Overview

Dataset statistics

Number of variables47
Number of observations333
Missing cells3607
Missing cells (%)23.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory131.5 KiB
Average record size in memory404.4 B

Variable types

Categorical19
Text7
DateTime4
Unsupported7
Numeric8
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-19297/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (97.1%)Imbalance
위생업태명 is highly imbalanced (63.2%)Imbalance
사용끝지하층 is highly imbalanced (51.6%)Imbalance
발한실여부 is highly imbalanced (93.8%)Imbalance
여성종사자수 is highly imbalanced (71.9%)Imbalance
인허가취소일자 has 333 (100.0%) missing valuesMissing
폐업일자 has 75 (22.5%) missing valuesMissing
휴업시작일자 has 333 (100.0%) missing valuesMissing
휴업종료일자 has 333 (100.0%) missing valuesMissing
재개업일자 has 333 (100.0%) missing valuesMissing
전화번호 has 70 (21.0%) missing valuesMissing
도로명주소 has 138 (41.4%) missing valuesMissing
도로명우편번호 has 140 (42.0%) missing valuesMissing
좌표정보(X) has 5 (1.5%) missing valuesMissing
좌표정보(Y) has 5 (1.5%) missing valuesMissing
건물지상층수 has 89 (26.7%) missing valuesMissing
건물지하층수 has 110 (33.0%) missing valuesMissing
사용시작지상층 has 122 (36.6%) missing valuesMissing
사용끝지상층 has 136 (40.8%) missing valuesMissing
발한실여부 has 57 (17.1%) missing valuesMissing
조건부허가신고사유 has 333 (100.0%) missing valuesMissing
조건부허가시작일자 has 333 (100.0%) missing valuesMissing
조건부허가종료일자 has 333 (100.0%) missing valuesMissing
남성종사자수 has 286 (85.9%) missing valuesMissing
다중이용업소여부 has 43 (12.9%) 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 85 (25.5%) zerosZeros
건물지하층수 has 112 (33.6%) zerosZeros
사용시작지상층 has 21 (6.3%) zerosZeros
사용끝지상층 has 13 (3.9%) zerosZeros
남성종사자수 has 38 (11.4%) zerosZeros

Reproduction

Analysis started2024-05-11 05:53:24.840692
Analysis finished2024-05-11 05:53:25.958587
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
3200000
333 

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 333
100.0%

Length

2024-05-11T14:53:26.042804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:26.167783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 333
100.0%

관리번호
Text

UNIQUE 

Distinct333
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-05-11T14:53:26.421411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters7326
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

Unique333 ?
Unique (%)100.0%

Sample

1st row3200000-206-1987-02991
2nd row3200000-206-1987-02992
3rd row3200000-206-1988-02993
4th row3200000-206-1990-02994
5th row3200000-206-1990-02995
ValueCountFrequency (%)
3200000-206-1987-02991 1
 
0.3%
3200000-206-2010-00014 1
 
0.3%
3200000-206-2011-00009 1
 
0.3%
3200000-206-2011-00008 1
 
0.3%
3200000-206-2011-00007 1
 
0.3%
3200000-206-2011-00006 1
 
0.3%
3200000-206-2011-00005 1
 
0.3%
3200000-206-2011-00004 1
 
0.3%
3200000-206-2011-00003 1
 
0.3%
3200000-206-2011-00002 1
 
0.3%
Other values (323) 323
97.0%
2024-05-11T14:53:26.913665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3617
49.4%
2 1082
 
14.8%
- 999
 
13.6%
3 468
 
6.4%
6 402
 
5.5%
1 328
 
4.5%
9 180
 
2.5%
4 65
 
0.9%
5 63
 
0.9%
8 62
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6327
86.4%
Dash Punctuation 999
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3617
57.2%
2 1082
 
17.1%
3 468
 
7.4%
6 402
 
6.4%
1 328
 
5.2%
9 180
 
2.8%
4 65
 
1.0%
5 63
 
1.0%
8 62
 
1.0%
7 60
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 999
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7326
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3617
49.4%
2 1082
 
14.8%
- 999
 
13.6%
3 468
 
6.4%
6 402
 
5.5%
1 328
 
4.5%
9 180
 
2.5%
4 65
 
0.9%
5 63
 
0.9%
8 62
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3617
49.4%
2 1082
 
14.8%
- 999
 
13.6%
3 468
 
6.4%
6 402
 
5.5%
1 328
 
4.5%
9 180
 
2.5%
4 65
 
0.9%
5 63
 
0.9%
8 62
 
0.8%
Distinct318
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum1987-06-03 00:00:00
Maximum2024-04-16 00:00:00
2024-05-11T14:53:27.545826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:27.770004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing333
Missing (%)100.0%
Memory size3.1 KiB
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
3
258 
1
75 

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 (%)
3 258
77.5%
1 75
 
22.5%

Length

2024-05-11T14:53:27.981894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:28.128784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 258
77.5%
1 75
 
22.5%

영업상태명
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
폐업
258 
영업/정상
75 

Length

Max length5
Median length2
Mean length2.6756757
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 258
77.5%
영업/정상 75
 
22.5%

Length

2024-05-11T14:53:28.283619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:28.428647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 258
77.5%
영업/정상 75
 
22.5%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2
258 
1
75 

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 (%)
2 258
77.5%
1 75
 
22.5%

Length

2024-05-11T14:53:28.588983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:28.747420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 258
77.5%
1 75
 
22.5%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
폐업
258 
영업
75 

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 258
77.5%
영업 75
 
22.5%

Length

2024-05-11T14:53:28.911104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:29.040869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 258
77.5%
영업 75
 
22.5%

폐업일자
Date

MISSING 

Distinct199
Distinct (%)77.1%
Missing75
Missing (%)22.5%
Memory size2.7 KiB
Minimum1995-11-16 00:00:00
Maximum2024-03-05 00:00:00
2024-05-11T14:53:29.233299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:29.487135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing333
Missing (%)100.0%
Memory size3.1 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing333
Missing (%)100.0%
Memory size3.1 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing333
Missing (%)100.0%
Memory size3.1 KiB

전화번호
Text

MISSING 

Distinct253
Distinct (%)96.2%
Missing70
Missing (%)21.0%
Memory size2.7 KiB
2024-05-11T14:53:29.933628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.315589
Min length6

Characters and Unicode

Total characters2713
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

Unique244 ?
Unique (%)92.8%

Sample

1st row02 8780151
2nd row02 8760493
3rd row02 8876695
4th row02 8716996
5th row02 8838973
ValueCountFrequency (%)
02 216
41.9%
883 4
 
0.8%
8716595 3
 
0.6%
8820121 2
 
0.4%
070 2
 
0.4%
031 2
 
0.4%
838 2
 
0.4%
862 2
 
0.4%
859 2
 
0.4%
8718545 2
 
0.4%
Other values (273) 279
54.1%
2024-05-11T14:53:30.538618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 456
16.8%
2 428
15.8%
8 394
14.5%
290
10.7%
7 205
7.6%
3 202
7.4%
5 181
 
6.7%
1 173
 
6.4%
6 150
 
5.5%
4 128
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2423
89.3%
Space Separator 290
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 456
18.8%
2 428
17.7%
8 394
16.3%
7 205
8.5%
3 202
8.3%
5 181
 
7.5%
1 173
 
7.1%
6 150
 
6.2%
4 128
 
5.3%
9 106
 
4.4%
Space Separator
ValueCountFrequency (%)
290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2713
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 456
16.8%
2 428
15.8%
8 394
14.5%
290
10.7%
7 205
7.6%
3 202
7.4%
5 181
 
6.7%
1 173
 
6.4%
6 150
 
5.5%
4 128
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2713
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 456
16.8%
2 428
15.8%
8 394
14.5%
290
10.7%
7 205
7.6%
3 202
7.4%
5 181
 
6.7%
1 173
 
6.4%
6 150
 
5.5%
4 128
 
4.7%
Distinct191
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-05-11T14:53:31.093444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.8648649
Min length3

Characters and Unicode

Total characters1620
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

Unique152 ?
Unique (%)45.6%

Sample

1st row40.00
2nd row127.50
3rd row.00
4th row.00
5th row.00
ValueCountFrequency (%)
00 49
 
14.7%
30.00 10
 
3.0%
66.00 10
 
3.0%
20.00 9
 
2.7%
33.00 7
 
2.1%
60.00 7
 
2.1%
23.00 5
 
1.5%
21.00 4
 
1.2%
40.00 4
 
1.2%
15.00 4
 
1.2%
Other values (181) 224
67.3%
2024-05-11T14:53:31.944261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 503
31.0%
. 333
20.6%
1 114
 
7.0%
2 107
 
6.6%
5 101
 
6.2%
6 98
 
6.0%
3 88
 
5.4%
9 80
 
4.9%
4 71
 
4.4%
8 63
 
3.9%
Other values (2) 62
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1284
79.3%
Other Punctuation 336
 
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 503
39.2%
1 114
 
8.9%
2 107
 
8.3%
5 101
 
7.9%
6 98
 
7.6%
3 88
 
6.9%
9 80
 
6.2%
4 71
 
5.5%
8 63
 
4.9%
7 59
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 333
99.1%
, 3
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 503
31.0%
. 333
20.6%
1 114
 
7.0%
2 107
 
6.6%
5 101
 
6.2%
6 98
 
6.0%
3 88
 
5.4%
9 80
 
4.9%
4 71
 
4.4%
8 63
 
3.9%
Other values (2) 62
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 503
31.0%
. 333
20.6%
1 114
 
7.0%
2 107
 
6.6%
5 101
 
6.2%
6 98
 
6.0%
3 88
 
5.4%
9 80
 
4.9%
4 71
 
4.4%
8 63
 
3.9%
Other values (2) 62
 
3.8%
Distinct103
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-05-11T14:53:32.505216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0810811
Min length6

Characters and Unicode

Total characters2025
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

Unique44 ?
Unique (%)13.2%

Sample

1st row151847
2nd row151835
3rd row151835
4th row151820
5th row151836
ValueCountFrequency (%)
151849 15
 
4.5%
151903 14
 
4.2%
151836 13
 
3.9%
151800 12
 
3.6%
151890 11
 
3.3%
151832 10
 
3.0%
151830 10
 
3.0%
151899 10
 
3.0%
151894 9
 
2.7%
151877 8
 
2.4%
Other values (93) 221
66.4%
2024-05-11T14:53:33.197174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 715
35.3%
5 373
18.4%
8 296
14.6%
0 134
 
6.6%
9 133
 
6.6%
3 106
 
5.2%
7 70
 
3.5%
4 66
 
3.3%
2 63
 
3.1%
6 42
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1998
98.7%
Dash Punctuation 27
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 715
35.8%
5 373
18.7%
8 296
14.8%
0 134
 
6.7%
9 133
 
6.7%
3 106
 
5.3%
7 70
 
3.5%
4 66
 
3.3%
2 63
 
3.2%
6 42
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2025
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 715
35.3%
5 373
18.4%
8 296
14.6%
0 134
 
6.6%
9 133
 
6.6%
3 106
 
5.2%
7 70
 
3.5%
4 66
 
3.3%
2 63
 
3.1%
6 42
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 715
35.3%
5 373
18.4%
8 296
14.6%
0 134
 
6.6%
9 133
 
6.6%
3 106
 
5.2%
7 70
 
3.5%
4 66
 
3.3%
2 63
 
3.1%
6 42
 
2.1%
Distinct301
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-05-11T14:53:33.643799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length38
Mean length24.408408
Min length18

Characters and Unicode

Total characters8128
Distinct characters164
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

Unique275 ?
Unique (%)82.6%

Sample

1st row서울특별시 관악구 봉천동 1575-9
2nd row서울특별시 관악구 봉천동 1596-6
3rd row서울특별시 관악구 봉천동 1595-1
4th row서울특별시 관악구 봉천동 945-6
5th row서울특별시 관악구 봉천동 862-2
ValueCountFrequency (%)
서울특별시 333
21.3%
관악구 333
21.3%
신림동 169
 
10.8%
봉천동 141
 
9.0%
남현동 23
 
1.5%
3층 10
 
0.6%
1층 9
 
0.6%
1673-21 8
 
0.5%
0동 8
 
0.5%
1638-32 7
 
0.4%
Other values (401) 519
33.3%
2024-05-11T14:53:34.260499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1499
18.4%
1 433
 
5.3%
357
 
4.4%
339
 
4.2%
337
 
4.1%
335
 
4.1%
334
 
4.1%
334
 
4.1%
334
 
4.1%
333
 
4.1%
Other values (154) 3493
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4339
53.4%
Decimal Number 1944
23.9%
Space Separator 1499
 
18.4%
Dash Punctuation 323
 
4.0%
Uppercase Letter 9
 
0.1%
Open Punctuation 6
 
0.1%
Close Punctuation 6
 
0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
357
 
8.2%
339
 
7.8%
337
 
7.8%
335
 
7.7%
334
 
7.7%
334
 
7.7%
334
 
7.7%
333
 
7.7%
333
 
7.7%
173
 
4.0%
Other values (135) 1130
26.0%
Decimal Number
ValueCountFrequency (%)
1 433
22.3%
6 221
11.4%
5 216
11.1%
2 209
10.8%
4 170
 
8.7%
3 165
 
8.5%
0 150
 
7.7%
7 142
 
7.3%
9 131
 
6.7%
8 107
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
B 7
77.8%
A 1
 
11.1%
D 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
/ 1
50.0%
Space Separator
ValueCountFrequency (%)
1499
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 323
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4339
53.4%
Common 3780
46.5%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
357
 
8.2%
339
 
7.8%
337
 
7.8%
335
 
7.7%
334
 
7.7%
334
 
7.7%
334
 
7.7%
333
 
7.7%
333
 
7.7%
173
 
4.0%
Other values (135) 1130
26.0%
Common
ValueCountFrequency (%)
1499
39.7%
1 433
 
11.5%
- 323
 
8.5%
6 221
 
5.8%
5 216
 
5.7%
2 209
 
5.5%
4 170
 
4.5%
3 165
 
4.4%
0 150
 
4.0%
7 142
 
3.8%
Other values (6) 252
 
6.7%
Latin
ValueCountFrequency (%)
B 7
77.8%
A 1
 
11.1%
D 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4339
53.4%
ASCII 3789
46.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1499
39.6%
1 433
 
11.4%
- 323
 
8.5%
6 221
 
5.8%
5 216
 
5.7%
2 209
 
5.5%
4 170
 
4.5%
3 165
 
4.4%
0 150
 
4.0%
7 142
 
3.7%
Other values (9) 261
 
6.9%
Hangul
ValueCountFrequency (%)
357
 
8.2%
339
 
7.8%
337
 
7.8%
335
 
7.7%
334
 
7.7%
334
 
7.7%
334
 
7.7%
333
 
7.7%
333
 
7.7%
173
 
4.0%
Other values (135) 1130
26.0%

도로명주소
Text

MISSING 

Distinct189
Distinct (%)96.9%
Missing138
Missing (%)41.4%
Memory size2.7 KiB
2024-05-11T14:53:34.736714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length42
Mean length31.589744
Min length21

Characters and Unicode

Total characters6160
Distinct characters168
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

Unique183 ?
Unique (%)93.8%

Sample

1st row서울특별시 관악구 쑥고개로 92 (봉천동)
2nd row서울특별시 관악구 신림로59길 23, 삼모스포렉스 813호 (신림동)
3rd row서울특별시 관악구 신림로 376, 201호 (신림동)
4th row서울특별시 관악구 남부순환로 1591 (신림동)
5th row서울특별시 관악구 남부순환로 1922 (봉천동, 5층)
ValueCountFrequency (%)
서울특별시 195
 
16.2%
관악구 195
 
16.2%
신림동 96
 
8.0%
봉천동 57
 
4.7%
남부순환로 47
 
3.9%
2층 14
 
1.2%
3층 14
 
1.2%
봉천로 13
 
1.1%
남현동 12
 
1.0%
4층 11
 
0.9%
Other values (339) 553
45.8%
2024-05-11T14:53:35.385580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1013
 
16.4%
1 240
 
3.9%
216
 
3.5%
216
 
3.5%
210
 
3.4%
201
 
3.3%
198
 
3.2%
197
 
3.2%
) 196
 
3.2%
( 196
 
3.2%
Other values (158) 3277
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3554
57.7%
Space Separator 1013
 
16.4%
Decimal Number 1003
 
16.3%
Close Punctuation 196
 
3.2%
Open Punctuation 196
 
3.2%
Other Punctuation 169
 
2.7%
Dash Punctuation 18
 
0.3%
Uppercase Letter 10
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
216
 
6.1%
216
 
6.1%
210
 
5.9%
201
 
5.7%
198
 
5.6%
197
 
5.5%
196
 
5.5%
195
 
5.5%
195
 
5.5%
179
 
5.0%
Other values (140) 1551
43.6%
Decimal Number
ValueCountFrequency (%)
1 240
23.9%
2 138
13.8%
0 125
12.5%
3 109
10.9%
5 88
 
8.8%
4 83
 
8.3%
8 64
 
6.4%
7 59
 
5.9%
9 53
 
5.3%
6 44
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
B 9
90.0%
A 1
 
10.0%
Space Separator
ValueCountFrequency (%)
1013
100.0%
Close Punctuation
ValueCountFrequency (%)
) 196
100.0%
Open Punctuation
ValueCountFrequency (%)
( 196
100.0%
Other Punctuation
ValueCountFrequency (%)
, 169
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3554
57.7%
Common 2596
42.1%
Latin 10
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
216
 
6.1%
216
 
6.1%
210
 
5.9%
201
 
5.7%
198
 
5.6%
197
 
5.5%
196
 
5.5%
195
 
5.5%
195
 
5.5%
179
 
5.0%
Other values (140) 1551
43.6%
Common
ValueCountFrequency (%)
1013
39.0%
1 240
 
9.2%
) 196
 
7.6%
( 196
 
7.6%
, 169
 
6.5%
2 138
 
5.3%
0 125
 
4.8%
3 109
 
4.2%
5 88
 
3.4%
4 83
 
3.2%
Other values (6) 239
 
9.2%
Latin
ValueCountFrequency (%)
B 9
90.0%
A 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3554
57.7%
ASCII 2606
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1013
38.9%
1 240
 
9.2%
) 196
 
7.5%
( 196
 
7.5%
, 169
 
6.5%
2 138
 
5.3%
0 125
 
4.8%
3 109
 
4.2%
5 88
 
3.4%
4 83
 
3.2%
Other values (8) 249
 
9.6%
Hangul
ValueCountFrequency (%)
216
 
6.1%
216
 
6.1%
210
 
5.9%
201
 
5.7%
198
 
5.6%
197
 
5.5%
196
 
5.5%
195
 
5.5%
195
 
5.5%
179
 
5.0%
Other values (140) 1551
43.6%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct83
Distinct (%)43.0%
Missing140
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean8774.285
Minimum8700
Maximum8865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T14:53:35.613008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8700
5-th percentile8702.6
Q18750
median8771
Q38793
95-th percentile8855
Maximum8865
Range165
Interquartile range (IQR)43

Descriptive statistics

Standard deviation42.148011
Coefficient of variation (CV)0.0048035835
Kurtosis-0.37890761
Mean8774.285
Median Absolute Deviation (MAD)22
Skewness0.30802099
Sum1693437
Variance1776.4548
MonotonicityNot monotonic
2024-05-11T14:53:35.827331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8793 7
 
2.1%
8739 7
 
2.1%
8768 5
 
1.5%
8772 5
 
1.5%
8806 5
 
1.5%
8766 5
 
1.5%
8771 5
 
1.5%
8754 5
 
1.5%
8807 4
 
1.2%
8765 4
 
1.2%
Other values (73) 141
42.3%
(Missing) 140
42.0%
ValueCountFrequency (%)
8700 3
0.9%
8701 4
1.2%
8702 3
0.9%
8703 1
 
0.3%
8704 1
 
0.3%
8705 1
 
0.3%
8708 4
1.2%
8711 1
 
0.3%
8714 1
 
0.3%
8720 2
0.6%
ValueCountFrequency (%)
8865 1
 
0.3%
8861 1
 
0.3%
8860 3
0.9%
8859 3
0.9%
8858 1
 
0.3%
8855 3
0.9%
8854 1
 
0.3%
8849 1
 
0.3%
8848 1
 
0.3%
8846 4
1.2%
Distinct324
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2024-05-11T14:53:36.218635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length15
Mean length7.8108108
Min length2

Characters and Unicode

Total characters2601
Distinct characters292
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

Unique315 ?
Unique (%)94.6%

Sample

1st row동방메인텍(주)
2nd row(주)에덴성업
3rd row태정기술산업주식회사
4th row서도공영주식회사
5th row화성개발주식회사
ValueCountFrequency (%)
주식회사 33
 
8.4%
5
 
1.3%
주)태성종합관리 2
 
0.5%
관진개발(주 2
 
0.5%
우리서비스 2
 
0.5%
주)필웅엔지니어링 2
 
0.5%
주)오케이기획 2
 
0.5%
세계기획테크 2
 
0.5%
주)한샘개발 2
 
0.5%
주)우진개발환경 2
 
0.5%
Other values (337) 338
86.2%
2024-05-11T14:53:36.936573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
238
 
9.2%
( 194
 
7.5%
) 194
 
7.5%
80
 
3.1%
62
 
2.4%
59
 
2.3%
55
 
2.1%
48
 
1.8%
46
 
1.8%
42
 
1.6%
Other values (282) 1583
60.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2106
81.0%
Open Punctuation 194
 
7.5%
Close Punctuation 194
 
7.5%
Space Separator 59
 
2.3%
Uppercase Letter 29
 
1.1%
Dash Punctuation 8
 
0.3%
Lowercase Letter 5
 
0.2%
Other Punctuation 4
 
0.2%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
238
 
11.3%
80
 
3.8%
62
 
2.9%
55
 
2.6%
48
 
2.3%
46
 
2.2%
42
 
2.0%
40
 
1.9%
40
 
1.9%
38
 
1.8%
Other values (254) 1417
67.3%
Uppercase Letter
ValueCountFrequency (%)
S 4
13.8%
C 4
13.8%
M 4
13.8%
T 3
10.3%
J 2
 
6.9%
B 2
 
6.9%
G 2
 
6.9%
I 1
 
3.4%
K 1
 
3.4%
P 1
 
3.4%
Other values (5) 5
17.2%
Lowercase Letter
ValueCountFrequency (%)
y 1
20.0%
s 1
20.0%
t 1
20.0%
e 1
20.0%
m 1
20.0%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
& 1
 
25.0%
Decimal Number
ValueCountFrequency (%)
4 1
50.0%
2 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 194
100.0%
Close Punctuation
ValueCountFrequency (%)
) 194
100.0%
Space Separator
ValueCountFrequency (%)
59
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2106
81.0%
Common 461
 
17.7%
Latin 34
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
238
 
11.3%
80
 
3.8%
62
 
2.9%
55
 
2.6%
48
 
2.3%
46
 
2.2%
42
 
2.0%
40
 
1.9%
40
 
1.9%
38
 
1.8%
Other values (254) 1417
67.3%
Latin
ValueCountFrequency (%)
S 4
 
11.8%
C 4
 
11.8%
M 4
 
11.8%
T 3
 
8.8%
J 2
 
5.9%
B 2
 
5.9%
G 2
 
5.9%
y 1
 
2.9%
s 1
 
2.9%
I 1
 
2.9%
Other values (10) 10
29.4%
Common
ValueCountFrequency (%)
( 194
42.1%
) 194
42.1%
59
 
12.8%
- 8
 
1.7%
. 3
 
0.7%
& 1
 
0.2%
4 1
 
0.2%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2106
81.0%
ASCII 495
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
238
 
11.3%
80
 
3.8%
62
 
2.9%
55
 
2.6%
48
 
2.3%
46
 
2.2%
42
 
2.0%
40
 
1.9%
40
 
1.9%
38
 
1.8%
Other values (254) 1417
67.3%
ASCII
ValueCountFrequency (%)
( 194
39.2%
) 194
39.2%
59
 
11.9%
- 8
 
1.6%
S 4
 
0.8%
C 4
 
0.8%
M 4
 
0.8%
. 3
 
0.6%
T 3
 
0.6%
J 2
 
0.4%
Other values (18) 20
 
4.0%
Distinct283
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum1999-11-05 00:00:00
Maximum2024-04-26 16:15:03
2024-05-11T14:53:37.175144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:37.418367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
I
234 
U
99 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 234
70.3%
U 99
29.7%

Length

2024-05-11T14:53:37.631757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:37.793138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 234
70.3%
u 99
29.7%
Distinct98
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-03 23:08:00
2024-05-11T14:53:37.965652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:53:38.226663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
건물위생관리업
332 
건물위생관리업 기타
 
1

Length

Max length10
Median length7
Mean length7.009009
Min length7

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 332
99.7%
건물위생관리업 기타 1
 
0.3%

Length

2024-05-11T14:53:38.473644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:38.666569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 333
99.7%
기타 1
 
0.3%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct252
Distinct (%)76.8%
Missing5
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean194265.96
Minimum191076.43
Maximum198325.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T14:53:38.885064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191076.43
5-th percentile191549.05
Q1192817.82
median193934.25
Q3195700.08
95-th percentile197955.94
Maximum198325.19
Range7248.7601
Interquartile range (IQR)2882.2594

Descriptive statistics

Standard deviation1823.1847
Coefficient of variation (CV)0.0093849928
Kurtosis-0.692625
Mean194265.96
Median Absolute Deviation (MAD)1495.8906
Skewness0.33441756
Sum63719236
Variance3324002.4
MonotonicityNot monotonic
2024-05-11T14:53:39.172750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196120.039741405 8
 
2.4%
193610.797201552 7
 
2.1%
193746.833837509 5
 
1.5%
195588.796492288 5
 
1.5%
195647.366393622 4
 
1.2%
196668.226137898 3
 
0.9%
197436.89654892 3
 
0.9%
195738.565943216 3
 
0.9%
193713.688152651 3
 
0.9%
191726.697584402 3
 
0.9%
Other values (242) 284
85.3%
(Missing) 5
 
1.5%
ValueCountFrequency (%)
191076.428759797 1
0.3%
191084.937997691 1
0.3%
191131.263415395 2
0.6%
191139.258430167 1
0.3%
191185.163772161 1
0.3%
191215.879312952 1
0.3%
191244.074940777 2
0.6%
191245.389750014 1
0.3%
191284.120259245 1
0.3%
191376.602183978 1
0.3%
ValueCountFrequency (%)
198325.188812014 1
 
0.3%
198284.078546351 1
 
0.3%
198278.931088754 2
0.6%
198264.099355157 1
 
0.3%
198257.694991475 1
 
0.3%
198210.417913572 1
 
0.3%
198196.96668588 1
 
0.3%
198142.028597565 1
 
0.3%
198059.477138778 2
0.6%
198051.385140733 3
0.9%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct252
Distinct (%)76.8%
Missing5
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean442080.37
Minimum439817
Maximum443513.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T14:53:39.437016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439817
5-th percentile440771.69
Q1441741.29
median442163.74
Q3442472.51
95-th percentile443030.24
Maximum443513.66
Range3696.6567
Interquartile range (IQR)731.22561

Descriptive statistics

Standard deviation650.68438
Coefficient of variation (CV)0.001471869
Kurtosis1.3369768
Mean442080.37
Median Absolute Deviation (MAD)328.90345
Skewness-0.84197081
Sum1.4500236 × 108
Variance423390.17
MonotonicityNot monotonic
2024-05-11T14:53:40.044418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
441980.475413186 8
 
2.4%
442281.285348422 7
 
2.1%
442510.775085572 5
 
1.5%
442098.372819317 5
 
1.5%
442105.342411703 4
 
1.2%
441640.722371723 3
 
0.9%
441472.608142257 3
 
0.9%
441381.722841201 3
 
0.9%
441850.367165377 3
 
0.9%
442072.615937286 3
 
0.9%
Other values (242) 284
85.3%
(Missing) 5
 
1.5%
ValueCountFrequency (%)
439816.999224208 1
0.3%
439993.033957335 1
0.3%
440060.367356882 1
0.3%
440079.037787497 1
0.3%
440175.725173063 1
0.3%
440176.635022518 1
0.3%
440205.2603058 1
0.3%
440263.768319604 2
0.6%
440285.417442582 1
0.3%
440374.894362337 2
0.6%
ValueCountFrequency (%)
443513.655954844 1
 
0.3%
443465.324057696 1
 
0.3%
443349.892172125 1
 
0.3%
443341.379446435 1
 
0.3%
443291.231245754 3
0.9%
443221.321009043 1
 
0.3%
443216.750634836 3
0.9%
443195.528279386 1
 
0.3%
443148.827256697 1
 
0.3%
443109.84937523 1
 
0.3%

위생업태명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
건물위생관리업
289 
<NA>
43 
건물위생관리업 기타
 
1

Length

Max length10
Median length7
Mean length6.6216216
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
건물위생관리업 289
86.8%
<NA> 43
 
12.9%
건물위생관리업 기타 1
 
0.3%

Length

2024-05-11T14:53:40.304150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:40.487389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건물위생관리업 290
86.8%
na 43
 
12.9%
기타 1
 
0.3%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)6.1%
Missing89
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean3.454918
Minimum0
Maximum26
Zeros85
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T14:53:40.653030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile10
Maximum26
Range26
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0053997
Coefficient of variation (CV)1.1593328
Kurtosis10.200197
Mean3.454918
Median Absolute Deviation (MAD)3
Skewness2.5150992
Sum843
Variance16.043227
MonotonicityNot monotonic
2024-05-11T14:53:40.837536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 85
25.5%
5 38
11.4%
3 37
11.1%
4 29
 
8.7%
6 13
 
3.9%
2 8
 
2.4%
7 7
 
2.1%
10 6
 
1.8%
1 5
 
1.5%
8 4
 
1.2%
Other values (5) 12
 
3.6%
(Missing) 89
26.7%
ValueCountFrequency (%)
0 85
25.5%
1 5
 
1.5%
2 8
 
2.4%
3 37
11.1%
4 29
 
8.7%
5 38
11.4%
6 13
 
3.9%
7 7
 
2.1%
8 4
 
1.2%
9 4
 
1.2%
ValueCountFrequency (%)
26 2
 
0.6%
23 1
 
0.3%
18 2
 
0.6%
13 3
 
0.9%
10 6
 
1.8%
9 4
 
1.2%
8 4
 
1.2%
7 7
 
2.1%
6 13
 
3.9%
5 38
11.4%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)3.1%
Missing110
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean0.65470852
Minimum0
Maximum6
Zeros112
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T14:53:41.030215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92612862
Coefficient of variation (CV)1.4145663
Kurtosis9.9458544
Mean0.65470852
Median Absolute Deviation (MAD)0
Skewness2.6631425
Sum146
Variance0.85771422
MonotonicityNot monotonic
2024-05-11T14:53:41.251994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 112
33.6%
1 96
28.8%
2 5
 
1.5%
3 4
 
1.2%
4 3
 
0.9%
5 2
 
0.6%
6 1
 
0.3%
(Missing) 110
33.0%
ValueCountFrequency (%)
0 112
33.6%
1 96
28.8%
2 5
 
1.5%
3 4
 
1.2%
4 3
 
0.9%
5 2
 
0.6%
6 1
 
0.3%
ValueCountFrequency (%)
6 1
 
0.3%
5 2
 
0.6%
4 3
 
0.9%
3 4
 
1.2%
2 5
 
1.5%
1 96
28.8%
0 112
33.6%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)6.2%
Missing122
Missing (%)36.6%
Infinite0
Infinite (%)0.0%
Mean3.0616114
Minimum0
Maximum15
Zeros21
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T14:53:41.425234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile8
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4959005
Coefficient of variation (CV)0.81522447
Kurtosis3.2757733
Mean3.0616114
Median Absolute Deviation (MAD)1
Skewness1.5061445
Sum646
Variance6.2295193
MonotonicityNot monotonic
2024-05-11T14:53:41.631340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 46
 
13.8%
1 38
 
11.4%
3 36
 
10.8%
4 27
 
8.1%
0 21
 
6.3%
5 16
 
4.8%
7 9
 
2.7%
8 7
 
2.1%
6 4
 
1.2%
10 4
 
1.2%
Other values (3) 3
 
0.9%
(Missing) 122
36.6%
ValueCountFrequency (%)
0 21
6.3%
1 38
11.4%
2 46
13.8%
3 36
10.8%
4 27
8.1%
5 16
 
4.8%
6 4
 
1.2%
7 9
 
2.7%
8 7
 
2.1%
9 1
 
0.3%
ValueCountFrequency (%)
15 1
 
0.3%
13 1
 
0.3%
10 4
 
1.2%
9 1
 
0.3%
8 7
 
2.1%
7 9
 
2.7%
6 4
 
1.2%
5 16
4.8%
4 27
8.1%
3 36
10.8%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)7.6%
Missing136
Missing (%)40.8%
Infinite0
Infinite (%)0.0%
Mean4.8730964
Minimum0
Maximum334
Zeros13
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T14:53:41.834748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum334
Range334
Interquartile range (IQR)2

Descriptive statistics

Standard deviation23.69791
Coefficient of variation (CV)4.8630087
Kurtosis192.68188
Mean4.8730964
Median Absolute Deviation (MAD)1
Skewness13.80726
Sum960
Variance561.59096
MonotonicityNot monotonic
2024-05-11T14:53:42.022891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 45
 
13.5%
1 35
 
10.5%
3 34
 
10.2%
4 27
 
8.1%
5 17
 
5.1%
0 13
 
3.9%
7 8
 
2.4%
8 6
 
1.8%
6 4
 
1.2%
10 3
 
0.9%
Other values (5) 5
 
1.5%
(Missing) 136
40.8%
ValueCountFrequency (%)
0 13
 
3.9%
1 35
10.5%
2 45
13.5%
3 34
10.2%
4 27
8.1%
5 17
 
5.1%
6 4
 
1.2%
7 8
 
2.4%
8 6
 
1.8%
9 1
 
0.3%
ValueCountFrequency (%)
334 1
 
0.3%
15 1
 
0.3%
13 1
 
0.3%
11 1
 
0.3%
10 3
 
0.9%
9 1
 
0.3%
8 6
 
1.8%
7 8
2.4%
6 4
 
1.2%
5 17
5.1%
Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
<NA>
228 
0
81 
1
23 
2
 
1

Length

Max length4
Median length4
Mean length3.0540541
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 228
68.5%
0 81
 
24.3%
1 23
 
6.9%
2 1
 
0.3%

Length

2024-05-11T14:53:42.238956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:42.413064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 228
68.5%
0 81
 
24.3%
1 23
 
6.9%
2 1
 
0.3%

사용끝지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
<NA>
241 
0
66 
1
 
24
2
 
1
5
 
1

Length

Max length4
Median length4
Mean length3.1711712
Min length1

Unique

Unique2 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 241
72.4%
0 66
 
19.8%
1 24
 
7.2%
2 1
 
0.3%
5 1
 
0.3%

Length

2024-05-11T14:53:42.608258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:42.804484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 241
72.4%
0 66
 
19.8%
1 24
 
7.2%
2 1
 
0.3%
5 1
 
0.3%

한실수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
172 
<NA>
161 

Length

Max length4
Median length1
Mean length2.4504505
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 (%)
0 172
51.7%
<NA> 161
48.3%

Length

2024-05-11T14:53:43.017799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:43.223265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 172
51.7%
na 161
48.3%

양실수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
172 
<NA>
161 

Length

Max length4
Median length1
Mean length2.4504505
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 (%)
0 172
51.7%
<NA> 161
48.3%

Length

2024-05-11T14:53:43.458860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:43.691900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 172
51.7%
na 161
48.3%

욕실수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
172 
<NA>
161 

Length

Max length4
Median length1
Mean length2.4504505
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 (%)
0 172
51.7%
<NA> 161
48.3%

Length

2024-05-11T14:53:43.901162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:44.074722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 172
51.7%
na 161
48.3%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.7%
Missing57
Missing (%)17.1%
Memory size798.0 B
False
274 
True
 
2
(Missing)
57 
ValueCountFrequency (%)
False 274
82.3%
True 2
 
0.6%
(Missing) 57
 
17.1%
2024-05-11T14:53:44.202861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
0
171 
<NA>
162 

Length

Max length4
Median length1
Mean length2.4594595
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 (%)
0 171
51.4%
<NA> 162
48.6%

Length

2024-05-11T14:53:44.393223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:44.572465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 171
51.4%
na 162
48.6%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing333
Missing (%)100.0%
Memory size3.1 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing333
Missing (%)100.0%
Memory size3.1 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing333
Missing (%)100.0%
Memory size3.1 KiB
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
<NA>
190 
임대
143 

Length

Max length4
Median length4
Mean length3.1411411
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 190
57.1%
임대 143
42.9%

Length

2024-05-11T14:53:44.744881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:44.956486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 190
57.1%
임대 143
42.9%

세탁기수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
<NA>
176 
0
157 

Length

Max length4
Median length4
Mean length2.5855856
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> 176
52.9%
0 157
47.1%

Length

2024-05-11T14:53:45.158036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:45.358339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 176
52.9%
0 157
47.1%

여성종사자수
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
<NA>
289 
0
39 
2
 
3
1
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.6036036
Min length1

Unique

Unique2 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 289
86.8%
0 39
 
11.7%
2 3
 
0.9%
1 1
 
0.3%
3 1
 
0.3%

Length

2024-05-11T14:53:45.590629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:45.823396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 289
86.8%
0 39
 
11.7%
2 3
 
0.9%
1 1
 
0.3%
3 1
 
0.3%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)17.0%
Missing286
Missing (%)85.9%
Infinite0
Infinite (%)0.0%
Mean7.9787234
Minimum0
Maximum300
Zeros38
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-11T14:53:46.014453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.7
Maximum300
Range300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation44.137379
Coefficient of variation (CV)5.5318848
Kurtosis44.277527
Mean7.9787234
Median Absolute Deviation (MAD)0
Skewness6.5901258
Sum375
Variance1948.1082
MonotonicityNot monotonic
2024-05-11T14:53:46.189873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 38
 
11.4%
3 3
 
0.9%
50 1
 
0.3%
300 1
 
0.3%
6 1
 
0.3%
1 1
 
0.3%
5 1
 
0.3%
4 1
 
0.3%
(Missing) 286
85.9%
ValueCountFrequency (%)
0 38
11.4%
1 1
 
0.3%
3 3
 
0.9%
4 1
 
0.3%
5 1
 
0.3%
6 1
 
0.3%
50 1
 
0.3%
300 1
 
0.3%
ValueCountFrequency (%)
300 1
 
0.3%
50 1
 
0.3%
6 1
 
0.3%
5 1
 
0.3%
4 1
 
0.3%
3 3
 
0.9%
1 1
 
0.3%
0 38
11.4%

회수건조수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
<NA>
193 
0
140 

Length

Max length4
Median length4
Mean length2.7387387
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> 193
58.0%
0 140
42.0%

Length

2024-05-11T14:53:46.444699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:46.648493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 193
58.0%
0 140
42.0%

침대수
Categorical

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
<NA>
195 
0
138 

Length

Max length4
Median length4
Mean length2.7567568
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> 195
58.6%
0 138
41.4%

Length

2024-05-11T14:53:46.817880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:53:46.993567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 195
58.6%
0 138
41.4%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing43
Missing (%)12.9%
Memory size798.0 B
False
290 
(Missing)
43 
ValueCountFrequency (%)
False 290
87.1%
(Missing) 43
 
12.9%
2024-05-11T14:53:47.138439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
032000003200000-206-1987-0299119870603<NA>3폐업2폐업20161128<NA><NA><NA>02 878015140.00151847서울특별시 관악구 봉천동 1575-9서울특별시 관악구 쑥고개로 92 (봉천동)8831동방메인텍(주)2006-11-03 00:00:00I2018-08-31 23:59:59.0건물위생관리업195209.480483441880.432568건물위생관리업5133<NA><NA><NA><NA><NA>N<NA><NA><NA><NA>임대<NA><NA><NA><NA><NA>N
132000003200000-206-1987-0299219870820<NA>3폐업2폐업20030326<NA><NA><NA>02 8760493127.50151835서울특별시 관악구 봉천동 1596-6<NA><NA>(주)에덴성업2003-03-27 00:00:00I2018-08-31 23:59:59.0건물위생관리업195764.838758441564.824906건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
232000003200000-206-1988-0299319880326<NA>3폐업2폐업20030326<NA><NA><NA>02 8876695.00151835서울특별시 관악구 봉천동 1595-1<NA><NA>태정기술산업주식회사2003-03-27 00:00:00I2018-08-31 23:59:59.0건물위생관리업195772.919373441498.239116건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
332000003200000-206-1990-0299419900105<NA>3폐업2폐업20030430<NA><NA><NA>02 8716996.00151820서울특별시 관악구 봉천동 945-6<NA><NA>서도공영주식회사2003-04-24 00:00:00I2018-08-31 23:59:59.0건물위생관리업194591.869955442311.948832건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
432000003200000-206-1990-0299519901130<NA>3폐업2폐업20030326<NA><NA><NA>02 8838973.00151836서울특별시 관악구 봉천동 862-2<NA><NA>화성개발주식회사2003-03-27 00:00:00I2018-08-31 23:59:59.0건물위생관리업195707.917935442075.680742건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
532000003200000-206-1991-0299519910423<NA>3폐업2폐업20030326<NA><NA><NA>02 8561412.00151899서울특별시 관악구 신림동 1570-9<NA><NA>라신산업2003-03-27 00:00:00I2018-08-31 23:59:59.0건물위생관리업192704.289178442230.088008건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
632000003200000-206-1991-0299719910731<NA>3폐업2폐업20030326<NA><NA><NA>02 8879101.00151849서울특별시 관악구 봉천동 1673-21<NA><NA>(주)부림방제2003-03-27 00:00:00I2018-08-31 23:59:59.0건물위생관리업196120.039741441980.475413건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
732000003200000-206-1992-0299819920313<NA>1영업/정상1영업<NA><NA><NA><NA>02 874540843.51151730서울특별시 관악구 신림동 1638-32 삼모스포렉스서울특별시 관악구 신림로59길 23, 삼모스포렉스 813호 (신림동)8776아태산업개발(주)2021-11-08 13:22:29U2021-11-10 02:40:00.0건물위생관리업193610.797202442281.285348건물위생관리업13399<NA><NA><NA><NA><NA>N<NA><NA><NA><NA>임대<NA><NA><NA><NA><NA>N
832000003200000-206-1992-0299919920509<NA>3폐업2폐업20030411<NA><NA><NA>02 7811231.00151832서울특별시 관악구 봉천동 1655-14<NA><NA>(주)한국PCO2003-04-15 00:00:00I2018-08-31 23:59:59.0건물위생관리업197081.704726441449.587673건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
932000003200000-206-1992-0300019920523<NA>3폐업2폐업20101228<NA><NA><NA>02 525258843.00151800서울특별시 관악구 남현동 1064-5<NA><NA>(주)공영기업2008-04-03 11:21:00I2018-08-31 23:59:59.0건물위생관리업198051.385141441512.027703건물위생관리업3133<NA><NA><NA><NA><NA>N<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
32332000003200000-206-2022-0000420221116<NA>1영업/정상1영업<NA><NA><NA><NA><NA>15.00151890서울특별시 관악구 신림동 1422-5 르네상스 복합쇼핑몰서울특별시 관악구 신림로 340, 르네상스 복합쇼핑몰 7층 705호 (신림동)8754에이플러스2022-11-16 10:47:27I2021-10-31 23:08:00.0건물위생관리업193746.833838442510.775086<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32432000003200000-206-2023-000012023-02-24<NA>1영업/정상1영업<NA><NA><NA><NA><NA>13.18151-050서울특별시 관악구 봉천동 1703 성현동아아파트서울특별시 관악구 관악로 285, 성현동아아파트 상가에이동 비층 108호 (봉천동, 성현동아아파트)8726먼지 쫓는 사람들2023-02-24 13:59:34I2022-12-01 22:06:00.0건물위생관리업195999.574887443044.700996<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32532000003200000-206-2023-000022023-08-07<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.00151-818서울특별시 관악구 봉천동 1624-22서울특별시 관악구 낙성대로4길 12-10, B01호 (봉천동)8790주식회사 청담클린존2023-09-27 14:04:16U2022-12-08 22:09:00.0건물위생관리업196228.164609441595.052521<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32632000003200000-206-2023-000032023-08-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>66.00151-926서울특별시 관악구 남현동 1054-1 금산빌딩서울특별시 관악구 남부순환로 2008, 금산빌딩 4층 401호 (남현동)8804주식회사 제이떠블유시스템(J.W System)2023-12-12 15:55:35U2022-11-01 23:04:00.0건물위생관리업197436.896549441472.608142<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32732000003200000-206-2023-000042023-08-22<NA>1영업/정상1영업<NA><NA><NA><NA><NA>46.38151-899서울특별시 관악구 신림동 1576-23서울특별시 관악구 남부순환로 1536, 103호 (신림동)8773서강크린2023-08-24 08:45:36U2022-12-07 22:06:00.0건물위생관리업192971.061048442290.047233<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32832000003200000-206-2023-000052023-11-02<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.00151-848서울특별시 관악구 봉천동 856-1 대우디오슈페리움2단지서울특별시 관악구 관악로 168, 4층 409가8호 (봉천동, 대우디오슈페리움2단지)8788독양 주식회사2024-04-26 16:15:03U2023-12-03 22:08:00.0건물위생관리업195785.625847442009.829042<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
32932000003200000-206-2023-000062023-11-13<NA>3폐업2폐업2023-12-15<NA><NA><NA><NA>5.00151-800서울특별시 관악구 남현동 1062-4 대한뉴팜서울특별시 관악구 남현3길 61, 대한뉴팜 2층 213호 (남현동)8806주식회사 다구스토2023-12-15 15:03:07U2022-11-01 23:07:00.0건물위생관리업198142.028598441468.240752<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33032000003200000-206-2024-000012024-02-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>9.90151-864서울특별시 관악구 신림동 343-3서울특별시 관악구 광신길 225, 1층 (신림동)8846우성사우나2024-02-01 16:49:40I2023-12-02 00:03:00.0건물위생관리업193721.863992440545.94642<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33132000003200000-206-2024-000022024-03-13<NA>1영업/정상1영업<NA><NA><NA><NA><NA>19.83151-892서울특별시 관악구 신림동 1432-178서울특별시 관악구 신림동1길 20-6, 2층 (신림동)8760와이케이클린 종합전문청소2024-03-13 10:37:54I2023-12-02 23:06:00.0건물위생관리업<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33232000003200000-206-2024-000032024-04-16<NA>1영업/정상1영업<NA><NA><NA><NA><NA>73.71151-820서울특별시 관악구 봉천동 945-14서울특별시 관악구 남부순환로 1714-1, 3층 (봉천동)8782주식회사 지명아이앤씨2024-04-16 14:11:10I2023-12-03 23:08:00.0건물위생관리업194656.948276442274.655023<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>