Overview

Dataset statistics

Number of variables17
Number of observations3061
Missing cells4505
Missing cells (%)8.7%
Duplicate rows144
Duplicate rows (%)4.7%
Total size in memory424.6 KiB
Average record size in memory142.0 B

Variable types

Categorical4
Numeric5
Text8

Dataset

Description시군구코드,처분일자,교부번호,업종명,업태명,업소명,소재지도로명,소재지지번,지도점검일자,행정처분상태,처분명,법적근거,위반일자,위반내용,처분내용,처분기간,영업장면적(㎡)
Author강남구
URLhttps://data.seoul.go.kr/dataList/OA-11298/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 144 (4.7%) duplicate rowsDuplicates
처분일자 is highly overall correlated with 지도점검일자 and 1 other fieldsHigh correlation
지도점검일자 is highly overall correlated with 처분일자 and 1 other fieldsHigh correlation
위반일자 is highly overall correlated with 처분일자 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 업태명High correlation
업태명 is highly overall correlated with 업종명High correlation
교부번호 has 120 (3.9%) missing valuesMissing
소재지도로명 has 1332 (43.5%) missing valuesMissing
처분기간 has 2934 (95.9%) missing valuesMissing
영업장면적(㎡) has 118 (3.9%) missing valuesMissing
위반일자 is highly skewed (γ1 = -52.0572853)Skewed
영업장면적(㎡) is highly skewed (γ1 = 23.58782368)Skewed
영업장면적(㎡) has 86 (2.8%) zerosZeros

Reproduction

Analysis started2024-05-18 03:34:12.141482
Analysis finished2024-05-18 03:34:28.683868
Duration16.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
3220000
3061 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3220000
2nd row3220000
3rd row3220000
4th row3220000
5th row3220000

Common Values

ValueCountFrequency (%)
3220000 3061
100.0%

Length

2024-05-18T12:34:29.134350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:34:29.490191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3220000 3061
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct833
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20115637
Minimum19960216
Maximum20240305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 KiB
2024-05-18T12:34:30.068965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19960216
5-th percentile19990921
Q120050825
median20130619
Q320180724
95-th percentile20211008
Maximum20240305
Range280089
Interquartile range (IQR)129899

Descriptive statistics

Standard deviation73574.665
Coefficient of variation (CV)0.0036575856
Kurtosis-1.0839705
Mean20115637
Median Absolute Deviation (MAD)59392
Skewness-0.41726933
Sum6.1573965 × 1010
Variance5.4132314 × 109
MonotonicityNot monotonic
2024-05-18T12:34:30.681483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190828 157
 
5.1%
20180917 97
 
3.2%
20180316 94
 
3.1%
20201028 73
 
2.4%
20170918 67
 
2.2%
20150817 61
 
2.0%
20180724 58
 
1.9%
20180823 58
 
1.9%
20151116 57
 
1.9%
20160216 54
 
1.8%
Other values (823) 2285
74.6%
ValueCountFrequency (%)
19960216 1
 
< 0.1%
19960417 5
 
0.2%
19960517 1
 
< 0.1%
19960523 2
 
0.1%
19960528 5
 
0.2%
19960614 4
 
0.1%
19960621 35
1.1%
19960624 2
 
0.1%
19960702 2
 
0.1%
19960712 1
 
< 0.1%
ValueCountFrequency (%)
20240305 12
0.4%
20240219 2
 
0.1%
20240216 2
 
0.1%
20240116 1
 
< 0.1%
20231205 1
 
< 0.1%
20231129 1
 
< 0.1%
20230816 1
 
< 0.1%
20230628 1
 
< 0.1%
20230619 7
0.2%
20230601 1
 
< 0.1%

교부번호
Text

MISSING 

Distinct1428
Distinct (%)48.6%
Missing120
Missing (%)3.9%
Memory size24.0 KiB
2024-05-18T12:34:31.717676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.3274396
Min length1

Characters and Unicode

Total characters18609
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique785 ?
Unique (%)26.7%

Sample

1st row06800410500072
2nd row0002
3rd row0002
4th row06800410600001
5th row06800410600001
ValueCountFrequency (%)
300 20
 
0.7%
0304 19
 
0.6%
282 17
 
0.6%
0115 15
 
0.5%
444 14
 
0.5%
0114 13
 
0.4%
58 12
 
0.4%
06800410500046 11
 
0.4%
0109 10
 
0.3%
06800410500056 10
 
0.3%
Other values (1418) 2800
95.2%
2024-05-18T12:34:33.453837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6868
36.9%
1 2302
 
12.4%
2 1773
 
9.5%
4 1373
 
7.4%
6 1212
 
6.5%
8 1101
 
5.9%
3 1079
 
5.8%
5 1063
 
5.7%
7 644
 
3.5%
- 595
 
3.2%
Other values (7) 599
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18008
96.8%
Dash Punctuation 595
 
3.2%
Other Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6868
38.1%
1 2302
 
12.8%
2 1773
 
9.8%
4 1373
 
7.6%
6 1212
 
6.7%
8 1101
 
6.1%
3 1079
 
6.0%
5 1063
 
5.9%
7 644
 
3.6%
9 593
 
3.3%
Other Letter
ValueCountFrequency (%)
수 1
16.7%
지 1
16.7%
서 1
16.7%
초 1
16.7%
구 1
16.7%
원 1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 595
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18603
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6868
36.9%
1 2302
 
12.4%
2 1773
 
9.5%
4 1373
 
7.4%
6 1212
 
6.5%
8 1101
 
5.9%
3 1079
 
5.8%
5 1063
 
5.7%
7 644
 
3.5%
- 595
 
3.2%
Hangul
ValueCountFrequency (%)
수 1
16.7%
지 1
16.7%
서 1
16.7%
초 1
16.7%
구 1
16.7%
원 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18603
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6868
36.9%
1 2302
 
12.4%
2 1773
 
9.5%
4 1373
 
7.4%
6 1212
 
6.5%
8 1101
 
5.9%
3 1079
 
5.8%
5 1063
 
5.7%
7 644
 
3.5%
- 595
 
3.2%
Hangul
ValueCountFrequency (%)
수 1
16.7%
지 1
16.7%
서 1
16.7%
초 1
16.7%
구 1
16.7%
원 1
16.7%

업종명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
숙박업(일반)
581 
위생관리용역업
483 
이용업
453 
피부미용업
440 
목욕장업
421 
Other values (19)
683 

Length

Max length23
Median length19
Mean length5.4599804
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row숙박업(일반)
2nd row숙박업(일반)
3rd row숙박업(일반)
4th row숙박업(일반)
5th row숙박업(일반)

Common Values

ValueCountFrequency (%)
숙박업(일반) 581
19.0%
위생관리용역업 483
15.8%
이용업 453
14.8%
피부미용업 440
14.4%
목욕장업 421
13.8%
미용업 154
 
5.0%
일반미용업 143
 
4.7%
종합미용업 108
 
3.5%
세탁업 95
 
3.1%
네일미용업 67
 
2.2%
Other values (14) 116
 
3.8%

Length

2024-05-18T12:34:34.042224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 581
17.9%
피부미용업 493
15.2%
위생관리용역업 483
14.9%
이용업 453
14.0%
목욕장업 421
13.0%
미용업 215
 
6.6%
일반미용업 188
 
5.8%
네일미용업 134
 
4.1%
종합미용업 108
 
3.3%
세탁업 95
 
2.9%
Other values (4) 72
 
2.2%

업태명
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
피부미용업
491 
위생관리용역업
480 
일반이용업
453 
여관업
435 
일반미용업
357 
Other values (18)
845 

Length

Max length14
Median length5
Mean length4.9431558
Min length2

Unique

Unique3 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
피부미용업 491
16.0%
위생관리용역업 480
15.7%
일반이용업 453
14.8%
여관업 435
14.2%
일반미용업 357
11.7%
공동탕업 259
8.5%
네일아트업 132
 
4.3%
한증막업 86
 
2.8%
일반세탁업 86
 
2.8%
관광호텔 85
 
2.8%
Other values (13) 197
6.4%

Length

2024-05-18T12:34:34.651346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
피부미용업 491
15.6%
위생관리용역업 483
15.3%
일반이용업 453
14.4%
여관업 435
13.8%
일반미용업 357
11.3%
공동탕업 259
8.2%
네일아트업 132
 
4.2%
기타 109
 
3.5%
일반세탁업 86
 
2.7%
한증막업 86
 
2.7%
Other values (12) 258
8.2%

업소명
Text

Distinct1753
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
2024-05-18T12:34:35.704077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length29
Mean length5.9790918
Min length1

Characters and Unicode

Total characters18302
Distinct characters661
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1119 ?
Unique (%)36.6%

Sample

1st row삼보여관
2nd row삼보여관
3rd row삼보여관
4th row미송
5th row미송
ValueCountFrequency (%)
주식회사 36
 
1.0%
에스테틱 27
 
0.7%
수사우나 20
 
0.6%
코리아이용원 19
 
0.5%
사우나 17
 
0.5%
뷰티 15
 
0.4%
주)이알에이피엠서비스 14
 
0.4%
호텔 13
 
0.4%
영동스파 13
 
0.4%
노블엔터테인먼트 12
 
0.3%
Other values (1953) 3424
94.8%
2024-05-18T12:34:37.181192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 725
 
4.0%
이 627
 
3.4%
550
 
3.0%
) 530
 
2.9%
( 529
 
2.9%
주 476
 
2.6%
사 298
 
1.6%
리 296
 
1.6%
관 267
 
1.5%
텔 259
 
1.4%
Other values (651) 13745
75.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15684
85.7%
Lowercase Letter 585
 
3.2%
Space Separator 550
 
3.0%
Close Punctuation 530
 
2.9%
Open Punctuation 529
 
2.9%
Uppercase Letter 313
 
1.7%
Decimal Number 66
 
0.4%
Other Punctuation 45
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 725
 
4.6%
이 627
 
4.0%
주 476
 
3.0%
사 298
 
1.9%
리 296
 
1.9%
관 267
 
1.7%
텔 259
 
1.7%
에 256
 
1.6%
우 249
 
1.6%
아 240
 
1.5%
Other values (582) 11991
76.5%
Uppercase Letter
ValueCountFrequency (%)
B 34
 
10.9%
A 30
 
9.6%
L 29
 
9.3%
S 26
 
8.3%
E 24
 
7.7%
M 22
 
7.0%
I 17
 
5.4%
R 17
 
5.4%
N 14
 
4.5%
C 11
 
3.5%
Other values (16) 89
28.4%
Lowercase Letter
ValueCountFrequency (%)
e 76
13.0%
a 59
 
10.1%
o 53
 
9.1%
n 48
 
8.2%
i 42
 
7.2%
l 39
 
6.7%
t 35
 
6.0%
u 29
 
5.0%
s 26
 
4.4%
y 22
 
3.8%
Other values (15) 156
26.7%
Decimal Number
ValueCountFrequency (%)
1 21
31.8%
2 14
21.2%
4 9
13.6%
0 6
 
9.1%
3 5
 
7.6%
8 3
 
4.5%
7 3
 
4.5%
6 3
 
4.5%
5 2
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 16
35.6%
？ 11
24.4%
& 9
20.0%
, 7
15.6%
: 1
 
2.2%
' 1
 
2.2%
Space Separator
ValueCountFrequency (%)
550
100.0%
Close Punctuation
ValueCountFrequency (%)
) 530
100.0%
Open Punctuation
ValueCountFrequency (%)
( 529
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15683
85.7%
Common 1720
 
9.4%
Latin 898
 
4.9%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 725
 
4.6%
이 627
 
4.0%
주 476
 
3.0%
사 298
 
1.9%
리 296
 
1.9%
관 267
 
1.7%
텔 259
 
1.7%
에 256
 
1.6%
우 249
 
1.6%
아 240
 
1.5%
Other values (581) 11990
76.5%
Latin
ValueCountFrequency (%)
e 76
 
8.5%
a 59
 
6.6%
o 53
 
5.9%
n 48
 
5.3%
i 42
 
4.7%
l 39
 
4.3%
t 35
 
3.9%
B 34
 
3.8%
A 30
 
3.3%
L 29
 
3.2%
Other values (41) 453
50.4%
Common
ValueCountFrequency (%)
550
32.0%
) 530
30.8%
( 529
30.8%
1 21
 
1.2%
. 16
 
0.9%
2 14
 
0.8%
？ 11
 
0.6%
4 9
 
0.5%
& 9
 
0.5%
, 7
 
0.4%
Other values (8) 24
 
1.4%
Han
ValueCountFrequency (%)
善 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15683
85.7%
ASCII 2607
 
14.2%
None 11
 
0.1%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 725
 
4.6%
이 627
 
4.0%
주 476
 
3.0%
사 298
 
1.9%
리 296
 
1.9%
관 267
 
1.7%
텔 259
 
1.7%
에 256
 
1.6%
우 249
 
1.6%
아 240
 
1.5%
Other values (581) 11990
76.5%
ASCII
ValueCountFrequency (%)
550
21.1%
) 530
20.3%
( 529
20.3%
e 76
 
2.9%
a 59
 
2.3%
o 53
 
2.0%
n 48
 
1.8%
i 42
 
1.6%
l 39
 
1.5%
t 35
 
1.3%
Other values (58) 646
24.8%
None
ValueCountFrequency (%)
？ 11
100.0%
CJK
ValueCountFrequency (%)
善 1
100.0%

소재지도로명
Text

MISSING 

Distinct1104
Distinct (%)63.9%
Missing1332
Missing (%)43.5%
Memory size24.0 KiB
2024-05-18T12:34:37.821939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length50
Mean length33.955466
Min length22

Characters and Unicode

Total characters58709
Distinct characters323
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

Unique774 ?
Unique (%)44.8%

Sample

1st row서울특별시 강남구 강남대로92길 15, (역삼동)
2nd row서울특별시 강남구 봉은사로 409, (삼성동)
3rd row서울특별시 강남구 강남대로92길 33, (역삼동)
4th row서울특별시 강남구 테헤란로37길 13-5, (역삼동)
5th row서울특별시 강남구 삼성로91길 32, (삼성동)
ValueCountFrequency (%)
서울특별시 1729
 
16.2%
강남구 1729
 
16.2%
역삼동 321
 
3.0%
논현동 242
 
2.3%
신사동 177
 
1.7%
지하1층 141
 
1.3%
삼성동 135
 
1.3%
청담동 120
 
1.1%
대치동 119
 
1.1%
지상2층 118
 
1.1%
Other values (1484) 5867
54.8%
2024-05-18T12:34:38.898133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8971
 
15.3%
, 2717
 
4.6%
1 2463
 
4.2%
남 1935
 
3.3%
동 1930
 
3.3%
강 1918
 
3.3%
구 1859
 
3.2%
서 1769
 
3.0%
로 1764
 
3.0%
시 1754
 
3.0%
Other values (313) 31629
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33353
56.8%
Decimal Number 9875
 
16.8%
Space Separator 8971
 
15.3%
Other Punctuation 2732
 
4.7%
Close Punctuation 1752
 
3.0%
Open Punctuation 1752
 
3.0%
Dash Punctuation 129
 
0.2%
Uppercase Letter 109
 
0.2%
Math Symbol 36
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 1935
 
5.8%
동 1930
 
5.8%
강 1918
 
5.8%
구 1859
 
5.6%
서 1769
 
5.3%
로 1764
 
5.3%
시 1754
 
5.3%
울 1737
 
5.2%
특 1729
 
5.2%
별 1729
 
5.2%
Other values (279) 15229
45.7%
Uppercase Letter
ValueCountFrequency (%)
B 54
49.5%
S 12
 
11.0%
A 7
 
6.4%
K 6
 
5.5%
T 6
 
5.5%
G 6
 
5.5%
L 4
 
3.7%
D 3
 
2.8%
P 2
 
1.8%
X 2
 
1.8%
Other values (6) 7
 
6.4%
Decimal Number
ValueCountFrequency (%)
1 2463
24.9%
2 1628
16.5%
3 1112
11.3%
0 948
 
9.6%
4 848
 
8.6%
5 782
 
7.9%
6 658
 
6.7%
8 625
 
6.3%
7 471
 
4.8%
9 340
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 2717
99.5%
. 12
 
0.4%
/ 3
 
0.1%
Space Separator
ValueCountFrequency (%)
8971
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1752
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1752
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 129
100.0%
Math Symbol
ValueCountFrequency (%)
~ 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33353
56.8%
Common 25247
43.0%
Latin 109
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 1935
 
5.8%
동 1930
 
5.8%
강 1918
 
5.8%
구 1859
 
5.6%
서 1769
 
5.3%
로 1764
 
5.3%
시 1754
 
5.3%
울 1737
 
5.2%
특 1729
 
5.2%
별 1729
 
5.2%
Other values (279) 15229
45.7%
Common
ValueCountFrequency (%)
8971
35.5%
, 2717
 
10.8%
1 2463
 
9.8%
) 1752
 
6.9%
( 1752
 
6.9%
2 1628
 
6.4%
3 1112
 
4.4%
0 948
 
3.8%
4 848
 
3.4%
5 782
 
3.1%
Other values (8) 2274
 
9.0%
Latin
ValueCountFrequency (%)
B 54
49.5%
S 12
 
11.0%
A 7
 
6.4%
K 6
 
5.5%
T 6
 
5.5%
G 6
 
5.5%
L 4
 
3.7%
D 3
 
2.8%
P 2
 
1.8%
X 2
 
1.8%
Other values (6) 7
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33353
56.8%
ASCII 25356
43.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8971
35.4%
, 2717
 
10.7%
1 2463
 
9.7%
) 1752
 
6.9%
( 1752
 
6.9%
2 1628
 
6.4%
3 1112
 
4.4%
0 948
 
3.7%
4 848
 
3.3%
5 782
 
3.1%
Other values (24) 2383
 
9.4%
Hangul
ValueCountFrequency (%)
남 1935
 
5.8%
동 1930
 
5.8%
강 1918
 
5.8%
구 1859
 
5.6%
서 1769
 
5.3%
로 1764
 
5.3%
시 1754
 
5.3%
울 1737
 
5.2%
특 1729
 
5.2%
별 1729
 
5.2%
Other values (279) 15229
45.7%

소재지지번
Text

Distinct1646
Distinct (%)53.8%
Missing1
Missing (%)< 0.1%
Memory size24.0 KiB
2024-05-18T12:34:39.650982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length49
Mean length28.433007
Min length20

Characters and Unicode

Total characters87005
Distinct characters326
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

Unique1014 ?
Unique (%)33.1%

Sample

1st row서울특별시 강남구 역삼동 779번지 5호
2nd row서울특별시 강남구 역삼동 779번지 5호
3rd row서울특별시 강남구 역삼동 779번지 5호
4th row서울특별시 강남구 역삼동 700번지 27호
5th row서울특별시 강남구 역삼동 700번지 27호
ValueCountFrequency (%)
서울특별시 3060
 
17.9%
강남구 3060
 
17.9%
역삼동 920
 
5.4%
논현동 566
 
3.3%
신사동 391
 
2.3%
삼성동 381
 
2.2%
대치동 296
 
1.7%
지하1층 274
 
1.6%
청담동 244
 
1.4%
1호 212
 
1.2%
Other values (1245) 7653
44.9%
2024-05-18T12:34:40.677977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21621
24.9%
지 4005
 
4.6%
1 3448
 
4.0%
동 3106
 
3.6%
서 3102
 
3.6%
남 3097
 
3.6%
강 3092
 
3.6%
구 3083
 
3.5%
시 3074
 
3.5%
울 3064
 
3.5%
Other values (316) 36313
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49025
56.3%
Space Separator 21621
24.9%
Decimal Number 15601
 
17.9%
Other Punctuation 249
 
0.3%
Open Punctuation 143
 
0.2%
Close Punctuation 141
 
0.2%
Dash Punctuation 108
 
0.1%
Uppercase Letter 77
 
0.1%
Math Symbol 40
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 4005
 
8.2%
동 3106
 
6.3%
서 3102
 
6.3%
남 3097
 
6.3%
강 3092
 
6.3%
구 3083
 
6.3%
시 3074
 
6.3%
울 3064
 
6.2%
번 3061
 
6.2%
별 3060
 
6.2%
Other values (287) 17281
35.2%
Decimal Number
ValueCountFrequency (%)
1 3448
22.1%
2 2211
14.2%
6 1554
10.0%
3 1315
 
8.4%
7 1303
 
8.4%
0 1267
 
8.1%
4 1192
 
7.6%
5 1173
 
7.5%
8 1124
 
7.2%
9 1014
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
B 41
53.2%
A 6
 
7.8%
T 5
 
6.5%
S 5
 
6.5%
G 5
 
6.5%
L 5
 
6.5%
D 4
 
5.2%
K 3
 
3.9%
P 2
 
2.6%
H 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 226
90.8%
. 19
 
7.6%
/ 4
 
1.6%
Math Symbol
ValueCountFrequency (%)
~ 39
97.5%
< 1
 
2.5%
Space Separator
ValueCountFrequency (%)
21621
100.0%
Open Punctuation
ValueCountFrequency (%)
( 143
100.0%
Close Punctuation
ValueCountFrequency (%)
) 141
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49025
56.3%
Common 37903
43.6%
Latin 77
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 4005
 
8.2%
동 3106
 
6.3%
서 3102
 
6.3%
남 3097
 
6.3%
강 3092
 
6.3%
구 3083
 
6.3%
시 3074
 
6.3%
울 3064
 
6.2%
번 3061
 
6.2%
별 3060
 
6.2%
Other values (287) 17281
35.2%
Common
ValueCountFrequency (%)
21621
57.0%
1 3448
 
9.1%
2 2211
 
5.8%
6 1554
 
4.1%
3 1315
 
3.5%
7 1303
 
3.4%
0 1267
 
3.3%
4 1192
 
3.1%
5 1173
 
3.1%
8 1124
 
3.0%
Other values (9) 1695
 
4.5%
Latin
ValueCountFrequency (%)
B 41
53.2%
A 6
 
7.8%
T 5
 
6.5%
S 5
 
6.5%
G 5
 
6.5%
L 5
 
6.5%
D 4
 
5.2%
K 3
 
3.9%
P 2
 
2.6%
H 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49025
56.3%
ASCII 37980
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21621
56.9%
1 3448
 
9.1%
2 2211
 
5.8%
6 1554
 
4.1%
3 1315
 
3.5%
7 1303
 
3.4%
0 1267
 
3.3%
4 1192
 
3.1%
5 1173
 
3.1%
8 1124
 
3.0%
Other values (19) 1772
 
4.7%
Hangul
ValueCountFrequency (%)
지 4005
 
8.2%
동 3106
 
6.3%
서 3102
 
6.3%
남 3097
 
6.3%
강 3092
 
6.3%
구 3083
 
6.3%
시 3074
 
6.3%
울 3064
 
6.2%
번 3061
 
6.2%
별 3060
 
6.2%
Other values (287) 17281
35.2%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct905
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20113019
Minimum19960216
Maximum20240112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 KiB
2024-05-18T12:34:41.092409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19960216
5-th percentile19990719
Q120050612
median20130411
Q320180405
95-th percentile20210701
Maximum20240112
Range279896
Interquartile range (IQR)129793

Descriptive statistics

Standard deviation73375.456
Coefficient of variation (CV)0.0036481573
Kurtosis-1.1037785
Mean20113019
Median Absolute Deviation (MAD)59291
Skewness-0.39984723
Sum6.156595 × 1010
Variance5.3839575 × 109
MonotonicityNot monotonic
2024-05-18T12:34:41.495403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190722 182
 
5.9%
20180701 155
 
5.1%
20171201 94
 
3.1%
20210701 93
 
3.0%
20200921 73
 
2.4%
20150120 71
 
2.3%
20141231 70
 
2.3%
20170605 67
 
2.2%
20161101 62
 
2.0%
20180405 60
 
2.0%
Other values (895) 2134
69.7%
ValueCountFrequency (%)
19960216 1
 
< 0.1%
19960317 3
 
0.1%
19960417 3
 
0.1%
19960423 1
 
< 0.1%
19960428 4
 
0.1%
19960514 2
 
0.1%
19960521 20
0.7%
19960523 1
 
< 0.1%
19960528 1
 
< 0.1%
19960602 2
 
0.1%
ValueCountFrequency (%)
20240112 1
 
< 0.1%
20240108 4
0.1%
20231127 1
 
< 0.1%
20230921 1
 
< 0.1%
20230628 1
 
< 0.1%
20230528 1
 
< 0.1%
20230517 1
 
< 0.1%
20230516 1
 
< 0.1%
20230515 5
0.2%
20230426 1
 
< 0.1%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
처분확정
3061 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
처분확정 3061
100.0%

Length

2024-05-18T12:34:41.790417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:34:42.105729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 3061
100.0%

처분명
Text

Distinct724
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
2024-05-18T12:34:42.712393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length92
Median length68
Mean length13.798105
Min length2

Characters and Unicode

Total characters42236
Distinct characters194
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique462 ?
Unique (%)15.1%

Sample

1st row경고
2nd row영업정지
3rd row영업정지
4th row경고
5th row영업정지 1월
ValueCountFrequency (%)
과태료부과 704
 
10.9%
경고 458
 
7.1%
20만원 429
 
6.7%
및 426
 
6.6%
과태료 314
 
4.9%
영업소폐쇄 283
 
4.4%
개선명령 275
 
4.3%
영업정지 223
 
3.5%
20만원(16만원 197
 
3.1%
부과 177
 
2.7%
Other values (825) 2959
45.9%
2024-05-18T12:34:44.362171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3394
 
8.0%
0 3136
 
7.4%
과 2923
 
6.9%
2 2566
 
6.1%
1 1808
 
4.3%
부 1728
 
4.1%
원 1724
 
4.1%
만 1620
 
3.8%
료 1606
 
3.8%
태 1404
 
3.3%
Other values (184) 20327
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24691
58.5%
Decimal Number 10006
23.7%
Space Separator 3394
 
8.0%
Other Punctuation 1747
 
4.1%
Open Punctuation 1032
 
2.4%
Close Punctuation 1024
 
2.4%
Math Symbol 236
 
0.6%
Dash Punctuation 104
 
0.2%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 2923
 
11.8%
부 1728
 
7.0%
원 1724
 
7.0%
만 1620
 
6.6%
료 1606
 
6.5%
태 1404
 
5.7%
업 1146
 
4.6%
영 1032
 
4.2%
지 854
 
3.5%
정 806
 
3.3%
Other values (155) 9848
39.9%
Decimal Number
ValueCountFrequency (%)
0 3136
31.3%
2 2566
25.6%
1 1808
18.1%
6 522
 
5.2%
3 504
 
5.0%
5 385
 
3.8%
7 301
 
3.0%
9 295
 
2.9%
4 290
 
2.9%
8 199
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 1353
77.4%
, 268
 
15.3%
% 92
 
5.3%
: 22
 
1.3%
' 3
 
0.2%
? 2
 
0.1%
* 2
 
0.1%
/ 2
 
0.1%
; 2
 
0.1%
＊ 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 221
93.6%
→ 11
 
4.7%
> 2
 
0.8%
< 2
 
0.8%
Space Separator
ValueCountFrequency (%)
3394
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1032
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24691
58.5%
Common 17545
41.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 2923
 
11.8%
부 1728
 
7.0%
원 1724
 
7.0%
만 1620
 
6.6%
료 1606
 
6.5%
태 1404
 
5.7%
업 1146
 
4.6%
영 1032
 
4.2%
지 854
 
3.5%
정 806
 
3.3%
Other values (155) 9848
39.9%
Common
ValueCountFrequency (%)
3394
19.3%
0 3136
17.9%
2 2566
14.6%
1 1808
10.3%
. 1353
 
7.7%
( 1032
 
5.9%
) 1024
 
5.8%
6 522
 
3.0%
3 504
 
2.9%
5 385
 
2.2%
Other values (19) 1821
10.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24691
58.5%
ASCII 17533
41.5%
Arrows 11
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3394
19.4%
0 3136
17.9%
2 2566
14.6%
1 1808
10.3%
. 1353
 
7.7%
( 1032
 
5.9%
) 1024
 
5.8%
6 522
 
3.0%
3 504
 
2.9%
5 385
 
2.2%
Other values (17) 1809
10.3%
Hangul
ValueCountFrequency (%)
과 2923
 
11.8%
부 1728
 
7.0%
원 1724
 
7.0%
만 1620
 
6.6%
료 1606
 
6.5%
태 1404
 
5.7%
업 1146
 
4.6%
영 1032
 
4.2%
지 854
 
3.5%
정 806
 
3.3%
Other values (155) 9848
39.9%
Arrows
ValueCountFrequency (%)
→ 11
100.0%
None
ValueCountFrequency (%)
＊ 1
100.0%

법적근거
Text

Distinct215
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
2024-05-18T12:34:45.013137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length37
Mean length9.217576
Min length1

Characters and Unicode

Total characters28215
Distinct characters73
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)3.4%

Sample

1st row공중위생법
2nd row공중위생관리법
3rd row공중위생관리법제11조
4th row공중위생법
5th row공중위생관리법
ValueCountFrequency (%)
법 1436
25.8%
제17조 864
15.5%
공중위생관리법 834
15.0%
공중위생법 348
 
6.2%
제22조제2항제6호 184
 
3.3%
제11조 175
 
3.1%
및 172
 
3.1%
제3조 127
 
2.3%
제11조제3항제2호 123
 
2.2%
제11조제1항 69
 
1.2%
Other values (165) 1240
22.3%
2024-05-18T12:34:46.086111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 3730
13.2%
법 3123
11.1%
1 2728
9.7%
조 2558
 
9.1%
2543
 
9.0%
위 1492
 
5.3%
생 1477
 
5.2%
중 1420
 
5.0%
공 1419
 
5.0%
관 1069
 
3.8%
Other values (63) 6656
23.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19569
69.4%
Decimal Number 5928
 
21.0%
Space Separator 2543
 
9.0%
Other Punctuation 172
 
0.6%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 3730
19.1%
법 3123
16.0%
조 2558
13.1%
위 1492
 
7.6%
생 1477
 
7.5%
중 1420
 
7.3%
공 1419
 
7.3%
관 1069
 
5.5%
리 1056
 
5.4%
항 832
 
4.3%
Other values (48) 1393
 
7.1%
Decimal Number
ValueCountFrequency (%)
1 2728
46.0%
7 1047
 
17.7%
2 959
 
16.2%
3 520
 
8.8%
4 276
 
4.7%
6 185
 
3.1%
9 111
 
1.9%
8 53
 
0.9%
0 48
 
0.8%
5 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2543
100.0%
Other Punctuation
ValueCountFrequency (%)
, 172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
T 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19569
69.4%
Common 8645
30.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 3730
19.1%
법 3123
16.0%
조 2558
13.1%
위 1492
 
7.6%
생 1477
 
7.5%
중 1420
 
7.3%
공 1419
 
7.3%
관 1069
 
5.5%
리 1056
 
5.4%
항 832
 
4.3%
Other values (48) 1393
 
7.1%
Common
ValueCountFrequency (%)
1 2728
31.6%
2543
29.4%
7 1047
 
12.1%
2 959
 
11.1%
3 520
 
6.0%
4 276
 
3.2%
6 185
 
2.1%
, 172
 
2.0%
9 111
 
1.3%
8 53
 
0.6%
Other values (4) 51
 
0.6%
Latin
ValueCountFrequency (%)
T 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19569
69.4%
ASCII 8646
30.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 3730
19.1%
법 3123
16.0%
조 2558
13.1%
위 1492
 
7.6%
생 1477
 
7.5%
중 1420
 
7.3%
공 1419
 
7.3%
관 1069
 
5.5%
리 1056
 
5.4%
항 832
 
4.3%
Other values (48) 1393
 
7.1%
ASCII
ValueCountFrequency (%)
1 2728
31.6%
2543
29.4%
7 1047
 
12.1%
2 959
 
11.1%
3 520
 
6.0%
4 276
 
3.2%
6 185
 
2.1%
, 172
 
2.0%
9 111
 
1.3%
8 53
 
0.6%
Other values (5) 52
 
0.6%

위반일자
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct906
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20106480
Minimum200406
Maximum20240112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.0 KiB
2024-05-18T12:34:46.658597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200406
5-th percentile19990831
Q120050612
median20130401
Q320180405
95-th percentile20210701
Maximum20240112
Range20039706
Interquartile range (IQR)129793

Descriptive statistics

Standard deviation367295.19
Coefficient of variation (CV)0.018267503
Kurtosis2821.9556
Mean20106480
Median Absolute Deviation (MAD)59286
Skewness-52.057285
Sum6.1545937 × 1010
Variance1.3490576 × 1011
MonotonicityNot monotonic
2024-05-18T12:34:47.242382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190722 180
 
5.9%
20180701 153
 
5.0%
20171201 93
 
3.0%
20141231 92
 
3.0%
20210701 86
 
2.8%
20200921 85
 
2.8%
20150101 71
 
2.3%
20170605 67
 
2.2%
20161101 62
 
2.0%
20180405 58
 
1.9%
Other values (896) 2114
69.1%
ValueCountFrequency (%)
200406 1
 
< 0.1%
19960204 1
 
< 0.1%
19960216 1
 
< 0.1%
19960417 5
 
0.2%
19960523 2
 
0.1%
19960528 5
 
0.2%
19960614 4
 
0.1%
19960621 35
1.1%
19960624 2
 
0.1%
19960702 2
 
0.1%
ValueCountFrequency (%)
20240112 1
 
< 0.1%
20240108 4
0.1%
20231127 1
 
< 0.1%
20231031 3
0.1%
20230921 1
 
< 0.1%
20230628 1
 
< 0.1%
20230528 1
 
< 0.1%
20230516 1
 
< 0.1%
20230515 6
0.2%
20230426 1
 
< 0.1%

위반내용
Text

Distinct734
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
2024-05-18T12:34:47.954679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length113
Median length63
Mean length12.361973
Min length1

Characters and Unicode

Total characters37840
Distinct characters351
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique454 ?
Unique (%)14.8%

Sample

1st row위생교육미필
2nd row청소년남녀혼숙
3rd row청소년혼숙
4th row위생교육미필
5th row도박방조
ValueCountFrequency (%)
위생교육 629
 
8.7%
미필 473
 
6.5%
위생교육미필 306
 
4.2%
2018 183
 
2.5%
및 178
 
2.5%
2017년 178
 
2.5%
미이수 142
 
2.0%
장소제공 132
 
1.8%
미이행 131
 
1.8%
폐업신고 119
 
1.6%
Other values (962) 4793
66.0%
2024-05-18T12:34:49.096742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4244
 
11.2%
위 1711
 
4.5%
미 1651
 
4.4%
2 1382
 
3.7%
0 1322
 
3.5%
교 1210
 
3.2%
육 1208
 
3.2%
생 1194
 
3.2%
1 1042
 
2.8%
필 925
 
2.4%
Other values (341) 21951
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27439
72.5%
Decimal Number 4839
 
12.8%
Space Separator 4244
 
11.2%
Open Punctuation 421
 
1.1%
Close Punctuation 421
 
1.1%
Other Punctuation 357
 
0.9%
Dash Punctuation 86
 
0.2%
Uppercase Letter 25
 
0.1%
Other Symbol 5
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
위 1711
 
6.2%
미 1651
 
6.0%
교 1210
 
4.4%
육 1208
 
4.4%
생 1194
 
4.4%
필 925
 
3.4%
업 888
 
3.2%
년 716
 
2.6%
이 683
 
2.5%
행 635
 
2.3%
Other values (308) 16618
60.6%
Decimal Number
ValueCountFrequency (%)
2 1382
28.6%
0 1322
27.3%
1 1042
21.5%
8 219
 
4.5%
7 210
 
4.3%
6 193
 
4.0%
3 149
 
3.1%
9 122
 
2.5%
4 100
 
2.1%
5 100
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
C 10
40.0%
V 4
 
16.0%
T 4
 
16.0%
R 2
 
8.0%
N 2
 
8.0%
I 2
 
8.0%
L 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
, 183
51.3%
. 132
37.0%
: 21
 
5.9%
/ 20
 
5.6%
? 1
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 419
99.5%
[ 2
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 419
99.5%
] 2
 
0.5%
Other Symbol
ValueCountFrequency (%)
㎖ 4
80.0%
㎡ 1
 
20.0%
Math Symbol
ValueCountFrequency (%)
> 1
50.0%
< 1
50.0%
Space Separator
ValueCountFrequency (%)
4244
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27439
72.5%
Common 10375
 
27.4%
Latin 26
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
위 1711
 
6.2%
미 1651
 
6.0%
교 1210
 
4.4%
육 1208
 
4.4%
생 1194
 
4.4%
필 925
 
3.4%
업 888
 
3.2%
년 716
 
2.6%
이 683
 
2.5%
행 635
 
2.3%
Other values (308) 16618
60.6%
Common
ValueCountFrequency (%)
4244
40.9%
2 1382
 
13.3%
0 1322
 
12.7%
1 1042
 
10.0%
( 419
 
4.0%
) 419
 
4.0%
8 219
 
2.1%
7 210
 
2.0%
6 193
 
1.9%
, 183
 
1.8%
Other values (15) 742
 
7.2%
Latin
ValueCountFrequency (%)
C 10
38.5%
V 4
 
15.4%
T 4
 
15.4%
R 2
 
7.7%
N 2
 
7.7%
I 2
 
7.7%
x 1
 
3.8%
L 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27439
72.5%
ASCII 10396
 
27.5%
CJK Compat 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4244
40.8%
2 1382
 
13.3%
0 1322
 
12.7%
1 1042
 
10.0%
( 419
 
4.0%
) 419
 
4.0%
8 219
 
2.1%
7 210
 
2.0%
6 193
 
1.9%
, 183
 
1.8%
Other values (21) 763
 
7.3%
Hangul
ValueCountFrequency (%)
위 1711
 
6.2%
미 1651
 
6.0%
교 1210
 
4.4%
육 1208
 
4.4%
생 1194
 
4.4%
필 925
 
3.4%
업 888
 
3.2%
년 716
 
2.6%
이 683
 
2.5%
행 635
 
2.3%
Other values (308) 16618
60.6%
CJK Compat
ValueCountFrequency (%)
㎖ 4
80.0%
㎡ 1
 
20.0%

처분내용
Text

Distinct724
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size24.0 KiB
2024-05-18T12:34:49.740442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length92
Median length68
Mean length13.798105
Min length2

Characters and Unicode

Total characters42236
Distinct characters194
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique462 ?
Unique (%)15.1%

Sample

1st row경고
2nd row영업정지
3rd row영업정지
4th row경고
5th row영업정지 1월
ValueCountFrequency (%)
과태료부과 704
 
10.9%
경고 458
 
7.1%
20만원 429
 
6.7%
및 426
 
6.6%
과태료 314
 
4.9%
영업소폐쇄 283
 
4.4%
개선명령 275
 
4.3%
영업정지 223
 
3.5%
20만원(16만원 197
 
3.1%
부과 177
 
2.7%
Other values (825) 2959
45.9%
2024-05-18T12:34:50.984720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3394
 
8.0%
0 3136
 
7.4%
과 2923
 
6.9%
2 2566
 
6.1%
1 1808
 
4.3%
부 1728
 
4.1%
원 1724
 
4.1%
만 1620
 
3.8%
료 1606
 
3.8%
태 1404
 
3.3%
Other values (184) 20327
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24691
58.5%
Decimal Number 10006
23.7%
Space Separator 3394
 
8.0%
Other Punctuation 1747
 
4.1%
Open Punctuation 1032
 
2.4%
Close Punctuation 1024
 
2.4%
Math Symbol 236
 
0.6%
Dash Punctuation 104
 
0.2%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 2923
 
11.8%
부 1728
 
7.0%
원 1724
 
7.0%
만 1620
 
6.6%
료 1606
 
6.5%
태 1404
 
5.7%
업 1146
 
4.6%
영 1032
 
4.2%
지 854
 
3.5%
정 806
 
3.3%
Other values (155) 9848
39.9%
Decimal Number
ValueCountFrequency (%)
0 3136
31.3%
2 2566
25.6%
1 1808
18.1%
6 522
 
5.2%
3 504
 
5.0%
5 385
 
3.8%
7 301
 
3.0%
9 295
 
2.9%
4 290
 
2.9%
8 199
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 1353
77.4%
, 268
 
15.3%
% 92
 
5.3%
: 22
 
1.3%
' 3
 
0.2%
? 2
 
0.1%
* 2
 
0.1%
/ 2
 
0.1%
; 2
 
0.1%
＊ 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 221
93.6%
→ 11
 
4.7%
> 2
 
0.8%
< 2
 
0.8%
Space Separator
ValueCountFrequency (%)
3394
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1032
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24691
58.5%
Common 17545
41.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 2923
 
11.8%
부 1728
 
7.0%
원 1724
 
7.0%
만 1620
 
6.6%
료 1606
 
6.5%
태 1404
 
5.7%
업 1146
 
4.6%
영 1032
 
4.2%
지 854
 
3.5%
정 806
 
3.3%
Other values (155) 9848
39.9%
Common
ValueCountFrequency (%)
3394
19.3%
0 3136
17.9%
2 2566
14.6%
1 1808
10.3%
. 1353
 
7.7%
( 1032
 
5.9%
) 1024
 
5.8%
6 522
 
3.0%
3 504
 
2.9%
5 385
 
2.2%
Other values (19) 1821
10.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24691
58.5%
ASCII 17533
41.5%
Arrows 11
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3394
19.4%
0 3136
17.9%
2 2566
14.6%
1 1808
10.3%
. 1353
 
7.7%
( 1032
 
5.9%
) 1024
 
5.8%
6 522
 
3.0%
3 504
 
2.9%
5 385
 
2.2%
Other values (17) 1809
10.3%
Hangul
ValueCountFrequency (%)
과 2923
 
11.8%
부 1728
 
7.0%
원 1724
 
7.0%
만 1620
 
6.6%
료 1606
 
6.5%
태 1404
 
5.7%
업 1146
 
4.6%
영 1032
 
4.2%
지 854
 
3.5%
정 806
 
3.3%
Other values (155) 9848
39.9%
Arrows
ValueCountFrequency (%)
→ 11
100.0%
None
ValueCountFrequency (%)
＊ 1
100.0%

처분기간
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)9.4%
Missing2934
Missing (%)95.9%
Infinite0
Infinite (%)0.0%
Mean13.519685
Minimum0
Maximum29
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.0 KiB
2024-05-18T12:34:51.511501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q110
median15
Q315
95-th percentile20
Maximum29
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.5054647
Coefficient of variation (CV)0.3332522
Kurtosis2.171812
Mean13.519685
Median Absolute Deviation (MAD)0
Skewness0.032496353
Sum1717
Variance20.299213
MonotonicityNot monotonic
2024-05-18T12:34:52.140067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
15 74
 
2.4%
10 31
 
1.0%
20 6
 
0.2%
5 5
 
0.2%
25 2
 
0.1%
3 2
 
0.1%
8 2
 
0.1%
1 1
 
< 0.1%
27 1
 
< 0.1%
29 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 2934
95.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
3 2
 
0.1%
5 5
 
0.2%
8 2
 
0.1%
10 31
1.0%
15 74
2.4%
20 6
 
0.2%
23 1
 
< 0.1%
25 2
 
0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
27 1
 
< 0.1%
25 2
 
0.1%
23 1
 
< 0.1%
20 6
 
0.2%
15 74
2.4%
10 31
1.0%
8 2
 
0.1%
5 5
 
0.2%
3 2
 
0.1%

영업장면적(㎡)
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1201
Distinct (%)40.8%
Missing118
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean410.27138
Minimum0
Maximum54085
Zeros86
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size27.0 KiB
2024-05-18T12:34:52.540038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q153
median119
Q3397
95-th percentile1577.052
Maximum54085
Range54085
Interquartile range (IQR)344

Descriptive statistics

Standard deviation1377.878
Coefficient of variation (CV)3.3584551
Kurtosis818.37653
Mean410.27138
Median Absolute Deviation (MAD)88
Skewness23.587824
Sum1207428.7
Variance1898547.8
MonotonicityNot monotonic
2024-05-18T12:34:53.164081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 86
 
2.8%
99.0 64
 
2.1%
33.0 35
 
1.1%
132.0 32
 
1.0%
66.0 26
 
0.8%
115.5 26
 
0.8%
432.0 20
 
0.7%
165.0 19
 
0.6%
30.0 19
 
0.6%
597.76 17
 
0.6%
Other values (1191) 2599
84.9%
(Missing) 118
 
3.9%
ValueCountFrequency (%)
0.0 86
2.8%
3.3 1
 
< 0.1%
4.0 1
 
< 0.1%
5.0 2
 
0.1%
5.22 1
 
< 0.1%
6.0 6
 
0.2%
6.6 4
 
0.1%
7.0 2
 
0.1%
7.14 1
 
< 0.1%
7.5 2
 
0.1%
ValueCountFrequency (%)
54085.0 1
 
< 0.1%
17142.35 1
 
< 0.1%
17066.77 3
 
0.1%
14093.0 2
 
0.1%
5817.0 8
0.3%
5547.38 1
 
< 0.1%
5202.24 1
 
< 0.1%
4944.75 1
 
< 0.1%
4939.66 2
 
0.1%
4290.0 1
 
< 0.1%

Interactions

2024-05-18T12:34:24.312687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:16.628043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:18.505083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:20.712040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:22.674566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:24.637762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:17.057681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:18.937368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:21.152493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:23.003362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:25.108997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:17.532604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:19.343387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:21.506080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:23.294310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:25.485040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:17.944932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:19.936911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:21.810611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:23.668899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:25.794095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:18.209891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:20.309511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:22.123509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:34:23.978422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T12:34:53.874075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명업태명지도점검일자위반일자처분기간영업장면적(㎡)
처분일자1.0000.6770.6670.995NaN0.6450.078
업종명0.6771.0000.9640.680NaN0.5860.000
업태명0.6670.9641.0000.665NaN0.7970.368
지도점검일자0.9950.6800.6651.000NaN0.6520.083
위반일자NaNNaNNaNNaN1.000NaNNaN
처분기간0.6450.5860.7970.652NaN1.000NaN
영업장면적(㎡)0.0780.0000.3680.083NaNNaN1.000
2024-05-18T12:34:54.303010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명업태명
업종명1.0000.680
업태명0.6801.000
2024-05-18T12:34:54.634397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자처분기간영업장면적(㎡)업종명업태명
처분일자1.0000.9980.997-0.479-0.2740.3230.317
지도점검일자0.9981.0000.999-0.488-0.2820.3250.315
위반일자0.9970.9991.000-0.484-0.2830.0000.000
처분기간-0.479-0.488-0.4841.000-0.0550.3550.371
영업장면적(㎡)-0.274-0.282-0.283-0.0551.0000.0000.190
업종명0.3230.3250.0000.3550.0001.0000.680
업태명0.3170.3150.0000.3710.1900.6801.000

Missing values

2024-05-18T12:34:26.446195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T12:34:27.703406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-18T12:34:28.364485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
032200001996062106800410500072숙박업(일반)여관업삼보여관<NA>서울특별시 강남구 역삼동 779번지 5호19960521처분확정경고공중위생법19960621위생교육미필경고<NA>0.0
13220000200308120002숙박업(일반)여관업삼보여관<NA>서울특별시 강남구 역삼동 779번지 5호20030720처분확정영업정지공중위생관리법20030720청소년남녀혼숙영업정지<NA>0.0
23220000200310070002숙박업(일반)여관업삼보여관<NA>서울특별시 강남구 역삼동 779번지 5호20030812처분확정영업정지공중위생관리법제11조20030812청소년혼숙영업정지<NA>0.0
332200001996062106800410600001숙박업(일반)여관업미송<NA>서울특별시 강남구 역삼동 700번지 27호19960521처분확정경고공중위생법19960621위생교육미필경고<NA>183.19
432200002002080806800410600001숙박업(일반)여관업미송<NA>서울특별시 강남구 역삼동 700번지 27호20020725처분확정영업정지 1월공중위생관리법20020725도박방조영업정지 1월<NA>183.19
532200002002080806800410600001숙박업(일반)여관업미송<NA>서울특별시 강남구 역삼동 700번지 27호20020725처분확정영업정지 1월공중위생관리법20020725도박방조영업정지 1월<NA>183.19
632200002003030306800410600001숙박업(일반)여관업미송<NA>서울특별시 강남구 역삼동 700번지 27호20020725처분확정2002.8.8건 행정처분집행 (2003.3.4~3.26)공중위생관리법20020725도박방조2002.8.8건 행정처분집행 (2003.3.4~3.26)<NA>183.19
732200002003030306800410600001숙박업(일반)여관업미송<NA>서울특별시 강남구 역삼동 700번지 27호20020725처분확정2002.8.8건 행정처분집행 (2003.3.4~3.26)공중위생관리법20020725도박방조2002.8.8건 행정처분집행 (2003.3.4~3.26)<NA>183.19
83220000200401070003숙박업(일반)여관업미송<NA>서울특별시 강남구 역삼동 700번지 27호20031118처분확정영업정지15일(2004.1.19-2.2)공중위생관리법20031118도박행위장소제공영업정지15일(2004.1.19-2.2)15183.19
93220000201107070003숙박업(일반)여관업미송<NA>서울특별시 강남구 역삼동 700번지 27호20110412처분확정영업정지1월공중위생관리법 제11조20110412성매매장소제공영업정지1월<NA>183.19
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
30513220000201908282015-00009일반미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업포원？(41shop)서울특별시 강남구 도산대로55길 45, 지상5층 (청담동, 십이.이.사.칠빌딩)서울특별시 강남구 청담동 84번지 24호 십이.이.사.칠빌딩20190722처분확정과태료부과 20만원(16만원 사전납부완료)법 제17조201907222018 위생교육 미필과태료부과 20만원(16만원 사전납부완료)<NA>25.0
30523220000202008032015-00129일반미용업, 네일미용업, 화장ㆍ분장 미용업일반미용업두쏠뷰티대치점서울특별시 강남구 도곡로 408, 지상3층 307,308호 (대치동, 디마크빌딩)서울특별시 강남구 대치동 1024번지 디마크빌딩 3층 307,308호20200401처분확정영업소폐쇄법 제11조제3항제2호20200401사업자등록 후 폐업신고 미이행영업소폐쇄<NA>178.12
30533220000201908282018-0005일반미용업, 네일미용업, 화장ㆍ분장 미용업일반미용업리썸1023서울특별시 강남구 언주로133길 28, 1층 (논현동)서울특별시 강남구 논현동 83번지 2호20190722처분확정과태료부과 20만원(16만원 사전납부완료)법 제17조201907222018 위생교육 미필과태료부과 20만원(16만원 사전납부완료)<NA>141.48
30543220000201908282016-00002피부미용업, 네일미용업, 화장ㆍ분장 미용업메이크업업세이미뷰티서울특별시 강남구 논현로106길 8, 2층 (역삼동)서울특별시 강남구 역삼동 659번지 12호20190722처분확정과태료부과 20만원(16만원 사전납부완료)법 제17조201907222018 위생교육 미필과태료부과 20만원(16만원 사전납부완료)<NA>53.0
30553220000202109102019-0009피부미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업그리다 아이래쉬서울특별시 강남구 역삼로 315, 개나리아파트 상가동 203호 (역삼동)서울특별시 강남구 역삼동 716번지 4호 개나리아파트20210823처분확정과징금부과법 제22조 제2항제2호20210823영업소 내 의료행위(반영구 눈썹문신)과징금부과<NA>36.3
30563220000202109102019-0009피부미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업그리다 아이래쉬서울특별시 강남구 역삼로 315, 개나리아파트 상가동 203호 (역삼동)서울특별시 강남구 역삼동 716번지 4호 개나리아파트20210823처분확정과태료부과 80만원법 제22조 제2항제2호20210823영업소 내 의료행위(반영구 눈썹문신)과태료부과 80만원<NA>36.3
30573220000201908282017-0068피부미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업나다움(NADAUM)서울특별시 강남구 도산대로12길 9, 3층 (논현동)서울특별시 강남구 논현동 5번지 15호20190722처분확정과태료부과 20만원(16만원 사전납부완료)법 제17조201907222018 위생교육 미필과태료부과 20만원(16만원 사전납부완료)<NA>9.9
30583220000201906212019-0001피부미용업, 네일미용업, 화장ㆍ분장 미용업네일아트업네일 더 샤인(Nail the shine)서울특별시 강남구 도산대로12길 14, 지상1층 102호 (논현동)서울특별시 강남구 논현동 12번지 15호20190604처분확정과징금564천원(영업정지2개월 갈음), 과태료500천원(400천원 사전납부 완료)법 제4조제4항 및 제7항20190604의료행위[반영구 화장(문신)]과징금564천원(영업정지2개월 갈음), 과태료500천원(400천원 사전납부 완료)<NA>27.0
30593220000202305052020-0026피부미용업, 네일미용업, 화장ㆍ분장 미용업메이크업업뷰티앤쉐입(beauty & shape)서울특별시 강남구 압구정로48길 34, 3층 302호 (신사동)서울특별시 강남구 신사동 642번지 15호20220101처분확정과태료부과 (사전처분 480,000원 부과)법 제22조제2항제6호202201012021년 위생교육 미이수 - 과태료부과과태료부과 (사전처분 480,000원 부과)<NA>115.5
30603220000202305112020-0002피부미용업, 네일미용업, 화장ㆍ분장 미용업피부미용업뷰티 더 끌리오서울특별시 강남구 도산대로51길 44, 3층 (신사동)서울특별시 강남구 신사동 657번지 28호20220101처분확정과태료부과법 제22조제2항제6호202201012021년도 위생교육 미이수과태료부과<NA>75.0

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
109322000020180123282목욕장업목욕장업 기타영동스파서울특별시 강남구 강남대로128길 10, 지상4,5층 (논현동)서울특별시 강남구 논현동 144번지 지상4,5층20170608처분확정영업정지법 제11조제1항제8호20170608성매매 알선영업정지<NA>597.768
130322000020210831300목욕장업한증막업수사우나서울특별시 강남구 언주로148길 13, (논현동,지상1층,지상2층)서울특별시 강남구 논현동 97번지 16호 지상1층,지상2층20210721처분확정영업정지 10일법 제83조제4항제2호20210721출입명부 부실작성영업정지 10일10432.06
1343220000202110290027목욕장업공동탕업라미드남자사우나서울특별시 강남구 봉은사로 410, (삼성동)서울특별시 강남구 삼성동 112번지 5호20210701처분확정과태료부과법 제22조제2항제6호202107012020년 위생교육 미필과태료부과<NA>245.636
14132200002024030558위생관리용역업위생관리용역업주식회사 노블엔터테인먼트서울특별시 강남구 봉은사로 129, 거평타운 1201호 (논현동)서울특별시 강남구 논현동 203번지 1호 거평타운20210701처분확정과태료부과법 제22조제2항제6호202107012020년 위생교육 미필과태료부과<NA>59.296
303220000200508180114목욕장업한증막업피지한증막<NA>서울특별시 강남구 대치동 932번지 2호20040620처분확정15일및과징금100만원공중위생관리법제11조20040619도박행위방조15일및과징금100만원15271.445
70322000020120702266목욕장업목욕장업 기타도토리짐서울특별시 강남구 논현로 641, (논현동,대우아이빌 지하1층 B01호)서울특별시 강남구 논현동 152번지 5호 대우아이빌 지하1층 B01호20120503처분확정영업소폐쇄공중위생관립버 제3조 및 동법시행규칙 제19조20120503신고를 하지 아니하고 영업소의 소재지를 변경영업소폐쇄<NA>292.444
953220000201412082014-00219일반미용업일반미용업박준뷰티랩서울특별시 강남구 삼성로 725, 지상2,3층 (청담동)서울특별시 강남구 청담동 31번지 12호20141104처분확정개선명령 및 과태료부과(40만원)-사전납부 감경부과법 제4조제4항 및 제7항20141104면허증 원본 미게시개선명령 및 과태료부과(40만원)-사전납부 감경부과<NA>0.04
1213220000201910252014-323피부미용업피부미용업수아미서울특별시 강남구 테헤란로 124, 지하1층 101호 (역삼동)서울특별시 강남구 역삼동 823번지 0호 지하1층-10120191002처분확정과징금부과564천원 부과(영업정지2개월 갈음)과태료500천원(400천원 사전납부완료)법 제4조제4항 및 제7항20191002의료행위(반영구 화장 문신)과징금부과564천원 부과(영업정지2개월 갈음)과태료500천원(400천원 사전납부완료)<NA>80.434
423220000200809110123목욕장업공동탕업한증막금사우나<NA>서울특별시 강남구 대치동 898번지20080303처분확정영업정지공중위생관리법제102008030322시이후 청소년출입영업정지10955.133
453220000200811240432이용업일반이용업성민<NA>서울특별시 강남구 역삼동 719번지 6호 지하1층20080920처분확정영업정지1월 갈음 과징금156만원부과공중위생관리법 제11조 및 동법생행규칙 제19조20080920무자격안마영업정지1월 갈음 과징금156만원부과<NA>148.53