Overview

Dataset statistics

Number of variables47
Number of observations451
Missing cells4161
Missing cells (%)19.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory178.1 KiB
Average record size in memory404.3 B

Variable types

Categorical20
Text7
DateTime4
Unsupported5
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
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-17447/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (51.3%)Imbalance
발한실여부 is highly imbalanced (63.6%)Imbalance
조건부허가시작일자 is highly imbalanced (97.7%)Imbalance
조건부허가종료일자 is highly imbalanced (97.7%)Imbalance
여성종사자수 is highly imbalanced (84.7%)Imbalance
남성종사자수 is highly imbalanced (84.7%)Imbalance
인허가취소일자 has 451 (100.0%) missing valuesMissing
폐업일자 has 107 (23.7%) missing valuesMissing
휴업시작일자 has 451 (100.0%) missing valuesMissing
휴업종료일자 has 451 (100.0%) missing valuesMissing
재개업일자 has 451 (100.0%) missing valuesMissing
전화번호 has 18 (4.0%) missing valuesMissing
도로명주소 has 218 (48.3%) missing valuesMissing
도로명우편번호 has 232 (51.4%) missing valuesMissing
좌표정보(X) has 36 (8.0%) missing valuesMissing
좌표정보(Y) has 36 (8.0%) missing valuesMissing
건물지상층수 has 134 (29.7%) missing valuesMissing
사용끝지상층 has 222 (49.2%) missing valuesMissing
한실수 has 136 (30.2%) missing valuesMissing
양실수 has 140 (31.0%) missing valuesMissing
욕실수 has 182 (40.4%) missing valuesMissing
발한실여부 has 106 (23.5%) missing valuesMissing
좌석수 has 233 (51.7%) missing valuesMissing
조건부허가신고사유 has 451 (100.0%) missing valuesMissing
다중이용업소여부 has 106 (23.5%) 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
건물지상층수 has 87 (19.3%) zerosZeros
한실수 has 108 (23.9%) zerosZeros
양실수 has 71 (15.7%) zerosZeros
욕실수 has 83 (18.4%) zerosZeros
좌석수 has 187 (41.5%) zerosZeros

Reproduction

Analysis started2024-04-06 09:54:37.190884
Analysis finished2024-04-06 09:54:38.551246
Duration1.36 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
3200000
451 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3200000
2nd row3200000
3rd row3200000
4th row3200000
5th row3200000

Common Values

ValueCountFrequency (%)
3200000 451
100.0%

Length

2024-04-06T18:54:38.670579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:38.859172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 451
100.0%

관리번호
Text

UNIQUE 

Distinct451
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-06T18:54:39.551038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters9922
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

Unique451 ?
Unique (%)100.0%

Sample

1st row3200000-201-1968-00041
2nd row3200000-201-1968-00310
3rd row3200000-201-1968-00315
4th row3200000-201-1968-00355
5th row3200000-201-1969-00049
ValueCountFrequency (%)
3200000-201-1968-00041 1
 
0.2%
3200000-201-1986-00388 1
 
0.2%
3200000-201-1999-00266 1
 
0.2%
3200000-201-1999-00246 1
 
0.2%
3200000-201-1999-00214 1
 
0.2%
3200000-201-1999-00184 1
 
0.2%
3200000-201-1999-00126 1
 
0.2%
3200000-201-1999-00095 1
 
0.2%
3200000-201-1999-00069 1
 
0.2%
3200000-201-1998-00398 1
 
0.2%
Other values (441) 441
97.8%
2024-04-06T18:54:40.599076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4190
42.2%
- 1353
 
13.6%
2 1259
 
12.7%
1 1100
 
11.1%
3 694
 
7.0%
9 495
 
5.0%
8 285
 
2.9%
7 202
 
2.0%
6 130
 
1.3%
4 113
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8569
86.4%
Dash Punctuation 1353
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4190
48.9%
2 1259
 
14.7%
1 1100
 
12.8%
3 694
 
8.1%
9 495
 
5.8%
8 285
 
3.3%
7 202
 
2.4%
6 130
 
1.5%
4 113
 
1.3%
5 101
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 1353
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9922
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4190
42.2%
- 1353
 
13.6%
2 1259
 
12.7%
1 1100
 
11.1%
3 694
 
7.0%
9 495
 
5.0%
8 285
 
2.9%
7 202
 
2.0%
6 130
 
1.3%
4 113
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4190
42.2%
- 1353
 
13.6%
2 1259
 
12.7%
1 1100
 
11.1%
3 694
 
7.0%
9 495
 
5.0%
8 285
 
2.9%
7 202
 
2.0%
6 130
 
1.3%
4 113
 
1.1%
Distinct415
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum1968-04-30 00:00:00
Maximum2023-09-22 00:00:00
2024-04-06T18:54:40.884522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:54:41.134007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing451
Missing (%)100.0%
Memory size4.1 KiB
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
3
344 
1
107 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
3 344
76.3%
1 107
 
23.7%

Length

2024-04-06T18:54:41.314806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:41.448983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 344
76.3%
1 107
 
23.7%

영업상태명
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
폐업
344 
영업/정상
107 

Length

Max length5
Median length2
Mean length2.7117517
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 344
76.3%
영업/정상 107
 
23.7%

Length

2024-04-06T18:54:41.672976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:41.847637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 344
76.3%
영업/정상 107
 
23.7%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2
344 
1
107 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 344
76.3%
1 107
 
23.7%

Length

2024-04-06T18:54:42.064871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:42.242419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 344
76.3%
1 107
 
23.7%
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
폐업
344 
영업
107 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 344
76.3%
영업 107
 
23.7%

Length

2024-04-06T18:54:42.405643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:42.553195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 344
76.3%
영업 107
 
23.7%

폐업일자
Date

MISSING 

Distinct285
Distinct (%)82.8%
Missing107
Missing (%)23.7%
Memory size3.7 KiB
Minimum1990-05-25 00:00:00
Maximum2024-04-01 00:00:00
2024-04-06T18:54:42.737017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:54:42.957557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing451
Missing (%)100.0%
Memory size4.1 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing451
Missing (%)100.0%
Memory size4.1 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing451
Missing (%)100.0%
Memory size4.1 KiB

전화번호
Text

MISSING 

Distinct364
Distinct (%)84.1%
Missing18
Missing (%)4.0%
Memory size3.7 KiB
2024-04-06T18:54:43.470546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.002309
Min length2

Characters and Unicode

Total characters4331
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

Unique354 ?
Unique (%)81.8%

Sample

1st row0208852537
2nd row0200000000
3rd row02 8834900
4th row02 8891302
5th row0200000000
ValueCountFrequency (%)
02 400
47.3%
00000 36
 
4.3%
0200000000 23
 
2.7%
877 5
 
0.6%
0 5
 
0.6%
878 3
 
0.4%
8855677 3
 
0.4%
8522968 2
 
0.2%
8771345 2
 
0.2%
8894894 2
 
0.2%
Other values (359) 365
43.1%
2024-04-06T18:54:44.320526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 991
22.9%
8 743
17.2%
2 616
14.2%
510
11.8%
7 344
 
7.9%
5 231
 
5.3%
3 198
 
4.6%
6 184
 
4.2%
9 183
 
4.2%
4 169
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3821
88.2%
Space Separator 510
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991
25.9%
8 743
19.4%
2 616
16.1%
7 344
 
9.0%
5 231
 
6.0%
3 198
 
5.2%
6 184
 
4.8%
9 183
 
4.8%
4 169
 
4.4%
1 162
 
4.2%
Space Separator
ValueCountFrequency (%)
510
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4331
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 991
22.9%
8 743
17.2%
2 616
14.2%
510
11.8%
7 344
 
7.9%
5 231
 
5.3%
3 198
 
4.6%
6 184
 
4.2%
9 183
 
4.2%
4 169
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 991
22.9%
8 743
17.2%
2 616
14.2%
510
11.8%
7 344
 
7.9%
5 231
 
5.3%
3 198
 
4.6%
6 184
 
4.2%
9 183
 
4.2%
4 169
 
3.9%
Distinct435
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-06T18:54:44.947568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.9002217
Min length3

Characters and Unicode

Total characters2661
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

Unique421 ?
Unique (%)93.3%

Sample

1st row65.15
2nd row62.99
3rd row47.91
4th row39.34
5th row61.25
ValueCountFrequency (%)
00 4
 
0.9%
45.87 2
 
0.4%
1,020.43 2
 
0.4%
137.34 2
 
0.4%
466.71 2
 
0.4%
411.86 2
 
0.4%
51.55 2
 
0.4%
42.05 2
 
0.4%
105.61 2
 
0.4%
360.34 2
 
0.4%
Other values (425) 429
95.1%
2024-04-06T18:54:45.797410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 451
16.9%
2 279
10.5%
4 270
10.1%
1 253
9.5%
3 234
8.8%
0 219
8.2%
6 204
7.7%
5 195
7.3%
9 189
7.1%
8 179
 
6.7%
Other values (2) 188
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2194
82.5%
Other Punctuation 467
 
17.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 279
12.7%
4 270
12.3%
1 253
11.5%
3 234
10.7%
0 219
10.0%
6 204
9.3%
5 195
8.9%
9 189
8.6%
8 179
8.2%
7 172
7.8%
Other Punctuation
ValueCountFrequency (%)
. 451
96.6%
, 16
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2661
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 451
16.9%
2 279
10.5%
4 270
10.1%
1 253
9.5%
3 234
8.8%
0 219
8.2%
6 204
7.7%
5 195
7.3%
9 189
7.1%
8 179
 
6.7%
Other values (2) 188
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 451
16.9%
2 279
10.5%
4 270
10.1%
1 253
9.5%
3 234
8.8%
0 219
8.2%
6 204
7.7%
5 195
7.3%
9 189
7.1%
8 179
 
6.7%
Other values (2) 188
7.1%
Distinct71
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-06T18:54:46.232574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2150776
Min length6

Characters and Unicode

Total characters2803
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 (%)5.8%

Sample

1st row151803
2nd row151050
3rd row151803
4th row151862
5th row151865
ValueCountFrequency (%)
151892 75
16.6%
151890 44
 
9.8%
151836 36
 
8.0%
151-892 31
 
6.9%
151930 25
 
5.5%
151891 19
 
4.2%
151-890 18
 
4.0%
151841 12
 
2.7%
151848 11
 
2.4%
151803 11
 
2.4%
Other values (61) 169
37.5%
2024-04-06T18:54:46.939465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 981
35.0%
5 491
17.5%
8 419
14.9%
9 263
 
9.4%
0 174
 
6.2%
2 128
 
4.6%
3 118
 
4.2%
- 97
 
3.5%
6 75
 
2.7%
4 47
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2706
96.5%
Dash Punctuation 97
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 981
36.3%
5 491
18.1%
8 419
15.5%
9 263
 
9.7%
0 174
 
6.4%
2 128
 
4.7%
3 118
 
4.4%
6 75
 
2.8%
4 47
 
1.7%
7 10
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 981
35.0%
5 491
17.5%
8 419
14.9%
9 263
 
9.4%
0 174
 
6.2%
2 128
 
4.6%
3 118
 
4.2%
- 97
 
3.5%
6 75
 
2.7%
4 47
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 981
35.0%
5 491
17.5%
8 419
14.9%
9 263
 
9.4%
0 174
 
6.2%
2 128
 
4.6%
3 118
 
4.2%
- 97
 
3.5%
6 75
 
2.7%
4 47
 
1.7%
Distinct422
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-06T18:54:47.465765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length29
Mean length22.443459
Min length18

Characters and Unicode

Total characters10122
Distinct characters49
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique400 ?
Unique (%)88.7%

Sample

1st row서울특별시 관악구 봉천동 14-0번지
2nd row서울특별시 관악구 봉천동 89-0번지
3rd row서울특별시 관악구 봉천동 9-0
4th row서울특별시 관악구 신림동 354-0번지
5th row서울특별시 관악구 신림동 395-12번지
ValueCountFrequency (%)
서울특별시 451
24.8%
관악구 451
24.8%
신림동 281
15.5%
봉천동 159
 
8.8%
남현동 11
 
0.6%
102-0번지 5
 
0.3%
89-0번지 5
 
0.3%
1432-14번지 3
 
0.2%
1432-28번지 2
 
0.1%
1432-29번지 2
 
0.1%
Other values (426) 446
24.6%
2024-04-06T18:54:48.246171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1710
 
16.9%
1 490
 
4.8%
452
 
4.5%
452
 
4.5%
451
 
4.5%
451
 
4.5%
- 451
 
4.5%
451
 
4.5%
451
 
4.5%
451
 
4.5%
Other values (39) 4312
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5623
55.6%
Decimal Number 2335
23.1%
Space Separator 1710
 
16.9%
Dash Punctuation 451
 
4.5%
Other Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
452
 
8.0%
452
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
319
 
5.7%
Other values (24) 1243
22.1%
Decimal Number
ValueCountFrequency (%)
1 490
21.0%
2 332
14.2%
3 323
13.8%
4 323
13.8%
6 205
8.8%
8 180
 
7.7%
5 144
 
6.2%
9 144
 
6.2%
0 108
 
4.6%
7 86
 
3.7%
Space Separator
ValueCountFrequency (%)
1710
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 451
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5623
55.6%
Common 4498
44.4%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
452
 
8.0%
452
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
319
 
5.7%
Other values (24) 1243
22.1%
Common
ValueCountFrequency (%)
1710
38.0%
1 490
 
10.9%
- 451
 
10.0%
2 332
 
7.4%
3 323
 
7.2%
4 323
 
7.2%
6 205
 
4.6%
8 180
 
4.0%
5 144
 
3.2%
9 144
 
3.2%
Other values (4) 196
 
4.4%
Latin
ValueCountFrequency (%)
K 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5623
55.6%
ASCII 4499
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1710
38.0%
1 490
 
10.9%
- 451
 
10.0%
2 332
 
7.4%
3 323
 
7.2%
4 323
 
7.2%
6 205
 
4.6%
8 180
 
4.0%
5 144
 
3.2%
9 144
 
3.2%
Other values (5) 197
 
4.4%
Hangul
ValueCountFrequency (%)
452
 
8.0%
452
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
451
 
8.0%
319
 
5.7%
Other values (24) 1243
22.1%

도로명주소
Text

MISSING 

Distinct225
Distinct (%)96.6%
Missing218
Missing (%)48.3%
Memory size3.7 KiB
2024-04-06T18:54:48.712051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length31
Mean length25.600858
Min length21

Characters and Unicode

Total characters5965
Distinct characters66
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

Unique217 ?
Unique (%)93.1%

Sample

1st row서울특별시 관악구 청림1길 11 (봉천동)
2nd row서울특별시 관악구 행운1나길 36 (봉천동)
3rd row서울특별시 관악구 청림길 3-1 (봉천동)
4th row서울특별시 관악구 행운1마길 21 (봉천동)
5th row서울특별시 관악구 양지길 11 (신림동, 1층)
ValueCountFrequency (%)
서울특별시 233
19.8%
관악구 233
19.8%
신림동 170
14.5%
봉천동 53
 
4.5%
신림동1길 32
 
2.7%
남부순환로 22
 
1.9%
신림로65길 19
 
1.6%
봉천로12길 15
 
1.3%
신림로64길 14
 
1.2%
남현동 10
 
0.9%
Other values (191) 373
31.8%
2024-04-06T18:54:49.353721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
941
 
15.8%
277
 
4.6%
266
 
4.5%
266
 
4.5%
256
 
4.3%
251
 
4.2%
236
 
4.0%
) 233
 
3.9%
233
 
3.9%
233
 
3.9%
Other values (56) 2773
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3545
59.4%
Space Separator 941
 
15.8%
Decimal Number 927
 
15.5%
Close Punctuation 233
 
3.9%
Open Punctuation 233
 
3.9%
Dash Punctuation 72
 
1.2%
Other Punctuation 11
 
0.2%
Uppercase Letter 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
277
 
7.8%
266
 
7.5%
266
 
7.5%
256
 
7.2%
251
 
7.1%
236
 
6.7%
233
 
6.6%
233
 
6.6%
233
 
6.6%
233
 
6.6%
Other values (38) 1061
29.9%
Decimal Number
ValueCountFrequency (%)
1 232
25.0%
2 150
16.2%
6 104
11.2%
5 102
11.0%
3 95
10.2%
4 71
 
7.7%
8 49
 
5.3%
9 43
 
4.6%
0 42
 
4.5%
7 39
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
941
100.0%
Close Punctuation
ValueCountFrequency (%)
) 233
100.0%
Open Punctuation
ValueCountFrequency (%)
( 233
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3545
59.4%
Common 2418
40.5%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
277
 
7.8%
266
 
7.5%
266
 
7.5%
256
 
7.2%
251
 
7.1%
236
 
6.7%
233
 
6.6%
233
 
6.6%
233
 
6.6%
233
 
6.6%
Other values (38) 1061
29.9%
Common
ValueCountFrequency (%)
941
38.9%
) 233
 
9.6%
( 233
 
9.6%
1 232
 
9.6%
2 150
 
6.2%
6 104
 
4.3%
5 102
 
4.2%
3 95
 
3.9%
- 72
 
3.0%
4 71
 
2.9%
Other values (6) 185
 
7.7%
Latin
ValueCountFrequency (%)
K 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3545
59.4%
ASCII 2420
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
941
38.9%
) 233
 
9.6%
( 233
 
9.6%
1 232
 
9.6%
2 150
 
6.2%
6 104
 
4.3%
5 102
 
4.2%
3 95
 
3.9%
- 72
 
3.0%
4 71
 
2.9%
Other values (8) 187
 
7.7%
Hangul
ValueCountFrequency (%)
277
 
7.8%
266
 
7.5%
266
 
7.5%
256
 
7.2%
251
 
7.1%
236
 
6.7%
233
 
6.6%
233
 
6.6%
233
 
6.6%
233
 
6.6%
Other values (38) 1061
29.9%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct26
Distinct (%)11.9%
Missing232
Missing (%)51.4%
Infinite0
Infinite (%)0.0%
Mean8768.7306
Minimum8707
Maximum8846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:49.632087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8707
5-th percentile8737
Q18754
median8760
Q38786
95-th percentile8807
Maximum8846
Range139
Interquartile range (IQR)32

Descriptive statistics

Standard deviation23.191376
Coefficient of variation (CV)0.0026447815
Kurtosis3.1917208
Mean8768.7306
Median Absolute Deviation (MAD)6
Skewness1.5237851
Sum1920352
Variance537.83993
MonotonicityNot monotonic
2024-04-06T18:54:49.881561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8760 57
 
12.6%
8754 32
 
7.1%
8759 18
 
4.0%
8787 15
 
3.3%
8788 13
 
2.9%
8753 12
 
2.7%
8776 10
 
2.2%
8846 9
 
2.0%
8807 8
 
1.8%
8786 8
 
1.8%
Other values (16) 37
 
8.2%
(Missing) 232
51.4%
ValueCountFrequency (%)
8707 1
 
0.2%
8732 3
 
0.7%
8733 1
 
0.2%
8737 8
 
1.8%
8745 1
 
0.2%
8753 12
 
2.7%
8754 32
7.1%
8755 2
 
0.4%
8758 1
 
0.2%
8759 18
4.0%
ValueCountFrequency (%)
8846 9
2.0%
8812 1
 
0.2%
8807 8
1.8%
8806 2
 
0.4%
8788 13
2.9%
8787 15
3.3%
8786 8
1.8%
8776 10
2.2%
8772 6
 
1.3%
8771 2
 
0.4%
Distinct405
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
2024-04-06T18:54:50.409036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length3.4611973
Min length1

Characters and Unicode

Total characters1561
Distinct characters315
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique368 ?
Unique (%)81.6%

Sample

1st row온천여관
2nd row동일
3rd row춘성
4th row동화
5th row고려
ValueCountFrequency (%)
호텔 14
 
2.9%
모텔 10
 
2.1%
관악 5
 
1.0%
천일 4
 
0.8%
제일 3
 
0.6%
신신 3
 
0.6%
꿈의궁전 3
 
0.6%
현대 3
 
0.6%
서울 3
 
0.6%
별장 2
 
0.4%
Other values (402) 436
89.7%
2024-04-06T18:54:51.389275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97
 
6.2%
94
 
6.0%
71
 
4.5%
38
 
2.4%
35
 
2.2%
35
 
2.2%
30
 
1.9%
24
 
1.5%
22
 
1.4%
22
 
1.4%
Other values (305) 1093
70.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1402
89.8%
Uppercase Letter 72
 
4.6%
Space Separator 35
 
2.2%
Decimal Number 18
 
1.2%
Lowercase Letter 15
 
1.0%
Open Punctuation 8
 
0.5%
Close Punctuation 8
 
0.5%
Other Punctuation 2
 
0.1%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
97
 
6.9%
94
 
6.7%
71
 
5.1%
38
 
2.7%
35
 
2.5%
30
 
2.1%
24
 
1.7%
22
 
1.6%
22
 
1.6%
22
 
1.6%
Other values (260) 947
67.5%
Uppercase Letter
ValueCountFrequency (%)
A 7
 
9.7%
L 6
 
8.3%
T 6
 
8.3%
S 6
 
8.3%
H 5
 
6.9%
E 5
 
6.9%
Y 4
 
5.6%
O 4
 
5.6%
N 3
 
4.2%
K 3
 
4.2%
Other values (12) 23
31.9%
Lowercase Letter
ValueCountFrequency (%)
e 3
20.0%
a 2
13.3%
w 2
13.3%
k 1
 
6.7%
t 1
 
6.7%
f 1
 
6.7%
r 1
 
6.7%
m 1
 
6.7%
n 1
 
6.7%
i 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
2 6
33.3%
7 3
16.7%
5 3
16.7%
1 2
 
11.1%
3 2
 
11.1%
9 1
 
5.6%
4 1
 
5.6%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1402
89.8%
Latin 88
 
5.6%
Common 71
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
97
 
6.9%
94
 
6.7%
71
 
5.1%
38
 
2.7%
35
 
2.5%
30
 
2.1%
24
 
1.7%
22
 
1.6%
22
 
1.6%
22
 
1.6%
Other values (260) 947
67.5%
Latin
ValueCountFrequency (%)
A 7
 
8.0%
L 6
 
6.8%
T 6
 
6.8%
S 6
 
6.8%
H 5
 
5.7%
E 5
 
5.7%
Y 4
 
4.5%
O 4
 
4.5%
N 3
 
3.4%
K 3
 
3.4%
Other values (24) 39
44.3%
Common
ValueCountFrequency (%)
35
49.3%
( 8
 
11.3%
) 8
 
11.3%
2 6
 
8.5%
7 3
 
4.2%
5 3
 
4.2%
1 2
 
2.8%
3 2
 
2.8%
. 2
 
2.8%
9 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1402
89.8%
ASCII 158
 
10.1%
Number Forms 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
97
 
6.9%
94
 
6.7%
71
 
5.1%
38
 
2.7%
35
 
2.5%
30
 
2.1%
24
 
1.7%
22
 
1.6%
22
 
1.6%
22
 
1.6%
Other values (260) 947
67.5%
ASCII
ValueCountFrequency (%)
35
22.2%
( 8
 
5.1%
) 8
 
5.1%
A 7
 
4.4%
L 6
 
3.8%
2 6
 
3.8%
T 6
 
3.8%
S 6
 
3.8%
H 5
 
3.2%
E 5
 
3.2%
Other values (34) 66
41.8%
Number Forms
ValueCountFrequency (%)
1
100.0%
Distinct330
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum1999-03-25 00:00:00
Maximum2024-04-01 10:21:23
2024-04-06T18:54:51.648191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:54:51.966436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
I
284 
U
167 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 284
63.0%
U 167
37.0%

Length

2024-04-06T18:54:52.188354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:52.361503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 284
63.0%
u 167
37.0%
Distinct85
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:03:00
2024-04-06T18:54:52.518455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:54:52.725460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
여관업
322 
여인숙업
91 
관광호텔
 
15
일반호텔
 
14
숙박업(생활)
 
7

Length

Max length7
Median length3
Mean length3.3414634
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 322
71.4%
여인숙업 91
 
20.2%
관광호텔 15
 
3.3%
일반호텔 14
 
3.1%
숙박업(생활) 7
 
1.6%
숙박업 기타 2
 
0.4%

Length

2024-04-06T18:54:53.056358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:53.259888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 322
71.1%
여인숙업 91
 
20.1%
관광호텔 15
 
3.3%
일반호텔 14
 
3.1%
숙박업(생활 7
 
1.5%
숙박업 2
 
0.4%
기타 2
 
0.4%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct376
Distinct (%)90.6%
Missing36
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean194356.92
Minimum191167.66
Maximum198254.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:53.611754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191167.66
5-th percentile192437.48
Q1193593.91
median193796.5
Q3195510.28
95-th percentile196198.87
Maximum198254.26
Range7086.5973
Interquartile range (IQR)1916.3786

Descriptive statistics

Standard deviation1262.0686
Coefficient of variation (CV)0.0064935616
Kurtosis0.71606724
Mean194356.92
Median Absolute Deviation (MAD)264.82004
Skewness0.74704116
Sum80658120
Variance1592817.2
MonotonicityNot monotonic
2024-04-06T18:54:54.389493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193688.561091051 3
 
0.7%
193973.523870439 3
 
0.7%
193549.841183338 3
 
0.7%
195428.165598837 2
 
0.4%
194009.195826023 2
 
0.4%
193632.986365286 2
 
0.4%
193608.44765448 2
 
0.4%
193578.811735682 2
 
0.4%
192356.455817365 2
 
0.4%
193859.618674702 2
 
0.4%
Other values (366) 392
86.9%
(Missing) 36
 
8.0%
ValueCountFrequency (%)
191167.664986193 1
0.2%
191182.000480527 1
0.2%
191214.160659729 1
0.2%
191623.66045939 1
0.2%
191932.8193939 1
0.2%
192082.654096615 2
0.4%
192221.284006412 1
0.2%
192307.483489665 1
0.2%
192309.058505788 1
0.2%
192312.717746236 1
0.2%
ValueCountFrequency (%)
198254.262302879 1
0.2%
198252.087552207 1
0.2%
198249.863476997 1
0.2%
198248.942426333 1
0.2%
198247.6698086 1
0.2%
198242.687159962 1
0.2%
198241.477700432 1
0.2%
198240.30568208 1
0.2%
198237.109694788 1
0.2%
198234.195911192 1
0.2%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct376
Distinct (%)90.6%
Missing36
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean442310.97
Minimum440317.17
Maximum443054.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:54.671326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum440317.17
5-th percentile441144.51
Q1442061.6
median442481.01
Q3442634.65
95-th percentile442792.2
Maximum443054.11
Range2736.9438
Interquartile range (IQR)573.04651

Descriptive statistics

Standard deviation472.44618
Coefficient of variation (CV)0.0010681313
Kurtosis2.3349647
Mean442310.97
Median Absolute Deviation (MAD)231.26083
Skewness-1.4995022
Sum1.8355905 × 108
Variance223205.39
MonotonicityNot monotonic
2024-04-06T18:54:54.916567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442773.052106287 3
 
0.7%
441047.593004422 3
 
0.7%
442753.65108589 3
 
0.7%
442047.154644404 2
 
0.4%
442731.582758392 2
 
0.4%
442578.829399716 2
 
0.4%
442632.12005209 2
 
0.4%
442712.268464354 2
 
0.4%
442143.241888941 2
 
0.4%
442562.87358396 2
 
0.4%
Other values (366) 392
86.9%
(Missing) 36
 
8.0%
ValueCountFrequency (%)
440317.168618377 1
0.2%
440539.727551719 1
0.2%
440620.085604179 1
0.2%
440781.50183404 1
0.2%
440849.341525169 1
0.2%
440905.462112996 1
0.2%
440938.389825273 1
0.2%
440957.820196328 1
0.2%
440960.230701304 1
0.2%
440995.873883907 1
0.2%
ValueCountFrequency (%)
443054.112395656 1
0.2%
443035.350947417 1
0.2%
443024.625382284 1
0.2%
442986.779833513 1
0.2%
442951.040886937 1
0.2%
442932.724872788 1
0.2%
442928.890303849 1
0.2%
442917.781997934 1
0.2%
442874.200824993 1
0.2%
442867.651020879 1
0.2%

위생업태명
Categorical

Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
여관업
247 
<NA>
106 
여인숙업
82 
일반호텔
 
6
관광호텔
 
6

Length

Max length7
Median length3
Mean length3.4789357
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 247
54.8%
<NA> 106
23.5%
여인숙업 82
 
18.2%
일반호텔 6
 
1.3%
관광호텔 6
 
1.3%
숙박업(생활) 4
 
0.9%

Length

2024-04-06T18:54:55.212519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:55.420431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여관업 247
54.8%
na 106
23.5%
여인숙업 82
 
18.2%
일반호텔 6
 
1.3%
관광호텔 6
 
1.3%
숙박업(생활 4
 
0.9%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)4.4%
Missing134
Missing (%)29.7%
Infinite0
Infinite (%)0.0%
Mean2.8990536
Minimum0
Maximum41
Zeros87
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:55.624293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q34
95-th percentile7
Maximum41
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1885722
Coefficient of variation (CV)1.0998666
Kurtosis64.018057
Mean2.8990536
Median Absolute Deviation (MAD)1
Skewness5.7373333
Sum919
Variance10.166993
MonotonicityNot monotonic
2024-04-06T18:54:55.823306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 87
19.3%
4 76
16.9%
3 55
12.2%
2 43
 
9.5%
5 21
 
4.7%
1 8
 
1.8%
7 6
 
1.3%
6 6
 
1.3%
8 5
 
1.1%
9 3
 
0.7%
Other values (4) 7
 
1.6%
(Missing) 134
29.7%
ValueCountFrequency (%)
0 87
19.3%
1 8
 
1.8%
2 43
9.5%
3 55
12.2%
4 76
16.9%
5 21
 
4.7%
6 6
 
1.3%
7 6
 
1.3%
8 5
 
1.1%
9 3
 
0.7%
ValueCountFrequency (%)
41 1
 
0.2%
12 1
 
0.2%
11 3
 
0.7%
10 2
 
0.4%
9 3
 
0.7%
8 5
 
1.1%
7 6
 
1.3%
6 6
 
1.3%
5 21
 
4.7%
4 76
16.9%
Distinct4
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
193 
<NA>
161 
0
95 
2
 
2

Length

Max length4
Median length1
Mean length2.0709534
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 193
42.8%
<NA> 161
35.7%
0 95
21.1%
2 2
 
0.4%

Length

2024-04-06T18:54:56.074323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:56.307606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 193
42.8%
na 161
35.7%
0 95
21.1%
2 2
 
0.4%
Distinct6
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
1
191 
<NA>
135 
0
88 
2
30 
3
 
6

Length

Max length4
Median length1
Mean length1.8980044
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row2
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
1 191
42.4%
<NA> 135
29.9%
0 88
19.5%
2 30
 
6.7%
3 6
 
1.3%
4 1
 
0.2%

Length

2024-04-06T18:54:56.490535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:56.684887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 191
42.4%
na 135
29.9%
0 88
19.5%
2 30
 
6.7%
3 6
 
1.3%
4 1
 
0.2%

사용끝지상층
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)5.7%
Missing222
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean3.7161572
Minimum0
Maximum12
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:56.872166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile8
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8901293
Coefficient of variation (CV)0.50862468
Kurtosis3.4500973
Mean3.7161572
Median Absolute Deviation (MAD)1
Skewness1.5200576
Sum851
Variance3.5725887
MonotonicityNot monotonic
2024-04-06T18:54:57.071381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 74
 
16.4%
3 51
 
11.3%
2 46
 
10.2%
5 20
 
4.4%
1 12
 
2.7%
7 6
 
1.3%
6 6
 
1.3%
8 5
 
1.1%
9 3
 
0.7%
10 3
 
0.7%
Other values (3) 3
 
0.7%
(Missing) 222
49.2%
ValueCountFrequency (%)
0 1
 
0.2%
1 12
 
2.7%
2 46
10.2%
3 51
11.3%
4 74
16.4%
5 20
 
4.4%
6 6
 
1.3%
7 6
 
1.3%
8 5
 
1.1%
9 3
 
0.7%
ValueCountFrequency (%)
12 1
 
0.2%
11 1
 
0.2%
10 3
 
0.7%
9 3
 
0.7%
8 5
 
1.1%
7 6
 
1.3%
6 6
 
1.3%
5 20
 
4.4%
4 74
16.4%
3 51
11.3%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
180 
1
161 
0
110 

Length

Max length4
Median length1
Mean length2.1973392
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 180
39.9%
1 161
35.7%
0 110
24.4%

Length

2024-04-06T18:54:57.294084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:57.487077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 180
39.9%
1 161
35.7%
0 110
24.4%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
267 
1
161 
0
 
23

Length

Max length4
Median length4
Mean length2.7760532
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 267
59.2%
1 161
35.7%
0 23
 
5.1%

Length

2024-04-06T18:54:57.707707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:54:57.880292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 267
59.2%
1 161
35.7%
0 23
 
5.1%

한실수
Real number (ℝ)

MISSING  ZEROS 

Distinct19
Distinct (%)6.0%
Missing136
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean3.2031746
Minimum0
Maximum38
Zeros108
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:58.051528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile10.3
Maximum38
Range38
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3343528
Coefficient of variation (CV)1.3531428
Kurtosis14.270071
Mean3.2031746
Median Absolute Deviation (MAD)2
Skewness2.7796502
Sum1009
Variance18.786614
MonotonicityNot monotonic
2024-04-06T18:54:58.242606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 108
23.9%
2 64
14.2%
3 31
 
6.9%
1 26
 
5.8%
9 16
 
3.5%
10 16
 
3.5%
4 12
 
2.7%
7 10
 
2.2%
6 8
 
1.8%
11 5
 
1.1%
Other values (9) 19
 
4.2%
(Missing) 136
30.2%
ValueCountFrequency (%)
0 108
23.9%
1 26
 
5.8%
2 64
14.2%
3 31
 
6.9%
4 12
 
2.7%
5 4
 
0.9%
6 8
 
1.8%
7 10
 
2.2%
8 4
 
0.9%
9 16
 
3.5%
ValueCountFrequency (%)
38 1
 
0.2%
24 1
 
0.2%
18 1
 
0.2%
17 1
 
0.2%
14 1
 
0.2%
13 2
 
0.4%
12 4
 
0.9%
11 5
 
1.1%
10 16
3.5%
9 16
3.5%

양실수
Real number (ℝ)

MISSING  ZEROS 

Distinct45
Distinct (%)14.5%
Missing140
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean13.466238
Minimum0
Maximum66
Zeros71
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:58.460230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q318
95-th percentile35
Maximum66
Range66
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.380146
Coefficient of variation (CV)0.84508727
Kurtosis1.6438024
Mean13.466238
Median Absolute Deviation (MAD)7
Skewness1.01412
Sum4188
Variance129.50773
MonotonicityNot monotonic
2024-04-06T18:54:58.701205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 71
15.7%
16 22
 
4.9%
13 20
 
4.4%
12 15
 
3.3%
17 15
 
3.3%
18 14
 
3.1%
14 12
 
2.7%
10 11
 
2.4%
20 10
 
2.2%
15 10
 
2.2%
Other values (35) 111
24.6%
(Missing) 140
31.0%
ValueCountFrequency (%)
0 71
15.7%
1 2
 
0.4%
2 5
 
1.1%
4 2
 
0.4%
5 2
 
0.4%
6 8
 
1.8%
7 6
 
1.3%
8 5
 
1.1%
9 9
 
2.0%
10 11
 
2.4%
ValueCountFrequency (%)
66 1
 
0.2%
49 1
 
0.2%
48 2
0.4%
45 3
0.7%
44 1
 
0.2%
42 1
 
0.2%
41 1
 
0.2%
40 1
 
0.2%
39 2
0.4%
37 1
 
0.2%

욕실수
Real number (ℝ)

MISSING  ZEROS 

Distinct42
Distinct (%)15.6%
Missing182
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean13.72119
Minimum0
Maximum48
Zeros83
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:58.940871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q319
95-th percentile34.6
Maximum48
Range48
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.779708
Coefficient of variation (CV)0.85850488
Kurtosis-0.18125502
Mean13.72119
Median Absolute Deviation (MAD)9
Skewness0.51535433
Sum3691
Variance138.76153
MonotonicityNot monotonic
2024-04-06T18:54:59.164951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 83
18.4%
19 25
 
5.5%
18 16
 
3.5%
17 14
 
3.1%
15 13
 
2.9%
16 10
 
2.2%
14 8
 
1.8%
12 7
 
1.6%
20 7
 
1.6%
10 7
 
1.6%
Other values (32) 79
17.5%
(Missing) 182
40.4%
ValueCountFrequency (%)
0 83
18.4%
2 2
 
0.4%
4 2
 
0.4%
5 1
 
0.2%
6 1
 
0.2%
7 3
 
0.7%
8 2
 
0.4%
9 2
 
0.4%
10 7
 
1.6%
11 5
 
1.1%
ValueCountFrequency (%)
48 3
0.7%
46 1
 
0.2%
45 1
 
0.2%
41 1
 
0.2%
40 1
 
0.2%
39 1
 
0.2%
38 2
0.4%
37 1
 
0.2%
35 3
0.7%
34 1
 
0.2%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.6%
Missing106
Missing (%)23.5%
Memory size1.0 KiB
True
321 
False
 
24
(Missing)
106 
ValueCountFrequency (%)
True 321
71.2%
False 24
 
5.3%
(Missing) 106
 
23.5%
2024-04-06T18:54:59.362933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct27
Distinct (%)12.4%
Missing233
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean13.348624
Minimum0
Maximum1025
Zeros187
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-04-06T18:54:59.529804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile56.6
Maximum1025
Range1025
Interquartile range (IQR)0

Descriptive statistics

Standard deviation78.666581
Coefficient of variation (CV)5.8932352
Kurtosis133.4379
Mean13.348624
Median Absolute Deviation (MAD)0
Skewness11.010953
Sum2910
Variance6188.4309
MonotonicityNot monotonic
2024-04-06T18:54:59.755107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 187
41.5%
28 2
 
0.4%
44 2
 
0.4%
60 2
 
0.4%
56 2
 
0.4%
32 2
 
0.4%
92 1
 
0.2%
24 1
 
0.2%
499 1
 
0.2%
34 1
 
0.2%
Other values (17) 17
 
3.8%
(Missing) 233
51.7%
ValueCountFrequency (%)
0 187
41.5%
17 1
 
0.2%
18 1
 
0.2%
20 1
 
0.2%
24 1
 
0.2%
25 1
 
0.2%
28 2
 
0.4%
31 1
 
0.2%
32 2
 
0.4%
33 1
 
0.2%
ValueCountFrequency (%)
1025 1
0.2%
499 1
0.2%
96 1
0.2%
92 1
0.2%
90 1
0.2%
82 1
0.2%
80 1
0.2%
78 1
0.2%
64 1
0.2%
60 2
0.4%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing451
Missing (%)100.0%
Memory size4.1 KiB

조건부허가시작일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
450 
20040224
 
1

Length

Max length8
Median length4
Mean length4.0088692
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 450
99.8%
20040224 1
 
0.2%

Length

2024-04-06T18:55:00.016328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:55:00.195324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 450
99.8%
20040224 1
 
0.2%

조건부허가종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
450 
20040224
 
1

Length

Max length8
Median length4
Mean length4.0088692
Min length4

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 450
99.8%
20040224 1
 
0.2%

Length

2024-04-06T18:55:00.385707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:55:00.589144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 450
99.8%
20040224 1
 
0.2%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
254 
자가
135 
임대
62 

Length

Max length4
Median length4
Mean length3.1263858
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 254
56.3%
자가 135
29.9%
임대 62
 
13.7%

Length

2024-04-06T18:55:00.776131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:55:00.967470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 254
56.3%
자가 135
29.9%
임대 62
 
13.7%

세탁기수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
331 
0
120 

Length

Max length4
Median length4
Mean length3.2017738
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 331
73.4%
0 120
 
26.6%

Length

2024-04-06T18:55:01.157927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:55:01.312942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 331
73.4%
0 120
 
26.6%

여성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
441 
0
 
10

Length

Max length4
Median length4
Mean length3.9334812
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> 441
97.8%
0 10
 
2.2%

Length

2024-04-06T18:55:01.598299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:55:01.833405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 441
97.8%
0 10
 
2.2%

남성종사자수
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
441 
0
 
10

Length

Max length4
Median length4
Mean length3.9334812
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> 441
97.8%
0 10
 
2.2%

Length

2024-04-06T18:55:02.036500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:55:02.204887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 441
97.8%
0 10
 
2.2%

회수건조수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
339 
0
112 

Length

Max length4
Median length4
Mean length3.2549889
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 339
75.2%
0 112
 
24.8%

Length

2024-04-06T18:55:02.406981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:55:02.586133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 339
75.2%
0 112
 
24.8%

침대수
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
<NA>
339 
0
112 

Length

Max length4
Median length4
Mean length3.2549889
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 339
75.2%
0 112
 
24.8%

Length

2024-04-06T18:55:02.817262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:55:03.094428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 339
75.2%
0 112
 
24.8%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing106
Missing (%)23.5%
Memory size1.0 KiB
False
345 
(Missing)
106 
ValueCountFrequency (%)
False 345
76.5%
(Missing) 106
 
23.5%
2024-04-06T18:55:03.222185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
032000003200000-201-1968-0004119680921<NA>3폐업2폐업19930716<NA><NA><NA>020885253765.15151803서울특별시 관악구 봉천동 14-0번지<NA><NA>온천여관2001-09-29 00:00:00I2018-08-31 23:59:59.0여관업196291.369381442797.855955여관업000<NA>0<NA>0010Y0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
132000003200000-201-1968-0031019680916<NA>3폐업2폐업19930117<NA><NA><NA>020000000062.99151050서울특별시 관악구 봉천동 89-0번지<NA><NA>동일2001-09-29 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업000<NA>0<NA>004Y0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
232000003200000-201-1968-0031519681219<NA>1영업/정상1영업<NA><NA><NA><NA>02 883490047.91151803서울특별시 관악구 봉천동 9-0서울특별시 관악구 청림1길 11 (봉천동)8732춘성2020-08-28 16:14:18U2020-08-30 02:40:00.0여관업196254.554748442951.040887여관업2022001200Y0<NA><NA><NA>자가0<NA><NA>00N
332000003200000-201-1968-0035519680430<NA>3폐업2폐업20100401<NA><NA><NA>02 889130239.34151862서울특별시 관악구 신림동 354-0번지<NA><NA>동화2004-01-19 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업1<NA>11<NA><NA>9<NA><NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
432000003200000-201-1969-0004919691229<NA>3폐업2폐업20030326<NA><NA><NA>020000000061.25151865서울특별시 관악구 신림동 395-12번지<NA><NA>고려2003-03-26 00:00:00I2018-08-31 23:59:59.0여관업193996.306835441171.347757여관업<NA><NA><NA><NA><NA><NA>9<NA><NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
532000003200000-201-1969-0030819691105<NA>3폐업2폐업20101110<NA><NA><NA>02 8737661203.37151855서울특별시 관악구 신림동 107-16번지<NA><NA>대원2007-02-09 00:00:00I2018-08-31 23:59:59.0여관업194228.629424441293.817084여관업2<NA>12<NA><NA>17<NA><NA>Y<NA><NA><NA><NA>자가<NA><NA><NA><NA><NA>N
632000003200000-201-1969-0031319690709<NA>3폐업2폐업20110706<NA><NA><NA>02 877682661.34151810서울특별시 관악구 봉천동 37-46번지<NA><NA>진주2010-08-25 16:46:22I2018-08-31 23:59:59.0여관업196179.417721442670.157041여관업2012001300Y0<NA><NA><NA>자가0<NA><NA>00N
732000003200000-201-1969-0031419690919<NA>3폐업2폐업20070322<NA><NA><NA>02 871327850.04151810서울특별시 관악구 봉천동 37-2번지<NA><NA>송도2007-02-09 00:00:00I2018-08-31 23:59:59.0여관업196215.512256442704.529149여관업2<NA>22<NA><NA>10<NA><NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
832000003200000-201-1969-0031619690419<NA>3폐업2폐업20030326<NA><NA><NA>02 0000030.53151050서울특별시 관악구 봉천동 89-0번지<NA><NA>신성2003-03-26 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업<NA><NA><NA><NA><NA><NA>6<NA><NA>Y<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
932000003200000-201-1969-0032319690228<NA>3폐업2폐업19970407<NA><NA><NA>02 877216341.72151050서울특별시 관악구 봉천동 158-0번지<NA><NA>광덕2001-09-29 00:00:00I2018-08-31 23:59:59.0여인숙업<NA><NA>여인숙업000<NA>0<NA>000Y0<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
44132000003200000-201-2020-0000120200213<NA>1영업/정상1영업<NA><NA><NA><NA>02 8771043268.34151892서울특별시 관악구 신림동 1433-99서울특별시 관악구 남부순환로 1593-5 (신림동)8759비둘기2022-12-22 15:02:04U2021-11-01 22:04:00.0여관업193513.175291442463.195804<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
44232000003200000-201-2021-0000120211020<NA>1영업/정상1영업<NA><NA><NA><NA><NA>466.71151899서울특별시 관악구 신림동 1567-16서울특별시 관악구 난곡로 280 (신림동)8772사이버2022-12-21 14:31:26U2021-11-01 22:03:00.0숙박업 기타192440.640506441861.051693<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
44332000003200000-201-2023-000012023-02-08<NA>1영업/정상1영업<NA><NA><NA><NA>02 877 26673853.16151-836서울특별시 관악구 봉천동 866-1서울특별시 관악구 관악로15길 17 (봉천동)8787하운드호텔2023-11-09 14:49:40U2022-10-31 23:01:00.0일반호텔195636.857061441997.826293<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
44432000003200000-214-1987-0000119870710<NA>1영업/정상1영업<NA><NA><NA><NA>02 8844655329.85151848서울특별시 관악구 봉천동 854-8번지서울특별시 관악구 남부순환로224길 31 (봉천동)8788조은집2018-01-15 15:28:46I2018-08-31 23:59:59.0숙박업(생활)195798.482688441918.410079숙박업(생활)41240001519Y0<NA><NA><NA>자가0<NA><NA>00N
44532000003200000-214-2003-0000120030106<NA>1영업/정상1영업<NA><NA><NA><NA>02 87222772,466.50151836서울특별시 관악구 봉천동 863-12번지서울특별시 관악구 관악로13길 8 (봉천동)8787노르웨이의숲2018-09-21 15:30:41U2018-09-21 23:59:59.0숙박업(생활)195666.53687441920.607975숙박업(생활)9119110490Y0<NA><NA><NA>자가0<NA><NA>00N
44632000003200000-214-2015-0000120150105<NA>3폐업2폐업20221101<NA><NA><NA><NA>861.50151890서울특별시 관악구 신림동 1421-5서울특별시 관악구 남부순환로185길 15 (신림동)8754코업2022-11-01 10:21:55U2021-11-01 00:03:00.0숙박업(생활)193926.887651442582.087759<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
44732000003200000-214-2019-0000120191121<NA>1영업/정상1영업<NA><NA><NA><NA><NA>930.62151899서울특별시 관악구 신림동 1569-6번지서울특별시 관악구 난곡로64길 7 (신림동)8772위드 어반2019-12-09 11:04:30U2019-12-11 02:40:00.0숙박업(생활)192424.148947442135.576964숙박업(생활)9119110420N0<NA><NA><NA>자가00000N
44832000003200000-214-2021-0000120210422<NA>1영업/정상1영업<NA><NA><NA><NA><NA>1,112.70151892서울특별시 관악구 신림동 1432-44 송호서울특별시 관악구 신림동3길 25, 송호 (신림동)8760송호2021-04-23 14:59:13I2021-04-24 00:22:57.0숙박업(생활)193558.091415442674.518363숙박업(생활)111<NA><NA><NA><NA>0480N0<NA><NA><NA><NA>00000N
44932000003200000-214-2022-000012022-03-03<NA>3폐업2폐업2023-12-26<NA><NA><NA><NA>945.66151-892서울특별시 관악구 신림동 1432-66서울특별시 관악구 신림로65길 10-16 (신림동)8760스위트빌Ⅲ2023-12-26 09:19:11U2022-11-01 22:08:00.0숙박업(생활)193641.437475442631.492617<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
45032000003200000-214-2023-000012023-09-22<NA>1영업/정상1영업<NA><NA><NA><NA>02 877 2667993.19151-836서울특별시 관악구 봉천동 866-1서울특별시 관악구 관악로15길 17, B동 (봉천동)8787생활숙박하운드호텔2023-11-08 10:39:57U2022-10-31 23:01:00.0숙박업(생활)195636.857061441997.826293<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>