Overview

Dataset statistics

Number of variables17
Number of observations90
Missing cells2
Missing cells (%)0.1%
Duplicate rows5
Duplicate rows (%)5.6%
Total size in memory12.6 KiB
Average record size in memory143.5 B

Variable types

Categorical9
Numeric4
Text4

Dataset

Description시군구코드,처분일자,교부번호,업종명,업태명,업소명,소재지도로명,소재지지번,지도점검일자,행정처분상태,처분명,법적근거,위반일자,위반내용,처분내용,처분기간,영업장면적(㎡)
Author강동구
URLhttps://data.seoul.go.kr/dataList/OA-10682/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 5 (5.6%) duplicate rowsDuplicates
처분내용 is highly overall correlated with 처분일자 and 7 other fieldsHigh correlation
처분명 is highly overall correlated with 처분일자 and 7 other fieldsHigh correlation
업태명 is highly overall correlated with 업종명 and 1 other fieldsHigh correlation
업종명 is highly overall correlated with 업태명 and 1 other fieldsHigh correlation
법적근거 is highly overall correlated with 처분일자 and 6 other fieldsHigh correlation
위반내용 is highly overall correlated with 처분일자 and 7 other fieldsHigh correlation
처분기간 is highly overall correlated with 처분일자 and 9 other fieldsHigh correlation
처분일자 is highly overall correlated with 지도점검일자 and 6 other fieldsHigh correlation
지도점검일자 is highly overall correlated with 처분일자 and 6 other fieldsHigh correlation
위반일자 is highly overall correlated with 처분일자 and 6 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 처분명 and 3 other fieldsHigh correlation
처분기간 is highly imbalanced (88.9%)Imbalance
영업장면적(㎡) has 2 (2.2%) missing valuesMissing
영업장면적(㎡) has 1 (1.1%) zerosZeros

Reproduction

Analysis started2024-05-18 03:04:46.545430
Analysis finished2024-05-18 03:04:56.326347
Duration9.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
3240000
90 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3240000
2nd row3240000
3rd row3240000
4th row3240000
5th row3240000

Common Values

ValueCountFrequency (%)
3240000 90
100.0%

Length

2024-05-18T12:04:56.523398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:04:56.842977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3240000 90
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210753
Minimum20200316
Maximum20240408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-18T12:04:57.212612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200316
5-th percentile20200624
Q120201028
median20201169
Q320220121
95-th percentile20231114
Maximum20240408
Range40092
Interquartile range (IQR)19092.75

Descriptive statistics

Standard deviation12730.223
Coefficient of variation (CV)0.00062987375
Kurtosis-0.49691103
Mean20210753
Median Absolute Deviation (MAD)545
Skewness0.95467168
Sum1.8189678 × 109
Variance1.6205858 × 108
MonotonicityNot monotonic
2024-05-18T12:04:57.778034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
20200624 8
 
8.9%
20201124 7
 
7.8%
20220121 6
 
6.7%
20201028 6
 
6.7%
20210201 3
 
3.3%
20200716 3
 
3.3%
20201214 3
 
3.3%
20230726 3
 
3.3%
20220303 3
 
3.3%
20201112 3
 
3.3%
Other values (35) 45
50.0%
ValueCountFrequency (%)
20200316 1
 
1.1%
20200624 8
8.9%
20200630 1
 
1.1%
20200716 3
 
3.3%
20200727 1
 
1.1%
20200731 1
 
1.1%
20200921 1
 
1.1%
20201026 1
 
1.1%
20201028 6
6.7%
20201029 2
 
2.2%
ValueCountFrequency (%)
20240408 1
1.1%
20240305 1
1.1%
20240201 1
1.1%
20240125 1
1.1%
20231120 1
1.1%
20231107 1
1.1%
20231018 2
2.2%
20230908 1
1.1%
20230825 2
2.2%
20230731 1
1.1%

교부번호
Text

Distinct73
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-05-18T12:04:58.457595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.2555556
Min length1

Characters and Unicode

Total characters473
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

Unique59 ?
Unique (%)65.6%

Sample

1st row60
2nd row60
3rd row74
4th row77
5th row77
ValueCountFrequency (%)
130 5
 
5.6%
37 2
 
2.2%
2018-00001 2
 
2.2%
58 2
 
2.2%
2017-00006 2
 
2.2%
1308 2
 
2.2%
87 2
 
2.2%
2017-00003 2
 
2.2%
84 2
 
2.2%
77 2
 
2.2%
Other values (63) 67
74.4%
2024-05-18T12:04:59.973797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 165
34.9%
1 76
16.1%
2 53
 
11.2%
- 33
 
7.0%
8 30
 
6.3%
7 27
 
5.7%
3 26
 
5.5%
6 21
 
4.4%
9 15
 
3.2%
5 15
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 440
93.0%
Dash Punctuation 33
 
7.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 165
37.5%
1 76
17.3%
2 53
 
12.0%
8 30
 
6.8%
7 27
 
6.1%
3 26
 
5.9%
6 21
 
4.8%
9 15
 
3.4%
5 15
 
3.4%
4 12
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 473
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 165
34.9%
1 76
16.1%
2 53
 
11.2%
- 33
 
7.0%
8 30
 
6.3%
7 27
 
5.7%
3 26
 
5.5%
6 21
 
4.4%
9 15
 
3.2%
5 15
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 165
34.9%
1 76
16.1%
2 53
 
11.2%
- 33
 
7.0%
8 30
 
6.3%
7 27
 
5.7%
3 26
 
5.5%
6 21
 
4.4%
9 15
 
3.2%
5 15
 
3.2%

업종명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size852.0 B
숙박업(일반)
24 
위생관리용역업
15 
목욕장업
14 
일반미용업
8 
피부미용업
7 
Other values (6)
22 

Length

Max length9
Median length7
Mean length5.4888889
Min length3

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
숙박업(일반) 24
26.7%
위생관리용역업 15
16.7%
목욕장업 14
15.6%
일반미용업 8
 
8.9%
피부미용업 7
 
7.8%
미용업 6
 
6.7%
네일미용업 6
 
6.7%
세탁업 4
 
4.4%
종합미용업 3
 
3.3%
이용업 2
 
2.2%

Length

2024-05-18T12:05:00.774345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
숙박업(일반 24
26.4%
위생관리용역업 15
16.5%
목욕장업 14
15.4%
일반미용업 8
 
8.8%
피부미용업 7
 
7.7%
미용업 7
 
7.7%
네일미용업 6
 
6.6%
세탁업 4
 
4.4%
종합미용업 3
 
3.3%
이용업 2
 
2.2%

업태명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size852.0 B
여관업
19 
위생관리용역업
15 
일반미용업
13 
공동탕업+찜질시설서비스영업
11 
네일아트업
8 
Other values (9)
24 

Length

Max length14
Median length7
Mean length5.8555556
Min length2

Unique

Unique2 ?
Unique (%)2.2%

Sample

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

Common Values

ValueCountFrequency (%)
여관업 19
21.1%
위생관리용역업 15
16.7%
일반미용업 13
14.4%
공동탕업+찜질시설서비스영업 11
12.2%
네일아트업 8
8.9%
피부미용업 7
 
7.8%
일반세탁업 4
 
4.4%
공동탕업 3
 
3.3%
관광호텔 2
 
2.2%
일반호텔 2
 
2.2%
Other values (4) 6
 
6.7%

Length

2024-05-18T12:05:01.394432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여관업 19
21.1%
위생관리용역업 15
16.7%
일반미용업 13
14.4%
공동탕업+찜질시설서비스영업 11
12.2%
네일아트업 8
8.9%
피부미용업 7
 
7.8%
일반세탁업 4
 
4.4%
공동탕업 3
 
3.3%
관광호텔 2
 
2.2%
일반호텔 2
 
2.2%
Other values (4) 6
 
6.7%

업소명
Text

Distinct74
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-05-18T12:05:02.333240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length5.9444444
Min length1

Characters and Unicode

Total characters535
Distinct characters182
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)67.8%

Sample

1st row러브이즈
2nd row러브이즈
3rd row연애시대모텔
4th row히트호텔(HIT)
5th row히트호텔(HIT)
ValueCountFrequency (%)
그린파크 5
 
4.5%
주식회사 4
 
3.6%
클리어 2
 
1.8%
한강허브대중탕 2
 
1.8%
천호점 2
 
1.8%
사우나 2
 
1.8%
라뷰호텔 2
 
1.8%
에이치네일샵 2
 
1.8%
spa 2
 
1.8%
nail 2
 
1.8%
Other values (76) 85
77.3%
2024-05-18T12:05:03.537892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
3.7%
사 15
 
2.8%
나 13
 
2.4%
어 12
 
2.2%
주 12
 
2.2%
( 11
 
2.1%
호 11
 
2.1%
) 11
 
2.1%
스 11
 
2.1%
텔 10
 
1.9%
Other values (172) 409
76.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 451
84.3%
Uppercase Letter 30
 
5.6%
Space Separator 20
 
3.7%
Open Punctuation 11
 
2.1%
Close Punctuation 11
 
2.1%
Other Punctuation 8
 
1.5%
Decimal Number 3
 
0.6%
Lowercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 15
 
3.3%
나 13
 
2.9%
어 12
 
2.7%
주 12
 
2.7%
호 11
 
2.4%
스 11
 
2.4%
텔 10
 
2.2%
우 9
 
2.0%
헤 8
 
1.8%
이 8
 
1.8%
Other values (151) 342
75.8%
Uppercase Letter
ValueCountFrequency (%)
A 6
20.0%
I 6
20.0%
N 4
13.3%
L 3
10.0%
T 3
10.0%
P 2
 
6.7%
S 2
 
6.7%
H 2
 
6.7%
J 1
 
3.3%
W 1
 
3.3%
Other Punctuation
ValueCountFrequency (%)
& 3
37.5%
, 2
25.0%
. 2
25.0%
' 1
 
12.5%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
4 1
33.3%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 451
84.3%
Common 53
 
9.9%
Latin 31
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 15
 
3.3%
나 13
 
2.9%
어 12
 
2.7%
주 12
 
2.7%
호 11
 
2.4%
스 11
 
2.4%
텔 10
 
2.2%
우 9
 
2.0%
헤 8
 
1.8%
이 8
 
1.8%
Other values (151) 342
75.8%
Latin
ValueCountFrequency (%)
A 6
19.4%
I 6
19.4%
N 4
12.9%
L 3
9.7%
T 3
9.7%
P 2
 
6.5%
S 2
 
6.5%
H 2
 
6.5%
J 1
 
3.2%
W 1
 
3.2%
Common
ValueCountFrequency (%)
20
37.7%
( 11
20.8%
) 11
20.8%
& 3
 
5.7%
, 2
 
3.8%
. 2
 
3.8%
' 1
 
1.9%
1 1
 
1.9%
4 1
 
1.9%
2 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 451
84.3%
ASCII 84
 
15.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
23.8%
( 11
13.1%
) 11
13.1%
A 6
 
7.1%
I 6
 
7.1%
N 4
 
4.8%
L 3
 
3.6%
T 3
 
3.6%
& 3
 
3.6%
, 2
 
2.4%
Other values (11) 15
17.9%
Hangul
ValueCountFrequency (%)
사 15
 
3.3%
나 13
 
2.9%
어 12
 
2.7%
주 12
 
2.7%
호 11
 
2.4%
스 11
 
2.4%
텔 10
 
2.2%
우 9
 
2.0%
헤 8
 
1.8%
이 8
 
1.8%
Other values (151) 342
75.8%

소재지도로명
Text

Distinct75
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-05-18T12:05:04.265142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length66
Median length44
Mean length31.933333
Min length23

Characters and Unicode

Total characters2874
Distinct characters116
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)70.0%

Sample

1st row서울특별시 강동구 천중로40길 71, (길동)
2nd row서울특별시 강동구 천중로40길 71, (길동)
3rd row서울특별시 강동구 진황도로 21-1, (천호동)
4th row서울특별시 강동구 구천면로24길 28, (천호동)
5th row서울특별시 강동구 구천면로24길 28, (천호동)
ValueCountFrequency (%)
서울특별시 90
 
16.8%
강동구 90
 
16.8%
천호동 28
 
5.2%
길동 18
 
3.4%
1층 12
 
2.2%
성내동 10
 
1.9%
명일동 9
 
1.7%
암사동 7
 
1.3%
올림픽로 7
 
1.3%
진황도로 7
 
1.3%
Other values (178) 259
48.2%
2024-05-18T12:05:05.516397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
447
 
15.6%
동 186
 
6.5%
, 127
 
4.4%
1 127
 
4.4%
구 101
 
3.5%
시 94
 
3.3%
강 94
 
3.3%
) 93
 
3.2%
( 93
 
3.2%
서 90
 
3.1%
Other values (106) 1422
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1599
55.6%
Decimal Number 496
 
17.3%
Space Separator 447
 
15.6%
Other Punctuation 127
 
4.4%
Close Punctuation 93
 
3.2%
Open Punctuation 93
 
3.2%
Dash Punctuation 19
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 186
 
11.6%
구 101
 
6.3%
시 94
 
5.9%
강 94
 
5.9%
서 90
 
5.6%
특 90
 
5.6%
별 90
 
5.6%
울 90
 
5.6%
로 90
 
5.6%
호 79
 
4.9%
Other values (91) 595
37.2%
Decimal Number
ValueCountFrequency (%)
1 127
25.6%
2 80
16.1%
0 61
12.3%
3 52
10.5%
4 40
 
8.1%
5 35
 
7.1%
7 33
 
6.7%
6 28
 
5.6%
9 20
 
4.0%
8 20
 
4.0%
Space Separator
ValueCountFrequency (%)
447
100.0%
Other Punctuation
ValueCountFrequency (%)
, 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 93
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1599
55.6%
Common 1275
44.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 186
 
11.6%
구 101
 
6.3%
시 94
 
5.9%
강 94
 
5.9%
서 90
 
5.6%
특 90
 
5.6%
별 90
 
5.6%
울 90
 
5.6%
로 90
 
5.6%
호 79
 
4.9%
Other values (91) 595
37.2%
Common
ValueCountFrequency (%)
447
35.1%
, 127
 
10.0%
1 127
 
10.0%
) 93
 
7.3%
( 93
 
7.3%
2 80
 
6.3%
0 61
 
4.8%
3 52
 
4.1%
4 40
 
3.1%
5 35
 
2.7%
Other values (5) 120
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1599
55.6%
ASCII 1275
44.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
447
35.1%
, 127
 
10.0%
1 127
 
10.0%
) 93
 
7.3%
( 93
 
7.3%
2 80
 
6.3%
0 61
 
4.8%
3 52
 
4.1%
4 40
 
3.1%
5 35
 
2.7%
Other values (5) 120
 
9.4%
Hangul
ValueCountFrequency (%)
동 186
 
11.6%
구 101
 
6.3%
시 94
 
5.9%
강 94
 
5.9%
서 90
 
5.6%
특 90
 
5.6%
별 90
 
5.6%
울 90
 
5.6%
로 90
 
5.6%
호 79
 
4.9%
Other values (91) 595
37.2%

소재지지번
Text

Distinct75
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
2024-05-18T12:05:06.157880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length39
Mean length29.233333
Min length20

Characters and Unicode

Total characters2631
Distinct characters103
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)70.0%

Sample

1st row서울특별시 강동구 길동 387번지 14호
2nd row서울특별시 강동구 길동 387번지 14호
3rd row서울특별시 강동구 천호동 557번지
4th row서울특별시 강동구 천호동 427번지 29호
5th row서울특별시 강동구 천호동 427번지 29호
ValueCountFrequency (%)
서울특별시 90
17.7%
강동구 90
17.7%
천호동 34
 
6.7%
길동 20
 
3.9%
성내동 11
 
2.2%
암사동 10
 
2.0%
명일동 9
 
1.8%
1층 9
 
1.8%
1호 9
 
1.8%
3호 7
 
1.4%
Other values (138) 220
43.2%
2024-05-18T12:05:07.413745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
635
24.1%
동 181
 
6.9%
호 123
 
4.7%
지 103
 
3.9%
시 94
 
3.6%
강 94
 
3.6%
1 94
 
3.6%
번 90
 
3.4%
울 90
 
3.4%
서 90
 
3.4%
Other values (93) 1037
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1446
55.0%
Space Separator 635
24.1%
Decimal Number 515
 
19.6%
Dash Punctuation 26
 
1.0%
Other Punctuation 5
 
0.2%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 181
12.5%
호 123
 
8.5%
지 103
 
7.1%
시 94
 
6.5%
강 94
 
6.5%
번 90
 
6.2%
울 90
 
6.2%
서 90
 
6.2%
별 90
 
6.2%
특 90
 
6.2%
Other values (78) 401
27.7%
Decimal Number
ValueCountFrequency (%)
1 94
18.3%
2 83
16.1%
4 79
15.3%
3 74
14.4%
0 45
8.7%
5 42
8.2%
8 32
 
6.2%
6 25
 
4.9%
7 21
 
4.1%
9 20
 
3.9%
Space Separator
ValueCountFrequency (%)
635
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1446
55.0%
Common 1185
45.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 181
12.5%
호 123
 
8.5%
지 103
 
7.1%
시 94
 
6.5%
강 94
 
6.5%
번 90
 
6.2%
울 90
 
6.2%
서 90
 
6.2%
별 90
 
6.2%
특 90
 
6.2%
Other values (78) 401
27.7%
Common
ValueCountFrequency (%)
635
53.6%
1 94
 
7.9%
2 83
 
7.0%
4 79
 
6.7%
3 74
 
6.2%
0 45
 
3.8%
5 42
 
3.5%
8 32
 
2.7%
- 26
 
2.2%
6 25
 
2.1%
Other values (5) 50
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1446
55.0%
ASCII 1185
45.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
635
53.6%
1 94
 
7.9%
2 83
 
7.0%
4 79
 
6.7%
3 74
 
6.2%
0 45
 
3.8%
5 42
 
3.5%
8 32
 
2.7%
- 26
 
2.2%
6 25
 
2.1%
Other values (5) 50
 
4.2%
Hangul
ValueCountFrequency (%)
동 181
12.5%
호 123
 
8.5%
지 103
 
7.1%
시 94
 
6.5%
강 94
 
6.5%
번 90
 
6.2%
울 90
 
6.2%
서 90
 
6.2%
별 90
 
6.2%
특 90
 
6.2%
Other values (78) 401
27.7%

지도점검일자
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20208479
Minimum20200204
Maximum20240205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-18T12:05:08.020256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200204
5-th percentile20200520
Q120200520
median20200762
Q320211194
95-th percentile20230715
Maximum20240205
Range40001
Interquartile range (IQR)10673.5

Descriptive statistics

Standard deviation11834.823
Coefficient of variation (CV)0.00058563651
Kurtosis0.070476982
Mean20208479
Median Absolute Deviation (MAD)347
Skewness1.2677316
Sum1.8187631 × 109
Variance1.4006303 × 108
MonotonicityNot monotonic
2024-05-18T12:05:08.653540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20200520 40
44.4%
20230703 11
 
12.2%
20211217 6
 
6.7%
20201211 3
 
3.3%
20210531 2
 
2.2%
20210929 2
 
2.2%
20230816 1
 
1.1%
20240205 1
 
1.1%
20230725 1
 
1.1%
20201110 1
 
1.1%
Other values (22) 22
24.4%
ValueCountFrequency (%)
20200204 1
 
1.1%
20200211 1
 
1.1%
20200514 1
 
1.1%
20200520 40
44.4%
20200523 1
 
1.1%
20200601 1
 
1.1%
20200924 1
 
1.1%
20201109 1
 
1.1%
20201110 1
 
1.1%
20201111 1
 
1.1%
ValueCountFrequency (%)
20240205 1
 
1.1%
20231225 1
 
1.1%
20231216 1
 
1.1%
20230816 1
 
1.1%
20230725 1
 
1.1%
20230703 11
12.2%
20230702 1
 
1.1%
20211217 6
6.7%
20211123 1
 
1.1%
20210929 2
 
2.2%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
처분확정
90 

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 90
100.0%

Length

2024-05-18T12:05:09.111939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:05:09.522581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 90
100.0%

처분명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
과태료부과 20만원
28 
영업소폐쇄
14 
과태료부과
11 
과징금부과
10 
개선명령(개선기한 : 2022.2.28.까지)
5 
Other values (16)
22 

Length

Max length26
Median length25
Mean length9.3777778
Min length2

Unique

Unique12 ?
Unique (%)13.3%

Sample

1st row과징금부과(기소유예감면 정지1개월로 경감)
2nd row과징금부과
3rd row과징금부과 (영업정지 1개월 갈음)
4th row과징금부과
5th row과징금부과

Common Values

ValueCountFrequency (%)
과태료부과 20만원 28
31.1%
영업소폐쇄 14
15.6%
과태료부과 11
 
12.2%
과징금부과 10
 
11.1%
개선명령(개선기한 : 2022.2.28.까지) 5
 
5.6%
개선명령 3
 
3.3%
직권말소 3
 
3.3%
경고 2
 
2.2%
영업정지 2
 
2.2%
과태료부과 10만원(경감처분 5만원) 1
 
1.1%
Other values (11) 11
 
12.2%

Length

2024-05-18T12:05:09.845988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
과태료부과 41
27.3%
20만원 28
18.7%
영업소폐쇄 14
 
9.3%
과징금부과 12
 
8.0%
개선명령(개선기한 6
 
4.0%
6
 
4.0%
2022.2.28.까지 5
 
3.3%
영업정지 4
 
2.7%
직권말소 4
 
2.7%
개선명령 3
 
2.0%
Other values (23) 27
18.0%

법적근거
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size852.0 B
법 제22조제2항제6호
38 
법 제11조제1항제8호
19 
법 제11조제1항제4호
11 
법 제11조제3항제2호
8 
법 제3조3항
4 
Other values (5)
10 

Length

Max length12
Median length12
Mean length11.6
Min length7

Unique

Unique2 ?
Unique (%)2.2%

Sample

1st row법 제11조제1항제8호
2nd row법 제11조제1항제8호
3rd row법 제11조제1항제8호
4th row법 제11조제1항제8호
5th row법 제11조제1항제8호

Common Values

ValueCountFrequency (%)
법 제22조제2항제6호 38
42.2%
법 제11조제1항제8호 19
21.1%
법 제11조제1항제4호 11
 
12.2%
법 제11조제3항제2호 8
 
8.9%
법 제3조3항 4
 
4.4%
법 제11조제3항제1호 4
 
4.4%
법 제82조제2항 2
 
2.2%
법 제83조제2항 2
 
2.2%
법 제11조제3항제3호 1
 
1.1%
법 제3조제4항 1
 
1.1%

Length

2024-05-18T12:05:10.200156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:05:10.719981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법 90
50.0%
제22조제2항제6호 38
21.1%
제11조제1항제8호 19
 
10.6%
제11조제1항제4호 11
 
6.1%
제11조제3항제2호 8
 
4.4%
제3조3항 4
 
2.2%
제11조제3항제1호 4
 
2.2%
제82조제2항 2
 
1.1%
제83조제2항 2
 
1.1%
제11조제3항제3호 1
 
0.6%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20205499
Minimum20191231
Maximum20231225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-18T12:05:11.272084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20191231
5-th percentile20191231
Q120191231
median20200762
Q320211140
95-th percentile20230703
Maximum20231225
Range39994
Interquartile range (IQR)19908.75

Descriptive statistics

Standard deviation13826.482
Coefficient of variation (CV)0.00068429302
Kurtosis-0.66360382
Mean20205499
Median Absolute Deviation (MAD)9531.5
Skewness0.73986938
Sum1.8184949 × 109
Variance1.911716 × 108
MonotonicityNot monotonic
2024-05-18T12:05:11.750571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
20191231 28
31.1%
20200520 12
13.3%
20230101 9
 
10.0%
20211217 5
 
5.6%
20210108 2
 
2.2%
20210701 2
 
2.2%
20210929 2
 
2.2%
20210514 2
 
2.2%
20221231 1
 
1.1%
20230316 1
 
1.1%
Other values (26) 26
28.9%
ValueCountFrequency (%)
20191231 28
31.1%
20200204 1
 
1.1%
20200211 1
 
1.1%
20200514 1
 
1.1%
20200520 12
13.3%
20200523 1
 
1.1%
20200601 1
 
1.1%
20200924 1
 
1.1%
20201109 1
 
1.1%
20201110 1
 
1.1%
ValueCountFrequency (%)
20231225 1
 
1.1%
20231216 1
 
1.1%
20230725 1
 
1.1%
20230720 1
 
1.1%
20230703 1
 
1.1%
20230702 1
 
1.1%
20230316 1
 
1.1%
20230101 9
10.0%
20221231 1
 
1.1%
20211217 5
5.6%

위반내용
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Memory size852.0 B
2019년 위생교육 미이수
24 
2022년 위생교육 미이수
9 
사업자 등록 폐업 사실 확인됨
8 
해당 소재지 영업종료 사실 확인됨
 
4
2019년 위생교육 미이수
 
3
Other values (39)
42 

Length

Max length136
Median length82
Mean length24.211111
Min length7

Unique

Unique37 ?
Unique (%)41.1%

Sample

1st row청소년 이성혼숙1차. 강동경찰서 수사과-9721호와 관련
2nd row청소년이성혼숙 장소제공 1차
3rd row청소년 이성혼숙 적발-서울강동경찰서 수사과 6399(2020.07.03.)호
4th row청소년이성혼숙
5th row청소년이성혼숙

Common Values

ValueCountFrequency (%)
2019년 위생교육 미이수 24
26.7%
2022년 위생교육 미이수 9
 
10.0%
사업자 등록 폐업 사실 확인됨 8
 
8.9%
해당 소재지 영업종료 사실 확인됨 4
 
4.4%
2019년 위생교육 미이수 3
 
3.3%
청소년이성혼숙 3
 
3.3%
청소년 이성혼숙 2
 
2.2%
2021. 1. 8. 23:36 경 천호대175길 11-9에서 청소년을 이성혼숙하게 함 1
 
1.1%
사업자등록말소 1
 
1.1%
청소년 이성혼숙 통보-서울강동경찰서 여성청소년과 4054(2020.07.02.)호 1
 
1.1%
Other values (34) 34
37.8%

Length

2024-05-18T12:05:12.311352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
위생교육 37
 
8.8%
미이수 36
 
8.6%
2019년 27
 
6.4%
청소년 14
 
3.3%
사실 12
 
2.9%
확인됨 12
 
2.9%
2022년 10
 
2.4%
폐업 10
 
2.4%
이성혼숙 10
 
2.4%
사업자 8
 
1.9%
Other values (168) 243
58.0%

처분내용
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
과태료부과 20만원
28 
영업소폐쇄
14 
과태료부과
11 
과징금부과
10 
개선명령(개선기한 : 2022.2.28.까지)
5 
Other values (16)
22 

Length

Max length26
Median length25
Mean length9.3777778
Min length2

Unique

Unique12 ?
Unique (%)13.3%

Sample

1st row과징금부과(기소유예감면 정지1개월로 경감)
2nd row과징금부과
3rd row과징금부과 (영업정지 1개월 갈음)
4th row과징금부과
5th row과징금부과

Common Values

ValueCountFrequency (%)
과태료부과 20만원 28
31.1%
영업소폐쇄 14
15.6%
과태료부과 11
 
12.2%
과징금부과 10
 
11.1%
개선명령(개선기한 : 2022.2.28.까지) 5
 
5.6%
개선명령 3
 
3.3%
직권말소 3
 
3.3%
경고 2
 
2.2%
영업정지 2
 
2.2%
과태료부과 10만원(경감처분 5만원) 1
 
1.1%
Other values (11) 11
 
12.2%

Length

2024-05-18T12:05:12.816339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
과태료부과 41
27.3%
20만원 28
18.7%
영업소폐쇄 14
 
9.3%
과징금부과 12
 
8.0%
개선명령(개선기한 6
 
4.0%
6
 
4.0%
2022.2.28.까지 5
 
3.3%
영업정지 4
 
2.7%
직권말소 4
 
2.7%
개선명령 3
 
2.0%
Other values (23) 27
18.0%

처분기간
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size852.0 B
<NA>
88 
0
 
1
45
 
1

Length

Max length4
Median length4
Mean length3.9444444
Min length1

Unique

Unique2 ?
Unique (%)2.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 88
97.8%
0 1
 
1.1%
45 1
 
1.1%

Length

2024-05-18T12:05:13.212985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:05:13.553569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 88
97.8%
0 1
 
1.1%
45 1
 
1.1%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct70
Distinct (%)79.5%
Missing2
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean414.57455
Minimum0
Maximum2983.87
Zeros1
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size942.0 B
2024-05-18T12:05:13.928817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.3155
Q130
median72.305
Q3589.65
95-th percentile1701.6465
Maximum2983.87
Range2983.87
Interquartile range (IQR)559.65

Descriptive statistics

Standard deviation603.00891
Coefficient of variation (CV)1.4545247
Kurtosis4.3511335
Mean414.57455
Median Absolute Deviation (MAD)60.07
Skewness2.0568306
Sum36482.56
Variance363619.74
MonotonicityNot monotonic
2024-05-18T12:05:14.359965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
442.8 5
 
5.6%
765.99 2
 
2.2%
1741.27 2
 
2.2%
34.24 2
 
2.2%
65.37 2
 
2.2%
36.75 2
 
2.2%
20.0 2
 
2.2%
30.0 2
 
2.2%
33.07 2
 
2.2%
16.5 2
 
2.2%
Other values (60) 65
72.2%
ValueCountFrequency (%)
0.0 1
1.1%
9.0 1
1.1%
9.9 1
1.1%
11.2 1
1.1%
13.2 1
1.1%
13.53 1
1.1%
13.68 1
1.1%
15.77 1
1.1%
16.5 2
2.2%
16.88 1
1.1%
ValueCountFrequency (%)
2983.87 1
1.1%
2224.98 2
2.2%
1741.27 2
2.2%
1628.06 1
1.1%
1524.8 2
2.2%
1422.48 2
2.2%
1107.2 1
1.1%
1077.0 1
1.1%
822.98 1
1.1%
805.11 1
1.1%

Interactions

2024-05-18T12:04:54.020318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:50.405360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:51.567727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:52.721985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:54.394191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:50.703846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:51.872067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:53.073870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:54.705060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:50.995677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:52.158104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:53.456207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:54.992695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:51.300060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:52.454335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:04:53.750068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T12:05:14.756581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
처분일자1.0000.5880.7030.7540.4280.7680.7680.8290.9410.8810.7910.9920.9410.0000.507
교부번호0.5881.0000.9970.9971.0001.0001.0000.9260.0000.9800.9240.0000.0000.0001.000
업종명0.7030.9971.0000.9740.9820.9800.9800.6670.5110.7770.6200.0000.511NaN0.450
업태명0.7540.9970.9741.0000.9910.9900.9900.7090.8300.8120.4620.6170.830NaN0.755
업소명0.4281.0000.9820.9911.0001.0001.0000.9030.0000.9920.9110.0000.0000.0001.000
소재지도로명0.7681.0000.9800.9901.0001.0001.0000.9300.8400.9920.9480.0000.8400.0001.000
소재지지번0.7681.0000.9800.9901.0001.0001.0000.9300.8400.9920.9480.0000.8400.0001.000
지도점검일자0.8290.9260.6670.7090.9030.9300.9301.0000.8970.8410.9711.0000.8970.0000.514
처분명0.9410.0000.5110.8300.0000.8400.8400.8971.0000.9300.9060.9971.000NaN0.849
법적근거0.8810.9800.7770.8120.9920.9920.9920.8410.9301.0000.7711.0000.930NaN0.604
위반일자0.7910.9240.6200.4620.9110.9480.9480.9710.9060.7711.0001.0000.9060.0000.283
위반내용0.9920.0000.0000.6170.0000.0000.0001.0000.9971.0001.0001.0000.9970.0000.955
처분내용0.9410.0000.5110.8300.0000.8400.8400.8971.0000.9300.9060.9971.000NaN0.849
처분기간0.0000.000NaNNaN0.0000.0000.0000.000NaNNaN0.0000.000NaN1.0000.000
영업장면적(㎡)0.5071.0000.4500.7551.0001.0001.0000.5140.8490.6040.2830.9550.8490.0001.000
2024-05-18T12:05:15.170159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분내용처분명업태명업종명법적근거위반내용처분기간
처분내용1.0001.0000.4000.1870.6450.7701.000
처분명1.0001.0000.4000.1870.6450.7701.000
업태명0.4000.4001.0000.8630.4870.1591.000
업종명0.1870.1870.8631.0000.4700.0001.000
법적근거0.6450.6450.4870.4701.0000.7581.000
위반내용0.7700.7700.1590.0000.7581.0001.000
처분기간1.0001.0001.0001.0001.0001.0001.000
2024-05-18T12:05:15.416783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자지도점검일자위반일자영업장면적(㎡)업종명업태명처분명법적근거위반내용처분내용처분기간
처분일자1.0000.9160.7900.2490.4590.4810.7160.5340.7000.7161.000
지도점검일자0.9161.0000.9350.2830.4500.4560.6500.6550.7310.6501.000
위반일자0.7900.9351.0000.3530.4100.4250.7070.5570.7360.7071.000
영업장면적(㎡)0.2490.2830.3531.0000.2240.4410.5150.3370.5590.5151.000
업종명0.4590.4500.4100.2241.0000.8630.1870.4700.0000.1871.000
업태명0.4810.4560.4250.4410.8631.0000.4000.4870.1590.4001.000
처분명0.7160.6500.7070.5150.1870.4001.0000.6450.7701.0001.000
법적근거0.5340.6550.5570.3370.4700.4870.6451.0000.7580.6451.000
위반내용0.7000.7310.7360.5590.0000.1590.7700.7581.0000.7701.000
처분내용0.7160.6500.7070.5150.1870.4001.0000.6450.7701.0001.000
처분기간1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-18T12:04:55.350373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T12:04:56.055691image/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.

Sample

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
032400002022030360숙박업(일반)여관업러브이즈서울특별시 강동구 천중로40길 71, (길동)서울특별시 강동구 길동 387번지 14호20210831처분확정과징금부과(기소유예감면 정지1개월로 경감)법 제11조제1항제8호20210701청소년 이성혼숙1차. 강동경찰서 수사과-9721호와 관련과징금부과(기소유예감면 정지1개월로 경감)<NA>765.99
132400002022030360숙박업(일반)여관업러브이즈서울특별시 강동구 천중로40길 71, (길동)서울특별시 강동구 길동 387번지 14호20210701처분확정과징금부과법 제11조제1항제8호20210701청소년이성혼숙 장소제공 1차과징금부과0765.99
232400002020072774숙박업(일반)여관업연애시대모텔서울특별시 강동구 진황도로 21-1, (천호동)서울특별시 강동구 천호동 557번지20200523처분확정과징금부과 (영업정지 1개월 갈음)법 제11조제1항제8호20200523청소년 이성혼숙 적발-서울강동경찰서 수사과 6399(2020.07.03.)호과징금부과 (영업정지 1개월 갈음)<NA>461.14
332400002021120777숙박업(일반)여관업히트호텔(HIT)서울특별시 강동구 구천면로24길 28, (천호동)서울특별시 강동구 천호동 427번지 29호20210929처분확정과징금부과법 제11조제1항제8호20210929청소년이성혼숙과징금부과<NA>1422.48
432400002021120777숙박업(일반)여관업히트호텔(HIT)서울특별시 강동구 구천면로24길 28, (천호동)서울특별시 강동구 천호동 427번지 29호20210929처분확정과징금부과법 제11조제1항제8호20210929청소년이성혼숙과징금부과<NA>1422.48
532400002021053181숙박업(일반)여인숙업신춘 여인숙서울특별시 강동구 올림픽로80길 56-4, (천호동)서울특별시 강동구 천호동 423번지 132호20210531처분확정영업신고사항 직권말소법 제3조3항20210531사업자등록말소영업신고사항 직권말소<NA>51.31
632400002020092184숙박업(일반)여관업온서울특별시 강동구 선사로 82, (천호동)서울특별시 강동구 천호동 328번지20200211처분확정과징금부과법 제11조제1항제8호20200211청소년 이성혼숙 통보-서울강동경찰서 여성청소년과 4054(2020.07.02.)호과징금부과<NA>448.74
732400002021060784숙박업(일반)여관업온서울특별시 강동구 선사로 82, (천호동)서울특별시 강동구 천호동 328번지20201218처분확정과징금부과법 제11조제1항제8호20201218청소년 이성혼숙 적발[서울강동경찰서 수사과-4676(2021.05.06.)호]과징금부과45448.74
8324000020200316111숙박업(일반)여관업예스모텔서울특별시 강동구 진황도로 24, (천호동)서울특별시 강동구 천호동 410번지 186호20200204처분확정과징금부과법 제11조제1항제8호202002042020.2.4. 01:05~03:25 청소년 이성혼숙 서울강동경찰서 수사과-1478호(2020.02.18.)과징금부과<NA>491.55
9324000020210121118숙박업(일반)여관업캠프서울특별시 강동구 구천면로24길 25, (천호동)서울특별시 강동구 천호동 416번지 3호20201228처분확정과태료부과법 제82조제2항20201228재난배상책임보험 미갱신과태료부과<NA>651.27
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)
803240000202010291315종합미용업기타채아네일서울특별시 강동구 올림픽로 664, 220호 (천호동, 대우한강베네시티상가)서울특별시 강동구 천호동 425번지 5호 대우한강베네시티상가-22020200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>13.53
813240000202011122017-00003종합미용업네일아트업클리어 NAIL & SPA서울특별시 강동구 천중로51길 102, 103호 (명일동)서울특별시 강동구 명일동 349번지 1호 -10320200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>36.75
823240000202011122017-00003종합미용업네일아트업클리어 NAIL & SPA서울특별시 강동구 천중로51길 102, 103호 (명일동)서울특별시 강동구 명일동 349번지 1호 -10320200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>36.75
833240000202011242016-00049네일미용업네일아트업IN.A.NAIL(인아네일)서울특별시 강동구 구천면로 336, 1호 (천호동)서울특별시 강동구 천호동 243번지 16호 -120200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>25.0
843240000202010282017-00013네일미용업네일아트업에이치네일샵서울특별시 강동구 성안로 22, 1층 (성내동)서울특별시 강동구 성내동 435번지 12호 1층20200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>65.37
853240000202010282017-00013네일미용업네일아트업에이치네일샵서울특별시 강동구 성안로 22, 1층 (성내동)서울특별시 강동구 성내동 435번지 12호 1층20200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>65.37
863240000202011242017-00007네일미용업네일아트업현대네일서울특별시 강동구 진황도로 18, 지2층 11호 (천호동, 현대프라자)서울특별시 강동구 천호동 410번지 105호 현대프라자 지2층-1120200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>16.5
873240000202011242019-00018네일미용업네일아트업늘,예쁘다서울특별시 강동구 양재대로 1474-11, 1층 (길동)서울특별시 강동구 길동 390번지 19호 1층20200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>34.24
883240000202011242019-00018네일미용업네일아트업늘,예쁘다서울특별시 강동구 양재대로 1474-11, 1층 (길동)서울특별시 강동구 길동 390번지 19호 1층20200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>34.24
893240000202010282019-00004화장ㆍ분장 미용업메이크업업바나나부띠끄서울특별시 강동구 올림픽로 806, 103호 (암사동, 까사팔공육)서울특별시 강동구 암사동 463번지 4호 까사팔공육-10320200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>26.0

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)# duplicates
03240000202010282017-00013네일미용업네일아트업에이치네일샵서울특별시 강동구 성안로 22, 1층 (성내동)서울특별시 강동구 성내동 435번지 12호 1층20200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>65.372
13240000202011122017-00003종합미용업네일아트업클리어 NAIL & SPA서울특별시 강동구 천중로51길 102, 103호 (명일동)서울특별시 강동구 명일동 349번지 1호 -10320200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>36.752
23240000202011131308미용업일반미용업언니네미발관서울특별시 강동구 고덕로 133, (암사동,204호)서울특별시 강동구 암사동 414번지 18호 204호20200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>33.072
33240000202011242019-00018네일미용업네일아트업늘,예쁘다서울특별시 강동구 양재대로 1474-11, 1층 (길동)서울특별시 강동구 길동 390번지 19호 1층20200520처분확정과태료부과 20만원법 제22조제2항제6호201912312019년 위생교육 미이수과태료부과 20만원<NA>34.242
432400002021120777숙박업(일반)여관업히트호텔(HIT)서울특별시 강동구 구천면로24길 28, (천호동)서울특별시 강동구 천호동 427번지 29호20210929처분확정과징금부과법 제11조제1항제8호20210929청소년이성혼숙과징금부과<NA>1422.482