양평동4가 161-12 선유로빌딩서울특별시 영등포구 양평로18길 6, 선유로빌딩 3층 (양평동4가)7206엠클린2022-11-02 10:05:50U2021-11-01 00:04:00.0세탁업 기타190823.18563448319.239689<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
55831800003180000-205-2022-000022022-05-09<NA>3폐업2폐업2023-02-17<NA><NA><NA><NA>28.53150-080서울특별시 영등포구 도림동 821 영등포아트자이아파트 상가동 109호서울특별시 영등포구 도영로 66, 상가동 109호 (도림동, 영등포아트자이아파트)7364마이 슈케어2023-02-17 14:40:23U2022-12-01 23:09:00.0운동화전문세탁업191113.04395445335.283133<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
55931800003180000-205-2022-0000320220520<NA>3폐업2폐업20220805<NA><NA><NA><NA>26.40150833서울특별시 영등포구 도림동 236-11 101호서울특별시 영등포구 도신로15다길 20, 1층 101호 (도림동)7374청하세탁소2022-08-05 09:53:15U2021-12-08 00:07:00.0일반세탁업190936.617894445087.892175<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
56031800003180000-205-2022-0000420221108<NA>1영업/정상1영업<NA><NA><NA><NA><NA>18.00150899서울특별시 영등포구 영등포동 618-199 101호서울특별시 영등포구 영신로17길 7-1, 101호 (영등포동)7366클린케이2022-11-08 11:31:41I2021-10-31 23:00:00.0일반세탁업191736.62368445735.592148<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
56131800003180000-205-2023-000012023-06-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>56.10150-853서울특별시 영등포구 신길동 452-8서울특별시 영등포구 영등포로84길 19, 1층 2호 (신길동)7355입체사2023-06-01 13:39:52I2022-12-06 00:03:00.0일반세탁업193035.367256445487.851981<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>