Overview

Dataset statistics

Number of variables47
Number of observations3154
Missing cells36613
Missing cells (%)24.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory405.0 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
Author노원구
URLhttps://data.seoul.go.kr/dataList/OA-17912/S/1/datasetView.do

Alerts

개방자치단체코드 has constant value ""Constant
발한실여부 has constant value ""Constant
다중이용업소여부 has constant value ""Constant
업태구분명 is highly imbalanced (50.3%)Imbalance
건물지하층수 is highly imbalanced (58.1%)Imbalance
사용끝지하층 is highly imbalanced (52.1%)Imbalance
남성종사자수 is highly imbalanced (53.3%)Imbalance
인허가취소일자 has 3154 (100.0%) missing valuesMissing
폐업일자 has 1292 (41.0%) missing valuesMissing
휴업시작일자 has 3154 (100.0%) missing valuesMissing
휴업종료일자 has 3154 (100.0%) missing valuesMissing
재개업일자 has 3154 (100.0%) missing valuesMissing
전화번호 has 1093 (34.7%) missing valuesMissing
도로명주소 has 936 (29.7%) missing valuesMissing
도로명우편번호 has 953 (30.2%) missing valuesMissing
좌표정보(X) has 99 (3.1%) missing valuesMissing
좌표정보(Y) has 99 (3.1%) missing valuesMissing
건물지상층수 has 1046 (33.2%) missing valuesMissing
사용시작지상층 has 1609 (51.0%) missing valuesMissing
사용끝지상층 has 1706 (54.1%) missing valuesMissing
발한실여부 has 646 (20.5%) missing valuesMissing
좌석수 has 741 (23.5%) missing valuesMissing
조건부허가신고사유 has 3154 (100.0%) missing valuesMissing
조건부허가시작일자 has 3154 (100.0%) missing valuesMissing
조건부허가종료일자 has 3154 (100.0%) missing valuesMissing
여성종사자수 has 2097 (66.5%) missing valuesMissing
침대수 has 1606 (50.9%) missing valuesMissing
다중이용업소여부 has 611 (19.4%) missing valuesMissing
사용시작지상층 is highly skewed (γ1 = 24.68043415)Skewed
사용끝지상층 is highly skewed (γ1 = 28.31874707)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 164 (5.2%) zerosZeros
건물지상층수 has 1796 (56.9%) zerosZeros
사용시작지상층 has 460 (14.6%) zerosZeros
사용끝지상층 has 192 (6.1%) zerosZeros
좌석수 has 211 (6.7%) zerosZeros
여성종사자수 has 804 (25.5%) zerosZeros
침대수 has 1192 (37.8%) zerosZeros

Reproduction

Analysis started2024-05-11 04:08:39.013291
Analysis finished2024-05-11 04:08:42.958142
Duration3.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
3100000
3154 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3100000
2nd row3100000
3rd row3100000
4th row3100000
5th row3100000

Common Values

ValueCountFrequency (%)
3100000 3154
100.0%

Length

2024-05-11T04:08:43.262934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:08:43.633748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3100000 3154
100.0%

관리번호
Text

UNIQUE 

Distinct3154
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
2024-05-11T04:08:44.160699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters69388
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

Unique3154 ?
Unique (%)100.0%

Sample

1st row3100000-204-1969-00482
2nd row3100000-204-1971-00454
3rd row3100000-204-1971-00665
4th row3100000-204-1972-00467
5th row3100000-204-1973-00468
ValueCountFrequency (%)
3100000-204-1969-00482 1
 
< 0.1%
3100000-211-2023-00018 1
 
< 0.1%
3100000-211-2024-00025 1
 
< 0.1%
3100000-212-2007-00002 1
 
< 0.1%
3100000-211-2024-00026 1
 
< 0.1%
3100000-211-2024-00027 1
 
< 0.1%
3100000-211-2024-00028 1
 
< 0.1%
3100000-211-2024-00029 1
 
< 0.1%
3100000-211-2024-00030 1
 
< 0.1%
3100000-211-2024-00031 1
 
< 0.1%
Other values (3144) 3144
99.7%
2024-05-11T04:08:45.482716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30591
44.1%
- 9462
 
13.6%
1 9139
 
13.2%
2 8045
 
11.6%
3 4449
 
6.4%
4 2278
 
3.3%
9 1885
 
2.7%
5 1078
 
1.6%
8 935
 
1.3%
6 782
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59926
86.4%
Dash Punctuation 9462
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30591
51.0%
1 9139
 
15.3%
2 8045
 
13.4%
3 4449
 
7.4%
4 2278
 
3.8%
9 1885
 
3.1%
5 1078
 
1.8%
8 935
 
1.6%
6 782
 
1.3%
7 744
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 9462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69388
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30591
44.1%
- 9462
 
13.6%
1 9139
 
13.2%
2 8045
 
11.6%
3 4449
 
6.4%
4 2278
 
3.3%
9 1885
 
2.7%
5 1078
 
1.6%
8 935
 
1.3%
6 782
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30591
44.1%
- 9462
 
13.6%
1 9139
 
13.2%
2 8045
 
11.6%
3 4449
 
6.4%
4 2278
 
3.3%
9 1885
 
2.7%
5 1078
 
1.6%
8 935
 
1.3%
6 782
 
1.1%
Distinct2359
Distinct (%)74.8%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
Minimum1969-05-29 00:00:00
Maximum2024-05-09 00:00:00
2024-05-11T04:08:45.943537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:08:46.456827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3154
Missing (%)100.0%
Memory size27.8 KiB
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
3
1862 
1
1292 

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 1862
59.0%
1 1292
41.0%

Length

2024-05-11T04:08:46.884357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:08:47.251997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1862
59.0%
1 1292
41.0%

영업상태명
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
폐업
1862 
영업/정상
1292 

Length

Max length5
Median length2
Mean length3.2289157
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 1862
59.0%
영업/정상 1292
41.0%

Length

2024-05-11T04:08:47.619730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:08:47.943454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1862
59.0%
영업/정상 1292
41.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
2
1862 
1
1292 

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 1862
59.0%
1 1292
41.0%

Length

2024-05-11T04:08:48.351420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:08:48.658886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1862
59.0%
1 1292
41.0%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
폐업
1862 
영업
1292 

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 1862
59.0%
영업 1292
41.0%

Length

2024-05-11T04:08:49.007535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:08:49.322238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 1862
59.0%
영업 1292
41.0%

폐업일자
Date

MISSING 

Distinct1503
Distinct (%)80.7%
Missing1292
Missing (%)41.0%
Memory size24.8 KiB
Minimum1992-11-03 00:00:00
Maximum2024-05-08 00:00:00
2024-05-11T04:08:49.670565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:08:50.125195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3154
Missing (%)100.0%
Memory size27.8 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3154
Missing (%)100.0%
Memory size27.8 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3154
Missing (%)100.0%
Memory size27.8 KiB

전화번호
Text

MISSING 

Distinct1865
Distinct (%)90.5%
Missing1093
Missing (%)34.7%
Memory size24.8 KiB
2024-05-11T04:08:50.802366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.649685
Min length2

Characters and Unicode

Total characters21949
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1721 ?
Unique (%)83.5%

Sample

1st row02 9731387
2nd row02 9329968
3rd row02 0
4th row02 9365146
5th row02 9366558
ValueCountFrequency (%)
02 1651
37.3%
930 47
 
1.1%
070 47
 
1.1%
933 41
 
0.9%
939 40
 
0.9%
931 34
 
0.8%
932 33
 
0.7%
00000 31
 
0.7%
977 31
 
0.7%
934 29
 
0.7%
Other values (1909) 2437
55.1%
2024-05-11T04:08:51.994266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3649
16.6%
2 3160
14.4%
3048
13.9%
9 2893
13.2%
3 2246
10.2%
7 1542
7.0%
1 1257
 
5.7%
5 1180
 
5.4%
8 1049
 
4.8%
6 970
 
4.4%
Other values (2) 955
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18898
86.1%
Space Separator 3048
 
13.9%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3649
19.3%
2 3160
16.7%
9 2893
15.3%
3 2246
11.9%
7 1542
8.2%
1 1257
 
6.7%
5 1180
 
6.2%
8 1049
 
5.6%
6 970
 
5.1%
4 952
 
5.0%
Space Separator
ValueCountFrequency (%)
3048
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3649
16.6%
2 3160
14.4%
3048
13.9%
9 2893
13.2%
3 2246
10.2%
7 1542
7.0%
1 1257
 
5.7%
5 1180
 
5.4%
8 1049
 
4.8%
6 970
 
4.4%
Other values (2) 955
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3649
16.6%
2 3160
14.4%
3048
13.9%
9 2893
13.2%
3 2246
10.2%
7 1542
7.0%
1 1257
 
5.7%
5 1180
 
5.4%
8 1049
 
4.8%
6 970
 
4.4%
Other values (2) 955
 
4.4%

소재지면적
Real number (ℝ)

ZEROS 

Distinct1547
Distinct (%)49.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.301881
Minimum0
Maximum661
Zeros164
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:08:52.608777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median28.46
Q345.36
95-th percentile113.796
Maximum661
Range661
Interquartile range (IQR)25.36

Descriptive statistics

Standard deviation41.517038
Coefficient of variation (CV)1.0301514
Kurtosis40.677655
Mean40.301881
Median Absolute Deviation (MAD)10.74
Skewness4.7271872
Sum127071.83
Variance1723.6644
MonotonicityNot monotonic
2024-05-11T04:08:53.273770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 164
 
5.2%
33.0 69
 
2.2%
26.4 36
 
1.1%
23.0 35
 
1.1%
20.0 30
 
1.0%
30.0 28
 
0.9%
19.8 26
 
0.8%
24.0 26
 
0.8%
23.1 26
 
0.8%
18.0 22
 
0.7%
Other values (1537) 2691
85.3%
ValueCountFrequency (%)
0.0 164
5.2%
3.3 1
 
< 0.1%
3.96 1
 
< 0.1%
6.0 2
 
0.1%
6.37 1
 
< 0.1%
6.5 1
 
< 0.1%
6.6 4
 
0.1%
6.67 1
 
< 0.1%
7.0 3
 
0.1%
7.54 1
 
< 0.1%
ValueCountFrequency (%)
661.0 1
< 0.1%
583.78 1
< 0.1%
423.0 2
0.1%
413.25 1
< 0.1%
412.0 1
< 0.1%
389.76 1
< 0.1%
362.0 1
< 0.1%
310.2 1
< 0.1%
295.68 1
< 0.1%
271.07 1
< 0.1%
Distinct177
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
2024-05-11T04:08:54.546500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.1176284
Min length6

Characters and Unicode

Total characters19295
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

Unique42 ?
Unique (%)1.3%

Sample

1st row139200
2nd row139863
3rd row139862
4th row139200
5th row139-821
ValueCountFrequency (%)
139240 218
 
6.9%
139816 155
 
4.9%
139200 132
 
4.2%
139821 103
 
3.3%
139837 94
 
3.0%
139800 89
 
2.8%
139832 83
 
2.6%
139804 79
 
2.5%
139838 69
 
2.2%
139861 64
 
2.0%
Other values (167) 2068
65.6%
2024-05-11T04:08:56.420132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4051
21.0%
3 3806
19.7%
9 3381
17.5%
8 2800
14.5%
0 1418
 
7.3%
2 1221
 
6.3%
4 776
 
4.0%
6 652
 
3.4%
7 436
 
2.3%
5 383
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18924
98.1%
Dash Punctuation 371
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4051
21.4%
3 3806
20.1%
9 3381
17.9%
8 2800
14.8%
0 1418
 
7.5%
2 1221
 
6.5%
4 776
 
4.1%
6 652
 
3.4%
7 436
 
2.3%
5 383
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19295
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4051
21.0%
3 3806
19.7%
9 3381
17.5%
8 2800
14.5%
0 1418
 
7.3%
2 1221
 
6.3%
4 776
 
4.0%
6 652
 
3.4%
7 436
 
2.3%
5 383
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4051
21.0%
3 3806
19.7%
9 3381
17.5%
8 2800
14.5%
0 1418
 
7.3%
2 1221
 
6.3%
4 776
 
4.0%
6 652
 
3.4%
7 436
 
2.3%
5 383
 
2.0%
Distinct2699
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
2024-05-11T04:08:57.299659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length45
Mean length27.167724
Min length17

Characters and Unicode

Total characters85687
Distinct characters327
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

Unique2377 ?
Unique (%)75.4%

Sample

1st row서울특별시 노원구 상계동 105-0번지
2nd row서울특별시 노원구 중계동 505번지 롯데상가 2층6호
3rd row서울특별시 노원구 중계동 426-0번지
4th row서울특별시 노원구 상계동 173-256번지
5th row서울특별시 노원구 상계동 1284-101
ValueCountFrequency (%)
서울특별시 3154
19.5%
노원구 3154
19.5%
상계동 1567
 
9.7%
공릉동 581
 
3.6%
중계동 537
 
3.3%
월계동 353
 
2.2%
1층 188
 
1.2%
2층 132
 
0.8%
하계동 120
 
0.7%
101호 71
 
0.4%
Other values (2913) 6309
39.0%
2024-05-11T04:08:58.869817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15074
 
17.6%
1 3785
 
4.4%
3482
 
4.1%
3255
 
3.8%
3213
 
3.7%
3210
 
3.7%
3172
 
3.7%
3170
 
3.7%
3163
 
3.7%
3154
 
3.7%
Other values (317) 41009
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49887
58.2%
Decimal Number 17780
 
20.7%
Space Separator 15074
 
17.6%
Dash Punctuation 2449
 
2.9%
Other Punctuation 226
 
0.3%
Open Punctuation 105
 
0.1%
Close Punctuation 104
 
0.1%
Uppercase Letter 57
 
0.1%
Lowercase Letter 3
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3482
 
7.0%
3255
 
6.5%
3213
 
6.4%
3210
 
6.4%
3172
 
6.4%
3170
 
6.4%
3163
 
6.3%
3154
 
6.3%
3154
 
6.3%
2751
 
5.5%
Other values (290) 18163
36.4%
Decimal Number
ValueCountFrequency (%)
1 3785
21.3%
2 2359
13.3%
3 2083
11.7%
0 2009
11.3%
5 1493
 
8.4%
6 1459
 
8.2%
4 1456
 
8.2%
7 1270
 
7.1%
9 1021
 
5.7%
8 845
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
A 28
49.1%
B 24
42.1%
T 2
 
3.5%
S 1
 
1.8%
K 1
 
1.8%
P 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
, 127
56.2%
@ 90
39.8%
. 8
 
3.5%
? 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
a 2
66.7%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
15074
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2449
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Close Punctuation
ValueCountFrequency (%)
) 104
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49887
58.2%
Common 35740
41.7%
Latin 60
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3482
 
7.0%
3255
 
6.5%
3213
 
6.4%
3210
 
6.4%
3172
 
6.4%
3170
 
6.4%
3163
 
6.3%
3154
 
6.3%
3154
 
6.3%
2751
 
5.5%
Other values (290) 18163
36.4%
Common
ValueCountFrequency (%)
15074
42.2%
1 3785
 
10.6%
- 2449
 
6.9%
2 2359
 
6.6%
3 2083
 
5.8%
0 2009
 
5.6%
5 1493
 
4.2%
6 1459
 
4.1%
4 1456
 
4.1%
7 1270
 
3.6%
Other values (9) 2303
 
6.4%
Latin
ValueCountFrequency (%)
A 28
46.7%
B 24
40.0%
T 2
 
3.3%
a 2
 
3.3%
S 1
 
1.7%
k 1
 
1.7%
K 1
 
1.7%
P 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49887
58.2%
ASCII 35800
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15074
42.1%
1 3785
 
10.6%
- 2449
 
6.8%
2 2359
 
6.6%
3 2083
 
5.8%
0 2009
 
5.6%
5 1493
 
4.2%
6 1459
 
4.1%
4 1456
 
4.1%
7 1270
 
3.5%
Other values (17) 2363
 
6.6%
Hangul
ValueCountFrequency (%)
3482
 
7.0%
3255
 
6.5%
3213
 
6.4%
3210
 
6.4%
3172
 
6.4%
3170
 
6.4%
3163
 
6.3%
3154
 
6.3%
3154
 
6.3%
2751
 
5.5%
Other values (290) 18163
36.4%

도로명주소
Text

MISSING 

Distinct2108
Distinct (%)95.0%
Missing936
Missing (%)29.7%
Memory size24.8 KiB
2024-05-11T04:08:59.743796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length54
Mean length37.091073
Min length22

Characters and Unicode

Total characters82268
Distinct characters330
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2013 ?
Unique (%)90.8%

Sample

1st row서울특별시 노원구 상계로26길 20 (상계동)
2nd row서울특별시 노원구 공릉로38길 12 (공릉동)
3rd row서울특별시 노원구 석계로 22 (월계동)
4th row서울특별시 노원구 동일로193길 18 (공릉동)
5th row서울특별시 노원구 상계로 297-3 (상계동, 지상1층)
ValueCountFrequency (%)
서울특별시 2218
 
14.3%
노원구 2218
 
14.3%
상계동 1003
 
6.5%
1층 498
 
3.2%
공릉동 395
 
2.5%
중계동 335
 
2.2%
동일로 306
 
2.0%
2층 275
 
1.8%
월계동 206
 
1.3%
한글비석로 172
 
1.1%
Other values (2266) 7872
50.8%
2024-05-11T04:09:01.141259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13292
 
16.2%
1 4051
 
4.9%
3432
 
4.2%
, 2752
 
3.3%
2 2670
 
3.2%
2479
 
3.0%
2458
 
3.0%
2457
 
3.0%
( 2358
 
2.9%
) 2357
 
2.9%
Other values (320) 43962
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45237
55.0%
Decimal Number 15700
 
19.1%
Space Separator 13292
 
16.2%
Other Punctuation 2807
 
3.4%
Open Punctuation 2358
 
2.9%
Close Punctuation 2357
 
2.9%
Dash Punctuation 446
 
0.5%
Uppercase Letter 68
 
0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3432
 
7.6%
2479
 
5.5%
2458
 
5.4%
2457
 
5.4%
2256
 
5.0%
2235
 
4.9%
2231
 
4.9%
2229
 
4.9%
2225
 
4.9%
2218
 
4.9%
Other values (291) 21017
46.5%
Decimal Number
ValueCountFrequency (%)
1 4051
25.8%
2 2670
17.0%
0 1758
11.2%
3 1662
10.6%
4 1321
 
8.4%
5 1005
 
6.4%
6 861
 
5.5%
7 849
 
5.4%
9 800
 
5.1%
8 723
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
B 34
50.0%
A 28
41.2%
C 1
 
1.5%
Y 1
 
1.5%
X 1
 
1.5%
P 1
 
1.5%
S 1
 
1.5%
T 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 2752
98.0%
@ 36
 
1.3%
. 17
 
0.6%
? 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
c 1
33.3%
k 1
33.3%
a 1
33.3%
Space Separator
ValueCountFrequency (%)
13292
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2358
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2357
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45237
55.0%
Common 36960
44.9%
Latin 71
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3432
 
7.6%
2479
 
5.5%
2458
 
5.4%
2457
 
5.4%
2256
 
5.0%
2235
 
4.9%
2231
 
4.9%
2229
 
4.9%
2225
 
4.9%
2218
 
4.9%
Other values (291) 21017
46.5%
Common
ValueCountFrequency (%)
13292
36.0%
1 4051
 
11.0%
, 2752
 
7.4%
2 2670
 
7.2%
( 2358
 
6.4%
) 2357
 
6.4%
0 1758
 
4.8%
3 1662
 
4.5%
4 1321
 
3.6%
5 1005
 
2.7%
Other values (8) 3734
 
10.1%
Latin
ValueCountFrequency (%)
B 34
47.9%
A 28
39.4%
c 1
 
1.4%
C 1
 
1.4%
Y 1
 
1.4%
X 1
 
1.4%
P 1
 
1.4%
k 1
 
1.4%
S 1
 
1.4%
T 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45237
55.0%
ASCII 37031
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13292
35.9%
1 4051
 
10.9%
, 2752
 
7.4%
2 2670
 
7.2%
( 2358
 
6.4%
) 2357
 
6.4%
0 1758
 
4.7%
3 1662
 
4.5%
4 1321
 
3.6%
5 1005
 
2.7%
Other values (19) 3805
 
10.3%
Hangul
ValueCountFrequency (%)
3432
 
7.6%
2479
 
5.5%
2458
 
5.4%
2457
 
5.4%
2256
 
5.0%
2235
 
4.9%
2231
 
4.9%
2229
 
4.9%
2225
 
4.9%
2218
 
4.9%
Other values (291) 21017
46.5%

도로명우편번호
Real number (ℝ)

MISSING 

Distinct258
Distinct (%)11.7%
Missing953
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean1743.8124
Minimum1600
Maximum1914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:01.830790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1600
5-th percentile1613
Q11675
median1726
Q31828
95-th percentile1894
Maximum1914
Range314
Interquartile range (IQR)153

Descriptive statistics

Standard deviation87.009565
Coefficient of variation (CV)0.049896174
Kurtosis-1.0961043
Mean1743.8124
Median Absolute Deviation (MAD)63
Skewness0.28561656
Sum3838131
Variance7570.6643
MonotonicityNot monotonic
2024-05-11T04:09:02.502174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1693 81
 
2.6%
1663 61
 
1.9%
1751 57
 
1.8%
1694 51
 
1.6%
1695 51
 
1.6%
1662 45
 
1.4%
1684 39
 
1.2%
1848 37
 
1.2%
1692 35
 
1.1%
1762 35
 
1.1%
Other values (248) 1709
54.2%
(Missing) 953
30.2%
ValueCountFrequency (%)
1600 1
 
< 0.1%
1601 5
 
0.2%
1603 3
 
0.1%
1604 30
1.0%
1605 4
 
0.1%
1606 11
 
0.3%
1607 9
 
0.3%
1608 13
0.4%
1609 8
 
0.3%
1610 8
 
0.3%
ValueCountFrequency (%)
1914 7
0.2%
1913 15
0.5%
1911 4
 
0.1%
1910 2
 
0.1%
1909 11
0.3%
1907 2
 
0.1%
1906 7
0.2%
1905 8
0.3%
1904 3
 
0.1%
1903 2
 
0.1%
Distinct2745
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
2024-05-11T04:09:03.277497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length5.6188966
Min length1

Characters and Unicode

Total characters17722
Distinct characters705
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2452 ?
Unique (%)77.7%

Sample

1st row꾀꼬리
2nd row이브헤어아트
3rd row현대헤어아트
4th row장미
5th row동궁
ValueCountFrequency (%)
헤어 80
 
2.1%
hair 26
 
0.7%
미용실 26
 
0.7%
노원점 26
 
0.7%
네일 22
 
0.6%
에스테틱 22
 
0.6%
블루클럽 17
 
0.4%
스킨케어 15
 
0.4%
뷰티 13
 
0.3%
11
 
0.3%
Other values (2877) 3559
93.2%
2024-05-11T04:09:04.602475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1315
 
7.4%
1230
 
6.9%
663
 
3.7%
479
 
2.7%
463
 
2.6%
419
 
2.4%
409
 
2.3%
283
 
1.6%
268
 
1.5%
261
 
1.5%
Other values (695) 11932
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15284
86.2%
Lowercase Letter 695
 
3.9%
Space Separator 663
 
3.7%
Uppercase Letter 511
 
2.9%
Other Punctuation 157
 
0.9%
Close Punctuation 134
 
0.8%
Open Punctuation 134
 
0.8%
Decimal Number 130
 
0.7%
Dash Punctuation 8
 
< 0.1%
Connector Punctuation 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1315
 
8.6%
1230
 
8.0%
479
 
3.1%
463
 
3.0%
419
 
2.7%
409
 
2.7%
283
 
1.9%
268
 
1.8%
261
 
1.7%
253
 
1.7%
Other values (615) 9904
64.8%
Lowercase Letter
ValueCountFrequency (%)
a 101
14.5%
e 76
10.9%
i 75
10.8%
l 54
 
7.8%
o 51
 
7.3%
n 51
 
7.3%
r 44
 
6.3%
u 33
 
4.7%
h 33
 
4.7%
t 30
 
4.3%
Other values (16) 147
21.2%
Uppercase Letter
ValueCountFrequency (%)
J 44
 
8.6%
A 41
 
8.0%
H 39
 
7.6%
N 38
 
7.4%
M 37
 
7.2%
S 34
 
6.7%
I 32
 
6.3%
O 31
 
6.1%
B 27
 
5.3%
E 27
 
5.3%
Other values (15) 161
31.5%
Decimal Number
ValueCountFrequency (%)
0 33
25.4%
2 23
17.7%
1 21
16.2%
5 11
 
8.5%
9 9
 
6.9%
8 9
 
6.9%
3 8
 
6.2%
4 7
 
5.4%
7 5
 
3.8%
6 4
 
3.1%
Other Punctuation
ValueCountFrequency (%)
? 45
28.7%
& 43
27.4%
. 31
19.7%
, 11
 
7.0%
# 10
 
6.4%
' 9
 
5.7%
: 5
 
3.2%
! 2
 
1.3%
1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 133
99.3%
] 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 133
99.3%
[ 1
 
0.7%
Space Separator
ValueCountFrequency (%)
663
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Number
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15275
86.2%
Common 1232
 
7.0%
Latin 1206
 
6.8%
Han 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1315
 
8.6%
1230
 
8.1%
479
 
3.1%
463
 
3.0%
419
 
2.7%
409
 
2.7%
283
 
1.9%
268
 
1.8%
261
 
1.7%
253
 
1.7%
Other values (611) 9895
64.8%
Latin
ValueCountFrequency (%)
a 101
 
8.4%
e 76
 
6.3%
i 75
 
6.2%
l 54
 
4.5%
o 51
 
4.2%
n 51
 
4.2%
J 44
 
3.6%
r 44
 
3.6%
A 41
 
3.4%
H 39
 
3.2%
Other values (41) 630
52.2%
Common
ValueCountFrequency (%)
663
53.8%
) 133
 
10.8%
( 133
 
10.8%
? 45
 
3.7%
& 43
 
3.5%
0 33
 
2.7%
. 31
 
2.5%
2 23
 
1.9%
1 21
 
1.7%
5 11
 
0.9%
Other values (19) 96
 
7.8%
Han
ValueCountFrequency (%)
6
66.7%
1
 
11.1%
1
 
11.1%
1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15275
86.2%
ASCII 2435
 
13.7%
CJK 9
 
0.1%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1315
 
8.6%
1230
 
8.1%
479
 
3.1%
463
 
3.0%
419
 
2.7%
409
 
2.7%
283
 
1.9%
268
 
1.8%
261
 
1.7%
253
 
1.7%
Other values (611) 9895
64.8%
ASCII
ValueCountFrequency (%)
663
27.2%
) 133
 
5.5%
( 133
 
5.5%
a 101
 
4.1%
e 76
 
3.1%
i 75
 
3.1%
l 54
 
2.2%
o 51
 
2.1%
n 51
 
2.1%
? 45
 
1.8%
Other values (67) 1053
43.2%
CJK
ValueCountFrequency (%)
6
66.7%
1
 
11.1%
1
 
11.1%
1
 
11.1%
None
ValueCountFrequency (%)
1
33.3%
1
33.3%
° 1
33.3%
Distinct2686
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
Minimum1999-01-16 00:00:00
Maximum2024-05-09 10:21:59
2024-05-11T04:09:05.166257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:09:05.730816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
I
1726 
U
1416 
D
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
I 1726
54.7%
U 1416
44.9%
D 12
 
0.4%

Length

2024-05-11T04:09:06.319991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:06.674138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
i 1726
54.7%
u 1416
44.9%
d 12
 
0.4%
Distinct753
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
Minimum2018-08-31 23:59:59
Maximum2023-12-05 00:04:00
2024-05-11T04:09:07.078103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T04:09:07.535264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
일반미용업
2235 
피부미용업
530 
네일아트업
287 
메이크업업
 
80
기타
 
21

Length

Max length6
Median length5
Mean length4.9803424
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
일반미용업 2235
70.9%
피부미용업 530
 
16.8%
네일아트업 287
 
9.1%
메이크업업 80
 
2.5%
기타 21
 
0.7%
미용업 기타 1
 
< 0.1%

Length

2024-05-11T04:09:08.198245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:08.616851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반미용업 2235
70.8%
피부미용업 530
 
16.8%
네일아트업 287
 
9.1%
메이크업업 80
 
2.5%
기타 22
 
0.7%
미용업 1
 
< 0.1%

좌표정보(X)
Real number (ℝ)

MISSING 

Distinct1229
Distinct (%)40.2%
Missing99
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean205937.15
Minimum203719.16
Maximum209288.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:08.992197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum203719.16
5-th percentile204674.4
Q1205359.55
median205958.28
Q3206515.86
95-th percentile207100.01
Maximum209288.47
Range5569.3109
Interquartile range (IQR)1156.3178

Descriptive statistics

Standard deviation763.60519
Coefficient of variation (CV)0.0037079526
Kurtosis-0.68561313
Mean205937.15
Median Absolute Deviation (MAD)589.89492
Skewness-0.05373648
Sum6.2913799 × 108
Variance583092.89
MonotonicityNot monotonic
2024-05-11T04:09:09.732545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206356.052509483 60
 
1.9%
206490.247358174 29
 
0.9%
205953.043735981 28
 
0.9%
205273.302426861 26
 
0.8%
205575.73673853 20
 
0.6%
205984.15211292 20
 
0.6%
205588.636016926 20
 
0.6%
205297.659202914 18
 
0.6%
205280.620692797 18
 
0.6%
205601.926027171 17
 
0.5%
Other values (1219) 2799
88.7%
(Missing) 99
 
3.1%
ValueCountFrequency (%)
203719.161728968 3
 
0.1%
203775.87563562 1
 
< 0.1%
203809.293429767 1
 
< 0.1%
203839.989956111 3
 
0.1%
203920.549325193 1
 
< 0.1%
203982.774049825 1
 
< 0.1%
203986.825383903 1
 
< 0.1%
204035.663906885 1
 
< 0.1%
204325.989587591 11
0.3%
204360.413814457 1
 
< 0.1%
ValueCountFrequency (%)
209288.472624641 1
 
< 0.1%
207924.967619729 1
 
< 0.1%
207849.086484736 1
 
< 0.1%
207841.50719447 1
 
< 0.1%
207819.06930737 1
 
< 0.1%
207808.613425989 1
 
< 0.1%
207794.259011921 6
0.2%
207749.307579592 1
 
< 0.1%
207746.151245202 2
 
0.1%
207642.144523897 2
 
0.1%

좌표정보(Y)
Real number (ℝ)

MISSING 

Distinct1229
Distinct (%)40.2%
Missing99
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean460615.07
Minimum456921.61
Maximum465253.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:10.152504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum456921.61
5-th percentile457474.39
Q1458469.88
median461164.12
Q3462097.09
95-th percentile463625.41
Maximum465253.15
Range8331.5425
Interquartile range (IQR)3627.207

Descriptive statistics

Standard deviation1993.6715
Coefficient of variation (CV)0.0043282812
Kurtosis-1.1146202
Mean460615.07
Median Absolute Deviation (MAD)1357.5347
Skewness-0.23803816
Sum1.407179 × 109
Variance3974726.2
MonotonicityNot monotonic
2024-05-11T04:09:10.603798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
462123.008334776 60
 
1.9%
460830.615360327 29
 
0.9%
459822.608944797 28
 
0.9%
461515.158002149 26
 
0.8%
461696.659067377 20
 
0.6%
457275.799282625 20
 
0.6%
459964.099651861 20
 
0.6%
461519.760233787 18
 
0.6%
461113.53904716 18
 
0.6%
459732.128595514 17
 
0.5%
Other values (1219) 2799
88.7%
(Missing) 99
 
3.1%
ValueCountFrequency (%)
456921.609527521 1
 
< 0.1%
456930.30571679 2
0.1%
456932.955961768 1
 
< 0.1%
456954.147370611 2
0.1%
456975.436848678 1
 
< 0.1%
456990.07335337 1
 
< 0.1%
456994.661617956 1
 
< 0.1%
456996.048176249 3
0.1%
457003.268496842 1
 
< 0.1%
457018.943974424 1
 
< 0.1%
ValueCountFrequency (%)
465253.152004 2
 
0.1%
465103.755134816 1
 
< 0.1%
464959.058464501 3
 
0.1%
464508.952781941 4
 
0.1%
464356.527197506 5
0.2%
464346.663669239 5
0.2%
464208.305428933 11
0.3%
464199.048415229 11
0.3%
464174.158420444 10
0.3%
464099.692139359 3
 
0.1%

위생업태명
Categorical

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
미용업
1296 
<NA>
611 
일반미용업
588 
피부미용업
342 
네일미용업
 
119
Other values (11)
198 

Length

Max length23
Median length19
Mean length4.3601776
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미용업 1296
41.1%
<NA> 611
19.4%
일반미용업 588
18.6%
피부미용업 342
 
10.8%
네일미용업 119
 
3.8%
종합미용업 77
 
2.4%
피부미용업, 네일미용업 28
 
0.9%
일반미용업, 화장ㆍ분장 미용업 19
 
0.6%
네일미용업, 화장ㆍ분장 미용업 19
 
0.6%
일반미용업, 네일미용업 14
 
0.4%
Other values (6) 41
 
1.3%

Length

2024-05-11T04:09:11.131629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미용업 1363
40.7%
일반미용업 643
19.2%
na 611
18.2%
피부미용업 391
 
11.7%
네일미용업 200
 
6.0%
종합미용업 77
 
2.3%
화장ㆍ분장 67
 
2.0%

건물지상층수
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)0.8%
Missing1046
Missing (%)33.2%
Infinite0
Infinite (%)0.0%
Mean0.5426945
Minimum0
Maximum66
Zeros1796
Zeros (%)56.9%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:11.601297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum66
Range66
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1284296
Coefficient of variation (CV)3.9219664
Kurtosis433.15275
Mean0.5426945
Median Absolute Deviation (MAD)0
Skewness15.73139
Sum1144
Variance4.5302124
MonotonicityNot monotonic
2024-05-11T04:09:11.982585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1796
56.9%
2 89
 
2.8%
4 57
 
1.8%
3 51
 
1.6%
1 50
 
1.6%
5 22
 
0.7%
6 13
 
0.4%
9 7
 
0.2%
7 7
 
0.2%
8 7
 
0.2%
Other values (6) 9
 
0.3%
(Missing) 1046
33.2%
ValueCountFrequency (%)
0 1796
56.9%
1 50
 
1.6%
2 89
 
2.8%
3 51
 
1.6%
4 57
 
1.8%
5 22
 
0.7%
6 13
 
0.4%
7 7
 
0.2%
8 7
 
0.2%
9 7
 
0.2%
ValueCountFrequency (%)
66 1
 
< 0.1%
20 1
 
< 0.1%
15 3
 
0.1%
13 2
 
0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
9 7
0.2%
8 7
0.2%
7 7
0.2%
6 13
0.4%

건물지하층수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
0
1909 
<NA>
1190 
1
 
47
2
 
6
4
 
1

Length

Max length4
Median length1
Mean length2.131896
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 1909
60.5%
<NA> 1190
37.7%
1 47
 
1.5%
2 6
 
0.2%
4 1
 
< 0.1%
3 1
 
< 0.1%

Length

2024-05-11T04:09:12.499130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:12.893461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1909
60.5%
na 1190
37.7%
1 47
 
1.5%
2 6
 
0.2%
4 1
 
< 0.1%
3 1
 
< 0.1%

사용시작지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct12
Distinct (%)0.8%
Missing1609
Missing (%)51.0%
Infinite0
Infinite (%)0.0%
Mean2.5320388
Minimum0
Maximum963
Zeros460
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:13.360125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum963
Range963
Interquartile range (IQR)2

Descriptive statistics

Standard deviation32.393135
Coefficient of variation (CV)12.793301
Kurtosis641.15006
Mean2.5320388
Median Absolute Deviation (MAD)1
Skewness24.680434
Sum3912
Variance1049.3152
MonotonicityNot monotonic
2024-05-11T04:09:13.905221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 625
 
19.8%
0 460
 
14.6%
2 351
 
11.1%
3 54
 
1.7%
4 20
 
0.6%
5 14
 
0.4%
6 7
 
0.2%
7 5
 
0.2%
9 3
 
0.1%
8 3
 
0.1%
Other values (2) 3
 
0.1%
(Missing) 1609
51.0%
ValueCountFrequency (%)
0 460
14.6%
1 625
19.8%
2 351
11.1%
3 54
 
1.7%
4 20
 
0.6%
5 14
 
0.4%
6 7
 
0.2%
7 5
 
0.2%
8 3
 
0.1%
9 3
 
0.1%
ValueCountFrequency (%)
963 1
 
< 0.1%
591 2
 
0.1%
9 3
 
0.1%
8 3
 
0.1%
7 5
 
0.2%
6 7
 
0.2%
5 14
 
0.4%
4 20
 
0.6%
3 54
 
1.7%
2 351
11.1%

사용끝지상층
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct16
Distinct (%)1.1%
Missing1706
Missing (%)54.1%
Infinite0
Infinite (%)0.0%
Mean2.390884
Minimum0
Maximum702
Zeros192
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:14.302764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum702
Range702
Interquartile range (IQR)1

Descriptive statistics

Standard deviation20.800775
Coefficient of variation (CV)8.7000355
Kurtosis904.06351
Mean2.390884
Median Absolute Deviation (MAD)0
Skewness28.318747
Sum3462
Variance432.67226
MonotonicityNot monotonic
2024-05-11T04:09:14.672784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 739
23.4%
2 396
 
12.6%
0 192
 
6.1%
3 59
 
1.9%
4 20
 
0.6%
5 15
 
0.5%
6 7
 
0.2%
7 5
 
0.2%
8 5
 
0.2%
9 3
 
0.1%
Other values (6) 7
 
0.2%
(Missing) 1706
54.1%
ValueCountFrequency (%)
0 192
 
6.1%
1 739
23.4%
2 396
12.6%
3 59
 
1.9%
4 20
 
0.6%
5 15
 
0.5%
6 7
 
0.2%
7 5
 
0.2%
8 5
 
0.2%
9 3
 
0.1%
ValueCountFrequency (%)
702 1
 
< 0.1%
210 1
 
< 0.1%
202 2
 
0.1%
102 1
 
< 0.1%
27 1
 
< 0.1%
10 1
 
< 0.1%
9 3
0.1%
8 5
0.2%
7 5
0.2%
6 7
0.2%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
<NA>
1895 
0
1225 
1
 
33
3
 
1

Length

Max length4
Median length4
Mean length2.8024731
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1895
60.1%
0 1225
38.8%
1 33
 
1.0%
3 1
 
< 0.1%

Length

2024-05-11T04:09:15.229939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:15.698778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1895
60.1%
0 1225
38.8%
1 33
 
1.0%
3 1
 
< 0.1%

사용끝지하층
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
<NA>
2157 
0
971 
1
 
25
3
 
1

Length

Max length4
Median length4
Mean length3.0516804
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2157
68.4%
0 971
30.8%
1 25
 
0.8%
3 1
 
< 0.1%

Length

2024-05-11T04:09:16.108573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:16.450086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2157
68.4%
0 971
30.8%
1 25
 
0.8%
3 1
 
< 0.1%

한실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
0
1938 
<NA>
1216 

Length

Max length4
Median length1
Mean length2.1566265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1938
61.4%
<NA> 1216
38.6%

Length

2024-05-11T04:09:16.957581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:17.297035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1938
61.4%
na 1216
38.6%

양실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
0
1938 
<NA>
1216 

Length

Max length4
Median length1
Mean length2.1566265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1938
61.4%
<NA> 1216
38.6%

Length

2024-05-11T04:09:17.698364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:18.072081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1938
61.4%
na 1216
38.6%

욕실수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
0
1938 
<NA>
1216 

Length

Max length4
Median length1
Mean length2.1566265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1938
61.4%
<NA> 1216
38.6%

Length

2024-05-11T04:09:18.578858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:18.955987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1938
61.4%
na 1216
38.6%

발한실여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing646
Missing (%)20.5%
Memory size6.3 KiB
False
2508 
(Missing)
646 
ValueCountFrequency (%)
False 2508
79.5%
(Missing) 646
 
20.5%
2024-05-11T04:09:19.260713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

좌석수
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)1.0%
Missing741
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean3.628678
Minimum0
Maximum70
Zeros211
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:19.681346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile8
Maximum70
Range70
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.022394
Coefficient of variation (CV)0.83291877
Kurtosis115.57991
Mean3.628678
Median Absolute Deviation (MAD)1
Skewness7.0436615
Sum8756
Variance9.1348655
MonotonicityNot monotonic
2024-05-11T04:09:20.272865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3 857
27.2%
4 448
14.2%
2 353
11.2%
0 211
 
6.7%
5 184
 
5.8%
6 109
 
3.5%
1 55
 
1.7%
8 48
 
1.5%
7 43
 
1.4%
10 37
 
1.2%
Other values (15) 68
 
2.2%
(Missing) 741
23.5%
ValueCountFrequency (%)
0 211
 
6.7%
1 55
 
1.7%
2 353
11.2%
3 857
27.2%
4 448
14.2%
5 184
 
5.8%
6 109
 
3.5%
7 43
 
1.4%
8 48
 
1.5%
9 17
 
0.5%
ValueCountFrequency (%)
70 1
 
< 0.1%
40 1
 
< 0.1%
38 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 4
0.1%
18 4
0.1%
17 1
 
< 0.1%
16 6
0.2%
15 4
0.1%

조건부허가신고사유
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3154
Missing (%)100.0%
Memory size27.8 KiB

조건부허가시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3154
Missing (%)100.0%
Memory size27.8 KiB

조건부허가종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3154
Missing (%)100.0%
Memory size27.8 KiB
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
<NA>
2377 
임대
736 
자가
 
41

Length

Max length4
Median length4
Mean length3.5072923
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> 2377
75.4%
임대 736
 
23.3%
자가 41
 
1.3%

Length

2024-05-11T04:09:20.879476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:21.228987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2377
75.4%
임대 736
 
23.3%
자가 41
 
1.3%

세탁기수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
0
1673 
<NA>
1481 

Length

Max length4
Median length1
Mean length2.4086874
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1673
53.0%
<NA> 1481
47.0%

Length

2024-05-11T04:09:21.641077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:21.939672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1673
53.0%
na 1481
47.0%

여성종사자수
Real number (ℝ)

MISSING  ZEROS 

Distinct15
Distinct (%)1.4%
Missing2097
Missing (%)66.5%
Infinite0
Infinite (%)0.0%
Mean0.49952696
Minimum0
Maximum24
Zeros804
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:22.240545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.525121
Coefficient of variation (CV)3.0531305
Kurtosis74.477729
Mean0.49952696
Median Absolute Deviation (MAD)0
Skewness7.0744668
Sum528
Variance2.3259941
MonotonicityNot monotonic
2024-05-11T04:09:22.635352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 804
 
25.5%
1 162
 
5.1%
2 45
 
1.4%
3 16
 
0.5%
6 6
 
0.2%
4 5
 
0.2%
5 5
 
0.2%
7 4
 
0.1%
10 3
 
0.1%
9 2
 
0.1%
Other values (5) 5
 
0.2%
(Missing) 2097
66.5%
ValueCountFrequency (%)
0 804
25.5%
1 162
 
5.1%
2 45
 
1.4%
3 16
 
0.5%
4 5
 
0.2%
5 5
 
0.2%
6 6
 
0.2%
7 4
 
0.1%
8 1
 
< 0.1%
9 2
 
0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
15 1
 
< 0.1%
13 1
 
< 0.1%
11 1
 
< 0.1%
10 3
0.1%
9 2
 
0.1%
8 1
 
< 0.1%
7 4
0.1%
6 6
0.2%
5 5
0.2%

남성종사자수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
<NA>
2143 
0
1000 
1
 
10
10
 
1

Length

Max length4
Median length4
Mean length3.038681
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2143
67.9%
0 1000
31.7%
1 10
 
0.3%
10 1
 
< 0.1%

Length

2024-05-11T04:09:23.047747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:23.383695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 2143
67.9%
0 1000
31.7%
1 10
 
0.3%
10 1
 
< 0.1%

회수건조수
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.8 KiB
0
1609 
<NA>
1545 

Length

Max length4
Median length1
Mean length2.4695625
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1609
51.0%
<NA> 1545
49.0%

Length

2024-05-11T04:09:23.769068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T04:09:24.110127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1609
51.0%
na 1545
49.0%

침대수
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.7%
Missing1606
Missing (%)50.9%
Infinite0
Infinite (%)0.0%
Mean0.65180879
Minimum0
Maximum10
Zeros1192
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size27.8 KiB
2024-05-11T04:09:24.512421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4443625
Coefficient of variation (CV)2.2159297
Kurtosis8.2162851
Mean0.65180879
Median Absolute Deviation (MAD)0
Skewness2.7167404
Sum1009
Variance2.0861829
MonotonicityNot monotonic
2024-05-11T04:09:24.906356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1192
37.8%
2 130
 
4.1%
3 80
 
2.5%
1 63
 
2.0%
4 34
 
1.1%
6 16
 
0.5%
5 15
 
0.5%
7 9
 
0.3%
8 6
 
0.2%
9 2
 
0.1%
(Missing) 1606
50.9%
ValueCountFrequency (%)
0 1192
37.8%
1 63
 
2.0%
2 130
 
4.1%
3 80
 
2.5%
4 34
 
1.1%
5 15
 
0.5%
6 16
 
0.5%
7 9
 
0.3%
8 6
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 2
 
0.1%
8 6
 
0.2%
7 9
 
0.3%
6 16
 
0.5%
5 15
 
0.5%
4 34
 
1.1%
3 80
2.5%
2 130
4.1%
1 63
2.0%

다중이용업소여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing611
Missing (%)19.4%
Memory size6.3 KiB
False
2543 
(Missing)
611 
ValueCountFrequency (%)
False 2543
80.6%
(Missing) 611
 
19.4%
2024-05-11T04:09:25.290079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
031000003100000-204-1969-0048219690529<NA>3폐업2폐업19971217<NA><NA><NA>02 973138712.0139200서울특별시 노원구 상계동 105-0번지<NA><NA>꾀꼬리2001-09-29 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
131000003100000-204-1971-0045419710812<NA>3폐업2폐업20100105<NA><NA><NA>02 93299680.0139863서울특별시 노원구 중계동 505번지 롯데상가 2층6호<NA><NA>이브헤어아트2005-11-04 00:00:00I2018-08-31 23:59:59.0일반미용업205445.843883459604.311547미용업<NA><NA><NA>1<NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
231000003100000-204-1971-0066519710628<NA>3폐업2폐업19970313<NA><NA><NA>02 011.0139862서울특별시 노원구 중계동 426-0번지<NA><NA>현대헤어아트2001-09-29 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
331000003100000-204-1972-0046719721212<NA>3폐업2폐업20030719<NA><NA><NA>02 936514610.75139200서울특별시 노원구 상계동 173-256번지<NA><NA>장미2003-07-19 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업<NA><NA><NA><NA><NA><NA><NA><NA><NA>N2<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
431000003100000-204-1973-004681973-12-13<NA>3폐업2폐업2024-01-04<NA><NA><NA>02 936655830.74139-821서울특별시 노원구 상계동 1284-101서울특별시 노원구 상계로26길 20 (상계동)1698동궁2024-01-04 16:24:29U2023-12-01 00:06:00.0일반미용업206216.974157461665.825848<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
531000003100000-204-1976-0062519760529<NA>1영업/정상1영업<NA><NA><NA><NA>020975041811.04139800서울특별시 노원구 공릉동 238-36서울특별시 노원구 공릉로38길 12 (공릉동)1819공주2021-08-16 11:19:24U2021-08-18 02:40:00.0일반미용업206997.248498457865.905402미용업002200000N3<NA><NA><NA>임대00000N
631000003100000-204-1978-0042019780106<NA>3폐업2폐업20210816<NA><NA><NA>02 910729211.4139841서울특별시 노원구 월계동 66-4서울특별시 노원구 석계로 22 (월계동)1895월계2021-08-16 09:32:19U2021-08-18 02:40:00.0일반미용업205617.37885457177.892011미용업001100000N2<NA><NA><NA>임대00000N
731000003100000-204-1978-0050719781205<NA>3폐업2폐업19950112<NA><NA><NA>020978813420.91139815서울특별시 노원구 상계동 389-599번지<NA><NA>향미2001-09-29 00:00:00I2018-08-31 23:59:59.0일반미용업206041.00018462002.709319미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
831000003100000-204-1979-0059319790510<NA>1영업/정상1영업<NA><NA><NA><NA>02 975 686224.42139240서울특별시 노원구 공릉동 379-24서울특별시 노원구 동일로193길 18 (공릉동)1852헵시바미장2021-08-16 11:20:17U2021-08-18 02:40:00.0일반미용업206248.631978458186.670789미용업001100000N3<NA><NA><NA>임대00000N
931000003100000-204-1980-0044419800502<NA>3폐업2폐업19940516<NA><NA><NA>02 917377718.2139847서울특별시 노원구 월계동 509-3번지<NA><NA>신신2001-09-29 00:00:00I2018-08-31 23:59:59.0일반미용업<NA><NA>미용업000<NA>0<NA>000N3<NA><NA><NA><NA><NA><NA><NA><NA><NA>N
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)위생업태명건물지상층수건물지하층수사용시작지상층사용끝지상층사용시작지하층사용끝지하층한실수양실수욕실수발한실여부좌석수조건부허가신고사유조건부허가시작일자조건부허가종료일자건물소유구분명세탁기수여성종사자수남성종사자수회수건조수침대수다중이용업소여부
314431000003100000-225-2022-000062022-11-30<NA>1영업/정상1영업<NA><NA><NA><NA><NA>24.25139-861서울특별시 노원구 중계동 363-2 제일약국서울특별시 노원구 한글비석로8길 46, 제일약국 상가동 104호 (중계동)1739스팀살롱 중계점2023-03-09 13:09:10U2022-12-02 23:01:00.0일반미용업206922.39164460609.708925<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
314531000003100000-225-2023-000012023-05-23<NA>1영업/정상1영업<NA><NA><NA><NA><NA>23.0139-240서울특별시 노원구 공릉동 561-37 명승화운트서울특별시 노원구 동일로180길 47-4, 1층 101호 (공릉동, 명승화운트)1846미장원입니다2023-05-23 11:26:21I2022-12-04 22:05:00.0일반미용업206724.397664457851.409743<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
314631000003100000-225-2023-000022023-08-10<NA>1영업/정상1영업<NA><NA><NA><NA><NA>123.45139-200서울특별시 노원구 상계동 624 상계주공15단지아파트서울특별시 노원구 동일로227길 26, 상가동 1층 102호 (상계동, 상계주공15단지아파트)1617크레파스헤어살롱2023-10-16 14:26:20I2022-10-30 23:08:00.0일반미용업204670.573678462933.316362<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
314731000003100000-226-2012-0000120120305<NA>1영업/정상1영업<NA><NA><NA><NA><NA>44.0139848서울특별시 노원구 월계동 276-18번지서울특별시 노원구 초안산로2라길 31, 3층 1호 (월계동)1867J네일&속눈썹2019-10-14 09:55:35U2019-10-16 02:40:00.0네일아트업205068.506536458759.220184피부미용업, 네일미용업, 화장ㆍ분장 미용업003300000N2<NA><NA><NA>임대0<NA><NA>00N
314831000003100000-226-2016-0000120161013<NA>1영업/정상1영업<NA><NA><NA><NA><NA>20.0139942서울특별시 노원구 상계동 730-2 상계주공3단지아파트 109호서울특별시 노원구 동일로 1383, 상가동 109호 (상계동, 상계주공3단지아파트)1762조현정토탈뷰티2020-11-16 11:24:15U2020-11-18 02:40:00.0네일아트업205280.620693461113.539047피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA>11000N0<NA><NA><NA><NA>00000N
314931000003100000-226-2016-0000220160512<NA>3폐업2폐업20220704<NA><NA><NA>02 939 778715.0139827서울특별시 노원구 상계동 692 상계주공7단지아파트 상가동 205호서울특별시 노원구 동일로 1456, 상가동 205호 (상계동, 상계주공7단지아파트)1690더뷰티 아미고2022-07-04 11:00:08U2021-12-07 00:06:00.0네일아트업205264.637478461827.579521<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
315031000003100000-226-2017-0000120170223<NA>3폐업2폐업20180115<NA><NA><NA><NA>13.0139832서울특별시 노원구 상계동 749-3번지 롯데프라자 204호서울특별시 노원구 동일로 1368, 2층 204호 (상계동, 롯데프라자)1756바비드샵2018-01-15 14:09:29I2018-08-31 23:59:59.0피부미용업205396.516588460932.28316피부미용업, 네일미용업, 화장ㆍ분장 미용업00<NA><NA><NA><NA>000N1<NA><NA><NA><NA>00002N
315131000003100000-226-2017-0000220170721<NA>1영업/정상1영업<NA><NA><NA><NA><NA>28.98139200서울특별시 노원구 상계동 173-1번지 108동 9호.10호서울특별시 노원구 한글비석로 399, 108동 1층 9호.10호 (상계동)1683루아네일2019-10-04 15:13:13U2019-10-06 02:40:00.0네일아트업206356.052509462123.008335피부미용업, 네일미용업, 화장ㆍ분장 미용업101100000N2<NA><NA><NA>임대01001N
315231000003100000-226-2018-0000120181116<NA>1영업/정상1영업<NA><NA><NA><NA>070 7867255627.8139800서울특별시 노원구 공릉동 120-5서울특별시 노원구 화랑로51길 18, 1층 (공릉동)1801오늘의네일2022-10-18 11:30:20I2021-10-30 22:00:00.0네일아트업207849.086485457964.699655<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
315331000003100000-226-2019-0000120190422<NA>1영업/정상1영업<NA><NA><NA><NA><NA>27.0139827서울특별시 노원구 상계동 693번지 미도빌딩서울특별시 노원구 동일로 1426, 미도빌딩 503호 (상계동)1694Nail Do 속눈썹2019-09-20 14:12:58U2019-09-22 02:40:00.0네일아트업205273.302427461515.158002피부미용업, 네일미용업, 화장ㆍ분장 미용업005500000N1<NA><NA><NA>임대00004N