Overview

Dataset statistics

Number of variables16
Number of observations98
Missing cells33
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 KiB
Average record size in memory136.3 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
Author강북구
URLhttps://data.seoul.go.kr/dataList/OA-10928/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 8 other fieldsHigh correlation
업태명 is highly overall correlated with 급수시설구분High correlation
지정취소사유 is highly overall correlated with 급수시설구분High correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
지정년도 is highly overall correlated with 신청일자 and 2 other fieldsHigh correlation
지정번호 is highly overall correlated with 급수시설구분High correlation
신청일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
취소일자 is highly overall correlated with 급수시설구분High correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
업태명 is highly imbalanced (62.3%)Imbalance
주된음식 has 33 (33.7%) missing valuesMissing
영업장면적(㎡) has 2 (2.0%) zerosZeros

Reproduction

Analysis started2024-05-11 05:35:15.082433
Analysis finished2024-05-11 05:35:24.140230
Duration9.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
3080000
98 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3080000
2nd row3080000
3rd row3080000
4th row3080000
5th row3080000

Common Values

ValueCountFrequency (%)
3080000 98
100.0%

Length

2024-05-11T14:35:24.246461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:24.380669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3080000 98
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.2653
Minimum2006
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-05-11T14:35:24.516142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2006
Q12006
median2007.5
Q32009
95-th percentile2014.15
Maximum2021
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.0647683
Coefficient of variation (CV)0.0015260774
Kurtosis3.9358957
Mean2008.2653
Median Absolute Deviation (MAD)1.5
Skewness1.8604724
Sum196810
Variance9.3928045
MonotonicityNot monotonic
2024-05-11T14:35:24.682917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2006 46
46.9%
2009 13
 
13.3%
2008 13
 
13.3%
2010 6
 
6.1%
2011 5
 
5.1%
2012 4
 
4.1%
2007 3
 
3.1%
2018 2
 
2.0%
2013 2
 
2.0%
2015 1
 
1.0%
Other values (3) 3
 
3.1%
ValueCountFrequency (%)
2006 46
46.9%
2007 3
 
3.1%
2008 13
 
13.3%
2009 13
 
13.3%
2010 6
 
6.1%
2011 5
 
5.1%
2012 4
 
4.1%
2013 2
 
2.0%
2014 1
 
1.0%
2015 1
 
1.0%
ValueCountFrequency (%)
2021 1
 
1.0%
2018 2
 
2.0%
2017 1
 
1.0%
2015 1
 
1.0%
2014 1
 
1.0%
2013 2
 
2.0%
2012 4
 
4.1%
2011 5
 
5.1%
2010 6
6.1%
2009 13
13.3%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.65306
Minimum1
Maximum1116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-05-11T14:35:24.884259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.85
Q112
median49.5
Q3136
95-th percentile333.9
Maximum1116
Range1115
Interquartile range (IQR)124

Descriptive statistics

Standard deviation238.61806
Coefficient of variation (CV)1.9777207
Kurtosis13.173204
Mean120.65306
Median Absolute Deviation (MAD)44.5
Skewness3.7130192
Sum11824
Variance56938.579
MonotonicityNot monotonic
2024-05-11T14:35:25.085869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 4
 
4.1%
1 4
 
4.1%
5 3
 
3.1%
12 3
 
3.1%
6 3
 
3.1%
7 3
 
3.1%
4 3
 
3.1%
13 2
 
2.0%
10 2
 
2.0%
30 2
 
2.0%
Other values (66) 69
70.4%
ValueCountFrequency (%)
1 4
4.1%
2 1
 
1.0%
3 4
4.1%
4 3
3.1%
5 3
3.1%
6 3
3.1%
7 3
3.1%
9 1
 
1.0%
10 2
2.0%
12 3
3.1%
ValueCountFrequency (%)
1116 1
1.0%
1112 1
1.0%
1111 1
1.0%
1110 1
1.0%
1104 1
1.0%
198 1
1.0%
196 1
1.0%
195 1
1.0%
193 1
1.0%
192 1
1.0%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20083012
Minimum20060627
Maximum20201005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-05-11T14:35:25.327037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060627
5-th percentile20060627
Q120060627
median20075616
Q320090910
95-th percentile20142618
Maximum20201005
Range140378
Interquartile range (IQR)30283

Descriptive statistics

Standard deviation29467.206
Coefficient of variation (CV)0.0014672702
Kurtosis2.9964027
Mean20083012
Median Absolute Deviation (MAD)14989
Skewness1.6739326
Sum1.9681352 × 109
Variance8.6831623 × 108
MonotonicityNot monotonic
2024-05-11T14:35:25.509485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20060627 46
46.9%
20090910 13
 
13.3%
20080701 13
 
13.3%
20100930 6
 
6.1%
20111020 4
 
4.1%
20121101 4
 
4.1%
20070530 2
 
2.0%
20171115 2
 
2.0%
20131129 2
 
2.0%
20151118 1
 
1.0%
Other values (5) 5
 
5.1%
ValueCountFrequency (%)
20060627 46
46.9%
20070530 2
 
2.0%
20070531 1
 
1.0%
20080701 13
 
13.3%
20090910 13
 
13.3%
20100930 6
 
6.1%
20111020 4
 
4.1%
20111028 1
 
1.0%
20121101 4
 
4.1%
20131129 2
 
2.0%
ValueCountFrequency (%)
20201005 1
 
1.0%
20171115 2
 
2.0%
20161018 1
 
1.0%
20151118 1
 
1.0%
20141118 1
 
1.0%
20131129 2
 
2.0%
20121101 4
4.1%
20111028 1
 
1.0%
20111020 4
4.1%
20100930 6
6.1%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20083462
Minimum20060701
Maximum20210106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-05-11T14:35:25.704257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060701
5-th percentile20060701
Q120060701
median20075807
Q320090910
95-th percentile20142717
Maximum20210106
Range149405
Interquartile range (IQR)30209

Descriptive statistics

Standard deviation30659.49
Coefficient of variation (CV)0.0015266038
Kurtosis3.7894059
Mean20083462
Median Absolute Deviation (MAD)15106
Skewness1.831975
Sum1.9681793 × 109
Variance9.4000433 × 108
MonotonicityNot monotonic
2024-05-11T14:35:25.875223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
20060701 46
46.9%
20090910 13
 
13.3%
20080908 13
 
13.3%
20101008 6
 
6.1%
20111028 5
 
5.1%
20121204 4
 
4.1%
20070706 3
 
3.1%
20180102 2
 
2.0%
20131217 2
 
2.0%
20151214 1
 
1.0%
Other values (3) 3
 
3.1%
ValueCountFrequency (%)
20060701 46
46.9%
20070706 3
 
3.1%
20080908 13
 
13.3%
20090910 13
 
13.3%
20101008 6
 
6.1%
20111028 5
 
5.1%
20121204 4
 
4.1%
20131217 2
 
2.0%
20141217 1
 
1.0%
20151214 1
 
1.0%
ValueCountFrequency (%)
20210106 1
 
1.0%
20180102 2
 
2.0%
20170103 1
 
1.0%
20151214 1
 
1.0%
20141217 1
 
1.0%
20131217 2
 
2.0%
20121204 4
 
4.1%
20111028 5
 
5.1%
20101008 6
6.1%
20090910 13
13.3%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20189589
Minimum20060701
Maximum20221031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-05-11T14:35:26.076421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060701
5-th percentile20091082
Q120183446
median20210923
Q320210923
95-th percentile20221031
Maximum20221031
Range160330
Interquartile range (IQR)27476.5

Descriptive statistics

Standard deviation44366.877
Coefficient of variation (CV)0.0021975127
Kurtosis1.6799298
Mean20189589
Median Absolute Deviation (MAD)0
Skewness-1.7797315
Sum1.9785797 × 109
Variance1.9684198 × 109
MonotonicityNot monotonic
2024-05-11T14:35:26.298649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20210923 60
61.2%
20221031 9
 
9.2%
20181221 6
 
6.1%
20101008 2
 
2.0%
20091112 2
 
2.0%
20060701 2
 
2.0%
20211224 2
 
2.0%
20071001 2
 
2.0%
20110429 1
 
1.0%
20160826 1
 
1.0%
Other values (11) 11
 
11.2%
ValueCountFrequency (%)
20060701 2
2.0%
20071001 2
2.0%
20090910 1
1.0%
20091112 2
2.0%
20100208 1
1.0%
20100603 1
1.0%
20101008 2
2.0%
20101119 1
1.0%
20110429 1
1.0%
20111116 1
1.0%
ValueCountFrequency (%)
20221031 9
 
9.2%
20211224 2
 
2.0%
20210929 1
 
1.0%
20210923 60
61.2%
20190123 1
 
1.0%
20181221 6
 
6.1%
20180514 1
 
1.0%
20180101 1
 
1.0%
20160826 1
 
1.0%
20120425 1
 
1.0%

업소명
Text

Distinct87
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size916.0 B
2024-05-11T14:35:26.866209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length6.7244898
Min length1

Characters and Unicode

Total characters659
Distinct characters211
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)79.6%

Sample

1st row전주 선지 순대 추어탕
2nd row번동숯불갈비
3rd row신고집찜 칼국수
4th row목포해물탕
5th row남원정통추어탕
ValueCountFrequency (%)
수유 3
 
2.0%
숯불닭갈비 3
 
2.0%
공릉동 3
 
2.0%
닭한마리 3
 
2.0%
수유역점 3
 
2.0%
감탄 3
 
2.0%
수유점 3
 
2.0%
박승광해물손칼국수 2
 
1.3%
추어탕 2
 
1.3%
전주 2
 
1.3%
Other values (112) 122
81.9%
2024-05-11T14:35:27.664092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51
 
7.7%
수 18
 
2.7%
점 15
 
2.3%
유 13
 
2.0%
마 11
 
1.7%
탕 11
 
1.7%
어 10
 
1.5%
갈 10
 
1.5%
비 10
 
1.5%
산 10
 
1.5%
Other values (201) 500
75.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 590
89.5%
Space Separator 51
 
7.7%
Decimal Number 7
 
1.1%
Close Punctuation 4
 
0.6%
Open Punctuation 4
 
0.6%
Other Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 18
 
3.1%
점 15
 
2.5%
유 13
 
2.2%
마 11
 
1.9%
탕 11
 
1.9%
어 10
 
1.7%
갈 10
 
1.7%
비 10
 
1.7%
산 10
 
1.7%
리 10
 
1.7%
Other values (192) 472
80.0%
Decimal Number
ValueCountFrequency (%)
0 2
28.6%
4 2
28.6%
1 2
28.6%
9 1
14.3%
Other Punctuation
ValueCountFrequency (%)
! 2
66.7%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
51
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 587
89.1%
Common 69
 
10.5%
Katakana 2
 
0.3%
Han 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 18
 
3.1%
점 15
 
2.6%
유 13
 
2.2%
마 11
 
1.9%
탕 11
 
1.9%
어 10
 
1.7%
갈 10
 
1.7%
비 10
 
1.7%
산 10
 
1.7%
리 10
 
1.7%
Other values (189) 469
79.9%
Common
ValueCountFrequency (%)
51
73.9%
) 4
 
5.8%
( 4
 
5.8%
0 2
 
2.9%
! 2
 
2.9%
4 2
 
2.9%
1 2
 
2.9%
9 1
 
1.4%
. 1
 
1.4%
Katakana
ValueCountFrequency (%)
タ 1
50.0%
ネ 1
50.0%
Han
ValueCountFrequency (%)
樂 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 587
89.1%
ASCII 69
 
10.5%
Katakana 2
 
0.3%
CJK Compat Ideographs 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51
73.9%
) 4
 
5.8%
( 4
 
5.8%
0 2
 
2.9%
! 2
 
2.9%
4 2
 
2.9%
1 2
 
2.9%
9 1
 
1.4%
. 1
 
1.4%
Hangul
ValueCountFrequency (%)
수 18
 
3.1%
점 15
 
2.6%
유 13
 
2.2%
마 11
 
1.9%
탕 11
 
1.9%
어 10
 
1.7%
갈 10
 
1.7%
비 10
 
1.7%
산 10
 
1.7%
리 10
 
1.7%
Other values (189) 469
79.9%
Katakana
ValueCountFrequency (%)
タ 1
50.0%
ネ 1
50.0%
CJK Compat Ideographs
ValueCountFrequency (%)
樂 1
100.0%

소재지도로명
Text

Distinct87
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size916.0 B
2024-05-11T14:35:28.230308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length39
Mean length29.510204
Min length23

Characters and Unicode

Total characters2892
Distinct characters95
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

Unique77 ?
Unique (%)78.6%

Sample

1st row서울특별시 강북구 오현로31길 173, (번동,(벌리길 85)(지상1층))
2nd row서울특별시 강북구 오현로32길 4-8, (번동)
3rd row서울특별시 강북구 한천로109길 72, 세종빌딩 2층 (번동)
4th row서울특별시 강북구 한천로140길 7, (수유동,(지상1층))
5th row서울특별시 강북구 삼양로 181, (미아동)
ValueCountFrequency (%)
서울특별시 98
18.5%
강북구 98
18.5%
수유동 37
 
7.0%
미아동 12
 
2.3%
1층 12
 
2.3%
번동 11
 
2.1%
한천로140길 8
 
1.5%
덕릉로 8
 
1.5%
4.19로 8
 
1.5%
5 7
 
1.3%
Other values (148) 231
43.6%
2024-05-11T14:35:28.960080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
432
 
14.9%
) 134
 
4.6%
( 134
 
4.6%
, 131
 
4.5%
1 130
 
4.5%
로 100
 
3.5%
울 100
 
3.5%
시 99
 
3.4%
동 99
 
3.4%
북 99
 
3.4%
Other values (85) 1434
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1575
54.5%
Decimal Number 464
 
16.0%
Space Separator 432
 
14.9%
Other Punctuation 143
 
4.9%
Close Punctuation 134
 
4.6%
Open Punctuation 134
 
4.6%
Dash Punctuation 9
 
0.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 100
 
6.3%
울 100
 
6.3%
시 99
 
6.3%
동 99
 
6.3%
북 99
 
6.3%
서 98
 
6.2%
별 98
 
6.2%
특 98
 
6.2%
구 98
 
6.2%
강 98
 
6.2%
Other values (68) 588
37.3%
Decimal Number
ValueCountFrequency (%)
1 130
28.0%
2 53
11.4%
4 45
 
9.7%
3 44
 
9.5%
9 42
 
9.1%
0 35
 
7.5%
7 32
 
6.9%
8 29
 
6.2%
6 27
 
5.8%
5 27
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 131
91.6%
. 12
 
8.4%
Space Separator
ValueCountFrequency (%)
432
100.0%
Close Punctuation
ValueCountFrequency (%)
) 134
100.0%
Open Punctuation
ValueCountFrequency (%)
( 134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1575
54.5%
Common 1316
45.5%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 100
 
6.3%
울 100
 
6.3%
시 99
 
6.3%
동 99
 
6.3%
북 99
 
6.3%
서 98
 
6.2%
별 98
 
6.2%
특 98
 
6.2%
구 98
 
6.2%
강 98
 
6.2%
Other values (68) 588
37.3%
Common
ValueCountFrequency (%)
432
32.8%
) 134
 
10.2%
( 134
 
10.2%
, 131
 
10.0%
1 130
 
9.9%
2 53
 
4.0%
4 45
 
3.4%
3 44
 
3.3%
9 42
 
3.2%
0 35
 
2.7%
Other values (6) 136
 
10.3%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1575
54.5%
ASCII 1317
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
432
32.8%
) 134
 
10.2%
( 134
 
10.2%
, 131
 
9.9%
1 130
 
9.9%
2 53
 
4.0%
4 45
 
3.4%
3 44
 
3.3%
9 42
 
3.2%
0 35
 
2.7%
Other values (7) 137
 
10.4%
Hangul
ValueCountFrequency (%)
로 100
 
6.3%
울 100
 
6.3%
시 99
 
6.3%
동 99
 
6.3%
북 99
 
6.3%
서 98
 
6.2%
별 98
 
6.2%
특 98
 
6.2%
구 98
 
6.2%
강 98
 
6.2%
Other values (68) 588
37.3%

소재지지번
Text

Distinct87
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size916.0 B
2024-05-11T14:35:29.570462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length38
Mean length29.377551
Min length22

Characters and Unicode

Total characters2879
Distinct characters83
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

Unique77 ?
Unique (%)78.6%

Sample

1st row서울특별시 강북구 번동 433번지 54호 (벌리길 85)(지상1층)
2nd row서울특별시 강북구 번동 306번지 38호
3rd row서울특별시 강북구 번동 168번지 6호 세종빌딩 2층
4th row서울특별시 강북구 수유동 177번지 20호 (지상1층)
5th row서울특별시 강북구 미아동 703번지 31호
ValueCountFrequency (%)
서울특별시 98
17.4%
강북구 98
17.4%
수유동 42
 
7.5%
미아동 28
 
5.0%
번동 20
 
3.6%
지상1층 16
 
2.8%
1호 13
 
2.3%
우이동 8
 
1.4%
5호 6
 
1.1%
1층 6
 
1.1%
Other values (155) 227
40.4%
2024-05-11T14:35:30.494246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
708
24.6%
1 140
 
4.9%
지 127
 
4.4%
번 118
 
4.1%
울 100
 
3.5%
구 100
 
3.5%
시 99
 
3.4%
북 99
 
3.4%
동 98
 
3.4%
서 98
 
3.4%
Other values (73) 1192
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1541
53.5%
Space Separator 708
24.6%
Decimal Number 535
 
18.6%
Open Punctuation 45
 
1.6%
Close Punctuation 45
 
1.6%
Other Punctuation 4
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 127
 
8.2%
번 118
 
7.7%
울 100
 
6.5%
구 100
 
6.5%
시 99
 
6.4%
북 99
 
6.4%
동 98
 
6.4%
서 98
 
6.4%
강 98
 
6.4%
별 98
 
6.4%
Other values (57) 506
32.8%
Decimal Number
ValueCountFrequency (%)
1 140
26.2%
4 62
11.6%
2 57
10.7%
5 49
 
9.2%
7 45
 
8.4%
3 43
 
8.0%
6 42
 
7.9%
8 37
 
6.9%
9 31
 
5.8%
0 29
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
, 1
 
25.0%
Space Separator
ValueCountFrequency (%)
708
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1541
53.5%
Common 1338
46.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 127
 
8.2%
번 118
 
7.7%
울 100
 
6.5%
구 100
 
6.5%
시 99
 
6.4%
북 99
 
6.4%
동 98
 
6.4%
서 98
 
6.4%
강 98
 
6.4%
별 98
 
6.4%
Other values (57) 506
32.8%
Common
ValueCountFrequency (%)
708
52.9%
1 140
 
10.5%
4 62
 
4.6%
2 57
 
4.3%
5 49
 
3.7%
( 45
 
3.4%
) 45
 
3.4%
7 45
 
3.4%
3 43
 
3.2%
6 42
 
3.1%
Other values (6) 102
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1541
53.5%
ASCII 1338
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
708
52.9%
1 140
 
10.5%
4 62
 
4.6%
2 57
 
4.3%
5 49
 
3.7%
( 45
 
3.4%
) 45
 
3.4%
7 45
 
3.4%
3 43
 
3.2%
6 42
 
3.1%
Other values (6) 102
 
7.6%
Hangul
ValueCountFrequency (%)
지 127
 
8.2%
번 118
 
7.7%
울 100
 
6.5%
구 100
 
6.5%
시 99
 
6.4%
북 99
 
6.4%
동 98
 
6.4%
서 98
 
6.4%
강 98
 
6.4%
별 98
 
6.4%
Other values (57) 506
32.8%

허가(신고)번호
Text

Distinct88
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size916.0 B
2024-05-11T14:35:30.864361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2156
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

Unique79 ?
Unique (%)80.6%

Sample

1st row3080000-101-1992-01042
2nd row3080000-101-2001-08455
3rd row3080000-101-2003-00055
4th row3080000-101-2005-00297
5th row3080000-101-2003-00406
ValueCountFrequency (%)
3080000-101-1992-04582 3
 
3.1%
3080000-101-1986-00344 2
 
2.0%
3080000-101-1996-06127 2
 
2.0%
3080000-101-2000-08078 2
 
2.0%
3080000-101-1999-07682 2
 
2.0%
3080000-101-2003-00005 2
 
2.0%
3080000-101-1995-06581 2
 
2.0%
3080000-101-2004-00263 2
 
2.0%
3080000-101-1998-04508 2
 
2.0%
3080000-101-2009-00161 1
 
1.0%
Other values (78) 78
79.6%
2024-05-11T14:35:31.477863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 868
40.3%
1 302
 
14.0%
- 294
 
13.6%
8 158
 
7.3%
3 135
 
6.3%
9 113
 
5.2%
2 86
 
4.0%
4 57
 
2.6%
5 56
 
2.6%
6 48
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1862
86.4%
Dash Punctuation 294
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 868
46.6%
1 302
 
16.2%
8 158
 
8.5%
3 135
 
7.3%
9 113
 
6.1%
2 86
 
4.6%
4 57
 
3.1%
5 56
 
3.0%
6 48
 
2.6%
7 39
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 294
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2156
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 868
40.3%
1 302
 
14.0%
- 294
 
13.6%
8 158
 
7.3%
3 135
 
6.3%
9 113
 
5.2%
2 86
 
4.0%
4 57
 
2.6%
5 56
 
2.6%
6 48
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2156
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 868
40.3%
1 302
 
14.0%
- 294
 
13.6%
8 158
 
7.3%
3 135
 
6.3%
9 113
 
5.2%
2 86
 
4.0%
4 57
 
2.6%
5 56
 
2.6%
6 48
 
2.2%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
한식
81 
일식
 
6
중국식
 
5
탕류(보신용)
 
2
기타
 
2
Other values (2)
 
2

Length

Max length10
Median length2
Mean length2.2653061
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row한식
2nd row한식
3rd row한식
4th row탕류(보신용)
5th row한식

Common Values

ValueCountFrequency (%)
한식 81
82.7%
일식 6
 
6.1%
중국식 5
 
5.1%
탕류(보신용) 2
 
2.0%
기타 2
 
2.0%
정종/대포집/소주방 1
 
1.0%
호프/통닭 1
 
1.0%

Length

2024-05-11T14:35:31.758398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:31.995029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 81
82.7%
일식 6
 
6.1%
중국식 5
 
5.1%
탕류(보신용 2
 
2.0%
기타 2
 
2.0%
정종/대포집/소주방 1
 
1.0%
호프/통닭 1
 
1.0%

지정취소사유
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size916.0 B
모범음식점 현황에 해당없음 확인
48 
<NA>
11 
행정처분
10 
중복
7 
모범음식점 현황에 해당없음
5 
Other values (12)
17 

Length

Max length17
Median length14
Mean length11.163265
Min length2

Unique

Unique9 ?
Unique (%)9.2%

Sample

1st row<NA>
2nd row모범음식점 현황에 해당없음 확인
3rd row모범음식점 현황에 해당없음 확인
4th row모범음식점 현황에 해당없음 확인
5th row모범음식점 현황에 해당없음 확인

Common Values

ValueCountFrequency (%)
모범음식점 현황에 해당없음 확인 48
49.0%
<NA> 11
 
11.2%
행정처분 10
 
10.2%
중복 7
 
7.1%
모범음식점 현황에 해당없음 5
 
5.1%
영업하고 있지 않음 3
 
3.1%
점수미달 3
 
3.1%
착오등록 2
 
2.0%
재지정 거부 1
 
1.0%
취소 5년경과 1
 
1.0%
Other values (7) 7
 
7.1%

Length

2024-05-11T14:35:32.335951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
모범음식점 53
20.0%
현황에 53
20.0%
해당없음 53
20.0%
확인 48
18.1%
na 11
 
4.2%
행정처분 10
 
3.8%
중복 7
 
2.6%
영업하고 3
 
1.1%
있지 3
 
1.1%
않음 3
 
1.1%
Other values (16) 21
 
7.9%

주된음식
Text

MISSING 

Distinct48
Distinct (%)73.8%
Missing33
Missing (%)33.7%
Memory size916.0 B
2024-05-11T14:35:32.666748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.5230769
Min length2

Characters and Unicode

Total characters229
Distinct characters79
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

Unique41 ?
Unique (%)63.1%

Sample

1st row삼겹살
2nd row해물찜
3rd row삼겹살
4th row냉면, 돈가스
5th row삼겹살
ValueCountFrequency (%)
돼지갈비 9
 
13.4%
삼겹살 4
 
6.0%
한정식 3
 
4.5%
보쌈 3
 
4.5%
장어구이 2
 
3.0%
칼국수 2
 
3.0%
냉면 2
 
3.0%
자장면 2
 
3.0%
해물탕 2
 
3.0%
버섯매운탕칼국수 1
 
1.5%
Other values (37) 37
55.2%
2024-05-11T14:35:33.335216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
갈 13
 
5.7%
비 13
 
5.7%
돼 10
 
4.4%
지 10
 
4.4%
탕 9
 
3.9%
국 9
 
3.9%
장 7
 
3.1%
살 6
 
2.6%
정 6
 
2.6%
식 6
 
2.6%
Other values (69) 140
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 225
98.3%
Space Separator 2
 
0.9%
Other Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
갈 13
 
5.8%
비 13
 
5.8%
돼 10
 
4.4%
지 10
 
4.4%
탕 9
 
4.0%
국 9
 
4.0%
장 7
 
3.1%
살 6
 
2.7%
정 6
 
2.7%
식 6
 
2.7%
Other values (67) 136
60.4%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 225
98.3%
Common 4
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
갈 13
 
5.8%
비 13
 
5.8%
돼 10
 
4.4%
지 10
 
4.4%
탕 9
 
4.0%
국 9
 
4.0%
장 7
 
3.1%
살 6
 
2.7%
정 6
 
2.7%
식 6
 
2.7%
Other values (67) 136
60.4%
Common
ValueCountFrequency (%)
2
50.0%
, 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 225
98.3%
ASCII 4
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
갈 13
 
5.8%
비 13
 
5.8%
돼 10
 
4.4%
지 10
 
4.4%
탕 9
 
4.0%
국 9
 
4.0%
장 7
 
3.1%
살 6
 
2.7%
정 6
 
2.7%
식 6
 
2.7%
Other values (67) 136
60.4%
ASCII
ValueCountFrequency (%)
2
50.0%
, 2
50.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.36612
Minimum0
Maximum420.81
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2024-05-11T14:35:33.604455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.498
Q172.7075
median92.675
Q3115.455
95-th percentile229.318
Maximum420.81
Range420.81
Interquartile range (IQR)42.7475

Descriptive statistics

Standard deviation67.696733
Coefficient of variation (CV)0.61338327
Kurtosis6.3889442
Mean110.36612
Median Absolute Deviation (MAD)22.775
Skewness2.2247793
Sum10815.88
Variance4582.8477
MonotonicityNot monotonic
2024-05-11T14:35:33.896158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.07 3
 
3.1%
0.0 2
 
2.0%
77.04 2
 
2.0%
82.5 2
 
2.0%
94.45 2
 
2.0%
101.29 2
 
2.0%
112.62 2
 
2.0%
64.74 2
 
2.0%
66.18 2
 
2.0%
94.19 2
 
2.0%
Other values (76) 77
78.6%
ValueCountFrequency (%)
0.0 2
2.0%
43.26 1
1.0%
48.0 1
1.0%
48.58 1
1.0%
49.66 1
1.0%
51.45 1
1.0%
57.4 1
1.0%
59.0 1
1.0%
59.07 1
1.0%
59.19 1
1.0%
ValueCountFrequency (%)
420.81 1
1.0%
360.0 1
1.0%
336.09 1
1.0%
280.22 1
1.0%
260.7 1
1.0%
223.78 1
1.0%
210.84 1
1.0%
206.0 1
1.0%
200.9 1
1.0%
194.08 1
1.0%

행정동명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size916.0 B
수유제3동
27 
우이동
17 
번제1동
12 
미아동
11 
송중동
8 
Other values (8)
23 

Length

Max length5
Median length4
Mean length3.8673469
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row번제2동
2nd row번제3동
3rd row번제3동
4th row수유제3동
5th row삼양동

Common Values

ValueCountFrequency (%)
수유제3동 27
27.6%
우이동 17
17.3%
번제1동 12
12.2%
미아동 11
11.2%
송중동 8
 
8.2%
송천동 6
 
6.1%
번제2동 4
 
4.1%
번제3동 4
 
4.1%
수유제1동 3
 
3.1%
삼양동 2
 
2.0%
Other values (3) 4
 
4.1%

Length

2024-05-11T14:35:34.163159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수유제3동 27
27.6%
우이동 17
17.3%
번제1동 12
12.2%
미아동 11
11.2%
송중동 8
 
8.2%
송천동 6
 
6.1%
번제2동 4
 
4.1%
번제3동 4
 
4.1%
수유제1동 3
 
3.1%
삼양동 2
 
2.0%
Other values (3) 4
 
4.1%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
상수도전용
84 
<NA>
14 

Length

Max length5
Median length5
Mean length4.8571429
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 84
85.7%
<NA> 14
 
14.3%

Length

2024-05-11T14:35:34.394096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:35:34.543466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 84
85.7%
na 14
 
14.3%

Interactions

2024-05-11T14:35:22.200560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:17.100905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:18.160122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:19.037742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:20.074247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:21.091796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:22.373743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:17.322598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:18.307774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:19.203677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:20.231321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:21.264906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:22.529285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:17.465496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:18.433488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:19.374882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:20.388995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:21.423877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:22.711282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:17.634077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:18.593867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:19.564728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:20.567193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:21.614155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:23.259434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:17.853765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:18.745659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:19.733173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:20.751203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:21.842729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:23.442552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:18.010398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:18.908076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:19.916021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:20.953699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:35:22.040212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:35:34.683179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명
지정년도1.0000.7010.9550.9500.0000.7150.0000.0000.0000.0000.2890.9280.0000.490
지정번호0.7011.0001.0001.0000.0000.0000.6270.6270.4510.0000.0000.8360.0960.398
신청일자0.9551.0001.0000.9990.0000.8240.7250.7250.7250.0000.2100.9810.0000.533
지정일자0.9501.0000.9991.0000.0000.8870.8530.8530.8530.3860.0190.9810.0000.316
취소일자0.0000.0000.0000.0001.0000.9380.1850.1850.9530.0000.8170.5100.0000.000
업소명0.7150.0000.8240.8870.9381.0001.0001.0001.0001.0000.9751.0000.9990.995
소재지도로명0.0000.6270.7250.8530.1851.0001.0001.0001.0001.0000.9811.0000.9991.000
소재지지번0.0000.6270.7250.8530.1851.0001.0001.0001.0001.0000.9811.0000.9991.000
허가(신고)번호0.0000.4510.7250.8530.9531.0001.0001.0001.0001.0000.9801.0001.0001.000
업태명0.0000.0000.0000.3860.0001.0001.0001.0001.0001.0000.0000.0000.0000.000
지정취소사유0.2890.0000.2100.0190.8170.9750.9810.9810.9800.0001.0000.8870.6040.000
주된음식0.9280.8360.9810.9810.5101.0001.0001.0001.0000.0000.8871.0000.0000.663
영업장면적(㎡)0.0000.0960.0000.0000.0000.9990.9990.9991.0000.0000.6040.0001.0000.160
행정동명0.4900.3980.5330.3160.0000.9951.0001.0001.0000.0000.0000.6630.1601.000
2024-05-11T14:35:34.929291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명지정취소사유행정동명
급수시설구분1.0001.0001.0001.000
업태명1.0001.0000.0000.000
지정취소사유1.0000.0001.0000.000
행정동명1.0000.0000.0001.000
2024-05-11T14:35:35.123973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명지정취소사유행정동명급수시설구분
지정년도1.000-0.2601.0001.0000.239-0.0400.0000.0450.2251.000
지정번호-0.2601.000-0.260-0.2600.146-0.0510.0000.0000.2301.000
신청일자1.000-0.2601.0001.0000.239-0.0400.0000.0000.2761.000
지정일자1.000-0.2601.0001.0000.239-0.0400.0000.0000.2051.000
취소일자0.2390.1460.2390.2391.0000.1270.1260.4940.0001.000
영업장면적(㎡)-0.040-0.051-0.040-0.0400.1271.0000.0000.2720.0541.000
업태명0.0000.0000.0000.0000.1260.0001.0000.0000.0001.000
지정취소사유0.0450.0000.0000.0000.4940.2720.0001.0000.0001.000
행정동명0.2250.2300.2760.2050.0000.0540.0000.0001.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T14:35:23.655277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:35:24.000388image/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
0308000020093200909102009091020101008전주 선지 순대 추어탕서울특별시 강북구 오현로31길 173, (번동,(벌리길 85)(지상1층))서울특별시 강북구 번동 433번지 54호 (벌리길 85)(지상1층)3080000-101-1992-01042한식<NA><NA>65.0번제2동상수도전용
13080000200685200606272006070120210923번동숯불갈비서울특별시 강북구 오현로32길 4-8, (번동)서울특별시 강북구 번동 306번지 38호3080000-101-2001-08455한식모범음식점 현황에 해당없음 확인삼겹살77.0번제3동상수도전용
23080000200684200606272006070120210923신고집찜 칼국수서울특별시 강북구 한천로109길 72, 세종빌딩 2층 (번동)서울특별시 강북구 번동 168번지 6호 세종빌딩 2층3080000-101-2003-00055한식모범음식점 현황에 해당없음 확인해물찜210.84번제3동<NA>
33080000200624200606272006070120210923목포해물탕서울특별시 강북구 한천로140길 7, (수유동,(지상1층))서울특별시 강북구 수유동 177번지 20호 (지상1층)3080000-101-2005-00297탕류(보신용)모범음식점 현황에 해당없음 확인삼겹살168.0수유제3동상수도전용
43080000200930200909102009091020210923남원정통추어탕서울특별시 강북구 삼양로 181, (미아동)서울특별시 강북구 미아동 703번지 31호3080000-101-2003-00406한식모범음식점 현황에 해당없음 확인<NA>91.52삼양동<NA>
5308000020111112201110202011102820210923본추 추어탕서울특별시 강북구 오패산로77길 18, (번동,울타리길 16 지상1층)서울특별시 강북구 번동 446번지 37호 울타리길 16 지상1층3080000-101-2009-00084한식모범음식점 현황에 해당없음 확인<NA>100.44번제1동<NA>
63080000200941200909102009091020210923육대장 수유(4.19탑)점서울특별시 강북구 4.19로 51, (수유동)서울특별시 강북구 수유동 567번지 16호 (지상1층)3080000-101-2006-00039한식모범음식점 현황에 해당없음 확인<NA>141.57우이동상수도전용
73080000201013201009302010100820210923어! 외양간에 참치가서울특별시 강북구 도봉로61길 5, (미아동,(목화길 5))서울특별시 강북구 미아동 206번지 1호 (목화길 5)3080000-101-2009-00165한식모범음식점 현황에 해당없음 확인<NA>360.0미아동상수도전용
8308000020124201211012012120420210923한탄강메기매운탕추어탕서울특별시 강북구 인수봉로 160, 1층 (수유동)서울특별시 강북구 수유동 455번지 32호 1층3080000-101-2011-00058한식모범음식점 현황에 해당없음 확인냉면, 돈가스128.56인수동상수도전용
930800002008192200807012008090820210923삼삼돌판 생모듬오겹살서울특별시 강북구 도봉로68길 11-5, (미아동,(꽃밭5길 20-8) 지상1층)서울특별시 강북구 미아동 187번지 55호 (꽃밭5길 20-8) 지상1층3080000-101-2008-00004한식모범음식점 현황에 해당없음 확인<NA>66.4미아동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
883080000200921200909102009091020221031홍콩반점0410 수유역점서울특별시 강북구 오패산로77길 3, 2층 (번동)서울특별시 강북구 번동 445번지 56호 2층3080000-101-2001-08713한식<NA>아구찜91.2번제1동상수도전용
89308000020111111201110282011102820210923깡돈서울특별시 강북구 오패산로77길 13, (번동)서울특별시 강북구 번동 445번지 51호3080000-101-2004-00141한식모범음식점 현황에 해당없음 확인<NA>0.0번제1동<NA>
903080000200683200606272006070120210923개성손만두서울특별시 강북구 한천로109길 59, (번동)서울특별시 강북구 번동 169번지 1호3080000-101-2001-08938한식모범음식점 현황에 해당없음 확인감자국161.3번제3동상수도전용
91308000020105201009302010100820210923생마차 수유역점서울특별시 강북구 한천로139길 29, (수유동)서울특별시 강북구 수유동 191번지 67호3080000-101-1994-00204한식모범음식점 현황에 해당없음 확인<NA>106.06수유제3동상수도전용
923080000200628200606272006070120210923여행호프서울특별시 강북구 4.19로 29, (수유동)서울특별시 강북구 수유동 570번지3080000-101-2001-08751한식모범음식점 현황에 해당없음 확인버섯매운탕칼국수74.2우이동상수도전용
933080000200638200606272006070120210923수유 감탄 숯불닭갈비서울특별시 강북구 덕릉로 94, (미아동,(지상1층))서울특별시 강북구 미아동 159번지 10호 (지상1층)3080000-101-1992-04582한식모범음식점 현황에 해당없음 확인돼지갈비95.07미아동상수도전용
9430800002008195200807012008090820210923수유 감탄 숯불닭갈비서울특별시 강북구 덕릉로 94, (미아동,(지상1층))서울특별시 강북구 미아동 159번지 10호 (지상1층)3080000-101-1992-04582한식모범음식점 현황에 해당없음 확인<NA>95.07미아동상수도전용
95308000020102201009302010100820210923수유 감탄 숯불닭갈비서울특별시 강북구 덕릉로 94, (미아동,(지상1층))서울특별시 강북구 미아동 159번지 10호 (지상1층)3080000-101-1992-04582한식모범음식점 현황에 해당없음 확인<NA>95.07미아동상수도전용
9630800002006115200606272006070120210923칸 스시 앤 이자카야서울특별시 강북구 도봉로89길 5, (수유동)서울특별시 강북구 수유동 191번지 25호3080000-101-1998-04508일식모범음식점 현황에 해당없음 확인참치168.96수유제3동상수도전용
9730800002008162200807012008090820210923칸 스시 앤 이자카야서울특별시 강북구 도봉로89길 5, (수유동)서울특별시 강북구 수유동 191번지 25호3080000-101-1998-04508일식모범음식점 현황에 해당없음 확인<NA>168.96수유제3동상수도전용