Overview

Dataset statistics

Number of variables47
Number of observations1466
Missing cells17266
Missing cells (%)25.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory578.5 KiB
Average record size in memory404.1 B

Variable types

Categorical18
Text7
DateTime4
Unsupported7
Numeric9
Boolean2

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),위생업태명,건물지상층수,건물지하층수,사용시작지상층,사용끝지상층,사용시작지하층,사용끝지하층,한실수,양실수,욕실수,발한실여부,좌석수,조건부허가신고사유,조건부허가시작일자,조건부허가종료일자,건물소유구분명,세탁기수,여성종사자수,남성종사자수,회수건조수,침대수,다중이용업소여부
Author종로구
URLhttps://data.seoul.go.kr/dataList/OA-17902/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
데이터갱신구분 is highly imbalanced (53.0%)Imbalance
업태구분명 is highly imbalanced (51.8%)Imbalance
사용시작지하층 is highly imbalanced (54.0%)Imbalance
사용끝지하층 is highly imbalanced (77.2%)Imbalance
발한실여부 is highly imbalanced (95.7%)Imbalance
건물소유구분명 is highly imbalanced (54.6%)Imbalance
여성종사자수 is highly imbalanced (71.8%)Imbalance
남성종사자수 is highly imbalanced (65.3%)Imbalance
다중이용업소여부 is highly imbalanced (98.3%)Imbalance
인허가취소일자 has 1466 (100.0%) missing valuesMissing
폐업일자 has 462 (31.5%) missing valuesMissing
휴업시작일자 has 1466 (100.0%) missing valuesMissing
휴업종료일자 has 1466 (100.0%) missing valuesMissing
재개업일자 has 1466 (100.0%) missing valuesMissing
전화번호 has 352 (24.0%) missing valuesMissing
소재지우편번호 has 24 (1.6%) missing valuesMissing
지번주소 has 24 (1.6%) missing valuesMissing
도로명주소 has 667 (45.5%) missing valuesMissing
도로명우편번호 has 672 (45.8%) missing valuesMissing
좌표정보(X) has 88 (6.0%) missing valuesMissing
좌표정보(Y) has 88 (6.0%) missing valuesMissing
건물지상층수 has 558 (38.1%) missing valuesMissing
건물지하층수 has 618 (42.2%) missing valuesMissing
사용시작지상층 has 750 (51.2%) missing valuesMissing
사용끝지상층 has 1092 (74.5%) missing valuesMissing
발한실여부 has 201 (13.7%) missing valuesMissing
좌석수 has 216 (14.7%) missing valuesMissing
조건부허가신고사유 has 1466 (100.0%) missing valuesMissing
조건부허가시작일자 has 1466 (100.0%) missing valuesMissing
조건부허가종료일자 has 1466 (100.0%) missing valuesMissing
침대수 has 1004 (68.5%) missing valuesMissing
다중이용업소여부 has 188 (12.8%) 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 733 (50.0%) zerosZeros
건물지하층수 has 797 (54.4%) zerosZeros
사용시작지상층 has 397 (27.1%) zerosZeros
사용끝지상층 has 83 (5.7%) zerosZeros
좌석수 has 115 (7.8%) zerosZeros
침대수 has 266 (18.1%) zerosZeros

Reproduction

Analysis started2024-04-06 13:41:21.792945
Analysis finished2024-04-06 13:41:22.716010
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3000000
1466 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000
2nd row3000000
3rd row3000000
4th row3000000
5th row3000000

Common Values

ValueCountFrequency (%)
3000000 1466
100.0%

Length

2024-04-06T22:41:22.770490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:22.853837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000 1466
100.0%

관리번호
Text

UNIQUE 

Distinct1466
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2024-04-06T22:41:23.001656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters32252
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

Unique1466 ?
Unique (%)100.0%

Sample

1st row3000000-204-1963-00845
2nd row3000000-204-1963-00862
3rd row3000000-204-1963-00873
4th row3000000-204-1963-01105
5th row3000000-204-1965-00841
ValueCountFrequency (%)
3000000-204-1963-00845 1
 
0.1%
3000000-211-2017-00015 1
 
0.1%
3000000-211-2017-00013 1
 
0.1%
3000000-211-2017-00012 1
 
0.1%
3000000-211-2017-00011 1
 
0.1%
3000000-211-2017-00010 1
 
0.1%
3000000-211-2017-00009 1
 
0.1%
3000000-211-2017-00008 1
 
0.1%
3000000-211-2017-00007 1
 
0.1%
3000000-211-2017-00006 1
 
0.1%
Other values (1456) 1456
99.3%
2024-04-06T22:41:23.283953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15148
47.0%
- 4398
 
13.6%
2 3273
 
10.1%
1 3087
 
9.6%
3 2102
 
6.5%
9 1326
 
4.1%
4 1196
 
3.7%
8 582
 
1.8%
5 398
 
1.2%
7 379
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27854
86.4%
Dash Punctuation 4398
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15148
54.4%
2 3273
 
11.8%
1 3087
 
11.1%
3 2102
 
7.5%
9 1326
 
4.8%
4 1196
 
4.3%
8 582
 
2.1%
5 398
 
1.4%
7 379
 
1.4%
6 363
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 4398
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15148
47.0%
- 4398
 
13.6%
2 3273
 
10.1%
1 3087
 
9.6%
3 2102
 
6.5%
9 1326
 
4.1%
4 1196
 
3.7%
8 582
 
1.8%
5 398
 
1.2%
7 379
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15148
47.0%
- 4398
 
13.6%
2 3273
 
10.1%
1 3087
 
9.6%
3 2102
 
6.5%
9 1326
 
4.1%
4 1196
 
3.7%
8 582
 
1.8%
5 398
 
1.2%
7 379
 
1.2%
Distinct1298
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Minimum1963-05-30 00:00:00
Maximum2024-04-03 00:00:00
2024-04-06T22:41:23.407905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:41:23.536095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1466
Missing (%)100.0%
Memory size13.0 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1004 
1
462 

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 1004
68.5%
1 462
31.5%

Length

2024-04-06T22:41:23.657322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:23.742987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1004
68.5%
1 462
31.5%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
폐업
1004 
영업/정상
462 

Length

Max length5
Median length2
Mean length2.9454297
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1004
68.5%
영업/정상 462
31.5%

Length

2024-04-06T22:41:23.839014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:23.926095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1004
68.5%
영업/정상 462
31.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2
1004 
1
462 

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 1004
68.5%
1 462
31.5%

Length

2024-04-06T22:41:24.014870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:24.104450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1004
68.5%
1 462
31.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
폐업
1004 
영업
462 

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 1004
68.5%
영업 462
31.5%

Length

2024-04-06T22:41:24.197895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:24.289573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1004
68.5%
영업 462
31.5%

폐업일자
Date

MISSING 

Distinct786
Distinct (%)78.3%
Missing462
Missing (%)31.5%
Memory size11.6 KiB
Minimum1991-03-15 00:00:00
Maximum2024-04-04 00:00:00
2024-04-06T22:41:24.389808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:41:24.507299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1466
Missing (%)100.0%
Memory size13.0 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1466
Missing (%)100.0%
Memory size13.0 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1466
Missing (%)100.0%
Memory size13.0 KiB

전화번호
Text

MISSING 

Distinct996
Distinct (%)89.4%
Missing352
Missing (%)24.0%
Memory size11.6 KiB
2024-04-06T22:41:24.736809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.38061
Min length2

Characters and Unicode

Total characters11564
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

Unique918 ?
Unique (%)82.4%

Sample

1st row0207628611
2nd row02 7634850
3rd row02 7635050
4th row02 7387574
5th row0222650948
ValueCountFrequency (%)
02 915
40.5%
070 17
 
0.8%
730 16
 
0.7%
379 15
 
0.7%
0200000000 13
 
0.6%
747 13
 
0.6%
733 13
 
0.6%
720 12
 
0.5%
742 11
 
0.5%
765 11
 
0.5%
Other values (1036) 1225
54.2%
2024-04-06T22:41:25.078888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1936
16.7%
2 1928
16.7%
1440
12.5%
7 1397
12.1%
3 1112
9.6%
6 848
7.3%
4 699
 
6.0%
5 668
 
5.8%
9 569
 
4.9%
8 486
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10124
87.5%
Space Separator 1440
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1936
19.1%
2 1928
19.0%
7 1397
13.8%
3 1112
11.0%
6 848
8.4%
4 699
 
6.9%
5 668
 
6.6%
9 569
 
5.6%
8 486
 
4.8%
1 481
 
4.8%
Space Separator
ValueCountFrequency (%)
1440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1936
16.7%
2 1928
16.7%
1440
12.5%
7 1397
12.1%
3 1112
9.6%
6 848
7.3%
4 699
 
6.0%
5 668
 
5.8%
9 569
 
4.9%
8 486
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1936
16.7%
2 1928
16.7%
1440
12.5%
7 1397
12.1%
3 1112
9.6%
6 848
7.3%
4 699
 
6.0%
5 668
 
5.8%
9 569
 
4.9%
8 486
 
4.2%
Distinct884
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2024-04-06T22:41:25.425554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.9877217
Min length3

Characters and Unicode

Total characters7312
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

Unique678 ?
Unique (%)46.2%

Sample

1st row18.90
2nd row34.36
3rd row14.70
4th row14.12
5th row36.86
ValueCountFrequency (%)
00 52
 
3.5%
21.00 19
 
1.3%
30.00 16
 
1.1%
19.80 15
 
1.0%
20.00 15
 
1.0%
33.00 15
 
1.0%
26.40 15
 
1.0%
18.00 14
 
1.0%
23.10 14
 
1.0%
23.00 12
 
0.8%
Other values (874) 1279
87.2%
2024-04-06T22:41:25.868103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1466
20.0%
0 1462
20.0%
1 769
10.5%
2 694
9.5%
3 547
 
7.5%
6 464
 
6.3%
4 441
 
6.0%
5 410
 
5.6%
8 401
 
5.5%
7 332
 
4.5%
Other values (2) 326
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5845
79.9%
Other Punctuation 1467
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1462
25.0%
1 769
13.2%
2 694
11.9%
3 547
 
9.4%
6 464
 
7.9%
4 441
 
7.5%
5 410
 
7.0%
8 401
 
6.9%
7 332
 
5.7%
9 325
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 1466
99.9%
, 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 7312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1466
20.0%
0 1462
20.0%
1 769
10.5%
2 694
9.5%
3 547
 
7.5%
6 464
 
6.3%
4 441
 
6.0%
5 410
 
5.6%
8 401
 
5.5%
7 332
 
4.5%
Other values (2) 326
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1466
20.0%
0 1462
20.0%
1 769
10.5%
2 694
9.5%
3 547
 
7.5%
6 464
 
6.3%
4 441
 
6.0%
5 410
 
5.6%
8 401
 
5.5%
7 332
 
4.5%
Other values (2) 326
 
4.5%

소재지우편번호
Text

MISSING 

Distinct198
Distinct (%)13.7%
Missing24
Missing (%)1.6%
Memory size11.6 KiB
2024-04-06T22:41:26.154910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0880721
Min length6

Characters and Unicode

Total characters8779
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

Unique50 ?
Unique (%)3.5%

Sample

1st row110850
2nd row110855
3rd row110522
4th row110044
5th row110833
ValueCountFrequency (%)
110522 57
 
4.0%
110837 40
 
2.8%
110847 38
 
2.6%
110840 35
 
2.4%
110827 31
 
2.1%
110043 30
 
2.1%
110862 30
 
2.1%
110813 30
 
2.1%
110122 29
 
2.0%
110121 28
 
1.9%
Other values (188) 1094
75.9%
2024-04-06T22:41:26.545100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3402
38.8%
0 2233
25.4%
8 694
 
7.9%
2 599
 
6.8%
4 419
 
4.8%
3 399
 
4.5%
5 360
 
4.1%
6 228
 
2.6%
7 223
 
2.5%
- 127
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8652
98.6%
Dash Punctuation 127
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3402
39.3%
0 2233
25.8%
8 694
 
8.0%
2 599
 
6.9%
4 419
 
4.8%
3 399
 
4.6%
5 360
 
4.2%
6 228
 
2.6%
7 223
 
2.6%
9 95
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8779
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3402
38.8%
0 2233
25.4%
8 694
 
7.9%
2 599
 
6.8%
4 419
 
4.8%
3 399
 
4.5%
5 360
 
4.1%
6 228
 
2.6%
7 223
 
2.5%
- 127
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8779
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3402
38.8%
0 2233
25.4%
8 694
 
7.9%
2 599
 
6.8%
4 419
 
4.8%
3 399
 
4.5%
5 360
 
4.1%
6 228
 
2.6%
7 223
 
2.5%
- 127
 
1.4%

지번주소
Text

MISSING 

Distinct1272
Distinct (%)88.2%
Missing24
Missing (%)1.6%
Memory size11.6 KiB
2024-04-06T22:41:26.775988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length42
Mean length23.628988
Min length15

Characters and Unicode

Total characters34073
Distinct characters236
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

Unique1140 ?
Unique (%)79.1%

Sample

1st row서울특별시 종로구 효제동 30-1번지
2nd row서울특별시 종로구 창신동 561-1번지
3rd row서울특별시 종로구 명륜2가 73-0번지
4th row서울특별시 종로구 필운동 276번지
5th row서울특별시 종로구 예지동 296-1번지
ValueCountFrequency (%)
서울특별시 1442
21.8%
종로구 1441
21.8%
창신동 206
 
3.1%
숭인동 124
 
1.9%
1층 101
 
1.5%
2층 92
 
1.4%
명륜2가 65
 
1.0%
평창동 63
 
1.0%
무악동 45
 
0.7%
종로1가 45
 
0.7%
Other values (1373) 2980
45.1%
2024-04-06T22:41:27.369709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6332
18.6%
1664
 
4.9%
1629
 
4.8%
1 1581
 
4.6%
1467
 
4.3%
1459
 
4.3%
1454
 
4.3%
1445
 
4.2%
1442
 
4.2%
1442
 
4.2%
Other values (226) 14158
41.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20473
60.1%
Space Separator 6332
 
18.6%
Decimal Number 6174
 
18.1%
Dash Punctuation 1003
 
2.9%
Uppercase Letter 27
 
0.1%
Other Punctuation 23
 
0.1%
Open Punctuation 20
 
0.1%
Close Punctuation 20
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1664
 
8.1%
1629
 
8.0%
1467
 
7.2%
1459
 
7.1%
1454
 
7.1%
1445
 
7.1%
1442
 
7.0%
1442
 
7.0%
1257
 
6.1%
1193
 
5.8%
Other values (200) 6021
29.4%
Decimal Number
ValueCountFrequency (%)
1 1581
25.6%
2 1067
17.3%
3 615
 
10.0%
0 548
 
8.9%
4 507
 
8.2%
5 477
 
7.7%
6 366
 
5.9%
8 362
 
5.9%
7 340
 
5.5%
9 311
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
B 10
37.0%
S 4
 
14.8%
D 4
 
14.8%
A 3
 
11.1%
K 3
 
11.1%
G 1
 
3.7%
C 1
 
3.7%
M 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 15
65.2%
/ 5
 
21.7%
@ 3
 
13.0%
Space Separator
ValueCountFrequency (%)
6332
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1003
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20473
60.1%
Common 13573
39.8%
Latin 27
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1664
 
8.1%
1629
 
8.0%
1467
 
7.2%
1459
 
7.1%
1454
 
7.1%
1445
 
7.1%
1442
 
7.0%
1442
 
7.0%
1257
 
6.1%
1193
 
5.8%
Other values (200) 6021
29.4%
Common
ValueCountFrequency (%)
6332
46.7%
1 1581
 
11.6%
2 1067
 
7.9%
- 1003
 
7.4%
3 615
 
4.5%
0 548
 
4.0%
4 507
 
3.7%
5 477
 
3.5%
6 366
 
2.7%
8 362
 
2.7%
Other values (8) 715
 
5.3%
Latin
ValueCountFrequency (%)
B 10
37.0%
S 4
 
14.8%
D 4
 
14.8%
A 3
 
11.1%
K 3
 
11.1%
G 1
 
3.7%
C 1
 
3.7%
M 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20473
60.1%
ASCII 13600
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6332
46.6%
1 1581
 
11.6%
2 1067
 
7.8%
- 1003
 
7.4%
3 615
 
4.5%
0 548
 
4.0%
4 507
 
3.7%
5 477
 
3.5%
6 366
 
2.7%
8 362
 
2.7%
Other values (16) 742
 
5.5%
Hangul
ValueCountFrequency (%)
1664
 
8.1%
1629
 
8.0%
1467
 
7.2%
1459
 
7.1%
1454
 
7.1%
1445
 
7.1%
1442
 
7.0%
1442
 
7.0%
1257
 
6.1%
1193
 
5.8%
Other values (200) 6021
29.4%

도로명주소
Text

MISSING 

Distinct747
Distinct (%)93.5%
Missing667
Missing (%)45.5%
Memory size11.6 KiB
2024-04-06T22:41:27.646840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length48
Mean length31.913642
Min length21

Characters and Unicode

Total characters25499
Distinct characters244
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

Unique706 ?
Unique (%)88.4%

Sample

1st row서울특별시 종로구 종로40가길 7-4 (종로5가)
2nd row서울특별시 종로구 자하문로9길 33-8 (누하동)
3rd row서울특별시 종로구 삼일대로32길 41 (운니동)
4th row서울특별시 종로구 지봉로 56 (숭인동)
5th row서울특별시 종로구 지봉로 55 (창신동)
ValueCountFrequency (%)
서울특별시 799
 
15.5%
종로구 799
 
15.5%
1층 170
 
3.3%
2층 128
 
2.5%
종로 118
 
2.3%
창신동 91
 
1.8%
숭인동 69
 
1.3%
3층 58
 
1.1%
19 56
 
1.1%
4층 50
 
1.0%
Other values (941) 2829
54.8%
2024-04-06T22:41:28.080542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4370
 
17.1%
1614
 
6.3%
1122
 
4.4%
1 1094
 
4.3%
817
 
3.2%
814
 
3.2%
810
 
3.2%
( 803
 
3.1%
) 803
 
3.1%
802
 
3.1%
Other values (234) 12450
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14402
56.5%
Space Separator 4370
 
17.1%
Decimal Number 4089
 
16.0%
Open Punctuation 803
 
3.1%
Close Punctuation 803
 
3.1%
Other Punctuation 799
 
3.1%
Dash Punctuation 187
 
0.7%
Uppercase Letter 44
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1614
 
11.2%
1122
 
7.8%
817
 
5.7%
814
 
5.7%
810
 
5.6%
802
 
5.6%
799
 
5.5%
799
 
5.5%
697
 
4.8%
559
 
3.9%
Other values (208) 5569
38.7%
Decimal Number
ValueCountFrequency (%)
1 1094
26.8%
2 735
18.0%
3 487
11.9%
4 403
 
9.9%
0 325
 
7.9%
5 293
 
7.2%
6 210
 
5.1%
9 208
 
5.1%
8 195
 
4.8%
7 139
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
B 29
65.9%
A 4
 
9.1%
S 3
 
6.8%
K 3
 
6.8%
L 1
 
2.3%
W 1
 
2.3%
J 1
 
2.3%
M 1
 
2.3%
C 1
 
2.3%
Other Punctuation
ValueCountFrequency (%)
, 798
99.9%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
4370
100.0%
Open Punctuation
ValueCountFrequency (%)
( 803
100.0%
Close Punctuation
ValueCountFrequency (%)
) 803
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 187
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14402
56.5%
Common 11053
43.3%
Latin 44
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1614
 
11.2%
1122
 
7.8%
817
 
5.7%
814
 
5.7%
810
 
5.6%
802
 
5.6%
799
 
5.5%
799
 
5.5%
697
 
4.8%
559
 
3.9%
Other values (208) 5569
38.7%
Common
ValueCountFrequency (%)
4370
39.5%
1 1094
 
9.9%
( 803
 
7.3%
) 803
 
7.3%
, 798
 
7.2%
2 735
 
6.6%
3 487
 
4.4%
4 403
 
3.6%
0 325
 
2.9%
5 293
 
2.7%
Other values (7) 942
 
8.5%
Latin
ValueCountFrequency (%)
B 29
65.9%
A 4
 
9.1%
S 3
 
6.8%
K 3
 
6.8%
L 1
 
2.3%
W 1
 
2.3%
J 1
 
2.3%
M 1
 
2.3%
C 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14402
56.5%
ASCII 11097
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4370
39.4%
1 1094
 
9.9%
( 803
 
7.2%
) 803
 
7.2%
, 798
 
7.2%
2 735
 
6.6%
3 487
 
4.4%
4 403
 
3.6%
0 325
 
2.9%
5 293
 
2.6%
Other values (16) 986
 
8.9%
Hangul
ValueCountFrequency (%)
1614
 
11.2%
1122
 
7.8%
817
 
5.7%
814
 
5.7%
810
 
5.6%
802
 
5.6%
799
 
5.5%
799
 
5.5%
697
 
4.8%
559
 
3.9%
Other values (208) 5569
38.7%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct145
Distinct (%)18.3%
Missing672
Missing (%)45.8%
Infinite0
Infinite (%)0.0%
Mean3108.5491
Minimum3004
Maximum3198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:28.211266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3004
5-th percentile3013
Q13073
median3109
Q33158
95-th percentile3190
Maximum3198
Range194
Interquartile range (IQR)85

Descriptive statistics

Standard deviation55.463888
Coefficient of variation (CV)0.017842371
Kurtosis-1.0794634
Mean3108.5491
Median Absolute Deviation (MAD)48
Skewness-0.14174067
Sum2468188
Variance3076.2429
MonotonicityNot monotonic
2024-04-06T22:41:28.339768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3157 39
 
2.7%
3174 27
 
1.8%
3190 21
 
1.4%
3189 19
 
1.3%
3077 17
 
1.2%
3041 17
 
1.2%
3073 16
 
1.1%
3040 15
 
1.0%
3168 15
 
1.0%
3113 13
 
0.9%
Other values (135) 595
40.6%
(Missing) 672
45.8%
ValueCountFrequency (%)
3004 2
 
0.1%
3006 2
 
0.1%
3007 2
 
0.1%
3008 11
0.8%
3009 6
0.4%
3010 6
0.4%
3011 2
 
0.1%
3012 8
0.5%
3013 6
0.4%
3014 2
 
0.1%
ValueCountFrequency (%)
3198 5
 
0.3%
3197 8
 
0.5%
3196 2
 
0.1%
3195 5
 
0.3%
3191 5
 
0.3%
3190 21
1.4%
3189 19
1.3%
3188 3
 
0.2%
3187 2
 
0.1%
3186 2
 
0.1%
Distinct1314
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
2024-04-06T22:41:28.624292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length28
Mean length5.3963165
Min length1

Characters and Unicode

Total characters7911
Distinct characters569
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1197 ?
Unique (%)81.7%

Sample

1st row목화
2nd row호산나
3rd row은하
4th row정원미용실
5th row나이스
ValueCountFrequency (%)
헤어 29
 
1.6%
미용실 22
 
1.2%
에스테틱 19
 
1.0%
네일 18
 
1.0%
hair 15
 
0.8%
헤어샵 14
 
0.8%
파랑머리 7
 
0.4%
7
 
0.4%
뷰티 7
 
0.4%
살롱 6
 
0.3%
Other values (1440) 1718
92.3%
2024-04-06T22:41:29.148582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
396
 
5.0%
364
 
4.6%
347
 
4.4%
273
 
3.5%
218
 
2.8%
198
 
2.5%
193
 
2.4%
190
 
2.4%
148
 
1.9%
109
 
1.4%
Other values (559) 5475
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6518
82.4%
Space Separator 396
 
5.0%
Lowercase Letter 388
 
4.9%
Uppercase Letter 344
 
4.3%
Open Punctuation 83
 
1.0%
Close Punctuation 83
 
1.0%
Other Punctuation 56
 
0.7%
Decimal Number 39
 
0.5%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
364
 
5.6%
347
 
5.3%
273
 
4.2%
218
 
3.3%
198
 
3.0%
193
 
3.0%
190
 
2.9%
148
 
2.3%
109
 
1.7%
105
 
1.6%
Other values (493) 4373
67.1%
Lowercase Letter
ValueCountFrequency (%)
a 54
13.9%
i 48
12.4%
r 35
9.0%
e 34
8.8%
o 29
 
7.5%
n 25
 
6.4%
s 23
 
5.9%
l 22
 
5.7%
h 20
 
5.2%
t 18
 
4.6%
Other values (15) 80
20.6%
Uppercase Letter
ValueCountFrequency (%)
A 49
 
14.2%
N 34
 
9.9%
I 26
 
7.6%
L 19
 
5.5%
S 17
 
4.9%
O 17
 
4.9%
H 16
 
4.7%
W 15
 
4.4%
P 14
 
4.1%
T 13
 
3.8%
Other values (15) 124
36.0%
Other Punctuation
ValueCountFrequency (%)
& 18
32.1%
? 13
23.2%
. 12
21.4%
, 9
16.1%
' 2
 
3.6%
# 2
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 10
25.6%
0 10
25.6%
3 7
17.9%
2 7
17.9%
5 4
 
10.3%
9 1
 
2.6%
Space Separator
ValueCountFrequency (%)
396
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6510
82.3%
Latin 732
 
9.3%
Common 661
 
8.4%
Han 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
364
 
5.6%
347
 
5.3%
273
 
4.2%
218
 
3.3%
198
 
3.0%
193
 
3.0%
190
 
2.9%
148
 
2.3%
109
 
1.7%
105
 
1.6%
Other values (486) 4365
67.1%
Latin
ValueCountFrequency (%)
a 54
 
7.4%
A 49
 
6.7%
i 48
 
6.6%
r 35
 
4.8%
N 34
 
4.6%
e 34
 
4.6%
o 29
 
4.0%
I 26
 
3.6%
n 25
 
3.4%
s 23
 
3.1%
Other values (40) 375
51.2%
Common
ValueCountFrequency (%)
396
59.9%
( 83
 
12.6%
) 83
 
12.6%
& 18
 
2.7%
? 13
 
2.0%
. 12
 
1.8%
1 10
 
1.5%
0 10
 
1.5%
, 9
 
1.4%
3 7
 
1.1%
Other values (6) 20
 
3.0%
Han
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6510
82.3%
ASCII 1393
 
17.6%
CJK 8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
396
28.4%
( 83
 
6.0%
) 83
 
6.0%
a 54
 
3.9%
A 49
 
3.5%
i 48
 
3.4%
r 35
 
2.5%
N 34
 
2.4%
e 34
 
2.4%
o 29
 
2.1%
Other values (56) 548
39.3%
Hangul
ValueCountFrequency (%)
364
 
5.6%
347
 
5.3%
273
 
4.2%
218
 
3.3%
198
 
3.0%
193
 
3.0%
190
 
2.9%
148
 
2.3%
109
 
1.7%
105
 
1.6%
Other values (486) 4365
67.1%
CJK
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Distinct1003
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Minimum1999-02-01 00:00:00
Maximum2024-04-04 14:41:50
2024-04-06T22:41:29.273164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:41:29.395898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
I
1165 
U
299 
D
 
2

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 1165
79.5%
U 299
 
20.4%
D 2
 
0.1%

Length

2024-04-06T22:41:29.515949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:29.600953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 1165
79.5%
u 299
 
20.4%
d 2
 
0.1%
Distinct360
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-04 00:06:00
2024-04-06T22:41:29.697247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T22:41:29.813238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
일반미용업
1088 
피부미용업
211 
네일아트업
 
95
메이크업업
 
37
기타
 
34

Length

Max length6
Median length5
Mean length4.931105
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 1088
74.2%
피부미용업 211
 
14.4%
네일아트업 95
 
6.5%
메이크업업 37
 
2.5%
기타 34
 
2.3%
미용업 기타 1
 
0.1%

Length

2024-04-06T22:41:29.934202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:30.040143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 1088
74.2%
피부미용업 211
 
14.4%
네일아트업 95
 
6.5%
메이크업업 37
 
2.5%
기타 35
 
2.4%
미용업 1
 
0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct891
Distinct (%)64.7%
Missing88
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean199012.47
Minimum195102.94
Maximum201960.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:30.159355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum195102.94
5-th percentile196435.31
Q1197445.45
median198955.39
Q3200483.75
95-th percentile201457.46
Maximum201960.95
Range6858.0141
Interquartile range (IQR)3038.2979

Descriptive statistics

Standard deviation1699.3704
Coefficient of variation (CV)0.0085390144
Kurtosis-1.2742762
Mean199012.47
Median Absolute Deviation (MAD)1516.8936
Skewness-0.0094132326
Sum2.7423919 × 108
Variance2887859.6
MonotonicityNot monotonic
2024-04-06T22:41:30.308273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198150.300374121 39
 
2.7%
197465.223921125 16
 
1.1%
197181.393301659 14
 
1.0%
200121.813720325 9
 
0.6%
197567.849954354 9
 
0.6%
196467.975088253 8
 
0.5%
201350.473375538 7
 
0.5%
200904.097306757 6
 
0.4%
198958.283792369 6
 
0.4%
197662.503910553 6
 
0.4%
Other values (881) 1258
85.8%
(Missing) 88
 
6.0%
ValueCountFrequency (%)
195102.937172367 1
 
0.1%
196036.761890225 3
0.2%
196065.157232057 1
 
0.1%
196092.743907822 5
0.3%
196099.306839597 2
 
0.1%
196112.95679095 1
 
0.1%
196116.651717549 2
 
0.1%
196130.554331044 3
0.2%
196132.751817734 1
 
0.1%
196155.334966507 1
 
0.1%
ValueCountFrequency (%)
201960.951300031 1
 
0.1%
201958.358743407 1
 
0.1%
201949.750623535 1
 
0.1%
201937.864355117 1
 
0.1%
201937.460305076 1
 
0.1%
201915.280860616 1
 
0.1%
201911.136750205 1
 
0.1%
201908.102083278 1
 
0.1%
201905.282909761 3
0.2%
201894.498459239 2
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct891
Distinct (%)64.7%
Missing88
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean452925.05
Minimum449607.85
Maximum456677.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:30.456797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum449607.85
5-th percentile451951.4
Q1452227.47
median452549.63
Q3453201.98
95-th percentile455922.45
Maximum456677.53
Range7069.6792
Interquartile range (IQR)974.50995

Descriptive statistics

Standard deviation1096.7644
Coefficient of variation (CV)0.0024215141
Kurtosis2.674096
Mean452925.05
Median Absolute Deviation (MAD)447.18363
Skewness1.7779271
Sum6.2413071 × 108
Variance1202892.1
MonotonicityNot monotonic
2024-04-06T22:41:30.611541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452019.212642931 39
 
2.7%
452366.613899898 16
 
1.1%
452458.826651573 14
 
1.0%
453518.007319338 9
 
0.6%
452567.159554494 9
 
0.6%
452566.134030104 8
 
0.5%
452279.7442603 7
 
0.5%
452258.945717944 6
 
0.4%
451941.721546298 6
 
0.4%
452189.09236786 6
 
0.4%
Other values (881) 1258
85.8%
(Missing) 88
 
6.0%
ValueCountFrequency (%)
449607.851240468 1
0.1%
451691.194358665 1
0.1%
451736.38443886 1
0.1%
451785.296574342 1
0.1%
451786.479083595 1
0.1%
451805.0 1
0.1%
451809.570431229 1
0.1%
451813.489945391 2
0.1%
451820.889548579 1
0.1%
451830.894858452 1
0.1%
ValueCountFrequency (%)
456677.530404469 1
 
0.1%
456604.764554737 1
 
0.1%
456582.534430668 2
0.1%
456579.266145313 1
 
0.1%
456510.847272901 3
0.2%
456478.436790904 4
0.3%
456382.613783585 1
 
0.1%
456364.152893731 3
0.2%
456309.265884524 1
 
0.1%
456292.062675988 1
 
0.1%

위생업태명
Categorical

Distinct14
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
미용업
761 
일반미용업
223 
<NA>
188 
피부미용업
146 
종합미용업
 
61
Other values (9)
87 

Length

Max length23
Median length3
Mean length4.1507503
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 761
51.9%
일반미용업 223
 
15.2%
<NA> 188
 
12.8%
피부미용업 146
 
10.0%
종합미용업 61
 
4.2%
네일미용업 40
 
2.7%
화장ㆍ분장 미용업 11
 
0.8%
네일미용업, 화장ㆍ분장 미용업 7
 
0.5%
일반미용업, 네일미용업 6
 
0.4%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 6
 
0.4%
Other values (4) 17
 
1.2%

Length

2024-04-06T22:41:30.776386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 795
51.4%
일반미용업 242
 
15.6%
na 188
 
12.2%
피부미용업 158
 
10.2%
네일미용업 69
 
4.5%
종합미용업 61
 
3.9%
화장ㆍ분장 34
 
2.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)1.9%
Missing558
Missing (%)38.1%
Infinite0
Infinite (%)0.0%
Mean0.66409692
Minimum0
Maximum24
Zeros733
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:30.902716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9323023
Coefficient of variation (CV)2.9096692
Kurtosis41.315737
Mean0.66409692
Median Absolute Deviation (MAD)0
Skewness5.3349379
Sum603
Variance3.7337922
MonotonicityNot monotonic
2024-04-06T22:41:31.016659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 733
50.0%
2 56
 
3.8%
1 33
 
2.3%
4 30
 
2.0%
3 25
 
1.7%
5 9
 
0.6%
6 5
 
0.3%
8 5
 
0.3%
9 3
 
0.2%
7 2
 
0.1%
Other values (7) 7
 
0.5%
(Missing) 558
38.1%
ValueCountFrequency (%)
0 733
50.0%
1 33
 
2.3%
2 56
 
3.8%
3 25
 
1.7%
4 30
 
2.0%
5 9
 
0.6%
6 5
 
0.3%
7 2
 
0.1%
8 5
 
0.3%
9 3
 
0.2%
ValueCountFrequency (%)
24 1
 
0.1%
17 1
 
0.1%
16 1
 
0.1%
15 1
 
0.1%
13 1
 
0.1%
12 1
 
0.1%
10 1
 
0.1%
9 3
0.2%
8 5
0.3%
7 2
 
0.1%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.9%
Missing618
Missing (%)42.2%
Infinite0
Infinite (%)0.0%
Mean0.10259434
Minimum0
Maximum7
Zeros797
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:31.128882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.54336645
Coefficient of variation (CV)5.2962615
Kurtosis89.266606
Mean0.10259434
Median Absolute Deviation (MAD)0
Skewness8.5209711
Sum87
Variance0.2952471
MonotonicityNot monotonic
2024-04-06T22:41:31.238199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 797
54.4%
1 35
 
2.4%
2 10
 
0.7%
7 2
 
0.1%
6 1
 
0.1%
3 1
 
0.1%
5 1
 
0.1%
4 1
 
0.1%
(Missing) 618
42.2%
ValueCountFrequency (%)
0 797
54.4%
1 35
 
2.4%
2 10
 
0.7%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 2
 
0.1%
ValueCountFrequency (%)
7 2
 
0.1%
6 1
 
0.1%
5 1
 
0.1%
4 1
 
0.1%
3 1
 
0.1%
2 10
 
0.7%
1 35
 
2.4%
0 797
54.4%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)1.8%
Missing750
Missing (%)51.2%
Infinite0
Infinite (%)0.0%
Mean0.97486034
Minimum0
Maximum17
Zeros397
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:31.350722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum17
Range17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6756368
Coefficient of variation (CV)1.7188481
Kurtosis22.753543
Mean0.97486034
Median Absolute Deviation (MAD)0
Skewness3.7422354
Sum698
Variance2.8077587
MonotonicityNot monotonic
2024-04-06T22:41:31.465612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 397
27.1%
1 145
 
9.9%
2 88
 
6.0%
3 43
 
2.9%
4 21
 
1.4%
5 11
 
0.8%
9 3
 
0.2%
8 2
 
0.1%
6 2
 
0.1%
11 1
 
0.1%
Other values (3) 3
 
0.2%
(Missing) 750
51.2%
ValueCountFrequency (%)
0 397
27.1%
1 145
 
9.9%
2 88
 
6.0%
3 43
 
2.9%
4 21
 
1.4%
5 11
 
0.8%
6 2
 
0.1%
8 2
 
0.1%
9 3
 
0.2%
11 1
 
0.1%
ValueCountFrequency (%)
17 1
 
0.1%
14 1
 
0.1%
12 1
 
0.1%
11 1
 
0.1%
9 3
 
0.2%
8 2
 
0.1%
6 2
 
0.1%
5 11
 
0.8%
4 21
1.4%
3 43
2.9%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)3.7%
Missing1092
Missing (%)74.5%
Infinite0
Infinite (%)0.0%
Mean2
Minimum0
Maximum102
Zeros83
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:31.584870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum102
Range102
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.5327444
Coefficient of variation (CV)2.7663722
Kurtosis288.00879
Mean2
Median Absolute Deviation (MAD)1
Skewness16.038373
Sum748
Variance30.61126
MonotonicityNot monotonic
2024-04-06T22:41:31.702757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 123
 
8.4%
2 88
 
6.0%
0 83
 
5.7%
3 40
 
2.7%
4 18
 
1.2%
5 11
 
0.8%
8 2
 
0.1%
9 2
 
0.1%
6 2
 
0.1%
11 1
 
0.1%
Other values (4) 4
 
0.3%
(Missing) 1092
74.5%
ValueCountFrequency (%)
0 83
5.7%
1 123
8.4%
2 88
6.0%
3 40
 
2.7%
4 18
 
1.2%
5 11
 
0.8%
6 2
 
0.1%
8 2
 
0.1%
9 2
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
102 1
 
0.1%
17 1
 
0.1%
14 1
 
0.1%
12 1
 
0.1%
11 1
 
0.1%
9 2
 
0.1%
8 2
 
0.1%
6 2
 
0.1%
5 11
0.8%
4 18
1.2%

사용시작지하층
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
<NA>
991 
0
431 
1
 
40
2
 
3
3
 
1

Length

Max length4
Median length4
Mean length3.0279673
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 991
67.6%
0 431
29.4%
1 40
 
2.7%
2 3
 
0.2%
3 1
 
0.1%

Length

2024-04-06T22:41:31.817725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:31.912592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 991
67.6%
0 431
29.4%
1 40
 
2.7%
2 3
 
0.2%
3 1
 
0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
<NA>
1314 
0
 
108
1
 
39
2
 
3
23
 
1

Length

Max length4
Median length4
Mean length3.6896317
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1314
89.6%
0 108
 
7.4%
1 39
 
2.7%
2 3
 
0.2%
23 1
 
0.1%
3 1
 
0.1%

Length

2024-04-06T22:41:32.012114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:32.127767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1314
89.6%
0 108
 
7.4%
1 39
 
2.7%
2 3
 
0.2%
23 1
 
0.1%
3 1
 
0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
819 
<NA>
647 

Length

Max length4
Median length1
Mean length2.3240109
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 819
55.9%
<NA> 647
44.1%

Length

2024-04-06T22:41:32.227042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:32.313740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 819
55.9%
na 647
44.1%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
819 
<NA>
647 

Length

Max length4
Median length1
Mean length2.3240109
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 819
55.9%
<NA> 647
44.1%

Length

2024-04-06T22:41:32.409596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:32.497954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 819
55.9%
na 647
44.1%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
819 
<NA>
647 

Length

Max length4
Median length1
Mean length2.3240109
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 819
55.9%
<NA> 647
44.1%

Length

2024-04-06T22:41:32.587877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:32.674561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 819
55.9%
na 647
44.1%

발한실여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing201
Missing (%)13.7%
Memory size3.0 KiB
False
1259 
True
 
6
(Missing)
201 
ValueCountFrequency (%)
False 1259
85.9%
True 6
 
0.4%
(Missing) 201
 
13.7%
2024-04-06T22:41:32.749386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)1.6%
Missing216
Missing (%)14.7%
Infinite0
Infinite (%)0.0%
Mean3.5488
Minimum0
Maximum26
Zeros115
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:32.831221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5802606
Coefficient of variation (CV)0.72707974
Kurtosis11.401808
Mean3.5488
Median Absolute Deviation (MAD)1
Skewness2.4057037
Sum4436
Variance6.6577448
MonotonicityNot monotonic
2024-04-06T22:41:32.929454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 388
26.5%
2 224
15.3%
4 206
14.1%
0 115
 
7.8%
5 102
 
7.0%
6 79
 
5.4%
1 38
 
2.6%
7 25
 
1.7%
8 22
 
1.5%
10 16
 
1.1%
Other values (10) 35
 
2.4%
(Missing) 216
14.7%
ValueCountFrequency (%)
0 115
 
7.8%
1 38
 
2.6%
2 224
15.3%
3 388
26.5%
4 206
14.1%
5 102
 
7.0%
6 79
 
5.4%
7 25
 
1.7%
8 22
 
1.5%
9 5
 
0.3%
ValueCountFrequency (%)
26 1
 
0.1%
20 2
 
0.1%
19 2
 
0.1%
16 2
 
0.1%
15 2
 
0.1%
14 4
 
0.3%
13 2
 
0.1%
12 9
0.6%
11 6
 
0.4%
10 16
1.1%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1466
Missing (%)100.0%
Memory size13.0 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1466
Missing (%)100.0%
Memory size13.0 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1466
Missing (%)100.0%
Memory size13.0 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
<NA>
1206 
임대
249 
자가
 
11

Length

Max length4
Median length4
Mean length3.6452933
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1206
82.3%
임대 249
 
17.0%
자가 11
 
0.8%

Length

2024-04-06T22:41:33.293382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:33.387265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1206
82.3%
임대 249
 
17.0%
자가 11
 
0.8%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
<NA>
970 
0
496 

Length

Max length4
Median length4
Mean length2.9849932
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> 970
66.2%
0 496
33.8%

Length

2024-04-06T22:41:33.489069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:33.581641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 970
66.2%
0 496
33.8%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
<NA>
1207 
0
248 
1
 
7
2
 
2
8
 
1

Length

Max length4
Median length4
Mean length3.4699864
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1207
82.3%
0 248
 
16.9%
1 7
 
0.5%
2 2
 
0.1%
8 1
 
0.1%
3 1
 
0.1%

Length

2024-04-06T22:41:33.675405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:33.770680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1207
82.3%
0 248
 
16.9%
1 7
 
0.5%
2 2
 
0.1%
8 1
 
0.1%
3 1
 
0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
<NA>
1208 
0
254 
1
 
3
2
 
1

Length

Max length4
Median length4
Mean length3.4720327
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1208
82.4%
0 254
 
17.3%
1 3
 
0.2%
2 1
 
0.1%

Length

2024-04-06T22:41:33.876700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:33.967092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1208
82.4%
0 254
 
17.3%
1 3
 
0.2%
2 1
 
0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
<NA>
1001 
0
465 

Length

Max length4
Median length4
Mean length3.0484311
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> 1001
68.3%
0 465
31.7%

Length

2024-04-06T22:41:34.064210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T22:41:34.152108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1001
68.3%
0 465
31.7%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)3.0%
Missing1004
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean1.5562771
Minimum0
Maximum20
Zeros266
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size13.0 KiB
2024-04-06T22:41:34.235459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6453564
Coefficient of variation (CV)1.6997979
Kurtosis7.9155507
Mean1.5562771
Median Absolute Deviation (MAD)0
Skewness2.4554057
Sum719
Variance6.9979106
MonotonicityNot monotonic
2024-04-06T22:41:34.334373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 266
 
18.1%
1 46
 
3.1%
2 46
 
3.1%
4 27
 
1.8%
3 26
 
1.8%
6 13
 
0.9%
5 9
 
0.6%
9 9
 
0.6%
7 8
 
0.5%
11 5
 
0.3%
Other values (4) 7
 
0.5%
(Missing) 1004
68.5%
ValueCountFrequency (%)
0 266
18.1%
1 46
 
3.1%
2 46
 
3.1%
3 26
 
1.8%
4 27
 
1.8%
5 9
 
0.6%
6 13
 
0.9%
7 8
 
0.5%
8 3
 
0.2%
9 9
 
0.6%
ValueCountFrequency (%)
20 1
 
0.1%
13 2
 
0.1%
11 5
 
0.3%
10 1
 
0.1%
9 9
 
0.6%
8 3
 
0.2%
7 8
 
0.5%
6 13
0.9%
5 9
 
0.6%
4 27
1.8%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing188
Missing (%)12.8%
Memory size3.0 KiB
False
1276 
True
 
2
(Missing)
188 
ValueCountFrequency (%)
False 1276
87.0%
True 2
 
0.1%
(Missing) 188
 
12.8%
2024-04-06T22:41:34.428944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030000003000000-204-1963-0084519630530<NA>3폐업2폐업19990804<NA><NA><NA>020762861118.90110850서울특별시 종로구 효제동 30-1번지<NA><NA>목화2001-11-20 00:00:00I2018-08-31 23:59:59.0일반미용업200130.508679452463.048466미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130000003000000-204-1963-0086219630606<NA>3폐업2폐업20040816<NA><NA><NA>02 763485034.36110855서울특별시 종로구 창신동 561-1번지<NA><NA>호산나2003-11-04 00:00:00I2018-08-31 23:59:59.0일반미용업200863.919391452188.262849미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230000003000000-204-1963-0087319630530<NA>3폐업2폐업19970421<NA><NA><NA>02 763505014.70110522서울특별시 종로구 명륜2가 73-0번지<NA><NA>은하2001-09-29 00:00:00I2018-08-31 23:59:59.0일반미용업199665.455258453680.142833미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330000003000000-204-1963-0110519630530<NA>3폐업2폐업20060217<NA><NA><NA>02 738757414.12110044서울특별시 종로구 필운동 276번지<NA><NA>정원미용실2004-06-02 00:00:00I2018-08-31 23:59:59.0일반미용업197311.19892452605.202432미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430000003000000-204-1965-0084119650302<NA>3폐업2폐업20050429<NA><NA><NA>022265094836.86110833서울특별시 종로구 예지동 296-1번지<NA><NA>나이스2003-02-28 00:00:00I2018-08-31 23:59:59.0일반미용업200021.977214451957.94858미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530000003000000-204-1966-0083019660105<NA>3폐업2폐업20021205<NA><NA><NA>02 735159817.92110070서울특별시 종로구 내수동 152-1번지<NA><NA>동아2002-12-05 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
630000003000000-204-1966-0083219661115<NA>3폐업2폐업19950712<NA><NA><NA>02 763045225.20110260서울특별시 종로구 가회동 64-1번지<NA><NA>도성2001-09-29 00:00:00I2018-08-31 23:59:59.0일반미용업198591.32576453230.01849미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
730000003000000-204-1966-0084219660110<NA>3폐업2폐업19981115<NA><NA><NA>02 762104323.00110530서울특별시 종로구 혜화동 108-1번지<NA><NA>세레나2001-09-29 00:00:00I2018-08-31 23:59:59.0일반미용업200009.198856453752.88467미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
830000003000000-204-1967-0083819670417<NA>1영업/정상1영업<NA><NA><NA><NA>022266629423.14110836서울특별시 종로구 종로5가 417-2번지서울특별시 종로구 종로40가길 7-4 (종로5가)3197희연2017-07-05 15:33:10I2018-08-31 23:59:59.0일반미용업200489.567671451980.178327미용업2111<NA><NA><NA><NA><NA>N4<NA><NA><NA>임대<NA><NA><NA><NA><NA>N
930000003000000-204-1967-0084619671019<NA>3폐업2폐업20081029<NA><NA><NA>02 763945556.00110410서울특별시 종로구 인의동 79-1번지<NA><NA>삼성2003-11-04 00:00:00I2018-08-31 23:59:59.0일반미용업199693.474085452394.669451미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
145630000003000000-225-2021-000012021-02-25<NA>1영업/정상1영업<NA><NA><NA><NA><NA>99.00110-053서울특별시 종로구 내자동 167-2 인왕빌딩서울특별시 종로구 사직로10길 17, 인왕빌딩 1,2층 (내자동)3169YAAD 야드2023-08-03 10:20:32U2022-12-08 00:05:00.0일반미용업197336.583985452534.336254<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
145730000003000000-225-2021-000022021-03-18<NA>1영업/정상1영업<NA><NA><NA><NA><NA>32.76110-841서울특별시 종로구 창신동 366서울특별시 종로구 종로 316-1, 2층 (창신동)3121란 헤어2024-03-05 14:58:49U2023-12-03 00:07:00.0일반미용업201077.908698452195.434878<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
145830000003000000-225-2021-0000320210419<NA>1영업/정상1영업<NA><NA><NA><NA><NA>81.29110837서울특별시 종로구 창신동 23-53서울특별시 종로구 지봉로 77-4, 2층 (창신동)3095헤어, 그대와2021-04-19 13:20:32I2021-04-21 00:22:58.0일반미용업201269.216313452753.591547일반미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N3<NA><NA><NA><NA>00000N
145930000003000000-225-2021-0000420210630<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.16110862서울특별시 종로구 숭인동 63-2서울특별시 종로구 지봉로8길 1-32, 1층 (숭인동)3109르솜 살롱2021-08-23 15:55:49U2021-08-25 02:40:00.0일반미용업201333.531619452629.81181일반미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N3<NA><NA><NA><NA>00001N
146030000003000000-225-2022-0000120221102<NA>1영업/정상1영업<NA><NA><NA><NA><NA>118.35110092서울특별시 종로구 홍파동 199 경희궁자이 2단지서울특별시 종로구 송월길 99, 상가동 1층 2152~2155호 (홍파동, 경희궁자이 2단지)3165박승철헤어스투디오 서대문역점2022-11-02 13:42:44I2021-11-01 00:05:00.0일반미용업196790.337498452053.372161<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
146130000003000000-226-2015-0000120150914<NA>1영업/정상1영업<NA><NA><NA><NA>0263885250660.31110071서울특별시 종로구 당주동 29서울특별시 종로구 새문안로 97, 9층 (당주동, 포시즌스호텔)3183더스파앳더포시즌스2021-02-05 11:03:08I2021-02-07 00:23:02.0메이크업업197764.0452030.0피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N5<NA><NA><NA>임대00008N
146230000003000000-226-2017-0000120170907<NA>1영업/정상1영업<NA><NA><NA><NA>02 762 072756.20110522서울특별시 종로구 명륜2가 176번지서울특별시 종로구 창경궁로 248-4, 2층 (명륜2가)3077와이키키2017-11-07 14:10:54I2018-08-31 23:59:59.0네일아트업199897.272384453457.758253피부미용업, 네일미용업, 화장ㆍ분장 미용업0022<NA><NA>000N1<NA><NA><NA>임대00001N
146330000003000000-226-2019-0000120190403<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.02110813서울특별시 종로구 무악동 46번지 경희궁 롯데캐슬 상가 지하1층 113호서울특별시 종로구 통일로 230, 상가 지하1층 113호 (무악동, 경희궁 롯데캐슬)3030요요살롱2019-04-03 10:53:28I2019-04-05 02:20:28.0네일아트업196392.222414452339.443피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA>11000N5<NA><NA><NA><NA>00001N
146430000003000000-226-2020-0000120200601<NA>1영업/정상1영업<NA><NA><NA><NA><NA>14.88110872서울특별시 종로구 내수동 72번지 경희궁의아침 3단지서울특별시 종로구 사직로8길 34, 경희궁의아침 3단지 163,164호 (내수동)3174You are beautiful2020-06-01 14:03:30I2020-06-04 00:23:18.0네일아트업197465.223921452366.6139피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N5<NA><NA><NA><NA>00001N
146530000003000000-226-2020-0000220200602<NA>1영업/정상1영업<NA><NA><NA><NA><NA>14.88110872서울특별시 종로구 내수동 72번지 경희궁의아침 3단지서울특별시 종로구 사직로8길 34, 경희궁의아침 3단지 165,166호 (내수동)3174너 참, 예쁘다2020-06-02 15:13:39I2020-06-04 00:23:18.0메이크업업197465.223921452366.6139피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N5<NA><NA><NA><NA>00001N