Overview

Dataset statistics

Number of variables47
Number of observations273
Missing cells2977
Missing cells (%)23.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory107.8 KiB
Average record size in memory404.5 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
Author강서구
URLhttps://data.seoul.go.kr/dataList/OA-17442/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (56.6%)Imbalance
욕실수 is highly imbalanced (56.2%)Imbalance
발한실여부 is highly imbalanced (92.1%)Imbalance
좌석수 is highly imbalanced (53.6%)Imbalance
건물소유구분명 is highly imbalanced (68.4%)Imbalance
여성종사자수 is highly imbalanced (64.9%)Imbalance
남성종사자수 is highly imbalanced (64.9%)Imbalance
인허가취소일자 has 273 (100.0%) missing valuesMissing
폐업일자 has 103 (37.7%) missing valuesMissing
휴업시작일자 has 273 (100.0%) missing valuesMissing
휴업종료일자 has 273 (100.0%) missing valuesMissing
재개업일자 has 273 (100.0%) missing valuesMissing
전화번호 has 8 (2.9%) missing valuesMissing
도로명주소 has 55 (20.1%) missing valuesMissing
도로명우편번호 has 64 (23.4%) missing valuesMissing
좌표정보(X) has 6 (2.2%) missing valuesMissing
좌표정보(Y) has 6 (2.2%) missing valuesMissing
건물지상층수 has 118 (43.2%) missing valuesMissing
건물지하층수 has 119 (43.6%) missing valuesMissing
사용시작지상층 has 121 (44.3%) missing valuesMissing
사용끝지상층 has 128 (46.9%) missing valuesMissing
한실수 has 130 (47.6%) missing valuesMissing
양실수 has 70 (25.6%) missing valuesMissing
발한실여부 has 69 (25.3%) missing valuesMissing
조건부허가신고사유 has 273 (100.0%) missing valuesMissing
조건부허가시작일자 has 273 (100.0%) missing valuesMissing
조건부허가종료일자 has 273 (100.0%) missing valuesMissing
다중이용업소여부 has 69 (25.3%) 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 64 (23.4%) zerosZeros
건물지하층수 has 65 (23.8%) zerosZeros
사용시작지상층 has 68 (24.9%) zerosZeros
사용끝지상층 has 64 (23.4%) zerosZeros
한실수 has 98 (35.9%) zerosZeros

Reproduction

Analysis started2024-05-11 06:25:15.986736
Analysis finished2024-05-11 06:25:17.309071
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
3150000
273 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3150000
2nd row3150000
3rd row3150000
4th row3150000
5th row3150000

Common Values

ValueCountFrequency (%)
3150000 273
100.0%

Length

2024-05-11T15:25:17.442989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:17.625250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3150000 273
100.0%

관리번호
Text

UNIQUE 

Distinct273
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-11T15:25:17.911144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters6006
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

Unique273 ?
Unique (%)100.0%

Sample

1st row3150000-201-1971-00001
2nd row3150000-201-1972-00001
3rd row3150000-201-1972-00002
4th row3150000-201-1973-00001
5th row3150000-201-1977-00001
ValueCountFrequency (%)
3150000-201-1971-00001 1
 
0.4%
3150000-201-2000-00029 1
 
0.4%
3150000-201-2000-00024 1
 
0.4%
3150000-201-2000-00025 1
 
0.4%
3150000-201-2000-00026 1
 
0.4%
3150000-201-2000-00027 1
 
0.4%
3150000-201-2000-00028 1
 
0.4%
3150000-201-1998-00014 1
 
0.4%
3150000-201-2000-00031 1
 
0.4%
3150000-201-2000-00030 1
 
0.4%
Other values (263) 263
96.3%
2024-05-11T15:25:18.606230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2573
42.8%
1 886
 
14.8%
- 819
 
13.6%
2 488
 
8.1%
3 346
 
5.8%
9 317
 
5.3%
5 312
 
5.2%
8 101
 
1.7%
4 67
 
1.1%
7 61
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5187
86.4%
Dash Punctuation 819
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2573
49.6%
1 886
 
17.1%
2 488
 
9.4%
3 346
 
6.7%
9 317
 
6.1%
5 312
 
6.0%
8 101
 
1.9%
4 67
 
1.3%
7 61
 
1.2%
6 36
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 819
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6006
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2573
42.8%
1 886
 
14.8%
- 819
 
13.6%
2 488
 
8.1%
3 346
 
5.8%
9 317
 
5.3%
5 312
 
5.2%
8 101
 
1.7%
4 67
 
1.1%
7 61
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2573
42.8%
1 886
 
14.8%
- 819
 
13.6%
2 488
 
8.1%
3 346
 
5.8%
9 317
 
5.3%
5 312
 
5.2%
8 101
 
1.7%
4 67
 
1.1%
7 61
 
1.0%
Distinct220
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum1971-11-12 00:00:00
Maximum2024-04-03 00:00:00
2024-05-11T15:25:18.867715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:25:19.121182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing273
Missing (%)100.0%
Memory size2.5 KiB
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
3
170 
1
103 

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 (%)
3 170
62.3%
1 103
37.7%

Length

2024-05-11T15:25:19.378940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:19.619503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 170
62.3%
1 103
37.7%

영업상태명
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
폐업
170 
영업/정상
103 

Length

Max length5
Median length2
Mean length3.1318681
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 170
62.3%
영업/정상 103
37.7%

Length

2024-05-11T15:25:19.827383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:20.027007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 170
62.3%
영업/정상 103
37.7%
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2
170 
1
103 

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 (%)
2 170
62.3%
1 103
37.7%

Length

2024-05-11T15:25:20.237813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:20.408234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 170
62.3%
1 103
37.7%
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
폐업
170 
영업
103 

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 170
62.3%
영업 103
37.7%

Length

2024-05-11T15:25:20.614651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:20.814100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 170
62.3%
영업 103
37.7%

폐업일자
Date

MISSING 

Distinct158
Distinct (%)92.9%
Missing103
Missing (%)37.7%
Memory size2.3 KiB
Minimum2000-03-01 00:00:00
Maximum2024-02-01 00:00:00
2024-05-11T15:25:21.021174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:25:21.278346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing273
Missing (%)100.0%
Memory size2.5 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing273
Missing (%)100.0%
Memory size2.5 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing273
Missing (%)100.0%
Memory size2.5 KiB

전화번호
Text

MISSING 

Distinct261
Distinct (%)98.5%
Missing8
Missing (%)2.9%
Memory size2.3 KiB
2024-05-11T15:25:22.088156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.128302
Min length10

Characters and Unicode

Total characters2684
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

Unique257 ?
Unique (%)97.0%

Sample

1st row0226646740
2nd row0226515956
3rd row02 6621414
4th row02 6621205
5th row0226033342
ValueCountFrequency (%)
02 23
 
7.9%
0226956984 2
 
0.7%
0226024117 2
 
0.7%
0226620669 2
 
0.7%
0236633256 2
 
0.7%
0226055573 1
 
0.3%
0226999982 1
 
0.3%
0226933239 1
 
0.3%
0226014166 1
 
0.3%
0226907098 1
 
0.3%
Other values (254) 254
87.6%
2024-05-11T15:25:22.751075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 636
23.7%
0 542
20.2%
6 428
15.9%
9 229
 
8.5%
3 169
 
6.3%
1 141
 
5.3%
7 137
 
5.1%
5 134
 
5.0%
4 122
 
4.5%
8 106
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2644
98.5%
Space Separator 40
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 636
24.1%
0 542
20.5%
6 428
16.2%
9 229
 
8.7%
3 169
 
6.4%
1 141
 
5.3%
7 137
 
5.2%
5 134
 
5.1%
4 122
 
4.6%
8 106
 
4.0%
Space Separator
ValueCountFrequency (%)
40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2684
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 636
23.7%
0 542
20.2%
6 428
15.9%
9 229
 
8.5%
3 169
 
6.3%
1 141
 
5.3%
7 137
 
5.1%
5 134
 
5.0%
4 122
 
4.5%
8 106
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2684
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 636
23.7%
0 542
20.2%
6 428
15.9%
9 229
 
8.5%
3 169
 
6.3%
1 141
 
5.3%
7 137
 
5.1%
5 134
 
5.0%
4 122
 
4.5%
8 106
 
3.9%
Distinct269
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-11T15:25:23.420322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.4139194
Min length5

Characters and Unicode

Total characters1751
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

Unique265 ?
Unique (%)97.1%

Sample

1st row92.56
2nd row151.07
3rd row82.76
4th row48.78
5th row169.59
ValueCountFrequency (%)
292.14 2
 
0.7%
149.75 2
 
0.7%
151.57 2
 
0.7%
446.95 2
 
0.7%
589.89 1
 
0.4%
656.83 1
 
0.4%
530.76 1
 
0.4%
623.00 1
 
0.4%
1,815.44 1
 
0.4%
586.25 1
 
0.4%
Other values (259) 259
94.9%
2024-05-11T15:25:24.409579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 273
15.6%
1 176
10.1%
3 172
9.8%
2 170
9.7%
4 165
9.4%
0 156
8.9%
5 139
7.9%
6 125
7.1%
8 120
6.9%
9 114
6.5%
Other values (2) 141
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1427
81.5%
Other Punctuation 324
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 176
12.3%
3 172
12.1%
2 170
11.9%
4 165
11.6%
0 156
10.9%
5 139
9.7%
6 125
8.8%
8 120
8.4%
9 114
8.0%
7 90
6.3%
Other Punctuation
ValueCountFrequency (%)
. 273
84.3%
, 51
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1751
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 273
15.6%
1 176
10.1%
3 172
9.8%
2 170
9.7%
4 165
9.4%
0 156
8.9%
5 139
7.9%
6 125
7.1%
8 120
6.9%
9 114
6.5%
Other values (2) 141
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1751
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 273
15.6%
1 176
10.1%
3 172
9.8%
2 170
9.7%
4 165
9.4%
0 156
8.9%
5 139
7.9%
6 125
7.1%
8 120
6.9%
9 114
6.5%
Other values (2) 141
8.1%
Distinct55
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-11T15:25:24.768166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1172161
Min length6

Characters and Unicode

Total characters1670
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

Unique26 ?
Unique (%)9.5%

Sample

1st row157812
2nd row157836
3rd row157853
4th row157812
5th row157871
ValueCountFrequency (%)
157910 57
20.9%
157909 34
 
12.5%
157866 29
 
10.6%
157916 12
 
4.4%
157-910 10
 
3.7%
157812 10
 
3.7%
157862 8
 
2.9%
157010 8
 
2.9%
157915 7
 
2.6%
157853 6
 
2.2%
Other values (45) 92
33.7%
2024-05-11T15:25:25.381408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 403
24.1%
5 300
18.0%
7 283
16.9%
9 197
11.8%
0 148
 
8.9%
8 128
 
7.7%
6 100
 
6.0%
2 48
 
2.9%
- 32
 
1.9%
3 21
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1638
98.1%
Dash Punctuation 32
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 403
24.6%
5 300
18.3%
7 283
17.3%
9 197
12.0%
0 148
 
9.0%
8 128
 
7.8%
6 100
 
6.1%
2 48
 
2.9%
3 21
 
1.3%
4 10
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1670
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 403
24.1%
5 300
18.0%
7 283
16.9%
9 197
11.8%
0 148
 
8.9%
8 128
 
7.7%
6 100
 
6.0%
2 48
 
2.9%
- 32
 
1.9%
3 21
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 403
24.1%
5 300
18.0%
7 283
16.9%
9 197
11.8%
0 148
 
8.9%
8 128
 
7.7%
6 100
 
6.0%
2 48
 
2.9%
- 32
 
1.9%
3 21
 
1.3%
Distinct267
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-11T15:25:25.917145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length35
Mean length22.450549
Min length18

Characters and Unicode

Total characters6129
Distinct characters93
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

Unique261 ?
Unique (%)95.6%

Sample

1st row서울특별시 강서구 공항동 53-20번지 .27
2nd row서울특별시 강서구 등촌동 506-13번지
3rd row서울특별시 강서구 방화동 621-2번지
4th row서울특별시 강서구 공항동 60-81번지
5th row서울특별시 강서구 화곡동 61-28번지
ValueCountFrequency (%)
서울특별시 273
23.8%
강서구 273
23.8%
화곡동 199
17.4%
염창동 16
 
1.4%
공항동 15
 
1.3%
방화동 15
 
1.3%
등촌동 10
 
0.9%
마곡동 9
 
0.8%
외발산동 6
 
0.5%
12 3
 
0.3%
Other values (309) 327
28.5%
2024-05-11T15:25:26.642464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1079
17.6%
547
 
8.9%
274
 
4.5%
274
 
4.5%
274
 
4.5%
273
 
4.5%
273
 
4.5%
273
 
4.5%
273
 
4.5%
- 266
 
4.3%
Other values (83) 2323
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3443
56.2%
Decimal Number 1300
 
21.2%
Space Separator 1079
 
17.6%
Dash Punctuation 266
 
4.3%
Other Punctuation 25
 
0.4%
Lowercase Letter 5
 
0.1%
Math Symbol 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
547
15.9%
274
8.0%
274
8.0%
274
8.0%
273
7.9%
273
7.9%
273
7.9%
273
7.9%
214
 
6.2%
209
 
6.1%
Other values (60) 559
16.2%
Decimal Number
ValueCountFrequency (%)
1 218
16.8%
2 216
16.6%
9 189
14.5%
3 116
8.9%
5 112
8.6%
0 104
8.0%
4 102
7.8%
6 92
7.1%
7 79
 
6.1%
8 72
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
k 2
40.0%
r 1
20.0%
y 1
20.0%
a 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 13
52.0%
, 12
48.0%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
P 1
50.0%
Space Separator
ValueCountFrequency (%)
1079
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 266
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3443
56.2%
Common 2679
43.7%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
547
15.9%
274
8.0%
274
8.0%
274
8.0%
273
7.9%
273
7.9%
273
7.9%
273
7.9%
214
 
6.2%
209
 
6.1%
Other values (60) 559
16.2%
Common
ValueCountFrequency (%)
1079
40.3%
- 266
 
9.9%
1 218
 
8.1%
2 216
 
8.1%
9 189
 
7.1%
3 116
 
4.3%
5 112
 
4.2%
0 104
 
3.9%
4 102
 
3.8%
6 92
 
3.4%
Other values (7) 185
 
6.9%
Latin
ValueCountFrequency (%)
k 2
28.6%
r 1
14.3%
S 1
14.3%
y 1
14.3%
P 1
14.3%
a 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3443
56.2%
ASCII 2686
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1079
40.2%
- 266
 
9.9%
1 218
 
8.1%
2 216
 
8.0%
9 189
 
7.0%
3 116
 
4.3%
5 112
 
4.2%
0 104
 
3.9%
4 102
 
3.8%
6 92
 
3.4%
Other values (13) 192
 
7.1%
Hangul
ValueCountFrequency (%)
547
15.9%
274
8.0%
274
8.0%
274
8.0%
273
7.9%
273
7.9%
273
7.9%
273
7.9%
214
 
6.2%
209
 
6.1%
Other values (60) 559
16.2%

도로명주소
Text

MISSING 

Distinct208
Distinct (%)95.4%
Missing55
Missing (%)20.1%
Memory size2.3 KiB
2024-05-11T15:25:26.985411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length47
Mean length26.178899
Min length22

Characters and Unicode

Total characters5707
Distinct characters120
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

Unique198 ?
Unique (%)90.8%

Sample

1st row서울특별시 강서구 까치산로 46 (화곡동)
2nd row서울특별시 강서구 양천로 701 (염창동)
3rd row서울특별시 강서구 초록마을로3길 49 (화곡동)
4th row서울특별시 강서구 화곡로42나길 6 (화곡동)
5th row서울특별시 강서구 까치산로15길 3 (화곡동)
ValueCountFrequency (%)
서울특별시 218
19.4%
강서구 218
19.4%
화곡동 149
 
13.3%
월정로20길 21
 
1.9%
곰달래로 17
 
1.5%
화곡로42길 15
 
1.3%
화곡로 14
 
1.2%
강서로5가길 14
 
1.2%
방화동 11
 
1.0%
강서로7길 11
 
1.0%
Other values (231) 433
38.6%
2024-05-11T15:25:27.501455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
904
 
15.8%
473
 
8.3%
254
 
4.5%
230
 
4.0%
226
 
4.0%
225
 
3.9%
219
 
3.8%
219
 
3.8%
219
 
3.8%
218
 
3.8%
Other values (110) 2520
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3437
60.2%
Space Separator 904
 
15.8%
Decimal Number 820
 
14.4%
Close Punctuation 218
 
3.8%
Open Punctuation 218
 
3.8%
Other Punctuation 58
 
1.0%
Dash Punctuation 40
 
0.7%
Math Symbol 5
 
0.1%
Lowercase Letter 5
 
0.1%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
473
13.8%
254
 
7.4%
230
 
6.7%
226
 
6.6%
225
 
6.5%
219
 
6.4%
219
 
6.4%
219
 
6.4%
218
 
6.3%
218
 
6.3%
Other values (86) 936
27.2%
Decimal Number
ValueCountFrequency (%)
1 161
19.6%
2 126
15.4%
4 96
11.7%
6 81
9.9%
5 79
9.6%
0 68
8.3%
3 66
8.0%
7 62
 
7.6%
8 54
 
6.6%
9 27
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
k 2
40.0%
y 1
20.0%
a 1
20.0%
r 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 46
79.3%
. 11
 
19.0%
/ 1
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
P 1
50.0%
Space Separator
ValueCountFrequency (%)
904
100.0%
Close Punctuation
ValueCountFrequency (%)
) 218
100.0%
Open Punctuation
ValueCountFrequency (%)
( 218
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3437
60.2%
Common 2263
39.7%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
473
13.8%
254
 
7.4%
230
 
6.7%
226
 
6.6%
225
 
6.5%
219
 
6.4%
219
 
6.4%
219
 
6.4%
218
 
6.3%
218
 
6.3%
Other values (86) 936
27.2%
Common
ValueCountFrequency (%)
904
39.9%
) 218
 
9.6%
( 218
 
9.6%
1 161
 
7.1%
2 126
 
5.6%
4 96
 
4.2%
6 81
 
3.6%
5 79
 
3.5%
0 68
 
3.0%
3 66
 
2.9%
Other values (8) 246
 
10.9%
Latin
ValueCountFrequency (%)
k 2
28.6%
S 1
14.3%
y 1
14.3%
P 1
14.3%
a 1
14.3%
r 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3437
60.2%
ASCII 2270
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
904
39.8%
) 218
 
9.6%
( 218
 
9.6%
1 161
 
7.1%
2 126
 
5.6%
4 96
 
4.2%
6 81
 
3.6%
5 79
 
3.5%
0 68
 
3.0%
3 66
 
2.9%
Other values (14) 253
 
11.1%
Hangul
ValueCountFrequency (%)
473
13.8%
254
 
7.4%
230
 
6.7%
226
 
6.6%
225
 
6.5%
219
 
6.4%
219
 
6.4%
219
 
6.4%
218
 
6.3%
218
 
6.3%
Other values (86) 936
27.2%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct46
Distinct (%)22.0%
Missing64
Missing (%)23.4%
Infinite0
Infinite (%)0.0%
Mean7706.7033
Minimum7505
Maximum7803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:27.794748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7505
5-th percentile7540
Q17663
median7762
Q37775
95-th percentile7778
Maximum7803
Range298
Interquartile range (IQR)112

Descriptive statistics

Standard deviation83.391673
Coefficient of variation (CV)0.010820667
Kurtosis-0.37996115
Mean7706.7033
Median Absolute Deviation (MAD)40
Skewness-0.8348399
Sum1610701
Variance6954.1712
MonotonicityNot monotonic
2024-05-11T15:25:28.055414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
7775 50
18.3%
7777 25
 
9.2%
7678 20
 
7.3%
7679 9
 
3.3%
7778 8
 
2.9%
7776 7
 
2.6%
7663 7
 
2.6%
7803 6
 
2.2%
7506 6
 
2.2%
7653 6
 
2.2%
Other values (36) 65
23.8%
(Missing) 64
23.4%
ValueCountFrequency (%)
7505 1
 
0.4%
7506 6
2.2%
7516 1
 
0.4%
7539 1
 
0.4%
7540 3
1.1%
7543 2
 
0.7%
7546 1
 
0.4%
7550 1
 
0.4%
7556 3
1.1%
7557 3
1.1%
ValueCountFrequency (%)
7803 6
 
2.2%
7789 1
 
0.4%
7788 2
 
0.7%
7786 1
 
0.4%
7778 8
 
2.9%
7777 25
9.2%
7776 7
 
2.6%
7775 50
18.3%
7768 1
 
0.4%
7767 1
 
0.4%
Distinct263
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-05-11T15:25:28.482299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length22
Mean length5.2564103
Min length1

Characters and Unicode

Total characters1435
Distinct characters273
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

Unique254 ?
Unique (%)93.0%

Sample

1st row김포여관
2nd row태평여관
3rd row서광여인숙
4th row경기여관
5th row온양여관
ValueCountFrequency (%)
호텔 9
 
2.8%
모텔 6
 
1.9%
hotel 4
 
1.2%
제일여관 3
 
0.9%
스테이 3
 
0.9%
레인보우 2
 
0.6%
서울 2
 
0.6%
로얄스퀘어호텔 2
 
0.6%
하임 2
 
0.6%
인터시티 2
 
0.6%
Other values (279) 288
89.2%
2024-05-11T15:25:29.208014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
129
 
9.0%
74
 
5.2%
61
 
4.3%
59
 
4.1%
58
 
4.0%
52
 
3.6%
50
 
3.5%
31
 
2.2%
28
 
2.0%
27
 
1.9%
Other values (263) 866
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1237
86.2%
Uppercase Letter 97
 
6.8%
Space Separator 50
 
3.5%
Open Punctuation 17
 
1.2%
Close Punctuation 17
 
1.2%
Decimal Number 11
 
0.8%
Lowercase Letter 5
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
129
 
10.4%
74
 
6.0%
61
 
4.9%
59
 
4.8%
58
 
4.7%
52
 
4.2%
31
 
2.5%
28
 
2.3%
27
 
2.2%
15
 
1.2%
Other values (225) 703
56.8%
Uppercase Letter
ValueCountFrequency (%)
E 13
13.4%
O 12
12.4%
L 12
12.4%
A 10
10.3%
T 10
10.3%
H 6
 
6.2%
S 5
 
5.2%
M 4
 
4.1%
I 3
 
3.1%
N 3
 
3.1%
Other values (13) 19
19.6%
Decimal Number
ValueCountFrequency (%)
2 3
27.3%
5 2
18.2%
0 2
18.2%
1 1
 
9.1%
6 1
 
9.1%
3 1
 
9.1%
9 1
 
9.1%
Lowercase Letter
ValueCountFrequency (%)
l 2
40.0%
e 1
20.0%
n 1
20.0%
o 1
20.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1237
86.2%
Latin 102
 
7.1%
Common 96
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
129
 
10.4%
74
 
6.0%
61
 
4.9%
59
 
4.8%
58
 
4.7%
52
 
4.2%
31
 
2.5%
28
 
2.3%
27
 
2.2%
15
 
1.2%
Other values (225) 703
56.8%
Latin
ValueCountFrequency (%)
E 13
12.7%
O 12
11.8%
L 12
11.8%
A 10
9.8%
T 10
9.8%
H 6
 
5.9%
S 5
 
4.9%
M 4
 
3.9%
I 3
 
2.9%
N 3
 
2.9%
Other values (17) 24
23.5%
Common
ValueCountFrequency (%)
50
52.1%
( 17
 
17.7%
) 17
 
17.7%
2 3
 
3.1%
5 2
 
2.1%
0 2
 
2.1%
1 1
 
1.0%
6 1
 
1.0%
3 1
 
1.0%
- 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1237
86.2%
ASCII 198
 
13.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
129
 
10.4%
74
 
6.0%
61
 
4.9%
59
 
4.8%
58
 
4.7%
52
 
4.2%
31
 
2.5%
28
 
2.3%
27
 
2.2%
15
 
1.2%
Other values (225) 703
56.8%
ASCII
ValueCountFrequency (%)
50
25.3%
( 17
 
8.6%
) 17
 
8.6%
E 13
 
6.6%
O 12
 
6.1%
L 12
 
6.1%
A 10
 
5.1%
T 10
 
5.1%
H 6
 
3.0%
S 5
 
2.5%
Other values (28) 46
23.2%
Distinct252
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2002-06-07 00:00:00
Maximum2024-05-08 11:57:46
2024-05-11T15:25:29.447476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:25:29.734679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
U
139 
I
134 

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 (%)
U 139
50.9%
I 134
49.1%

Length

2024-05-11T15:25:29.975526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:30.174180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
u 139
50.9%
i 134
49.1%
Distinct124
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 23:00:00
2024-05-11T15:25:30.393052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:25:30.646430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
여관업
216 
관광호텔
30 
일반호텔
 
9
숙박업(생활)
 
9
여인숙업
 
8

Length

Max length7
Median length3
Mean length3.3150183
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 216
79.1%
관광호텔 30
 
11.0%
일반호텔 9
 
3.3%
숙박업(생활) 9
 
3.3%
여인숙업 8
 
2.9%
숙박업 기타 1
 
0.4%

Length

2024-05-11T15:25:30.879157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:31.069992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 216
78.8%
관광호텔 30
 
10.9%
일반호텔 9
 
3.3%
숙박업(생활 9
 
3.3%
여인숙업 8
 
2.9%
숙박업 1
 
0.4%
기타 1
 
0.4%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct248
Distinct (%)92.9%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean186021.17
Minimum182524.82
Maximum189200.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:31.295824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182524.82
5-th percentile183132.35
Q1185746.44
median186317.8
Q3186455.64
95-th percentile188530.17
Maximum189200.15
Range6675.3239
Interquartile range (IQR)709.20169

Descriptive statistics

Standard deviation1319.4288
Coefficient of variation (CV)0.0070928961
Kurtosis1.2026302
Mean186021.17
Median Absolute Deviation (MAD)455.67653
Skewness-0.45847517
Sum49667653
Variance1740892.5
MonotonicityNot monotonic
2024-05-11T15:25:31.565776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
185863.097607353 2
 
0.7%
185744.439209715 2
 
0.7%
185729.290624774 2
 
0.7%
186402.942701702 2
 
0.7%
183863.692502412 2
 
0.7%
183993.393147385 2
 
0.7%
186358.332102311 2
 
0.7%
188530.174258909 2
 
0.7%
186415.131479404 2
 
0.7%
185841.652551744 2
 
0.7%
Other values (238) 247
90.5%
(Missing) 6
 
2.2%
ValueCountFrequency (%)
182524.823835629 1
0.4%
182944.648231601 1
0.4%
182955.836242934 1
0.4%
182971.470840788 1
0.4%
183007.440662947 1
0.4%
183042.639859252 1
0.4%
183043.30388526 1
0.4%
183080.627664394 1
0.4%
183085.230480374 1
0.4%
183091.368220272 1
0.4%
ValueCountFrequency (%)
189200.147733153 1
0.4%
189172.250232972 1
0.4%
189165.464226546 1
0.4%
189152.269637156 1
0.4%
189028.13490462 1
0.4%
188963.907416791 1
0.4%
188951.80143117 1
0.4%
188939.792834998 1
0.4%
188921.439575928 1
0.4%
188883.085593065 1
0.4%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct248
Distinct (%)92.9%
Missing6
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean448953.15
Minimum447360.48
Maximum452334.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:31.827240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum447360.48
5-th percentile447412.63
Q1447491.93
median449348.46
Q3449859.42
95-th percentile451278.3
Maximum452334.06
Range4973.5718
Interquartile range (IQR)2367.4904

Descriptive statistics

Standard deviation1414.8588
Coefficient of variation (CV)0.0031514621
Kurtosis-0.98052331
Mean448953.15
Median Absolute Deviation (MAD)1583.6858
Skewness0.3778988
Sum1.1987049 × 108
Variance2001825.5
MonotonicityNot monotonic
2024-05-11T15:25:32.110514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447532.962112156 2
 
0.7%
447538.415878275 2
 
0.7%
447764.779091241 2
 
0.7%
447426.274158648 2
 
0.7%
449443.610735042 2
 
0.7%
449605.865701793 2
 
0.7%
449480.163884546 2
 
0.7%
449991.138785439 2
 
0.7%
447426.977456533 2
 
0.7%
447544.394615942 2
 
0.7%
Other values (238) 247
90.5%
(Missing) 6
 
2.2%
ValueCountFrequency (%)
447360.483643506 1
0.4%
447372.387977136 1
0.4%
447372.878934869 1
0.4%
447381.257869472 1
0.4%
447391.437190535 1
0.4%
447391.774332306 1
0.4%
447396.45923655 1
0.4%
447398.844380128 1
0.4%
447400.382804237 1
0.4%
447401.426813655 1
0.4%
ValueCountFrequency (%)
452334.055492356 1
0.4%
452332.290803158 1
0.4%
452309.796144652 1
0.4%
452305.906181726 1
0.4%
452253.772178 1
0.4%
452208.873036934 1
0.4%
451786.0 1
0.4%
451719.56016977 1
0.4%
451677.575370424 1
0.4%
451668.92061568 1
0.4%

위생업태명
Categorical

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
여관업
165 
<NA>
69 
관광호텔
20 
여인숙업
 
8
일반호텔
 
7

Length

Max length7
Median length3
Mean length3.4395604
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 165
60.4%
<NA> 69
25.3%
관광호텔 20
 
7.3%
여인숙업 8
 
2.9%
일반호텔 7
 
2.6%
숙박업(생활) 4
 
1.5%

Length

2024-05-11T15:25:32.388395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:32.582057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 165
60.4%
na 69
25.3%
관광호텔 20
 
7.3%
여인숙업 8
 
2.9%
일반호텔 7
 
2.6%
숙박업(생활 4
 
1.5%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)9.7%
Missing118
Missing (%)43.2%
Infinite0
Infinite (%)0.0%
Mean3.4774194
Minimum0
Maximum15
Zeros64
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:32.757169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile12
Maximum15
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8668345
Coefficient of variation (CV)1.1119839
Kurtosis0.60710043
Mean3.4774194
Median Absolute Deviation (MAD)3
Skewness1.0866929
Sum539
Variance14.952409
MonotonicityNot monotonic
2024-05-11T15:25:32.963780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 64
23.4%
3 21
 
7.7%
4 20
 
7.3%
5 10
 
3.7%
6 9
 
3.3%
8 6
 
2.2%
9 5
 
1.8%
7 5
 
1.8%
10 3
 
1.1%
13 3
 
1.1%
Other values (5) 9
 
3.3%
(Missing) 118
43.2%
ValueCountFrequency (%)
0 64
23.4%
1 2
 
0.7%
3 21
 
7.7%
4 20
 
7.3%
5 10
 
3.7%
6 9
 
3.3%
7 5
 
1.8%
8 6
 
2.2%
9 5
 
1.8%
10 3
 
1.1%
ValueCountFrequency (%)
15 2
 
0.7%
14 2
 
0.7%
13 3
 
1.1%
12 2
 
0.7%
11 1
 
0.4%
10 3
 
1.1%
9 5
1.8%
8 6
2.2%
7 5
1.8%
6 9
3.3%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)3.9%
Missing119
Missing (%)43.6%
Infinite0
Infinite (%)0.0%
Mean0.78571429
Minimum0
Maximum5
Zeros65
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:33.162100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93560149
Coefficient of variation (CV)1.1907655
Kurtosis5.332454
Mean0.78571429
Median Absolute Deviation (MAD)1
Skewness1.9454977
Sum121
Variance0.87535014
MonotonicityNot monotonic
2024-05-11T15:25:33.351391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 72
26.4%
0 65
23.8%
3 7
 
2.6%
2 7
 
2.6%
5 2
 
0.7%
4 1
 
0.4%
(Missing) 119
43.6%
ValueCountFrequency (%)
0 65
23.8%
1 72
26.4%
2 7
 
2.6%
3 7
 
2.6%
4 1
 
0.4%
5 2
 
0.7%
ValueCountFrequency (%)
5 2
 
0.7%
4 1
 
0.4%
3 7
 
2.6%
2 7
 
2.6%
1 72
26.4%
0 65
23.8%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)7.2%
Missing121
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean1.9342105
Minimum0
Maximum10
Zeros68
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:33.609685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile6.45
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4647745
Coefficient of variation (CV)1.2743052
Kurtosis0.96814177
Mean1.9342105
Median Absolute Deviation (MAD)1
Skewness1.2786758
Sum294
Variance6.0751133
MonotonicityNot monotonic
2024-05-11T15:25:33.895121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 68
24.9%
1 26
 
9.5%
4 17
 
6.2%
3 13
 
4.8%
6 8
 
2.9%
5 7
 
2.6%
2 5
 
1.8%
7 2
 
0.7%
10 2
 
0.7%
9 2
 
0.7%
(Missing) 121
44.3%
ValueCountFrequency (%)
0 68
24.9%
1 26
 
9.5%
2 5
 
1.8%
3 13
 
4.8%
4 17
 
6.2%
5 7
 
2.6%
6 8
 
2.9%
7 2
 
0.7%
8 2
 
0.7%
9 2
 
0.7%
ValueCountFrequency (%)
10 2
 
0.7%
9 2
 
0.7%
8 2
 
0.7%
7 2
 
0.7%
6 8
 
2.9%
5 7
 
2.6%
4 17
6.2%
3 13
4.8%
2 5
 
1.8%
1 26
9.5%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)9.7%
Missing128
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean2.9862069
Minimum0
Maximum13
Zeros64
Zeros (%)23.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:34.129484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile9.8
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3270487
Coefficient of variation (CV)1.1141387
Kurtosis0.24884168
Mean2.9862069
Median Absolute Deviation (MAD)3
Skewness0.95064736
Sum433
Variance11.069253
MonotonicityNot monotonic
2024-05-11T15:25:34.402063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 64
23.4%
4 19
 
7.0%
3 16
 
5.9%
6 10
 
3.7%
5 10
 
3.7%
8 6
 
2.2%
7 5
 
1.8%
2 3
 
1.1%
10 3
 
1.1%
1 2
 
0.7%
Other values (4) 7
 
2.6%
(Missing) 128
46.9%
ValueCountFrequency (%)
0 64
23.4%
1 2
 
0.7%
2 3
 
1.1%
3 16
 
5.9%
4 19
 
7.0%
5 10
 
3.7%
6 10
 
3.7%
7 5
 
1.8%
8 6
 
2.2%
9 2
 
0.7%
ValueCountFrequency (%)
13 2
 
0.7%
12 1
 
0.4%
11 2
 
0.7%
10 3
 
1.1%
9 2
 
0.7%
8 6
 
2.2%
7 5
 
1.8%
6 10
3.7%
5 10
3.7%
4 19
7.0%
Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
137 
0
82 
1
50 
3
 
3
2
 
1

Length

Max length4
Median length4
Mean length2.5054945
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 137
50.2%
0 82
30.0%
1 50
 
18.3%
3 3
 
1.1%
2 1
 
0.4%

Length

2024-05-11T15:25:34.723877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:34.907510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 137
50.2%
0 82
30.0%
1 50
 
18.3%
3 3
 
1.1%
2 1
 
0.4%
Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
144 
0
78 
1
46 
3
 
4
2
 
1

Length

Max length4
Median length4
Mean length2.5824176
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 144
52.7%
0 78
28.6%
1 46
 
16.8%
3 4
 
1.5%
2 1
 
0.4%

Length

2024-05-11T15:25:35.117842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:35.322187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 144
52.7%
0 78
28.6%
1 46
 
16.8%
3 4
 
1.5%
2 1
 
0.4%

한실수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)8.4%
Missing130
Missing (%)47.6%
Infinite0
Infinite (%)0.0%
Mean1.2377622
Minimum0
Maximum19
Zeros98
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:35.490179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6.9
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.805824
Coefficient of variation (CV)2.2668522
Kurtosis16.272821
Mean1.2377622
Median Absolute Deviation (MAD)0
Skewness3.6014811
Sum177
Variance7.8726485
MonotonicityNot monotonic
2024-05-11T15:25:35.669252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 98
35.9%
2 12
 
4.4%
1 12
 
4.4%
5 5
 
1.8%
3 3
 
1.1%
4 3
 
1.1%
9 2
 
0.7%
7 2
 
0.7%
6 2
 
0.7%
8 2
 
0.7%
Other values (2) 2
 
0.7%
(Missing) 130
47.6%
ValueCountFrequency (%)
0 98
35.9%
1 12
 
4.4%
2 12
 
4.4%
3 3
 
1.1%
4 3
 
1.1%
5 5
 
1.8%
6 2
 
0.7%
7 2
 
0.7%
8 2
 
0.7%
9 2
 
0.7%
ValueCountFrequency (%)
19 1
 
0.4%
16 1
 
0.4%
9 2
 
0.7%
8 2
 
0.7%
7 2
 
0.7%
6 2
 
0.7%
5 5
1.8%
4 3
 
1.1%
3 3
 
1.1%
2 12
4.4%

양실수
Real number (ℝ)

MISSING 

Distinct54
Distinct (%)26.6%
Missing70
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean26.62069
Minimum0
Maximum355
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-05-11T15:25:35.896597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q114
median19
Q327.5
95-th percentile57
Maximum355
Range355
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation33.057091
Coefficient of variation (CV)1.2417819
Kurtosis59.980968
Mean26.62069
Median Absolute Deviation (MAD)6
Skewness6.9380965
Sum5404
Variance1092.7713
MonotonicityNot monotonic
2024-05-11T15:25:36.591484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 17
 
6.2%
20 14
 
5.1%
10 12
 
4.4%
11 11
 
4.0%
15 11
 
4.0%
14 10
 
3.7%
19 10
 
3.7%
12 8
 
2.9%
24 8
 
2.9%
16 7
 
2.6%
Other values (44) 95
34.8%
(Missing) 70
25.6%
ValueCountFrequency (%)
0 1
 
0.4%
3 1
 
0.4%
5 1
 
0.4%
6 2
 
0.7%
8 2
 
0.7%
9 2
 
0.7%
10 12
4.4%
11 11
4.0%
12 8
2.9%
13 3
 
1.1%
ValueCountFrequency (%)
355 1
0.4%
260 1
0.4%
118 1
0.4%
100 1
0.4%
97 1
0.4%
88 1
0.4%
82 1
0.4%
69 1
0.4%
67 1
0.4%
58 1
0.4%

욕실수
Categorical

IMBALANCE 

Distinct6
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
155 
0
113 
19
 
2
29
 
1
18
 
1

Length

Max length4
Median length4
Mean length2.7216117
Min length1

Unique

Unique3 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 155
56.8%
0 113
41.4%
19 2
 
0.7%
29 1
 
0.4%
18 1
 
0.4%
35 1
 
0.4%

Length

2024-05-11T15:25:36.811546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:37.029671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 155
56.8%
0 113
41.4%
19 2
 
0.7%
29 1
 
0.4%
18 1
 
0.4%
35 1
 
0.4%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)1.0%
Missing69
Missing (%)25.3%
Memory size678.0 B
False
202 
True
 
2
(Missing)
69 
ValueCountFrequency (%)
False 202
74.0%
True 2
 
0.7%
(Missing) 69
 
25.3%
2024-05-11T15:25:37.227835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
156 
0
114 
29
 
1
12
 
1
35
 
1

Length

Max length4
Median length4
Mean length2.7252747
Min length1

Unique

Unique3 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 156
57.1%
0 114
41.8%
29 1
 
0.4%
12 1
 
0.4%
35 1
 
0.4%

Length

2024-05-11T15:25:37.387417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:37.643329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 156
57.1%
0 114
41.8%
29 1
 
0.4%
12 1
 
0.4%
35 1
 
0.4%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing273
Missing (%)100.0%
Memory size2.5 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing273
Missing (%)100.0%
Memory size2.5 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing273
Missing (%)100.0%
Memory size2.5 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
249 
자가
 
18
임대
 
6

Length

Max length4
Median length4
Mean length3.8241758
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> 249
91.2%
자가 18
 
6.6%
임대 6
 
2.2%

Length

2024-05-11T15:25:37.934937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:38.156572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 249
91.2%
자가 18
 
6.6%
임대 6
 
2.2%

세탁기수
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
164 
0
109 

Length

Max length4
Median length4
Mean length2.8021978
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> 164
60.1%
0 109
39.9%

Length

2024-05-11T15:25:38.378857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:38.608942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 164
60.1%
0 109
39.9%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
255 
0
 
18

Length

Max length4
Median length4
Mean length3.8021978
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> 255
93.4%
0 18
 
6.6%

Length

2024-05-11T15:25:38.767975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:38.918240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 255
93.4%
0 18
 
6.6%

남성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
255 
0
 
18

Length

Max length4
Median length4
Mean length3.8021978
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> 255
93.4%
0 18
 
6.6%

Length

2024-05-11T15:25:39.084306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:39.275989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 255
93.4%
0 18
 
6.6%

회수건조수
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
167 
0
106 

Length

Max length4
Median length4
Mean length2.8351648
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> 167
61.2%
0 106
38.8%

Length

2024-05-11T15:25:39.454504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:39.640143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 167
61.2%
0 106
38.8%

침대수
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
<NA>
167 
0
106 

Length

Max length4
Median length4
Mean length2.8351648
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> 167
61.2%
0 106
38.8%

Length

2024-05-11T15:25:39.827942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:25:39.984344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 167
61.2%
0 106
38.8%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.5%
Missing69
Missing (%)25.3%
Memory size678.0 B
False
204 
(Missing)
69 
ValueCountFrequency (%)
False 204
74.7%
(Missing) 69
 
25.3%
2024-05-11T15:25:40.159980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031500003150000-201-1971-0000119711112<NA>3폐업2폐업20071115<NA><NA><NA>022664674092.56157812서울특별시 강서구 공항동 53-20번지 .27<NA><NA>김포여관2004-12-06 00:00:00I2018-08-31 23:59:59.0여관업<NA><NA>여관업<NA><NA><NA><NA><NA><NA><NA>14<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
131500003150000-201-1972-0000119720323<NA>3폐업2폐업20071227<NA><NA><NA>0226515956151.07157836서울특별시 강서구 등촌동 506-13번지<NA><NA>태평여관2004-12-06 00:00:00I2018-08-31 23:59:59.0여인숙업187885.829027449804.589159여인숙업1<NA>11<NA><NA>35<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231500003150000-201-1972-0000219720525<NA>3폐업2폐업20020514<NA><NA><NA>02 662141482.76157853서울특별시 강서구 방화동 621-2번지<NA><NA>서광여인숙2002-06-07 00:00:00I2018-08-31 23:59:59.0여인숙업182944.648232451129.7358여인숙업000<NA>0<NA>0120N0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
331500003150000-201-1973-0000119731003<NA>3폐업2폐업20090923<NA><NA><NA>02 662120548.78157812서울특별시 강서구 공항동 60-81번지<NA><NA>경기여관2009-05-25 13:33:56I2018-08-31 23:59:59.0여관업183159.681454450648.953157여관업<NA><NA><NA><NA><NA><NA><NA>19<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431500003150000-201-1977-0000119770806<NA>3폐업2폐업20161013<NA><NA><NA>0226033342169.59157871서울특별시 강서구 화곡동 61-28번지서울특별시 강서구 까치산로 46 (화곡동)7722온양여관2015-06-23 10:17:21I2018-08-31 23:59:59.0여관업186231.092557449002.648542여관업<NA><NA><NA><NA><NA><NA>46<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
531500003150000-201-1978-0000119781126<NA>3폐업2폐업20030529<NA><NA><NA>022664428651.36157812서울특별시 강서구 공항동 52-21번지 (2층)<NA><NA>제일여관2003-05-29 00:00:00I2018-08-31 23:59:59.0여관업183132.116103450891.824912여관업3122<NA><NA>33<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
631500003150000-201-1980-0000319800930<NA>3폐업2폐업20210706<NA><NA><NA>0236643708643.30157861서울특별시 강서구 염창동 250-21서울특별시 강서구 양천로 701 (염창동)7540나그랑2021-07-06 15:50:15U2021-07-08 02:40:00.0여관업188790.644148449774.896439여관업31230092029N29<NA><NA><NA>임대00000N
731500003150000-201-1980-0000619800930<NA>3폐업2폐업20110714<NA><NA><NA>0236623109297.52157801서울특별시 강서구 가양동 131-4번지 2통5반<NA><NA>삼호2004-02-05 00:00:00I2018-08-31 23:59:59.0여관업185993.853911451719.56017여관업<NA><NA><NA><NA><NA><NA><NA>13<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831500003150000-201-1980-0000719800930<NA>1영업/정상1영업<NA><NA><NA><NA>0226914206664.36157866서울특별시 강서구 화곡동 27-12번지서울특별시 강서구 초록마을로3길 49 (화곡동)7678쟈스민모텔2018-02-20 16:24:50I2018-08-31 23:59:59.0여관업186435.126837449345.559809여관업0000000180N0<NA><NA><NA><NA>0<NA><NA>00N
931500003150000-201-1980-0000819800930<NA>3폐업2폐업20111017<NA><NA><NA>0226044303329.20157910서울특별시 강서구 화곡동 920-7번지<NA><NA>포시즌2004-12-06 00:00:00I2018-08-31 23:59:59.0여관업185984.895966447559.458273여관업<NA><NA><NA><NA><NA><NA><NA>15<NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
26331500003150000-201-2023-000042023-11-16<NA>3폐업2폐업2024-02-01<NA><NA><NA>0226905555902.30157-210서울특별시 강서구 마곡동 759-2서울특별시 강서구 마곡중앙5로 22, 지하1/지상10층 (마곡동)7788웰튼메디컬호스텔2024-02-01 16:13:27U2023-12-02 00:03:00.0숙박업 기타184496.437997451677.57537<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
26431500003150000-214-2015-0000120151117<NA>3폐업2폐업20170713<NA><NA><NA>0264701200961.52157210서울특별시 강서구 마곡동 797-11번지서울특별시 강서구 마곡중앙6로 76-3 (마곡동)7803우림엠에스 인터시티2017-07-13 14:46:03I2018-08-31 23:59:59.0숙박업(생활)185417.0450847.0숙박업(생활)145000003550N0<NA><NA><NA>임대0<NA><NA>00N
26531500003150000-214-2015-0000220151120<NA>1영업/정상1영업<NA><NA><NA><NA>0226990221980.74157868서울특별시 강서구 화곡동 72-1번지서울특별시 강서구 화곡로 248-13 (화곡동)7678필 호스텔2018-02-20 15:45:14I2018-08-31 23:59:59.0숙박업(생활)186358.332102449480.163885숙박업(생활)5100000270N0<NA><NA><NA><NA>00000N
26631500003150000-214-2017-0000120170118<NA>3폐업2폐업20220405<NA><NA><NA>02 216190007,963.44157210서울특별시 강서구 마곡동 760-1 (힐스테이트 에코 마곡나루역)서울특별시 강서구 마곡중앙로 161-11, 지하1~14층 (마곡동, 가양1동, 힐스테이트 에코 마곡나루역)7788라마다앙코르서울마곡2022-04-05 13:57:57U2021-12-04 00:07:00.0숙박업(생활)184618.0451786.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
26731500003150000-214-2017-000022017-02-14<NA>1영업/정상1영업<NA><NA><NA><NA><NA>6471.22157-210서울특별시 강서구 마곡동 797-12서울특별시 강서구 마곡동로4길 23 (마곡동)7803더퍼스트스테이 호텔2024-04-02 11:24:22U2023-12-04 00:07:00.0숙박업(생활)185458.359508450832.655325<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
26831500003150000-214-2017-0000320170529<NA>3폐업2폐업20200304<NA><NA><NA>02 673360004,951.49157240서울특별시 강서구 공항동 1375번지서울특별시 강서구 공항대로 30 (공항동)7623로얄스퀘어호텔2020-03-04 14:35:53U2020-03-06 02:40:00.0숙박업(생활)183171.101582450946.909086숙박업(생활)72172201000N0<NA><NA><NA>자가0<NA><NA>00N
26931500003150000-214-2017-000042017-08-09<NA>1영업/정상1영업<NA><NA><NA><NA>02366485006356.74157-210서울특별시 강서구 마곡동 797-11서울특별시 강서구 마곡중앙6로 76-3 (마곡동)7803인터시티 서울호텔2023-04-14 13:23:24U2022-12-03 23:06:00.0숙박업(생활)185424.394815450845.917955<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
27031500003150000-214-2017-0000520170822<NA>3폐업2폐업20211021<NA><NA><NA>02 266090002,866.56157290서울특별시 강서구 외발산동 426 스위트동, 3~4층서울특별시 강서구 방화대로 94, 3~4층 (외발산동, 스위트동)7506메이필드 스위트호텔2021-10-21 11:42:10U2021-10-23 02:40:00.0숙박업(생활)183863.692502449443.610735숙박업(생활)0034000310N0<NA><NA><NA><NA>00000N
27131500003150000-214-2020-000012020-08-11<NA>1영업/정상1영업<NA><NA><NA><NA>02673360055243.86157-240서울특별시 강서구 공항동 1375 로얄스퀘어호텔서울서울특별시 강서구 공항대로 30 (공항동, 로얄스퀘어호텔서울)7623로얄스퀘어호텔 서울2023-08-09 15:13:14U2022-12-07 23:01:00.0숙박업(생활)183171.101582450946.909086<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
27231500003150000-214-2024-000012024-04-03<NA>1영업/정상1영업<NA><NA><NA><NA><NA>2173.94157-210서울특별시 강서구 마곡동 797-11 인터시티365서울특별시 강서구 마곡중앙6로 76-3, 인터시티365 (마곡동)7803인터시티365레지던스2024-04-03 16:47:56I2023-12-04 00:07:00.0숙박업(생활)185424.394815450845.917955<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>