Overview

Dataset statistics

Number of variables47
Number of observations853
Missing cells9133
Missing cells (%)22.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory336.7 KiB
Average record size in memory404.2 B

Variable types

Categorical20
Text7
DateTime4
Unsupported7
Numeric7
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-19295/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (63.7%)Imbalance
사용시작지하층 is highly imbalanced (69.5%)Imbalance
사용끝지하층 is highly imbalanced (71.7%)Imbalance
여성종사자수 is highly imbalanced (71.1%)Imbalance
남성종사자수 is highly imbalanced (69.7%)Imbalance
인허가취소일자 has 853 (100.0%) missing valuesMissing
폐업일자 has 276 (32.4%) missing valuesMissing
휴업시작일자 has 853 (100.0%) missing valuesMissing
휴업종료일자 has 853 (100.0%) missing valuesMissing
재개업일자 has 853 (100.0%) missing valuesMissing
전화번호 has 200 (23.4%) missing valuesMissing
소재지우편번호 has 12 (1.4%) missing valuesMissing
지번주소 has 12 (1.4%) missing valuesMissing
도로명주소 has 239 (28.0%) missing valuesMissing
도로명우편번호 has 246 (28.8%) missing valuesMissing
좌표정보(X) has 11 (1.3%) missing valuesMissing
좌표정보(Y) has 11 (1.3%) missing valuesMissing
건물지상층수 has 374 (43.8%) missing valuesMissing
건물지하층수 has 383 (44.9%) missing valuesMissing
사용시작지상층 has 484 (56.7%) missing valuesMissing
사용끝지상층 has 583 (68.3%) missing valuesMissing
발한실여부 has 177 (20.8%) missing valuesMissing
조건부허가신고사유 has 853 (100.0%) missing valuesMissing
조건부허가시작일자 has 853 (100.0%) missing valuesMissing
조건부허가종료일자 has 853 (100.0%) missing valuesMissing
다중이용업소여부 has 154 (18.1%) missing valuesMissing
도로명우편번호 is highly skewed (γ1 = 24.58832529)Skewed
좌표정보(Y) is highly skewed (γ1 = -28.24590045)Skewed
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 319 (37.4%) zerosZeros
건물지하층수 has 370 (43.4%) zerosZeros
사용시작지상층 has 53 (6.2%) zerosZeros
사용끝지상층 has 42 (4.9%) zerosZeros

Reproduction

Analysis started2024-05-11 06:29:26.233889
Analysis finished2024-05-11 06:29:28.075913
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
3180000
853 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3180000 853
100.0%

Length

2024-05-11T15:29:28.192824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:28.383276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 853
100.0%

관리번호
Text

UNIQUE 

Distinct853
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2024-05-11T15:29:28.679434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters18766
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

Unique853 ?
Unique (%)100.0%

Sample

1st row3180000-206-1987-02472
2nd row3180000-206-1987-02474
3rd row3180000-206-1987-02475
4th row3180000-206-1987-02478
5th row3180000-206-1987-02480
ValueCountFrequency (%)
3180000-206-1987-02472 1
 
0.1%
3180000-206-2012-00032 1
 
0.1%
3180000-206-2013-00019 1
 
0.1%
3180000-206-2013-00020 1
 
0.1%
3180000-206-2013-00021 1
 
0.1%
3180000-206-2013-00022 1
 
0.1%
3180000-206-2013-00023 1
 
0.1%
3180000-206-2013-00024 1
 
0.1%
3180000-206-2013-00025 1
 
0.1%
3180000-206-2013-00026 1
 
0.1%
Other values (843) 843
98.8%
2024-05-11T15:29:29.351899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8182
43.6%
- 2559
 
13.6%
2 2170
 
11.6%
1 1677
 
8.9%
3 1189
 
6.3%
8 1024
 
5.5%
6 1017
 
5.4%
9 305
 
1.6%
5 243
 
1.3%
4 240
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16207
86.4%
Dash Punctuation 2559
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8182
50.5%
2 2170
 
13.4%
1 1677
 
10.3%
3 1189
 
7.3%
8 1024
 
6.3%
6 1017
 
6.3%
9 305
 
1.9%
5 243
 
1.5%
4 240
 
1.5%
7 160
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 2559
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8182
43.6%
- 2559
 
13.6%
2 2170
 
11.6%
1 1677
 
8.9%
3 1189
 
6.3%
8 1024
 
5.5%
6 1017
 
5.4%
9 305
 
1.6%
5 243
 
1.3%
4 240
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8182
43.6%
- 2559
 
13.6%
2 2170
 
11.6%
1 1677
 
8.9%
3 1189
 
6.3%
8 1024
 
5.5%
6 1017
 
5.4%
9 305
 
1.6%
5 243
 
1.3%
4 240
 
1.3%
Distinct768
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
Minimum1987-03-03 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T15:29:29.631577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:29.930328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing853
Missing (%)100.0%
Memory size7.6 KiB
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
3
577 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 577
67.6%
1 276
32.4%

Length

2024-05-11T15:29:30.164380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:30.381226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 577
67.6%
1 276
32.4%

영업상태명
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
폐업
577 
영업/정상
276 

Length

Max length5
Median length2
Mean length2.9706917
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 577
67.6%
영업/정상 276
32.4%

Length

2024-05-11T15:29:30.591452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:30.829829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 577
67.6%
영업/정상 276
32.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2
577 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 577
67.6%
1 276
32.4%

Length

2024-05-11T15:29:31.002481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:31.198160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 577
67.6%
1 276
32.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
폐업
577 
영업
276 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 577
67.6%
영업 276
32.4%

Length

2024-05-11T15:29:31.413890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:31.591814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 577
67.6%
영업 276
32.4%

폐업일자
Date

MISSING 

Distinct467
Distinct (%)80.9%
Missing276
Missing (%)32.4%
Memory size6.8 KiB
Minimum1996-03-29 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T15:29:31.814149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:32.479831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing853
Missing (%)100.0%
Memory size7.6 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing853
Missing (%)100.0%
Memory size7.6 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing853
Missing (%)100.0%
Memory size7.6 KiB

전화번호
Text

MISSING 

Distinct635
Distinct (%)97.2%
Missing200
Missing (%)23.4%
Memory size6.8 KiB
2024-05-11T15:29:32.990158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.254211
Min length2

Characters and Unicode

Total characters6696
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

Unique619 ?
Unique (%)94.8%

Sample

1st row02 7846531
2nd row02 7806876
3rd row02 7822290
4th row02 8463312
5th row02 7826393
ValueCountFrequency (%)
02 332
30.2%
070 15
 
1.4%
031 6
 
0.5%
761 5
 
0.5%
785 4
 
0.4%
784 3
 
0.3%
835 3
 
0.3%
769 3
 
0.3%
0226332597 3
 
0.3%
780 3
 
0.3%
Other values (693) 724
65.8%
2024-05-11T15:29:33.760472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1165
17.4%
0 1099
16.4%
7 641
9.6%
6 616
9.2%
575
8.6%
8 555
8.3%
3 514
7.7%
1 481
7.2%
5 413
 
6.2%
4 357
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6121
91.4%
Space Separator 575
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1165
19.0%
0 1099
18.0%
7 641
10.5%
6 616
10.1%
8 555
9.1%
3 514
8.4%
1 481
7.9%
5 413
 
6.7%
4 357
 
5.8%
9 280
 
4.6%
Space Separator
ValueCountFrequency (%)
575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6696
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1165
17.4%
0 1099
16.4%
7 641
9.6%
6 616
9.2%
575
8.6%
8 555
8.3%
3 514
7.7%
1 481
7.2%
5 413
 
6.2%
4 357
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6696
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1165
17.4%
0 1099
16.4%
7 641
9.6%
6 616
9.2%
575
8.6%
8 555
8.3%
3 514
7.7%
1 481
7.2%
5 413
 
6.2%
4 357
 
5.3%
Distinct529
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2024-05-11T15:29:34.407952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.1031653
Min length3

Characters and Unicode

Total characters4353
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

Unique434 ?
Unique (%)50.9%

Sample

1st row105.25
2nd row51.55
3rd row83.25
4th row.00
5th row78.33
ValueCountFrequency (%)
00 53
 
6.2%
33.00 28
 
3.3%
66.00 18
 
2.1%
9.00 14
 
1.6%
60.00 13
 
1.5%
30.00 13
 
1.5%
99.00 12
 
1.4%
45.00 10
 
1.2%
40.00 9
 
1.1%
132.00 8
 
0.9%
Other values (519) 675
79.1%
2024-05-11T15:29:35.352652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1154
26.5%
. 853
19.6%
1 348
 
8.0%
3 340
 
7.8%
6 290
 
6.7%
2 283
 
6.5%
5 266
 
6.1%
4 237
 
5.4%
9 222
 
5.1%
8 189
 
4.3%
Other values (2) 171
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3494
80.3%
Other Punctuation 859
 
19.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1154
33.0%
1 348
 
10.0%
3 340
 
9.7%
6 290
 
8.3%
2 283
 
8.1%
5 266
 
7.6%
4 237
 
6.8%
9 222
 
6.4%
8 189
 
5.4%
7 165
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 853
99.3%
, 6
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1154
26.5%
. 853
19.6%
1 348
 
8.0%
3 340
 
7.8%
6 290
 
6.7%
2 283
 
6.5%
5 266
 
6.1%
4 237
 
5.4%
9 222
 
5.1%
8 189
 
4.3%
Other values (2) 171
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1154
26.5%
. 853
19.6%
1 348
 
8.0%
3 340
 
7.8%
6 290
 
6.7%
2 283
 
6.5%
5 266
 
6.1%
4 237
 
5.4%
9 222
 
5.1%
8 189
 
4.3%
Other values (2) 171
 
3.9%

소재지우편번호
Text

MISSING 

Distinct187
Distinct (%)22.2%
Missing12
Missing (%)1.4%
Memory size6.8 KiB
2024-05-11T15:29:35.783331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1212842
Min length6

Characters and Unicode

Total characters5148
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

Unique70 ?
Unique (%)8.3%

Sample

1st row150877
2nd row150886
3rd row150708
4th row150839
5th row150868
ValueCountFrequency (%)
150037 28
 
3.3%
150102 23
 
2.7%
150871 23
 
2.7%
150870 21
 
2.5%
150035 19
 
2.3%
150890 19
 
2.3%
150103 17
 
2.0%
150809 15
 
1.8%
150803 15
 
1.8%
150836 15
 
1.8%
Other values (177) 646
76.8%
2024-05-11T15:29:36.704455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1278
24.8%
1 1094
21.3%
5 972
18.9%
8 601
11.7%
3 290
 
5.6%
7 218
 
4.2%
9 187
 
3.6%
6 151
 
2.9%
4 132
 
2.6%
2 123
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5046
98.0%
Dash Punctuation 102
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1278
25.3%
1 1094
21.7%
5 972
19.3%
8 601
11.9%
3 290
 
5.7%
7 218
 
4.3%
9 187
 
3.7%
6 151
 
3.0%
4 132
 
2.6%
2 123
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5148
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1278
24.8%
1 1094
21.3%
5 972
18.9%
8 601
11.7%
3 290
 
5.6%
7 218
 
4.2%
9 187
 
3.6%
6 151
 
2.9%
4 132
 
2.6%
2 123
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1278
24.8%
1 1094
21.3%
5 972
18.9%
8 601
11.7%
3 290
 
5.6%
7 218
 
4.2%
9 187
 
3.6%
6 151
 
2.9%
4 132
 
2.6%
2 123
 
2.4%

지번주소
Text

MISSING 

Distinct815
Distinct (%)96.9%
Missing12
Missing (%)1.4%
Memory size6.8 KiB
2024-05-11T15:29:37.185074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length38
Mean length29.360285
Min length18

Characters and Unicode

Total characters24692
Distinct characters300
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique791 ?
Unique (%)94.1%

Sample

1st row서울특별시 영등포구 여의도동 24-3 공무원연금 401호
2nd row서울특별시 영등포구 여의도동 36-2 맨하탄빌딩1005호
3rd row서울특별시 영등포구 여의도동 25-5 동화빌딩 1403호
4th row서울특별시 영등포구 신길동 110-21
5th row서울특별시 영등포구 여의도동 10-0
ValueCountFrequency (%)
서울특별시 840
 
18.2%
영등포구 838
 
18.2%
여의도동 228
 
4.9%
신길동 84
 
1.8%
대림동 73
 
1.6%
당산동6가 39
 
0.8%
당산동3가 36
 
0.8%
문래동3가 34
 
0.7%
영등포동7가 31
 
0.7%
양평동4가 28
 
0.6%
Other values (1195) 2379
51.6%
2024-05-11T15:29:37.922533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4258
 
17.2%
1 1036
 
4.2%
989
 
4.0%
971
 
3.9%
967
 
3.9%
939
 
3.8%
859
 
3.5%
846
 
3.4%
842
 
3.4%
841
 
3.4%
Other values (290) 12144
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14368
58.2%
Decimal Number 5217
 
21.1%
Space Separator 4258
 
17.2%
Dash Punctuation 703
 
2.8%
Uppercase Letter 74
 
0.3%
Open Punctuation 23
 
0.1%
Close Punctuation 23
 
0.1%
Other Punctuation 18
 
0.1%
Lowercase Letter 6
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
989
 
6.9%
971
 
6.8%
967
 
6.7%
939
 
6.5%
859
 
6.0%
846
 
5.9%
842
 
5.9%
841
 
5.9%
841
 
5.9%
840
 
5.8%
Other values (251) 5433
37.8%
Uppercase Letter
ValueCountFrequency (%)
B 21
28.4%
K 15
20.3%
A 9
12.2%
T 6
 
8.1%
S 4
 
5.4%
E 4
 
5.4%
D 4
 
5.4%
R 2
 
2.7%
H 2
 
2.7%
P 2
 
2.7%
Other values (5) 5
 
6.8%
Decimal Number
ValueCountFrequency (%)
1 1036
19.9%
2 758
14.5%
3 705
13.5%
4 574
11.0%
0 564
10.8%
5 442
8.5%
6 384
 
7.4%
7 290
 
5.6%
9 242
 
4.6%
8 222
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
n 2
33.3%
k 1
16.7%
p 1
16.7%
i 1
16.7%
v 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 7
38.9%
/ 6
33.3%
& 3
16.7%
. 2
 
11.1%
Space Separator
ValueCountFrequency (%)
4258
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 703
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14368
58.2%
Common 10244
41.5%
Latin 80
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
989
 
6.9%
971
 
6.8%
967
 
6.7%
939
 
6.5%
859
 
6.0%
846
 
5.9%
842
 
5.9%
841
 
5.9%
841
 
5.9%
840
 
5.8%
Other values (251) 5433
37.8%
Latin
ValueCountFrequency (%)
B 21
26.2%
K 15
18.8%
A 9
11.2%
T 6
 
7.5%
S 4
 
5.0%
E 4
 
5.0%
D 4
 
5.0%
n 2
 
2.5%
R 2
 
2.5%
H 2
 
2.5%
Other values (10) 11
13.8%
Common
ValueCountFrequency (%)
4258
41.6%
1 1036
 
10.1%
2 758
 
7.4%
3 705
 
6.9%
- 703
 
6.9%
4 574
 
5.6%
0 564
 
5.5%
5 442
 
4.3%
6 384
 
3.7%
7 290
 
2.8%
Other values (9) 530
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14368
58.2%
ASCII 10324
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4258
41.2%
1 1036
 
10.0%
2 758
 
7.3%
3 705
 
6.8%
- 703
 
6.8%
4 574
 
5.6%
0 564
 
5.5%
5 442
 
4.3%
6 384
 
3.7%
7 290
 
2.8%
Other values (29) 610
 
5.9%
Hangul
ValueCountFrequency (%)
989
 
6.9%
971
 
6.8%
967
 
6.7%
939
 
6.5%
859
 
6.0%
846
 
5.9%
842
 
5.9%
841
 
5.9%
841
 
5.9%
840
 
5.8%
Other values (251) 5433
37.8%

도로명주소
Text

MISSING 

Distinct601
Distinct (%)97.9%
Missing239
Missing (%)28.0%
Memory size6.8 KiB
2024-05-11T15:29:38.434011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length48
Mean length37.416938
Min length23

Characters and Unicode

Total characters22974
Distinct characters283
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique588 ?
Unique (%)95.8%

Sample

1st row서울특별시 영등포구 국제금융로6길 33 (여의도동,맨하탄빌딩1005호)
2nd row서울특별시 영등포구 여의나루로 71, 1403호 (여의도동,동화빌딩)
3rd row서울특별시 영등포구 국제금융로8길 27-9, 908호 (여의도동, 동북빌딩)
4th row서울특별시 영등포구 디지털로48길 23 (대림동,성원빌딩101호)
5th row서울특별시 영등포구 신길로 197-1 (신길동,3층)
ValueCountFrequency (%)
서울특별시 613
 
15.0%
영등포구 611
 
14.9%
여의도동 114
 
2.8%
신길동 50
 
1.2%
2층 46
 
1.1%
대림동 38
 
0.9%
1층 38
 
0.9%
4층 37
 
0.9%
영등포로 25
 
0.6%
3층 25
 
0.6%
Other values (1005) 2498
61.0%
2024-05-11T15:29:39.348956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3482
 
15.2%
850
 
3.7%
1 794
 
3.5%
777
 
3.4%
775
 
3.4%
, 715
 
3.1%
704
 
3.1%
629
 
2.7%
628
 
2.7%
) 627
 
2.7%
Other values (273) 12993
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13456
58.6%
Decimal Number 3905
 
17.0%
Space Separator 3482
 
15.2%
Other Punctuation 723
 
3.1%
Close Punctuation 627
 
2.7%
Open Punctuation 627
 
2.7%
Dash Punctuation 82
 
0.4%
Uppercase Letter 68
 
0.3%
Lowercase Letter 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
850
 
6.3%
777
 
5.8%
775
 
5.8%
704
 
5.2%
629
 
4.7%
628
 
4.7%
621
 
4.6%
616
 
4.6%
615
 
4.6%
614
 
4.6%
Other values (238) 6627
49.2%
Uppercase Letter
ValueCountFrequency (%)
B 20
29.4%
K 16
23.5%
A 6
 
8.8%
T 5
 
7.4%
E 4
 
5.9%
S 3
 
4.4%
D 3
 
4.4%
N 2
 
2.9%
H 2
 
2.9%
P 2
 
2.9%
Other values (4) 5
 
7.4%
Decimal Number
ValueCountFrequency (%)
1 794
20.3%
2 548
14.0%
0 487
12.5%
3 464
11.9%
4 395
10.1%
5 293
 
7.5%
6 292
 
7.5%
7 286
 
7.3%
8 189
 
4.8%
9 157
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 715
98.9%
/ 4
 
0.6%
& 3
 
0.4%
. 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
n 2
66.7%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
3482
100.0%
Close Punctuation
ValueCountFrequency (%)
) 627
100.0%
Open Punctuation
ValueCountFrequency (%)
( 627
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13456
58.6%
Common 9447
41.1%
Latin 71
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
850
 
6.3%
777
 
5.8%
775
 
5.8%
704
 
5.2%
629
 
4.7%
628
 
4.7%
621
 
4.6%
616
 
4.6%
615
 
4.6%
614
 
4.6%
Other values (238) 6627
49.2%
Common
ValueCountFrequency (%)
3482
36.9%
1 794
 
8.4%
, 715
 
7.6%
) 627
 
6.6%
( 627
 
6.6%
2 548
 
5.8%
0 487
 
5.2%
3 464
 
4.9%
4 395
 
4.2%
5 293
 
3.1%
Other values (9) 1015
 
10.7%
Latin
ValueCountFrequency (%)
B 20
28.2%
K 16
22.5%
A 6
 
8.5%
T 5
 
7.0%
E 4
 
5.6%
S 3
 
4.2%
D 3
 
4.2%
n 2
 
2.8%
N 2
 
2.8%
H 2
 
2.8%
Other values (6) 8
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13456
58.6%
ASCII 9518
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3482
36.6%
1 794
 
8.3%
, 715
 
7.5%
) 627
 
6.6%
( 627
 
6.6%
2 548
 
5.8%
0 487
 
5.1%
3 464
 
4.9%
4 395
 
4.2%
5 293
 
3.1%
Other values (25) 1086
 
11.4%
Hangul
ValueCountFrequency (%)
850
 
6.3%
777
 
5.8%
775
 
5.8%
704
 
5.2%
629
 
4.7%
628
 
4.7%
621
 
4.6%
616
 
4.6%
615
 
4.6%
614
 
4.6%
Other values (238) 6627
49.2%

도로명우편번호
Real number (ℝ)

MISSING  SKEWED 

Distinct161
Distinct (%)26.5%
Missing246
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean7385.3707
Minimum7201
Maximum61087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-05-11T15:29:39.579359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7201
5-th percentile7213
Q17241.5
median7286
Q37333
95-th percentile7410
Maximum61087
Range53886
Interquartile range (IQR)91.5

Descriptive statistics

Standard deviation2184.7321
Coefficient of variation (CV)0.29581888
Kurtosis605.37847
Mean7385.3707
Median Absolute Deviation (MAD)47
Skewness24.588325
Sum4482920
Variance4773054.4
MonotonicityNot monotonic
2024-05-11T15:29:39.823778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7333 30
 
3.5%
7238 26
 
3.0%
7222 23
 
2.7%
7250 20
 
2.3%
7345 17
 
2.0%
7327 16
 
1.9%
7331 13
 
1.5%
7332 13
 
1.5%
7275 13
 
1.5%
7264 11
 
1.3%
Other values (151) 425
49.8%
(Missing) 246
28.8%
ValueCountFrequency (%)
7201 1
 
0.1%
7202 2
 
0.2%
7203 2
 
0.2%
7204 2
 
0.2%
7205 1
 
0.1%
7206 6
0.7%
7207 2
 
0.2%
7208 4
0.5%
7209 4
0.5%
7212 3
0.4%
ValueCountFrequency (%)
61087 1
 
0.1%
8376 1
 
0.1%
7965 1
 
0.1%
7446 1
 
0.1%
7442 3
0.4%
7437 1
 
0.1%
7435 1
 
0.1%
7434 1
 
0.1%
7433 2
0.2%
7432 3
0.4%
Distinct824
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2024-05-11T15:29:40.374007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length22
Mean length8.3376319
Min length2

Characters and Unicode

Total characters7112
Distinct characters432
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

Unique798 ?
Unique (%)93.6%

Sample

1st row(주)고암
2nd row아세아환경(주)
3rd row(주)삼호실업
4th row(주)대경성업
5th row(주)호천
ValueCountFrequency (%)
주식회사 89
 
8.8%
서비스 5
 
0.5%
5
 
0.5%
주)제이엘티엠 4
 
0.4%
주)휴먼링크 3
 
0.3%
유한책임회사 2
 
0.2%
리에이즈 2
 
0.2%
주)스마트파트너스원 2
 
0.2%
인창산업 2
 
0.2%
주)고암 2
 
0.2%
Other values (872) 895
88.5%
2024-05-11T15:29:41.162328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
680
 
9.6%
) 581
 
8.2%
( 576
 
8.1%
259
 
3.6%
188
 
2.6%
166
 
2.3%
159
 
2.2%
142
 
2.0%
125
 
1.8%
123
 
1.7%
Other values (422) 4113
57.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5634
79.2%
Close Punctuation 581
 
8.2%
Open Punctuation 576
 
8.1%
Space Separator 159
 
2.2%
Uppercase Letter 92
 
1.3%
Lowercase Letter 42
 
0.6%
Other Punctuation 17
 
0.2%
Decimal Number 8
 
0.1%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
680
 
12.1%
259
 
4.6%
188
 
3.3%
166
 
2.9%
142
 
2.5%
125
 
2.2%
123
 
2.2%
102
 
1.8%
80
 
1.4%
77
 
1.4%
Other values (371) 3692
65.5%
Uppercase Letter
ValueCountFrequency (%)
C 14
15.2%
S 12
13.0%
E 10
10.9%
M 8
8.7%
T 7
 
7.6%
I 6
 
6.5%
N 6
 
6.5%
B 4
 
4.3%
O 4
 
4.3%
A 3
 
3.3%
Other values (11) 18
19.6%
Lowercase Letter
ValueCountFrequency (%)
e 8
19.0%
n 6
14.3%
a 5
11.9%
o 3
 
7.1%
c 3
 
7.1%
m 3
 
7.1%
i 2
 
4.8%
g 2
 
4.8%
l 2
 
4.8%
h 2
 
4.8%
Other values (6) 6
14.3%
Decimal Number
ValueCountFrequency (%)
1 2
25.0%
7 1
12.5%
2 1
12.5%
3 1
12.5%
6 1
12.5%
4 1
12.5%
9 1
12.5%
Other Punctuation
ValueCountFrequency (%)
. 9
52.9%
& 7
41.2%
, 1
 
5.9%
Close Punctuation
ValueCountFrequency (%)
) 581
100.0%
Open Punctuation
ValueCountFrequency (%)
( 576
100.0%
Space Separator
ValueCountFrequency (%)
159
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5634
79.2%
Common 1344
 
18.9%
Latin 134
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
680
 
12.1%
259
 
4.6%
188
 
3.3%
166
 
2.9%
142
 
2.5%
125
 
2.2%
123
 
2.2%
102
 
1.8%
80
 
1.4%
77
 
1.4%
Other values (371) 3692
65.5%
Latin
ValueCountFrequency (%)
C 14
 
10.4%
S 12
 
9.0%
E 10
 
7.5%
e 8
 
6.0%
M 8
 
6.0%
T 7
 
5.2%
I 6
 
4.5%
N 6
 
4.5%
n 6
 
4.5%
a 5
 
3.7%
Other values (27) 52
38.8%
Common
ValueCountFrequency (%)
) 581
43.2%
( 576
42.9%
159
 
11.8%
. 9
 
0.7%
& 7
 
0.5%
- 3
 
0.2%
1 2
 
0.1%
7 1
 
0.1%
2 1
 
0.1%
, 1
 
0.1%
Other values (4) 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5634
79.2%
ASCII 1478
 
20.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
680
 
12.1%
259
 
4.6%
188
 
3.3%
166
 
2.9%
142
 
2.5%
125
 
2.2%
123
 
2.2%
102
 
1.8%
80
 
1.4%
77
 
1.4%
Other values (371) 3692
65.5%
ASCII
ValueCountFrequency (%)
) 581
39.3%
( 576
39.0%
159
 
10.8%
C 14
 
0.9%
S 12
 
0.8%
E 10
 
0.7%
. 9
 
0.6%
e 8
 
0.5%
M 8
 
0.5%
& 7
 
0.5%
Other values (41) 94
 
6.4%
Distinct769
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
Minimum1999-09-06 00:00:00
Maximum2024-05-09 16:12:02
2024-05-11T15:29:41.424607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:41.712938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
I
625 
U
228 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 625
73.3%
U 228
 
26.7%

Length

2024-05-11T15:29:41.945862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:42.191940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 625
73.3%
u 228
 
26.7%
Distinct277
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:02:00
2024-05-11T15:29:42.443586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:29:42.723105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
건물위생관리업
794 
건물위생관리업 기타
 
59

Length

Max length10
Median length7
Mean length7.2075029
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 794
93.1%
건물위생관리업 기타 59
 
6.9%

Length

2024-05-11T15:29:42.966627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:43.255749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 853
93.5%
기타 59
 
6.5%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct511
Distinct (%)60.7%
Missing11
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean191771.31
Minimum187289.83
Maximum194632.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-05-11T15:29:43.573673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum187289.83
5-th percentile189940.38
Q1190825.66
median191540.58
Q3192879.85
95-th percentile193818.79
Maximum194632.53
Range7342.6998
Interquartile range (IQR)2054.1861

Descriptive statistics

Standard deviation1277.2768
Coefficient of variation (CV)0.0066604163
Kurtosis-0.67390698
Mean191771.31
Median Absolute Deviation (MAD)958.00368
Skewness0.2888241
Sum1.6147145 × 108
Variance1631436
MonotonicityNot monotonic
2024-05-11T15:29:43.949417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194561.746032498 14
 
1.6%
190555.768991595 13
 
1.5%
193469.554731741 13
 
1.5%
191634.434595481 10
 
1.2%
189682.022243843 10
 
1.2%
190996.357288859 7
 
0.8%
193798.948251805 7
 
0.8%
189700.355755718 6
 
0.7%
190364.652010662 6
 
0.7%
192959.271161366 6
 
0.7%
Other values (501) 750
87.9%
(Missing) 11
 
1.3%
ValueCountFrequency (%)
187289.826565 1
 
0.1%
187897.979059132 1
 
0.1%
189549.847307536 2
 
0.2%
189574.962072527 1
 
0.1%
189586.236800721 1
 
0.1%
189607.598899153 2
 
0.2%
189651.949080387 2
 
0.2%
189662.247717869 1
 
0.1%
189679.286130733 2
 
0.2%
189682.022243843 10
1.2%
ValueCountFrequency (%)
194632.526367463 3
 
0.4%
194592.276750438 1
 
0.1%
194561.746032498 14
1.6%
194530.535390096 5
 
0.6%
194504.656267957 1
 
0.1%
194028.635844427 3
 
0.4%
193999.840415079 1
 
0.1%
193989.272586157 2
 
0.2%
193870.812916718 1
 
0.1%
193861.272368256 3
 
0.4%

좌표정보(Y)
Real number (ℝ)

MISSING  SKEWED 

Distinct511
Distinct (%)60.7%
Missing11
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean446150.37
Minimum189707.52
Maximum449021.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-05-11T15:29:44.329153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189707.52
5-th percentile443942.16
Q1445998.49
median446595.21
Q3447178.67
95-th percentile448172.84
Maximum449021.44
Range259313.92
Interquartile range (IQR)1180.183

Descriptive statistics

Standard deviation8927.951
Coefficient of variation (CV)0.02001108
Kurtosis812.17795
Mean446150.37
Median Absolute Deviation (MAD)584.04739
Skewness-28.2459
Sum3.7565861 × 108
Variance79708309
MonotonicityNot monotonic
2024-05-11T15:29:44.672346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446364.318286465 14
 
1.6%
446698.814322782 13
 
1.5%
446508.068667777 13
 
1.5%
446595.213058616 10
 
1.2%
446823.857632163 10
 
1.2%
445841.377603245 7
 
0.8%
446579.652565611 7
 
0.8%
446888.903509382 6
 
0.7%
447158.946262059 6
 
0.7%
447637.656082358 6
 
0.7%
Other values (501) 750
87.9%
(Missing) 11
 
1.3%
ValueCountFrequency (%)
189707.523875 1
 
0.1%
442708.140860448 1
 
0.1%
442756.531513655 1
 
0.1%
442888.411210573 1
 
0.1%
442905.116903156 3
0.4%
443035.744665571 2
0.2%
443048.18952177 3
0.4%
443056.576924345 1
 
0.1%
443090.653895762 1
 
0.1%
443129.751254213 1
 
0.1%
ValueCountFrequency (%)
449021.440559054 1
0.1%
448906.42052912 1
0.1%
448896.010728335 1
0.1%
448779.57454276 1
0.1%
448728.619441197 1
0.1%
448667.181702536 1
0.1%
448656.726986041 2
0.2%
448548.48004759 1
0.1%
448527.841857699 1
0.1%
448507.891439415 1
0.1%

위생업태명
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
건물위생관리업
647 
<NA>
154 
건물위생관리업 기타
 
52

Length

Max length10
Median length7
Mean length6.6412661
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 647
75.8%
<NA> 154
 
18.1%
건물위생관리업 기타 52
 
6.1%

Length

2024-05-11T15:29:44.907787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:45.130620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 699
77.2%
na 154
 
17.0%
기타 52
 
5.7%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)4.6%
Missing374
Missing (%)43.8%
Infinite0
Infinite (%)0.0%
Mean2.2421712
Minimum0
Maximum23
Zeros319
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-05-11T15:29:45.336835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile11
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1387818
Coefficient of variation (CV)1.8458813
Kurtosis4.9903872
Mean2.2421712
Median Absolute Deviation (MAD)0
Skewness2.2289907
Sum1074
Variance17.129515
MonotonicityNot monotonic
2024-05-11T15:29:45.580604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 319
37.4%
5 32
 
3.8%
4 28
 
3.3%
3 21
 
2.5%
10 13
 
1.5%
2 9
 
1.1%
1 8
 
0.9%
11 8
 
0.9%
8 6
 
0.7%
15 5
 
0.6%
Other values (12) 30
 
3.5%
(Missing) 374
43.8%
ValueCountFrequency (%)
0 319
37.4%
1 8
 
0.9%
2 9
 
1.1%
3 21
 
2.5%
4 28
 
3.3%
5 32
 
3.8%
6 3
 
0.4%
7 5
 
0.6%
8 6
 
0.7%
9 4
 
0.5%
ValueCountFrequency (%)
23 1
 
0.1%
21 1
 
0.1%
20 2
 
0.2%
18 2
 
0.2%
17 1
 
0.1%
16 1
 
0.1%
15 5
0.6%
14 4
0.5%
13 2
 
0.2%
12 4
0.5%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)2.3%
Missing383
Missing (%)44.9%
Infinite0
Infinite (%)0.0%
Mean0.49574468
Minimum0
Maximum15
Zeros370
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-05-11T15:29:45.796924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4451533
Coefficient of variation (CV)2.9151161
Kurtosis40.644891
Mean0.49574468
Median Absolute Deviation (MAD)0
Skewness5.3913076
Sum233
Variance2.088468
MonotonicityNot monotonic
2024-05-11T15:29:46.002073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 370
43.4%
1 60
 
7.0%
4 18
 
2.1%
3 7
 
0.8%
2 7
 
0.8%
6 2
 
0.2%
5 2
 
0.2%
14 1
 
0.1%
8 1
 
0.1%
7 1
 
0.1%
(Missing) 383
44.9%
ValueCountFrequency (%)
0 370
43.4%
1 60
 
7.0%
2 7
 
0.8%
3 7
 
0.8%
4 18
 
2.1%
5 2
 
0.2%
6 2
 
0.2%
7 1
 
0.1%
8 1
 
0.1%
14 1
 
0.1%
ValueCountFrequency (%)
15 1
 
0.1%
14 1
 
0.1%
8 1
 
0.1%
7 1
 
0.1%
6 2
 
0.2%
5 2
 
0.2%
4 18
 
2.1%
3 7
 
0.8%
2 7
 
0.8%
1 60
7.0%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)5.4%
Missing484
Missing (%)56.7%
Infinite0
Infinite (%)0.0%
Mean5.4254743
Minimum0
Maximum503
Zeros53
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-05-11T15:29:46.467173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile12
Maximum503
Range503
Interquartile range (IQR)4

Descriptive statistics

Standard deviation26.280686
Coefficient of variation (CV)4.8439426
Kurtosis351.89214
Mean5.4254743
Median Absolute Deviation (MAD)2
Skewness18.54745
Sum2002
Variance690.67446
MonotonicityNot monotonic
2024-05-11T15:29:46.752292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 66
 
7.7%
0 53
 
6.2%
3 44
 
5.2%
4 43
 
5.0%
1 41
 
4.8%
5 31
 
3.6%
6 16
 
1.9%
8 15
 
1.8%
7 11
 
1.3%
9 11
 
1.3%
Other values (10) 38
 
4.5%
(Missing) 484
56.7%
ValueCountFrequency (%)
0 53
6.2%
1 41
4.8%
2 66
7.7%
3 44
5.2%
4 43
5.0%
5 31
3.6%
6 16
 
1.9%
7 11
 
1.3%
8 15
 
1.8%
9 11
 
1.3%
ValueCountFrequency (%)
503 1
 
0.1%
35 1
 
0.1%
24 1
 
0.1%
20 1
 
0.1%
15 2
 
0.2%
14 3
 
0.4%
13 4
 
0.5%
12 7
0.8%
11 8
0.9%
10 10
1.2%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)9.3%
Missing583
Missing (%)68.3%
Infinite0
Infinite (%)0.0%
Mean16.696296
Minimum0
Maximum904
Zeros42
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size7.6 KiB
2024-05-11T15:29:47.018055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile13.55
Maximum904
Range904
Interquartile range (IQR)4

Descriptive statistics

Standard deviation85.946493
Coefficient of variation (CV)5.1476382
Kurtosis64.902692
Mean16.696296
Median Absolute Deviation (MAD)2
Skewness7.7498948
Sum4508
Variance7386.7996
MonotonicityNot monotonic
2024-05-11T15:29:47.305040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 46
 
5.4%
0 42
 
4.9%
3 33
 
3.9%
4 32
 
3.8%
1 23
 
2.7%
5 21
 
2.5%
6 13
 
1.5%
8 12
 
1.4%
10 9
 
1.1%
7 8
 
0.9%
Other values (15) 31
 
3.6%
(Missing) 583
68.3%
ValueCountFrequency (%)
0 42
4.9%
1 23
2.7%
2 46
5.4%
3 33
3.9%
4 32
3.8%
5 21
2.5%
6 13
 
1.5%
7 8
 
0.9%
8 12
 
1.4%
9 7
 
0.8%
ValueCountFrequency (%)
904 1
0.1%
706 1
0.1%
509 1
0.1%
503 1
0.1%
307 1
0.1%
302 1
0.1%
201 1
0.1%
35 1
0.1%
20 1
0.1%
15 2
0.2%

사용시작지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
738 
0
78 
1
 
35
2
 
1
103
 
1

Length

Max length4
Median length4
Mean length3.5978898
Min length1

Unique

Unique2 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 738
86.5%
0 78
 
9.1%
1 35
 
4.1%
2 1
 
0.1%
103 1
 
0.1%

Length

2024-05-11T15:29:47.586122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:47.901764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 738
86.5%
0 78
 
9.1%
1 35
 
4.1%
2 1
 
0.1%
103 1
 
0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
768 
0
 
57
1
 
27
2
 
1

Length

Max length4
Median length4
Mean length3.7010551
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 768
90.0%
0 57
 
6.7%
1 27
 
3.2%
2 1
 
0.1%

Length

2024-05-11T15:29:48.145191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:48.382821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 768
90.0%
0 57
 
6.7%
1 27
 
3.2%
2 1
 
0.1%

한실수
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
431 
0
422 

Length

Max length4
Median length4
Mean length2.5158265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 431
50.5%
0 422
49.5%

Length

2024-05-11T15:29:48.628154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:48.856996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 431
50.5%
0 422
49.5%

양실수
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
431 
0
422 

Length

Max length4
Median length4
Mean length2.5158265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 431
50.5%
0 422
49.5%

Length

2024-05-11T15:29:49.122527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:49.310049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 431
50.5%
0 422
49.5%

욕실수
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
431 
0
422 

Length

Max length4
Median length4
Mean length2.5158265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 431
50.5%
0 422
49.5%

Length

2024-05-11T15:29:49.935019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:50.145747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 431
50.5%
0 422
49.5%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing177
Missing (%)20.8%
Memory size1.8 KiB
False
676 
(Missing)
177 
ValueCountFrequency (%)
False 676
79.2%
(Missing) 177
 
20.8%
2024-05-11T15:29:50.309151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
431 
0
422 

Length

Max length4
Median length4
Mean length2.5158265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 431
50.5%
0 422
49.5%

Length

2024-05-11T15:29:50.482782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:50.644834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 431
50.5%
0 422
49.5%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing853
Missing (%)100.0%
Memory size7.6 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing853
Missing (%)100.0%
Memory size7.6 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing853
Missing (%)100.0%
Memory size7.6 KiB
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
593 
임대
250 
자가
 
10

Length

Max length4
Median length4
Mean length3.3903869
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> 593
69.5%
임대 250
29.3%
자가 10
 
1.2%

Length

2024-05-11T15:29:50.834177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:51.060433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 593
69.5%
임대 250
29.3%
자가 10
 
1.2%

세탁기수
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
453 
0
400 

Length

Max length4
Median length4
Mean length2.5932005
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> 453
53.1%
0 400
46.9%

Length

2024-05-11T15:29:51.297504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:51.482948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 453
53.1%
0 400
46.9%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
707 
0
135 
1
 
6
2
 
3
3
 
1

Length

Max length4
Median length4
Mean length3.4865182
Min length1

Unique

Unique2 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 707
82.9%
0 135
 
15.8%
1 6
 
0.7%
2 3
 
0.4%
3 1
 
0.1%
4 1
 
0.1%

Length

2024-05-11T15:29:51.671162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:51.871432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 707
82.9%
0 135
 
15.8%
1 6
 
0.7%
2 3
 
0.4%
3 1
 
0.1%
4 1
 
0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
707 
0
129 
1
 
8
2
 
6
3
 
2

Length

Max length4
Median length4
Mean length3.4865182
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 707
82.9%
0 129
 
15.1%
1 8
 
0.9%
2 6
 
0.7%
3 2
 
0.2%
4 1
 
0.1%

Length

2024-05-11T15:29:52.048657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:52.196626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 707
82.9%
0 129
 
15.1%
1 8
 
0.9%
2 6
 
0.7%
3 2
 
0.2%
4 1
 
0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
496 
0
357 

Length

Max length4
Median length4
Mean length2.7444314
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> 496
58.1%
0 357
41.9%

Length

2024-05-11T15:29:52.423192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:52.630487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 496
58.1%
0 357
41.9%

침대수
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
<NA>
514 
0
339 

Length

Max length4
Median length4
Mean length2.8077374
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> 514
60.3%
0 339
39.7%

Length

2024-05-11T15:29:52.807023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:29:53.008401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 514
60.3%
0 339
39.7%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing154
Missing (%)18.1%
Memory size1.8 KiB
False
699 
(Missing)
154 
ValueCountFrequency (%)
False 699
81.9%
(Missing) 154
 
18.1%
2024-05-11T15:29:53.210284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031800003180000-206-1987-0247219870602<NA>3폐업2폐업19961217<NA><NA><NA>02 7846531105.25150877서울특별시 영등포구 여의도동 24-3 공무원연금 401호<NA><NA>(주)고암2001-08-02 00:00:00I2018-08-31 23:59:59.0건물위생관리업193523.965234446930.57677건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131800003180000-206-1987-0247419870303<NA>1영업/정상1영업<NA><NA><NA><NA>02 780687651.55150886서울특별시 영등포구 여의도동 36-2 맨하탄빌딩1005호서울특별시 영등포구 국제금융로6길 33 (여의도동,맨하탄빌딩1005호)7331아세아환경(주)2017-04-14 11:00:16I2018-08-31 23:59:59.0건물위생관리업193469.554732446508.068668건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231800003180000-206-1987-0247519870604<NA>3폐업2폐업20220830<NA><NA><NA>02 782229083.25150708서울특별시 영등포구 여의도동 25-5 동화빌딩 1403호서울특별시 영등포구 여의나루로 71, 1403호 (여의도동,동화빌딩)7327(주)삼호실업2022-09-02 21:10:09U2021-12-09 00:04:00.0건물위생관리업193472.910163446842.048446<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
331800003180000-206-1987-0247819870819<NA>3폐업2폐업20021231<NA><NA><NA>02 8463312.00150839서울특별시 영등포구 신길동 110-21<NA><NA>(주)대경성업2003-04-03 00:00:00I2018-08-31 23:59:59.0건물위생관리업192643.466253445575.089252건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431800003180000-206-1987-0248019871215<NA>3폐업2폐업20050531<NA><NA><NA>02 782639378.33150868서울특별시 영등포구 여의도동 10-0<NA><NA>(주)호천2003-07-07 00:00:00I2018-08-31 23:59:59.0건물위생관리업193411.101987447538.816738건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531800003180000-206-1987-0248119871228<NA>3폐업2폐업20041122<NA><NA><NA>02 785636371.40150890서울특별시 영등포구 여의도동 44-15 1108호<NA><NA>(주)제일안전관리2004-12-13 00:00:00I2018-08-31 23:59:59.0건물위생관리업193840.710102446413.386546건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631800003180000-206-1988-0248419881220<NA>3폐업2폐업19970731<NA><NA><NA>02 7803851.00150869서울특별시 영등포구 여의도동 12-5<NA><NA>(주)내경2001-08-02 00:00:00I2018-08-31 23:59:59.0건물위생관리업193224.517898447474.871772건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
731800003180000-206-1989-0000119890906<NA>3폐업2폐업20170526<NA><NA><NA>02 3122570354.71150891서울특별시 영등포구 여의도동 45-14 동북빌딩 908호서울특별시 영등포구 국제금융로8길 27-9, 908호 (여의도동, 동북빌딩)7332명신아이엔에스(주)2017-05-26 11:55:05I2018-08-31 23:59:59.0건물위생관리업193636.76878446396.975636건물위생관리업000<NA>0<NA>000N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831800003180000-206-1990-0248719901001<NA>1영업/정상1영업<NA><NA><NA><NA>02 831123892.56150824서울특별시 영등포구 대림동 1025-73 성원빌딩101호서울특별시 영등포구 디지털로48길 23 (대림동,성원빌딩101호)7446성원산업(주)2009-05-06 16:10:24I2018-08-31 23:59:59.0건물위생관리업191226.384665442888.411211건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
931800003180000-206-1991-0248319911219<NA>3폐업2폐업20070308<NA><NA><NA>0226713934148.00150808서울특별시 영등포구 당산동6가 121-99<NA><NA>수도종합방제(주)2005-05-23 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
84331800003180000-206-2024-000022024-01-12<NA>1영업/정상1영업<NA><NA><NA><NA><NA>23.44150-841서울특별시 영등포구 신길동 310-25서울특별시 영등포구 가마산로61길 20-16, 1층 (신길동)7383새날 E&C2024-01-12 11:50:56I2023-11-30 23:04:00.0건물위생관리업191543.813211444976.752745<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
84431800003180000-206-2024-000032024-02-13<NA>1영업/정상1영업<NA><NA><NA><NA><NA>68.17150-102서울특별시 영등포구 양평동2가 37-1 동아프라임밸리 902호서울특별시 영등포구 영등포로5길 19, 동아프라임밸리 9층 902호 (양평동2가)7275한국장애인고용산업주식회사2024-02-13 16:06:12I2023-12-01 23:05:00.0건물위생관리업189700.355756446888.903509<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
84531800003180000-206-2024-000042024-02-14<NA>1영업/정상1영업<NA><NA><NA><NA>02 783136045.00150-886서울특별시 영등포구 여의도동 36-2 1005호서울특별시 영등포구 국제금융로6길 33, 10층 1005호 (여의도동)7331에이비엠에스(주)2024-02-14 10:54:43I2023-12-01 23:06:00.0건물위생관리업193469.554732446508.068668<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
84631800003180000-206-2024-000052024-02-27<NA>1영업/정상1영업<NA><NA><NA><NA>022678555086.73150-809서울특별시 영등포구 당산동6가 304-2 당산동홍익빌딩 801호서울특별시 영등포구 당산로 233, 당산동홍익빌딩 8층 801호 (당산동6가)7222주식회사 다옴씨엔앤2024-02-27 11:23:20I2023-12-01 22:09:00.0건물위생관리업191269.011197448039.161845<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
84731800003180000-206-2024-000062024-03-04<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.00150-841서울특별시 영등포구 신길동 298-15서울특별시 영등포구 가마산로65길 32, 2층 (신길동)7384청소토피아2024-03-04 17:05:15I2023-12-03 00:06:00.0건물위생관리업191605.461826445138.671507<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
84831800003180000-206-2024-000072024-03-21<NA>1영업/정상1영업<NA><NA><NA><NA>027827892153.16150-890서울특별시 영등포구 여의도동 44-2 태양빌딩 405호서울특별시 영등포구 여의대방로67길 22, 태양빌딩 405, 406호 (여의도동)7333한국카히스캅시큐리티 유한책임회사2024-03-21 10:54:53I2023-12-02 22:03:00.0건물위생관리업193730.450951446509.09836<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
84931800003180000-206-2024-000082024-03-27<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.00150-840서울특별시 영등포구 신길동 154-9 101호서울특별시 영등포구 도신로56길 12, 1층 101호 (신길동)7348참그린2024-03-27 14:47:18I2023-12-02 22:09:00.0건물위생관리업192497.07483445393.800275<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
85031800003180000-206-2024-000092024-03-28<NA>1영업/정상1영업<NA><NA><NA><NA>0707113318646.28150-878서울특별시 영등포구 여의도동 25-12 신송센타빌딩 B201호서울특별시 영등포구 여의나루로 57, 신송센타빌딩 지하2층 B201호 (여의도동)7327(주)엔와이종합관리2024-03-28 11:44:04I2023-12-02 21:00:00.0건물위생관리업193340.185783446774.020261<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
85131800003180000-206-2024-000102024-04-29<NA>1영업/정상1영업<NA><NA><NA><NA><NA>4.00150-919서울특별시 영등포구 문래동4가 67 리버뷰 신안인스빌 109동 203-11호서울특별시 영등포구 경인로77길 49, 109동 2층 203-11호 (문래동4가, 리버뷰 신안인스빌)7287디오씨엔에스2024-04-29 14:55:57I2023-12-05 00:01:00.0건물위생관리업190079.219797445778.895202<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
85231800003180000-206-2024-000112024-05-08<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.00150-840서울특별시 영등포구 신길동 146-5서울특별시 영등포구 도신로58길 26-1, 1층 (신길동)7349뉴-해피크린2024-05-08 16:40:53I2023-12-04 23:00:00.0건물위생관리업192575.748515445276.881037<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>