Overview

Dataset statistics

Number of variables18
Number of observations6583
Missing cells13954
Missing cells (%)11.8%
Duplicate rows245
Duplicate rows (%)3.7%
Total size in memory970.9 KiB
Average record size in memory151.0 B

Variable types

Categorical4
Numeric6
Text8

Dataset

Description시군구코드,처분일자,교부번호,업종명,업태명,업소명,소재지도로명,소재지지번,지도점검일자,행정처분상태,처분명,법적근거,위반일자,위반내용,처분내용,처분기간,영업장면적(㎡),운영형태
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-10835/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
행정처분상태 has constant value ""Constant
Dataset has 245 (3.7%) duplicate rowsDuplicates
운영형태 is highly overall correlated with 지도점검일자 and 2 other fieldsHigh correlation
업종명 is highly overall correlated with 운영형태High correlation
처분일자 is highly overall correlated with 교부번호 and 2 other fieldsHigh correlation
교부번호 is highly overall correlated with 처분일자 and 2 other fieldsHigh correlation
지도점검일자 is highly overall correlated with 처분일자 and 3 other fieldsHigh correlation
위반일자 is highly overall correlated with 처분일자 and 2 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 운영형태High correlation
업종명 is highly imbalanced (56.0%)Imbalance
운영형태 is highly imbalanced (98.7%)Imbalance
소재지도로명 has 3739 (56.8%) missing valuesMissing
처분기간 has 5858 (89.0%) missing valuesMissing
영업장면적(㎡) has 4322 (65.7%) missing valuesMissing
지도점검일자 is highly skewed (γ1 = -58.55211223)Skewed

Reproduction

Analysis started2024-05-18 00:01:03.817658
Analysis finished2024-05-18 00:01:23.508243
Duration19.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
3140000
6583 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3140000
2nd row3140000
3rd row3140000
4th row3140000
5th row3140000

Common Values

ValueCountFrequency (%)
3140000 6583
100.0%

Length

2024-05-18T09:01:23.717095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:01:24.033054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 6583
100.0%

처분일자
Real number (ℝ)

HIGH CORRELATION 

Distinct2255
Distinct (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20119607
Minimum19940125
Maximum20240514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2024-05-18T09:01:24.354108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19940125
5-th percentile20030520
Q120070427
median20111213
Q320170131
95-th percentile20230303
Maximum20240514
Range300389
Interquartile range (IQR)99704

Descriptive statistics

Standard deviation59850.953
Coefficient of variation (CV)0.0029747575
Kurtosis-0.99304829
Mean20119607
Median Absolute Deviation (MAD)41105
Skewness0.19114748
Sum1.3244738 × 1011
Variance3.5821366 × 109
MonotonicityDecreasing
2024-05-18T09:01:24.802369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210727 182
 
2.8%
20230303 108
 
1.6%
20051215 74
 
1.1%
20070403 73
 
1.1%
20070209 59
 
0.9%
20070502 54
 
0.8%
20111107 51
 
0.8%
20070330 49
 
0.7%
20051130 48
 
0.7%
20130725 46
 
0.7%
Other values (2245) 5839
88.7%
ValueCountFrequency (%)
19940125 1
 
< 0.1%
20011016 1
 
< 0.1%
20011120 19
0.3%
20020102 3
 
< 0.1%
20020117 1
 
< 0.1%
20020120 1
 
< 0.1%
20020124 12
0.2%
20020127 1
 
< 0.1%
20020128 4
 
0.1%
20020130 1
 
< 0.1%
ValueCountFrequency (%)
20240514 1
 
< 0.1%
20240416 2
 
< 0.1%
20240409 2
 
< 0.1%
20240404 1
 
< 0.1%
20240328 1
 
< 0.1%
20240327 2
 
< 0.1%
20240321 2
 
< 0.1%
20240320 5
0.1%
20240312 2
 
< 0.1%
20240311 7
0.1%

교부번호
Real number (ℝ)

HIGH CORRELATION 

Distinct3662
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0039097 × 1010
Minimum1.9730073 × 1010
Maximum2.0230098 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2024-05-18T09:01:25.355015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9730073 × 1010
5-th percentile1.9910074 × 1010
Q11.9990074 × 1010
median2.0030074 × 1010
Q32.0100073 × 1010
95-th percentile2.0170073 × 1010
Maximum2.0230098 × 1010
Range5.0002493 × 108
Interquartile range (IQR)1.0999954 × 108

Descriptive statistics

Standard deviation79122887
Coefficient of variation (CV)0.0039484257
Kurtosis0.18452575
Mean2.0039097 × 1010
Median Absolute Deviation (MAD)50000820
Skewness-0.23844847
Sum1.3191738 × 1014
Variance6.2604313 × 1015
MonotonicityNot monotonic
2024-05-18T09:01:25.804962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20010077455 44
 
0.7%
19990073517 33
 
0.5%
20010073352 24
 
0.4%
20020073448 24
 
0.4%
20090073924 24
 
0.4%
20010073990 21
 
0.3%
20120073684 19
 
0.3%
19970073253 18
 
0.3%
20020073795 18
 
0.3%
19890073110 18
 
0.3%
Other values (3652) 6340
96.3%
ValueCountFrequency (%)
19730073001 1
 
< 0.1%
19730073002 3
 
< 0.1%
19770073018 1
 
< 0.1%
19780073004 8
0.1%
19780073005 3
 
< 0.1%
19780073007 5
0.1%
19780073009 2
 
< 0.1%
19790073009 6
0.1%
19790073021 9
0.1%
19800073020 2
 
< 0.1%
ValueCountFrequency (%)
20230097934 1
< 0.1%
20230097457 1
< 0.1%
20230097356 2
< 0.1%
20230097271 1
< 0.1%
20230097002 2
< 0.1%
20220090277 1
< 0.1%
20220090179 1
< 0.1%
20220090091 1
< 0.1%
20220090002 1
< 0.1%
20220089981 1
< 0.1%

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
일반음식점
4485 
단란주점
467 
휴게음식점
 
428
즉석판매제조가공업
 
351
식품제조가공업
 
198
Other values (16)
654 

Length

Max length13
Median length5
Mean length5.4952149
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row휴게음식점
2nd row일반음식점
3rd row일반음식점
4th row일반음식점
5th row일반음식점

Common Values

ValueCountFrequency (%)
일반음식점 4485
68.1%
단란주점 467
 
7.1%
휴게음식점 428
 
6.5%
즉석판매제조가공업 351
 
5.3%
식품제조가공업 198
 
3.0%
건강기능식품일반판매업 161
 
2.4%
식품등 수입판매업 107
 
1.6%
제과점영업 97
 
1.5%
유통전문판매업 71
 
1.1%
식품소분업 54
 
0.8%
Other values (11) 164
 
2.5%

Length

2024-05-18T09:01:26.241740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반음식점 4485
67.0%
단란주점 467
 
7.0%
휴게음식점 428
 
6.4%
즉석판매제조가공업 351
 
5.2%
식품제조가공업 198
 
3.0%
건강기능식품일반판매업 161
 
2.4%
식품등 107
 
1.6%
수입판매업 107
 
1.6%
제과점영업 97
 
1.4%
유통전문판매업 71
 
1.1%
Other values (12) 218
 
3.3%

업태명
Text

Distinct61
Distinct (%)0.9%
Missing25
Missing (%)0.4%
Memory size51.6 KiB
2024-05-18T09:01:26.854252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length4.1447088
Min length2

Characters and Unicode

Total characters27181
Distinct characters142
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.2%

Sample

1st row일반조리판매
2nd row한식
3rd row한식
4th row기타
5th row기타
ValueCountFrequency (%)
한식 1437
21.3%
호프/통닭 1225
18.1%
기타 471
 
7.0%
단란주점 467
 
6.9%
즉석판매제조가공업 351
 
5.2%
분식 312
 
4.6%
중국식 245
 
3.6%
식품제조가공업 198
 
2.9%
까페 167
 
2.5%
일식 157
 
2.3%
Other values (52) 1723
25.5%
2024-05-18T09:01:27.976710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 2891
 
10.6%
통 1492
 
5.5%
한 1437
 
5.3%
닭 1382
 
5.1%
/ 1345
 
4.9%
호 1225
 
4.5%
프 1225
 
4.5%
업 1096
 
4.0%
판 842
 
3.1%
매 842
 
3.1%
Other values (132) 13404
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25142
92.5%
Other Punctuation 1352
 
5.0%
Close Punctuation 246
 
0.9%
Open Punctuation 246
 
0.9%
Space Separator 195
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 2891
 
11.5%
통 1492
 
5.9%
한 1437
 
5.7%
닭 1382
 
5.5%
호 1225
 
4.9%
프 1225
 
4.9%
업 1096
 
4.4%
판 842
 
3.3%
매 842
 
3.3%
점 683
 
2.7%
Other values (126) 12027
47.8%
Other Punctuation
ValueCountFrequency (%)
/ 1345
99.5%
, 6
 
0.4%
. 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 246
100.0%
Open Punctuation
ValueCountFrequency (%)
( 246
100.0%
Space Separator
ValueCountFrequency (%)
195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25142
92.5%
Common 2039
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 2891
 
11.5%
통 1492
 
5.9%
한 1437
 
5.7%
닭 1382
 
5.5%
호 1225
 
4.9%
프 1225
 
4.9%
업 1096
 
4.4%
판 842
 
3.3%
매 842
 
3.3%
점 683
 
2.7%
Other values (126) 12027
47.8%
Common
ValueCountFrequency (%)
/ 1345
66.0%
) 246
 
12.1%
( 246
 
12.1%
195
 
9.6%
, 6
 
0.3%
. 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25142
92.5%
ASCII 2039
 
7.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 2891
 
11.5%
통 1492
 
5.9%
한 1437
 
5.7%
닭 1382
 
5.5%
호 1225
 
4.9%
프 1225
 
4.9%
업 1096
 
4.4%
판 842
 
3.3%
매 842
 
3.3%
점 683
 
2.7%
Other values (126) 12027
47.8%
ASCII
ValueCountFrequency (%)
/ 1345
66.0%
) 246
 
12.1%
( 246
 
12.1%
195
 
9.6%
, 6
 
0.3%
. 1
 
< 0.1%

업소명
Text

Distinct3654
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
2024-05-18T09:01:28.712857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length5.4488835
Min length1

Characters and Unicode

Total characters35870
Distinct characters886
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2444 ?
Unique (%)37.1%

Sample

1st row달콤왕가탕후루 목동강서고점
2nd row한라맥주 오목교점
3rd row한라맥주 오목교점
4th row포트캔커피
5th row포트캔커피
ValueCountFrequency (%)
만리장성 49
 
0.7%
24시 44
 
0.6%
한강단란주점 31
 
0.4%
목동점 30
 
0.4%
디엔틱 24
 
0.3%
대항탕수제비포장마차 20
 
0.3%
제이(j 20
 
0.3%
주)와이앤에스지 19
 
0.3%
아방궁 19
 
0.3%
아우토반 18
 
0.3%
Other values (3815) 6761
96.1%
2024-05-18T09:01:30.200924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 959
 
2.7%
스 670
 
1.9%
점 640
 
1.8%
리 597
 
1.7%
주 583
 
1.6%
) 515
 
1.4%
( 514
 
1.4%
식 496
 
1.4%
마 487
 
1.4%
453
 
1.3%
Other values (876) 29956
83.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32809
91.5%
Uppercase Letter 519
 
1.4%
Close Punctuation 515
 
1.4%
Open Punctuation 514
 
1.4%
Decimal Number 474
 
1.3%
Space Separator 453
 
1.3%
Lowercase Letter 449
 
1.3%
Other Punctuation 128
 
0.4%
Dash Punctuation 7
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 959
 
2.9%
스 670
 
2.0%
점 640
 
2.0%
리 597
 
1.8%
주 583
 
1.8%
식 496
 
1.5%
마 487
 
1.5%
동 423
 
1.3%
장 411
 
1.3%
아 385
 
1.2%
Other values (803) 27158
82.8%
Uppercase Letter
ValueCountFrequency (%)
S 50
 
9.6%
J 33
 
6.4%
B 32
 
6.2%
N 32
 
6.2%
O 32
 
6.2%
K 31
 
6.0%
C 30
 
5.8%
I 28
 
5.4%
E 28
 
5.4%
A 24
 
4.6%
Other values (16) 199
38.3%
Lowercase Letter
ValueCountFrequency (%)
e 63
14.0%
r 48
10.7%
a 47
10.5%
o 42
 
9.4%
t 36
 
8.0%
i 25
 
5.6%
n 24
 
5.3%
c 23
 
5.1%
f 19
 
4.2%
l 16
 
3.6%
Other values (12) 106
23.6%
Decimal Number
ValueCountFrequency (%)
0 112
23.6%
2 97
20.5%
4 84
17.7%
7 42
 
8.9%
1 42
 
8.9%
8 35
 
7.4%
5 23
 
4.9%
3 22
 
4.6%
9 9
 
1.9%
6 8
 
1.7%
Other Punctuation
ValueCountFrequency (%)
& 56
43.8%
. 29
22.7%
? 19
 
14.8%
, 8
 
6.2%
; 6
 
4.7%
! 5
 
3.9%
' 3
 
2.3%
/ 1
 
0.8%
？ 1
 
0.8%
Close Punctuation
ValueCountFrequency (%)
) 515
100.0%
Open Punctuation
ValueCountFrequency (%)
( 514
100.0%
Space Separator
ValueCountFrequency (%)
453
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Symbol
ValueCountFrequency (%)
℃ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32804
91.5%
Common 2093
 
5.8%
Latin 968
 
2.7%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 959
 
2.9%
스 670
 
2.0%
점 640
 
2.0%
리 597
 
1.8%
주 583
 
1.8%
식 496
 
1.5%
마 487
 
1.5%
동 423
 
1.3%
장 411
 
1.3%
아 385
 
1.2%
Other values (800) 27153
82.8%
Latin
ValueCountFrequency (%)
e 63
 
6.5%
S 50
 
5.2%
r 48
 
5.0%
a 47
 
4.9%
o 42
 
4.3%
t 36
 
3.7%
J 33
 
3.4%
B 32
 
3.3%
N 32
 
3.3%
O 32
 
3.3%
Other values (38) 553
57.1%
Common
ValueCountFrequency (%)
) 515
24.6%
( 514
24.6%
453
21.6%
0 112
 
5.4%
2 97
 
4.6%
4 84
 
4.0%
& 56
 
2.7%
7 42
 
2.0%
1 42
 
2.0%
8 35
 
1.7%
Other values (15) 143
 
6.8%
Han
ValueCountFrequency (%)
明 3
60.0%
酒 1
 
20.0%
松 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32802
91.4%
ASCII 3059
 
8.5%
CJK 5
 
< 0.1%
Compat Jamo 2
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 959
 
2.9%
스 670
 
2.0%
점 640
 
2.0%
리 597
 
1.8%
주 583
 
1.8%
식 496
 
1.5%
마 487
 
1.5%
동 423
 
1.3%
장 411
 
1.3%
아 385
 
1.2%
Other values (799) 27151
82.8%
ASCII
ValueCountFrequency (%)
) 515
16.8%
( 514
16.8%
453
14.8%
0 112
 
3.7%
2 97
 
3.2%
4 84
 
2.7%
e 63
 
2.1%
& 56
 
1.8%
S 50
 
1.6%
r 48
 
1.6%
Other values (61) 1067
34.9%
CJK
ValueCountFrequency (%)
明 3
60.0%
酒 1
 
20.0%
松 1
 
20.0%
Compat Jamo
ValueCountFrequency (%)
ㅇ 2
100.0%
Letterlike Symbols
ValueCountFrequency (%)
℃ 1
100.0%
None
ValueCountFrequency (%)
？ 1
100.0%

소재지도로명
Text

MISSING 

Distinct1481
Distinct (%)52.1%
Missing3739
Missing (%)56.8%
Memory size51.6 KiB
2024-05-18T09:01:30.940942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length54
Mean length33.100211
Min length22

Characters and Unicode

Total characters94137
Distinct characters292
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

Unique900 ?
Unique (%)31.6%

Sample

1st row서울특별시 양천구 목동중앙남로 22, 1층 (목동)
2nd row서울특별시 양천구 오목로 337-2, 1층 (목동)
3rd row서울특별시 양천구 오목로 337-2, 1층 (목동)
4th row서울특별시 양천구 오목로 138, 신정스포렉스 101일부,102호 (신정동)
5th row서울특별시 양천구 오목로 138, 신정스포렉스 101일부,102호 (신정동)
ValueCountFrequency (%)
서울특별시 2844
 
15.7%
양천구 2844
 
15.7%
목동 905
 
5.0%
1층 860
 
4.7%
신정동 784
 
4.3%
신월동 744
 
4.1%
지상1층 296
 
1.6%
목동동로 271
 
1.5%
지하1층 253
 
1.4%
오목로 234
 
1.3%
Other values (1388) 8112
44.7%
2024-05-18T09:01:32.473597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15303
 
16.3%
동 4607
 
4.9%
1 4498
 
4.8%
, 4379
 
4.7%
서 3131
 
3.3%
) 3054
 
3.2%
( 3054
 
3.2%
로 3043
 
3.2%
양 2985
 
3.2%
시 2900
 
3.1%
Other values (282) 47183
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53387
56.7%
Space Separator 15303
 
16.3%
Decimal Number 14357
 
15.3%
Other Punctuation 4395
 
4.7%
Close Punctuation 3054
 
3.2%
Open Punctuation 3054
 
3.2%
Dash Punctuation 438
 
0.5%
Uppercase Letter 123
 
0.1%
Math Symbol 22
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4607
 
8.6%
서 3131
 
5.9%
로 3043
 
5.7%
양 2985
 
5.6%
시 2900
 
5.4%
천 2886
 
5.4%
울 2851
 
5.3%
구 2848
 
5.3%
특 2844
 
5.3%
별 2844
 
5.3%
Other values (250) 22448
42.0%
Uppercase Letter
ValueCountFrequency (%)
B 47
38.2%
A 37
30.1%
C 15
 
12.2%
S 10
 
8.1%
T 3
 
2.4%
K 2
 
1.6%
D 2
 
1.6%
G 2
 
1.6%
I 2
 
1.6%
H 1
 
0.8%
Other values (2) 2
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 4498
31.3%
2 2149
15.0%
3 1628
 
11.3%
0 1398
 
9.7%
5 970
 
6.8%
4 946
 
6.6%
7 887
 
6.2%
6 743
 
5.2%
9 610
 
4.2%
8 528
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 4379
99.6%
/ 8
 
0.2%
. 7
 
0.2%
& 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
15303
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3054
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3054
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 438
100.0%
Math Symbol
ValueCountFrequency (%)
~ 22
100.0%
Lowercase Letter
ValueCountFrequency (%)
l 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53387
56.7%
Common 40623
43.2%
Latin 127
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4607
 
8.6%
서 3131
 
5.9%
로 3043
 
5.7%
양 2985
 
5.6%
시 2900
 
5.4%
천 2886
 
5.4%
울 2851
 
5.3%
구 2848
 
5.3%
특 2844
 
5.3%
별 2844
 
5.3%
Other values (250) 22448
42.0%
Common
ValueCountFrequency (%)
15303
37.7%
1 4498
 
11.1%
, 4379
 
10.8%
) 3054
 
7.5%
( 3054
 
7.5%
2 2149
 
5.3%
3 1628
 
4.0%
0 1398
 
3.4%
5 970
 
2.4%
4 946
 
2.3%
Other values (9) 3244
 
8.0%
Latin
ValueCountFrequency (%)
B 47
37.0%
A 37
29.1%
C 15
 
11.8%
S 10
 
7.9%
l 4
 
3.1%
T 3
 
2.4%
K 2
 
1.6%
D 2
 
1.6%
G 2
 
1.6%
I 2
 
1.6%
Other values (3) 3
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53387
56.7%
ASCII 40750
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15303
37.6%
1 4498
 
11.0%
, 4379
 
10.7%
) 3054
 
7.5%
( 3054
 
7.5%
2 2149
 
5.3%
3 1628
 
4.0%
0 1398
 
3.4%
5 970
 
2.4%
4 946
 
2.3%
Other values (22) 3371
 
8.3%
Hangul
ValueCountFrequency (%)
동 4607
 
8.6%
서 3131
 
5.9%
로 3043
 
5.7%
양 2985
 
5.6%
시 2900
 
5.4%
천 2886
 
5.4%
울 2851
 
5.3%
구 2848
 
5.3%
특 2844
 
5.3%
별 2844
 
5.3%
Other values (250) 22448
42.0%

소재지지번
Text

Distinct3354
Distinct (%)51.0%
Missing1
Missing (%)< 0.1%
Memory size51.6 KiB
2024-05-18T09:01:33.412856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length54
Mean length30.164084
Min length21

Characters and Unicode

Total characters198540
Distinct characters339
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

Unique2056 ?
Unique (%)31.2%

Sample

1st row서울특별시 양천구 목동 766번지 16호 1층
2nd row서울특별시 양천구 목동 406번지 296호 1층
3rd row서울특별시 양천구 목동 406번지 296호 1층
4th row서울특별시 양천구 신정동 945번지 2호 신정스포렉스
5th row서울특별시 양천구 신정동 945번지 2호 신정스포렉스
ValueCountFrequency (%)
서울특별시 6582
 
16.8%
양천구 6582
 
16.8%
목동 2419
 
6.2%
신정동 2334
 
6.0%
신월동 1847
 
4.7%
1층 874
 
2.2%
1호 727
 
1.9%
지하1층 449
 
1.1%
2호 436
 
1.1%
9호 359
 
0.9%
Other values (2253) 16522
42.2%
2024-05-18T09:01:34.980671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48075
24.2%
1 10683
 
5.4%
지 8293
 
4.2%
동 7256
 
3.7%
호 6957
 
3.5%
서 6726
 
3.4%
양 6667
 
3.4%
시 6664
 
3.4%
천 6640
 
3.3%
울 6591
 
3.3%
Other values (329) 83988
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107915
54.4%
Space Separator 48075
24.2%
Decimal Number 39466
 
19.9%
Close Punctuation 904
 
0.5%
Open Punctuation 904
 
0.5%
Dash Punctuation 547
 
0.3%
Other Punctuation 410
 
0.2%
Uppercase Letter 281
 
0.1%
Math Symbol 31
 
< 0.1%
Lowercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 8293
 
7.7%
동 7256
 
6.7%
호 6957
 
6.4%
서 6726
 
6.2%
양 6667
 
6.2%
시 6664
 
6.2%
천 6640
 
6.2%
울 6591
 
6.1%
구 6589
 
6.1%
번 6587
 
6.1%
Other values (291) 38945
36.1%
Uppercase Letter
ValueCountFrequency (%)
B 139
49.5%
A 62
22.1%
C 22
 
7.8%
V 16
 
5.7%
S 10
 
3.6%
T 9
 
3.2%
P 7
 
2.5%
D 5
 
1.8%
G 3
 
1.1%
I 2
 
0.7%
Other values (4) 6
 
2.1%
Decimal Number
ValueCountFrequency (%)
1 10683
27.1%
2 5119
13.0%
0 4232
 
10.7%
9 3677
 
9.3%
3 3150
 
8.0%
4 2972
 
7.5%
6 2823
 
7.2%
7 2458
 
6.2%
5 2306
 
5.8%
8 2046
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 378
92.2%
/ 17
 
4.1%
. 11
 
2.7%
: 2
 
0.5%
@ 1
 
0.2%
& 1
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
l 4
66.7%
b 2
33.3%
Space Separator
ValueCountFrequency (%)
48075
100.0%
Close Punctuation
ValueCountFrequency (%)
) 904
100.0%
Open Punctuation
ValueCountFrequency (%)
( 904
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 547
100.0%
Math Symbol
ValueCountFrequency (%)
~ 31
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107915
54.4%
Common 90337
45.5%
Latin 288
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 8293
 
7.7%
동 7256
 
6.7%
호 6957
 
6.4%
서 6726
 
6.2%
양 6667
 
6.2%
시 6664
 
6.2%
천 6640
 
6.2%
울 6591
 
6.1%
구 6589
 
6.1%
번 6587
 
6.1%
Other values (291) 38945
36.1%
Common
ValueCountFrequency (%)
48075
53.2%
1 10683
 
11.8%
2 5119
 
5.7%
0 4232
 
4.7%
9 3677
 
4.1%
3 3150
 
3.5%
4 2972
 
3.3%
6 2823
 
3.1%
7 2458
 
2.7%
5 2306
 
2.6%
Other values (11) 4842
 
5.4%
Latin
ValueCountFrequency (%)
B 139
48.3%
A 62
21.5%
C 22
 
7.6%
V 16
 
5.6%
S 10
 
3.5%
T 9
 
3.1%
P 7
 
2.4%
D 5
 
1.7%
l 4
 
1.4%
G 3
 
1.0%
Other values (7) 11
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107915
54.4%
ASCII 90624
45.6%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48075
53.0%
1 10683
 
11.8%
2 5119
 
5.6%
0 4232
 
4.7%
9 3677
 
4.1%
3 3150
 
3.5%
4 2972
 
3.3%
6 2823
 
3.1%
7 2458
 
2.7%
5 2306
 
2.5%
Other values (27) 5129
 
5.7%
Hangul
ValueCountFrequency (%)
지 8293
 
7.7%
동 7256
 
6.7%
호 6957
 
6.4%
서 6726
 
6.2%
양 6667
 
6.2%
시 6664
 
6.2%
천 6640
 
6.2%
울 6591
 
6.1%
구 6589
 
6.1%
번 6587
 
6.1%
Other values (291) 38945
36.1%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

지도점검일자
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2356
Distinct (%)35.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20113756
Minimum2001103
Maximum20240313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2024-05-18T09:01:35.642013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001103
5-th percentile20030403
Q120070125
median20111027
Q320161209
95-th percentile20221231
Maximum20240313
Range18239210
Interquartile range (IQR)91084

Descriptive statistics

Standard deviation262511.77
Coefficient of variation (CV)0.013051355
Kurtosis3775.7072
Mean20113756
Median Absolute Deviation (MAD)49775
Skewness-58.552112
Sum1.3240885 × 1011
Variance6.8912428 × 1010
MonotonicityNot monotonic
2024-05-18T09:01:36.330090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210607 181
 
2.7%
20061129 117
 
1.8%
20221231 108
 
1.6%
20110901 99
 
1.5%
20070125 79
 
1.2%
20061229 66
 
1.0%
20051017 57
 
0.9%
20070228 57
 
0.9%
20110824 40
 
0.6%
20200909 38
 
0.6%
Other values (2346) 5741
87.2%
ValueCountFrequency (%)
2001103 1
 
< 0.1%
10021026 1
 
< 0.1%
19940105 1
 
< 0.1%
20010728 1
 
< 0.1%
20010811 1
 
< 0.1%
20010828 1
 
< 0.1%
20010831 1
 
< 0.1%
20010906 6
0.1%
20010907 1
 
< 0.1%
20010911 2
 
< 0.1%
ValueCountFrequency (%)
20240313 1
 
< 0.1%
20240307 1
 
< 0.1%
20240304 4
0.1%
20240227 2
 
< 0.1%
20240226 2
 
< 0.1%
20240221 2
 
< 0.1%
20240220 2
 
< 0.1%
20240219 5
0.1%
20240213 1
 
< 0.1%
20240208 2
 
< 0.1%

행정처분상태
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
처분확정
6583 

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 6583
100.0%

Length

2024-05-18T09:01:36.995653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:01:37.355687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분확정 6583
100.0%

처분명
Text

Distinct891
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
2024-05-18T09:01:38.217637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length69
Mean length9.0267355
Min length2

Characters and Unicode

Total characters59423
Distinct characters232
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)7.3%

Sample

1st row시정명령
2nd row과징금부과 1,980만원
3rd row과징금부과 1,980만원
4th row과징금부과
5th row과징금부과
ValueCountFrequency (%)
영업소폐쇄 1220
 
11.9%
시정명령 1117
 
10.9%
영업정지 1032
 
10.1%
과태료부과 859
 
8.4%
시설개수명령 340
 
3.3%
부과 277
 
2.7%
20만원 263
 
2.6%
과태료 257
 
2.5%
및 204
 
2.0%
과징금부과 190
 
1.9%
Other values (912) 4452
43.6%
2024-05-18T09:01:39.666937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 4272
 
7.2%
3633
 
6.1%
업 3251
 
5.5%
영 3222
 
5.4%
정 3055
 
5.1%
0 2910
 
4.9%
부 2361
 
4.0%
지 1867
 
3.1%
2 1862
 
3.1%
시 1714
 
2.9%
Other values (222) 31276
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42747
71.9%
Decimal Number 9018
 
15.2%
Space Separator 3633
 
6.1%
Other Punctuation 1539
 
2.6%
Open Punctuation 988
 
1.7%
Close Punctuation 982
 
1.7%
Dash Punctuation 392
 
0.7%
Math Symbol 122
 
0.2%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 4272
 
10.0%
업 3251
 
7.6%
영 3222
 
7.5%
정 3055
 
7.1%
부 2361
 
5.5%
지 1867
 
4.4%
시 1714
 
4.0%
원 1709
 
4.0%
소 1684
 
3.9%
료 1677
 
3.9%
Other values (199) 17935
42.0%
Decimal Number
ValueCountFrequency (%)
0 2910
32.3%
2 1862
20.6%
1 1496
16.6%
5 623
 
6.9%
4 498
 
5.5%
3 464
 
5.1%
6 431
 
4.8%
7 309
 
3.4%
8 244
 
2.7%
9 181
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 820
53.3%
, 380
24.7%
% 211
 
13.7%
: 88
 
5.7%
/ 40
 
2.6%
Math Symbol
ValueCountFrequency (%)
~ 118
96.7%
+ 2
 
1.6%
= 2
 
1.6%
Space Separator
ValueCountFrequency (%)
3633
100.0%
Open Punctuation
ValueCountFrequency (%)
( 988
100.0%
Close Punctuation
ValueCountFrequency (%)
) 982
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 392
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42747
71.9%
Common 16674
 
28.1%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 4272
 
10.0%
업 3251
 
7.6%
영 3222
 
7.5%
정 3055
 
7.1%
부 2361
 
5.5%
지 1867
 
4.4%
시 1714
 
4.0%
원 1709
 
4.0%
소 1684
 
3.9%
료 1677
 
3.9%
Other values (199) 17935
42.0%
Common
ValueCountFrequency (%)
3633
21.8%
0 2910
17.5%
2 1862
11.2%
1 1496
9.0%
( 988
 
5.9%
) 982
 
5.9%
. 820
 
4.9%
5 623
 
3.7%
4 498
 
3.0%
3 464
 
2.8%
Other values (12) 2398
14.4%
Latin
ValueCountFrequency (%)
X 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42742
71.9%
ASCII 16676
 
28.1%
Compat Jamo 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 4272
 
10.0%
업 3251
 
7.6%
영 3222
 
7.5%
정 3055
 
7.1%
부 2361
 
5.5%
지 1867
 
4.4%
시 1714
 
4.0%
원 1709
 
4.0%
소 1684
 
3.9%
료 1677
 
3.9%
Other values (198) 17930
41.9%
ASCII
ValueCountFrequency (%)
3633
21.8%
0 2910
17.5%
2 1862
11.2%
1 1496
9.0%
( 988
 
5.9%
) 982
 
5.9%
. 820
 
4.9%
5 623
 
3.7%
4 498
 
3.0%
3 464
 
2.8%
Other values (13) 2400
14.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 5
100.0%

법적근거
Text

Distinct601
Distinct (%)9.1%
Missing2
Missing (%)< 0.1%
Memory size51.6 KiB
2024-05-18T09:01:40.365414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length64
Median length61
Mean length13.337031
Min length4

Characters and Unicode

Total characters87771
Distinct characters134
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

Unique284 ?
Unique (%)4.3%

Sample

1st row법 제71조, 법 제74조 및 법 제75조
2nd row법 제75조
3rd row법 제75조
4th row법 제71조 및 법 제75조
5th row법 제71조 및 법 제75조
ValueCountFrequency (%)
법 3364
18.4%
식품위생법 3001
16.4%
및 1434
 
7.8%
제75조 1092
 
6.0%
제71조 834
 
4.6%
제37조 536
 
2.9%
식품위생법제58조 431
 
2.4%
제101조제2항제1호 393
 
2.1%
제31조 352
 
1.9%
제44조 312
 
1.7%
Other values (404) 6543
35.8%
2024-05-18T09:01:41.833229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11922
13.6%
제 10825
12.3%
조 8935
10.2%
법 8072
 
9.2%
1 5203
 
5.9%
식 4446
 
5.1%
품 4438
 
5.1%
위 4404
 
5.0%
생 4341
 
4.9%
7 3886
 
4.4%
Other values (124) 21299
24.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53212
60.6%
Decimal Number 20986
 
23.9%
Space Separator 11922
 
13.6%
Other Punctuation 880
 
1.0%
Close Punctuation 383
 
0.4%
Open Punctuation 382
 
0.4%
Dash Punctuation 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 10825
20.3%
조 8935
16.8%
법 8072
15.2%
식 4446
8.4%
품 4438
8.3%
위 4404
8.3%
생 4341
8.2%
항 1638
 
3.1%
및 1476
 
2.8%
호 903
 
1.7%
Other values (106) 3734
 
7.0%
Decimal Number
ValueCountFrequency (%)
1 5203
24.8%
7 3886
18.5%
2 2500
11.9%
5 2224
10.6%
3 2120
10.1%
4 1902
 
9.1%
0 1162
 
5.5%
8 1028
 
4.9%
6 854
 
4.1%
9 107
 
0.5%
Other Punctuation
ValueCountFrequency (%)
, 867
98.5%
. 13
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 381
99.5%
] 2
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 380
99.5%
[ 2
 
0.5%
Space Separator
ValueCountFrequency (%)
11922
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53212
60.6%
Common 34559
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 10825
20.3%
조 8935
16.8%
법 8072
15.2%
식 4446
8.4%
품 4438
8.3%
위 4404
8.3%
생 4341
8.2%
항 1638
 
3.1%
및 1476
 
2.8%
호 903
 
1.7%
Other values (106) 3734
 
7.0%
Common
ValueCountFrequency (%)
11922
34.5%
1 5203
15.1%
7 3886
 
11.2%
2 2500
 
7.2%
5 2224
 
6.4%
3 2120
 
6.1%
4 1902
 
5.5%
0 1162
 
3.4%
8 1028
 
3.0%
, 867
 
2.5%
Other values (8) 1745
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53212
60.6%
ASCII 34559
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11922
34.5%
1 5203
15.1%
7 3886
 
11.2%
2 2500
 
7.2%
5 2224
 
6.4%
3 2120
 
6.1%
4 1902
 
5.5%
0 1162
 
3.4%
8 1028
 
3.0%
, 867
 
2.5%
Other values (8) 1745
 
5.0%
Hangul
ValueCountFrequency (%)
제 10825
20.3%
조 8935
16.8%
법 8072
15.2%
식 4446
8.4%
품 4438
8.3%
위 4404
8.3%
생 4341
8.2%
항 1638
 
3.1%
및 1476
 
2.8%
호 903
 
1.7%
Other values (106) 3734
 
7.0%

위반일자
Real number (ℝ)

HIGH CORRELATION 

Distinct2367
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20118047
Minimum19940105
Maximum21030809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2024-05-18T09:01:42.281538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19940105
5-th percentile20030329
Q120070130
median20111019
Q320161209
95-th percentile20221231
Maximum21030809
Range1090704
Interquartile range (IQR)91079

Descriptive statistics

Standard deviation60786.348
Coefficient of variation (CV)0.0030214835
Kurtosis6.5740899
Mean20118047
Median Absolute Deviation (MAD)49783
Skewness0.67636438
Sum1.324371 × 1011
Variance3.6949801 × 109
MonotonicityNot monotonic
2024-05-18T09:01:42.767341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20210607 181
 
2.7%
20061129 114
 
1.7%
20221231 108
 
1.6%
20110901 99
 
1.5%
20070125 72
 
1.1%
20061229 65
 
1.0%
20070228 57
 
0.9%
20051017 56
 
0.9%
20190101 50
 
0.8%
20191231 43
 
0.7%
Other values (2357) 5738
87.2%
ValueCountFrequency (%)
19940105 1
< 0.1%
20001016 1
< 0.1%
20010519 1
< 0.1%
20010728 1
< 0.1%
20010828 1
< 0.1%
20010831 1
< 0.1%
20010906 1
< 0.1%
20010907 1
< 0.1%
20010911 1
< 0.1%
20010915 1
< 0.1%
ValueCountFrequency (%)
21030809 1
 
< 0.1%
20240313 1
 
< 0.1%
20240307 1
 
< 0.1%
20240304 4
0.1%
20240227 2
< 0.1%
20240226 2
< 0.1%
20240221 2
< 0.1%
20240220 2
< 0.1%
20240219 4
0.1%
20240218 1
 
< 0.1%

위반내용
Text

Distinct2373
Distinct (%)36.1%
Missing7
Missing (%)0.1%
Memory size51.6 KiB
2024-05-18T09:01:43.487194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length313
Median length166
Mean length20.719282
Min length1

Characters and Unicode

Total characters136250
Distinct characters684
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1474 ?
Unique (%)22.4%

Sample

1st row영업장 외 영업 1차
2nd row청소년 주류제공 - 1차
3rd row청소년 주류제공 - 1차
4th row소비기한이 경과된 제품 조리목적 보관-1차
5th row소비기한이 경과된 제품 조리목적 보관-1차
ValueCountFrequency (%)
1차 531
 
1.9%
영업시설의 497
 
1.7%
전부를 495
 
1.7%
철거 477
 
1.7%
457
 
1.6%
건강진단 373
 
1.3%
위생교육 332
 
1.2%
미 331
 
1.2%
및 306
 
1.1%
멸실 300
 
1.1%
Other values (4211) 24438
85.6%
2024-05-18T09:01:44.759505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22535
 
16.5%
업 4367
 
3.2%
1 3209
 
2.4%
영 3157
 
2.3%
2 2453
 
1.8%
시 2310
 
1.7%
0 2277
 
1.7%
( 2084
 
1.5%
) 2084
 
1.5%
미 1967
 
1.4%
Other values (674) 89807
65.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 94643
69.5%
Space Separator 22535
 
16.5%
Decimal Number 10872
 
8.0%
Other Punctuation 2906
 
2.1%
Open Punctuation 2104
 
1.5%
Close Punctuation 2104
 
1.5%
Dash Punctuation 634
 
0.5%
Lowercase Letter 306
 
0.2%
Uppercase Letter 94
 
0.1%
Other Symbol 30
 
< 0.1%
Other values (2) 22
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 4367
 
4.6%
영 3157
 
3.3%
시 2310
 
2.4%
미 1967
 
2.1%
이 1831
 
1.9%
위 1711
 
1.8%
설 1700
 
1.8%
고 1582
 
1.7%
주 1522
 
1.6%
차 1353
 
1.4%
Other values (599) 73143
77.3%
Lowercase Letter
ValueCountFrequency (%)
w 42
13.7%
k 34
11.1%
c 31
10.1%
o 28
9.2%
r 25
8.2%
e 22
 
7.2%
g 20
 
6.5%
t 16
 
5.2%
d 14
 
4.6%
i 12
 
3.9%
Other values (11) 62
20.3%
Uppercase Letter
ValueCountFrequency (%)
O 20
21.3%
E 11
11.7%
T 6
 
6.4%
G 5
 
5.3%
R 5
 
5.3%
A 5
 
5.3%
H 5
 
5.3%
C 5
 
5.3%
N 4
 
4.3%
L 4
 
4.3%
Other values (11) 24
25.5%
Decimal Number
ValueCountFrequency (%)
1 3209
29.5%
2 2453
22.6%
0 2277
20.9%
3 606
 
5.6%
7 455
 
4.2%
5 416
 
3.8%
4 415
 
3.8%
8 359
 
3.3%
9 353
 
3.2%
6 329
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 1653
56.9%
, 604
 
20.8%
: 347
 
11.9%
/ 210
 
7.2%
* 71
 
2.4%
? 13
 
0.4%
% 4
 
0.1%
？ 2
 
0.1%
# 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 2084
99.0%
[ 16
 
0.8%
「 2
 
0.1%
［ 2
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 2084
99.0%
] 16
 
0.8%
」 2
 
0.1%
］ 2
 
0.1%
Other Symbol
ValueCountFrequency (%)
㎡ 20
66.7%
○ 10
33.3%
Space Separator
ValueCountFrequency (%)
22535
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 634
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94643
69.5%
Common 41207
30.2%
Latin 400
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 4367
 
4.6%
영 3157
 
3.3%
시 2310
 
2.4%
미 1967
 
2.1%
이 1831
 
1.9%
위 1711
 
1.8%
설 1700
 
1.8%
고 1582
 
1.7%
주 1522
 
1.6%
차 1353
 
1.4%
Other values (599) 73143
77.3%
Latin
ValueCountFrequency (%)
w 42
 
10.5%
k 34
 
8.5%
c 31
 
7.8%
o 28
 
7.0%
r 25
 
6.2%
e 22
 
5.5%
g 20
 
5.0%
O 20
 
5.0%
t 16
 
4.0%
d 14
 
3.5%
Other values (32) 148
37.0%
Common
ValueCountFrequency (%)
22535
54.7%
1 3209
 
7.8%
2 2453
 
6.0%
0 2277
 
5.5%
( 2084
 
5.1%
) 2084
 
5.1%
. 1653
 
4.0%
- 634
 
1.5%
3 606
 
1.5%
, 604
 
1.5%
Other values (23) 3068
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 94584
69.4%
ASCII 41567
30.5%
Compat Jamo 59
 
< 0.1%
CJK Compat 20
 
< 0.1%
Geometric Shapes 10
 
< 0.1%
None 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22535
54.2%
1 3209
 
7.7%
2 2453
 
5.9%
0 2277
 
5.5%
( 2084
 
5.0%
) 2084
 
5.0%
. 1653
 
4.0%
- 634
 
1.5%
3 606
 
1.5%
, 604
 
1.5%
Other values (58) 3428
 
8.2%
Hangul
ValueCountFrequency (%)
업 4367
 
4.6%
영 3157
 
3.3%
시 2310
 
2.4%
미 1967
 
2.1%
이 1831
 
1.9%
위 1711
 
1.8%
설 1700
 
1.8%
고 1582
 
1.7%
주 1522
 
1.6%
차 1353
 
1.4%
Other values (596) 73084
77.3%
Compat Jamo
ValueCountFrequency (%)
ㅇ 55
93.2%
ㆍ 3
 
5.1%
ㅏ 1
 
1.7%
CJK Compat
ValueCountFrequency (%)
㎡ 20
100.0%
Geometric Shapes
ValueCountFrequency (%)
○ 10
100.0%
None
ValueCountFrequency (%)
？ 2
20.0%
「 2
20.0%
」 2
20.0%
］ 2
20.0%
［ 2
20.0%

처분내용
Text

Distinct891
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
2024-05-18T09:01:45.385104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length83
Median length69
Mean length9.0267355
Min length2

Characters and Unicode

Total characters59423
Distinct characters232
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique480 ?
Unique (%)7.3%

Sample

1st row시정명령
2nd row과징금부과 1,980만원
3rd row과징금부과 1,980만원
4th row과징금부과
5th row과징금부과
ValueCountFrequency (%)
영업소폐쇄 1220
 
11.9%
시정명령 1117
 
10.9%
영업정지 1032
 
10.1%
과태료부과 859
 
8.4%
시설개수명령 340
 
3.3%
부과 277
 
2.7%
20만원 263
 
2.6%
과태료 257
 
2.5%
및 204
 
2.0%
과징금부과 190
 
1.9%
Other values (912) 4452
43.6%
2024-05-18T09:01:46.971604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 4272
 
7.2%
3633
 
6.1%
업 3251
 
5.5%
영 3222
 
5.4%
정 3055
 
5.1%
0 2910
 
4.9%
부 2361
 
4.0%
지 1867
 
3.1%
2 1862
 
3.1%
시 1714
 
2.9%
Other values (222) 31276
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42747
71.9%
Decimal Number 9018
 
15.2%
Space Separator 3633
 
6.1%
Other Punctuation 1539
 
2.6%
Open Punctuation 988
 
1.7%
Close Punctuation 982
 
1.7%
Dash Punctuation 392
 
0.7%
Math Symbol 122
 
0.2%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 4272
 
10.0%
업 3251
 
7.6%
영 3222
 
7.5%
정 3055
 
7.1%
부 2361
 
5.5%
지 1867
 
4.4%
시 1714
 
4.0%
원 1709
 
4.0%
소 1684
 
3.9%
료 1677
 
3.9%
Other values (199) 17935
42.0%
Decimal Number
ValueCountFrequency (%)
0 2910
32.3%
2 1862
20.6%
1 1496
16.6%
5 623
 
6.9%
4 498
 
5.5%
3 464
 
5.1%
6 431
 
4.8%
7 309
 
3.4%
8 244
 
2.7%
9 181
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 820
53.3%
, 380
24.7%
% 211
 
13.7%
: 88
 
5.7%
/ 40
 
2.6%
Math Symbol
ValueCountFrequency (%)
~ 118
96.7%
+ 2
 
1.6%
= 2
 
1.6%
Space Separator
ValueCountFrequency (%)
3633
100.0%
Open Punctuation
ValueCountFrequency (%)
( 988
100.0%
Close Punctuation
ValueCountFrequency (%)
) 982
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 392
100.0%
Uppercase Letter
ValueCountFrequency (%)
X 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42747
71.9%
Common 16674
 
28.1%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 4272
 
10.0%
업 3251
 
7.6%
영 3222
 
7.5%
정 3055
 
7.1%
부 2361
 
5.5%
지 1867
 
4.4%
시 1714
 
4.0%
원 1709
 
4.0%
소 1684
 
3.9%
료 1677
 
3.9%
Other values (199) 17935
42.0%
Common
ValueCountFrequency (%)
3633
21.8%
0 2910
17.5%
2 1862
11.2%
1 1496
9.0%
( 988
 
5.9%
) 982
 
5.9%
. 820
 
4.9%
5 623
 
3.7%
4 498
 
3.0%
3 464
 
2.8%
Other values (12) 2398
14.4%
Latin
ValueCountFrequency (%)
X 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42742
71.9%
ASCII 16676
 
28.1%
Compat Jamo 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 4272
 
10.0%
업 3251
 
7.6%
영 3222
 
7.5%
정 3055
 
7.1%
부 2361
 
5.5%
지 1867
 
4.4%
시 1714
 
4.0%
원 1709
 
4.0%
소 1684
 
3.9%
료 1677
 
3.9%
Other values (198) 17930
41.9%
ASCII
ValueCountFrequency (%)
3633
21.8%
0 2910
17.5%
2 1862
11.2%
1 1496
9.0%
( 988
 
5.9%
) 982
 
5.9%
. 820
 
4.9%
5 623
 
3.7%
4 498
 
3.0%
3 464
 
2.8%
Other values (13) 2400
14.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 5
100.0%

처분기간
Real number (ℝ)

MISSING 

Distinct21
Distinct (%)2.9%
Missing5858
Missing (%)89.0%
Infinite0
Infinite (%)0.0%
Mean12.58069
Minimum0
Maximum30
Zeros20
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2024-05-18T09:01:47.437307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median15
Q315
95-th percentile20
Maximum30
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.3131924
Coefficient of variation (CV)0.42232918
Kurtosis1.4072141
Mean12.58069
Median Absolute Deviation (MAD)0
Skewness0.21714639
Sum9121
Variance28.230013
MonotonicityNot monotonic
2024-05-18T09:01:48.005933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
15 394
 
6.0%
7 165
 
2.5%
10 54
 
0.8%
0 20
 
0.3%
20 15
 
0.2%
17 14
 
0.2%
30 14
 
0.2%
5 11
 
0.2%
22 10
 
0.2%
3 7
 
0.1%
Other values (11) 21
 
0.3%
(Missing) 5858
89.0%
ValueCountFrequency (%)
0 20
 
0.3%
2 6
 
0.1%
3 7
 
0.1%
4 1
 
< 0.1%
5 11
 
0.2%
6 1
 
< 0.1%
7 165
2.5%
8 1
 
< 0.1%
10 54
 
0.8%
11 3
 
< 0.1%
ValueCountFrequency (%)
30 14
 
0.2%
29 2
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
22 10
 
0.2%
21 2
 
< 0.1%
20 15
 
0.2%
17 14
 
0.2%
16 1
 
< 0.1%
15 394
6.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct827
Distinct (%)36.6%
Missing4322
Missing (%)65.7%
Infinite0
Infinite (%)0.0%
Mean104.46088
Minimum0
Maximum2064.61
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size58.0 KiB
2024-05-18T09:01:48.492451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.2
Q138.52
median79.79
Q3122.4
95-th percentile248.33
Maximum2064.61
Range2064.61
Interquartile range (IQR)83.88

Descriptive statistics

Standard deviation142.88902
Coefficient of variation (CV)1.3678711
Kurtosis63.20978
Mean104.46088
Median Absolute Deviation (MAD)42.19
Skewness6.7650905
Sum236186.06
Variance20417.273
MonotonicityNot monotonic
2024-05-18T09:01:49.049766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.4 73
 
1.1%
23.1 34
 
0.5%
148.84 33
 
0.5%
99.0 31
 
0.5%
29.7 31
 
0.5%
33.0 27
 
0.4%
92.4 26
 
0.4%
49.5 23
 
0.3%
66.0 21
 
0.3%
19.8 21
 
0.3%
Other values (817) 1941
29.5%
(Missing) 4322
65.7%
ValueCountFrequency (%)
0.0 5
0.1%
2.25 1
 
< 0.1%
3.3 3
< 0.1%
3.38 1
 
< 0.1%
3.5 1
 
< 0.1%
5.0 1
 
< 0.1%
5.41 1
 
< 0.1%
6.5 2
 
< 0.1%
6.6 3
< 0.1%
7.0 1
 
< 0.1%
ValueCountFrequency (%)
2064.61 1
 
< 0.1%
1845.0 1
 
< 0.1%
1697.05 3
< 0.1%
1419.0 1
 
< 0.1%
1390.0 3
< 0.1%
1012.35 3
< 0.1%
983.0 1
 
< 0.1%
951.0 3
< 0.1%
838.69 2
< 0.1%
828.45 2
< 0.1%

운영형태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
<NA>
6570 
직영
 
12
(조합)위탁
 
1

Length

Max length6
Median length4
Mean length3.9966581
Min length2

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> 6570
99.8%
직영 12
 
0.2%
(조합)위탁 1
 
< 0.1%

Length

2024-05-18T09:01:49.522309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:01:49.870589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6570
99.8%
직영 12
 
0.2%
조합)위탁 1
 
< 0.1%

Interactions

2024-05-18T09:01:19.592846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:09.295419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:11.325689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:13.342431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:15.468219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:17.510008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:19.965278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:09.639248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:11.789023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:13.679278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:15.776762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:17.924641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:20.343867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:09.914728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:12.164235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:13.974632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:16.046438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:18.303159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:20.658313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:10.274207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:12.458589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:14.293677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:16.343386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:18.674089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:20.937645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:10.619759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:12.764201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:14.635584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:16.672796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:19.053140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:21.263632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:11.000549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:13.050401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:15.035534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:17.033460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:01:19.331913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T09:01:50.200050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자교부번호업종명업태명지도점검일자위반일자처분기간영업장면적(㎡)운영형태
처분일자1.0000.5580.3500.5150.0660.9480.3710.1440.000
교부번호0.5581.0000.5800.7050.0000.5320.2270.0330.383
업종명0.3500.5801.0001.0000.0000.2290.3660.255NaN
업태명0.5150.7051.0001.0000.0000.4270.4950.6901.000
지도점검일자0.0660.0000.0000.0001.0000.040NaNNaNNaN
위반일자0.9480.5320.2290.4270.0401.0000.5210.0000.000
처분기간0.3710.2270.3660.495NaN0.5211.0000.000NaN
영업장면적(㎡)0.1440.0330.2550.690NaN0.0000.0001.000NaN
운영형태0.0000.383NaN1.000NaN0.000NaNNaN1.000
2024-05-18T09:01:50.607684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운영형태업종명
운영형태1.0001.000
업종명1.0001.000
2024-05-18T09:01:50.914096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자교부번호지도점검일자위반일자처분기간영업장면적(㎡)업종명운영형태
처분일자1.0000.5661.0000.999-0.0520.0460.1420.000
교부번호0.5661.0000.5670.566-0.019-0.0910.2540.369
지도점검일자1.0000.5671.0000.999-0.0520.0450.0001.000
위반일자0.9990.5660.9991.000-0.0520.0460.1250.000
처분기간-0.052-0.019-0.052-0.0521.000-0.0760.2070.000
영업장면적(㎡)0.046-0.0910.0450.046-0.0761.0000.1121.000
업종명0.1420.2540.0000.1250.2070.1121.0001.000
운영형태0.0000.3691.0000.0000.0001.0001.0001.000

Missing values

2024-05-18T09:01:22.043502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T09:01:22.765297image/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.
2024-05-18T09:01:23.245622image/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(㎡)운영형태
031400002024051420230097934휴게음식점일반조리판매달콤왕가탕후루 목동강서고점서울특별시 양천구 목동중앙남로 22, 1층 (목동)서울특별시 양천구 목동 766번지 16호 1층20231101처분확정시정명령법 제71조, 법 제74조 및 법 제75조20231101영업장 외 영업 1차시정명령<NA><NA><NA>
131400002024041620230097356일반음식점한식한라맥주 오목교점서울특별시 양천구 오목로 337-2, 1층 (목동)서울특별시 양천구 목동 406번지 296호 1층20231015처분확정과징금부과 1,980만원법 제75조20231015청소년 주류제공 - 1차과징금부과 1,980만원3061.11<NA>
231400002024041620230097356일반음식점한식한라맥주 오목교점서울특별시 양천구 오목로 337-2, 1층 (목동)서울특별시 양천구 목동 406번지 296호 1층20231015처분확정과징금부과 1,980만원법 제75조20231015청소년 주류제공 - 1차과징금부과 1,980만원30<NA><NA>
331400002024040920200073387일반음식점기타포트캔커피서울특별시 양천구 오목로 138, 신정스포렉스 101일부,102호 (신정동)서울특별시 양천구 신정동 945번지 2호 신정스포렉스20240304처분확정과징금부과법 제71조 및 법 제75조20240304소비기한이 경과된 제품 조리목적 보관-1차과징금부과15116.24<NA>
431400002024040920200073387일반음식점기타포트캔커피서울특별시 양천구 오목로 138, 신정스포렉스 101일부,102호 (신정동)서울특별시 양천구 신정동 945번지 2호 신정스포렉스20240304처분확정과징금부과법 제71조 및 법 제75조20240304소비기한이 경과된 제품 조리목적 보관-1차과징금부과15<NA><NA>
531400002024040420070073400제과점영업제과점영업마인츠돔 과자점서울특별시 양천구 오목로 300, (목동,목동현대하이이페리온(2차) 205동 119호)서울특별시 양천구 목동 961번지 목동현대하이이페리온(2차) 205동 119호20240313처분확정과징금부과법 제71조, 법 제72조, 법 제75조 및 법 제76조20240313이물(너트) 혼입과징금부과285.0<NA>
631400002024032820020073181일반음식점분식한솥도시락서울특별시 양천구 오목로 203, (신정동)서울특별시 양천구 신정동 907번지 11호20240307처분확정시정명령법 제71조, 법 제72조, 법 제75조 및 법 제76조20240307이물(합성섬유 추정)혼입-1차시정명령<NA>26.4<NA>
731400002024032720190073837일반음식점중국식마라영웅 마라탕?마라샹궈 전문점서울특별시 양천구 은행정로7길 10, 1층 01호 (신정동)서울특별시 양천구 신정동 980번지 4호20240227처분확정시정명령법 제71조, 법 제72조 및 법 제75조20240227이물(종이류추정)혼입-1차시정명령<NA><NA><NA>
831400002024032720190073837일반음식점중국식마라영웅 마라탕?마라샹궈 전문점서울특별시 양천구 은행정로7길 10, 1층 01호 (신정동)서울특별시 양천구 신정동 980번지 4호20240227처분확정시정명령법 제71조, 법 제72조 및 법 제75조20240227이물(종이류추정)혼입-1차시정명령<NA>51.78<NA>
931400002024032120170347027유통전문판매업유통전문판매업(주)지앤건강생활서울특별시 양천구 공항대로 644, 5층,6층 (목동)서울특별시 양천구 목동 515번지 12호 5층, 6층20240304처분확정시정명령법 제71조, 법 제72조 및 법 제75조20240304굽네 닭가슴살 김치만두 이물 혼입(플라스틱) - 1차시정명령<NA>488.56<NA>
시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)운영형태
657331400002001112019990073352일반음식점분식고래호프<NA>서울특별시 양천구 신월동 932번지 11호20010728처분확정시설개수명령, 영업정지15일갈음과징금60만원식품위생법제31조20010728무도장설치(1차)영업장음향기기설치(2차)시설개수명령, 영업정지15일갈음과징금60만원15137.84<NA>
657431400002001112019950073354일반음식점호프/통닭하이트호프<NA>서울특별시 양천구 신월동 442번지 4호20011013처분확정영업정지2월식품위생법31조20011013청소년주류제공(1차)영업정지2월<NA><NA><NA>
657531400002001112019960073048단란주점단란주점코리아타운<NA>서울특별시 양천구 신정동 1183번지 10호20011007처분확정영업정지1월갈음 과징금180만원식품위생법제31조20011007무도장설치(2차위반)영업정지1월갈음 과징금180만원<NA>141.51<NA>
657631400002001112020010073543일반음식점호프/통닭해피데이꼬치<NA>서울특별시 양천구 신정동 207번지 19호2001103처분확정영업정지1월 갈음과징금120만원식품위생법제31조20011003청소년주류제공1차영업정지1월 갈음과징금120만원<NA><NA><NA>
657731400002001112020010073652일반음식점분식형어디가신월동점<NA>서울특별시 양천구 신월동 166번지 4호20011007처분확정영업정지(2001.11.29-1.28)식품위생법제31조20011007청소년주류제공1차영업정지(2001.11.29-1.28)<NA><NA><NA>
657831400002001112019980073781일반음식점정종/대포집/소주방보스<NA>서울특별시 양천구 신정동 1024번지 17호20010906처분확정영업정지1월갈음과징금300만원식품위생법제31조20020206단란주점영업,영업장음향기기설치(1차)영업정지1월갈음과징금300만원<NA><NA><NA>
657931400002001112019940073792일반음식점호프/통닭머니뭐니<NA>서울특별시 양천구 신정동 1183번지 5호20011007처분확정영업정지15일갈음과징금90만원식품위생법제31조20011007주류전문판매(1차)영업정지15일갈음과징금90만원15<NA><NA>
658031400002001112019910073601일반음식점경양식탈렌트<NA>서울특별시 양천구 신정동 1018번지 13호20010906처분확정영업정지1월갈음과징금300만원식품위생법22조20020318단란주점형태영업영업정지1월갈음과징금300만원<NA>105.3<NA>
658131400002001101620000073627일반음식점분식코리아나호프<NA>서울특별시 양천구 신정동 1030번지 6호20010911처분확정영업소폐쇄식품위생법21조20010911무도장설치 4차영업소폐쇄<NA>250.8<NA>
658231400001994012519930073620단란주점단란주점카니발<NA>서울특별시 양천구 신정동 973번지 30호19940105처분확정시설개수명령식품위생법 제21조19940105시설기준위반시설개수명령<NA>114.74<NA>

Duplicate rows

Most frequently occurring

시군구코드처분일자교부번호업종명업태명업소명소재지도로명소재지지번지도점검일자행정처분상태처분명법적근거위반일자위반내용처분내용처분기간영업장면적(㎡)운영형태# duplicates
6131400002009102019980073516일반음식점중국식북경반점<NA>서울특별시 양천구 신월동 474번지 9호20091030처분확정영업소폐쇄식품위생법 제37조20090929시설물멸실영업소폐쇄<NA><NA><NA>4
6531400002009121020000073307일반음식점한식해성식당<NA>서울특별시 양천구 신월동 110번지 29호20091015처분확정영업소폐쇄식품위생법 제37조20091015폐업신고를 하지 않고 영업시설물 전부를 철거영업소폐쇄<NA><NA><NA>4
14231400002017072420130073158일반음식점분식목동해밀서울특별시 양천구 목동동로 339, 1층 110호 (목동, 목동트윈빌)서울특별시 양천구 목동 905번지 22호 목동트윈빌 1층-11020170621처분확정과태료부과법 제101조제2항 제1호20170621건강진단을 받지 아니한 종원업과태료부과<NA><NA><NA>4
19531400002020031319960073588일반음식점한식이해림아구찜서울특별시 양천구 신목로 42, (신정동)서울특별시 양천구 신정동 296번지 103호20200228처분확정영업정지법 제71조 및 법 제75조20200228손님에게 제공되었던 음식을 재보관영업정지15<NA><NA>4
20031400002020033119960073588일반음식점한식이해림아구찜서울특별시 양천구 신목로 42, (신정동)서울특별시 양천구 신정동 296번지 103호20200228처분확정영업정지법 제71조 및 법 제75조20200228손님에게 제공되었던 음식을 재보관영업정지15<NA><NA>4
20431400002020052920150073715일반음식점일식뉴욕바닷가재서울특별시 양천구 남부순환로 575, 1동 제1층 104호 (신월동)서울특별시 양천구 신월동 547번지 9호 1동 제1층-10420200102처분확정영업정지법 제71조 및 법 제75조20200506유통기한이 경과된 제품을 보관영업정지15122.4<NA>4
20531400002020052920150073715일반음식점일식뉴욕바닷가재서울특별시 양천구 남부순환로 575, 1동 제1층 104호 (신월동)서울특별시 양천구 신월동 547번지 9호 1동 제1층-10420200102처분확정영업정지법 제71조 및 법 제75조20200506유통기한이 경과된 제품을 보관영업정지15<NA><NA>4
20631400002020061220150073715일반음식점일식뉴욕바닷가재서울특별시 양천구 남부순환로 575, 1동 제1층 104호 (신월동)서울특별시 양천구 신월동 547번지 9호 1동 제1층-10420200102처분확정영업정지법 제71조 및 법 제75조20200506유통기한이 경과된 제품을 보관영업정지15122.4<NA>4
20731400002020061220150073715일반음식점일식뉴욕바닷가재서울특별시 양천구 남부순환로 575, 1동 제1층 104호 (신월동)서울특별시 양천구 신월동 547번지 9호 1동 제1층-10420200102처분확정영업정지법 제71조 및 법 제75조20200506유통기한이 경과된 제품을 보관영업정지15<NA><NA>4
22031400002020111219960073588일반음식점한식이해림아구찜서울특별시 양천구 신목로 42, (신정동)서울특별시 양천구 신정동 296번지 103호20200228처분확정과징금부과법 제71조 및 법 제75조20200228손님에게 제공되었던 음식을 재보관과징금부과<NA><NA><NA>4