Overview

Dataset statistics

Number of variables47
Number of observations1966
Missing cells19880
Missing cells (%)21.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory777.7 KiB
Average record size in memory405.1 B

Variable types

Categorical18
Text6
DateTime4
Unsupported7
Numeric10
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
Author중구
URLhttps://data.seoul.go.kr/dataList/OA-17903/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
데이터갱신구분 is highly imbalanced (54.0%)Imbalance
사용시작지하층 is highly imbalanced (67.6%)Imbalance
사용끝지하층 is highly imbalanced (67.8%)Imbalance
여성종사자수 is highly imbalanced (77.2%)Imbalance
남성종사자수 is highly imbalanced (64.3%)Imbalance
다중이용업소여부 is highly imbalanced (98.7%)Imbalance
인허가취소일자 has 1966 (100.0%) missing valuesMissing
폐업일자 has 612 (31.1%) missing valuesMissing
휴업시작일자 has 1966 (100.0%) missing valuesMissing
휴업종료일자 has 1966 (100.0%) missing valuesMissing
재개업일자 has 1966 (100.0%) missing valuesMissing
전화번호 has 573 (29.1%) missing valuesMissing
도로명주소 has 829 (42.2%) missing valuesMissing
도로명우편번호 has 839 (42.7%) missing valuesMissing
좌표정보(X) has 596 (30.3%) missing valuesMissing
좌표정보(Y) has 596 (30.3%) missing valuesMissing
건물지상층수 has 257 (13.1%) missing valuesMissing
건물지하층수 has 257 (13.1%) missing valuesMissing
사용시작지상층 has 257 (13.1%) missing valuesMissing
사용끝지상층 has 257 (13.1%) missing valuesMissing
발한실여부 has 274 (13.9%) missing valuesMissing
좌석수 has 257 (13.1%) missing valuesMissing
조건부허가신고사유 has 1966 (100.0%) missing valuesMissing
조건부허가시작일자 has 1966 (100.0%) missing valuesMissing
조건부허가종료일자 has 1966 (100.0%) missing valuesMissing
침대수 has 257 (13.1%) missing valuesMissing
다중이용업소여부 has 257 (13.1%) 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
소재지면적 has 219 (11.1%) zerosZeros
건물지상층수 has 1597 (81.2%) zerosZeros
건물지하층수 has 1672 (85.0%) zerosZeros
사용시작지상층 has 988 (50.3%) zerosZeros
사용끝지상층 has 1043 (53.1%) zerosZeros
좌석수 has 422 (21.5%) zerosZeros
침대수 has 1409 (71.7%) zerosZeros

Reproduction

Analysis started2024-05-11 05:44:43.006960
Analysis finished2024-05-11 05:44:45.043084
Duration2.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
3010000
1966 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3010000
2nd row3010000
3rd row3010000
4th row3010000
5th row3010000

Common Values

ValueCountFrequency (%)
3010000 1966
100.0%

Length

2024-05-11T14:44:45.130093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:45.247571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3010000 1966
100.0%

관리번호
Text

UNIQUE 

Distinct1966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2024-05-11T14:44:45.538974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters43252
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

Unique1966 ?
Unique (%)100.0%

Sample

1st row3010000-204-1911-00395
2nd row3010000-204-1961-00857
3rd row3010000-204-1962-00897
4th row3010000-204-1964-00728
5th row3010000-204-1964-00775
ValueCountFrequency (%)
3010000-204-1911-00395 1
 
0.1%
3010000-212-2009-00018 1
 
0.1%
3010000-212-2009-00016 1
 
0.1%
3010000-212-2009-00015 1
 
0.1%
3010000-212-2009-00014 1
 
0.1%
3010000-212-2009-00013 1
 
0.1%
3010000-212-2009-00012 1
 
0.1%
3010000-212-2009-00011 1
 
0.1%
3010000-212-2009-00010 1
 
0.1%
3010000-212-2009-00009 1
 
0.1%
Other values (1956) 1956
99.5%
2024-05-11T14:44:45.981021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18706
43.2%
- 5898
 
13.6%
1 5894
 
13.6%
2 4569
 
10.6%
3 2752
 
6.4%
4 1468
 
3.4%
9 1443
 
3.3%
8 765
 
1.8%
5 687
 
1.6%
7 556
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37354
86.4%
Dash Punctuation 5898
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18706
50.1%
1 5894
 
15.8%
2 4569
 
12.2%
3 2752
 
7.4%
4 1468
 
3.9%
9 1443
 
3.9%
8 765
 
2.0%
5 687
 
1.8%
7 556
 
1.5%
6 514
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 5898
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18706
43.2%
- 5898
 
13.6%
1 5894
 
13.6%
2 4569
 
10.6%
3 2752
 
6.4%
4 1468
 
3.4%
9 1443
 
3.3%
8 765
 
1.8%
5 687
 
1.6%
7 556
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18706
43.2%
- 5898
 
13.6%
1 5894
 
13.6%
2 4569
 
10.6%
3 2752
 
6.4%
4 1468
 
3.4%
9 1443
 
3.3%
8 765
 
1.8%
5 687
 
1.6%
7 556
 
1.3%
Distinct1598
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
Minimum1911-11-11 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T14:44:46.200659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:44:46.418944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1966
Missing (%)100.0%
Memory size17.4 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
3
1354 
1
612 

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 1354
68.9%
1 612
31.1%

Length

2024-05-11T14:44:46.629806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:46.762615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1354
68.9%
1 612
31.1%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
폐업
1354 
영업/정상
612 

Length

Max length5
Median length2
Mean length2.9338759
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1354
68.9%
영업/정상 612
31.1%

Length

2024-05-11T14:44:46.911554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:47.053352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1354
68.9%
영업/정상 612
31.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2
1354 
1
612 

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 1354
68.9%
1 612
31.1%

Length

2024-05-11T14:44:47.210998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:47.366389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1354
68.9%
1 612
31.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
폐업
1354 
영업
612 

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 1354
68.9%
영업 612
31.1%

Length

2024-05-11T14:44:47.508739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:47.649905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1354
68.9%
영업 612
31.1%

폐업일자
Date

MISSING 

Distinct1081
Distinct (%)79.8%
Missing612
Missing (%)31.1%
Memory size15.5 KiB
Minimum1992-04-09 00:00:00
Maximum2024-04-24 00:00:00
2024-05-11T14:44:47.859739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:44:48.085666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1966
Missing (%)100.0%
Memory size17.4 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1966
Missing (%)100.0%
Memory size17.4 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1966
Missing (%)100.0%
Memory size17.4 KiB

전화번호
Text

MISSING 

Distinct1194
Distinct (%)85.7%
Missing573
Missing (%)29.1%
Memory size15.5 KiB
2024-05-11T14:44:48.494411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.400574
Min length1

Characters and Unicode

Total characters14488
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

Unique1115 ?
Unique (%)80.0%

Sample

1st row0200000000
2nd row02 00000
3rd row0207558626
4th row0222358680
5th row02 7763090
ValueCountFrequency (%)
02 777
33.1%
0200000000 51
 
2.2%
00000 42
 
1.8%
318 23
 
1.0%
070 16
 
0.7%
752 13
 
0.6%
777 12
 
0.5%
0 11
 
0.5%
776 9
 
0.4%
773 8
 
0.3%
Other values (1229) 1386
59.0%
2024-05-11T14:44:49.126932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3185
22.0%
0 2839
19.6%
7 1423
9.8%
1404
9.7%
3 1269
 
8.8%
5 934
 
6.4%
6 796
 
5.5%
8 772
 
5.3%
1 738
 
5.1%
9 567
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13084
90.3%
Space Separator 1404
 
9.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3185
24.3%
0 2839
21.7%
7 1423
10.9%
3 1269
 
9.7%
5 934
 
7.1%
6 796
 
6.1%
8 772
 
5.9%
1 738
 
5.6%
9 567
 
4.3%
4 561
 
4.3%
Space Separator
ValueCountFrequency (%)
1404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14488
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3185
22.0%
0 2839
19.6%
7 1423
9.8%
1404
9.7%
3 1269
 
8.8%
5 934
 
6.4%
6 796
 
5.5%
8 772
 
5.3%
1 738
 
5.1%
9 567
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14488
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3185
22.0%
0 2839
19.6%
7 1423
9.8%
1404
9.7%
3 1269
 
8.8%
5 934
 
6.4%
6 796
 
5.5%
8 772
 
5.3%
1 738
 
5.1%
9 567
 
3.9%

소재지면적
Real number (ℝ)

ZEROS 

Distinct950
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.044507
Minimum0
Maximum664.78
Zeros219
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:49.354514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119
median33
Q368.1125
95-th percentile188.24
Maximum664.78
Range664.78
Interquartile range (IQR)49.1125

Descriptive statistics

Standard deviation67.416599
Coefficient of variation (CV)1.2029118
Kurtosis13.837983
Mean56.044507
Median Absolute Deviation (MAD)19
Skewness3.0645135
Sum110183.5
Variance4544.9978
MonotonicityNot monotonic
2024-05-11T14:44:49.596371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 219
 
11.1%
33.0 71
 
3.6%
16.5 36
 
1.8%
23.1 30
 
1.5%
66.0 28
 
1.4%
49.5 27
 
1.4%
20.0 26
 
1.3%
19.8 25
 
1.3%
26.4 23
 
1.2%
13.2 18
 
0.9%
Other values (940) 1463
74.4%
ValueCountFrequency (%)
0.0 219
11.1%
3.0 1
 
0.1%
3.22 1
 
0.1%
3.3 5
 
0.3%
3.83 1
 
0.1%
3.84 4
 
0.2%
3.88 1
 
0.1%
4.64 1
 
0.1%
5.22 1
 
0.1%
6.0 1
 
0.1%
ValueCountFrequency (%)
664.78 1
0.1%
545.45 1
0.1%
537.0 2
0.1%
498.35 1
0.1%
495.87 1
0.1%
462.0 1
0.1%
457.71 1
0.1%
450.0 1
0.1%
429.0 1
0.1%
413.6 1
0.1%
Distinct216
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2024-05-11T14:44:50.087227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0859613
Min length6

Characters and Unicode

Total characters11965
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

Unique52 ?
Unique (%)2.6%

Sample

1st row100440
2nd row100450
3rd row100051
4th row100-450
5th row100011
ValueCountFrequency (%)
100450 281
 
14.3%
100011 79
 
4.0%
100810 61
 
3.1%
100861 58
 
3.0%
100022 56
 
2.8%
100440 54
 
2.7%
100851 45
 
2.3%
100819 44
 
2.2%
100070 37
 
1.9%
100012 32
 
1.6%
Other values (206) 1219
62.0%
2024-05-11T14:44:50.838499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5221
43.6%
1 2810
23.5%
8 970
 
8.1%
4 689
 
5.8%
5 539
 
4.5%
2 532
 
4.4%
9 301
 
2.5%
3 272
 
2.3%
6 256
 
2.1%
7 206
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11796
98.6%
Dash Punctuation 169
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5221
44.3%
1 2810
23.8%
8 970
 
8.2%
4 689
 
5.8%
5 539
 
4.6%
2 532
 
4.5%
9 301
 
2.6%
3 272
 
2.3%
6 256
 
2.2%
7 206
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 169
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11965
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5221
43.6%
1 2810
23.5%
8 970
 
8.1%
4 689
 
5.8%
5 539
 
4.5%
2 532
 
4.4%
9 301
 
2.5%
3 272
 
2.3%
6 256
 
2.1%
7 206
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5221
43.6%
1 2810
23.5%
8 970
 
8.1%
4 689
 
5.8%
5 539
 
4.5%
2 532
 
4.4%
9 301
 
2.5%
3 272
 
2.3%
6 256
 
2.1%
7 206
 
1.7%
Distinct1733
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2024-05-11T14:44:51.323006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length48
Mean length24.658698
Min length14

Characters and Unicode

Total characters48479
Distinct characters303
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1548 ?
Unique (%)78.7%

Sample

1st row서울특별시 중구 황학동 1560-0번지
2nd row서울특별시 중구 신당동 347-0번지
3rd row서울특별시 중구 회현동1가 7625-0번지
4th row서울특별시 중구 신당동 432-520
5th row서울특별시 중구 충무로1가 23-10번지
ValueCountFrequency (%)
서울특별시 1966
20.9%
중구 1966
20.9%
신당동 620
 
6.6%
1층 164
 
1.7%
명동2가 158
 
1.7%
2층 134
 
1.4%
3층 122
 
1.3%
충무로2가 120
 
1.3%
황학동 105
 
1.1%
을지로6가 103
 
1.1%
Other values (1839) 3934
41.9%
2024-05-11T14:44:52.039220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9061
18.7%
1 2205
 
4.5%
2035
 
4.2%
2011
 
4.1%
1988
 
4.1%
1984
 
4.1%
1967
 
4.1%
1967
 
4.1%
1966
 
4.1%
2 1914
 
3.9%
Other values (293) 21381
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27242
56.2%
Decimal Number 10169
 
21.0%
Space Separator 9061
 
18.7%
Dash Punctuation 1686
 
3.5%
Uppercase Letter 81
 
0.2%
Close Punctuation 78
 
0.2%
Open Punctuation 78
 
0.2%
Other Punctuation 47
 
0.1%
Lowercase Letter 28
 
0.1%
Math Symbol 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2035
 
7.5%
2011
 
7.4%
1988
 
7.3%
1984
 
7.3%
1967
 
7.2%
1967
 
7.2%
1966
 
7.2%
1813
 
6.7%
1557
 
5.7%
1550
 
5.7%
Other values (242) 8404
30.8%
Uppercase Letter
ValueCountFrequency (%)
B 19
23.5%
A 8
9.9%
S 8
9.9%
K 5
 
6.2%
L 5
 
6.2%
T 5
 
6.2%
E 5
 
6.2%
C 4
 
4.9%
P 4
 
4.9%
M 4
 
4.9%
Other values (10) 14
17.3%
Lowercase Letter
ValueCountFrequency (%)
e 6
21.4%
a 4
14.3%
c 3
10.7%
n 2
 
7.1%
m 2
 
7.1%
p 2
 
7.1%
r 2
 
7.1%
t 2
 
7.1%
b 2
 
7.1%
l 1
 
3.6%
Other values (2) 2
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 2205
21.7%
2 1914
18.8%
3 1257
12.4%
4 929
9.1%
5 828
 
8.1%
0 811
 
8.0%
6 719
 
7.1%
8 563
 
5.5%
7 518
 
5.1%
9 425
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 41
87.2%
@ 3
 
6.4%
. 2
 
4.3%
? 1
 
2.1%
Space Separator
ValueCountFrequency (%)
9061
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1686
100.0%
Close Punctuation
ValueCountFrequency (%)
) 78
100.0%
Open Punctuation
ValueCountFrequency (%)
( 78
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27242
56.2%
Common 21128
43.6%
Latin 109
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2035
 
7.5%
2011
 
7.4%
1988
 
7.3%
1984
 
7.3%
1967
 
7.2%
1967
 
7.2%
1966
 
7.2%
1813
 
6.7%
1557
 
5.7%
1550
 
5.7%
Other values (242) 8404
30.8%
Latin
ValueCountFrequency (%)
B 19
17.4%
A 8
 
7.3%
S 8
 
7.3%
e 6
 
5.5%
K 5
 
4.6%
L 5
 
4.6%
T 5
 
4.6%
E 5
 
4.6%
C 4
 
3.7%
P 4
 
3.7%
Other values (22) 40
36.7%
Common
ValueCountFrequency (%)
9061
42.9%
1 2205
 
10.4%
2 1914
 
9.1%
- 1686
 
8.0%
3 1257
 
5.9%
4 929
 
4.4%
5 828
 
3.9%
0 811
 
3.8%
6 719
 
3.4%
8 563
 
2.7%
Other values (9) 1155
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27242
56.2%
ASCII 21237
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9061
42.7%
1 2205
 
10.4%
2 1914
 
9.0%
- 1686
 
7.9%
3 1257
 
5.9%
4 929
 
4.4%
5 828
 
3.9%
0 811
 
3.8%
6 719
 
3.4%
8 563
 
2.7%
Other values (41) 1264
 
6.0%
Hangul
ValueCountFrequency (%)
2035
 
7.5%
2011
 
7.4%
1988
 
7.3%
1984
 
7.3%
1967
 
7.2%
1967
 
7.2%
1966
 
7.2%
1813
 
6.7%
1557
 
5.7%
1550
 
5.7%
Other values (242) 8404
30.8%

도로명주소
Text

MISSING 

Distinct1069
Distinct (%)94.0%
Missing829
Missing (%)42.2%
Memory size15.5 KiB
2024-05-11T14:44:52.523943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length54
Mean length31.914688
Min length20

Characters and Unicode

Total characters36287
Distinct characters308
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1008 ?
Unique (%)88.7%

Sample

1st row서울특별시 중구 동호로11아길 1 (신당동)
2nd row서울특별시 중구 퇴계로28길 14 (남학동,1층)
3rd row서울특별시 중구 다동길 21, 3층 (다동)
4th row서울특별시 중구 난계로13길 26 (황학동)
5th row서울특별시 중구 명동10길 31-1 (명동2가)
ValueCountFrequency (%)
서울특별시 1137
 
15.5%
중구 1137
 
15.5%
신당동 286
 
3.9%
1층 210
 
2.9%
2층 145
 
2.0%
3층 131
 
1.8%
퇴계로 102
 
1.4%
다산로 90
 
1.2%
명동2가 79
 
1.1%
을지로6가 71
 
1.0%
Other values (1174) 3924
53.7%
2024-05-11T14:44:53.070478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6176
 
17.0%
1 1501
 
4.1%
2 1237
 
3.4%
1226
 
3.4%
1211
 
3.3%
, 1208
 
3.3%
1203
 
3.3%
1196
 
3.3%
) 1180
 
3.3%
( 1180
 
3.3%
Other values (298) 18969
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19844
54.7%
Decimal Number 6373
 
17.6%
Space Separator 6176
 
17.0%
Other Punctuation 1211
 
3.3%
Close Punctuation 1180
 
3.3%
Open Punctuation 1180
 
3.3%
Dash Punctuation 203
 
0.6%
Uppercase Letter 88
 
0.2%
Lowercase Letter 21
 
0.1%
Math Symbol 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1226
 
6.2%
1211
 
6.1%
1203
 
6.1%
1196
 
6.0%
1169
 
5.9%
1159
 
5.8%
1158
 
5.8%
1138
 
5.7%
1138
 
5.7%
883
 
4.4%
Other values (249) 8363
42.1%
Uppercase Letter
ValueCountFrequency (%)
B 30
34.1%
S 8
 
9.1%
A 8
 
9.1%
K 4
 
4.5%
C 4
 
4.5%
E 4
 
4.5%
M 4
 
4.5%
T 4
 
4.5%
L 4
 
4.5%
I 3
 
3.4%
Other values (10) 15
17.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
23.8%
c 3
14.3%
a 2
 
9.5%
t 2
 
9.5%
n 2
 
9.5%
r 2
 
9.5%
k 1
 
4.8%
l 1
 
4.8%
p 1
 
4.8%
m 1
 
4.8%
Decimal Number
ValueCountFrequency (%)
1 1501
23.6%
2 1237
19.4%
3 753
11.8%
4 608
9.5%
0 597
 
9.4%
6 413
 
6.5%
5 366
 
5.7%
8 366
 
5.7%
7 326
 
5.1%
9 206
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 1208
99.8%
. 2
 
0.2%
@ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
6176
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1180
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1180
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 203
100.0%
Math Symbol
ValueCountFrequency (%)
~ 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19844
54.7%
Common 16334
45.0%
Latin 109
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1226
 
6.2%
1211
 
6.1%
1203
 
6.1%
1196
 
6.0%
1169
 
5.9%
1159
 
5.8%
1158
 
5.8%
1138
 
5.7%
1138
 
5.7%
883
 
4.4%
Other values (249) 8363
42.1%
Latin
ValueCountFrequency (%)
B 30
27.5%
S 8
 
7.3%
A 8
 
7.3%
e 5
 
4.6%
K 4
 
3.7%
C 4
 
3.7%
E 4
 
3.7%
M 4
 
3.7%
T 4
 
3.7%
L 4
 
3.7%
Other values (21) 34
31.2%
Common
ValueCountFrequency (%)
6176
37.8%
1 1501
 
9.2%
2 1237
 
7.6%
, 1208
 
7.4%
) 1180
 
7.2%
( 1180
 
7.2%
3 753
 
4.6%
4 608
 
3.7%
0 597
 
3.7%
6 413
 
2.5%
Other values (8) 1481
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19844
54.7%
ASCII 16443
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6176
37.6%
1 1501
 
9.1%
2 1237
 
7.5%
, 1208
 
7.3%
) 1180
 
7.2%
( 1180
 
7.2%
3 753
 
4.6%
4 608
 
3.7%
0 597
 
3.6%
6 413
 
2.5%
Other values (39) 1590
 
9.7%
Hangul
ValueCountFrequency (%)
1226
 
6.2%
1211
 
6.1%
1203
 
6.1%
1196
 
6.0%
1169
 
5.9%
1159
 
5.8%
1158
 
5.8%
1138
 
5.7%
1138
 
5.7%
883
 
4.4%
Other values (249) 8363
42.1%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct117
Distinct (%)10.4%
Missing839
Missing (%)42.7%
Infinite0
Infinite (%)0.0%
Mean4565.6584
Minimum4500
Maximum4637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:53.220526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4500
5-th percentile4515
Q14536
median4564
Q34594
95-th percentile4629
Maximum4637
Range137
Interquartile range (IQR)58

Descriptive statistics

Standard deviation34.9596
Coefficient of variation (CV)0.0076570774
Kurtosis-0.95944799
Mean4565.6584
Median Absolute Deviation (MAD)28
Skewness0.20873265
Sum5145497
Variance1222.1736
MonotonicityNot monotonic
2024-05-11T14:44:53.373877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4536 121
 
6.2%
4537 65
 
3.3%
4564 39
 
2.0%
4595 32
 
1.6%
4561 30
 
1.5%
4631 29
 
1.5%
4534 29
 
1.5%
4563 29
 
1.5%
4526 27
 
1.4%
4590 26
 
1.3%
Other values (107) 700
35.6%
(Missing) 839
42.7%
ValueCountFrequency (%)
4500 4
0.2%
4501 4
0.2%
4502 7
0.4%
4503 5
0.3%
4504 4
0.2%
4505 4
0.2%
4506 6
0.3%
4507 7
0.4%
4508 1
 
0.1%
4509 6
0.3%
ValueCountFrequency (%)
4637 7
 
0.4%
4635 1
 
0.1%
4634 6
 
0.3%
4633 4
 
0.2%
4632 4
 
0.2%
4631 29
1.5%
4630 3
 
0.2%
4629 17
0.9%
4627 3
 
0.2%
4625 4
 
0.2%
Distinct1734
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2024-05-11T14:44:53.671018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length27
Mean length5.9069176
Min length1

Characters and Unicode

Total characters11613
Distinct characters611
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1558 ?
Unique (%)79.2%

Sample

1st row신영
2nd row헤어라인
3rd row궁원
4th row명진
5th row나경테크닉스
ValueCountFrequency (%)
헤어 30
 
1.2%
미용실 24
 
1.0%
에스테틱 22
 
0.9%
네일 20
 
0.8%
hair 19
 
0.8%
nail 14
 
0.6%
명동점 11
 
0.4%
10
 
0.4%
이철헤어커커 9
 
0.4%
리안헤어 9
 
0.4%
Other values (1890) 2304
93.2%
2024-05-11T14:44:54.221376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
583
 
5.0%
548
 
4.7%
507
 
4.4%
357
 
3.1%
327
 
2.8%
255
 
2.2%
250
 
2.2%
233
 
2.0%
209
 
1.8%
156
 
1.3%
Other values (601) 8188
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9359
80.6%
Uppercase Letter 644
 
5.5%
Lowercase Letter 639
 
5.5%
Space Separator 507
 
4.4%
Open Punctuation 151
 
1.3%
Close Punctuation 151
 
1.3%
Decimal Number 76
 
0.7%
Other Punctuation 75
 
0.6%
Dash Punctuation 8
 
0.1%
Math Symbol 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
583
 
6.2%
548
 
5.9%
357
 
3.8%
327
 
3.5%
255
 
2.7%
250
 
2.7%
233
 
2.5%
209
 
2.2%
156
 
1.7%
147
 
1.6%
Other values (528) 6294
67.3%
Uppercase Letter
ValueCountFrequency (%)
A 66
 
10.2%
S 51
 
7.9%
I 44
 
6.8%
N 43
 
6.7%
O 42
 
6.5%
E 38
 
5.9%
L 34
 
5.3%
R 32
 
5.0%
T 31
 
4.8%
H 28
 
4.3%
Other values (15) 235
36.5%
Lowercase Letter
ValueCountFrequency (%)
a 79
12.4%
e 72
11.3%
i 69
10.8%
n 65
10.2%
o 54
8.5%
r 40
 
6.3%
l 40
 
6.3%
s 37
 
5.8%
t 31
 
4.9%
h 28
 
4.4%
Other values (13) 124
19.4%
Decimal Number
ValueCountFrequency (%)
2 18
23.7%
0 13
17.1%
1 11
14.5%
3 9
11.8%
4 7
 
9.2%
7 5
 
6.6%
9 4
 
5.3%
8 4
 
5.3%
5 3
 
3.9%
6 2
 
2.6%
Other Punctuation
ValueCountFrequency (%)
& 23
30.7%
. 20
26.7%
? 15
20.0%
' 6
 
8.0%
# 5
 
6.7%
, 2
 
2.7%
: 2
 
2.7%
; 2
 
2.7%
Space Separator
ValueCountFrequency (%)
507
100.0%
Open Punctuation
ValueCountFrequency (%)
( 151
100.0%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9356
80.6%
Latin 1283
 
11.0%
Common 971
 
8.4%
Han 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
583
 
6.2%
548
 
5.9%
357
 
3.8%
327
 
3.5%
255
 
2.7%
250
 
2.7%
233
 
2.5%
209
 
2.2%
156
 
1.7%
147
 
1.6%
Other values (526) 6291
67.2%
Latin
ValueCountFrequency (%)
a 79
 
6.2%
e 72
 
5.6%
i 69
 
5.4%
A 66
 
5.1%
n 65
 
5.1%
o 54
 
4.2%
S 51
 
4.0%
I 44
 
3.4%
N 43
 
3.4%
O 42
 
3.3%
Other values (38) 698
54.4%
Common
ValueCountFrequency (%)
507
52.2%
( 151
 
15.6%
) 151
 
15.6%
& 23
 
2.4%
. 20
 
2.1%
2 18
 
1.9%
? 15
 
1.5%
0 13
 
1.3%
1 11
 
1.1%
3 9
 
0.9%
Other values (15) 53
 
5.5%
Han
ValueCountFrequency (%)
2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9354
80.5%
ASCII 2254
 
19.4%
CJK 3
 
< 0.1%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
583
 
6.2%
548
 
5.9%
357
 
3.8%
327
 
3.5%
255
 
2.7%
250
 
2.7%
233
 
2.5%
209
 
2.2%
156
 
1.7%
147
 
1.6%
Other values (524) 6289
67.2%
ASCII
ValueCountFrequency (%)
507
22.5%
( 151
 
6.7%
) 151
 
6.7%
a 79
 
3.5%
e 72
 
3.2%
i 69
 
3.1%
A 66
 
2.9%
n 65
 
2.9%
o 54
 
2.4%
S 51
 
2.3%
Other values (63) 989
43.9%
CJK
ValueCountFrequency (%)
2
66.7%
1
33.3%
Compat Jamo
ValueCountFrequency (%)
1
50.0%
1
50.0%
Distinct1459
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
Minimum1999-01-09 00:00:00
Maximum2024-05-09 13:30:08
2024-05-11T14:44:54.396127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:44:54.552747image/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 size15.5 KiB
I
1588 
U
372 
D
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 1588
80.8%
U 372
 
18.9%
D 6
 
0.3%

Length

2024-05-11T14:44:54.737863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:54.853769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 1588
80.8%
u 372
 
18.9%
d 6
 
0.3%
Distinct479
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T14:44:55.260792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:44:55.408320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
일반미용업
1387 
피부미용업
373 
네일아트업
168 
메이크업업
 
22
기타
 
16

Length

Max length5
Median length5
Mean length4.9755849
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 1387
70.5%
피부미용업 373
 
19.0%
네일아트업 168
 
8.5%
메이크업업 22
 
1.1%
기타 16
 
0.8%

Length

2024-05-11T14:44:55.584329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:55.716387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 1387
70.5%
피부미용업 373
 
19.0%
네일아트업 168
 
8.5%
메이크업업 22
 
1.1%
기타 16
 
0.8%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct768
Distinct (%)56.1%
Missing596
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean199695.29
Minimum196629.53
Maximum202215.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:55.862806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum196629.53
5-th percentile197215.47
Q1198497.41
median199756.12
Q3201023.83
95-th percentile201798.06
Maximum202215.56
Range5586.0345
Interquartile range (IQR)2526.4174

Descriptive statistics

Standard deviation1483.5043
Coefficient of variation (CV)0.0074288398
Kurtosis-1.3408467
Mean199695.29
Median Absolute Deviation (MAD)1260.1156
Skewness-0.11346071
Sum2.7358255 × 108
Variance2200785
MonotonicityNot monotonic
2024-05-11T14:44:56.012248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200750.455125653 36
 
1.8%
201823.908977364 24
 
1.2%
200664.582936542 19
 
1.0%
198259.65357739 14
 
0.7%
200573.641273539 13
 
0.7%
200613.510297669 13
 
0.7%
196864.942838297 11
 
0.6%
198495.752148427 10
 
0.5%
201442.039019479 8
 
0.4%
198847.421844455 7
 
0.4%
Other values (758) 1215
61.8%
(Missing) 596
30.3%
ValueCountFrequency (%)
196629.529289096 1
0.1%
196651.474616736 1
0.1%
196697.116895888 1
0.1%
196700.055871163 2
0.1%
196708.30647209 1
0.1%
196711.099340642 1
0.1%
196713.052052044 1
0.1%
196722.521964636 1
0.1%
196743.056161634 1
0.1%
196746.506418769 2
0.1%
ValueCountFrequency (%)
202215.563754519 1
0.1%
202128.136971071 1
0.1%
202069.665572698 1
0.1%
202013.322106429 1
0.1%
201993.227929317 1
0.1%
201992.413564529 1
0.1%
201983.407147251 2
0.1%
201978.359014791 2
0.1%
201965.260934935 1
0.1%
201958.204672457 1
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct768
Distinct (%)56.1%
Missing596
Missing (%)30.3%
Infinite0
Infinite (%)0.0%
Mean451053.57
Minimum449638.82
Maximum452076.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:56.174251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum449638.82
5-th percentile450055.51
Q1450817.95
median451099.88
Q3451387.12
95-th percentile451781.87
Maximum452076.82
Range2437.9944
Interquartile range (IQR)569.17008

Descriptive statistics

Standard deviation513.80548
Coefficient of variation (CV)0.001139123
Kurtosis0.51584131
Mean451053.57
Median Absolute Deviation (MAD)284.54768
Skewness-0.73153348
Sum6.1794339 × 108
Variance263996.07
MonotonicityNot monotonic
2024-05-11T14:44:56.357621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
449638.824308081 36
 
1.8%
452076.818664092 24
 
1.2%
451781.8683954 19
 
1.0%
451392.198218657 14
 
0.7%
451621.036467544 13
 
0.7%
451817.515366883 13
 
0.7%
450649.69804319 11
 
0.6%
451241.680693735 10
 
0.5%
451619.845549151 8
 
0.4%
451049.288478768 7
 
0.4%
Other values (758) 1215
61.8%
(Missing) 596
30.3%
ValueCountFrequency (%)
449638.824308081 36
1.8%
449670.613249189 1
 
0.1%
449687.143213423 3
 
0.2%
449703.239755874 1
 
0.1%
449714.824280955 1
 
0.1%
449738.991311072 1
 
0.1%
449777.515840267 2
 
0.1%
449797.113764066 1
 
0.1%
449809.744684888 1
 
0.1%
449939.260432874 2
 
0.1%
ValueCountFrequency (%)
452076.818664092 24
1.2%
452009.399759421 1
 
0.1%
452005.950500534 2
 
0.1%
451892.262353485 1
 
0.1%
451880.176539918 1
 
0.1%
451875.379111464 1
 
0.1%
451874.637536958 2
 
0.1%
451854.430056257 1
 
0.1%
451851.573474084 1
 
0.1%
451843.187476545 1
 
0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
미용업
820 
일반미용업
384 
피부미용업
261 
<NA>
257 
종합미용업
115 
Other values (11)
129 

Length

Max length23
Median length16
Mean length4.4440488
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 820
41.7%
일반미용업 384
19.5%
피부미용업 261
 
13.3%
<NA> 257
 
13.1%
종합미용업 115
 
5.8%
네일미용업 54
 
2.7%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 16
 
0.8%
피부미용업, 네일미용업 14
 
0.7%
일반미용업, 네일미용업 12
 
0.6%
화장ㆍ분장 미용업 9
 
0.5%
Other values (6) 24
 
1.2%

Length

2024-05-11T14:44:56.544977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 867
41.3%
일반미용업 423
20.1%
피부미용업 284
 
13.5%
na 257
 
12.2%
종합미용업 115
 
5.5%
네일미용업 108
 
5.1%
화장ㆍ분장 47
 
2.2%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)0.7%
Missing257
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.23756583
Minimum0
Maximum17
Zeros1597
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:56.677203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1171298
Coefficient of variation (CV)4.7024012
Kurtosis68.396323
Mean0.23756583
Median Absolute Deviation (MAD)0
Skewness7.0373905
Sum406
Variance1.2479791
MonotonicityNot monotonic
2024-05-11T14:44:56.786461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1597
81.2%
2 23
 
1.2%
3 22
 
1.1%
5 20
 
1.0%
1 19
 
1.0%
4 14
 
0.7%
6 8
 
0.4%
10 2
 
0.1%
15 1
 
0.1%
12 1
 
0.1%
Other values (2) 2
 
0.1%
(Missing) 257
 
13.1%
ValueCountFrequency (%)
0 1597
81.2%
1 19
 
1.0%
2 23
 
1.2%
3 22
 
1.1%
4 14
 
0.7%
5 20
 
1.0%
6 8
 
0.4%
7 1
 
0.1%
10 2
 
0.1%
12 1
 
0.1%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
12 1
 
0.1%
10 2
 
0.1%
7 1
 
0.1%
6 8
 
0.4%
5 20
1.0%
4 14
0.7%
3 22
1.1%
2 23
1.2%

건물지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)0.4%
Missing257
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.042715038
Minimum0
Maximum13
Zeros1672
Zeros (%)85.0%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:56.921605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43947608
Coefficient of variation (CV)10.288557
Kurtosis495.79034
Mean0.042715038
Median Absolute Deviation (MAD)0
Skewness19.602805
Sum73
Variance0.19313923
MonotonicityNot monotonic
2024-05-11T14:44:57.091976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1672
85.0%
1 26
 
1.3%
2 4
 
0.2%
4 4
 
0.2%
13 1
 
0.1%
7 1
 
0.1%
3 1
 
0.1%
(Missing) 257
 
13.1%
ValueCountFrequency (%)
0 1672
85.0%
1 26
 
1.3%
2 4
 
0.2%
3 1
 
0.1%
4 4
 
0.2%
7 1
 
0.1%
13 1
 
0.1%
ValueCountFrequency (%)
13 1
 
0.1%
7 1
 
0.1%
4 4
 
0.2%
3 1
 
0.1%
2 4
 
0.2%
1 26
 
1.3%
0 1672
85.0%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct17
Distinct (%)1.0%
Missing257
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean1.2428321
Minimum0
Maximum17
Zeros988
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:57.229475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum17
Range17
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1785392
Coefficient of variation (CV)1.752883
Kurtosis9.986784
Mean1.2428321
Median Absolute Deviation (MAD)0
Skewness2.786496
Sum2124
Variance4.7460332
MonotonicityNot monotonic
2024-05-11T14:44:57.377796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 988
50.3%
1 240
 
12.2%
2 163
 
8.3%
3 128
 
6.5%
4 70
 
3.6%
5 43
 
2.2%
7 16
 
0.8%
6 16
 
0.8%
9 12
 
0.6%
10 8
 
0.4%
Other values (7) 25
 
1.3%
(Missing) 257
 
13.1%
ValueCountFrequency (%)
0 988
50.3%
1 240
 
12.2%
2 163
 
8.3%
3 128
 
6.5%
4 70
 
3.6%
5 43
 
2.2%
6 16
 
0.8%
7 16
 
0.8%
8 8
 
0.4%
9 12
 
0.6%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 3
 
0.2%
13 5
 
0.3%
12 3
 
0.2%
11 4
 
0.2%
10 8
0.4%
9 12
0.6%
8 8
0.4%
7 16
0.8%

사용끝지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct18
Distinct (%)1.1%
Missing257
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean1.1977765
Minimum0
Maximum26
Zeros1043
Zeros (%)53.1%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:57.523178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2573263
Coefficient of variation (CV)1.8845973
Kurtosis16.953305
Mean1.1977765
Median Absolute Deviation (MAD)0
Skewness3.3079244
Sum2047
Variance5.095522
MonotonicityNot monotonic
2024-05-11T14:44:57.655529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 1043
53.1%
1 214
 
10.9%
2 146
 
7.4%
3 114
 
5.8%
4 73
 
3.7%
5 41
 
2.1%
6 17
 
0.9%
7 16
 
0.8%
9 11
 
0.6%
10 8
 
0.4%
Other values (8) 26
 
1.3%
(Missing) 257
 
13.1%
ValueCountFrequency (%)
0 1043
53.1%
1 214
 
10.9%
2 146
 
7.4%
3 114
 
5.8%
4 73
 
3.7%
5 41
 
2.1%
6 17
 
0.9%
7 16
 
0.8%
8 8
 
0.4%
9 11
 
0.6%
ValueCountFrequency (%)
26 1
 
0.1%
17 1
 
0.1%
15 1
 
0.1%
14 3
 
0.2%
13 5
0.3%
12 3
 
0.2%
11 4
 
0.2%
10 8
0.4%
9 11
0.6%
8 8
0.4%

사용시작지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1632 
<NA>
257 
1
 
56
2
 
13
3
 
5

Length

Max length4
Median length1
Mean length1.3921668
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1632
83.0%
<NA> 257
 
13.1%
1 56
 
2.8%
2 13
 
0.7%
3 5
 
0.3%
4 3
 
0.2%

Length

2024-05-11T14:44:57.813560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:57.961899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1632
83.0%
na 257
 
13.1%
1 56
 
2.8%
2 13
 
0.7%
3 5
 
0.3%
4 3
 
0.2%

사용끝지하층
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1633 
<NA>
257 
1
 
56
2
 
12
3
 
5

Length

Max length4
Median length1
Mean length1.3921668
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1633
83.1%
<NA> 257
 
13.1%
1 56
 
2.8%
2 12
 
0.6%
3 5
 
0.3%
4 3
 
0.2%

Length

2024-05-11T14:44:58.129347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:58.259945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1633
83.1%
na 257
 
13.1%
1 56
 
2.8%
2 12
 
0.6%
3 5
 
0.3%
4 3
 
0.2%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1709 
<NA>
257 

Length

Max length4
Median length1
Mean length1.3921668
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1709
86.9%
<NA> 257
 
13.1%

Length

2024-05-11T14:44:58.443431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:58.605023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1709
86.9%
na 257
 
13.1%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1709 
<NA>
257 

Length

Max length4
Median length1
Mean length1.3921668
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1709
86.9%
<NA> 257
 
13.1%

Length

2024-05-11T14:44:58.824970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:59.015723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1709
86.9%
na 257
 
13.1%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1709 
<NA>
257 

Length

Max length4
Median length1
Mean length1.3921668
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1709
86.9%
<NA> 257
 
13.1%

Length

2024-05-11T14:44:59.196297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:59.382675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1709
86.9%
na 257
 
13.1%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing274
Missing (%)13.9%
Memory size4.0 KiB
False
1692 
(Missing)
274 
ValueCountFrequency (%)
False 1692
86.1%
(Missing) 274
 
13.9%
2024-05-11T14:44:59.503228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct31
Distinct (%)1.8%
Missing257
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean3.8063195
Minimum0
Maximum52
Zeros422
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:44:59.653859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum52
Range52
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1748166
Coefficient of variation (CV)1.0968119
Kurtosis27.096874
Mean3.8063195
Median Absolute Deviation (MAD)2
Skewness3.6415774
Sum6505
Variance17.429094
MonotonicityNot monotonic
2024-05-11T14:44:59.839665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 422
21.5%
3 342
17.4%
4 244
12.4%
2 179
9.1%
5 133
 
6.8%
6 118
 
6.0%
8 71
 
3.6%
10 37
 
1.9%
7 35
 
1.8%
1 24
 
1.2%
Other values (21) 104
 
5.3%
(Missing) 257
13.1%
ValueCountFrequency (%)
0 422
21.5%
1 24
 
1.2%
2 179
9.1%
3 342
17.4%
4 244
12.4%
5 133
 
6.8%
6 118
 
6.0%
7 35
 
1.8%
8 71
 
3.6%
9 20
 
1.0%
ValueCountFrequency (%)
52 1
0.1%
50 1
0.1%
40 1
0.1%
33 1
0.1%
30 1
0.1%
29 1
0.1%
26 1
0.1%
25 1
0.1%
23 2
0.1%
22 1
0.1%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1966
Missing (%)100.0%
Memory size17.4 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1966
Missing (%)100.0%
Memory size17.4 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1966
Missing (%)100.0%
Memory size17.4 KiB
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
<NA>
1430 
임대
531 
자가
 
5

Length

Max length4
Median length4
Mean length3.4547304
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> 1430
72.7%
임대 531
 
27.0%
자가 5
 
0.3%

Length

2024-05-11T14:45:00.039124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:45:00.198139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1430
72.7%
임대 531
 
27.0%
자가 5
 
0.3%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1709 
<NA>
257 

Length

Max length4
Median length1
Mean length1.3921668
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1709
86.9%
<NA> 257
 
13.1%

Length

2024-05-11T14:45:00.348133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:45:00.462932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1709
86.9%
na 257
 
13.1%

여성종사자수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1704 
<NA>
257 
2
 
2
16
 
1
3
 
1

Length

Max length4
Median length1
Mean length1.3926755
Min length1

Unique

Unique3 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 1704
86.7%
<NA> 257
 
13.1%
2 2
 
0.1%
16 1
 
0.1%
3 1
 
0.1%
1 1
 
0.1%

Length

2024-05-11T14:45:00.583933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:45:00.754484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1704
86.7%
na 257
 
13.1%
2 2
 
0.1%
16 1
 
0.1%
3 1
 
0.1%
1 1
 
0.1%

남성종사자수
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1708 
<NA>
257 
2
 
1

Length

Max length4
Median length1
Mean length1.3921668
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 1708
86.9%
<NA> 257
 
13.1%
2 1
 
0.1%

Length

2024-05-11T14:45:00.927829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:45:01.121747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1708
86.9%
na 257
 
13.1%
2 1
 
0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
0
1709 
<NA>
257 

Length

Max length4
Median length1
Mean length1.3921668
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1709
86.9%
<NA> 257
 
13.1%

Length

2024-05-11T14:45:01.305089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:45:01.490071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1709
86.9%
na 257
 
13.1%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)1.3%
Missing257
Missing (%)13.1%
Infinite0
Infinite (%)0.0%
Mean0.97132826
Minimum0
Maximum32
Zeros1409
Zeros (%)71.7%
Negative0
Negative (%)0.0%
Memory size17.4 KiB
2024-05-11T14:45:01.659947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum32
Range32
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7776284
Coefficient of variation (CV)2.8596187
Kurtosis25.186543
Mean0.97132826
Median Absolute Deviation (MAD)0
Skewness4.2390616
Sum1660
Variance7.7152196
MonotonicityNot monotonic
2024-05-11T14:45:01.858580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 1409
71.7%
2 52
 
2.6%
5 35
 
1.8%
3 33
 
1.7%
1 31
 
1.6%
4 28
 
1.4%
6 27
 
1.4%
8 22
 
1.1%
7 20
 
1.0%
9 14
 
0.7%
Other values (12) 38
 
1.9%
(Missing) 257
 
13.1%
ValueCountFrequency (%)
0 1409
71.7%
1 31
 
1.6%
2 52
 
2.6%
3 33
 
1.7%
4 28
 
1.4%
5 35
 
1.8%
6 27
 
1.4%
7 20
 
1.0%
8 22
 
1.1%
9 14
 
0.7%
ValueCountFrequency (%)
32 1
 
0.1%
29 1
 
0.1%
21 1
 
0.1%
20 1
 
0.1%
18 2
 
0.1%
17 3
0.2%
16 1
 
0.1%
14 7
0.4%
13 2
 
0.1%
12 7
0.4%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing257
Missing (%)13.1%
Memory size4.0 KiB
False
1707 
True
 
2
(Missing)
257 
ValueCountFrequency (%)
False 1707
86.8%
True 2
 
0.1%
(Missing) 257
 
13.1%
2024-05-11T14:45:02.045235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030100003010000-204-1911-0039519111111<NA>3폐업2폐업19940623<NA><NA><NA>02000000000.0100440서울특별시 중구 황학동 1560-0번지<NA><NA>신영2001-10-08 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N0<NA><NA><NA><NA>00000N
130100003010000-204-1961-0085719610817<NA>3폐업2폐업19980623<NA><NA><NA>02 0000011.02100450서울특별시 중구 신당동 347-0번지<NA><NA>헤어라인2001-10-08 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N0<NA><NA><NA><NA>00000N
230100003010000-204-1962-0089719620823<NA>3폐업2폐업19971103<NA><NA><NA>020755862627.75100051서울특별시 중구 회현동1가 7625-0번지<NA><NA>궁원2001-10-08 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N5<NA><NA><NA><NA>00000N
330100003010000-204-1964-007281964-02-12<NA>3폐업2폐업2023-01-02<NA><NA><NA>022235868012.24100-450서울특별시 중구 신당동 432-520서울특별시 중구 동호로11아길 1 (신당동)4602명진2023-01-02 09:45:58U2022-12-04 22:06:00.0일반미용업200451.432529449959.754292<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
430100003010000-204-1964-0077519640423<NA>3폐업2폐업19951117<NA><NA><NA>02 7763090210.7100011서울특별시 중구 충무로1가 23-10번지<NA><NA>나경테크닉스2001-10-08 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N23<NA><NA><NA><NA>00000N
530100003010000-204-1964-0096519640825<NA>3폐업2폐업19981202<NA><NA><NA>02 753740420.81100060서울특별시 중구 남창동 48-2번지<NA><NA>금수2001-10-08 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N4<NA><NA><NA><NA>00000N
630100003010000-204-1965-0075819651111<NA>3폐업2폐업19980904<NA><NA><NA>02 23883550.0100440서울특별시 중구 황학동 1763-0번지<NA><NA>진성2001-10-08 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N0<NA><NA><NA><NA>00000N
730100003010000-204-1967-0074319670125<NA>3폐업2폐업19941013<NA><NA><NA>020274093213.2100271서울특별시 중구 필동1가 23-11번지<NA><NA>세진2001-10-08 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N2<NA><NA><NA><NA>00000N
830100003010000-204-1967-0089919670516<NA>3폐업2폐업19951106<NA><NA><NA>02 265407432.4100330서울특별시 중구 주교동 154-0번지<NA><NA>평양2001-10-08 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N3<NA><NA><NA><NA>00000N
930100003010000-204-1968-0071019681211<NA>3폐업2폐업20010630<NA><NA><NA>02000000000.0100091서울특별시 중구 남대문로1가 14-0번지<NA><NA>조흥은행구내2001-07-05 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000000000N0<NA><NA><NA><NA>00000N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
195630100003010000-225-2021-0000220210602<NA>1영업/정상1영업<NA><NA><NA><NA><NA>27.0100834서울특별시 중구 신당동 377-35서울특별시 중구 다산로19길 23, 1층 (신당동)4607써니살롱(sunny salon)2021-06-02 17:58:17I2021-06-04 00:22:55.0일반미용업200870.608282450557.065548일반미용업, 네일미용업, 화장ㆍ분장 미용업000000000N2<NA><NA><NA><NA>00000N
195730100003010000-225-2023-000012023-05-30<NA>1영업/정상1영업<NA><NA><NA><NA>02 22316633165.0100-840서울특별시 중구 신당동 372-430 태권도한국체육관서울특별시 중구 다산로8길 27, 태권도한국체육관 2층 (신당동)4596파란헤어 약수점2023-05-30 13:28:54I2022-12-06 00:01:00.0일반미용업200904.437006450057.34207<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
195830100003010000-225-2023-000022023-07-21<NA>1영업/정상1영업<NA><NA><NA><NA><NA>128.94100-809서울특별시 중구 명동2가 31-5서울특별시 중구 명동10길 38, 3층 (명동2가)4536헤어더뷰 명동점2023-07-21 14:41:18I2022-12-06 22:03:00.0일반미용업198653.047335451099.892275<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
195930100003010000-225-2023-000032023-08-01<NA>1영업/정상1영업<NA><NA><NA><NA><NA>17.97100-092서울특별시 중구 남대문로2가 9-9서울특별시 중구 소공로 지하 102, 55호 (남대문로2가)4532프라자 헤어샵2023-08-01 17:25:32I2022-12-08 00:03:00.0일반미용업198354.121882451309.736294<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
196030100003010000-226-2018-0000120180504<NA>3폐업2폐업20210618<NA><NA><NA><NA>15.0100885서울특별시 중구 신당동 193-29 1층서울특별시 중구 다산로 289, 1층 (신당동)4568Nail-My.B2021-06-18 09:19:09U2021-06-20 02:40:00.0네일아트업201200.506104451798.681897피부미용업, 네일미용업, 화장ㆍ분장 미용업001100000N4<NA><NA><NA><NA>00001N
196130100003010000-226-2018-0000220181207<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.6100041서울특별시 중구 남산동1가 10-1번지 4층서울특별시 중구 퇴계로18길 24, 4층 (남산동1가)4631에코 에스테틱2018-12-07 13:57:14I2018-12-09 02:20:10.0피부미용업198611.501988450841.621885피부미용업, 네일미용업, 화장ㆍ분장 미용업004400000N6<NA><NA><NA><NA>00000N
196230100003010000-226-2019-0000120190422<NA>1영업/정상1영업<NA><NA><NA><NA>02 22508045537.0100856서울특별시 중구 장충동2가 201번지 반얀트리 클럽 앤 스파 서울서울특별시 중구 장충단로 60, 반얀트리 클럽 앤 스파 서울 3층 (장충동2가)4605반얀트리 스파2019-04-22 14:31:32I2019-04-24 02:20:29.0피부미용업199977.759937449777.51584피부미용업, 네일미용업, 화장ㆍ분장 미용업003300000N18<NA><NA><NA><NA>00000N
196330100003010000-226-2020-0000120200520<NA>1영업/정상1영업<NA><NA><NA><NA>02 318 420019.82100092서울특별시 중구 남대문로2가 9-9번지 명동지하쇼핑센터서울특별시 중구 남대문로 지하 72, 명동지하쇼핑센터 지하1층 마-5호 (남대문로2가)4535네일스퀘어2020-05-20 17:52:39I2020-05-22 00:23:19.0네일아트업198354.121882451309.736294피부미용업, 네일미용업, 화장ㆍ분장 미용업000011000N5<NA><NA><NA><NA>00001N
196430100003010000-226-2023-000012023-07-19<NA>1영업/정상1영업<NA><NA><NA><NA><NA>17.32100-420서울특별시 중구 무학동 74-3서울특별시 중구 퇴계로74길 8, 1층 (무학동)4611요거네일2023-07-19 13:36:44I2022-12-06 22:01:00.0네일아트업201232.149725451387.119876<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
196530100003010000-226-2024-000012024-03-06<NA>1영업/정상1영업<NA><NA><NA><NA><NA>18.0100-300서울특별시 중구 초동 33-6서울특별시 중구 충무로 36, 2층 (초동)4556수야디나 네일&래쉬2024-03-06 11:05:33I2023-12-03 00:08:00.0네일아트업199318.008333451326.669333<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>