Overview

Dataset statistics

Number of variables47
Number of observations1954
Missing cells25714
Missing cells (%)28.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory773.0 KiB
Average record size in memory405.1 B

Variable types

Categorical16
Text6
DateTime4
Unsupported7
Numeric12
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
Author용산구
URLhttps://data.seoul.go.kr/dataList/OA-17904/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
업태구분명 is highly imbalanced (51.1%)Imbalance
사용끝지하층 is highly imbalanced (73.5%)Imbalance
건물소유구분명 is highly imbalanced (64.0%)Imbalance
다중이용업소여부 is highly imbalanced (98.7%)Imbalance
인허가취소일자 has 1954 (100.0%) missing valuesMissing
폐업일자 has 654 (33.5%) missing valuesMissing
휴업시작일자 has 1954 (100.0%) missing valuesMissing
휴업종료일자 has 1954 (100.0%) missing valuesMissing
재개업일자 has 1954 (100.0%) missing valuesMissing
전화번호 has 606 (31.0%) missing valuesMissing
도로명주소 has 786 (40.2%) missing valuesMissing
도로명우편번호 has 793 (40.6%) missing valuesMissing
좌표정보(X) has 96 (4.9%) missing valuesMissing
좌표정보(Y) has 96 (4.9%) missing valuesMissing
건물지상층수 has 562 (28.8%) missing valuesMissing
건물지하층수 has 672 (34.4%) missing valuesMissing
사용시작지상층 has 893 (45.7%) missing valuesMissing
사용끝지상층 has 1548 (79.2%) missing valuesMissing
발한실여부 has 309 (15.8%) missing valuesMissing
좌석수 has 350 (17.9%) missing valuesMissing
조건부허가신고사유 has 1954 (100.0%) missing valuesMissing
조건부허가시작일자 has 1954 (100.0%) missing valuesMissing
조건부허가종료일자 has 1954 (100.0%) missing valuesMissing
여성종사자수 has 1534 (78.5%) missing valuesMissing
남성종사자수 has 1537 (78.7%) missing valuesMissing
침대수 has 1296 (66.3%) missing valuesMissing
다중이용업소여부 has 300 (15.4%) missing valuesMissing
건물지하층수 is highly skewed (γ1 = 29.55718632)Skewed
사용끝지상층 is highly skewed (γ1 = 20.11514163)Skewed
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 83 (4.2%) zerosZeros
건물지상층수 has 1117 (57.2%) zerosZeros
건물지하층수 has 1224 (62.6%) zerosZeros
사용시작지상층 has 657 (33.6%) zerosZeros
사용끝지상층 has 97 (5.0%) zerosZeros
좌석수 has 215 (11.0%) zerosZeros
여성종사자수 has 345 (17.7%) zerosZeros
남성종사자수 has 406 (20.8%) zerosZeros
침대수 has 450 (23.0%) zerosZeros

Reproduction

Analysis started2024-05-11 03:22:34.001059
Analysis finished2024-05-11 03:22:36.277902
Duration2.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
3020000
1954 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000
2nd row3020000
3rd row3020000
4th row3020000
5th row3020000

Common Values

ValueCountFrequency (%)
3020000 1954
100.0%

Length

2024-05-11T03:22:36.478481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:22:36.796134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000 1954
100.0%

관리번호
Text

UNIQUE 

Distinct1954
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
2024-05-11T03:22:37.341272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters42988
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

Unique1954 ?
Unique (%)100.0%

Sample

1st row3020000-204-1960-00962
2nd row3020000-204-1960-01025
3rd row3020000-204-1962-00529
4th row3020000-204-1963-01023
5th row3020000-204-1964-00978
ValueCountFrequency (%)
3020000-204-1960-00962 1
 
0.1%
3020000-211-2020-00016 1
 
0.1%
3020000-211-2020-00014 1
 
0.1%
3020000-211-2020-00013 1
 
0.1%
3020000-211-2020-00012 1
 
0.1%
3020000-211-2020-00011 1
 
0.1%
3020000-211-2020-00010 1
 
0.1%
3020000-211-2020-00009 1
 
0.1%
3020000-211-2020-00008 1
 
0.1%
3020000-211-2020-00007 1
 
0.1%
Other values (1944) 1944
99.5%
2024-05-11T03:22:38.479079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18627
43.3%
2 6482
 
15.1%
- 5862
 
13.6%
1 4230
 
9.8%
3 2662
 
6.2%
9 1525
 
3.5%
4 1250
 
2.9%
8 659
 
1.5%
5 614
 
1.4%
6 546
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37126
86.4%
Dash Punctuation 5862
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18627
50.2%
2 6482
 
17.5%
1 4230
 
11.4%
3 2662
 
7.2%
9 1525
 
4.1%
4 1250
 
3.4%
8 659
 
1.8%
5 614
 
1.7%
6 546
 
1.5%
7 531
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 5862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42988
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18627
43.3%
2 6482
 
15.1%
- 5862
 
13.6%
1 4230
 
9.8%
3 2662
 
6.2%
9 1525
 
3.5%
4 1250
 
2.9%
8 659
 
1.5%
5 614
 
1.4%
6 546
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18627
43.3%
2 6482
 
15.1%
- 5862
 
13.6%
1 4230
 
9.8%
3 2662
 
6.2%
9 1525
 
3.5%
4 1250
 
2.9%
8 659
 
1.5%
5 614
 
1.4%
6 546
 
1.3%
Distinct1625
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
Minimum1960-12-30 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T03:22:38.948984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:22:39.562759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1954
Missing (%)100.0%
Memory size17.3 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
3
1300 
1
654 

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 1300
66.5%
1 654
33.5%

Length

2024-05-11T03:22:40.018642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:22:40.429008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1300
66.5%
1 654
33.5%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
폐업
1300 
영업/정상
654 

Length

Max length5
Median length2
Mean length3.0040942
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1300
66.5%
영업/정상 654
33.5%

Length

2024-05-11T03:22:40.986716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:22:41.614584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1300
66.5%
영업/정상 654
33.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
2
1300 
1
654 

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 1300
66.5%
1 654
33.5%

Length

2024-05-11T03:22:42.195604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:22:42.943231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1300
66.5%
1 654
33.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
폐업
1300 
영업
654 

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 1300
66.5%
영업 654
33.5%

Length

2024-05-11T03:22:43.342644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:22:43.896690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1300
66.5%
영업 654
33.5%

폐업일자
Date

MISSING 

Distinct1061
Distinct (%)81.6%
Missing654
Missing (%)33.5%
Memory size15.4 KiB
Minimum1985-12-31 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T03:22:44.845475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:22:45.703443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1954
Missing (%)100.0%
Memory size17.3 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1954
Missing (%)100.0%
Memory size17.3 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1954
Missing (%)100.0%
Memory size17.3 KiB

전화번호
Text

MISSING 

Distinct1196
Distinct (%)88.7%
Missing606
Missing (%)31.0%
Memory size15.4 KiB
2024-05-11T03:22:46.783021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.10905
Min length2

Characters and Unicode

Total characters13627
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

Unique1094 ?
Unique (%)81.2%

Sample

1st row02 7936966
2nd row0207536014
3rd row02 7926694
4th row02 7943767
5th row0207952883
ValueCountFrequency (%)
02 979
38.5%
749 22
 
0.9%
070 16
 
0.6%
794 15
 
0.6%
00000 15
 
0.6%
797 15
 
0.6%
790 14
 
0.6%
798 12
 
0.5%
792 12
 
0.5%
793 12
 
0.5%
Other values (1215) 1430
56.3%
2024-05-11T03:22:48.064954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2504
18.4%
7 1993
14.6%
2 1975
14.5%
1463
10.7%
9 1304
9.6%
3 812
 
6.0%
1 764
 
5.6%
4 738
 
5.4%
5 732
 
5.4%
8 677
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12164
89.3%
Space Separator 1463
 
10.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2504
20.6%
7 1993
16.4%
2 1975
16.2%
9 1304
10.7%
3 812
 
6.7%
1 764
 
6.3%
4 738
 
6.1%
5 732
 
6.0%
8 677
 
5.6%
6 665
 
5.5%
Space Separator
ValueCountFrequency (%)
1463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13627
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2504
18.4%
7 1993
14.6%
2 1975
14.5%
1463
10.7%
9 1304
9.6%
3 812
 
6.0%
1 764
 
5.6%
4 738
 
5.4%
5 732
 
5.4%
8 677
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13627
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2504
18.4%
7 1993
14.6%
2 1975
14.5%
1463
10.7%
9 1304
9.6%
3 812
 
6.0%
1 764
 
5.6%
4 738
 
5.4%
5 732
 
5.4%
8 677
 
5.0%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1143
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.718567
Minimum0
Maximum553.97
Zeros83
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:22:48.532612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.552
Q118.0875
median29.16
Q351.65
95-th percentile132.5165
Maximum553.97
Range553.97
Interquartile range (IQR)33.5625

Descriptive statistics

Standard deviation47.206641
Coefficient of variation (CV)1.0797847
Kurtosis23.88397
Mean43.718567
Median Absolute Deviation (MAD)12.66
Skewness3.833872
Sum85426.08
Variance2228.4669
MonotonicityNot monotonic
2024-05-11T03:22:49.015612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 83
 
4.2%
33.0 57
 
2.9%
26.4 28
 
1.4%
16.5 27
 
1.4%
19.8 23
 
1.2%
23.1 20
 
1.0%
49.5 19
 
1.0%
13.2 17
 
0.9%
30.0 16
 
0.8%
66.0 12
 
0.6%
Other values (1133) 1652
84.5%
ValueCountFrequency (%)
0.0 83
4.2%
3.3 4
 
0.2%
3.98 1
 
0.1%
6.5 1
 
0.1%
6.6 2
 
0.1%
7.0 1
 
0.1%
7.2 1
 
0.1%
7.5 1
 
0.1%
8.0 1
 
0.1%
8.25 1
 
0.1%
ValueCountFrequency (%)
553.97 1
0.1%
530.48 1
0.1%
467.55 1
0.1%
362.82 1
0.1%
358.57 1
0.1%
346.78 1
0.1%
329.0 1
0.1%
327.6 1
0.1%
322.49 1
0.1%
315.01 1
0.1%
Distinct188
Distinct (%)9.6%
Missing2
Missing (%)0.1%
Memory size15.4 KiB
2024-05-11T03:22:49.863240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1055328
Min length6

Characters and Unicode

Total characters11918
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

Unique39 ?
Unique (%)2.0%

Sample

1st row140871
2nd row140833
3rd row140883
4th row140858
5th row140823
ValueCountFrequency (%)
140823 86
 
4.4%
140832 68
 
3.5%
140132 68
 
3.5%
140133 58
 
3.0%
140863 52
 
2.7%
140833 49
 
2.5%
140887 48
 
2.5%
140858 47
 
2.4%
140861 42
 
2.2%
140854 41
 
2.1%
Other values (178) 1393
71.4%
2024-05-11T03:22:51.206501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2607
21.9%
1 2533
21.3%
4 2246
18.8%
8 1668
14.0%
3 675
 
5.7%
2 617
 
5.2%
9 400
 
3.4%
5 350
 
2.9%
6 328
 
2.8%
7 288
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11712
98.3%
Dash Punctuation 206
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2607
22.3%
1 2533
21.6%
4 2246
19.2%
8 1668
14.2%
3 675
 
5.8%
2 617
 
5.3%
9 400
 
3.4%
5 350
 
3.0%
6 328
 
2.8%
7 288
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11918
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2607
21.9%
1 2533
21.3%
4 2246
18.8%
8 1668
14.0%
3 675
 
5.7%
2 617
 
5.2%
9 400
 
3.4%
5 350
 
2.9%
6 328
 
2.8%
7 288
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2607
21.9%
1 2533
21.3%
4 2246
18.8%
8 1668
14.0%
3 675
 
5.7%
2 617
 
5.2%
9 400
 
3.4%
5 350
 
2.9%
6 328
 
2.8%
7 288
 
2.4%
Distinct1613
Distinct (%)82.6%
Missing2
Missing (%)0.1%
Memory size15.4 KiB
2024-05-11T03:22:52.058289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length47
Mean length25.170082
Min length16

Characters and Unicode

Total characters49132
Distinct characters269
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

Unique1401 ?
Unique (%)71.8%

Sample

1st row서울특별시 용산구 한강로2가 179-0번지
2nd row서울특별시 용산구 용산동2가 2-0번지
3rd row서울특별시 용산구 한강로3가 65-54번지
4th row서울특별시 용산구 이태원동 13-0번지
5th row서울특별시 용산구 보광동 238-25번지
ValueCountFrequency (%)
서울특별시 1952
21.5%
용산구 1951
21.5%
한남동 275
 
3.0%
이태원동 247
 
2.7%
보광동 179
 
2.0%
이촌동 167
 
1.8%
1층 165
 
1.8%
한강로2가 118
 
1.3%
용산동2가 101
 
1.1%
후암동 99
 
1.1%
Other values (1673) 3813
42.1%
2024-05-11T03:22:53.280800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8668
 
17.6%
2276
 
4.6%
2238
 
4.6%
2019
 
4.1%
2 1990
 
4.1%
1974
 
4.0%
1962
 
4.0%
1953
 
4.0%
1952
 
4.0%
1952
 
4.0%
Other values (259) 22148
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28704
58.4%
Decimal Number 9805
 
20.0%
Space Separator 8668
 
17.6%
Dash Punctuation 1771
 
3.6%
Uppercase Letter 55
 
0.1%
Open Punctuation 46
 
0.1%
Close Punctuation 46
 
0.1%
Other Punctuation 32
 
0.1%
Lowercase Letter 3
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2276
 
7.9%
2238
 
7.8%
2019
 
7.0%
1974
 
6.9%
1962
 
6.8%
1953
 
6.8%
1952
 
6.8%
1952
 
6.8%
1708
 
6.0%
1642
 
5.7%
Other values (226) 9028
31.5%
Uppercase Letter
ValueCountFrequency (%)
B 20
36.4%
G 7
 
12.7%
L 7
 
12.7%
A 6
 
10.9%
C 4
 
7.3%
D 4
 
7.3%
K 1
 
1.8%
P 1
 
1.8%
E 1
 
1.8%
S 1
 
1.8%
Other values (3) 3
 
5.5%
Decimal Number
ValueCountFrequency (%)
2 1990
20.3%
1 1951
19.9%
3 1207
12.3%
0 903
9.2%
4 812
8.3%
5 727
 
7.4%
6 712
 
7.3%
7 581
 
5.9%
8 469
 
4.8%
9 453
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
j 1
33.3%
c 1
33.3%
b 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 29
90.6%
. 3
 
9.4%
Space Separator
ValueCountFrequency (%)
8668
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1771
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28704
58.4%
Common 20370
41.5%
Latin 58
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2276
 
7.9%
2238
 
7.8%
2019
 
7.0%
1974
 
6.9%
1962
 
6.8%
1953
 
6.8%
1952
 
6.8%
1952
 
6.8%
1708
 
6.0%
1642
 
5.7%
Other values (226) 9028
31.5%
Common
ValueCountFrequency (%)
8668
42.6%
2 1990
 
9.8%
1 1951
 
9.6%
- 1771
 
8.7%
3 1207
 
5.9%
0 903
 
4.4%
4 812
 
4.0%
5 727
 
3.6%
6 712
 
3.5%
7 581
 
2.9%
Other values (7) 1048
 
5.1%
Latin
ValueCountFrequency (%)
B 20
34.5%
G 7
 
12.1%
L 7
 
12.1%
A 6
 
10.3%
C 4
 
6.9%
D 4
 
6.9%
K 1
 
1.7%
P 1
 
1.7%
E 1
 
1.7%
S 1
 
1.7%
Other values (6) 6
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28704
58.4%
ASCII 20428
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8668
42.4%
2 1990
 
9.7%
1 1951
 
9.6%
- 1771
 
8.7%
3 1207
 
5.9%
0 903
 
4.4%
4 812
 
4.0%
5 727
 
3.6%
6 712
 
3.5%
7 581
 
2.8%
Other values (23) 1106
 
5.4%
Hangul
ValueCountFrequency (%)
2276
 
7.9%
2238
 
7.8%
2019
 
7.0%
1974
 
6.9%
1962
 
6.8%
1953
 
6.8%
1952
 
6.8%
1952
 
6.8%
1708
 
6.0%
1642
 
5.7%
Other values (226) 9028
31.5%

도로명주소
Text

MISSING 

Distinct1098
Distinct (%)94.0%
Missing786
Missing (%)40.2%
Memory size15.4 KiB
2024-05-11T03:22:54.149595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length53
Mean length33.923801
Min length21

Characters and Unicode

Total characters39623
Distinct characters276
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

Unique1034 ?
Unique (%)88.5%

Sample

1st row서울특별시 용산구 이촌로75길 6 (이촌동)
2nd row서울특별시 용산구 이촌로 224 (이촌동)
3rd row서울특별시 용산구 청파로45길 34 (청파동2가)
4th row서울특별시 용산구 이태원로27길 101 (한남동, 1층 101호)
5th row서울특별시 용산구 이촌로 264, 지층 12호 (이촌동, 삼익상가)
ValueCountFrequency (%)
서울특별시 1168
 
15.2%
용산구 1167
 
15.2%
1층 294
 
3.8%
2층 170
 
2.2%
한남동 155
 
2.0%
이태원동 129
 
1.7%
한강대로 120
 
1.6%
이촌동 101
 
1.3%
한강로2가 93
 
1.2%
보광동 78
 
1.0%
Other values (1132) 4221
54.8%
2024-05-11T03:22:55.502184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6542
 
16.5%
1 1710
 
4.3%
1432
 
3.6%
1394
 
3.5%
1372
 
3.5%
1310
 
3.3%
, 1258
 
3.2%
2 1256
 
3.2%
( 1196
 
3.0%
) 1196
 
3.0%
Other values (266) 20957
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22562
56.9%
Space Separator 6542
 
16.5%
Decimal Number 6519
 
16.5%
Other Punctuation 1261
 
3.2%
Open Punctuation 1196
 
3.0%
Close Punctuation 1196
 
3.0%
Dash Punctuation 216
 
0.5%
Uppercase Letter 113
 
0.3%
Lowercase Letter 14
 
< 0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1432
 
6.3%
1394
 
6.2%
1372
 
6.1%
1310
 
5.8%
1195
 
5.3%
1177
 
5.2%
1169
 
5.2%
1168
 
5.2%
1168
 
5.2%
1053
 
4.7%
Other values (228) 10124
44.9%
Decimal Number
ValueCountFrequency (%)
1 1710
26.2%
2 1256
19.3%
3 652
 
10.0%
0 628
 
9.6%
4 543
 
8.3%
5 438
 
6.7%
6 372
 
5.7%
7 366
 
5.6%
9 321
 
4.9%
8 233
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
B 70
61.9%
A 11
 
9.7%
L 7
 
6.2%
G 7
 
6.2%
C 7
 
6.2%
D 7
 
6.2%
K 1
 
0.9%
R 1
 
0.9%
N 1
 
0.9%
U 1
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
b 4
28.6%
r 2
14.3%
j 1
 
7.1%
c 1
 
7.1%
e 1
 
7.1%
w 1
 
7.1%
o 1
 
7.1%
t 1
 
7.1%
k 1
 
7.1%
a 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 1258
99.8%
. 2
 
0.2%
? 1
 
0.1%
Space Separator
ValueCountFrequency (%)
6542
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1196
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1196
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 216
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22562
56.9%
Common 16934
42.7%
Latin 127
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1432
 
6.3%
1394
 
6.2%
1372
 
6.1%
1310
 
5.8%
1195
 
5.3%
1177
 
5.2%
1169
 
5.2%
1168
 
5.2%
1168
 
5.2%
1053
 
4.7%
Other values (228) 10124
44.9%
Latin
ValueCountFrequency (%)
B 70
55.1%
A 11
 
8.7%
L 7
 
5.5%
G 7
 
5.5%
C 7
 
5.5%
D 7
 
5.5%
b 4
 
3.1%
r 2
 
1.6%
K 1
 
0.8%
R 1
 
0.8%
Other values (10) 10
 
7.9%
Common
ValueCountFrequency (%)
6542
38.6%
1 1710
 
10.1%
, 1258
 
7.4%
2 1256
 
7.4%
( 1196
 
7.1%
) 1196
 
7.1%
3 652
 
3.9%
0 628
 
3.7%
4 543
 
3.2%
5 438
 
2.6%
Other values (8) 1515
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22562
56.9%
ASCII 17061
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6542
38.3%
1 1710
 
10.0%
, 1258
 
7.4%
2 1256
 
7.4%
( 1196
 
7.0%
) 1196
 
7.0%
3 652
 
3.8%
0 628
 
3.7%
4 543
 
3.2%
5 438
 
2.6%
Other values (28) 1642
 
9.6%
Hangul
ValueCountFrequency (%)
1432
 
6.3%
1394
 
6.2%
1372
 
6.1%
1310
 
5.8%
1195
 
5.3%
1177
 
5.2%
1169
 
5.2%
1168
 
5.2%
1168
 
5.2%
1053
 
4.7%
Other values (228) 10124
44.9%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct119
Distinct (%)10.2%
Missing793
Missing (%)40.6%
Infinite0
Infinite (%)0.0%
Mean4368.8984
Minimum4004
Maximum4428
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:22:56.174162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4004
5-th percentile4310
Q14337
median4376
Q34401
95-th percentile4426
Maximum4428
Range424
Interquartile range (IQR)64

Descriptive statistics

Standard deviation38.815501
Coefficient of variation (CV)0.0088845054
Kurtosis5.3311561
Mean4368.8984
Median Absolute Deviation (MAD)31
Skewness-0.79329174
Sum5072291
Variance1506.6431
MonotonicityNot monotonic
2024-05-11T03:22:56.638028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4378 74
 
3.8%
4426 41
 
2.1%
4313 40
 
2.0%
4427 38
 
1.9%
4391 28
 
1.4%
4394 27
 
1.4%
4345 27
 
1.4%
4315 25
 
1.3%
4387 25
 
1.3%
4385 24
 
1.2%
Other values (109) 812
41.6%
(Missing) 793
40.6%
ValueCountFrequency (%)
4004 1
 
0.1%
4300 6
0.3%
4301 2
 
0.1%
4302 1
 
0.1%
4303 5
0.3%
4304 3
 
0.2%
4305 9
0.5%
4306 1
 
0.1%
4307 8
0.4%
4308 3
 
0.2%
ValueCountFrequency (%)
4428 3
 
0.2%
4427 38
1.9%
4426 41
2.1%
4425 2
 
0.1%
4424 4
 
0.2%
4423 13
 
0.7%
4421 1
 
0.1%
4420 24
1.2%
4419 23
1.2%
4417 7
 
0.4%
Distinct1723
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
2024-05-11T03:22:57.557533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length28
Mean length6.0844422
Min length1

Characters and Unicode

Total characters11889
Distinct characters639
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1559 ?
Unique (%)79.8%

Sample

1st row김정혜헤어라인
2nd row선천
3rd row유미미용실
4th row가나다
5th row비너스
ValueCountFrequency (%)
미용실 38
 
1.6%
헤어 21
 
0.9%
용산점 18
 
0.7%
hair 18
 
0.7%
13
 
0.5%
nail 13
 
0.5%
에스테틱 10
 
0.4%
블루클럽 9
 
0.4%
9
 
0.4%
네일 9
 
0.4%
Other values (1895) 2274
93.5%
2024-05-11T03:22:59.460232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
637
 
5.4%
606
 
5.1%
478
 
4.0%
393
 
3.3%
321
 
2.7%
293
 
2.5%
274
 
2.3%
262
 
2.2%
251
 
2.1%
) 175
 
1.5%
Other values (629) 8199
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9634
81.0%
Lowercase Letter 760
 
6.4%
Uppercase Letter 535
 
4.5%
Space Separator 478
 
4.0%
Close Punctuation 175
 
1.5%
Open Punctuation 173
 
1.5%
Other Punctuation 76
 
0.6%
Decimal Number 53
 
0.4%
Math Symbol 2
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
637
 
6.6%
606
 
6.3%
393
 
4.1%
321
 
3.3%
293
 
3.0%
274
 
2.8%
262
 
2.7%
251
 
2.6%
144
 
1.5%
140
 
1.5%
Other values (556) 6313
65.5%
Lowercase Letter
ValueCountFrequency (%)
a 114
15.0%
i 91
12.0%
e 80
10.5%
r 55
 
7.2%
l 54
 
7.1%
o 50
 
6.6%
n 46
 
6.1%
h 41
 
5.4%
t 35
 
4.6%
u 33
 
4.3%
Other values (15) 161
21.2%
Uppercase Letter
ValueCountFrequency (%)
A 65
 
12.1%
N 45
 
8.4%
L 40
 
7.5%
E 39
 
7.3%
B 36
 
6.7%
S 30
 
5.6%
O 29
 
5.4%
H 27
 
5.0%
I 27
 
5.0%
M 23
 
4.3%
Other values (15) 174
32.5%
Decimal Number
ValueCountFrequency (%)
1 19
35.8%
2 10
18.9%
0 9
17.0%
3 6
 
11.3%
5 3
 
5.7%
9 2
 
3.8%
8 2
 
3.8%
7 1
 
1.9%
4 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
? 32
42.1%
& 18
23.7%
. 8
 
10.5%
' 8
 
10.5%
# 5
 
6.6%
, 3
 
3.9%
% 1
 
1.3%
: 1
 
1.3%
Space Separator
ValueCountFrequency (%)
478
100.0%
Close Punctuation
ValueCountFrequency (%)
) 175
100.0%
Open Punctuation
ValueCountFrequency (%)
( 173
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9634
81.0%
Latin 1295
 
10.9%
Common 960
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
637
 
6.6%
606
 
6.3%
393
 
4.1%
321
 
3.3%
293
 
3.0%
274
 
2.8%
262
 
2.7%
251
 
2.6%
144
 
1.5%
140
 
1.5%
Other values (556) 6313
65.5%
Latin
ValueCountFrequency (%)
a 114
 
8.8%
i 91
 
7.0%
e 80
 
6.2%
A 65
 
5.0%
r 55
 
4.2%
l 54
 
4.2%
o 50
 
3.9%
n 46
 
3.6%
N 45
 
3.5%
h 41
 
3.2%
Other values (40) 654
50.5%
Common
ValueCountFrequency (%)
478
49.8%
) 175
 
18.2%
( 173
 
18.0%
? 32
 
3.3%
1 19
 
2.0%
& 18
 
1.9%
2 10
 
1.0%
0 9
 
0.9%
. 8
 
0.8%
' 8
 
0.8%
Other values (13) 30
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9634
81.0%
ASCII 2255
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
637
 
6.6%
606
 
6.3%
393
 
4.1%
321
 
3.3%
293
 
3.0%
274
 
2.8%
262
 
2.7%
251
 
2.6%
144
 
1.5%
140
 
1.5%
Other values (556) 6313
65.5%
ASCII
ValueCountFrequency (%)
478
21.2%
) 175
 
7.8%
( 173
 
7.7%
a 114
 
5.1%
i 91
 
4.0%
e 80
 
3.5%
A 65
 
2.9%
r 55
 
2.4%
l 54
 
2.4%
o 50
 
2.2%
Other values (63) 920
40.8%
Distinct1466
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
Minimum1999-01-06 00:00:00
Maximum2024-05-09 10:24:06
2024-05-11T03:23:00.211529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:23:00.797730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
I
1513 
U
433 
D
 
8

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 1513
77.4%
U 433
 
22.2%
D 8
 
0.4%

Length

2024-05-11T03:23:01.385879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:01.787555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 1513
77.4%
u 433
 
22.2%
d 8
 
0.4%
Distinct551
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:09:00
2024-05-11T03:23:02.360251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:23:02.965502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
일반미용업
1478 
피부미용업
265 
네일아트업
164 
메이크업업
 
43
기타
 
4

Length

Max length5
Median length5
Mean length4.9938588
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 1478
75.6%
피부미용업 265
 
13.6%
네일아트업 164
 
8.4%
메이크업업 43
 
2.2%
기타 4
 
0.2%

Length

2024-05-11T03:23:03.562936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:04.061903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 1478
75.6%
피부미용업 265
 
13.6%
네일아트업 164
 
8.4%
메이크업업 43
 
2.2%
기타 4
 
0.2%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1078
Distinct (%)58.0%
Missing96
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean198181.02
Minimum192097.52
Maximum201187.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:04.809772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum192097.52
5-th percentile196271.89
Q1197037.18
median197804.89
Q3199585.49
95-th percentile200480.93
Maximum201187.7
Range9090.1765
Interquartile range (IQR)2548.304

Descriptive statistics

Standard deviation1417.9178
Coefficient of variation (CV)0.0071546599
Kurtosis-1.068681
Mean198181.02
Median Absolute Deviation (MAD)1097.8293
Skewness0.20185953
Sum3.6822033 × 108
Variance2010490.8
MonotonicityNot monotonic
2024-05-11T03:23:05.475761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
196861.350875235 29
 
1.5%
197527.309705578 18
 
0.9%
197811.954980013 18
 
0.9%
196762.077394917 16
 
0.8%
197011.0 16
 
0.8%
197450.277644237 16
 
0.8%
197011.737528847 15
 
0.8%
195547.140326252 14
 
0.7%
197221.439324261 13
 
0.7%
197071.403205622 13
 
0.7%
Other values (1068) 1690
86.5%
(Missing) 96
 
4.9%
ValueCountFrequency (%)
192097.519545642 1
 
0.1%
195547.140326252 14
0.7%
195563.535062555 2
 
0.1%
195563.788982635 2
 
0.1%
195624.541967859 1
 
0.1%
195626.865584206 2
 
0.1%
195636.891564481 1
 
0.1%
195698.926268495 1
 
0.1%
195719.498339194 2
 
0.1%
195751.834976202 2
 
0.1%
ValueCountFrequency (%)
201187.696032399 1
0.1%
201085.355495115 1
0.1%
201043.530898034 1
0.1%
200976.869287647 1
0.1%
200939.299261016 1
0.1%
200922.970395183 1
0.1%
200905.051752862 1
0.1%
200851.73321483 1
0.1%
200837.136549128 2
0.1%
200834.388023075 2
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1078
Distinct (%)58.0%
Missing96
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean448120.31
Minimum446114.16
Maximum450313.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:06.077616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum446114.16
5-th percentile446427.14
Q1447430.1
median448048.37
Q3448814.78
95-th percentile449713.65
Maximum450313.41
Range4199.258
Interquartile range (IQR)1384.6826

Descriptive statistics

Standard deviation954.68394
Coefficient of variation (CV)0.0021304188
Kurtosis-0.60359376
Mean448120.31
Median Absolute Deviation (MAD)650.77696
Skewness0.038638242
Sum8.3260754 × 108
Variance911421.43
MonotonicityNot monotonic
2024-05-11T03:23:06.628615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447226.720509901 29
 
1.5%
446787.344790036 18
 
0.9%
446248.255892098 18
 
0.9%
447480.039577359 16
 
0.8%
447404.0 16
 
0.8%
446437.197878548 16
 
0.8%
447430.100250997 15
 
0.8%
447976.775939762 14
 
0.7%
446524.508385914 13
 
0.7%
447073.62284187 13
 
0.7%
Other values (1068) 1690
86.5%
(Missing) 96
 
4.9%
ValueCountFrequency (%)
446114.155238838 6
 
0.3%
446178.85892605 4
 
0.2%
446194.350546744 2
 
0.1%
446202.36502252 2
 
0.1%
446221.69071399 1
 
0.1%
446233.81723078 5
 
0.3%
446242.346265603 1
 
0.1%
446243.198701013 2
 
0.1%
446246.110967125 4
 
0.2%
446248.255892098 18
0.9%
ValueCountFrequency (%)
450313.413235717 1
0.1%
450272.711607398 1
0.1%
450247.06018934 2
0.1%
450244.186706939 1
0.1%
450218.180711465 1
0.1%
450213.426129678 1
0.1%
450186.758623284 2
0.1%
450171.953371765 1
0.1%
450170.223836736 1
0.1%
450126.951675139 2
0.1%

위생업태명
Categorical

Distinct17
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
미용업
700 
일반미용업
574 
<NA>
300 
피부미용업
168 
종합미용업
 
63
Other values (12)
149 

Length

Max length23
Median length19
Mean length4.6816786
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 700
35.8%
일반미용업 574
29.4%
<NA> 300
15.4%
피부미용업 168
 
8.6%
종합미용업 63
 
3.2%
네일미용업 52
 
2.7%
피부미용업, 네일미용업 23
 
1.2%
일반미용업, 네일미용업, 화장ㆍ분장 미용업 17
 
0.9%
일반미용업, 화장ㆍ분장 미용업 12
 
0.6%
네일미용업, 화장ㆍ분장 미용업 9
 
0.5%
Other values (7) 36
 
1.8%

Length

2024-05-11T03:23:07.113268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 763
35.7%
일반미용업 616
28.9%
na 300
 
14.1%
피부미용업 214
 
10.0%
네일미용업 116
 
5.4%
종합미용업 63
 
3.0%
화장ㆍ분장 63
 
3.0%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)1.1%
Missing562
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean0.62931034
Minimum0
Maximum42
Zeros1117
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:07.617463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.45
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4822481
Coefficient of variation (CV)3.9443943
Kurtosis175.38871
Mean0.62931034
Median Absolute Deviation (MAD)0
Skewness11.741904
Sum876
Variance6.1615558
MonotonicityNot monotonic
2024-05-11T03:23:08.007196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1117
57.2%
2 82
 
4.2%
1 77
 
3.9%
3 46
 
2.4%
4 34
 
1.7%
5 20
 
1.0%
6 4
 
0.2%
7 3
 
0.2%
10 2
 
0.1%
17 1
 
0.1%
Other values (6) 6
 
0.3%
(Missing) 562
28.8%
ValueCountFrequency (%)
0 1117
57.2%
1 77
 
3.9%
2 82
 
4.2%
3 46
 
2.4%
4 34
 
1.7%
5 20
 
1.0%
6 4
 
0.2%
7 3
 
0.2%
9 1
 
0.1%
10 2
 
0.1%
ValueCountFrequency (%)
42 1
 
0.1%
40 1
 
0.1%
39 1
 
0.1%
37 1
 
0.1%
17 1
 
0.1%
12 1
 
0.1%
10 2
0.1%
9 1
 
0.1%
7 3
0.2%
6 4
0.2%

건물지하층수
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct8
Distinct (%)0.6%
Missing672
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean0.099063963
Minimum0
Maximum40
Zeros1224
Zeros (%)62.6%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:08.544372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1966708
Coefficient of variation (CV)12.079779
Kurtosis969.92908
Mean0.099063963
Median Absolute Deviation (MAD)0
Skewness29.557186
Sum127
Variance1.432021
MonotonicityNot monotonic
2024-05-11T03:23:09.073657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1224
62.6%
1 45
 
2.3%
2 5
 
0.3%
3 4
 
0.2%
4 1
 
0.1%
40 1
 
0.1%
7 1
 
0.1%
9 1
 
0.1%
(Missing) 672
34.4%
ValueCountFrequency (%)
0 1224
62.6%
1 45
 
2.3%
2 5
 
0.3%
3 4
 
0.2%
4 1
 
0.1%
7 1
 
0.1%
9 1
 
0.1%
40 1
 
0.1%
ValueCountFrequency (%)
40 1
 
0.1%
9 1
 
0.1%
7 1
 
0.1%
4 1
 
0.1%
3 4
 
0.2%
2 5
 
0.3%
1 45
 
2.3%
0 1224
62.6%

사용시작지상층
Real number (ℝ)

MISSING  ZEROS 

Distinct9
Distinct (%)0.8%
Missing893
Missing (%)45.7%
Infinite0
Infinite (%)0.0%
Mean0.61545712
Minimum0
Maximum8
Zeros657
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:09.629132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0635942
Coefficient of variation (CV)1.7281369
Kurtosis11.775958
Mean0.61545712
Median Absolute Deviation (MAD)0
Skewness2.8827771
Sum653
Variance1.1312326
MonotonicityNot monotonic
2024-05-11T03:23:10.147932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 657
33.6%
1 263
 
13.5%
2 91
 
4.7%
3 26
 
1.3%
5 11
 
0.6%
4 5
 
0.3%
6 4
 
0.2%
8 3
 
0.2%
7 1
 
0.1%
(Missing) 893
45.7%
ValueCountFrequency (%)
0 657
33.6%
1 263
13.5%
2 91
 
4.7%
3 26
 
1.3%
4 5
 
0.3%
5 11
 
0.6%
6 4
 
0.2%
7 1
 
0.1%
8 3
 
0.2%
ValueCountFrequency (%)
8 3
 
0.2%
7 1
 
0.1%
6 4
 
0.2%
5 11
 
0.6%
4 5
 
0.3%
3 26
 
1.3%
2 91
 
4.7%
1 263
13.5%
0 657
33.6%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct10
Distinct (%)2.5%
Missing1548
Missing (%)79.2%
Infinite0
Infinite (%)0.0%
Mean2.9359606
Minimum0
Maximum701
Zeros97
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:10.588095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum701
Range701
Interquartile range (IQR)1

Descriptive statistics

Standard deviation34.749557
Coefficient of variation (CV)11.835839
Kurtosis405.07429
Mean2.9359606
Median Absolute Deviation (MAD)1
Skewness20.115142
Sum1192
Variance1207.5317
MonotonicityNot monotonic
2024-05-11T03:23:11.007193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 201
 
10.3%
0 97
 
5.0%
2 67
 
3.4%
3 24
 
1.2%
5 10
 
0.5%
4 3
 
0.2%
701 1
 
0.1%
7 1
 
0.1%
6 1
 
0.1%
9 1
 
0.1%
(Missing) 1548
79.2%
ValueCountFrequency (%)
0 97
5.0%
1 201
10.3%
2 67
 
3.4%
3 24
 
1.2%
4 3
 
0.2%
5 10
 
0.5%
6 1
 
0.1%
7 1
 
0.1%
9 1
 
0.1%
701 1
 
0.1%
ValueCountFrequency (%)
701 1
 
0.1%
9 1
 
0.1%
7 1
 
0.1%
6 1
 
0.1%
5 10
 
0.5%
4 3
 
0.2%
3 24
 
1.2%
2 67
 
3.4%
1 201
10.3%
0 97
5.0%
Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
<NA>
1182 
0
708 
1
 
51
2
 
13

Length

Max length4
Median length4
Mean length2.814739
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
<NA> 1182
60.5%
0 708
36.2%
1 51
 
2.6%
2 13
 
0.7%

Length

2024-05-11T03:23:11.438347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:11.880862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1182
60.5%
0 708
36.2%
1 51
 
2.6%
2 13
 
0.7%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
<NA>
1776 
0
 
134
1
 
36
2
 
8

Length

Max length4
Median length4
Mean length3.7267144
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> 1776
90.9%
0 134
 
6.9%
1 36
 
1.8%
2 8
 
0.4%

Length

2024-05-11T03:23:12.466756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:12.991085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1776
90.9%
0 134
 
6.9%
1 36
 
1.8%
2 8
 
0.4%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
0
1263 
<NA>
691 

Length

Max length4
Median length1
Mean length2.0609007
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1263
64.6%
<NA> 691
35.4%

Length

2024-05-11T03:23:13.605365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:14.077318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1263
64.6%
na 691
35.4%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
0
1263 
<NA>
691 

Length

Max length4
Median length1
Mean length2.0609007
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1263
64.6%
<NA> 691
35.4%

Length

2024-05-11T03:23:14.666945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:15.154078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1263
64.6%
na 691
35.4%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
0
1263 
<NA>
691 

Length

Max length4
Median length1
Mean length2.0609007
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1263
64.6%
<NA> 691
35.4%

Length

2024-05-11T03:23:15.700640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:16.371824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1263
64.6%
na 691
35.4%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)0.1%
Missing309
Missing (%)15.8%
Memory size3.9 KiB
False
1645 
(Missing)
309 
ValueCountFrequency (%)
False 1645
84.2%
(Missing) 309
 
15.8%
2024-05-11T03:23:16.860981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)1.4%
Missing350
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean3.3996259
Minimum0
Maximum32
Zeros215
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:17.776622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7189912
Coefficient of variation (CV)0.79979129
Kurtosis15.184753
Mean3.3996259
Median Absolute Deviation (MAD)1
Skewness2.6790913
Sum5453
Variance7.3929131
MonotonicityNot monotonic
2024-05-11T03:23:18.410064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 523
26.8%
2 287
14.7%
4 236
12.1%
0 215
11.0%
5 114
 
5.8%
6 69
 
3.5%
7 34
 
1.7%
8 32
 
1.6%
1 23
 
1.2%
10 18
 
0.9%
Other values (12) 53
 
2.7%
(Missing) 350
17.9%
ValueCountFrequency (%)
0 215
11.0%
1 23
 
1.2%
2 287
14.7%
3 523
26.8%
4 236
12.1%
5 114
 
5.8%
6 69
 
3.5%
7 34
 
1.7%
8 32
 
1.6%
9 17
 
0.9%
ValueCountFrequency (%)
32 1
 
0.1%
24 1
 
0.1%
21 2
 
0.1%
18 3
 
0.2%
17 1
 
0.1%
16 3
 
0.2%
15 1
 
0.1%
14 6
0.3%
13 3
 
0.2%
12 12
0.6%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1954
Missing (%)100.0%
Memory size17.3 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1954
Missing (%)100.0%
Memory size17.3 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1954
Missing (%)100.0%
Memory size17.3 KiB

건물소유구분명
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
<NA>
1707 
임대
240 
자가
 
7

Length

Max length4
Median length4
Mean length3.7471853
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> 1707
87.4%
임대 240
 
12.3%
자가 7
 
0.4%

Length

2024-05-11T03:23:19.360645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:20.184906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1707
87.4%
임대 240
 
12.3%
자가 7
 
0.4%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
<NA>
1263 
0
691 

Length

Max length4
Median length4
Mean length2.9390993
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> 1263
64.6%
0 691
35.4%

Length

2024-05-11T03:23:20.712890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:21.173320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1263
64.6%
0 691
35.4%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct10
Distinct (%)2.4%
Missing1534
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0.35
Minimum0
Maximum14
Zeros345
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:21.513320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.284709
Coefficient of variation (CV)3.6705973
Kurtosis58.183586
Mean0.35
Median Absolute Deviation (MAD)0
Skewness6.9721739
Sum147
Variance1.6504773
MonotonicityNot monotonic
2024-05-11T03:23:21.808216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 345
 
17.7%
1 55
 
2.8%
2 11
 
0.6%
5 3
 
0.2%
14 1
 
0.1%
10 1
 
0.1%
12 1
 
0.1%
8 1
 
0.1%
4 1
 
0.1%
7 1
 
0.1%
(Missing) 1534
78.5%
ValueCountFrequency (%)
0 345
17.7%
1 55
 
2.8%
2 11
 
0.6%
4 1
 
0.1%
5 3
 
0.2%
7 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
12 1
 
0.1%
14 1
 
0.1%
ValueCountFrequency (%)
14 1
 
0.1%
12 1
 
0.1%
10 1
 
0.1%
8 1
 
0.1%
7 1
 
0.1%
5 3
 
0.2%
4 1
 
0.1%
2 11
 
0.6%
1 55
 
2.8%
0 345
17.7%

남성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)1.4%
Missing1537
Missing (%)78.7%
Infinite0
Infinite (%)0.0%
Mean0.059952038
Minimum0
Maximum5
Zeros406
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:22.135119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.44802236
Coefficient of variation (CV)7.473013
Kurtosis89.328198
Mean0.059952038
Median Absolute Deviation (MAD)0
Skewness9.1292779
Sum25
Variance0.20072404
MonotonicityNot monotonic
2024-05-11T03:23:22.553483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 406
 
20.8%
1 6
 
0.3%
5 2
 
0.1%
4 1
 
0.1%
3 1
 
0.1%
2 1
 
0.1%
(Missing) 1537
78.7%
ValueCountFrequency (%)
0 406
20.8%
1 6
 
0.3%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 2
 
0.1%
ValueCountFrequency (%)
5 2
 
0.1%
4 1
 
0.1%
3 1
 
0.1%
2 1
 
0.1%
1 6
 
0.3%
0 406
20.8%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
<NA>
1283 
0
671 

Length

Max length4
Median length4
Mean length2.9698055
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> 1283
65.7%
0 671
34.3%

Length

2024-05-11T03:23:22.971996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:23:23.357287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1283
65.7%
0 671
34.3%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)2.0%
Missing1296
Missing (%)66.3%
Infinite0
Infinite (%)0.0%
Mean0.98480243
Minimum0
Maximum12
Zeros450
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2024-05-11T03:23:23.632788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9459391
Coefficient of variation (CV)1.975969
Kurtosis7.1489414
Mean0.98480243
Median Absolute Deviation (MAD)0
Skewness2.5648952
Sum648
Variance3.7866789
MonotonicityNot monotonic
2024-05-11T03:23:23.998698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 450
 
23.0%
2 66
 
3.4%
1 52
 
2.7%
3 25
 
1.3%
4 18
 
0.9%
5 16
 
0.8%
7 9
 
0.5%
6 9
 
0.5%
8 6
 
0.3%
9 2
 
0.1%
Other values (3) 5
 
0.3%
(Missing) 1296
66.3%
ValueCountFrequency (%)
0 450
23.0%
1 52
 
2.7%
2 66
 
3.4%
3 25
 
1.3%
4 18
 
0.9%
5 16
 
0.8%
6 9
 
0.5%
7 9
 
0.5%
8 6
 
0.3%
9 2
 
0.1%
ValueCountFrequency (%)
12 1
 
0.1%
11 2
 
0.1%
10 2
 
0.1%
9 2
 
0.1%
8 6
 
0.3%
7 9
 
0.5%
6 9
 
0.5%
5 16
0.8%
4 18
0.9%
3 25
1.3%

다중이용업소여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing300
Missing (%)15.4%
Memory size3.9 KiB
False
1652 
True
 
2
(Missing)
300 
ValueCountFrequency (%)
False 1652
84.5%
True 2
 
0.1%
(Missing) 300
 
15.4%
2024-05-11T03:23:24.226340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
030200003020000-204-1960-0096219601230<NA>3폐업2폐업19950627<NA><NA><NA>02 79369660.0140871서울특별시 용산구 한강로2가 179-0번지<NA><NA>김정혜헤어라인2001-09-27 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
130200003020000-204-1960-0102519601231<NA>3폐업2폐업19940517<NA><NA><NA>020753601421.28140833서울특별시 용산구 용산동2가 2-0번지<NA><NA>선천2001-09-27 00:00:00I2018-08-31 23:59:59.0일반미용업198200.056058448877.743778미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
230200003020000-204-1962-0052919620118<NA>3폐업2폐업20050120<NA><NA><NA>02 792669423.01140883서울특별시 용산구 한강로3가 65-54번지<NA><NA>유미미용실1999-06-18 00:00:00I2018-08-31 23:59:59.0일반미용업196756.675436446758.473859미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
330200003020000-204-1963-0102319630628<NA>3폐업2폐업19950412<NA><NA><NA>02 794376721.0140858서울특별시 용산구 이태원동 13-0번지<NA><NA>가나다2001-09-27 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
430200003020000-204-1964-0097819641016<NA>3폐업2폐업20020917<NA><NA><NA>020795288320.7140823서울특별시 용산구 보광동 238-25번지<NA><NA>비너스2002-09-24 00:00:00I2018-08-31 23:59:59.0일반미용업199722.09222447593.557975미용업000<NA>0<NA>000N4<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
530200003020000-204-1965-0101519650710<NA>3폐업2폐업19951117<NA><NA><NA>02 754587217.36140902서울특별시 용산구 후암동 404-0번지<NA><NA>대성2001-09-27 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
630200003020000-204-1967-0099419670914<NA>3폐업2폐업19940622<NA><NA><NA>02 797769315.15140895서울특별시 용산구 한남동 732-25번지<NA><NA>코스모스2001-09-27 00:00:00I2018-08-31 23:59:59.0일반미용업199671.078758447935.537305미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
730200003020000-204-1967-0101319670227<NA>3폐업2폐업20010302<NA><NA><NA>020793324312.8140160서울특별시 용산구 남영동 54번지<NA><NA>미지2003-02-20 00:00:00I2018-08-31 23:59:59.0일반미용업197601.525042448967.140758미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
830200003020000-204-1968-0097019680824<NA>3폐업2폐업19991005<NA><NA><NA>02 717302917.2140846서울특별시 용산구 원효로1가 27-25번지<NA><NA>순애1999-10-05 00:00:00I2018-08-31 23:59:59.0일반미용업196897.368624448543.271211미용업000<NA>0<NA>000N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
930200003020000-204-1968-0100219680612<NA>3폐업2폐업20020701<NA><NA><NA>020713146015.28140832서울특별시 용산구 용문동 28-21번지<NA><NA>금강1999-03-10 00:00:00I2018-08-31 23:59:59.0일반미용업196431.643261448310.569216미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
194430200003020000-226-2017-000012017-08-03<NA>1영업/정상1영업<NA><NA><NA><NA><NA>57.67140-871서울특별시 용산구 한강로2가 342 지하1층 163호서울특별시 용산구 한강대로 95, 지하1층 163호 (한강로2가)4378네일마젠타2024-04-03 10:00:41U2023-12-04 00:05:00.0네일아트업197011.737529447430.100251<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
194530200003020000-226-2017-0000220170926<NA>3폐업2폐업20190311<NA><NA><NA>02 749968940.0140040서울특별시 용산구 산천동 202번지 삼성테마트 120호서울특별시 용산구 원효로 51, 120호 (산천동, 삼성테마트)4359지나뷰티2019-03-11 13:05:43U2019-03-13 02:40:00.0피부미용업195547.140326447976.77594피부미용업, 네일미용업, 화장ㆍ분장 미용업0022<NA><NA>000N0<NA><NA><NA>임대01001N
194630200003020000-226-2017-0000320171031<NA>1영업/정상1영업<NA><NA><NA><NA>02 790353330.0140863서울특별시 용산구 이태원동 74-13번지 1층서울특별시 용산구 이태원로20길 21, 1층 (이태원동)4391유스(Youth)뷰티2018-10-01 10:03:04I2018-10-03 02:35:52.0네일아트업199254.388891447907.927107피부미용업, 네일미용업, 화장ㆍ분장 미용업0011<NA><NA>000N2<NA><NA><NA>임대01000N
194730200003020000-226-2018-0000120181018<NA>3폐업2폐업20201211<NA><NA><NA>02 3272200329.31140111서울특별시 용산구 원효로1가 133-3 리첸시아 용산서울특별시 용산구 백범로 341, A동 1층 121호 (원효로1가, 리첸시아 용산)4315네일스토리2020-12-11 11:41:12U2020-12-13 02:40:00.0네일아트업197045.849468448447.558967피부미용업, 네일미용업, 화장ㆍ분장 미용업101<NA><NA><NA>000N3<NA><NA><NA><NA>00001N
194830200003020000-226-2018-0000220181109<NA>1영업/정상1영업<NA><NA><NA><NA><NA>40.86140100서울특별시 용산구 문배동 11-14번지 이안용산 101동 107호서울특별시 용산구 백범로90길 74, 101동 107호 (문배동, 이안용산)4315Nailash (네일래쉬)2018-11-09 14:48:17I2018-11-11 02:36:57.0네일아트업197248.654675448360.574655피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N5<NA><NA><NA><NA>00002N
194930200003020000-226-2019-0000120190114<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.96140889서울특별시 용산구 한남동 96-3번지 신성미소시티서울특별시 용산구 독서당로 39, 101동 1층 108-1호 (한남동, 신성미소시티)4410네일샵,입니다2019-01-14 15:43:19I2019-01-16 02:20:50.0네일아트업200559.366155447696.887166피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N9<NA><NA><NA><NA>00001N
195030200003020000-226-2021-0000120210405<NA>1영업/정상1영업<NA><NA><NA><NA><NA>149.1140780서울특별시 용산구 한강로3가 40-999 용산역서울특별시 용산구 한강대로23길 55, 에이치디씨 아이파크몰 리빙파크 8층 (한강로3가)4377반디인하우스 용산아이파크몰점2021-04-05 17:11:14I2021-04-07 00:22:58.0네일아트업196762.077395447480.039577피부미용업, 네일미용업, 화장ㆍ분장 미용업008<NA><NA><NA>000N8<NA><NA><NA>임대00005N
195130200003020000-226-2021-0000220210830<NA>1영업/정상1영업<NA><NA><NA><NA><NA>82.4140882서울특별시 용산구 한강로3가 63-70서울특별시 용산구 서빙고로 17, 용산센트럴파크 해링턴스퀘어 판매시설동 1층 24호 (한강로3가)4387래쉬블랑2021-08-30 11:42:42I2021-09-01 00:22:50.0메이크업업197071.403206447073.622842피부미용업, 네일미용업, 화장ㆍ분장 미용업000000000N5<NA><NA><NA><NA>00005N
195230200003020000-226-2021-0000320211231<NA>1영업/정상1영업<NA><NA><NA><NA><NA>33.0140012서울특별시 용산구 한강로2가 420 용산푸르지오써밋서울특별시 용산구 한강대로 69, 상가동 B110-1호 (한강로2가, 용산푸르지오써밋)4378네일노마드2021-12-31 14:21:05I2022-01-02 00:22:41.0네일아트업196861.350875447226.72051피부미용업, 네일미용업, 화장ㆍ분장 미용업000000000N5<NA><NA><NA><NA>00001N
195330200003020000-226-2023-000012023-06-14<NA>1영업/정상1영업<NA><NA><NA><NA><NA>52.0140-133서울특별시 용산구 청파동3가 24-73서울특별시 용산구 청파로45길 12-1, 1층 (청파동3가)4313손톱까끼2023-06-14 14:35:56I2022-12-05 23:06:00.0피부미용업197273.276416449131.435814<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>