Overview

Dataset statistics

Number of variables47
Number of observations485
Missing cells4703
Missing cells (%)20.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory191.5 KiB
Average record size in memory404.3 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
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-17445/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
건물지하층수 is highly imbalanced (60.4%)Imbalance
사용시작지하층 is highly imbalanced (67.3%)Imbalance
사용끝지하층 is highly imbalanced (69.1%)Imbalance
좌석수 is highly imbalanced (71.9%)Imbalance
건물소유구분명 is highly imbalanced (69.5%)Imbalance
여성종사자수 is highly imbalanced (69.9%)Imbalance
남성종사자수 is highly imbalanced (69.2%)Imbalance
인허가취소일자 has 485 (100.0%) missing valuesMissing
폐업일자 has 221 (45.6%) missing valuesMissing
휴업시작일자 has 485 (100.0%) missing valuesMissing
휴업종료일자 has 485 (100.0%) missing valuesMissing
재개업일자 has 485 (100.0%) missing valuesMissing
전화번호 has 48 (9.9%) missing valuesMissing
소재지우편번호 has 5 (1.0%) missing valuesMissing
지번주소 has 5 (1.0%) missing valuesMissing
도로명주소 has 173 (35.7%) missing valuesMissing
도로명우편번호 has 193 (39.8%) missing valuesMissing
좌표정보(X) has 54 (11.1%) missing valuesMissing
좌표정보(Y) has 54 (11.1%) missing valuesMissing
건물지상층수 has 69 (14.2%) missing valuesMissing
사용시작지상층 has 69 (14.2%) missing valuesMissing
사용끝지상층 has 69 (14.2%) missing valuesMissing
한실수 has 69 (14.2%) missing valuesMissing
양실수 has 69 (14.2%) missing valuesMissing
욕실수 has 69 (14.2%) missing valuesMissing
발한실여부 has 69 (14.2%) missing valuesMissing
조건부허가신고사유 has 485 (100.0%) missing valuesMissing
조건부허가시작일자 has 485 (100.0%) missing valuesMissing
조건부허가종료일자 has 485 (100.0%) missing valuesMissing
다중이용업소여부 has 69 (14.2%) 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 371 (76.5%) zerosZeros
사용시작지상층 has 368 (75.9%) zerosZeros
사용끝지상층 has 375 (77.3%) zerosZeros
한실수 has 201 (41.4%) zerosZeros
양실수 has 261 (53.8%) zerosZeros
욕실수 has 384 (79.2%) zerosZeros

Reproduction

Analysis started2024-05-11 06:31:58.590541
Analysis finished2024-05-11 06:31:59.718328
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
3180000
485 

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 485
100.0%

Length

2024-05-11T15:31:59.823648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:31:59.961213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 485
100.0%

관리번호
Text

UNIQUE 

Distinct485
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2024-05-11T15:32:00.205687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters10670
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

Unique485 ?
Unique (%)100.0%

Sample

1st row3180000-201-1965-00214
2nd row3180000-201-1966-00218
3rd row3180000-201-1966-00220
4th row3180000-201-1967-00236
5th row3180000-201-1968-00286
ValueCountFrequency (%)
3180000-201-1965-00214 1
 
0.2%
3180000-201-2003-00167 1
 
0.2%
3180000-201-2003-00257 1
 
0.2%
3180000-201-2003-00256 1
 
0.2%
3180000-201-2003-00255 1
 
0.2%
3180000-201-2003-00254 1
 
0.2%
3180000-201-2003-00253 1
 
0.2%
3180000-201-2003-00252 1
 
0.2%
3180000-201-2003-00251 1
 
0.2%
3180000-201-2003-00250 1
 
0.2%
Other values (475) 475
97.9%
2024-05-11T15:32:00.696991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4521
42.4%
- 1455
 
13.6%
1 1338
 
12.5%
2 1143
 
10.7%
3 921
 
8.6%
8 632
 
5.9%
9 208
 
1.9%
7 123
 
1.2%
4 120
 
1.1%
6 105
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9215
86.4%
Dash Punctuation 1455
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4521
49.1%
1 1338
 
14.5%
2 1143
 
12.4%
3 921
 
10.0%
8 632
 
6.9%
9 208
 
2.3%
7 123
 
1.3%
4 120
 
1.3%
6 105
 
1.1%
5 104
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 1455
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10670
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4521
42.4%
- 1455
 
13.6%
1 1338
 
12.5%
2 1143
 
10.7%
3 921
 
8.6%
8 632
 
5.9%
9 208
 
1.9%
7 123
 
1.2%
4 120
 
1.1%
6 105
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4521
42.4%
- 1455
 
13.6%
1 1338
 
12.5%
2 1143
 
10.7%
3 921
 
8.6%
8 632
 
5.9%
9 208
 
1.9%
7 123
 
1.2%
4 120
 
1.1%
6 105
 
1.0%
Distinct181
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum1965-02-05 00:00:00
Maximum2024-04-18 00:00:00
2024-05-11T15:32:00.976911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:32:01.227555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing485
Missing (%)100.0%
Memory size4.4 KiB
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
3
264 
1
221 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 264
54.4%
1 221
45.6%

Length

2024-05-11T15:32:01.496121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:01.637847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 264
54.4%
1 221
45.6%

영업상태명
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
폐업
264 
영업/정상
221 

Length

Max length5
Median length2
Mean length3.3670103
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 264
54.4%
영업/정상 221
45.6%

Length

2024-05-11T15:32:01.836101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:01.988141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 264
54.4%
영업/정상 221
45.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2
264 
1
221 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 264
54.4%
1 221
45.6%

Length

2024-05-11T15:32:02.127819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:02.251794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 264
54.4%
1 221
45.6%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
폐업
264 
영업
221 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 264
54.4%
영업 221
45.6%

Length

2024-05-11T15:32:02.396610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:02.538715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 264
54.4%
영업 221
45.6%

폐업일자
Date

MISSING 

Distinct212
Distinct (%)80.3%
Missing221
Missing (%)45.6%
Memory size3.9 KiB
Minimum1989-03-03 00:00:00
Maximum2023-12-20 00:00:00
2024-05-11T15:32:02.710652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:32:02.918111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing485
Missing (%)100.0%
Memory size4.4 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing485
Missing (%)100.0%
Memory size4.4 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing485
Missing (%)100.0%
Memory size4.4 KiB

전화번호
Text

MISSING 

Distinct434
Distinct (%)99.3%
Missing48
Missing (%)9.9%
Memory size3.9 KiB
2024-05-11T15:32:03.284928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.7139588
Min length2

Characters and Unicode

Total characters3808
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

Unique433 ?
Unique (%)99.1%

Sample

1st row0206786684
2nd row0206782036
3rd row0200000000
4th row0206786578
5th row0206337243
ValueCountFrequency (%)
02 36
 
7.6%
0200000000 4
 
0.8%
26786671 1
 
0.2%
0206786684 1
 
0.2%
0226783336 1
 
0.2%
26771047 1
 
0.2%
26339267 1
 
0.2%
26322511 1
 
0.2%
26339654 1
 
0.2%
26750151 1
 
0.2%
Other values (426) 426
89.9%
2024-05-11T15:32:03.804905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 618
16.2%
0 535
14.0%
6 495
13.0%
3 445
11.7%
8 392
10.3%
7 380
10.0%
4 273
7.2%
1 221
 
5.8%
5 214
 
5.6%
9 181
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3754
98.6%
Space Separator 54
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 618
16.5%
0 535
14.3%
6 495
13.2%
3 445
11.9%
8 392
10.4%
7 380
10.1%
4 273
7.3%
1 221
 
5.9%
5 214
 
5.7%
9 181
 
4.8%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3808
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 618
16.2%
0 535
14.0%
6 495
13.0%
3 445
11.7%
8 392
10.3%
7 380
10.0%
4 273
7.2%
1 221
 
5.8%
5 214
 
5.6%
9 181
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 618
16.2%
0 535
14.0%
6 495
13.0%
3 445
11.7%
8 392
10.3%
7 380
10.0%
4 273
7.2%
1 221
 
5.8%
5 214
 
5.6%
9 181
 
4.8%
Distinct330
Distinct (%)68.5%
Missing3
Missing (%)0.6%
Memory size3.9 KiB
2024-05-11T15:32:04.228182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length6
Mean length5.8589212
Min length3

Characters and Unicode

Total characters2824
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

Unique290 ?
Unique (%)60.2%

Sample

1st row20.76
2nd row45.54
3rd row.00
4th row.00
5th row89.94
ValueCountFrequency (%)
116.00 31
 
6.4%
00 27
 
5.6%
99.00 22
 
4.6%
122.00 11
 
2.3%
66.00 9
 
1.9%
131.00 7
 
1.5%
132.00 5
 
1.0%
127.00 5
 
1.0%
133.00 4
 
0.8%
188.00 4
 
0.8%
Other values (320) 357
74.1%
2024-05-11T15:32:04.816865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 633
22.4%
. 482
17.1%
1 329
11.7%
2 216
 
7.6%
9 193
 
6.8%
6 190
 
6.7%
3 166
 
5.9%
5 161
 
5.7%
8 142
 
5.0%
7 133
 
4.7%
Other values (2) 179
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2293
81.2%
Other Punctuation 531
 
18.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 633
27.6%
1 329
14.3%
2 216
 
9.4%
9 193
 
8.4%
6 190
 
8.3%
3 166
 
7.2%
5 161
 
7.0%
8 142
 
6.2%
7 133
 
5.8%
4 130
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 482
90.8%
, 49
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2824
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 633
22.4%
. 482
17.1%
1 329
11.7%
2 216
 
7.6%
9 193
 
6.8%
6 190
 
6.7%
3 166
 
5.9%
5 161
 
5.7%
8 142
 
5.0%
7 133
 
4.7%
Other values (2) 179
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2824
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 633
22.4%
. 482
17.1%
1 329
11.7%
2 216
 
7.6%
9 193
 
6.8%
6 190
 
6.7%
3 166
 
5.9%
5 161
 
5.7%
8 142
 
5.0%
7 133
 
4.7%
Other values (2) 179
 
6.3%

소재지우편번호
Text

MISSING 

Distinct93
Distinct (%)19.4%
Missing5
Missing (%)1.0%
Memory size3.9 KiB
2024-05-11T15:32:05.166979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0916667
Min length6

Characters and Unicode

Total characters2924
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

Unique38 ?
Unique (%)7.9%

Sample

1st row150030
2nd row150030
3rd row150030
4th row150030
5th row150806
ValueCountFrequency (%)
150033 76
 
15.8%
150841 30
 
6.2%
150031 24
 
5.0%
150800 23
 
4.8%
150034 18
 
3.8%
150-033 17
 
3.5%
150030 16
 
3.3%
150839 16
 
3.3%
150035 15
 
3.1%
150867 14
 
2.9%
Other values (83) 231
48.1%
2024-05-11T15:32:05.648250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 866
29.6%
1 568
19.4%
5 531
18.2%
3 342
 
11.7%
8 246
 
8.4%
4 87
 
3.0%
6 72
 
2.5%
9 68
 
2.3%
2 61
 
2.1%
- 44
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2880
98.5%
Dash Punctuation 44
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 866
30.1%
1 568
19.7%
5 531
18.4%
3 342
 
11.9%
8 246
 
8.5%
4 87
 
3.0%
6 72
 
2.5%
9 68
 
2.4%
2 61
 
2.1%
7 39
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2924
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 866
29.6%
1 568
19.4%
5 531
18.2%
3 342
 
11.7%
8 246
 
8.4%
4 87
 
3.0%
6 72
 
2.5%
9 68
 
2.3%
2 61
 
2.1%
- 44
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2924
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 866
29.6%
1 568
19.4%
5 531
18.2%
3 342
 
11.7%
8 246
 
8.4%
4 87
 
3.0%
6 72
 
2.5%
9 68
 
2.3%
2 61
 
2.1%
- 44
 
1.5%

지번주소
Text

MISSING 

Distinct457
Distinct (%)95.2%
Missing5
Missing (%)1.0%
Memory size3.9 KiB
2024-05-11T15:32:05.942084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length34
Mean length24.139583
Min length18

Characters and Unicode

Total characters11587
Distinct characters75
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique435 ?
Unique (%)90.6%

Sample

1st row서울특별시 영등포구 영등포동 411-0번지
2nd row서울특별시 영등포구 영등포동 411-13번지
3rd row서울특별시 영등포구 영등포동 433-1번지
4th row서울특별시 영등포구 영등포동 509-0번지
5th row서울특별시 영등포구 당산동4가 36-6번지
ValueCountFrequency (%)
서울특별시 480
24.6%
영등포구 480
24.6%
영등포동3가 93
 
4.8%
신길동 88
 
4.5%
당산동1가 28
 
1.4%
영등포동 27
 
1.4%
양평동4가 26
 
1.3%
대림동 24
 
1.2%
영등포동2가 24
 
1.2%
영등포동1가 24
 
1.2%
Other values (497) 657
33.7%
2024-05-11T15:32:06.412901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1886
16.3%
704
 
6.1%
704
 
6.1%
703
 
6.1%
481
 
4.2%
480
 
4.1%
480
 
4.1%
480
 
4.1%
480
 
4.1%
480
 
4.1%
Other values (65) 4709
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7095
61.2%
Decimal Number 2165
 
18.7%
Space Separator 1886
 
16.3%
Dash Punctuation 424
 
3.7%
Other Punctuation 8
 
0.1%
Uppercase Letter 7
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
704
9.9%
704
9.9%
703
9.9%
481
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
Other values (43) 1623
22.9%
Decimal Number
ValueCountFrequency (%)
1 454
21.0%
3 355
16.4%
2 318
14.7%
4 236
10.9%
5 171
 
7.9%
6 138
 
6.4%
0 136
 
6.3%
9 128
 
5.9%
7 122
 
5.6%
8 107
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
S 2
28.6%
H 1
14.3%
P 1
14.3%
I 1
14.3%
V 1
14.3%
J 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 7
87.5%
& 1
 
12.5%
Space Separator
ValueCountFrequency (%)
1886
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 424
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7095
61.2%
Common 4485
38.7%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
704
9.9%
704
9.9%
703
9.9%
481
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
Other values (43) 1623
22.9%
Common
ValueCountFrequency (%)
1886
42.1%
1 454
 
10.1%
- 424
 
9.5%
3 355
 
7.9%
2 318
 
7.1%
4 236
 
5.3%
5 171
 
3.8%
6 138
 
3.1%
0 136
 
3.0%
9 128
 
2.9%
Other values (6) 239
 
5.3%
Latin
ValueCountFrequency (%)
S 2
28.6%
H 1
14.3%
P 1
14.3%
I 1
14.3%
V 1
14.3%
J 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7095
61.2%
ASCII 4492
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1886
42.0%
1 454
 
10.1%
- 424
 
9.4%
3 355
 
7.9%
2 318
 
7.1%
4 236
 
5.3%
5 171
 
3.8%
6 138
 
3.1%
0 136
 
3.0%
9 128
 
2.8%
Other values (12) 246
 
5.5%
Hangul
ValueCountFrequency (%)
704
9.9%
704
9.9%
703
9.9%
481
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
480
 
6.8%
Other values (43) 1623
22.9%

도로명주소
Text

MISSING 

Distinct303
Distinct (%)97.1%
Missing173
Missing (%)35.7%
Memory size3.9 KiB
2024-05-11T15:32:06.724008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length40
Mean length28.621795
Min length23

Characters and Unicode

Total characters8930
Distinct characters103
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

Unique294 ?
Unique (%)94.2%

Sample

1st row서울특별시 영등포구 디지털로 337 (대림동)
2nd row서울특별시 영등포구 영등포로47길 11 (영등포동2가)
3rd row서울특별시 영등포구 영중로4길 40 (영등포동3가)
4th row서울특별시 영등포구 도림로 279 (신길동)
5th row서울특별시 영등포구 영등포로46길 7-6 (영등포동3가)
ValueCountFrequency (%)
서울특별시 312
19.7%
영등포구 312
19.7%
영등포동3가 64
 
4.0%
신길동 60
 
3.8%
당산동1가 22
 
1.4%
영등포동2가 17
 
1.1%
영등포동1가 16
 
1.0%
대림동 16
 
1.0%
영중로10길 16
 
1.0%
영중로4길 14
 
0.9%
Other values (350) 735
46.4%
2024-05-11T15:32:07.180665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1272
 
14.2%
590
 
6.6%
512
 
5.7%
510
 
5.7%
319
 
3.6%
318
 
3.6%
315
 
3.5%
( 313
 
3.5%
) 313
 
3.5%
312
 
3.5%
Other values (93) 4156
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5474
61.3%
Decimal Number 1383
 
15.5%
Space Separator 1272
 
14.2%
Open Punctuation 313
 
3.5%
Close Punctuation 313
 
3.5%
Dash Punctuation 136
 
1.5%
Other Punctuation 28
 
0.3%
Uppercase Letter 9
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
590
 
10.8%
512
 
9.4%
510
 
9.3%
319
 
5.8%
318
 
5.8%
315
 
5.8%
312
 
5.7%
312
 
5.7%
312
 
5.7%
312
 
5.7%
Other values (69) 1662
30.4%
Decimal Number
ValueCountFrequency (%)
1 308
22.3%
3 214
15.5%
2 179
12.9%
4 155
11.2%
5 108
 
7.8%
8 107
 
7.7%
6 105
 
7.6%
7 85
 
6.1%
0 75
 
5.4%
9 47
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
S 2
22.2%
B 2
22.2%
J 1
11.1%
P 1
11.1%
H 1
11.1%
I 1
11.1%
V 1
11.1%
Other Punctuation
ValueCountFrequency (%)
, 27
96.4%
& 1
 
3.6%
Space Separator
ValueCountFrequency (%)
1272
100.0%
Open Punctuation
ValueCountFrequency (%)
( 313
100.0%
Close Punctuation
ValueCountFrequency (%)
) 313
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 136
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5474
61.3%
Common 3447
38.6%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
590
 
10.8%
512
 
9.4%
510
 
9.3%
319
 
5.8%
318
 
5.8%
315
 
5.8%
312
 
5.7%
312
 
5.7%
312
 
5.7%
312
 
5.7%
Other values (69) 1662
30.4%
Common
ValueCountFrequency (%)
1272
36.9%
( 313
 
9.1%
) 313
 
9.1%
1 308
 
8.9%
3 214
 
6.2%
2 179
 
5.2%
4 155
 
4.5%
- 136
 
3.9%
5 108
 
3.1%
8 107
 
3.1%
Other values (7) 342
 
9.9%
Latin
ValueCountFrequency (%)
S 2
22.2%
B 2
22.2%
J 1
11.1%
P 1
11.1%
H 1
11.1%
I 1
11.1%
V 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5474
61.3%
ASCII 3456
38.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1272
36.8%
( 313
 
9.1%
) 313
 
9.1%
1 308
 
8.9%
3 214
 
6.2%
2 179
 
5.2%
4 155
 
4.5%
- 136
 
3.9%
5 108
 
3.1%
8 107
 
3.1%
Other values (14) 351
 
10.2%
Hangul
ValueCountFrequency (%)
590
 
10.8%
512
 
9.4%
510
 
9.3%
319
 
5.8%
318
 
5.8%
315
 
5.8%
312
 
5.7%
312
 
5.7%
312
 
5.7%
312
 
5.7%
Other values (69) 1662
30.4%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct83
Distinct (%)28.4%
Missing193
Missing (%)39.8%
Infinite0
Infinite (%)0.0%
Mean7303.411
Minimum7202
Maximum7445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:07.748577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7202
5-th percentile7213
Q17257
median7303
Q37316.5
95-th percentile7434.35
Maximum7445
Range243
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation61.322164
Coefficient of variation (CV)0.0083963733
Kurtosis0.014728946
Mean7303.411
Median Absolute Deviation (MAD)40
Skewness0.7847454
Sum2132596
Variance3760.4079
MonotonicityNot monotonic
2024-05-11T15:32:07.975868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7304 36
 
7.4%
7303 18
 
3.7%
7306 16
 
3.3%
7250 12
 
2.5%
7252 12
 
2.5%
7266 10
 
2.1%
7206 9
 
1.9%
7251 9
 
1.9%
7302 8
 
1.6%
7267 6
 
1.2%
Other values (73) 156
32.2%
(Missing) 193
39.8%
ValueCountFrequency (%)
7202 1
 
0.2%
7205 2
 
0.4%
7206 9
1.9%
7208 1
 
0.2%
7213 4
0.8%
7218 1
 
0.2%
7222 2
 
0.4%
7223 2
 
0.4%
7230 1
 
0.2%
7236 1
 
0.2%
ValueCountFrequency (%)
7445 1
 
0.2%
7444 6
1.2%
7443 1
 
0.2%
7442 4
0.8%
7436 3
0.6%
7433 1
 
0.2%
7432 1
 
0.2%
7429 1
 
0.2%
7427 1
 
0.2%
7426 5
1.0%
Distinct457
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2024-05-11T15:32:08.365200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length39
Mean length5.0845361
Min length1

Characters and Unicode

Total characters2466
Distinct characters317
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

Unique430 ?
Unique (%)88.7%

Sample

1st row흥일
2nd row경북
3rd row동신
4th row청명
5th row제일
ValueCountFrequency (%)
호텔 23
 
3.9%
모텔 8
 
1.3%
hotel 7
 
1.2%
서울 4
 
0.7%
여관 4
 
0.7%
여의도 3
 
0.5%
부산 3
 
0.5%
알프스모텔 2
 
0.3%
아랑여인숙 2
 
0.3%
영등포점 2
 
0.3%
Other values (500) 536
90.2%
2024-05-11T15:32:08.898762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
194
 
7.9%
111
 
4.5%
111
 
4.5%
110
 
4.5%
92
 
3.7%
85
 
3.4%
83
 
3.4%
68
 
2.8%
52
 
2.1%
48
 
1.9%
Other values (307) 1512
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1997
81.0%
Uppercase Letter 149
 
6.0%
Lowercase Letter 143
 
5.8%
Space Separator 111
 
4.5%
Open Punctuation 24
 
1.0%
Close Punctuation 24
 
1.0%
Decimal Number 10
 
0.4%
Other Punctuation 6
 
0.2%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
194
 
9.7%
111
 
5.6%
110
 
5.5%
92
 
4.6%
85
 
4.3%
83
 
4.2%
68
 
3.4%
52
 
2.6%
48
 
2.4%
37
 
1.9%
Other values (252) 1117
55.9%
Uppercase Letter
ValueCountFrequency (%)
H 15
 
10.1%
E 14
 
9.4%
O 14
 
9.4%
R 12
 
8.1%
S 10
 
6.7%
A 10
 
6.7%
L 8
 
5.4%
T 8
 
5.4%
N 7
 
4.7%
M 5
 
3.4%
Other values (16) 46
30.9%
Lowercase Letter
ValueCountFrequency (%)
e 19
13.3%
o 18
12.6%
a 15
10.5%
t 14
9.8%
s 11
7.7%
u 11
7.7%
l 10
7.0%
r 9
 
6.3%
i 7
 
4.9%
d 6
 
4.2%
Other values (8) 23
16.1%
Decimal Number
ValueCountFrequency (%)
5 3
30.0%
2 2
20.0%
3 2
20.0%
0 1
 
10.0%
1 1
 
10.0%
6 1
 
10.0%
Space Separator
ValueCountFrequency (%)
111
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1997
81.0%
Latin 292
 
11.8%
Common 177
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
194
 
9.7%
111
 
5.6%
110
 
5.5%
92
 
4.6%
85
 
4.3%
83
 
4.2%
68
 
3.4%
52
 
2.6%
48
 
2.4%
37
 
1.9%
Other values (252) 1117
55.9%
Latin
ValueCountFrequency (%)
e 19
 
6.5%
o 18
 
6.2%
a 15
 
5.1%
H 15
 
5.1%
E 14
 
4.8%
O 14
 
4.8%
t 14
 
4.8%
R 12
 
4.1%
s 11
 
3.8%
u 11
 
3.8%
Other values (34) 149
51.0%
Common
ValueCountFrequency (%)
111
62.7%
( 24
 
13.6%
) 24
 
13.6%
. 6
 
3.4%
5 3
 
1.7%
- 2
 
1.1%
2 2
 
1.1%
3 2
 
1.1%
0 1
 
0.6%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1997
81.0%
ASCII 469
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
194
 
9.7%
111
 
5.6%
110
 
5.5%
92
 
4.6%
85
 
4.3%
83
 
4.2%
68
 
3.4%
52
 
2.6%
48
 
2.4%
37
 
1.9%
Other values (252) 1117
55.9%
ASCII
ValueCountFrequency (%)
111
23.7%
( 24
 
5.1%
) 24
 
5.1%
e 19
 
4.1%
o 18
 
3.8%
a 15
 
3.2%
H 15
 
3.2%
E 14
 
3.0%
O 14
 
3.0%
t 14
 
3.0%
Other values (45) 201
42.9%
Distinct370
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum1999-05-21 00:00:00
Maximum2024-05-09 10:58:08
2024-05-11T15:32:09.109942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:32:09.316814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
I
259 
U
226 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 259
53.4%
U 226
46.6%

Length

2024-05-11T15:32:09.526087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:09.664834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 259
53.4%
u 226
46.6%
Distinct186
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:02:00
2024-05-11T15:32:09.904590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:32:10.228799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
여관업
287 
여인숙업
117 
관광호텔
45 
일반호텔
 
19
숙박업(생활)
 
11

Length

Max length7
Median length3
Mean length3.5010309
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 287
59.2%
여인숙업 117
24.1%
관광호텔 45
 
9.3%
일반호텔 19
 
3.9%
숙박업(생활) 11
 
2.3%
숙박업 기타 6
 
1.2%

Length

2024-05-11T15:32:10.522119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:10.695463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 287
58.5%
여인숙업 117
23.8%
관광호텔 45
 
9.2%
일반호텔 19
 
3.9%
숙박업(생활 11
 
2.2%
숙박업 6
 
1.2%
기타 6
 
1.2%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct391
Distinct (%)90.7%
Missing54
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean191597.16
Minimum189552.02
Maximum193769.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:10.871107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189552.02
5-th percentile190272.74
Q1191131.07
median191744.06
Q3191970.83
95-th percentile192855
Maximum193769.3
Range4217.2733
Interquartile range (IQR)839.75775

Descriptive statistics

Standard deviation723.40765
Coefficient of variation (CV)0.00377567
Kurtosis0.36162886
Mean191597.16
Median Absolute Deviation (MAD)327.2815
Skewness-0.25046406
Sum82578376
Variance523318.63
MonotonicityNot monotonic
2024-05-11T15:32:11.049245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191141.65399723 3
 
0.6%
192866.856097244 3
 
0.6%
191660.077150645 3
 
0.6%
190760.799466343 3
 
0.6%
191819.089904742 2
 
0.4%
191815.12760407 2
 
0.4%
191846.027534849 2
 
0.4%
191802.848762399 2
 
0.4%
191907.330286112 2
 
0.4%
192016.315942134 2
 
0.4%
Other values (381) 407
83.9%
(Missing) 54
 
11.1%
ValueCountFrequency (%)
189552.021760333 1
0.2%
189670.698184757 1
0.2%
189680.769993867 1
0.2%
189706.839953833 1
0.2%
189745.178404513 1
0.2%
189758.735655025 1
0.2%
189797.015370392 1
0.2%
189814.291077523 1
0.2%
189850.75199983 1
0.2%
189987.291142716 1
0.2%
ValueCountFrequency (%)
193769.295072608 1
0.2%
193592.000380036 1
0.2%
193326.931018911 1
0.2%
193321.88651945 1
0.2%
193262.936546062 1
0.2%
193259.936965933 1
0.2%
193231.14264784 1
0.2%
193213.946938054 1
0.2%
193042.092915795 1
0.2%
193019.645823279 2
0.4%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct391
Distinct (%)90.7%
Missing54
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean446173.15
Minimum442906.4
Maximum448747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:11.235149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum442906.4
5-th percentile443946.31
Q1445735.46
median446248.94
Q3446683.09
95-th percentile448270.6
Maximum448747
Range5840.5966
Interquartile range (IQR)947.63398

Descriptive statistics

Standard deviation1151.2169
Coefficient of variation (CV)0.0025802021
Kurtosis0.90385214
Mean446173.15
Median Absolute Deviation (MAD)458.86724
Skewness-0.57680435
Sum1.9230063 × 108
Variance1325300.4
MonotonicityNot monotonic
2024-05-11T15:32:11.419081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446522.091108078 3
 
0.6%
447341.334688661 3
 
0.6%
446283.352067071 3
 
0.6%
445806.11689565 3
 
0.6%
446422.561224404 2
 
0.4%
446186.872818441 2
 
0.4%
446162.842268686 2
 
0.4%
446211.836831489 2
 
0.4%
446195.468440609 2
 
0.4%
446140.25090082 2
 
0.4%
Other values (381) 407
83.9%
(Missing) 54
 
11.1%
ValueCountFrequency (%)
442906.403356289 1
0.2%
442915.2384894 1
0.2%
442947.77006931 1
0.2%
442960.721824841 1
0.2%
443007.167758628 1
0.2%
443017.977832154 1
0.2%
443020.437188604 1
0.2%
443034.084648369 1
0.2%
443073.934901159 2
0.4%
443104.21516993 1
0.2%
ValueCountFrequency (%)
448747.0 1
0.2%
448495.794800958 1
0.2%
448495.324746171 1
0.2%
448477.522453196 1
0.2%
448399.407884689 1
0.2%
448383.642303233 1
0.2%
448357.631636357 1
0.2%
448324.752249228 1
0.2%
448321.071816685 1
0.2%
448314.317593948 2
0.4%

위생업태명
Categorical

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
여관업
250 
여인숙업
113 
<NA>
69 
관광호텔
28 
일반호텔
 
13
Other values (2)
 
12

Length

Max length7
Median length3
Mean length3.5505155
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 250
51.5%
여인숙업 113
23.3%
<NA> 69
 
14.2%
관광호텔 28
 
5.8%
일반호텔 13
 
2.7%
숙박업(생활) 8
 
1.6%
숙박업 기타 4
 
0.8%

Length

2024-05-11T15:32:11.639775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:11.806200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 250
51.1%
여인숙업 113
23.1%
na 69
 
14.1%
관광호텔 28
 
5.7%
일반호텔 13
 
2.7%
숙박업(생활 8
 
1.6%
숙박업 4
 
0.8%
기타 4
 
0.8%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)3.6%
Missing69
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean0.81490385
Minimum0
Maximum20
Zeros371
Zeros (%)76.5%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:11.956706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8261889
Coefficient of variation (CV)3.4681256
Kurtosis17.427182
Mean0.81490385
Median Absolute Deviation (MAD)0
Skewness4.0334679
Sum339
Variance7.9873436
MonotonicityNot monotonic
2024-05-11T15:32:12.132427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 371
76.5%
8 7
 
1.4%
3 6
 
1.2%
2 5
 
1.0%
10 5
 
1.0%
11 3
 
0.6%
13 3
 
0.6%
1 3
 
0.6%
6 3
 
0.6%
4 2
 
0.4%
Other values (5) 8
 
1.6%
(Missing) 69
 
14.2%
ValueCountFrequency (%)
0 371
76.5%
1 3
 
0.6%
2 5
 
1.0%
3 6
 
1.2%
4 2
 
0.4%
6 3
 
0.6%
7 2
 
0.4%
8 7
 
1.4%
9 1
 
0.2%
10 5
 
1.0%
ValueCountFrequency (%)
20 2
 
0.4%
17 1
 
0.2%
13 3
0.6%
12 2
 
0.4%
11 3
0.6%
10 5
1.0%
9 1
 
0.2%
8 7
1.4%
7 2
 
0.4%
6 3
0.6%

건물지하층수
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
383 
<NA>
69 
1
 
22
2
 
7
3
 
3

Length

Max length4
Median length1
Mean length1.4268041
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 383
79.0%
<NA> 69
 
14.2%
1 22
 
4.5%
2 7
 
1.4%
3 3
 
0.6%
8 1
 
0.2%

Length

2024-05-11T15:32:12.325279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:12.478550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 383
79.0%
na 69
 
14.2%
1 22
 
4.5%
2 7
 
1.4%
3 3
 
0.6%
8 1
 
0.2%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)2.9%
Missing69
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean0.41826923
Minimum0
Maximum30
Zeros368
Zeros (%)75.9%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:12.575558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1177512
Coefficient of variation (CV)5.0631294
Kurtosis103.96405
Mean0.41826923
Median Absolute Deviation (MAD)0
Skewness9.0928463
Sum174
Variance4.4848703
MonotonicityNot monotonic
2024-05-11T15:32:12.700932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 368
75.9%
1 22
 
4.5%
2 12
 
2.5%
3 3
 
0.6%
5 2
 
0.4%
4 2
 
0.4%
12 2
 
0.4%
13 1
 
0.2%
14 1
 
0.2%
9 1
 
0.2%
Other values (2) 2
 
0.4%
(Missing) 69
 
14.2%
ValueCountFrequency (%)
0 368
75.9%
1 22
 
4.5%
2 12
 
2.5%
3 3
 
0.6%
4 2
 
0.4%
5 2
 
0.4%
9 1
 
0.2%
11 1
 
0.2%
12 2
 
0.4%
13 1
 
0.2%
ValueCountFrequency (%)
30 1
 
0.2%
14 1
 
0.2%
13 1
 
0.2%
12 2
 
0.4%
11 1
 
0.2%
9 1
 
0.2%
5 2
 
0.4%
4 2
 
0.4%
3 3
 
0.6%
2 12
2.5%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)3.6%
Missing69
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean0.69230769
Minimum0
Maximum20
Zeros375
Zeros (%)77.3%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:12.866254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6024156
Coefficient of variation (CV)3.7590448
Kurtosis23.577673
Mean0.69230769
Median Absolute Deviation (MAD)0
Skewness4.6060586
Sum288
Variance6.7725672
MonotonicityNot monotonic
2024-05-11T15:32:13.007898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 375
77.3%
3 5
 
1.0%
8 5
 
1.0%
2 4
 
0.8%
10 4
 
0.8%
1 4
 
0.8%
6 4
 
0.8%
4 3
 
0.6%
11 3
 
0.6%
5 2
 
0.4%
Other values (5) 7
 
1.4%
(Missing) 69
 
14.2%
ValueCountFrequency (%)
0 375
77.3%
1 4
 
0.8%
2 4
 
0.8%
3 5
 
1.0%
4 3
 
0.6%
5 2
 
0.4%
6 4
 
0.8%
8 5
 
1.0%
9 2
 
0.4%
10 4
 
0.8%
ValueCountFrequency (%)
20 2
 
0.4%
17 1
 
0.2%
15 1
 
0.2%
12 1
 
0.2%
11 3
0.6%
10 4
0.8%
9 2
 
0.4%
8 5
1.0%
6 4
0.8%
5 2
 
0.4%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
398 
<NA>
69 
1
 
15
3
 
1
2
 
1

Length

Max length4
Median length1
Mean length1.4268041
Min length1

Unique

Unique3 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 398
82.1%
<NA> 69
 
14.2%
1 15
 
3.1%
3 1
 
0.2%
2 1
 
0.2%
6 1
 
0.2%

Length

2024-05-11T15:32:13.160493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:13.303390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 398
82.1%
na 69
 
14.2%
1 15
 
3.1%
3 1
 
0.2%
2 1
 
0.2%
6 1
 
0.2%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
403 
<NA>
69 
1
 
9
3
 
2
2
 
1

Length

Max length4
Median length1
Mean length1.4268041
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 403
83.1%
<NA> 69
 
14.2%
1 9
 
1.9%
3 2
 
0.4%
2 1
 
0.2%
5 1
 
0.2%

Length

2024-05-11T15:32:13.443459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:13.572587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 403
83.1%
na 69
 
14.2%
1 9
 
1.9%
3 2
 
0.4%
2 1
 
0.2%
5 1
 
0.2%

한실수
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)6.0%
Missing69
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean5.7788462
Minimum0
Maximum97
Zeros201
Zeros (%)41.4%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:13.725794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q310
95-th percentile15
Maximum97
Range97
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.9984417
Coefficient of variation (CV)1.3840897
Kurtosis42.540851
Mean5.7788462
Median Absolute Deviation (MAD)3
Skewness4.4894672
Sum2404
Variance63.97507
MonotonicityNot monotonic
2024-05-11T15:32:13.859035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 201
41.4%
10 44
 
9.1%
8 27
 
5.6%
9 25
 
5.2%
11 18
 
3.7%
12 17
 
3.5%
7 15
 
3.1%
13 12
 
2.5%
14 11
 
2.3%
3 6
 
1.2%
Other values (15) 40
 
8.2%
(Missing) 69
 
14.2%
ValueCountFrequency (%)
0 201
41.4%
2 2
 
0.4%
3 6
 
1.2%
4 2
 
0.4%
5 5
 
1.0%
6 5
 
1.0%
7 15
 
3.1%
8 27
 
5.6%
9 25
 
5.2%
10 44
 
9.1%
ValueCountFrequency (%)
97 1
 
0.2%
49 1
 
0.2%
38 1
 
0.2%
34 1
 
0.2%
33 2
 
0.4%
23 1
 
0.2%
20 1
 
0.2%
18 5
1.0%
17 2
 
0.4%
16 5
1.0%

양실수
Real number (ℝ)

MISSING  ZEROS 

Distinct60
Distinct (%)14.4%
Missing69
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean12.242788
Minimum0
Maximum316
Zeros261
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:14.008090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile51
Maximum316
Range316
Interquartile range (IQR)16

Descriptive statistics

Standard deviation29.846857
Coefficient of variation (CV)2.4379133
Kurtosis46.380283
Mean12.242788
Median Absolute Deviation (MAD)0
Skewness5.8647748
Sum5093
Variance890.83489
MonotonicityNot monotonic
2024-05-11T15:32:14.171230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 261
53.8%
14 10
 
2.1%
12 8
 
1.6%
16 7
 
1.4%
20 7
 
1.4%
15 7
 
1.4%
13 6
 
1.2%
19 6
 
1.2%
24 6
 
1.2%
27 5
 
1.0%
Other values (50) 93
 
19.2%
(Missing) 69
 
14.2%
ValueCountFrequency (%)
0 261
53.8%
4 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
8 3
 
0.6%
9 3
 
0.6%
10 5
 
1.0%
11 4
 
0.8%
12 8
 
1.6%
13 6
 
1.2%
ValueCountFrequency (%)
316 1
0.2%
283 1
0.2%
174 1
0.2%
159 1
0.2%
149 1
0.2%
133 1
0.2%
130 1
0.2%
106 1
0.2%
98 1
0.2%
88 1
0.2%

욕실수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)3.1%
Missing69
Missing (%)14.2%
Infinite0
Infinite (%)0.0%
Mean1.3846154
Minimum0
Maximum182
Zeros384
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2024-05-11T15:32:14.308929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum182
Range182
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.5314902
Coefficient of variation (CV)6.883854
Kurtosis312.43074
Mean1.3846154
Median Absolute Deviation (MAD)0
Skewness16.633086
Sum576
Variance90.849305
MonotonicityNot monotonic
2024-05-11T15:32:14.432777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 384
79.2%
13 5
 
1.0%
10 4
 
0.8%
15 4
 
0.8%
18 4
 
0.8%
11 3
 
0.6%
8 3
 
0.6%
16 3
 
0.6%
7 2
 
0.4%
182 1
 
0.2%
Other values (3) 3
 
0.6%
(Missing) 69
 
14.2%
ValueCountFrequency (%)
0 384
79.2%
7 2
 
0.4%
8 3
 
0.6%
9 1
 
0.2%
10 4
 
0.8%
11 3
 
0.6%
12 1
 
0.2%
13 5
 
1.0%
15 4
 
0.8%
16 3
 
0.6%
ValueCountFrequency (%)
182 1
 
0.2%
18 4
0.8%
17 1
 
0.2%
16 3
0.6%
15 4
0.8%
13 5
1.0%
12 1
 
0.2%
11 3
0.6%
10 4
0.8%
9 1
 
0.2%

발한실여부
Boolean

MISSING 

Distinct2
Distinct (%)0.5%
Missing69
Missing (%)14.2%
Memory size1.1 KiB
False
369 
True
47 
(Missing)
69 
ValueCountFrequency (%)
False 369
76.1%
True 47
 
9.7%
(Missing) 69
 
14.2%
2024-05-11T15:32:14.571375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
413 
<NA>
69 
10
 
1
13
 
1
11
 
1

Length

Max length4
Median length1
Mean length1.4329897
Min length1

Unique

Unique3 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 413
85.2%
<NA> 69
 
14.2%
10 1
 
0.2%
13 1
 
0.2%
11 1
 
0.2%

Length

2024-05-11T15:32:14.723458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:14.865886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 413
85.2%
na 69
 
14.2%
10 1
 
0.2%
13 1
 
0.2%
11 1
 
0.2%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing485
Missing (%)100.0%
Memory size4.4 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing485
Missing (%)100.0%
Memory size4.4 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing485
Missing (%)100.0%
Memory size4.4 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
<NA>
445 
자가
 
28
임대
 
12

Length

Max length4
Median length4
Mean length3.8350515
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> 445
91.8%
자가 28
 
5.8%
임대 12
 
2.5%

Length

2024-05-11T15:32:15.051227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:15.534820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 445
91.8%
자가 28
 
5.8%
임대 12
 
2.5%

세탁기수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
416 
<NA>
69 

Length

Max length4
Median length1
Mean length1.4268041
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 416
85.8%
<NA> 69
 
14.2%

Length

2024-05-11T15:32:15.659024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:15.780047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 416
85.8%
na 69
 
14.2%

여성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
410 
<NA>
69 
2
 
4
1
 
1
10
 
1

Length

Max length4
Median length1
Mean length1.428866
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 410
84.5%
<NA> 69
 
14.2%
2 4
 
0.8%
1 1
 
0.2%
10 1
 
0.2%

Length

2024-05-11T15:32:15.914706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:16.051202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 410
84.5%
na 69
 
14.2%
2 4
 
0.8%
1 1
 
0.2%
10 1
 
0.2%

남성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
409 
<NA>
69 
2
 
4
1
 
2
10
 
1

Length

Max length4
Median length1
Mean length1.428866
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 409
84.3%
<NA> 69
 
14.2%
2 4
 
0.8%
1 2
 
0.4%
10 1
 
0.2%

Length

2024-05-11T15:32:16.198904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:16.343204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 409
84.3%
na 69
 
14.2%
2 4
 
0.8%
1 2
 
0.4%
10 1
 
0.2%

회수건조수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
416 
<NA>
69 

Length

Max length4
Median length1
Mean length1.4268041
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 416
85.8%
<NA> 69
 
14.2%

Length

2024-05-11T15:32:16.492005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:16.631226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 416
85.8%
na 69
 
14.2%

침대수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
0
416 
<NA>
69 

Length

Max length4
Median length1
Mean length1.4268041
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 416
85.8%
<NA> 69
 
14.2%

Length

2024-05-11T15:32:16.779786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:16.948310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 416
85.8%
na 69
 
14.2%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing69
Missing (%)14.2%
Memory size1.1 KiB
False
416 
(Missing)
69 
ValueCountFrequency (%)
False 416
85.8%
(Missing) 69
 
14.2%
2024-05-11T15:32:17.091025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031800003180000-201-1965-0021419650205<NA>3폐업2폐업19980310<NA><NA><NA>020678668420.76150030서울특별시 영등포구 영등포동 411-0번지<NA><NA>흥일2001-08-02 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업000000000N0<NA><NA><NA><NA>00000N
131800003180000-201-1966-0021819660719<NA>3폐업2폐업19971230<NA><NA><NA>020678203645.54150030서울특별시 영등포구 영등포동 411-13번지<NA><NA>경북2001-08-02 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업000000000N0<NA><NA><NA><NA>00000N
231800003180000-201-1966-0022019660927<NA>3폐업2폐업19980310<NA><NA><NA>0200000000.00150030서울특별시 영등포구 영등포동 433-1번지<NA><NA>동신2001-08-02 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업000000000N0<NA><NA><NA><NA>00000N
331800003180000-201-1967-0023619670523<NA>3폐업2폐업19980310<NA><NA><NA>0206786578.00150030서울특별시 영등포구 영등포동 509-0번지<NA><NA>청명2001-08-02 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업000000000N0<NA><NA><NA><NA>00000N
431800003180000-201-1968-0028619680705<NA>3폐업2폐업19940831<NA><NA><NA>020633724389.94150806서울특별시 영등포구 당산동4가 36-6번지<NA><NA>제일2001-08-02 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업000000000N0<NA><NA><NA><NA>00000N
531800003180000-201-1968-0034519680413<NA>3폐업2폐업19980310<NA><NA><NA>02 8420601.00150861서울특별시 영등포구 신길동 4916-10번지<NA><NA>선광2001-08-02 00:00:00I2018-08-31 23:59:59.0여인숙업192247.377011444834.996066여인숙업000000000N0<NA><NA><NA><NA>00000N
631800003180000-201-1969-0007919691223<NA>3폐업2폐업19990324<NA><NA><NA>020842074064.11150898서울특별시 영등포구 영등포동 577-9번지<NA><NA>연흥2001-08-02 00:00:00I2018-08-31 23:59:59.0여관업<NA><NA>여관업00000010010N0<NA><NA><NA><NA>00000N
731800003180000-201-1969-0009219691230<NA>3폐업2폐업19961004<NA><NA><NA>020678629783.09150034서울특별시 영등포구 영등포동4가 113-3번지<NA><NA>진일2001-08-02 00:00:00I2018-08-31 23:59:59.0여관업191359.560107446287.703433여관업00000013013N0<NA><NA><NA><NA>00000N
831800003180000-201-1969-0030219690617<NA>3폐업2폐업19960108<NA><NA><NA>020000000037.36150102서울특별시 영등포구 양평동2가 11-1번지<NA><NA>장단2001-08-02 00:00:00I2018-08-31 23:59:59.0여인숙업189706.839954446724.565431여인숙업000000000N0<NA><NA><NA><NA>00000N
931800003180000-201-1970-0033219700225<NA>3폐업2폐업19930622<NA><NA><NA>020846862772.01150050서울특별시 영등포구 신길동 41-7번지<NA><NA>신일2001-08-02 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업000000000N0<NA><NA><NA><NA>00000N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
47531800003180000-214-2003-0000220030226<NA>1영업/정상1영업<NA><NA><NA><NA>26346151992.72150804서울특별시 영등포구 당산동3가 388-3번지서울특별시 영등포구 당산로33길 5 (당산동3가)7256조이빌2018-12-24 13:15:50U2018-12-26 02:40:00.0숙박업(생활)190752.32588447208.361607숙박업(생활)0000000280N0<NA><NA><NA><NA>00000N
47631800003180000-214-2003-0000320030226<NA>1영업/정상1영업<NA><NA><NA><NA><NA>277.07150092서울특별시 영등포구 문래동2가 31-3번지서울특별시 영등포구 경인로 709-2 (문래동2가)7289제이제이 하우스2018-08-06 09:45:45I2018-08-31 23:59:59.0숙박업(생활)190448.090511445493.753765숙박업(생활)0024001000N0<NA><NA><NA>임대00000N
47731800003180000-214-2003-0000420030226<NA>1영업/정상1영업<NA><NA><NA><NA><NA>131.00150037서울특별시 영등포구 영등포동7가 76번지서울특별시 영등포구 영중로 98-1 (영등포동7가)7246코코빌2018-08-22 18:21:02I2018-08-31 23:59:59.0숙박업(생활)191589.828853446972.265255숙박업(생활)0000001100N0<NA><NA><NA><NA>00000N
47831800003180000-214-2013-0000120131029<NA>1영업/정상1영업<NA><NA><NA><NA>02 26787848132.00150033서울특별시 영등포구 영등포동3가 10-29번지서울특별시 영등포구 영중로2길 7 (영등포동3가)7304삼일여관2019-04-05 11:18:49U2019-04-07 02:40:00.0숙박업(생활)191740.142023446094.578904숙박업(생활)302300080N0<NA><NA><NA>임대00000N
47931800003180000-214-2016-0000120160901<NA>3폐업2폐업20200413<NA><NA><NA>201411117,817.06<NA><NA>서울특별시 영등포구 양평로28길 11 (양평동6가)7202토브 호텔 앤 레지던스2020-04-13 15:08:44U2020-04-15 02:40:00.0숙박업(생활)190261.0448747.0숙박업(생활)1201200001490N0<NA><NA><NA><NA>0101000N
48031800003180000-214-2018-0000120180110<NA>1영업/정상1영업<NA><NA><NA><NA><NA>265.46150834서울특별시 영등포구 문래동3가 54-30번지서울특별시 영등포구 도림로128가길 4 (문래동3가)7299문래 게스트하우스2019-01-15 16:28:10U2019-01-17 02:40:00.0숙박업(생활)190760.799466445806.116896숙박업(생활)302300000N0<NA><NA><NA><NA>00000N
48131800003180000-214-2019-0000120191216<NA>1영업/정상1영업<NA><NA><NA><NA><NA>317.23150034서울특별시 영등포구 영등포동4가 426-2번지서울특별시 영등포구 경인로 823-2, 3층,4층,5층층 (영등포동4가)7305프라임레지던스2019-12-16 17:37:47I2019-12-18 00:23:26.0숙박업(생활)191478.014119445967.326555숙박업(생활)000000000N0<NA><NA><NA><NA>00000N
48231800003180000-214-2022-000012022-10-25<NA>1영업/정상1영업<NA><NA><NA><NA><NA>3439.17150-872서울특별시 영등포구 여의도동 15-13 호텔 더 디자이너스 여의도서울특별시 영등포구 국회대로68길 24, 라포르테 블랑 여의도 (여의도동)7237더코노셔2023-02-14 13:52:59U2022-12-01 23:06:00.0숙박업(생활)192866.856097447341.334689<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
48331800003180000-214-2024-000012024-03-21<NA>1영업/정상1영업<NA><NA><NA><NA><NA>467.01150-832서울특별시 영등포구 도림동 202-48서울특별시 영등포구 도영로11길 29 (도림동)7373스프링플라워2024-03-21 10:33:50I2023-12-02 22:03:00.0숙박업(생활)190705.322158445302.156117<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
48431800003180000-214-2024-000022024-04-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>944.48150-103서울특별시 영등포구 양평동3가 33-1서울특별시 영등포구 선유서로31길 6-12 (양평동3가)7269센트럴타워(CENTRAL TOWER)2024-04-25 14:25:41U2023-12-03 22:07:00.0숙박업(생활)189850.752447211.49036<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>