Overview

Dataset statistics

Number of variables16
Number of observations87
Missing cells5
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory136.5 B

Variable types

Categorical5
Numeric6
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,지정취소사유,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author양천구
URLhttps://data.seoul.go.kr/dataList/OA-10851/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 신청일자 and 3 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 지정취소사유High correlation
지정취소사유 is highly overall correlated with 지정년도 and 4 other fieldsHigh correlation
업태명 is highly imbalanced (54.2%)Imbalance
급수시설구분 is highly imbalanced (54.3%)Imbalance
주된음식 has 5 (5.7%) missing valuesMissing

Reproduction

Analysis started2024-05-04 05:42:27.780528
Analysis finished2024-05-04 05:42:44.145443
Duration16.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
3140000
87 

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 87
100.0%

Length

2024-05-04T05:42:44.314775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:42:44.869925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3140000 87
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.8391
Minimum2001
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T05:42:45.265073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12001
median2003
Q32007
95-th percentile2016.4
Maximum2018
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7663392
Coefficient of variation (CV)0.0023774174
Kurtosis1.173561
Mean2004.8391
Median Absolute Deviation (MAD)2
Skewness1.436287
Sum174421
Variance22.71799
MonotonicityNot monotonic
2024-05-04T05:42:45.753994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2001 26
29.9%
2002 15
17.2%
2003 8
 
9.2%
2005 7
 
8.0%
2004 5
 
5.7%
2008 4
 
4.6%
2009 4
 
4.6%
2013 3
 
3.4%
2007 3
 
3.4%
2018 3
 
3.4%
Other values (6) 9
 
10.3%
ValueCountFrequency (%)
2001 26
29.9%
2002 15
17.2%
2003 8
 
9.2%
2004 5
 
5.7%
2005 7
 
8.0%
2006 3
 
3.4%
2007 3
 
3.4%
2008 4
 
4.6%
2009 4
 
4.6%
2011 1
 
1.1%
ValueCountFrequency (%)
2018 3
3.4%
2017 2
2.3%
2015 1
 
1.1%
2014 1
 
1.1%
2013 3
3.4%
2012 1
 
1.1%
2011 1
 
1.1%
2009 4
4.6%
2008 4
4.6%
2007 3
3.4%

지정번호
Real number (ℝ)

Distinct65
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.563218
Minimum1
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T05:42:46.147866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q112
median36
Q377.5
95-th percentile158.5
Maximum199
Range198
Interquartile range (IQR)65.5

Descriptive statistics

Standard deviation49.436288
Coefficient of variation (CV)0.94051105
Kurtosis0.2448574
Mean52.563218
Median Absolute Deviation (MAD)27
Skewness1.0643045
Sum4573
Variance2443.9465
MonotonicityNot monotonic
2024-05-04T05:42:46.613166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 4
 
4.6%
1 4
 
4.6%
6 3
 
3.4%
9 3
 
3.4%
21 2
 
2.3%
23 2
 
2.3%
38 2
 
2.3%
10 2
 
2.3%
55 2
 
2.3%
61 2
 
2.3%
Other values (55) 61
70.1%
ValueCountFrequency (%)
1 4
4.6%
2 1
 
1.1%
3 1
 
1.1%
4 1
 
1.1%
5 1
 
1.1%
6 3
3.4%
7 2
2.3%
8 1
 
1.1%
9 3
3.4%
10 2
2.3%
ValueCountFrequency (%)
199 1
1.1%
174 1
1.1%
166 1
1.1%
162 1
1.1%
160 1
1.1%
155 1
1.1%
137 1
1.1%
132 1
1.1%
131 1
1.1%
121 1
1.1%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20049046
Minimum20010601
Maximum20181101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T05:42:46.999939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010601
5-th percentile20010601
Q120010601
median20030602
Q320070611
95-th percentile20165084
Maximum20181101
Range170500
Interquartile range (IQR)60010

Descriptive statistics

Standard deviation47838.71
Coefficient of variation (CV)0.0023860841
Kurtosis1.1698997
Mean20049046
Median Absolute Deviation (MAD)20001
Skewness1.4365569
Sum1.744267 × 109
Variance2.2885422 × 109
MonotonicityNot monotonic
2024-05-04T05:42:47.387279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20010601 26
29.9%
20020402 15
17.2%
20030602 8
 
9.2%
20050620 7
 
8.0%
20090820 4
 
4.6%
20131001 3
 
3.4%
20060710 3
 
3.4%
20040531 3
 
3.4%
20080901 3
 
3.4%
20171031 2
 
2.3%
Other values (10) 13
14.9%
ValueCountFrequency (%)
20010601 26
29.9%
20020402 15
17.2%
20030602 8
 
9.2%
20040521 2
 
2.3%
20040531 3
 
3.4%
20050620 7
 
8.0%
20060710 3
 
3.4%
20070601 1
 
1.1%
20070621 2
 
2.3%
20080901 3
 
3.4%
ValueCountFrequency (%)
20181101 2
2.3%
20181015 1
 
1.1%
20171031 2
2.3%
20151207 1
 
1.1%
20141112 1
 
1.1%
20131001 3
3.4%
20121002 1
 
1.1%
20110908 1
 
1.1%
20090820 4
4.6%
20081110 1
 
1.1%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20049176
Minimum20010703
Maximum20181130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T05:42:47.841949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010703
5-th percentile20010703
Q120010703
median20030801
Q320070921
95-th percentile20165157
Maximum20181130
Range170427
Interquartile range (IQR)60218

Descriptive statistics

Standard deviation47836.572
Coefficient of variation (CV)0.002385962
Kurtosis1.1647253
Mean20049176
Median Absolute Deviation (MAD)20098
Skewness1.43465
Sum1.7442783 × 109
Variance2.2883376 × 109
MonotonicityNot monotonic
2024-05-04T05:42:48.227045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20010703 26
29.9%
20020501 15
17.2%
20030801 8
 
9.2%
20050804 7
 
8.0%
20040719 5
 
5.7%
20090916 4
 
4.6%
20081021 3
 
3.4%
20131209 3
 
3.4%
20070921 3
 
3.4%
20060830 3
 
3.4%
Other values (8) 10
 
11.5%
ValueCountFrequency (%)
20010703 26
29.9%
20020501 15
17.2%
20030801 8
 
9.2%
20040719 5
 
5.7%
20050804 7
 
8.0%
20060830 3
 
3.4%
20070921 3
 
3.4%
20081021 3
 
3.4%
20081126 1
 
1.1%
20090916 4
 
4.6%
ValueCountFrequency (%)
20181130 2
2.3%
20181129 1
 
1.1%
20171129 2
2.3%
20151221 1
 
1.1%
20141222 1
 
1.1%
20131209 3
3.4%
20121031 1
 
1.1%
20111031 1
 
1.1%
20090916 4
4.6%
20081126 1
 
1.1%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20097967
Minimum20030404
Maximum20231122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T05:42:48.628777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030404
5-th percentile20030802
Q120050410
median20081021
Q320120616
95-th percentile20221130
Maximum20231122
Range200718
Interquartile range (IQR)70206.5

Descriptive statistics

Standard deviation59972.79
Coefficient of variation (CV)0.0029840227
Kurtosis-0.27241056
Mean20097967
Median Absolute Deviation (MAD)30614
Skewness0.88819658
Sum1.7485232 × 109
Variance3.5967355 × 109
MonotonicityDecreasing
2024-05-04T05:42:49.108206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111019 11
 
12.6%
20100618 7
 
8.0%
20070921 5
 
5.7%
20121031 4
 
4.6%
20221130 3
 
3.4%
20201221 3
 
3.4%
20081021 3
 
3.4%
20030801 2
 
2.3%
20231122 2
 
2.3%
20211206 2
 
2.3%
Other values (45) 45
51.7%
ValueCountFrequency (%)
20030404 1
1.1%
20030424 1
1.1%
20030612 1
1.1%
20030801 2
2.3%
20030805 1
1.1%
20031007 1
1.1%
20031020 1
1.1%
20031104 1
1.1%
20031117 1
1.1%
20040129 1
1.1%
ValueCountFrequency (%)
20231122 2
2.3%
20230214 1
 
1.1%
20221130 3
3.4%
20211206 2
2.3%
20201221 3
3.4%
20200529 1
 
1.1%
20191130 1
 
1.1%
20181130 1
 
1.1%
20171129 1
 
1.1%
20161221 1
 
1.1%

업소명
Text

Distinct77
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-05-04T05:42:49.766251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length6.045977
Min length2

Characters and Unicode

Total characters526
Distinct characters213
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)78.2%

Sample

1st row최고야식당
2nd row은성가든
3rd row가랑삼계탕
4th row두성전주콩나물국밥 양천구청점
5th row한무쇼핑(주)목동점
ValueCountFrequency (%)
신월동큰집 3
 
3.1%
억조1978 2
 
2.1%
오목교점 2
 
2.1%
원두막 2
 
2.1%
등촌샤브칼국수 2
 
2.1%
뉴욕바닷가재 2
 
2.1%
황토골 2
 
2.1%
양천옥설렁탕 2
 
2.1%
은성가든 2
 
2.1%
상도연탄갈비 2
 
2.1%
Other values (76) 76
78.4%
2024-05-04T05:42:50.772881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 15
 
2.9%
동 14
 
2.7%
목 11
 
2.1%
10
 
1.9%
국 9
 
1.7%
주 8
 
1.5%
리 8
 
1.5%
갈 8
 
1.5%
가 7
 
1.3%
두 7
 
1.3%
Other values (203) 429
81.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 500
95.1%
Decimal Number 11
 
2.1%
Space Separator 10
 
1.9%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 15
 
3.0%
동 14
 
2.8%
목 11
 
2.2%
국 9
 
1.8%
주 8
 
1.6%
리 8
 
1.6%
갈 8
 
1.6%
가 7
 
1.4%
두 7
 
1.4%
비 7
 
1.4%
Other values (193) 406
81.2%
Decimal Number
ValueCountFrequency (%)
3 2
18.2%
8 2
18.2%
9 2
18.2%
7 2
18.2%
1 2
18.2%
5 1
9.1%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 500
95.1%
Common 26
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 15
 
3.0%
동 14
 
2.8%
목 11
 
2.2%
국 9
 
1.8%
주 8
 
1.6%
리 8
 
1.6%
갈 8
 
1.6%
가 7
 
1.4%
두 7
 
1.4%
비 7
 
1.4%
Other values (193) 406
81.2%
Common
ValueCountFrequency (%)
10
38.5%
3 2
 
7.7%
) 2
 
7.7%
8 2
 
7.7%
( 2
 
7.7%
9 2
 
7.7%
7 2
 
7.7%
1 2
 
7.7%
. 1
 
3.8%
5 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 500
95.1%
ASCII 26
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 15
 
3.0%
동 14
 
2.8%
목 11
 
2.2%
국 9
 
1.8%
주 8
 
1.6%
리 8
 
1.6%
갈 8
 
1.6%
가 7
 
1.4%
두 7
 
1.4%
비 7
 
1.4%
Other values (193) 406
81.2%
ASCII
ValueCountFrequency (%)
10
38.5%
3 2
 
7.7%
) 2
 
7.7%
8 2
 
7.7%
( 2
 
7.7%
9 2
 
7.7%
7 2
 
7.7%
1 2
 
7.7%
. 1
 
3.8%
5 1
 
3.8%

소재지도로명
Text

Distinct79
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-05-04T05:42:51.337399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length43
Mean length30.724138
Min length22

Characters and Unicode

Total characters2673
Distinct characters112
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

Unique72 ?
Unique (%)82.8%

Sample

1st row서울특별시 양천구 화곡로4길 12, 1층 (신월동)
2nd row서울특별시 양천구 신월로 210, 2층 (신월동)
3rd row서울특별시 양천구 목동로 210, 타운빌딩 2층 201호 (목동)
4th row서울특별시 양천구 목동서로 377, 이스타빌 A동 1층 111~111가호 (신정동)
5th row서울특별시 양천구 목동동로 257, (목동, 현대백화점 지하4층)
ValueCountFrequency (%)
서울특별시 87
16.4%
양천구 87
16.4%
목동 31
 
5.8%
1층 31
 
5.8%
신정동 26
 
4.9%
신월동 23
 
4.3%
목동동로 13
 
2.4%
2층 8
 
1.5%
오목로 7
 
1.3%
신정중앙로 6
 
1.1%
Other values (155) 212
39.9%
2024-05-04T05:42:52.380754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
445
 
16.6%
동 147
 
5.5%
1 129
 
4.8%
, 113
 
4.2%
서 94
 
3.5%
로 93
 
3.5%
( 90
 
3.4%
) 90
 
3.4%
양 89
 
3.3%
시 88
 
3.3%
Other values (102) 1295
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1516
56.7%
Space Separator 445
 
16.6%
Decimal Number 393
 
14.7%
Other Punctuation 113
 
4.2%
Open Punctuation 90
 
3.4%
Close Punctuation 90
 
3.4%
Dash Punctuation 12
 
0.4%
Math Symbol 10
 
0.4%
Uppercase Letter 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 147
 
9.7%
서 94
 
6.2%
로 93
 
6.1%
양 89
 
5.9%
시 88
 
5.8%
천 87
 
5.7%
구 87
 
5.7%
울 87
 
5.7%
특 87
 
5.7%
별 87
 
5.7%
Other values (84) 570
37.6%
Decimal Number
ValueCountFrequency (%)
1 129
32.8%
2 70
17.8%
0 38
 
9.7%
4 30
 
7.6%
3 27
 
6.9%
6 27
 
6.9%
5 24
 
6.1%
8 19
 
4.8%
7 15
 
3.8%
9 14
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
A 2
50.0%
Space Separator
ValueCountFrequency (%)
445
100.0%
Other Punctuation
ValueCountFrequency (%)
, 113
100.0%
Open Punctuation
ValueCountFrequency (%)
( 90
100.0%
Close Punctuation
ValueCountFrequency (%)
) 90
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1516
56.7%
Common 1153
43.1%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 147
 
9.7%
서 94
 
6.2%
로 93
 
6.1%
양 89
 
5.9%
시 88
 
5.8%
천 87
 
5.7%
구 87
 
5.7%
울 87
 
5.7%
특 87
 
5.7%
별 87
 
5.7%
Other values (84) 570
37.6%
Common
ValueCountFrequency (%)
445
38.6%
1 129
 
11.2%
, 113
 
9.8%
( 90
 
7.8%
) 90
 
7.8%
2 70
 
6.1%
0 38
 
3.3%
4 30
 
2.6%
3 27
 
2.3%
6 27
 
2.3%
Other values (6) 94
 
8.2%
Latin
ValueCountFrequency (%)
B 2
50.0%
A 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1516
56.7%
ASCII 1157
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
445
38.5%
1 129
 
11.1%
, 113
 
9.8%
( 90
 
7.8%
) 90
 
7.8%
2 70
 
6.1%
0 38
 
3.3%
4 30
 
2.6%
3 27
 
2.3%
6 27
 
2.3%
Other values (8) 98
 
8.5%
Hangul
ValueCountFrequency (%)
동 147
 
9.7%
서 94
 
6.2%
로 93
 
6.1%
양 89
 
5.9%
시 88
 
5.8%
천 87
 
5.7%
구 87
 
5.7%
울 87
 
5.7%
특 87
 
5.7%
별 87
 
5.7%
Other values (84) 570
37.6%

소재지지번
Text

Distinct79
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-05-04T05:42:52.850213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length41
Mean length29.011494
Min length21

Characters and Unicode

Total characters2524
Distinct characters98
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

Unique72 ?
Unique (%)82.8%

Sample

1st row서울특별시 양천구 신월동 52번지 3호 1층
2nd row서울특별시 양천구 신월동 606번지 9호 2층
3rd row서울특별시 양천구 목동 810번지 1호 타운빌딩 2층-201
4th row서울특별시 양천구 신정동 323번지 9호 A 이스타빌-111~111가
5th row서울특별시 양천구 목동 916번지 현대백화점 지하4층
ValueCountFrequency (%)
서울특별시 87
17.1%
양천구 87
17.1%
목동 33
 
6.5%
신정동 29
 
5.7%
신월동 25
 
4.9%
1층 21
 
4.1%
1호 10
 
2.0%
2호 10
 
2.0%
296번지 7
 
1.4%
6호 6
 
1.2%
Other values (137) 193
38.0%
2024-05-04T05:42:53.713638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
623
24.7%
1 118
 
4.7%
지 98
 
3.9%
동 91
 
3.6%
서 88
 
3.5%
시 88
 
3.5%
번 87
 
3.4%
천 87
 
3.4%
구 87
 
3.4%
울 87
 
3.4%
Other values (88) 1070
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1374
54.4%
Space Separator 623
24.7%
Decimal Number 497
 
19.7%
Dash Punctuation 11
 
0.4%
Math Symbol 5
 
0.2%
Other Punctuation 4
 
0.2%
Uppercase Letter 4
 
0.2%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 98
 
7.1%
동 91
 
6.6%
서 88
 
6.4%
시 88
 
6.4%
번 87
 
6.3%
천 87
 
6.3%
구 87
 
6.3%
울 87
 
6.3%
특 87
 
6.3%
별 87
 
6.3%
Other values (70) 487
35.4%
Decimal Number
ValueCountFrequency (%)
1 118
23.7%
2 81
16.3%
9 58
11.7%
0 55
11.1%
4 39
 
7.8%
6 39
 
7.8%
5 32
 
6.4%
7 30
 
6.0%
3 25
 
5.0%
8 20
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
50.0%
A 2
50.0%
Space Separator
ValueCountFrequency (%)
623
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1374
54.4%
Common 1146
45.4%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 98
 
7.1%
동 91
 
6.6%
서 88
 
6.4%
시 88
 
6.4%
번 87
 
6.3%
천 87
 
6.3%
구 87
 
6.3%
울 87
 
6.3%
특 87
 
6.3%
별 87
 
6.3%
Other values (70) 487
35.4%
Common
ValueCountFrequency (%)
623
54.4%
1 118
 
10.3%
2 81
 
7.1%
9 58
 
5.1%
0 55
 
4.8%
4 39
 
3.4%
6 39
 
3.4%
5 32
 
2.8%
7 30
 
2.6%
3 25
 
2.2%
Other values (6) 46
 
4.0%
Latin
ValueCountFrequency (%)
B 2
50.0%
A 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1374
54.4%
ASCII 1150
45.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
623
54.2%
1 118
 
10.3%
2 81
 
7.0%
9 58
 
5.0%
0 55
 
4.8%
4 39
 
3.4%
6 39
 
3.4%
5 32
 
2.8%
7 30
 
2.6%
3 25
 
2.2%
Other values (8) 50
 
4.3%
Hangul
ValueCountFrequency (%)
지 98
 
7.1%
동 91
 
6.6%
서 88
 
6.4%
시 88
 
6.4%
번 87
 
6.3%
천 87
 
6.3%
구 87
 
6.3%
울 87
 
6.3%
특 87
 
6.3%
별 87
 
6.3%
Other values (70) 487
35.4%

허가(신고)번호
Text

Distinct79
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Memory size828.0 B
2024-05-04T05:42:54.336931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1914
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

Unique72 ?
Unique (%)82.8%

Sample

1st row3140000-101-2015-00079
2nd row3140000-101-2001-08743
3rd row3140000-101-2003-00281
4th row3140000-101-2007-00294
5th row3140000-105-2002-00002
ValueCountFrequency (%)
3140000-101-1999-06971 3
 
3.4%
3140000-101-2001-08878 2
 
2.3%
3140000-101-1994-01243 2
 
2.3%
3140000-101-1998-01956 2
 
2.3%
3140000-101-1999-06953 2
 
2.3%
3140000-101-2001-08743 2
 
2.3%
3140000-101-1996-02989 2
 
2.3%
3140000-101-1995-02958 1
 
1.1%
3140000-101-1992-00319 1
 
1.1%
3140000-101-2001-09034 1
 
1.1%
Other values (69) 69
79.3%
2024-05-04T05:42:55.263110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 667
34.8%
1 367
19.2%
- 261
 
13.6%
9 133
 
6.9%
4 121
 
6.3%
3 116
 
6.1%
2 92
 
4.8%
8 48
 
2.5%
7 40
 
2.1%
5 37
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1653
86.4%
Dash Punctuation 261
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 667
40.4%
1 367
22.2%
9 133
 
8.0%
4 121
 
7.3%
3 116
 
7.0%
2 92
 
5.6%
8 48
 
2.9%
7 40
 
2.4%
5 37
 
2.2%
6 32
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1914
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 667
34.8%
1 367
19.2%
- 261
 
13.6%
9 133
 
6.9%
4 121
 
6.3%
3 116
 
6.1%
2 92
 
4.8%
8 48
 
2.5%
7 40
 
2.1%
5 37
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1914
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 667
34.8%
1 367
19.2%
- 261
 
13.6%
9 133
 
6.9%
4 121
 
6.3%
3 116
 
6.1%
2 92
 
4.8%
8 48
 
2.5%
7 40
 
2.1%
5 37
 
1.9%

업태명
Categorical

IMBALANCE 

Distinct9
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size828.0 B
한식
66 
중국식
 
5
기타
 
5
경양식
 
4
일식
 
2
Other values (4)
 
5

Length

Max length8
Median length2
Mean length2.2643678
Min length2

Unique

Unique3 ?
Unique (%)3.4%

Sample

1st row한식
2nd row한식
3rd row한식
4th row한식
5th row산업체

Common Values

ValueCountFrequency (%)
한식 66
75.9%
중국식 5
 
5.7%
기타 5
 
5.7%
경양식 4
 
4.6%
일식 2
 
2.3%
식육(숯불구이) 2
 
2.3%
산업체 1
 
1.1%
뷔페식 1
 
1.1%
분식 1
 
1.1%

Length

2024-05-04T05:42:55.851758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:42:56.199996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 66
75.9%
중국식 5
 
5.7%
기타 5
 
5.7%
경양식 4
 
4.6%
일식 2
 
2.3%
식육(숯불구이 2
 
2.3%
산업체 1
 
1.1%
뷔페식 1
 
1.1%
분식 1
 
1.1%

지정취소사유
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Memory size828.0 B
영업자지위승계
33 
지정기준 부적합
5 
지정기준미달
5 
서울시 위생등급 평가
5 
지정기준에 부적합
5 
Other values (26)
34 

Length

Max length22
Median length21
Mean length8.0229885
Min length2

Unique

Unique20 ?
Unique (%)23.0%

Sample

1st row모범업소 기준 미달
2nd row모범업소 기준 미달
3rd row행정처분
4th row지정거부
5th row지정기준 미달(HACCP 없음)

Common Values

ValueCountFrequency (%)
영업자지위승계 33
37.9%
지정기준 부적합 5
 
5.7%
지정기준미달 5
 
5.7%
서울시 위생등급 평가 5
 
5.7%
지정기준에 부적합 5
 
5.7%
지정거부 3
 
3.4%
업종변경 3
 
3.4%
기준미달 2
 
2.3%
폐업 2
 
2.3%
행정처분 2
 
2.3%
Other values (21) 22
25.3%

Length

2024-05-04T05:42:56.651517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영업자지위승계 33
25.2%
부적합 11
 
8.4%
지정기준 6
 
4.6%
지정기준미달 5
 
3.8%
서울시 5
 
3.8%
위생등급 5
 
3.8%
평가 5
 
3.8%
지정기준에 5
 
3.8%
지정거부 4
 
3.1%
업종변경 3
 
2.3%
Other values (39) 49
37.4%

주된음식
Text

MISSING 

Distinct50
Distinct (%)61.0%
Missing5
Missing (%)5.7%
Memory size828.0 B
2024-05-04T05:42:57.113710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length3.3658537
Min length2

Characters and Unicode

Total characters276
Distinct characters91
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

Unique35 ?
Unique (%)42.7%

Sample

1st row된장찌개
2nd row갈비탕
3rd row한방삼계탕
4th row들깨칼국수
5th row탕수육
ValueCountFrequency (%)
돼지갈비 8
 
9.6%
갈비탕 5
 
6.0%
갈비 4
 
4.8%
삼겹살 4
 
4.8%
칼국수 3
 
3.6%
소금구이 3
 
3.6%
냉면 3
 
3.6%
해장국 3
 
3.6%
바닷가재 2
 
2.4%
스테이크 2
 
2.4%
Other values (40) 46
55.4%
2024-05-04T05:42:57.996123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 22
 
8.0%
갈 20
 
7.2%
탕 15
 
5.4%
지 10
 
3.6%
돼 8
 
2.9%
삼 8
 
2.9%
국 8
 
2.9%
살 7
 
2.5%
장 7
 
2.5%
수 7
 
2.5%
Other values (81) 164
59.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
99.3%
Other Punctuation 1
 
0.4%
Space Separator 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 22
 
8.0%
갈 20
 
7.3%
탕 15
 
5.5%
지 10
 
3.6%
돼 8
 
2.9%
삼 8
 
2.9%
국 8
 
2.9%
살 7
 
2.6%
장 7
 
2.6%
수 7
 
2.6%
Other values (79) 162
59.1%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 274
99.3%
Common 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 22
 
8.0%
갈 20
 
7.3%
탕 15
 
5.5%
지 10
 
3.6%
돼 8
 
2.9%
삼 8
 
2.9%
국 8
 
2.9%
살 7
 
2.6%
장 7
 
2.6%
수 7
 
2.6%
Other values (79) 162
59.1%
Common
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 274
99.3%
ASCII 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 22
 
8.0%
갈 20
 
7.3%
탕 15
 
5.5%
지 10
 
3.6%
돼 8
 
2.9%
삼 8
 
2.9%
국 8
 
2.9%
살 7
 
2.6%
장 7
 
2.6%
수 7
 
2.6%
Other values (79) 162
59.1%
ASCII
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.39632
Minimum36.3
Maximum1080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2024-05-04T05:42:58.435335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.3
5-th percentile65.848
Q186.81
median102
Q3177.5
95-th percentile610.714
Maximum1080
Range1043.7
Interquartile range (IQR)90.69

Descriptive statistics

Standard deviation191.24431
Coefficient of variation (CV)1.0542899
Kurtosis8.1605111
Mean181.39632
Median Absolute Deviation (MAD)28.72
Skewness2.7885425
Sum15781.48
Variance36574.384
MonotonicityNot monotonic
2024-05-04T05:42:58.878700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.81 3
 
3.4%
82.5 2
 
2.3%
795.45 2
 
2.3%
92.4 2
 
2.3%
111.78 2
 
2.3%
297.0 2
 
2.3%
521.38 2
 
2.3%
132.0 2
 
2.3%
198.0 2
 
2.3%
330.0 2
 
2.3%
Other values (65) 66
75.9%
ValueCountFrequency (%)
36.3 1
1.1%
42.04 1
1.1%
45.65 1
1.1%
63.0 1
1.1%
65.74 1
1.1%
66.1 1
1.1%
69.12 1
1.1%
71.27 1
1.1%
71.38 1
1.1%
73.28 1
1.1%
ValueCountFrequency (%)
1080.0 1
1.1%
838.69 1
1.1%
795.45 2
2.3%
649.0 1
1.1%
521.38 2
2.3%
416.0 1
1.1%
375.8 1
1.1%
330.0 2
2.3%
310.57 1
1.1%
297.0 2
2.3%

행정동명
Categorical

Distinct17
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size828.0 B
목1동
17 
신정4동
13 
신정2동
11 
신월5동
9 
목4동
7 
Other values (12)
30 

Length

Max length4
Median length4
Mean length3.6206897
Min length3

Unique

Unique3 ?
Unique (%)3.4%

Sample

1st row신월5동
2nd row신월2동
3rd row목1동
4th row신정7동
5th row목1동

Common Values

ValueCountFrequency (%)
목1동 17
19.5%
신정4동 13
14.9%
신정2동 11
12.6%
신월5동 9
10.3%
목4동 7
8.0%
신월2동 4
 
4.6%
목3동 3
 
3.4%
신월1동 3
 
3.4%
신월6동 3
 
3.4%
신정7동 3
 
3.4%
Other values (7) 14
16.1%

Length

2024-05-04T05:42:59.290453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목1동 17
19.5%
신정4동 13
14.9%
신정2동 11
12.6%
신월5동 9
10.3%
목4동 7
8.0%
신월2동 4
 
4.6%
신월4동 3
 
3.4%
목2동 3
 
3.4%
목5동 3
 
3.4%
신월6동 3
 
3.4%
Other values (7) 14
16.1%

급수시설구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size828.0 B
상수도전용
72 
<NA>
14 
간이상수도
 
1

Length

Max length5
Median length5
Mean length4.8390805
Min length4

Unique

Unique1 ?
Unique (%)1.1%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 72
82.8%
<NA> 14
 
16.1%
간이상수도 1
 
1.1%

Length

2024-05-04T05:42:59.663594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:42:59.984474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 72
82.8%
na 14
 
16.1%
간이상수도 1
 
1.1%

Interactions

2024-05-04T05:42:41.565232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:32.604236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:34.178534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:36.429883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:38.232800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:39.881634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:41.814824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:32.815572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:34.562952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:36.762140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:38.512506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:40.148026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:42.075792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:33.104013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:35.127657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:37.071767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:38.817760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:40.436921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:42.348842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:33.369491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:35.519771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:37.360458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:39.103518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:40.707993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:42.602720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:33.637194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:35.814476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:37.695289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:39.368567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:40.974645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:42.869724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:33.947650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:36.137188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:37.987996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:39.630896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T05:42:41.306765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T05:43:00.239622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
지정년도1.0000.4281.0001.0000.8490.5100.9320.9320.9320.5500.9170.7970.4630.4990.000
지정번호0.4281.0000.4280.4280.0000.9230.9100.9100.9100.0000.4450.1010.4510.6460.000
신청일자1.0000.4281.0001.0000.8490.5100.9320.9320.9320.5500.9170.7970.4630.4990.000
지정일자1.0000.4281.0001.0000.8490.5100.9320.9320.9320.5500.9170.7970.4630.4990.000
취소일자0.8490.0000.8490.8491.0000.8650.8810.8810.8810.3240.9930.3800.3240.5550.000
업소명0.5100.9230.5100.5100.8651.0001.0001.0001.0000.9700.9820.9980.9960.9941.000
소재지도로명0.9320.9100.9320.9320.8811.0001.0001.0001.0001.0000.9790.9981.0001.0001.000
소재지지번0.9320.9100.9320.9320.8811.0001.0001.0001.0001.0000.9790.9981.0001.0001.000
허가(신고)번호0.9320.9100.9320.9320.8811.0001.0001.0001.0001.0000.9790.9981.0001.0001.000
업태명0.5500.0000.5500.5500.3240.9701.0001.0001.0001.0000.7710.9570.7520.0000.000
지정취소사유0.9170.4450.9170.9170.9930.9820.9790.9790.9790.7711.0000.8050.8820.6970.000
주된음식0.7970.1010.7970.7970.3800.9980.9980.9980.9980.9570.8051.0000.6620.6560.000
영업장면적(㎡)0.4630.4510.4630.4630.3240.9961.0001.0001.0000.7520.8820.6621.0000.0000.000
행정동명0.4990.6460.4990.4990.5550.9941.0001.0001.0000.0000.6970.6560.0001.0000.000
급수시설구분0.0000.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.0001.000
2024-05-04T05:43:00.669368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명행정동명급수시설구분지정취소사유
업태명1.0000.0000.0000.349
행정동명0.0001.0000.0000.233
급수시설구분0.0000.0001.0000.000
지정취소사유0.3490.2330.0001.000
2024-05-04T05:43:01.046354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명지정취소사유행정동명급수시설구분
지정년도1.000-0.4391.0001.0000.5450.1860.2740.5440.1630.000
지정번호-0.4391.000-0.435-0.439-0.251-0.0170.0000.1170.2980.000
신청일자1.000-0.4351.0001.0000.5450.1830.2740.5440.1630.000
지정일자1.000-0.4391.0001.0000.5450.1850.2740.5440.1630.000
취소일자0.545-0.2510.5450.5451.0000.1650.1100.7390.2410.000
영업장면적(㎡)0.186-0.0170.1830.1850.1651.0000.4920.5070.0000.000
업태명0.2740.0000.2740.2740.1100.4921.0000.3490.0000.000
지정취소사유0.5440.1170.5440.5440.7390.5070.3491.0000.2330.000
행정동명0.1630.2980.1630.1630.2410.0000.0000.2331.0000.000
급수시설구분0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-05-04T05:42:43.248274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T05:42:43.908166image/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

시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
03140000201712201710312017112920231122최고야식당서울특별시 양천구 화곡로4길 12, 1층 (신월동)서울특별시 양천구 신월동 52번지 3호 1층3140000-101-2015-00079한식모범업소 기준 미달된장찌개82.5신월5동<NA>
1314000020179201710312017112920231122은성가든서울특별시 양천구 신월로 210, 2층 (신월동)서울특별시 양천구 신월동 606번지 9호 2층3140000-101-2001-08743한식모범업소 기준 미달갈비탕330.0신월2동상수도전용
231400002004131200405312004071920230214가랑삼계탕서울특별시 양천구 목동로 210, 타운빌딩 2층 201호 (목동)서울특별시 양천구 목동 810번지 1호 타운빌딩 2층-2013140000-101-2003-00281한식행정처분한방삼계탕158.4목1동상수도전용
3314000020139201310012013120920221130두성전주콩나물국밥 양천구청점서울특별시 양천구 목동서로 377, 이스타빌 A동 1층 111~111가호 (신정동)서울특별시 양천구 신정동 323번지 9호 A 이스타빌-111~111가3140000-101-2007-00294한식지정거부들깨칼국수103.24신정7동<NA>
4314000020071200706012007092120221130한무쇼핑(주)목동점서울특별시 양천구 목동동로 257, (목동, 현대백화점 지하4층)서울특별시 양천구 목동 916번지 현대백화점 지하4층3140000-105-2002-00002산업체지정기준 미달(HACCP 없음)<NA>1080.0목1동상수도전용
531400002018114201811012018113020221130정짜장서울특별시 양천구 공항대로 634, 지상2층 (목동)서울특별시 양천구 목동 515번지 지상2층3140000-101-2017-00228중국식지정거부탕수육90.0목2동<NA>
6314000020111201109082011103120211206엉터리생고기 이대목동점서울특별시 양천구 목동동로 423, (목동, 이화프라자2층201,202호)서울특별시 양천구 목동 909번지 8호 이화프라자2층201,202호3140000-101-1996-04758한식폐업등심112.84목5동상수도전용
7314000020096200908202009091620211206개성손만두서울특별시 양천구 목동중앙로 101, (목동)서울특별시 양천구 목동 752번지 8호3140000-101-2007-00006한식지위승계 후 취소소갈비118.8목2동상수도전용
8314000020125201210022012103120201221억조1978서울특별시 양천구 목동동로10길 24, 2~3층 (신정동)서울특별시 양천구 신정동 296번지 24호3140000-101-1998-01956한식점검불가갈비795.45신정2동상수도전용
931400002018117201810152018112920201221애슐리퀸즈목동행복한백화점서울특별시 양천구 목동동로 309, 중소기업유통센터(행복한백화점) 5층 (목동)서울특별시 양천구 목동 917번지 6호 중소기업유통센터(행복한백화점)3140000-101-2011-00245뷔페식지정거부, 위생등급제 실시셀러드, 스테이크649.0목1동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
773140000200323200306022003080120031117은성가든서울특별시 양천구 신월로 210, 2층 (신월동)서울특별시 양천구 신월동 606번지 9호 2층3140000-101-2001-08743한식영업자지위승계갈비330.0신월2동상수도전용
783140000200155200106012001070320031104린궁즈서울특별시 양천구 목동동로8길 3, 2층 (신정동)서울특별시 양천구 신정동 294번지 23호 2층3140000-101-1999-07175중국식영업자지위승계생삼겹살131.97신정2동상수도전용
793140000200253200204022002050120031020엉터리해장국 신월5동점서울특별시 양천구 가로공원로 119, 1층 (신월동)서울특별시 양천구 신월동 81번지 21호 1층3140000-101-2000-08134한식영업자지위승계소갈비181.8신월5동상수도전용
803140000200223200204022002050120031007황토골서울특별시 양천구 가로공원로 115, (신월동,1,2층)서울특별시 양천구 신월동 50번지 3호 1,2층3140000-101-1996-02989한식영업자지위승계해장국94.85신월5동상수도전용
813140000200326200306022003080120030805곰배령감자탕서울특별시 양천구 신월로15길 15, (신월동)서울특별시 양천구 신월동 529번지 2호3140000-101-2002-00161한식영업자지위승계꼼장어36.3신월4동상수도전용
8231400002001137200106012001070320030801곰달래감자탕서울특별시 양천구 남부순환로 368, 1층 (신월동)서울특별시 양천구 신월동 166번지 10호 1층3140000-101-1987-00819한식기준미달돼지갈비156.48신월3동상수도전용
8331400002001132200106012001070320030801목련나무집서울특별시 양천구 오목로11길 5-6, 가동 (신월동)서울특별시 양천구 신월동 489번지 11호 가3140000-101-1997-01808한식기준미달삼계탕71.38신월2동상수도전용
8431400002001162200106012001070320030612콩사랑전주콩나물국밥서울특별시 양천구 가로공원로 128, (신월동, 1층)서울특별시 양천구 신월동 96번지 1호 1층3140000-101-1999-06952한식영업자지위승계갈비탕83.2신월1동상수도전용
853140000200181200106012001070320030424원두막서울특별시 양천구 신목로 56, 2층 (신정동)서울특별시 양천구 신정동 296번지 77호 2층3140000-101-1999-06953한식영업자지위승계냉면521.38신정2동상수도전용
8631400002001105200106012001070320030404육쌈냉면서울특별시 양천구 신정중앙로 107, (신정동)서울특별시 양천구 신정동 900번지 13호3140000-101-1990-04088분식영업자지위승계막국수86.49신정4동상수도전용