Overview

Dataset statistics

Number of variables47
Number of observations503
Missing cells5039
Missing cells (%)21.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory198.6 KiB
Average record size in memory404.3 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
Author강북구
URLhttps://data.seoul.go.kr/dataList/OA-17960/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (83.0%)Imbalance
위생업태명 is highly imbalanced (67.7%)Imbalance
사용끝지상층 is highly imbalanced (61.5%)Imbalance
사용시작지하층 is highly imbalanced (50.6%)Imbalance
사용끝지하층 is highly imbalanced (76.3%)Imbalance
건물소유구분명 is highly imbalanced (93.4%)Imbalance
여성종사자수 is highly imbalanced (68.2%)Imbalance
남성종사자수 is highly imbalanced (68.2%)Imbalance
회수건조수 is highly imbalanced (70.6%)Imbalance
인허가취소일자 has 503 (100.0%) missing valuesMissing
폐업일자 has 130 (25.8%) missing valuesMissing
휴업시작일자 has 503 (100.0%) missing valuesMissing
휴업종료일자 has 503 (100.0%) missing valuesMissing
재개업일자 has 503 (100.0%) missing valuesMissing
전화번호 has 57 (11.3%) missing valuesMissing
도로명주소 has 226 (44.9%) missing valuesMissing
도로명우편번호 has 231 (45.9%) missing valuesMissing
좌표정보(X) has 29 (5.8%) missing valuesMissing
좌표정보(Y) has 29 (5.8%) missing valuesMissing
건물지상층수 has 288 (57.3%) missing valuesMissing
발한실여부 has 54 (10.7%) missing valuesMissing
조건부허가신고사유 has 503 (100.0%) missing valuesMissing
조건부허가시작일자 has 503 (100.0%) missing valuesMissing
조건부허가종료일자 has 503 (100.0%) missing valuesMissing
세탁기수 has 422 (83.9%) missing valuesMissing
다중이용업소여부 has 50 (9.9%) 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 182 (36.2%) zerosZeros
세탁기수 has 38 (7.6%) zerosZeros

Reproduction

Analysis started2024-05-11 06:49:33.404486
Analysis finished2024-05-11 06:49:34.441772
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
3080000
503 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3080000
2nd row3080000
3rd row3080000
4th row3080000
5th row3080000

Common Values

ValueCountFrequency (%)
3080000 503
100.0%

Length

2024-05-11T15:49:34.520780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:34.651819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3080000 503
100.0%

관리번호
Text

UNIQUE 

Distinct503
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2024-05-11T15:49:34.880265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters11066
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

Unique503 ?
Unique (%)100.0%

Sample

1st row3080000-205-1987-01753
2nd row3080000-205-1987-01754
3rd row3080000-205-1987-01761
4th row3080000-205-1987-01769
5th row3080000-205-1987-01775
ValueCountFrequency (%)
3080000-205-1987-01753 1
 
0.2%
3080000-205-1997-01908 1
 
0.2%
3080000-205-1999-02091 1
 
0.2%
3080000-205-1999-02090 1
 
0.2%
3080000-205-1999-02089 1
 
0.2%
3080000-205-1999-02088 1
 
0.2%
3080000-205-1999-02087 1
 
0.2%
3080000-205-1999-02086 1
 
0.2%
3080000-205-1999-02085 1
 
0.2%
3080000-205-1999-02084 1
 
0.2%
Other values (493) 493
98.0%
2024-05-11T15:49:35.405600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4396
39.7%
- 1509
 
13.6%
2 945
 
8.5%
8 869
 
7.9%
1 779
 
7.0%
9 760
 
6.9%
3 645
 
5.8%
5 645
 
5.8%
7 276
 
2.5%
6 122
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9557
86.4%
Dash Punctuation 1509
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4396
46.0%
2 945
 
9.9%
8 869
 
9.1%
1 779
 
8.2%
9 760
 
8.0%
3 645
 
6.7%
5 645
 
6.7%
7 276
 
2.9%
6 122
 
1.3%
4 120
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 1509
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11066
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4396
39.7%
- 1509
 
13.6%
2 945
 
8.5%
8 869
 
7.9%
1 779
 
7.0%
9 760
 
6.9%
3 645
 
5.8%
5 645
 
5.8%
7 276
 
2.5%
6 122
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11066
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4396
39.7%
- 1509
 
13.6%
2 945
 
8.5%
8 869
 
7.9%
1 779
 
7.0%
9 760
 
6.9%
3 645
 
5.8%
5 645
 
5.8%
7 276
 
2.5%
6 122
 
1.1%
Distinct388
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum1987-05-15 00:00:00
Maximum2024-03-28 00:00:00
2024-05-11T15:49:35.636988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:49:35.871404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing503
Missing (%)100.0%
Memory size4.5 KiB
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
3
373 
1
130 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 373
74.2%
1 130
 
25.8%

Length

2024-05-11T15:49:36.078908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:36.214786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 373
74.2%
1 130
 
25.8%

영업상태명
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
폐업
373 
영업/정상
130 

Length

Max length5
Median length2
Mean length2.7753479
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 373
74.2%
영업/정상 130
 
25.8%

Length

2024-05-11T15:49:36.396939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:36.575905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 373
74.2%
영업/정상 130
 
25.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2
373 
1
130 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 373
74.2%
1 130
 
25.8%

Length

2024-05-11T15:49:36.745491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:36.886737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 373
74.2%
1 130
 
25.8%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
폐업
373 
영업
130 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 373
74.2%
영업 130
 
25.8%

Length

2024-05-11T15:49:37.037745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:37.179562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 373
74.2%
영업 130
 
25.8%

폐업일자
Date

MISSING 

Distinct332
Distinct (%)89.0%
Missing130
Missing (%)25.8%
Memory size4.1 KiB
Minimum1992-08-12 00:00:00
Maximum2024-04-30 00:00:00
2024-05-11T15:49:37.348199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:49:37.570744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing503
Missing (%)100.0%
Memory size4.5 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing503
Missing (%)100.0%
Memory size4.5 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing503
Missing (%)100.0%
Memory size4.5 KiB

전화번호
Text

MISSING 

Distinct424
Distinct (%)95.1%
Missing57
Missing (%)11.3%
Memory size4.1 KiB
2024-05-11T15:49:37.933660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.199552
Min length10

Characters and Unicode

Total characters4549
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

Unique405 ?
Unique (%)90.8%

Sample

1st row0209885434
2nd row0209811384
3rd row02 9043469
4th row02 9836705
5th row02 9804683
ValueCountFrequency (%)
02 318
39.7%
982 5
 
0.6%
945 4
 
0.5%
9893573 3
 
0.4%
9058052 3
 
0.4%
900 3
 
0.4%
987 3
 
0.4%
9897794 3
 
0.4%
988 3
 
0.4%
9898768 2
 
0.2%
Other values (434) 454
56.7%
2024-05-11T15:49:38.496163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 892
19.6%
9 734
16.1%
2 664
14.6%
8 478
10.5%
404
8.9%
5 249
 
5.5%
7 233
 
5.1%
1 233
 
5.1%
4 227
 
5.0%
3 225
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4145
91.1%
Space Separator 404
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 892
21.5%
9 734
17.7%
2 664
16.0%
8 478
11.5%
5 249
 
6.0%
7 233
 
5.6%
1 233
 
5.6%
4 227
 
5.5%
3 225
 
5.4%
6 210
 
5.1%
Space Separator
ValueCountFrequency (%)
404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4549
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 892
19.6%
9 734
16.1%
2 664
14.6%
8 478
10.5%
404
8.9%
5 249
 
5.5%
7 233
 
5.1%
1 233
 
5.1%
4 227
 
5.0%
3 225
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 892
19.6%
9 734
16.1%
2 664
14.6%
8 478
10.5%
404
8.9%
5 249
 
5.5%
7 233
 
5.1%
1 233
 
5.1%
4 227
 
5.0%
3 225
 
4.9%
Distinct269
Distinct (%)53.7%
Missing2
Missing (%)0.4%
Memory size4.1 KiB
2024-05-11T15:49:39.070946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.9241517
Min length3

Characters and Unicode

Total characters2467
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

Unique219 ?
Unique (%)43.7%

Sample

1st row11.00
2nd row49.00
3rd row.00
4th row15.77
5th row26.00
ValueCountFrequency (%)
00 38
 
7.6%
15.00 19
 
3.8%
20.00 16
 
3.2%
14.00 11
 
2.2%
19.00 10
 
2.0%
33.00 9
 
1.8%
13.00 9
 
1.8%
16.00 8
 
1.6%
22.00 8
 
1.6%
26.40 8
 
1.6%
Other values (259) 365
72.9%
2024-05-11T15:49:39.797860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 654
26.5%
. 501
20.3%
1 285
11.6%
2 235
 
9.5%
3 149
 
6.0%
5 139
 
5.6%
4 114
 
4.6%
6 114
 
4.6%
9 100
 
4.1%
8 97
 
3.9%
Other values (2) 79
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1963
79.6%
Other Punctuation 504
 
20.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 654
33.3%
1 285
14.5%
2 235
 
12.0%
3 149
 
7.6%
5 139
 
7.1%
4 114
 
5.8%
6 114
 
5.8%
9 100
 
5.1%
8 97
 
4.9%
7 76
 
3.9%
Other Punctuation
ValueCountFrequency (%)
. 501
99.4%
, 3
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2467
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 654
26.5%
. 501
20.3%
1 285
11.6%
2 235
 
9.5%
3 149
 
6.0%
5 139
 
5.6%
4 114
 
4.6%
6 114
 
4.6%
9 100
 
4.1%
8 97
 
3.9%
Other values (2) 79
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 654
26.5%
. 501
20.3%
1 285
11.6%
2 235
 
9.5%
3 149
 
6.0%
5 139
 
5.6%
4 114
 
4.6%
6 114
 
4.6%
9 100
 
4.1%
8 97
 
3.9%
Other values (2) 79
 
3.2%
Distinct82
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2024-05-11T15:49:40.156311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0477137
Min length6

Characters and Unicode

Total characters3042
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

Unique19 ?
Unique (%)3.8%

Sample

1st row142-827
2nd row142805
3rd row142070
4th row142816
5th row142815
ValueCountFrequency (%)
142100 21
 
4.2%
142809 17
 
3.4%
142876 16
 
3.2%
142875 16
 
3.2%
142874 16
 
3.2%
142877 15
 
3.0%
142805 15
 
3.0%
142804 13
 
2.6%
142872 13
 
2.6%
142803 13
 
2.6%
Other values (72) 348
69.2%
2024-05-11T15:49:40.682471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 643
21.1%
2 609
20.0%
4 575
18.9%
8 553
18.2%
0 200
 
6.6%
7 175
 
5.8%
6 113
 
3.7%
5 59
 
1.9%
9 47
 
1.5%
3 44
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3018
99.2%
Dash Punctuation 24
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 643
21.3%
2 609
20.2%
4 575
19.1%
8 553
18.3%
0 200
 
6.6%
7 175
 
5.8%
6 113
 
3.7%
5 59
 
2.0%
9 47
 
1.6%
3 44
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3042
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 643
21.1%
2 609
20.0%
4 575
18.9%
8 553
18.2%
0 200
 
6.6%
7 175
 
5.8%
6 113
 
3.7%
5 59
 
1.9%
9 47
 
1.5%
3 44
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 643
21.1%
2 609
20.0%
4 575
18.9%
8 553
18.2%
0 200
 
6.6%
7 175
 
5.8%
6 113
 
3.7%
5 59
 
1.9%
9 47
 
1.5%
3 44
 
1.4%
Distinct475
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2024-05-11T15:49:41.093478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length46
Mean length23.840954
Min length18

Characters and Unicode

Total characters11992
Distinct characters107
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

Unique451 ?
Unique (%)89.7%

Sample

1st row서울특별시 강북구 미아동 674-21
2nd row서울특별시 강북구 미아동 469-47번지 (이터골길 48)
3rd row서울특별시 강북구 수유동 320-7번지
4th row서울특별시 강북구 미아동 320-28번지
5th row서울특별시 강북구 미아동 318-5번지 성북프라자 107호
ValueCountFrequency (%)
서울특별시 503
23.1%
강북구 503
23.1%
미아동 236
 
10.9%
수유동 182
 
8.4%
번동 70
 
3.2%
1층 17
 
0.8%
우이동 15
 
0.7%
1353번지 7
 
0.3%
102호 6
 
0.3%
상가 5
 
0.2%
Other values (558) 629
28.9%
2024-05-11T15:49:41.710529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2109
 
17.6%
522
 
4.4%
509
 
4.2%
508
 
4.2%
505
 
4.2%
503
 
4.2%
503
 
4.2%
503
 
4.2%
503
 
4.2%
503
 
4.2%
Other values (97) 5324
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6714
56.0%
Decimal Number 2627
 
21.9%
Space Separator 2109
 
17.6%
Dash Punctuation 477
 
4.0%
Uppercase Letter 28
 
0.2%
Close Punctuation 15
 
0.1%
Open Punctuation 15
 
0.1%
Other Punctuation 5
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
522
 
7.8%
509
 
7.6%
508
 
7.6%
505
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
496
 
7.4%
Other values (75) 1659
24.7%
Decimal Number
ValueCountFrequency (%)
1 484
18.4%
2 340
12.9%
3 316
12.0%
4 293
11.2%
5 258
9.8%
7 225
8.6%
0 207
7.9%
6 191
 
7.3%
8 173
 
6.6%
9 140
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
B 9
32.1%
S 7
25.0%
K 7
25.0%
A 5
17.9%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
2109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 477
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6714
56.0%
Common 5248
43.8%
Latin 30
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
522
 
7.8%
509
 
7.6%
508
 
7.6%
505
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
496
 
7.4%
Other values (75) 1659
24.7%
Common
ValueCountFrequency (%)
2109
40.2%
1 484
 
9.2%
- 477
 
9.1%
2 340
 
6.5%
3 316
 
6.0%
4 293
 
5.6%
5 258
 
4.9%
7 225
 
4.3%
0 207
 
3.9%
6 191
 
3.6%
Other values (6) 348
 
6.6%
Latin
ValueCountFrequency (%)
B 9
30.0%
S 7
23.3%
K 7
23.3%
A 5
16.7%
s 1
 
3.3%
k 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6714
56.0%
ASCII 5278
44.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2109
40.0%
1 484
 
9.2%
- 477
 
9.0%
2 340
 
6.4%
3 316
 
6.0%
4 293
 
5.6%
5 258
 
4.9%
7 225
 
4.3%
0 207
 
3.9%
6 191
 
3.6%
Other values (12) 378
 
7.2%
Hangul
ValueCountFrequency (%)
522
 
7.8%
509
 
7.6%
508
 
7.6%
505
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
503
 
7.5%
496
 
7.4%
Other values (75) 1659
24.7%

도로명주소
Text

MISSING 

Distinct269
Distinct (%)97.1%
Missing226
Missing (%)44.9%
Memory size4.1 KiB
2024-05-11T15:49:42.236336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length48
Mean length27.490975
Min length21

Characters and Unicode

Total characters7615
Distinct characters111
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

Unique262 ?
Unique (%)94.6%

Sample

1st row서울특별시 강북구 삼양로24가길 3 (미아동)
2nd row서울특별시 강북구 도봉로33길 18, 107호 (미아동, 성북프라자)
3rd row서울특별시 강북구 월계로7길 17 (미아동)
4th row서울특별시 강북구 도봉로10라길 10 (미아동)
5th row서울특별시 강북구 솔샘로64길 39 (미아동)
ValueCountFrequency (%)
서울특별시 277
18.4%
강북구 277
18.4%
미아동 117
 
7.8%
수유동 100
 
6.7%
번동 38
 
2.5%
1층 30
 
2.0%
우이동 12
 
0.8%
19 10
 
0.7%
22 8
 
0.5%
11 7
 
0.5%
Other values (367) 626
41.7%
2024-05-11T15:49:42.884572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1225
 
16.1%
1 311
 
4.1%
294
 
3.9%
) 283
 
3.7%
( 283
 
3.7%
279
 
3.7%
279
 
3.7%
277
 
3.6%
277
 
3.6%
277
 
3.6%
Other values (101) 3830
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4426
58.1%
Decimal Number 1264
 
16.6%
Space Separator 1225
 
16.1%
Close Punctuation 283
 
3.7%
Open Punctuation 283
 
3.7%
Other Punctuation 98
 
1.3%
Dash Punctuation 23
 
0.3%
Uppercase Letter 13
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
294
 
6.6%
279
 
6.3%
279
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
Other values (82) 1635
36.9%
Decimal Number
ValueCountFrequency (%)
1 311
24.6%
2 184
14.6%
3 144
11.4%
4 103
 
8.1%
0 103
 
8.1%
5 102
 
8.1%
7 98
 
7.8%
6 78
 
6.2%
9 74
 
5.9%
8 67
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
B 5
38.5%
K 3
23.1%
S 3
23.1%
A 2
 
15.4%
Space Separator
ValueCountFrequency (%)
1225
100.0%
Close Punctuation
ValueCountFrequency (%)
) 283
100.0%
Open Punctuation
ValueCountFrequency (%)
( 283
100.0%
Other Punctuation
ValueCountFrequency (%)
, 98
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4426
58.1%
Common 3176
41.7%
Latin 13
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
294
 
6.6%
279
 
6.3%
279
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
Other values (82) 1635
36.9%
Common
ValueCountFrequency (%)
1225
38.6%
1 311
 
9.8%
) 283
 
8.9%
( 283
 
8.9%
2 184
 
5.8%
3 144
 
4.5%
4 103
 
3.2%
0 103
 
3.2%
5 102
 
3.2%
7 98
 
3.1%
Other values (5) 340
 
10.7%
Latin
ValueCountFrequency (%)
B 5
38.5%
K 3
23.1%
S 3
23.1%
A 2
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4426
58.1%
ASCII 3189
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1225
38.4%
1 311
 
9.8%
) 283
 
8.9%
( 283
 
8.9%
2 184
 
5.8%
3 144
 
4.5%
4 103
 
3.2%
0 103
 
3.2%
5 102
 
3.2%
7 98
 
3.1%
Other values (9) 353
 
11.1%
Hangul
ValueCountFrequency (%)
294
 
6.6%
279
 
6.3%
279
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
277
 
6.3%
Other values (82) 1635
36.9%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct148
Distinct (%)54.4%
Missing231
Missing (%)45.9%
Infinite0
Infinite (%)0.0%
Mean1116.8934
Minimum1002
Maximum1236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T15:49:43.097857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile1017.75
Q11058
median1114.5
Q31172.25
95-th percentile1219
Maximum1236
Range234
Interquartile range (IQR)114.25

Descriptive statistics

Standard deviation64.685327
Coefficient of variation (CV)0.0579154
Kurtosis-1.1740754
Mean1116.8934
Median Absolute Deviation (MAD)57
Skewness0.050665221
Sum303795
Variance4184.1915
MonotonicityNot monotonic
2024-05-11T15:49:43.282735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1055 6
 
1.2%
1081 6
 
1.2%
1178 5
 
1.0%
1169 5
 
1.0%
1066 4
 
0.8%
1157 4
 
0.8%
1076 4
 
0.8%
1045 4
 
0.8%
1053 4
 
0.8%
1005 4
 
0.8%
Other values (138) 226
44.9%
(Missing) 231
45.9%
ValueCountFrequency (%)
1002 2
0.4%
1003 1
 
0.2%
1004 2
0.4%
1005 4
0.8%
1006 1
 
0.2%
1010 2
0.4%
1014 1
 
0.2%
1015 1
 
0.2%
1020 2
0.4%
1021 1
 
0.2%
ValueCountFrequency (%)
1236 1
0.2%
1235 1
0.2%
1234 2
0.4%
1233 1
0.2%
1230 2
0.4%
1229 1
0.2%
1226 1
0.2%
1225 1
0.2%
1223 1
0.2%
1222 1
0.2%
Distinct359
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2024-05-11T15:49:43.662807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length4.1928429
Min length2

Characters and Unicode

Total characters2109
Distinct characters253
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

Unique288 ?
Unique (%)57.3%

Sample

1st row삼양사
2nd row백조사
3rd row장미사
4th row제일사
5th row현대사
ValueCountFrequency (%)
백양사 21
 
4.1%
제일사 13
 
2.5%
백조사 6
 
1.2%
현대사 6
 
1.2%
현대세탁소 6
 
1.2%
명동사 6
 
1.2%
금성사 5
 
1.0%
중앙사 5
 
1.0%
삼양사 5
 
1.0%
벽산세탁소 4
 
0.8%
Other values (357) 437
85.0%
2024-05-11T15:49:44.293900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
263
 
12.5%
179
 
8.5%
176
 
8.3%
101
 
4.8%
64
 
3.0%
45
 
2.1%
44
 
2.1%
40
 
1.9%
36
 
1.7%
35
 
1.7%
Other values (243) 1126
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2069
98.1%
Space Separator 11
 
0.5%
Decimal Number 11
 
0.5%
Close Punctuation 6
 
0.3%
Open Punctuation 6
 
0.3%
Uppercase Letter 4
 
0.2%
Dash Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
263
 
12.7%
179
 
8.7%
176
 
8.5%
101
 
4.9%
64
 
3.1%
45
 
2.2%
44
 
2.1%
40
 
1.9%
36
 
1.7%
35
 
1.7%
Other values (232) 1086
52.5%
Decimal Number
ValueCountFrequency (%)
8 4
36.4%
2 3
27.3%
4 2
18.2%
1 2
18.2%
Uppercase Letter
ValueCountFrequency (%)
K 2
50.0%
S 2
50.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2069
98.1%
Common 36
 
1.7%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
263
 
12.7%
179
 
8.7%
176
 
8.5%
101
 
4.9%
64
 
3.1%
45
 
2.2%
44
 
2.1%
40
 
1.9%
36
 
1.7%
35
 
1.7%
Other values (232) 1086
52.5%
Common
ValueCountFrequency (%)
11
30.6%
) 6
16.7%
( 6
16.7%
8 4
 
11.1%
2 3
 
8.3%
4 2
 
5.6%
1 2
 
5.6%
- 1
 
2.8%
. 1
 
2.8%
Latin
ValueCountFrequency (%)
K 2
50.0%
S 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2069
98.1%
ASCII 40
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
263
 
12.7%
179
 
8.7%
176
 
8.5%
101
 
4.9%
64
 
3.1%
45
 
2.2%
44
 
2.1%
40
 
1.9%
36
 
1.7%
35
 
1.7%
Other values (232) 1086
52.5%
ASCII
ValueCountFrequency (%)
11
27.5%
) 6
15.0%
( 6
15.0%
8 4
 
10.0%
2 3
 
7.5%
K 2
 
5.0%
S 2
 
5.0%
4 2
 
5.0%
1 2
 
5.0%
- 1
 
2.5%
Distinct308
Distinct (%)61.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum1999-01-14 00:00:00
Maximum2024-04-30 07:47:52
2024-05-11T15:49:44.797079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:49:44.976759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
I
410 
U
93 

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 410
81.5%
U 93
 
18.5%

Length

2024-05-11T15:49:45.163304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:45.311246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 410
81.5%
u 93
 
18.5%
Distinct87
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:02:00
2024-05-11T15:49:45.462117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:49:45.630129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
일반세탁업
477 
빨래방업
 
21
세탁업 기타
 
3
운동화전문세탁업
 
2

Length

Max length8
Median length5
Mean length4.9761431
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반세탁업 477
94.8%
빨래방업 21
 
4.2%
세탁업 기타 3
 
0.6%
운동화전문세탁업 2
 
0.4%

Length

2024-05-11T15:49:45.858803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:46.015701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 477
94.3%
빨래방업 21
 
4.2%
세탁업 3
 
0.6%
기타 3
 
0.6%
운동화전문세탁업 2
 
0.4%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct410
Distinct (%)86.5%
Missing29
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean201959.52
Minimum200623.69
Maximum204157.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T15:49:46.203471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200623.69
5-th percentile201023.08
Q1201447.68
median201900.92
Q3202436.22
95-th percentile203085.26
Maximum204157.84
Range3534.151
Interquartile range (IQR)988.53787

Descriptive statistics

Standard deviation676.36958
Coefficient of variation (CV)0.0033490354
Kurtosis-0.15443594
Mean201959.52
Median Absolute Deviation (MAD)492.05142
Skewness0.42082846
Sum95728813
Variance457475.81
MonotonicityNot monotonic
2024-05-11T15:49:46.395456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201045.611312321 7
 
1.4%
201606.445832012 6
 
1.2%
201730.788828487 3
 
0.6%
201760.433753367 3
 
0.6%
202567.355602159 3
 
0.6%
201285.643558955 3
 
0.6%
202672.901538892 3
 
0.6%
201572.983515913 3
 
0.6%
202022.430958864 3
 
0.6%
202105.012427981 3
 
0.6%
Other values (400) 437
86.9%
(Missing) 29
 
5.8%
ValueCountFrequency (%)
200623.692487553 1
0.2%
200626.320844864 1
0.2%
200644.415224027 1
0.2%
200665.844382263 1
0.2%
200697.482832695 1
0.2%
200737.896471856 1
0.2%
200748.157044965 1
0.2%
200782.752017835 2
0.4%
200838.620918829 1
0.2%
200857.414238432 1
0.2%
ValueCountFrequency (%)
204157.843492258 1
0.2%
204089.673173372 1
0.2%
204083.177112537 1
0.2%
203932.991517795 2
0.4%
203631.978357584 1
0.2%
203627.347902029 1
0.2%
203557.454895202 2
0.4%
203368.306925527 1
0.2%
203311.703852798 1
0.2%
203297.056941693 1
0.2%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct410
Distinct (%)86.5%
Missing29
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean458731.61
Minimum456528.98
Maximum462319.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T15:49:46.652810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum456528.98
5-th percentile456915.14
Q1457769.79
median458619.32
Q3459684.62
95-th percentile460505.87
Maximum462319.7
Range5790.7176
Interquartile range (IQR)1914.8272

Descriptive statistics

Standard deviation1209.3422
Coefficient of variation (CV)0.0026362738
Kurtosis-0.34569784
Mean458731.61
Median Absolute Deviation (MAD)969.28609
Skewness0.33464711
Sum2.1743879 × 108
Variance1462508.5
MonotonicityNot monotonic
2024-05-11T15:49:46.824678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
457292.961457264 7
 
1.4%
460001.632075829 6
 
1.2%
460147.541149157 3
 
0.6%
456845.373829971 3
 
0.6%
457368.453124798 3
 
0.6%
459085.058867574 3
 
0.6%
459690.910438225 3
 
0.6%
457576.362841212 3
 
0.6%
458041.360353398 3
 
0.6%
457098.242432148 3
 
0.6%
Other values (400) 437
86.9%
(Missing) 29
 
5.8%
ValueCountFrequency (%)
456528.977767312 1
0.2%
456553.934080847 1
0.2%
456586.330666247 2
0.4%
456629.812299466 1
0.2%
456646.209087973 1
0.2%
456650.873462856 1
0.2%
456695.005274783 1
0.2%
456708.188821403 1
0.2%
456743.812404766 1
0.2%
456771.532102847 1
0.2%
ValueCountFrequency (%)
462319.695335156 1
0.2%
462265.485180691 1
0.2%
462236.627031963 1
0.2%
462160.312358665 1
0.2%
462133.582810923 1
0.2%
462072.887842384 1
0.2%
461824.130497677 1
0.2%
461812.404269483 1
0.2%
461795.889637783 1
0.2%
461719.192835803 1
0.2%

위생업태명
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
일반세탁업
431 
<NA>
50 
빨래방업
 
19
운동화전문세탁업
 
2
세탁업 기타
 
1

Length

Max length8
Median length5
Mean length4.8767396
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
일반세탁업 431
85.7%
<NA> 50
 
9.9%
빨래방업 19
 
3.8%
운동화전문세탁업 2
 
0.4%
세탁업 기타 1
 
0.2%

Length

2024-05-11T15:49:47.009564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:47.159831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반세탁업 431
85.5%
na 50
 
9.9%
빨래방업 19
 
3.8%
운동화전문세탁업 2
 
0.4%
세탁업 1
 
0.2%
기타 1
 
0.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)2.8%
Missing288
Missing (%)57.3%
Infinite0
Infinite (%)0.0%
Mean0.44186047
Minimum0
Maximum5
Zeros182
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T15:49:47.291621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1500168
Coefficient of variation (CV)2.6026695
Kurtosis5.0813406
Mean0.44186047
Median Absolute Deviation (MAD)0
Skewness2.5345705
Sum95
Variance1.3225386
MonotonicityNot monotonic
2024-05-11T15:49:47.442527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 182
36.2%
3 11
 
2.2%
4 10
 
2.0%
1 8
 
1.6%
2 2
 
0.4%
5 2
 
0.4%
(Missing) 288
57.3%
ValueCountFrequency (%)
0 182
36.2%
1 8
 
1.6%
2 2
 
0.4%
3 11
 
2.2%
4 10
 
2.0%
5 2
 
0.4%
ValueCountFrequency (%)
5 2
 
0.4%
4 10
 
2.0%
3 11
 
2.2%
2 2
 
0.4%
1 8
 
1.6%
0 182
36.2%
Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
297 
0
189 
1
 
14
2
 
3

Length

Max length4
Median length4
Mean length2.7713718
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 297
59.0%
0 189
37.6%
1 14
 
2.8%
2 3
 
0.6%

Length

2024-05-11T15:49:47.641352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:47.811780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 297
59.0%
0 189
37.6%
1 14
 
2.8%
2 3
 
0.6%
Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
317 
0
142 
1
43 
2
 
1

Length

Max length4
Median length4
Mean length2.8906561
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 317
63.0%
0 142
28.2%
1 43
 
8.5%
2 1
 
0.2%

Length

2024-05-11T15:49:47.991045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:48.171476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 317
63.0%
0 142
28.2%
1 43
 
8.5%
2 1
 
0.2%

사용끝지상층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
429 
1
 
42
0
 
30
2
 
2

Length

Max length4
Median length4
Mean length3.5586481
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> 429
85.3%
1 42
 
8.3%
0 30
 
6.0%
2 2
 
0.4%

Length

2024-05-11T15:49:48.341775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:48.545769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 429
85.3%
1 42
 
8.3%
0 30
 
6.0%
2 2
 
0.4%

사용시작지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
349 
0
146 
1
 
7
2
 
1

Length

Max length4
Median length4
Mean length3.0815109
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 349
69.4%
0 146
29.0%
1 7
 
1.4%
2 1
 
0.2%

Length

2024-05-11T15:49:48.745466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:48.961315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 349
69.4%
0 146
29.0%
1 7
 
1.4%
2 1
 
0.2%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
462 
0
 
33
1
 
7
2
 
1

Length

Max length4
Median length4
Mean length3.7554672
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> 462
91.8%
0 33
 
6.6%
1 7
 
1.4%
2 1
 
0.2%

Length

2024-05-11T15:49:49.137888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:49.290942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 462
91.8%
0 33
 
6.6%
1 7
 
1.4%
2 1
 
0.2%

한실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
309 
0
194 

Length

Max length4
Median length4
Mean length2.8429423
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 309
61.4%
0 194
38.6%

Length

2024-05-11T15:49:49.477080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:49.644695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 309
61.4%
0 194
38.6%

양실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
309 
0
194 

Length

Max length4
Median length4
Mean length2.8429423
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 309
61.4%
0 194
38.6%

Length

2024-05-11T15:49:49.844531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:50.019238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 309
61.4%
0 194
38.6%

욕실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
309 
0
194 

Length

Max length4
Median length4
Mean length2.8429423
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 309
61.4%
0 194
38.6%

Length

2024-05-11T15:49:50.214576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:50.396974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 309
61.4%
0 194
38.6%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing54
Missing (%)10.7%
Memory size1.1 KiB
False
449 
(Missing)
54 
ValueCountFrequency (%)
False 449
89.3%
(Missing) 54
 
10.7%
2024-05-11T15:49:50.543156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
309 
0
194 

Length

Max length4
Median length4
Mean length2.8429423
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 309
61.4%
0 194
38.6%

Length

2024-05-11T15:49:50.715995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:50.880370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 309
61.4%
0 194
38.6%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing503
Missing (%)100.0%
Memory size4.5 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing503
Missing (%)100.0%
Memory size4.5 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing503
Missing (%)100.0%
Memory size4.5 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
497 
임대
 
4
자가
 
2

Length

Max length4
Median length4
Mean length3.9761431
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 497
98.8%
임대 4
 
0.8%
자가 2
 
0.4%

Length

2024-05-11T15:49:51.074061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:51.291628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 497
98.8%
임대 4
 
0.8%
자가 2
 
0.4%

세탁기수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)7.4%
Missing422
Missing (%)83.9%
Infinite0
Infinite (%)0.0%
Mean1.2098765
Minimum0
Maximum5
Zeros38
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2024-05-11T15:49:51.439033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3667118
Coefficient of variation (CV)1.1296292
Kurtosis-0.45033296
Mean1.2098765
Median Absolute Deviation (MAD)1
Skewness0.78400503
Sum98
Variance1.8679012
MonotonicityNot monotonic
2024-05-11T15:49:51.620155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 38
 
7.6%
2 18
 
3.6%
1 10
 
2.0%
3 9
 
1.8%
4 5
 
1.0%
5 1
 
0.2%
(Missing) 422
83.9%
ValueCountFrequency (%)
0 38
7.6%
1 10
 
2.0%
2 18
3.6%
3 9
 
1.8%
4 5
 
1.0%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 5
 
1.0%
3 9
 
1.8%
2 18
3.6%
1 10
 
2.0%
0 38
7.6%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
474 
0
 
29

Length

Max length4
Median length4
Mean length3.8270378
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> 474
94.2%
0 29
 
5.8%

Length

2024-05-11T15:49:51.827330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:51.992684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 474
94.2%
0 29
 
5.8%

남성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
474 
0
 
29

Length

Max length4
Median length4
Mean length3.8270378
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> 474
94.2%
0 29
 
5.8%

Length

2024-05-11T15:49:52.179581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:52.347972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 474
94.2%
0 29
 
5.8%

회수건조수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
437 
0
51 
1
 
13
2
 
1
5
 
1

Length

Max length4
Median length4
Mean length3.6063618
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 437
86.9%
0 51
 
10.1%
1 13
 
2.6%
2 1
 
0.2%
5 1
 
0.2%

Length

2024-05-11T15:49:52.519868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:52.676239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 437
86.9%
0 51
 
10.1%
1 13
 
2.6%
2 1
 
0.2%
5 1
 
0.2%

침대수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
<NA>
439 
0
64 

Length

Max length4
Median length4
Mean length3.6182903
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> 439
87.3%
0 64
 
12.7%

Length

2024-05-11T15:49:52.853115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:49:53.023187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 439
87.3%
0 64
 
12.7%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing50
Missing (%)9.9%
Memory size1.1 KiB
False
453 
(Missing)
50 
ValueCountFrequency (%)
False 453
90.1%
(Missing) 50
 
9.9%
2024-05-11T15:49:53.150635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030800003080000-205-1987-017531987-07-22<NA>3폐업2폐업2024-04-30<NA><NA><NA>020988543411.00142-827서울특별시 강북구 미아동 674-21서울특별시 강북구 삼양로24가길 3 (미아동)1202삼양사2024-04-30 07:47:52U2023-12-05 00:02:00.0일반세탁업201924.308991457234.166774<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
130800003080000-205-1987-0175419871012<NA>3폐업2폐업20111228<NA><NA><NA>020981138449.00142805서울특별시 강북구 미아동 469-47번지 (이터골길 48)<NA><NA>백조사2008-01-10 14:19:59I2018-08-31 23:59:59.0일반세탁업202326.718945456956.992584일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230800003080000-205-1987-0176119870702<NA>3폐업2폐업19960124<NA><NA><NA>02 9043469.00142070서울특별시 강북구 수유동 320-7번지<NA><NA>장미사2001-09-26 00:00:00I2018-08-31 23:59:59.0일반세탁업201271.914291460365.785605일반세탁업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330800003080000-205-1987-0176919870708<NA>3폐업2폐업20021231<NA><NA><NA>02 983670515.77142816서울특별시 강북구 미아동 320-28번지<NA><NA>제일사2002-03-21 00:00:00I2018-08-31 23:59:59.0일반세탁업201937.183005457651.770438일반세탁업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430800003080000-205-1987-0177519870708<NA>1영업/정상1영업<NA><NA><NA><NA>02 980468326.00142815서울특별시 강북구 미아동 318-5번지 성북프라자 107호서울특별시 강북구 도봉로33길 18, 107호 (미아동, 성북프라자)1175현대사2014-01-06 15:51:40I2018-08-31 23:59:59.0일반세탁업202266.448738457691.802407일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530800003080000-205-1987-0177619870722<NA>3폐업2폐업20190318<NA><NA><NA>020989253916.00142804서울특별시 강북구 미아동 45-4번지서울특별시 강북구 월계로7길 17 (미아동)1221신일사2019-03-18 14:12:19U2019-03-20 02:40:00.0일반세탁업202815.323282456528.977767일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
630800003080000-205-1987-0177919870722<NA>3폐업2폐업20100423<NA><NA><NA>020981278931.00142801서울특별시 강북구 미아동 3-264번지<NA><NA>대웅세탁소2006-08-10 00:00:00I2018-08-31 23:59:59.0일반세탁업202949.35765457154.744565일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
730800003080000-205-1987-0178119870826<NA>3폐업2폐업20140701<NA><NA><NA>020980629212.00142800서울특별시 강북구 미아동 90-57번지서울특별시 강북구 도봉로10라길 10 (미아동)1216일진사2009-10-05 15:24:30I2018-08-31 23:59:59.0일반세탁업202707.446508457014.722052일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
830800003080000-205-1987-0178219870715<NA>1영업/정상1영업<NA><NA><NA><NA>02 988692323.00142805서울특별시 강북구 미아동 460-57번지서울특별시 강북구 솔샘로64길 39 (미아동)1205신성사2004-04-08 00:00:00I2018-08-31 23:59:59.0일반세탁업202453.551683457039.326759일반세탁업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
930800003080000-205-1987-0178719870902<NA>1영업/정상1영업<NA><NA><NA><NA>02 980783020.46142810서울특별시 강북구 미아동 218-4번지서울특별시 강북구 도봉로67길 43 (미아동)1119경남세탁소2012-11-21 11:43:13I2018-08-31 23:59:59.0일반세탁업201899.678122458610.322759일반세탁업000000000N0<NA><NA><NA><NA>0<NA><NA>00N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
49330800003080000-205-2018-0000520181231<NA>1영업/정상1영업<NA><NA><NA><NA><NA>82.08142876서울특별시 강북구 수유동 167-75번지 1층서울특별시 강북구 도봉로101길 35, 1층 (수유동)1055코리아세탁2018-12-31 12:22:57I2019-01-02 02:20:35.0일반세탁업202473.343644460146.775074일반세탁업00<NA><NA><NA><NA>000N0<NA><NA><NA><NA>20050N
49430800003080000-205-2019-0000120190924<NA>3폐업2폐업20220520<NA><NA><NA><NA>20.00142805서울특별시 강북구 미아동 416-78 1층서울특별시 강북구 솔샘로64나길 42, 1층 (미아동)1204성심사2022-05-20 13:53:03U2021-12-04 22:02:00.0일반세탁업202171.908363457117.235029<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
49530800003080000-205-2020-0000120200506<NA>1영업/정상1영업<NA><NA><NA><NA><NA>21.69142888서울특별시 강북구 수유동 726-2번지 1층서울특별시 강북구 노해로27길 66, 1층 (수유동)1045우리신발세탁소2020-05-06 14:53:10I2020-05-08 00:23:40.0운동화전문세탁업201783.625174460510.614098운동화전문세탁업00<NA><NA><NA><NA>000N0<NA><NA><NA><NA>00010N
49630800003080000-205-2020-0000220201022<NA>1영업/정상1영업<NA><NA><NA><NA><NA>25.00142876서울특별시 강북구 수유동 180-14서울특별시 강북구 한천로148길 32, 1층 (수유동)1053충남세탁2022-02-16 13:53:50U2022-02-18 02:40:00.0일반세탁업202141.21101460108.378142일반세탁업00<NA><NA><NA><NA>000N0<NA><NA><NA><NA>20010N
49730800003080000-205-2021-0000120210217<NA>1영업/정상1영업<NA><NA><NA><NA>02 945 5557.00142060서울특별시 강북구 번동 242-4서울특별시 강북구 한천로105길 7, 번동1단지주공아파트상가 205호 (번동)1230웰빙세탁2021-02-17 15:45:03I2021-02-19 00:23:01.0일반세탁업204083.177113458197.619196일반세탁업002200000N0<NA><NA><NA><NA>1<NA><NA>10N
49830800003080000-205-2022-0000120220721<NA>1영업/정상1영업<NA><NA><NA><NA>02 1800018442.09142883서울특별시 강북구 수유동 554-93서울특별시 강북구 인수봉로 274-1, 1층 (수유동)1031주식회사 호미아2022-07-21 15:14:38I2021-12-06 22:03:00.0세탁업 기타200914.734029460099.328199<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
49930800003080000-205-2023-000012023-07-06<NA>1영업/정상1영업<NA><NA><NA><NA><NA>65.36142-885서울특별시 강북구 수유동 434-10 1층 101호서울특별시 강북구 인수봉로57길 7, 1층 101호 (수유동)1023유모차카시트세탁뽀송강북점2023-07-06 16:26:56I2022-12-07 00:08:00.0세탁업 기타201028.204061459423.354761<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
50030800003080000-205-2023-000022023-08-25<NA>1영업/정상1영업<NA><NA><NA><NA><NA>30.00142-812서울특별시 강북구 미아동 302-35 1층서울특별시 강북구 도봉로49길 50, 1층 (미아동)1169하얀사2023-08-25 11:03:42I2022-12-07 22:07:00.0일반세탁업202022.430959458041.360353<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
50130800003080000-205-2024-000012024-02-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA>36.00142-871서울특별시 강북구 우이동 41-9 1층서울특별시 강북구 삼양로 625-1, 1층 (우이동)1004퍼시픽크린2024-02-02 14:35:33I2023-12-02 00:04:00.0일반세탁업201134.577942461824.130498<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
50230800003080000-205-2024-000022024-03-28<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.00142-809서울특별시 강북구 미아동 134-47 1층서울특별시 강북구 도봉로30길 22, 1층 (미아동)1161조광세탁소2024-03-28 11:59:54I2023-12-02 21:00:00.0일반세탁업202551.565409457634.010974<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>