Overview

Dataset statistics

Number of variables47
Number of observations549
Missing cells6666
Missing cells (%)25.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory216.7 KiB
Average record size in memory404.2 B

Variable types

Categorical18
Text7
DateTime4
Unsupported7
Numeric9
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
Author마포구
URLhttps://data.seoul.go.kr/dataList/OA-19290/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (96.5%)Imbalance
위생업태명 is highly imbalanced (57.8%)Imbalance
사용끝지하층 is highly imbalanced (58.2%)Imbalance
건물소유구분명 is highly imbalanced (53.0%)Imbalance
인허가취소일자 has 549 (100.0%) missing valuesMissing
폐업일자 has 155 (28.2%) missing valuesMissing
휴업시작일자 has 549 (100.0%) missing valuesMissing
휴업종료일자 has 549 (100.0%) missing valuesMissing
재개업일자 has 549 (100.0%) missing valuesMissing
전화번호 has 133 (24.2%) missing valuesMissing
도로명주소 has 171 (31.1%) missing valuesMissing
도로명우편번호 has 172 (31.3%) missing valuesMissing
좌표정보(X) has 18 (3.3%) missing valuesMissing
좌표정보(Y) has 18 (3.3%) missing valuesMissing
건물지상층수 has 210 (38.3%) missing valuesMissing
건물지하층수 has 224 (40.8%) missing valuesMissing
사용시작지상층 has 280 (51.0%) missing valuesMissing
사용끝지상층 has 344 (62.7%) missing valuesMissing
발한실여부 has 99 (18.0%) missing valuesMissing
조건부허가신고사유 has 549 (100.0%) missing valuesMissing
조건부허가시작일자 has 549 (100.0%) missing valuesMissing
조건부허가종료일자 has 549 (100.0%) missing valuesMissing
여성종사자수 has 454 (82.7%) missing valuesMissing
남성종사자수 has 451 (82.1%) missing valuesMissing
다중이용업소여부 has 88 (16.0%) missing valuesMissing
관리번호 has unique valuesUnique
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가신고사유 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조건부허가종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물지상층수 has 252 (45.9%) zerosZeros
건물지하층수 has 262 (47.7%) zerosZeros
사용시작지상층 has 68 (12.4%) zerosZeros
사용끝지상층 has 23 (4.2%) zerosZeros
여성종사자수 has 77 (14.0%) zerosZeros
남성종사자수 has 61 (11.1%) zerosZeros

Reproduction

Analysis started2024-04-06 10:53:10.826234
Analysis finished2024-04-06 10:53:12.475756
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
3130000
549 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3130000
2nd row3130000
3rd row3130000
4th row3130000
5th row3130000

Common Values

ValueCountFrequency (%)
3130000 549
100.0%

Length

2024-04-06T19:53:12.618956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:12.838998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3130000 549
100.0%

관리번호
Text

UNIQUE 

Distinct549
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-04-06T19:53:13.140388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters12078
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

Unique549 ?
Unique (%)100.0%

Sample

1st row3130000-206-1987-01953
2nd row3130000-206-1987-01955
3rd row3130000-206-1987-01956
4th row3130000-206-1988-01957
5th row3130000-206-1988-01958
ValueCountFrequency (%)
3130000-206-1987-01953 1
 
0.2%
3130000-206-2011-00019 1
 
0.2%
3130000-206-2012-00021 1
 
0.2%
3130000-206-2012-00020 1
 
0.2%
3130000-206-2012-00019 1
 
0.2%
3130000-206-2012-00018 1
 
0.2%
3130000-206-2012-00017 1
 
0.2%
3130000-206-2012-00016 1
 
0.2%
3130000-206-2012-00015 1
 
0.2%
3130000-206-2009-00022 1
 
0.2%
Other values (539) 539
98.2%
2024-04-06T19:53:13.766436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5210
43.1%
- 1647
 
13.6%
2 1322
 
10.9%
3 1249
 
10.3%
1 1179
 
9.8%
6 668
 
5.5%
9 335
 
2.8%
5 125
 
1.0%
7 116
 
1.0%
8 114
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10431
86.4%
Dash Punctuation 1647
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5210
49.9%
2 1322
 
12.7%
3 1249
 
12.0%
1 1179
 
11.3%
6 668
 
6.4%
9 335
 
3.2%
5 125
 
1.2%
7 116
 
1.1%
8 114
 
1.1%
4 113
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12078
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5210
43.1%
- 1647
 
13.6%
2 1322
 
10.9%
3 1249
 
10.3%
1 1179
 
9.8%
6 668
 
5.5%
9 335
 
2.8%
5 125
 
1.0%
7 116
 
1.0%
8 114
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5210
43.1%
- 1647
 
13.6%
2 1322
 
10.9%
3 1249
 
10.3%
1 1179
 
9.8%
6 668
 
5.5%
9 335
 
2.8%
5 125
 
1.0%
7 116
 
1.0%
8 114
 
0.9%
Distinct502
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1987-06-01 00:00:00
Maximum2024-03-26 00:00:00
2024-04-06T19:53:14.076754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:53:14.337554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
3
394 
1
155 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 394
71.8%
1 155
 
28.2%

Length

2024-04-06T19:53:14.559255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:14.717499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 394
71.8%
1 155
 
28.2%

영업상태명
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
폐업
394 
영업/정상
155 

Length

Max length5
Median length2
Mean length2.8469945
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 394
71.8%
영업/정상 155
 
28.2%

Length

2024-04-06T19:53:14.894518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:15.051862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 394
71.8%
영업/정상 155
 
28.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2
394 
1
155 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 394
71.8%
1 155
 
28.2%

Length

2024-04-06T19:53:15.246468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:15.401610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 394
71.8%
1 155
 
28.2%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
폐업
394 
영업
155 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 394
71.8%
영업 155
 
28.2%

Length

2024-04-06T19:53:15.579176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:15.720219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 394
71.8%
영업 155
 
28.2%

폐업일자
Date

MISSING 

Distinct285
Distinct (%)72.3%
Missing155
Missing (%)28.2%
Memory size4.4 KiB
Minimum1992-01-22 00:00:00
Maximum2024-03-15 00:00:00
2024-04-06T19:53:15.892968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:53:16.128286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

전화번호
Text

MISSING 

Distinct402
Distinct (%)96.6%
Missing133
Missing (%)24.2%
Memory size4.4 KiB
2024-04-06T19:53:16.622781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.329327
Min length2

Characters and Unicode

Total characters4297
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

Unique389 ?
Unique (%)93.5%

Sample

1st row02 7184221
2nd row0207190813
3rd row0232720217
4th row0207025618
5th row0207190311
ValueCountFrequency (%)
02 252
33.7%
332 5
 
0.7%
322 4
 
0.5%
701 4
 
0.5%
717 4
 
0.5%
333 4
 
0.5%
711 4
 
0.5%
070 3
 
0.4%
302 3
 
0.4%
326 3
 
0.4%
Other values (438) 462
61.8%
2024-04-06T19:53:17.329488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 792
18.4%
2 731
17.0%
3 531
12.4%
453
10.5%
1 377
8.8%
7 338
7.9%
5 250
 
5.8%
6 243
 
5.7%
4 229
 
5.3%
8 197
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3844
89.5%
Space Separator 453
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 792
20.6%
2 731
19.0%
3 531
13.8%
1 377
9.8%
7 338
8.8%
5 250
 
6.5%
6 243
 
6.3%
4 229
 
6.0%
8 197
 
5.1%
9 156
 
4.1%
Space Separator
ValueCountFrequency (%)
453
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 792
18.4%
2 731
17.0%
3 531
12.4%
453
10.5%
1 377
8.8%
7 338
7.9%
5 250
 
5.8%
6 243
 
5.7%
4 229
 
5.3%
8 197
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 792
18.4%
2 731
17.0%
3 531
12.4%
453
10.5%
1 377
8.8%
7 338
7.9%
5 250
 
5.8%
6 243
 
5.7%
4 229
 
5.3%
8 197
 
4.6%
Distinct388
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-04-06T19:53:17.951985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.0473588
Min length3

Characters and Unicode

Total characters2771
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

Unique336 ?
Unique (%)61.2%

Sample

1st row109.09
2nd row.00
3rd row96.26
4th row926.10
5th row.00
ValueCountFrequency (%)
00 51
 
9.3%
66.00 16
 
2.9%
33.00 9
 
1.6%
3.30 7
 
1.3%
20.00 7
 
1.3%
60.00 6
 
1.1%
10.00 6
 
1.1%
70.00 5
 
0.9%
30.00 4
 
0.7%
82.50 4
 
0.7%
Other values (378) 434
79.1%
2024-04-06T19:53:18.800382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 619
22.3%
. 549
19.8%
1 256
9.2%
2 220
 
7.9%
6 203
 
7.3%
3 198
 
7.1%
5 178
 
6.4%
4 151
 
5.4%
8 145
 
5.2%
9 126
 
4.5%
Other values (2) 126
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2214
79.9%
Other Punctuation 557
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 619
28.0%
1 256
11.6%
2 220
 
9.9%
6 203
 
9.2%
3 198
 
8.9%
5 178
 
8.0%
4 151
 
6.8%
8 145
 
6.5%
9 126
 
5.7%
7 118
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 549
98.6%
, 8
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2771
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 619
22.3%
. 549
19.8%
1 256
9.2%
2 220
 
7.9%
6 203
 
7.3%
3 198
 
7.1%
5 178
 
6.4%
4 151
 
5.4%
8 145
 
5.2%
9 126
 
4.5%
Other values (2) 126
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 619
22.3%
. 549
19.8%
1 256
9.2%
2 220
 
7.9%
6 203
 
7.3%
3 198
 
7.1%
5 178
 
6.4%
4 151
 
5.4%
8 145
 
5.2%
9 126
 
4.5%
Other values (2) 126
 
4.5%
Distinct151
Distinct (%)27.7%
Missing3
Missing (%)0.5%
Memory size4.4 KiB
2024-04-06T19:53:19.384630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1007326
Min length6

Characters and Unicode

Total characters3331
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

Unique53 ?
Unique (%)9.7%

Sample

1st row121810
2nd row121731
3rd row121715
4th row121050
5th row121874
ValueCountFrequency (%)
121815 21
 
3.8%
121812 19
 
3.5%
121904 18
 
3.3%
121846 16
 
2.9%
121050 12
 
2.2%
121850 12
 
2.2%
121843 12
 
2.2%
121896 12
 
2.2%
121897 11
 
2.0%
121816 10
 
1.8%
Other values (141) 403
73.8%
2024-04-06T19:53:20.200692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1248
37.5%
2 631
18.9%
8 494
 
14.8%
0 186
 
5.6%
4 142
 
4.3%
7 135
 
4.1%
5 133
 
4.0%
9 113
 
3.4%
6 103
 
3.1%
3 91
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3276
98.3%
Dash Punctuation 55
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1248
38.1%
2 631
19.3%
8 494
 
15.1%
0 186
 
5.7%
4 142
 
4.3%
7 135
 
4.1%
5 133
 
4.1%
9 113
 
3.4%
6 103
 
3.1%
3 91
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1248
37.5%
2 631
18.9%
8 494
 
14.8%
0 186
 
5.6%
4 142
 
4.3%
7 135
 
4.1%
5 133
 
4.0%
9 113
 
3.4%
6 103
 
3.1%
3 91
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1248
37.5%
2 631
18.9%
8 494
 
14.8%
0 186
 
5.6%
4 142
 
4.3%
7 135
 
4.1%
5 133
 
4.0%
9 113
 
3.4%
6 103
 
3.1%
3 91
 
2.7%
Distinct525
Distinct (%)96.2%
Missing3
Missing (%)0.5%
Memory size4.4 KiB
2024-04-06T19:53:20.658801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length38
Mean length26.415751
Min length17

Characters and Unicode

Total characters14423
Distinct characters244
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)92.7%

Sample

1st row서울특별시 마포구 대흥동 325-78 4층
2nd row서울특별시 마포구 도화동 536-0 정우빌딩 311호
3rd row서울특별시 마포구 도화동 51-1 성우빌딩 15층 1호 전체
4th row서울특별시 마포구 마포동 136-1
5th row서울특별시 마포구 염리동 168-9 보험공단 15층동 1512호
ValueCountFrequency (%)
서울특별시 546
 
18.8%
마포구 545
 
18.8%
도화동 87
 
3.0%
성산동 70
 
2.4%
서교동 65
 
2.2%
공덕동 50
 
1.7%
2층 44
 
1.5%
합정동 34
 
1.2%
3층 33
 
1.1%
상암동 32
 
1.1%
Other values (756) 1391
48.0%
2024-04-06T19:53:21.429592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2767
19.2%
636
 
4.4%
1 626
 
4.3%
621
 
4.3%
589
 
4.1%
580
 
4.0%
551
 
3.8%
551
 
3.8%
551
 
3.8%
548
 
3.8%
Other values (234) 6403
44.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7965
55.2%
Decimal Number 3090
 
21.4%
Space Separator 2767
 
19.2%
Dash Punctuation 421
 
2.9%
Uppercase Letter 59
 
0.4%
Close Punctuation 50
 
0.3%
Open Punctuation 49
 
0.3%
Other Punctuation 16
 
0.1%
Lowercase Letter 5
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
636
 
8.0%
621
 
7.8%
589
 
7.4%
580
 
7.3%
551
 
6.9%
551
 
6.9%
551
 
6.9%
548
 
6.9%
546
 
6.9%
222
 
2.8%
Other values (196) 2570
32.3%
Uppercase Letter
ValueCountFrequency (%)
B 11
18.6%
D 7
11.9%
C 6
10.2%
K 5
8.5%
M 5
8.5%
G 5
8.5%
L 4
 
6.8%
A 4
 
6.8%
T 3
 
5.1%
S 3
 
5.1%
Other values (5) 6
10.2%
Decimal Number
ValueCountFrequency (%)
1 626
20.3%
2 444
14.4%
3 383
12.4%
5 328
10.6%
4 318
10.3%
0 309
10.0%
6 223
 
7.2%
7 179
 
5.8%
8 141
 
4.6%
9 139
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 9
56.2%
. 6
37.5%
/ 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
b 3
60.0%
s 1
 
20.0%
k 1
 
20.0%
Close Punctuation
ValueCountFrequency (%)
) 49
98.0%
] 1
 
2.0%
Open Punctuation
ValueCountFrequency (%)
( 48
98.0%
[ 1
 
2.0%
Space Separator
ValueCountFrequency (%)
2767
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 421
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7965
55.2%
Common 6393
44.3%
Latin 65
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
636
 
8.0%
621
 
7.8%
589
 
7.4%
580
 
7.3%
551
 
6.9%
551
 
6.9%
551
 
6.9%
548
 
6.9%
546
 
6.9%
222
 
2.8%
Other values (196) 2570
32.3%
Common
ValueCountFrequency (%)
2767
43.3%
1 626
 
9.8%
2 444
 
6.9%
- 421
 
6.6%
3 383
 
6.0%
5 328
 
5.1%
4 318
 
5.0%
0 309
 
4.8%
6 223
 
3.5%
7 179
 
2.8%
Other values (9) 395
 
6.2%
Latin
ValueCountFrequency (%)
B 11
16.9%
D 7
10.8%
C 6
9.2%
K 5
7.7%
M 5
7.7%
G 5
7.7%
L 4
 
6.2%
A 4
 
6.2%
T 3
 
4.6%
S 3
 
4.6%
Other values (9) 12
18.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7965
55.2%
ASCII 6457
44.8%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2767
42.9%
1 626
 
9.7%
2 444
 
6.9%
- 421
 
6.5%
3 383
 
5.9%
5 328
 
5.1%
4 318
 
4.9%
0 309
 
4.8%
6 223
 
3.5%
7 179
 
2.8%
Other values (27) 459
 
7.1%
Hangul
ValueCountFrequency (%)
636
 
8.0%
621
 
7.8%
589
 
7.4%
580
 
7.3%
551
 
6.9%
551
 
6.9%
551
 
6.9%
548
 
6.9%
546
 
6.9%
222
 
2.8%
Other values (196) 2570
32.3%
Number Forms
ValueCountFrequency (%)
1
100.0%

도로명주소
Text

MISSING 

Distinct371
Distinct (%)98.1%
Missing171
Missing (%)31.1%
Memory size4.4 KiB
2024-04-06T19:53:21.954453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length45
Mean length34.349206
Min length22

Characters and Unicode

Total characters12984
Distinct characters256
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique364 ?
Unique (%)96.3%

Sample

1st row서울특별시 마포구 독막로 241 (대흥동,4층)
2nd row서울특별시 마포구 마포대로 49, 성우빌딩 15층 1호 (도화동)
3rd row서울특별시 마포구 마포대로 196 (아현동,고려아카데미텔 422)
4th row서울특별시 마포구 마포대로 127, 301호 (공덕동, 풍림브이아이피텔)
5th row서울특별시 마포구 마포대로 12 (마포동,한신빌딩 211호)
ValueCountFrequency (%)
서울특별시 378
 
15.1%
마포구 377
 
15.0%
마포대로 66
 
2.6%
도화동 47
 
1.9%
성산동 44
 
1.8%
2층 37
 
1.5%
서교동 34
 
1.4%
공덕동 32
 
1.3%
월드컵북로 30
 
1.2%
3층 30
 
1.2%
Other values (665) 1434
57.2%
2024-04-06T19:53:22.643947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2133
 
16.4%
1 496
 
3.8%
494
 
3.8%
485
 
3.7%
454
 
3.5%
440
 
3.4%
, 436
 
3.4%
( 397
 
3.1%
) 397
 
3.1%
390
 
3.0%
Other values (246) 6862
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7490
57.7%
Space Separator 2133
 
16.4%
Decimal Number 2025
 
15.6%
Other Punctuation 437
 
3.4%
Open Punctuation 398
 
3.1%
Close Punctuation 398
 
3.1%
Uppercase Letter 60
 
0.5%
Dash Punctuation 39
 
0.3%
Lowercase Letter 3
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
494
 
6.6%
485
 
6.5%
454
 
6.1%
440
 
5.9%
390
 
5.2%
383
 
5.1%
382
 
5.1%
382
 
5.1%
378
 
5.0%
356
 
4.8%
Other values (211) 3346
44.7%
Uppercase Letter
ValueCountFrequency (%)
B 11
18.3%
C 9
15.0%
D 9
15.0%
M 7
11.7%
K 5
8.3%
S 4
 
6.7%
A 4
 
6.7%
L 2
 
3.3%
G 2
 
3.3%
T 2
 
3.3%
Other values (5) 5
8.3%
Decimal Number
ValueCountFrequency (%)
1 496
24.5%
2 287
14.2%
0 239
11.8%
3 219
10.8%
4 176
 
8.7%
5 157
 
7.8%
6 132
 
6.5%
8 124
 
6.1%
7 108
 
5.3%
9 87
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 436
99.8%
. 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 397
99.7%
[ 1
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 397
99.7%
] 1
 
0.3%
Space Separator
ValueCountFrequency (%)
2133
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 39
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 3
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7490
57.7%
Common 5430
41.8%
Latin 64
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
494
 
6.6%
485
 
6.5%
454
 
6.1%
440
 
5.9%
390
 
5.2%
383
 
5.1%
382
 
5.1%
382
 
5.1%
378
 
5.0%
356
 
4.8%
Other values (211) 3346
44.7%
Common
ValueCountFrequency (%)
2133
39.3%
1 496
 
9.1%
, 436
 
8.0%
( 397
 
7.3%
) 397
 
7.3%
2 287
 
5.3%
0 239
 
4.4%
3 219
 
4.0%
4 176
 
3.2%
5 157
 
2.9%
Other values (8) 493
 
9.1%
Latin
ValueCountFrequency (%)
B 11
17.2%
C 9
14.1%
D 9
14.1%
M 7
10.9%
K 5
7.8%
S 4
 
6.2%
A 4
 
6.2%
b 3
 
4.7%
L 2
 
3.1%
G 2
 
3.1%
Other values (7) 8
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7490
57.7%
ASCII 5493
42.3%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2133
38.8%
1 496
 
9.0%
, 436
 
7.9%
( 397
 
7.2%
) 397
 
7.2%
2 287
 
5.2%
0 239
 
4.4%
3 219
 
4.0%
4 176
 
3.2%
5 157
 
2.9%
Other values (24) 556
 
10.1%
Hangul
ValueCountFrequency (%)
494
 
6.6%
485
 
6.5%
454
 
6.1%
440
 
5.9%
390
 
5.2%
383
 
5.1%
382
 
5.1%
382
 
5.1%
378
 
5.0%
356
 
4.8%
Other values (211) 3346
44.7%
Number Forms
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct142
Distinct (%)37.7%
Missing172
Missing (%)31.3%
Infinite0
Infinite (%)0.0%
Mean4075.5411
Minimum3902
Maximum6109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:22.975272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3902
5-th percentile3923.2
Q13990
median4075
Q34158
95-th percentile4206.2
Maximum6109
Range2207
Interquartile range (IQR)168

Descriptive statistics

Standard deviation140.18348
Coefficient of variation (CV)0.034396287
Kurtosis116.95984
Mean4075.5411
Median Absolute Deviation (MAD)84
Skewness8.0669914
Sum1536479
Variance19651.409
MonotonicityNot monotonic
2024-04-06T19:53:23.225260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4158 15
 
2.7%
4168 13
 
2.4%
3909 9
 
1.6%
4157 9
 
1.6%
3938 9
 
1.6%
4173 8
 
1.5%
3978 8
 
1.5%
4083 7
 
1.3%
4177 7
 
1.3%
3994 7
 
1.3%
Other values (132) 285
51.9%
(Missing) 172
31.3%
ValueCountFrequency (%)
3902 1
 
0.2%
3908 3
 
0.5%
3909 9
1.6%
3911 2
 
0.4%
3918 3
 
0.5%
3920 1
 
0.2%
3924 2
 
0.4%
3925 4
0.7%
3926 1
 
0.2%
3929 2
 
0.4%
ValueCountFrequency (%)
6109 1
 
0.2%
4214 3
0.5%
4213 1
 
0.2%
4212 3
0.5%
4211 1
 
0.2%
4210 1
 
0.2%
4209 3
0.5%
4208 1
 
0.2%
4207 5
0.9%
4206 4
0.7%
Distinct537
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2024-04-06T19:53:23.529533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length27
Mean length8.3260474
Min length2

Characters and Unicode

Total characters4571
Distinct characters376
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

Unique525 ?
Unique (%)95.6%

Sample

1st row(주)보경실업
2nd row(주)우양기술용역
3rd row백상개발(주)
4th row산마루실업
5th row협성개발공영(주)
ValueCountFrequency (%)
주식회사 43
 
6.9%
3
 
0.5%
주)인광엔지니어링 2
 
0.3%
ltd 2
 
0.3%
컴퍼니 2
 
0.3%
크린 2
 
0.3%
주)비엠휴먼솔루션 2
 
0.3%
주)에이치알스타 2
 
0.3%
주)인플러스 2
 
0.3%
미래환경 2
 
0.3%
Other values (555) 562
90.1%
2024-04-06T19:53:24.505387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
445
 
9.7%
( 387
 
8.5%
) 387
 
8.5%
184
 
4.0%
129
 
2.8%
122
 
2.7%
80
 
1.8%
77
 
1.7%
76
 
1.7%
66
 
1.4%
Other values (366) 2618
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3633
79.5%
Open Punctuation 387
 
8.5%
Close Punctuation 387
 
8.5%
Space Separator 77
 
1.7%
Uppercase Letter 40
 
0.9%
Lowercase Letter 25
 
0.5%
Decimal Number 10
 
0.2%
Other Punctuation 9
 
0.2%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
445
 
12.2%
184
 
5.1%
129
 
3.6%
122
 
3.4%
80
 
2.2%
76
 
2.1%
66
 
1.8%
57
 
1.6%
57
 
1.6%
53
 
1.5%
Other values (324) 2364
65.1%
Uppercase Letter
ValueCountFrequency (%)
S 6
15.0%
T 6
15.0%
M 4
10.0%
C 4
10.0%
B 3
7.5%
L 2
 
5.0%
R 2
 
5.0%
I 2
 
5.0%
K 2
 
5.0%
H 2
 
5.0%
Other values (6) 7
17.5%
Lowercase Letter
ValueCountFrequency (%)
t 5
20.0%
i 3
12.0%
o 3
12.0%
d 2
 
8.0%
c 2
 
8.0%
s 2
 
8.0%
h 2
 
8.0%
e 1
 
4.0%
p 1
 
4.0%
a 1
 
4.0%
Other values (3) 3
12.0%
Decimal Number
ValueCountFrequency (%)
2 3
30.0%
1 3
30.0%
0 1
 
10.0%
6 1
 
10.0%
8 1
 
10.0%
5 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 6
66.7%
, 2
 
22.2%
& 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 387
100.0%
Close Punctuation
ValueCountFrequency (%)
) 387
100.0%
Space Separator
ValueCountFrequency (%)
77
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3633
79.5%
Common 873
 
19.1%
Latin 65
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
445
 
12.2%
184
 
5.1%
129
 
3.6%
122
 
3.4%
80
 
2.2%
76
 
2.1%
66
 
1.8%
57
 
1.6%
57
 
1.6%
53
 
1.5%
Other values (324) 2364
65.1%
Latin
ValueCountFrequency (%)
S 6
 
9.2%
T 6
 
9.2%
t 5
 
7.7%
M 4
 
6.2%
C 4
 
6.2%
B 3
 
4.6%
i 3
 
4.6%
o 3
 
4.6%
d 2
 
3.1%
L 2
 
3.1%
Other values (19) 27
41.5%
Common
ValueCountFrequency (%)
( 387
44.3%
) 387
44.3%
77
 
8.8%
. 6
 
0.7%
2 3
 
0.3%
1 3
 
0.3%
- 3
 
0.3%
, 2
 
0.2%
& 1
 
0.1%
0 1
 
0.1%
Other values (3) 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3633
79.5%
ASCII 938
 
20.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
445
 
12.2%
184
 
5.1%
129
 
3.6%
122
 
3.4%
80
 
2.2%
76
 
2.1%
66
 
1.8%
57
 
1.6%
57
 
1.6%
53
 
1.5%
Other values (324) 2364
65.1%
ASCII
ValueCountFrequency (%)
( 387
41.3%
) 387
41.3%
77
 
8.2%
. 6
 
0.6%
S 6
 
0.6%
T 6
 
0.6%
t 5
 
0.5%
M 4
 
0.4%
C 4
 
0.4%
2 3
 
0.3%
Other values (32) 53
 
5.7%
Distinct472
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum1999-02-08 00:00:00
Maximum2024-04-01 14:05:16
2024-04-06T19:53:24.734271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:53:24.993603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
I
386 
U
163 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 386
70.3%
U 163
29.7%

Length

2024-04-06T19:53:25.275290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:25.479502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 386
70.3%
u 163
29.7%
Distinct175
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:03:00
2024-04-06T19:53:25.758672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T19:53:26.021935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
건물위생관리업
547 
건물위생관리업 기타
 
2

Length

Max length10
Median length7
Mean length7.010929
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 547
99.6%
건물위생관리업 기타 2
 
0.4%

Length

2024-04-06T19:53:26.424512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:26.611699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 549
99.6%
기타 2
 
0.4%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct336
Distinct (%)63.3%
Missing18
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean193687.32
Minimum189315.37
Maximum202991.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:26.835512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189315.37
5-th percentile190403.29
Q1192335.8
median193787.25
Q3195237.64
95-th percentile196021.38
Maximum202991.53
Range13676.162
Interquartile range (IQR)2901.8446

Descriptive statistics

Standard deviation1771.4725
Coefficient of variation (CV)0.0091460427
Kurtosis0.25252446
Mean193687.32
Median Absolute Deviation (MAD)1450.393
Skewness-0.040463498
Sum1.0284797 × 108
Variance3138114.8
MonotonicityNot monotonic
2024-04-06T19:53:27.077988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195324.793653981 11
 
2.0%
195184.335899857 10
 
1.8%
195237.642733133 9
 
1.6%
194974.587674037 9
 
1.6%
195748.06444032 8
 
1.5%
195703.425296812 7
 
1.3%
192513.961688702 7
 
1.3%
194978.350112608 6
 
1.1%
196057.949965838 6
 
1.1%
195214.309334733 6
 
1.1%
Other values (326) 452
82.3%
(Missing) 18
 
3.3%
ValueCountFrequency (%)
189315.370584751 2
0.4%
189409.319972063 1
 
0.2%
189624.641758403 1
 
0.2%
189855.433985731 1
 
0.2%
190070.994854984 1
 
0.2%
190086.625449279 1
 
0.2%
190125.564768858 2
0.4%
190144.143203955 1
 
0.2%
190182.067334395 3
0.5%
190204.923593825 2
0.4%
ValueCountFrequency (%)
202991.532710332 1
 
0.2%
196462.740666971 1
 
0.2%
196306.548908007 1
 
0.2%
196286.512049256 1
 
0.2%
196214.48977946 1
 
0.2%
196135.832498348 1
 
0.2%
196121.004601938 1
 
0.2%
196119.942703319 5
0.9%
196078.108047045 3
0.5%
196069.938748134 2
 
0.4%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct336
Distinct (%)63.3%
Missing18
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean449981.55
Minimum445088.97
Maximum453872.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:27.335093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum445088.97
5-th percentile448487.37
Q1449083.06
median449796.7
Q3450684.14
95-th percentile452650.33
Maximum453872.43
Range8783.4563
Interquartile range (IQR)1601.0776

Descriptive statistics

Standard deviation1184.8328
Coefficient of variation (CV)0.0026330698
Kurtosis0.8735759
Mean449981.55
Median Absolute Deviation (MAD)815.06317
Skewness0.70629199
Sum2.389402 × 108
Variance1403828.8
MonotonicityNot monotonic
2024-04-06T19:53:27.607500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448885.430093289 11
 
2.0%
448780.26877488 10
 
1.8%
448733.404817253 9
 
1.6%
448229.063825491 9
 
1.6%
449145.578789461 8
 
1.5%
449330.800717721 7
 
1.3%
450966.302367257 7
 
1.3%
448487.374314355 6
 
1.1%
449897.213071289 6
 
1.1%
448572.253976483 6
 
1.1%
Other values (326) 452
82.3%
(Missing) 18
 
3.3%
ValueCountFrequency (%)
445088.969441836 1
 
0.2%
448229.063825491 9
1.6%
448260.481051368 1
 
0.2%
448407.752664604 4
0.7%
448437.479009838 1
 
0.2%
448460.50414883 2
 
0.4%
448463.997345199 2
 
0.4%
448476.859046692 3
 
0.5%
448487.374314355 6
1.1%
448489.519681016 1
 
0.2%
ValueCountFrequency (%)
453872.42578746 1
 
0.2%
453647.349314742 2
0.4%
453614.583448105 1
 
0.2%
453459.525588653 1
 
0.2%
453231.427685063 1
 
0.2%
453176.817119902 1
 
0.2%
453141.676626027 3
0.5%
453090.149821248 2
0.4%
452911.904225602 1
 
0.2%
452758.642764785 4
0.7%

위생업태명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
건물위생관리업
459 
<NA>
88 
건물위생관리업 기타
 
2

Length

Max length10
Median length7
Mean length6.5300546
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 459
83.6%
<NA> 88
 
16.0%
건물위생관리업 기타 2
 
0.4%

Length

2024-04-06T19:53:27.857805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:28.090348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 461
83.7%
na 88
 
16.0%
기타 2
 
0.4%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)5.6%
Missing210
Missing (%)38.3%
Infinite0
Infinite (%)0.0%
Mean2.0147493
Minimum0
Maximum25
Zeros252
Zeros (%)45.9%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:28.360059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13.2
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.7682773
Coefficient of variation (CV)2.3666852
Kurtosis10.430396
Mean2.0147493
Median Absolute Deviation (MAD)0
Skewness3.1728341
Sum683
Variance22.736468
MonotonicityNot monotonic
2024-04-06T19:53:28.537860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 252
45.9%
4 19
 
3.5%
5 16
 
2.9%
3 9
 
1.6%
6 8
 
1.5%
2 6
 
1.1%
18 4
 
0.7%
25 4
 
0.7%
15 3
 
0.5%
10 3
 
0.5%
Other values (9) 15
 
2.7%
(Missing) 210
38.3%
ValueCountFrequency (%)
0 252
45.9%
1 1
 
0.2%
2 6
 
1.1%
3 9
 
1.6%
4 19
 
3.5%
5 16
 
2.9%
6 8
 
1.5%
7 2
 
0.4%
8 3
 
0.5%
9 2
 
0.4%
ValueCountFrequency (%)
25 4
0.7%
24 2
0.4%
21 1
 
0.2%
20 1
 
0.2%
18 4
0.7%
17 2
0.4%
15 3
0.5%
13 1
 
0.2%
10 3
0.5%
9 2
0.4%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)2.5%
Missing224
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean0.42153846
Minimum0
Maximum32
Zeros262
Zeros (%)47.7%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:28.824775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum32
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9572982
Coefficient of variation (CV)4.6432256
Kurtosis210.64245
Mean0.42153846
Median Absolute Deviation (MAD)0
Skewness13.329619
Sum137
Variance3.8310161
MonotonicityNot monotonic
2024-04-06T19:53:29.065727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 262
47.7%
1 44
 
8.0%
3 8
 
1.5%
2 4
 
0.7%
5 3
 
0.5%
4 2
 
0.4%
6 1
 
0.2%
32 1
 
0.2%
(Missing) 224
40.8%
ValueCountFrequency (%)
0 262
47.7%
1 44
 
8.0%
2 4
 
0.7%
3 8
 
1.5%
4 2
 
0.4%
5 3
 
0.5%
6 1
 
0.2%
32 1
 
0.2%
ValueCountFrequency (%)
32 1
 
0.2%
6 1
 
0.2%
5 3
 
0.5%
4 2
 
0.4%
3 8
 
1.5%
2 4
 
0.7%
1 44
 
8.0%
0 262
47.7%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)7.4%
Missing280
Missing (%)51.0%
Infinite0
Infinite (%)0.0%
Mean3.7509294
Minimum0
Maximum21
Zeros68
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:29.297099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile14
Maximum21
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.345315
Coefficient of variation (CV)1.1584636
Kurtosis2.2232068
Mean3.7509294
Median Absolute Deviation (MAD)2
Skewness1.6477438
Sum1009
Variance18.881762
MonotonicityNot monotonic
2024-04-06T19:53:29.481052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 68
 
12.4%
2 47
 
8.6%
3 31
 
5.6%
4 29
 
5.3%
1 24
 
4.4%
5 15
 
2.7%
6 8
 
1.5%
7 6
 
1.1%
11 6
 
1.1%
13 5
 
0.9%
Other values (10) 30
 
5.5%
(Missing) 280
51.0%
ValueCountFrequency (%)
0 68
12.4%
1 24
 
4.4%
2 47
8.6%
3 31
5.6%
4 29
5.3%
5 15
 
2.7%
6 8
 
1.5%
7 6
 
1.1%
8 4
 
0.7%
9 5
 
0.9%
ValueCountFrequency (%)
21 1
 
0.2%
18 1
 
0.2%
17 3
0.5%
16 3
0.5%
15 5
0.9%
14 3
0.5%
13 5
0.9%
12 2
 
0.4%
11 6
1.1%
10 3
0.5%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)9.8%
Missing344
Missing (%)62.7%
Infinite0
Infinite (%)0.0%
Mean4.5121951
Minimum0
Maximum21
Zeros23
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:29.666403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile15
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.4739001
Coefficient of variation (CV)0.99151299
Kurtosis1.6688851
Mean4.5121951
Median Absolute Deviation (MAD)2
Skewness1.5330962
Sum925
Variance20.015782
MonotonicityNot monotonic
2024-04-06T19:53:29.901499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 42
 
7.7%
3 29
 
5.3%
4 27
 
4.9%
0 23
 
4.2%
1 21
 
3.8%
5 13
 
2.4%
6 7
 
1.3%
7 6
 
1.1%
15 5
 
0.9%
9 5
 
0.9%
Other values (10) 27
 
4.9%
(Missing) 344
62.7%
ValueCountFrequency (%)
0 23
4.2%
1 21
3.8%
2 42
7.7%
3 29
5.3%
4 27
4.9%
5 13
 
2.4%
6 7
 
1.3%
7 6
 
1.1%
8 3
 
0.5%
9 5
 
0.9%
ValueCountFrequency (%)
21 1
 
0.2%
18 1
 
0.2%
17 3
0.5%
16 3
0.5%
15 5
0.9%
14 3
0.5%
13 5
0.9%
12 1
 
0.2%
11 5
0.9%
10 2
 
0.4%
Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
429 
0
104 
1
 
16

Length

Max length4
Median length4
Mean length3.3442623
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 429
78.1%
0 104
 
18.9%
1 16
 
2.9%

Length

2024-04-06T19:53:30.112301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:30.293798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 429
78.1%
0 104
 
18.9%
1 16
 
2.9%

사용끝지하층
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
476 
0
58 
1
 
15

Length

Max length4
Median length4
Mean length3.6010929
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> 476
86.7%
0 58
 
10.6%
1 15
 
2.7%

Length

2024-04-06T19:53:30.485942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:30.673200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 476
86.7%
0 58
 
10.6%
1 15
 
2.7%

한실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
290 
<NA>
259 

Length

Max length4
Median length1
Mean length2.4153005
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 290
52.8%
<NA> 259
47.2%

Length

2024-04-06T19:53:30.874889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:31.100456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 290
52.8%
na 259
47.2%

양실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
290 
<NA>
259 

Length

Max length4
Median length1
Mean length2.4153005
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 290
52.8%
<NA> 259
47.2%

Length

2024-04-06T19:53:31.362164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:31.534772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 290
52.8%
na 259
47.2%

욕실수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
290 
<NA>
259 

Length

Max length4
Median length1
Mean length2.4153005
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 290
52.8%
<NA> 259
47.2%

Length

2024-04-06T19:53:31.738991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:32.051207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 290
52.8%
na 259
47.2%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing99
Missing (%)18.0%
Memory size1.2 KiB
False
450 
(Missing)
99 
ValueCountFrequency (%)
False 450
82.0%
(Missing) 99
 
18.0%
2024-04-06T19:53:32.201716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
0
290 
<NA>
259 

Length

Max length4
Median length1
Mean length2.4153005
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 290
52.8%
<NA> 259
47.2%

Length

2024-04-06T19:53:32.389159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:32.557062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 290
52.8%
na 259
47.2%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing549
Missing (%)100.0%
Memory size5.0 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
447 
임대
97 
자가
 
5

Length

Max length4
Median length4
Mean length3.6284153
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> 447
81.4%
임대 97
 
17.7%
자가 5
 
0.9%

Length

2024-04-06T19:53:32.741848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:32.918424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 447
81.4%
임대 97
 
17.7%
자가 5
 
0.9%

세탁기수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
305 
0
244 

Length

Max length4
Median length4
Mean length2.6666667
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> 305
55.6%
0 244
44.4%

Length

2024-04-06T19:53:33.100641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:33.286070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 305
55.6%
0 244
44.4%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)9.5%
Missing454
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean1.2842105
Minimum0
Maximum47
Zeros77
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:33.439099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.3
Maximum47
Range47
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.8394534
Coefficient of variation (CV)4.5471154
Kurtosis47.128817
Mean1.2842105
Median Absolute Deviation (MAD)0
Skewness6.6641833
Sum122
Variance34.099216
MonotonicityNot monotonic
2024-04-06T19:53:33.645935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 77
 
14.0%
1 8
 
1.5%
4 3
 
0.5%
7 2
 
0.4%
31 1
 
0.2%
5 1
 
0.2%
3 1
 
0.2%
47 1
 
0.2%
2 1
 
0.2%
(Missing) 454
82.7%
ValueCountFrequency (%)
0 77
14.0%
1 8
 
1.5%
2 1
 
0.2%
3 1
 
0.2%
4 3
 
0.5%
5 1
 
0.2%
7 2
 
0.4%
31 1
 
0.2%
47 1
 
0.2%
ValueCountFrequency (%)
47 1
 
0.2%
31 1
 
0.2%
7 2
 
0.4%
5 1
 
0.2%
4 3
 
0.5%
3 1
 
0.2%
2 1
 
0.2%
1 8
 
1.5%
0 77
14.0%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)13.3%
Missing451
Missing (%)82.1%
Infinite0
Infinite (%)0.0%
Mean3.5
Minimum0
Maximum150
Zeros61
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2024-04-06T19:53:33.826550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile15.3
Maximum150
Range150
Interquartile range (IQR)1

Descriptive statistics

Standard deviation15.916826
Coefficient of variation (CV)4.5476647
Kurtosis75.82899
Mean3.5
Median Absolute Deviation (MAD)0
Skewness8.3333221
Sum343
Variance253.34536
MonotonicityNot monotonic
2024-04-06T19:53:34.016691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 61
 
11.1%
1 13
 
2.4%
2 8
 
1.5%
3 6
 
1.1%
30 2
 
0.4%
17 1
 
0.2%
15 1
 
0.2%
150 1
 
0.2%
25 1
 
0.2%
8 1
 
0.2%
Other values (3) 3
 
0.5%
(Missing) 451
82.1%
ValueCountFrequency (%)
0 61
11.1%
1 13
 
2.4%
2 8
 
1.5%
3 6
 
1.1%
4 1
 
0.2%
6 1
 
0.2%
8 1
 
0.2%
11 1
 
0.2%
15 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
150 1
 
0.2%
30 2
 
0.4%
25 1
 
0.2%
17 1
 
0.2%
15 1
 
0.2%
11 1
 
0.2%
8 1
 
0.2%
6 1
 
0.2%
4 1
 
0.2%
3 6
1.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
327 
0
222 

Length

Max length4
Median length4
Mean length2.7868852
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> 327
59.6%
0 222
40.4%

Length

2024-04-06T19:53:34.236681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:34.384879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 327
59.6%
0 222
40.4%

침대수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
342 
0
207 

Length

Max length4
Median length4
Mean length2.8688525
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> 342
62.3%
0 207
37.7%

Length

2024-04-06T19:53:34.549177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T19:53:34.714154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 342
62.3%
0 207
37.7%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing88
Missing (%)16.0%
Memory size1.2 KiB
False
461 
(Missing)
88 
ValueCountFrequency (%)
False 461
84.0%
(Missing) 88
 
16.0%
2024-04-06T19:53:34.844158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031300003130000-206-1987-0195319870601<NA>3폐업2폐업20210315<NA><NA><NA>02 7184221109.09121810서울특별시 마포구 대흥동 325-78 4층서울특별시 마포구 독막로 241 (대흥동,4층)4096(주)보경실업2021-03-15 15:05:19U2021-03-17 02:40:00.0건물위생관리업194681.263318449408.790228건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131300003130000-206-1987-0195519870825<NA>3폐업2폐업20030220<NA><NA><NA>0207190813.00121731서울특별시 마포구 도화동 536-0 정우빌딩 311호<NA><NA>(주)우양기술용역2003-03-24 00:00:00I2018-08-31 23:59:59.0건물위생관리업195277.885389448919.647035건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231300003130000-206-1987-0195619870814<NA>1영업/정상1영업<NA><NA><NA><NA>023272021796.26121715서울특별시 마포구 도화동 51-1 성우빌딩 15층 1호 전체서울특별시 마포구 마포대로 49, 성우빌딩 15층 1호 (도화동)4158백상개발(주)2022-08-03 16:41:21U2021-12-08 00:05:00.0건물위생관리업195237.642733448733.404817<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
331300003130000-206-1988-0195719880507<NA>3폐업2폐업19920122<NA><NA><NA>0207025618926.10121050서울특별시 마포구 마포동 136-1<NA><NA>산마루실업1999-02-08 00:00:00I2018-08-31 23:59:59.0건물위생관리업195019.399817448407.752665건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431300003130000-206-1988-0195819880729<NA>3폐업2폐업20030220<NA><NA><NA>0207190311.00121874서울특별시 마포구 염리동 168-9 보험공단 15층동 1512호<NA><NA>협성개발공영(주)2003-03-24 00:00:00I2018-08-31 23:59:59.0건물위생관리업195292.972628449083.060282건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531300003130000-206-1988-0195919880806<NA>3폐업2폐업20160411<NA><NA><NA>0203135593158.80121861서울특별시 마포구 아현동 437-3 고려아카데미텔 422서울특별시 마포구 마포대로 196 (아현동,고려아카데미텔 422)4206세한디앤에스(주)2008-04-07 16:04:39I2018-08-31 23:59:59.0건물위생관리업196057.949966449897.213071건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631300003130000-206-1988-0196019880819<NA>3폐업2폐업19940506<NA><NA><NA>020701106455.30121815서울특별시 마포구 도화동 538-0 성지빌딩 121호<NA><NA>한고개발(주)2002-02-20 00:00:00I2018-08-31 23:59:59.0건물위생관리업195184.3359448780.268775건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731300003130000-206-1988-0196119880801<NA>3폐업2폐업20160913<NA><NA><NA>020717126635.80121805서울특별시 마포구 공덕동 404서울특별시 마포구 마포대로 127, 301호 (공덕동, 풍림브이아이피텔)4144(주)영신엔지니어링2016-09-19 09:38:05I2018-08-31 23:59:59.0건물위생관리업195703.425297449330.800718건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831300003130000-206-1989-0196219890126<NA>3폐업2폐업20061231<NA><NA><NA>0207131304.00121736서울특별시 마포구 마포동 136-1 한신빌딩 211호서울특별시 마포구 마포대로 12 (마포동,한신빌딩 211호)4175한승멀틴(주)2007-03-29 00:00:00I2018-08-31 23:59:59.0건물위생관리업195019.399817448407.752665건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931300003130000-206-1989-0196319890518<NA>3폐업2폐업20030220<NA><NA><NA>0207020451.00121881서울특별시 마포구 창전동 11-1 6층<NA><NA>(주)고암2003-03-24 00:00:00I2018-08-31 23:59:59.0건물위생관리업<NA><NA>건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
53931300003130000-206-2023-000082023-10-24<NA>1영업/정상1영업<NA><NA><NA><NA><NA>220.76121-897서울특별시 마포구 합정동 366-1 THE VOID[더 보이드] 지하1층서울특별시 마포구 성지1길 30, THE VOID[더 보이드] 지하1층 (합정동)4072주식회사 일잘하는친구들2023-10-24 17:12:05I2022-10-30 22:06:00.0건물위생관리업192426.375119449520.574853<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54031300003130000-206-2023-000092023-10-26<NA>1영업/정상1영업<NA><NA><NA><NA><NA>3.00121-856서울특별시 마포구 신수동 459 밤섬경남아너스빌 109동 b101-123호서울특별시 마포구 독막로28길 10, 109동 지하층 b101-123호 (신수동, 밤섬경남아너스빌)4089지엘크린 주식회사2024-03-15 17:06:34U2023-12-02 23:07:00.0건물위생관리업194301.304866449388.053901<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54131300003130000-206-2023-000102023-12-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>9.90121-893서울특별시 마포구 서교동 373-6 4층 2호 1번, 6번서울특별시 마포구 양화로12길 23, 4층 2호 1번, 6번호 (서교동)4043키비주얼나인2023-12-01 10:03:24I2022-11-02 00:03:00.0건물위생관리업192716.491597449982.653533<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54231300003130000-206-2023-000112023-12-12<NA>1영업/정상1영업<NA><NA><NA><NA><NA>49.59121-702서울특별시 마포구 도화동 250-4 근신빌딩 본관 3층 301호서울특별시 마포구 삼개로 16, 근신빌딩 본관동 3층 301호 (도화동)4173태산종합관리 주식회사2023-12-12 15:55:21I2022-11-01 23:04:00.0건물위생관리업195258.466477448540.434893<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54331300003130000-206-2024-000012024-01-25<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.00121-805서울특별시 마포구 공덕동 404 풍림브이아이피텔 1116호서울특별시 마포구 마포대로 127, 풍림브이아이피텔 1116호 (공덕동)4144주식회사 인웍스2024-01-25 11:07:13I2023-11-30 22:07:00.0건물위생관리업195703.425297449330.800718<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54431300003130000-206-2024-000022024-02-29<NA>1영업/정상1영업<NA><NA><NA><NA><NA>3.30121-850서울특별시 마포구 성산동 591-4 대명비첸시티오피스텔 B105호서울특별시 마포구 월드컵로 196, 대명비첸시티오피스텔 B1층 105-C100호 (성산동)3938건양2024-02-29 14:29:31I2023-12-03 00:02:00.0건물위생관리업191337.868997451395.153093<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54531300003130000-206-2024-000032024-03-06<NA>1영업/정상1영업<NA><NA><NA><NA>02 31410181105.00121-846서울특별시 마포구 성산동 239-9 중산빌딩서울특별시 마포구 성미산로 64, 중산빌딩 5층 (성산동)3999(주)삼경기업2024-03-06 16:42:49I2023-12-03 00:08:00.0건물위생관리업192436.244584450800.375312<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54631300003130000-206-2024-000042024-03-13<NA>1영업/정상1영업<NA><NA><NA><NA><NA>3.30121-894서울특별시 마포구 서교동 375-25 2층 61호서울특별시 마포구 양화로15안길 19, 2층 61호 (서교동)4031해송시스템2024-03-13 17:20:20I2023-12-02 23:06:00.0건물위생관리업192730.376891450187.395199<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54731300003130000-206-2024-000052024-03-20<NA>1영업/정상1영업<NA><NA><NA><NA><NA>12.00121-844서울특별시 마포구 성산동 86-4 204호서울특별시 마포구 성산로 154, 204호 (성산동)3966마포크린2024-03-20 14:27:03I2023-12-02 22:02:00.0건물위생관리업192039.820018451386.82634<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
54831300003130000-206-2024-000062024-03-26<NA>1영업/정상1영업<NA><NA><NA><NA><NA>39.45121-807서울특별시 마포구 노고산동 109-47서울특별시 마포구 서강로16길 25, 1층 (노고산동)4109빌서비스2024-03-26 14:25:11I2023-12-02 22:08:00.0건물위생관리업194225.503409450041.142653<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>