Overview

Dataset statistics

Number of variables16
Number of observations134
Missing cells11
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.8 KiB
Average record size in memory136.0 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
Author서대문구
URLhttps://data.seoul.go.kr/dataList/OA-2379/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 신청일자 and 1 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정취소사유High correlation
지정취소사유 is highly overall correlated with 취소일자High correlation
급수시설구분 is highly imbalanced (53.5%)Imbalance
주된음식 has 10 (7.5%) missing valuesMissing
지정번호 has 4 (3.0%) zerosZeros

Reproduction

Analysis started2024-05-11 06:08:54.893619
Analysis finished2024-05-11 06:09:04.348616
Duration9.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
3120000
134 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3120000
2nd row3120000
3rd row3120000
4th row3120000
5th row3120000

Common Values

ValueCountFrequency (%)
3120000 134
100.0%

Length

2024-05-11T15:09:04.498899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:09:04.678641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3120000 134
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.2313
Minimum1987
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:09:04.831233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile2004
Q12004
median2005
Q32010
95-th percentile2014
Maximum2014
Range27
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.8911677
Coefficient of variation (CV)0.0024379879
Kurtosis5.2923112
Mean2006.2313
Median Absolute Deviation (MAD)1
Skewness-1.4227088
Sum268835
Variance23.923521
MonotonicityDecreasing
2024-05-11T15:09:05.046771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2004 59
44.0%
2011 14
 
10.4%
2008 12
 
9.0%
2014 11
 
8.2%
2005 9
 
6.7%
2010 6
 
4.5%
2007 6
 
4.5%
2012 5
 
3.7%
2006 4
 
3.0%
1987 4
 
3.0%
Other values (2) 4
 
3.0%
ValueCountFrequency (%)
1987 4
 
3.0%
1999 2
 
1.5%
2004 59
44.0%
2005 9
 
6.7%
2006 4
 
3.0%
2007 6
 
4.5%
2008 12
 
9.0%
2009 2
 
1.5%
2010 6
 
4.5%
2011 14
 
10.4%
ValueCountFrequency (%)
2014 11
 
8.2%
2012 5
 
3.7%
2011 14
 
10.4%
2010 6
 
4.5%
2009 2
 
1.5%
2008 12
 
9.0%
2007 6
 
4.5%
2006 4
 
3.0%
2005 9
 
6.7%
2004 59
44.0%

지정번호
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.477612
Minimum0
Maximum163
Zeros4
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:09:05.248424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q117
median48
Q3101.75
95-th percentile149.35
Maximum163
Range163
Interquartile range (IQR)84.75

Descriptive statistics

Standard deviation50.867174
Coefficient of variation (CV)0.81416643
Kurtosis-1.1114124
Mean62.477612
Median Absolute Deviation (MAD)37
Skewness0.49745533
Sum8372
Variance2587.4694
MonotonicityNot monotonic
2024-05-11T15:09:05.446464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
3.0%
9 4
 
3.0%
12 3
 
2.2%
13 3
 
2.2%
146 3
 
2.2%
38 3
 
2.2%
7 3
 
2.2%
141 3
 
2.2%
18 3
 
2.2%
64 3
 
2.2%
Other values (67) 102
76.1%
ValueCountFrequency (%)
0 4
3.0%
1 2
1.5%
3 2
1.5%
4 3
2.2%
5 1
 
0.7%
6 2
1.5%
7 3
2.2%
8 1
 
0.7%
9 4
3.0%
11 2
1.5%
ValueCountFrequency (%)
163 1
 
0.7%
162 1
 
0.7%
157 2
1.5%
152 1
 
0.7%
151 1
 
0.7%
150 1
 
0.7%
149 2
1.5%
148 1
 
0.7%
146 3
2.2%
145 1
 
0.7%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20075387
Minimum19870513
Maximum20141010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:09:05.616409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870513
5-th percentile20041018
Q120041018
median20090312
Q320100701
95-th percentile20141010
Maximum20141010
Range270497
Interquartile range (IQR)59683

Descriptive statistics

Standard deviation48356.232
Coefficient of variation (CV)0.0024087323
Kurtosis7.5253271
Mean20075387
Median Absolute Deviation (MAD)29609
Skewness-2.1175709
Sum2.6901018 × 109
Variance2.3383252 × 109
MonotonicityNot monotonic
2024-05-11T15:09:05.812648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20041018 36
26.9%
20090312 30
22.4%
20100701 12
 
9.0%
20141010 11
 
8.2%
20111020 8
 
6.0%
20121030 7
 
5.2%
19870513 4
 
3.0%
20050719 4
 
3.0%
20090327 4
 
3.0%
20060703 3
 
2.2%
Other values (11) 15
11.2%
ValueCountFrequency (%)
19870513 4
 
3.0%
20041018 36
26.9%
20041020 2
 
1.5%
20050216 1
 
0.7%
20050506 1
 
0.7%
20050719 4
 
3.0%
20060703 3
 
2.2%
20070619 3
 
2.2%
20080926 2
 
1.5%
20081026 1
 
0.7%
ValueCountFrequency (%)
20141010 11
8.2%
20121030 7
5.2%
20111022 1
 
0.7%
20111020 8
6.0%
20111016 1
 
0.7%
20111014 1
 
0.7%
20111011 1
 
0.7%
20111010 1
 
0.7%
20100701 12
9.0%
20090327 4
 
3.0%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20063260
Minimum19870513
Maximum20141105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:09:05.996420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870513
5-th percentile20041020
Q120041020
median20050612
Q320100726
95-th percentile20141105
Maximum20141105
Range270592
Interquartile range (IQR)59706

Descriptive statistics

Standard deviation49007.172
Coefficient of variation (CV)0.0024426326
Kurtosis5.3037829
Mean20063260
Median Absolute Deviation (MAD)9592.5
Skewness-1.4240059
Sum2.6884768 × 109
Variance2.4017029 × 109
MonotonicityNot monotonic
2024-05-11T15:09:06.183098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20041020 59
44.0%
20111201 14
 
10.4%
20141105 11
 
8.2%
20080926 11
 
8.2%
20050719 7
 
5.2%
20070625 6
 
4.5%
20121101 5
 
3.7%
20100726 5
 
3.7%
20060706 4
 
3.0%
19870513 4
 
3.0%
Other values (6) 8
 
6.0%
ValueCountFrequency (%)
19870513 4
 
3.0%
19990410 2
 
1.5%
20041020 59
44.0%
20050216 1
 
0.7%
20050506 1
 
0.7%
20050719 7
 
5.2%
20060706 4
 
3.0%
20070625 6
 
4.5%
20080926 11
 
8.2%
20081026 1
 
0.7%
ValueCountFrequency (%)
20141105 11
8.2%
20121101 5
 
3.7%
20111201 14
10.4%
20100726 5
 
3.7%
20100701 1
 
0.7%
20090710 2
 
1.5%
20081026 1
 
0.7%
20080926 11
8.2%
20070625 6
4.5%
20060706 4
 
3.0%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20167499
Minimum19961021
Maximum20230719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:09:06.356843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19961021
5-th percentile20050482
Q120100726
median20221108
Q320221108
95-th percentile20221108
Maximum20230719
Range269698
Interquartile range (IQR)120382

Descriptive statistics

Standard deviation70650.584
Coefficient of variation (CV)0.0035031902
Kurtosis-0.75161922
Mean20167499
Median Absolute Deviation (MAD)0
Skewness-0.82475424
Sum2.7024449 × 109
Variance4.991505 × 109
MonotonicityNot monotonic
2024-05-11T15:09:06.561611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
20221108 77
57.5%
20100726 14
 
10.4%
20111201 4
 
3.0%
20100326 2
 
1.5%
20091217 2
 
1.5%
20150115 2
 
1.5%
20070625 2
 
1.5%
20060811 2
 
1.5%
20050512 2
 
1.5%
20050421 1
 
0.7%
Other values (26) 26
 
19.4%
ValueCountFrequency (%)
19961021 1
0.7%
19981020 1
0.7%
20030605 1
0.7%
20050223 1
0.7%
20050415 1
0.7%
20050421 1
0.7%
20050427 1
0.7%
20050512 2
1.5%
20050524 1
0.7%
20050630 1
0.7%
ValueCountFrequency (%)
20230719 1
 
0.7%
20221108 77
57.5%
20201218 1
 
0.7%
20201103 1
 
0.7%
20201028 1
 
0.7%
20200122 1
 
0.7%
20160404 1
 
0.7%
20160330 1
 
0.7%
20150115 2
 
1.5%
20121012 1
 
0.7%

업소명
Text

Distinct92
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T15:09:07.057874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length6.4925373
Min length1

Characters and Unicode

Total characters870
Distinct characters232
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)42.5%

Sample

1st row벙커원
2nd row이코노스시
3rd row이화수전통육개장(충정로점)
4th row한촌설렁탕 북가좌점
5th row인도야시장
ValueCountFrequency (%)
신촌점 5
 
2.6%
고기랑 3
 
1.6%
이코노스시 3
 
1.6%
이디야 3
 
1.6%
장어세상 3
 
1.6%
법성포 3
 
1.6%
영광굴비 3
 
1.6%
가화만사성 3
 
1.6%
포티드 3
 
1.6%
감자탕 2
 
1.0%
Other values (117) 160
83.8%
2024-05-11T15:09:07.659700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
57
 
6.6%
점 28
 
3.2%
촌 20
 
2.3%
이 18
 
2.1%
비 17
 
2.0%
신 17
 
2.0%
갈 14
 
1.6%
장 13
 
1.5%
가 11
 
1.3%
집 11
 
1.3%
Other values (222) 664
76.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 770
88.5%
Space Separator 57
 
6.6%
Uppercase Letter 12
 
1.4%
Open Punctuation 9
 
1.0%
Close Punctuation 9
 
1.0%
Lowercase Letter 8
 
0.9%
Decimal Number 5
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 28
 
3.6%
촌 20
 
2.6%
이 18
 
2.3%
비 17
 
2.2%
신 17
 
2.2%
갈 14
 
1.8%
장 13
 
1.7%
가 11
 
1.4%
집 11
 
1.4%
연 11
 
1.4%
Other values (206) 610
79.2%
Uppercase Letter
ValueCountFrequency (%)
A 4
33.3%
N 2
16.7%
E 2
16.7%
B 2
16.7%
G 2
16.7%
Lowercase Letter
ValueCountFrequency (%)
e 2
25.0%
f 2
25.0%
a 2
25.0%
c 2
25.0%
Decimal Number
ValueCountFrequency (%)
0 2
40.0%
4 1
20.0%
2 1
20.0%
1 1
20.0%
Space Separator
ValueCountFrequency (%)
57
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 770
88.5%
Common 80
 
9.2%
Latin 20
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 28
 
3.6%
촌 20
 
2.6%
이 18
 
2.3%
비 17
 
2.2%
신 17
 
2.2%
갈 14
 
1.8%
장 13
 
1.7%
가 11
 
1.4%
집 11
 
1.4%
연 11
 
1.4%
Other values (206) 610
79.2%
Latin
ValueCountFrequency (%)
A 4
20.0%
e 2
10.0%
f 2
10.0%
a 2
10.0%
N 2
10.0%
c 2
10.0%
E 2
10.0%
B 2
10.0%
G 2
10.0%
Common
ValueCountFrequency (%)
57
71.2%
( 9
 
11.2%
) 9
 
11.2%
0 2
 
2.5%
4 1
 
1.2%
2 1
 
1.2%
1 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 770
88.5%
ASCII 100
 
11.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
57
57.0%
( 9
 
9.0%
) 9
 
9.0%
A 4
 
4.0%
e 2
 
2.0%
f 2
 
2.0%
a 2
 
2.0%
0 2
 
2.0%
N 2
 
2.0%
c 2
 
2.0%
Other values (6) 9
 
9.0%
Hangul
ValueCountFrequency (%)
점 28
 
3.6%
촌 20
 
2.6%
이 18
 
2.3%
비 17
 
2.2%
신 17
 
2.2%
갈 14
 
1.8%
장 13
 
1.7%
가 11
 
1.4%
집 11
 
1.4%
연 11
 
1.4%
Other values (206) 610
79.2%

소재지도로명
Text

Distinct93
Distinct (%)69.9%
Missing1
Missing (%)0.7%
Memory size1.2 KiB
2024-05-11T15:09:08.021341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length37
Mean length29.827068
Min length24

Characters and Unicode

Total characters3967
Distinct characters96
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)43.6%

Sample

1st row서울특별시 서대문구 충정로 20, 1층 (충정로3가)
2nd row서울특별시 서대문구 신촌역로 14, (대현동)
3rd row서울특별시 서대문구 충정로 18, 1층 (충정로3가)
4th row서울특별시 서대문구 응암로 67, (북가좌동)
5th row서울특별시 서대문구 연세로 20, (창천동,지상3층)
ValueCountFrequency (%)
서울특별시 133
18.0%
서대문구 133
18.0%
창천동 39
 
5.3%
1층 30
 
4.1%
명물길 19
 
2.6%
연희동 18
 
2.4%
2층 12
 
1.6%
연희로 9
 
1.2%
연희맛로 9
 
1.2%
홍은동 8
 
1.1%
Other values (148) 330
44.6%
2024-05-11T15:09:09.136268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
607
 
15.3%
서 266
 
6.7%
, 183
 
4.6%
1 158
 
4.0%
( 153
 
3.9%
) 153
 
3.9%
대 145
 
3.7%
문 134
 
3.4%
특 133
 
3.4%
별 133
 
3.4%
Other values (86) 1902
47.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2320
58.5%
Space Separator 607
 
15.3%
Decimal Number 516
 
13.0%
Other Punctuation 183
 
4.6%
Open Punctuation 153
 
3.9%
Close Punctuation 153
 
3.9%
Dash Punctuation 33
 
0.8%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 266
 
11.5%
대 145
 
6.2%
문 134
 
5.8%
특 133
 
5.7%
별 133
 
5.7%
시 133
 
5.7%
울 133
 
5.7%
구 133
 
5.7%
동 124
 
5.3%
로 123
 
5.3%
Other values (70) 863
37.2%
Decimal Number
ValueCountFrequency (%)
1 158
30.6%
2 101
19.6%
3 59
 
11.4%
5 38
 
7.4%
6 37
 
7.2%
7 31
 
6.0%
4 30
 
5.8%
9 22
 
4.3%
0 21
 
4.1%
8 19
 
3.7%
Space Separator
ValueCountFrequency (%)
607
100.0%
Other Punctuation
ValueCountFrequency (%)
, 183
100.0%
Open Punctuation
ValueCountFrequency (%)
( 153
100.0%
Close Punctuation
ValueCountFrequency (%)
) 153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2320
58.5%
Common 1647
41.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 266
 
11.5%
대 145
 
6.2%
문 134
 
5.8%
특 133
 
5.7%
별 133
 
5.7%
시 133
 
5.7%
울 133
 
5.7%
구 133
 
5.7%
동 124
 
5.3%
로 123
 
5.3%
Other values (70) 863
37.2%
Common
ValueCountFrequency (%)
607
36.9%
, 183
 
11.1%
1 158
 
9.6%
( 153
 
9.3%
) 153
 
9.3%
2 101
 
6.1%
3 59
 
3.6%
5 38
 
2.3%
6 37
 
2.2%
- 33
 
2.0%
Other values (6) 125
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2320
58.5%
ASCII 1647
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
607
36.9%
, 183
 
11.1%
1 158
 
9.6%
( 153
 
9.3%
) 153
 
9.3%
2 101
 
6.1%
3 59
 
3.6%
5 38
 
2.3%
6 37
 
2.2%
- 33
 
2.0%
Other values (6) 125
 
7.6%
Hangul
ValueCountFrequency (%)
서 266
 
11.5%
대 145
 
6.2%
문 134
 
5.8%
특 133
 
5.7%
별 133
 
5.7%
시 133
 
5.7%
울 133
 
5.7%
구 133
 
5.7%
동 124
 
5.3%
로 123
 
5.3%
Other values (70) 863
37.2%

소재지지번
Text

Distinct93
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T15:09:09.542568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length39
Mean length29.223881
Min length24

Characters and Unicode

Total characters3916
Distinct characters69
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)43.3%

Sample

1st row서울특별시 서대문구 충정로3가 270번지 1층
2nd row서울특별시 서대문구 대현동 101번지 5호
3rd row서울특별시 서대문구 충정로3가 360번지 24호 1층
4th row서울특별시 서대문구 북가좌동 307번지 1호
5th row서울특별시 서대문구 창천동 9번지 20호 지상3층
ValueCountFrequency (%)
서울특별시 134
 
17.6%
서대문구 134
 
17.6%
창천동 52
 
6.8%
1층 28
 
3.7%
연희동 22
 
2.9%
5호 12
 
1.6%
홍은동 11
 
1.4%
지상1층 10
 
1.3%
2층 9
 
1.2%
2번지 9
 
1.2%
Other values (131) 340
44.7%
2024-05-11T15:09:10.128112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
957
24.4%
서 268
 
6.8%
지 168
 
4.3%
1 163
 
4.2%
대 142
 
3.6%
별 134
 
3.4%
번 134
 
3.4%
특 134
 
3.4%
문 134
 
3.4%
구 134
 
3.4%
Other values (59) 1548
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2221
56.7%
Space Separator 957
24.4%
Decimal Number 667
 
17.0%
Close Punctuation 22
 
0.6%
Open Punctuation 22
 
0.6%
Other Punctuation 16
 
0.4%
Dash Punctuation 9
 
0.2%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 268
 
12.1%
지 168
 
7.6%
대 142
 
6.4%
별 134
 
6.0%
번 134
 
6.0%
특 134
 
6.0%
문 134
 
6.0%
구 134
 
6.0%
울 134
 
6.0%
시 134
 
6.0%
Other values (43) 705
31.7%
Decimal Number
ValueCountFrequency (%)
1 163
24.4%
2 112
16.8%
3 95
14.2%
5 66
9.9%
8 43
 
6.4%
0 42
 
6.3%
7 39
 
5.8%
4 38
 
5.7%
6 36
 
5.4%
9 33
 
4.9%
Space Separator
ValueCountFrequency (%)
957
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2221
56.7%
Common 1695
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 268
 
12.1%
지 168
 
7.6%
대 142
 
6.4%
별 134
 
6.0%
번 134
 
6.0%
특 134
 
6.0%
문 134
 
6.0%
구 134
 
6.0%
울 134
 
6.0%
시 134
 
6.0%
Other values (43) 705
31.7%
Common
ValueCountFrequency (%)
957
56.5%
1 163
 
9.6%
2 112
 
6.6%
3 95
 
5.6%
5 66
 
3.9%
8 43
 
2.5%
0 42
 
2.5%
7 39
 
2.3%
4 38
 
2.2%
6 36
 
2.1%
Other values (6) 104
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2221
56.7%
ASCII 1695
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
957
56.5%
1 163
 
9.6%
2 112
 
6.6%
3 95
 
5.6%
5 66
 
3.9%
8 43
 
2.5%
0 42
 
2.5%
7 39
 
2.3%
4 38
 
2.2%
6 36
 
2.1%
Other values (6) 104
 
6.1%
Hangul
ValueCountFrequency (%)
서 268
 
12.1%
지 168
 
7.6%
대 142
 
6.4%
별 134
 
6.0%
번 134
 
6.0%
특 134
 
6.0%
문 134
 
6.0%
구 134
 
6.0%
울 134
 
6.0%
시 134
 
6.0%
Other values (43) 705
31.7%

허가(신고)번호
Text

Distinct94
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2024-05-11T15:09:10.441580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2948
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)44.0%

Sample

1st row3120000-101-2002-00590
2nd row3120000-101-1994-04213
3rd row3120000-101-2000-03500
4th row3120000-101-1994-05535
5th row3120000-101-2002-00539
ValueCountFrequency (%)
3120000-101-1992-05434 3
 
2.2%
3120000-101-1994-04213 3
 
2.2%
3120000-101-1977-01871 3
 
2.2%
3120000-101-1999-03820 3
 
2.2%
3120000-101-1996-01666 3
 
2.2%
3120000-101-2000-04932 2
 
1.5%
3120000-101-1998-00658 2
 
1.5%
3120000-101-1991-01548 2
 
1.5%
3120000-101-1992-03609 2
 
1.5%
3120000-101-1995-01585 2
 
1.5%
Other values (84) 109
81.3%
2024-05-11T15:09:10.993951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 998
33.9%
1 592
20.1%
- 402
13.6%
2 241
 
8.2%
3 206
 
7.0%
9 205
 
7.0%
5 71
 
2.4%
4 62
 
2.1%
6 59
 
2.0%
8 57
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2546
86.4%
Dash Punctuation 402
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 998
39.2%
1 592
23.3%
2 241
 
9.5%
3 206
 
8.1%
9 205
 
8.1%
5 71
 
2.8%
4 62
 
2.4%
6 59
 
2.3%
8 57
 
2.2%
7 55
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 402
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2948
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 998
33.9%
1 592
20.1%
- 402
13.6%
2 241
 
8.2%
3 206
 
7.0%
9 205
 
7.0%
5 71
 
2.4%
4 62
 
2.1%
6 59
 
2.0%
8 57
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2948
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 998
33.9%
1 592
20.1%
- 402
13.6%
2 241
 
8.2%
3 206
 
7.0%
9 205
 
7.0%
5 71
 
2.4%
4 62
 
2.1%
6 59
 
2.0%
8 57
 
1.9%

업태명
Categorical

Distinct11
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
한식
81 
일식
13 
중국식
11 
경양식
9 
분식
 
8
Other values (6)
12 

Length

Max length15
Median length2
Mean length2.6044776
Min length2

Unique

Unique2 ?
Unique (%)1.5%

Sample

1st row호프/통닭
2nd row정종/대포집/소주방
3rd row한식
4th row한식
5th row기타

Common Values

ValueCountFrequency (%)
한식 81
60.4%
일식 13
 
9.7%
중국식 11
 
8.2%
경양식 9
 
6.7%
분식 8
 
6.0%
정종/대포집/소주방 3
 
2.2%
기타 3
 
2.2%
통닭(치킨) 2
 
1.5%
외국음식전문점(인도,태국등) 2
 
1.5%
호프/통닭 1
 
0.7%

Length

2024-05-11T15:09:11.227559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 81
60.4%
일식 13
 
9.7%
중국식 11
 
8.2%
경양식 9
 
6.7%
분식 8
 
6.0%
정종/대포집/소주방 3
 
2.2%
기타 3
 
2.2%
통닭(치킨 2
 
1.5%
외국음식전문점(인도,태국등 2
 
1.5%
호프/통닭 1
 
0.7%

지정취소사유
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
새올대장정리
77 
기준미달
14 
행정처분
13 
영업자지위승계
13 
위생등급부적합
 
4
Other values (11)
13 

Length

Max length31
Median length6
Mean length6.1567164
Min length2

Unique

Unique9 ?
Unique (%)6.7%

Sample

1st row새올대장정리
2nd row새올대장정리
3rd row새올대장정리
4th row행정처분
5th row새올대장정리

Common Values

ValueCountFrequency (%)
새올대장정리 77
57.5%
기준미달 14
 
10.4%
행정처분 13
 
9.7%
영업자지위승계 13
 
9.7%
위생등급부적합 4
 
3.0%
지위승계 2
 
1.5%
자진폐업 2
 
1.5%
지정기준미달(2004.07.) 1
 
0.7%
지정기준미달(2004.07) 1
 
0.7%
멸실 1
 
0.7%
Other values (6) 6
 
4.5%

Length

2024-05-11T15:09:11.473212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
새올대장정리 77
55.8%
기준미달 14
 
10.1%
행정처분 13
 
9.4%
영업자지위승계 13
 
9.4%
위생등급부적합 4
 
2.9%
지위승계 3
 
2.2%
자진폐업 2
 
1.4%
지정기준미달(2004.07 2
 
1.4%
200931200000052290 1
 
0.7%
미달 1
 
0.7%
Other values (8) 8
 
5.8%

주된음식
Text

MISSING 

Distinct69
Distinct (%)55.6%
Missing10
Missing (%)7.5%
Memory size1.2 KiB
2024-05-11T15:09:11.855631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length3.2903226
Min length1

Characters and Unicode

Total characters408
Distinct characters102
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

Unique46 ?
Unique (%)37.1%

Sample

1st row햄버거
2nd row회덮밥
3rd row설렁탕
4th row설렁탕
5th row카레덮밥
ValueCountFrequency (%)
한식 16
 
12.7%
일식 7
 
5.6%
돼지갈비 6
 
4.8%
갈비탕 4
 
3.2%
자장면 4
 
3.2%
된장찌개 3
 
2.4%
한정식 3
 
2.4%
삼겹살 3
 
2.4%
참치회 3
 
2.4%
갈비 3
 
2.4%
Other values (61) 74
58.7%
2024-05-11T15:09:12.478851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
식 34
 
8.3%
비 20
 
4.9%
한 19
 
4.7%
탕 18
 
4.4%
갈 18
 
4.4%
회 13
 
3.2%
밥 13
 
3.2%
지 13
 
3.2%
장 12
 
2.9%
. 10
 
2.5%
Other values (92) 238
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 393
96.3%
Other Punctuation 12
 
2.9%
Space Separator 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 34
 
8.7%
비 20
 
5.1%
한 19
 
4.8%
탕 18
 
4.6%
갈 18
 
4.6%
회 13
 
3.3%
밥 13
 
3.3%
지 13
 
3.3%
장 12
 
3.1%
일 8
 
2.0%
Other values (89) 225
57.3%
Other Punctuation
ValueCountFrequency (%)
. 10
83.3%
, 2
 
16.7%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 393
96.3%
Common 15
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 34
 
8.7%
비 20
 
5.1%
한 19
 
4.8%
탕 18
 
4.6%
갈 18
 
4.6%
회 13
 
3.3%
밥 13
 
3.3%
지 13
 
3.3%
장 12
 
3.1%
일 8
 
2.0%
Other values (89) 225
57.3%
Common
ValueCountFrequency (%)
. 10
66.7%
3
 
20.0%
, 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 393
96.3%
ASCII 15
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
식 34
 
8.7%
비 20
 
5.1%
한 19
 
4.8%
탕 18
 
4.6%
갈 18
 
4.6%
회 13
 
3.3%
밥 13
 
3.3%
지 13
 
3.3%
장 12
 
3.1%
일 8
 
2.0%
Other values (89) 225
57.3%
ASCII
ValueCountFrequency (%)
. 10
66.7%
3
 
20.0%
, 2
 
13.3%

영업장면적(㎡)
Real number (ℝ)

Distinct92
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.3653
Minimum26.4
Maximum470.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2024-05-11T15:09:12.729758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.4
5-th percentile43.631
Q181.9
median103.77
Q3147.8
95-th percentile293.59
Maximum470.03
Range443.63
Interquartile range (IQR)65.9

Descriptive statistics

Standard deviation83.6313
Coefficient of variation (CV)0.64647399
Kurtosis3.168136
Mean129.3653
Median Absolute Deviation (MAD)29.305
Skewness1.7130531
Sum17334.95
Variance6994.1943
MonotonicityNot monotonic
2024-05-11T15:09:12.983800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226.08 3
 
2.2%
83.85 3
 
2.2%
251.19 3
 
2.2%
87.36 3
 
2.2%
99.0 3
 
2.2%
125.6 3
 
2.2%
82.5 3
 
2.2%
51.82 2
 
1.5%
87.19 2
 
1.5%
194.23 2
 
1.5%
Other values (82) 107
79.9%
ValueCountFrequency (%)
26.4 2
1.5%
31.21 1
0.7%
43.19 2
1.5%
43.41 2
1.5%
43.75 1
0.7%
49.0 2
1.5%
51.15 1
0.7%
51.82 2
1.5%
53.0 1
0.7%
53.4 1
0.7%
ValueCountFrequency (%)
470.03 1
 
0.7%
403.88 2
1.5%
401.31 1
 
0.7%
340.0 1
 
0.7%
325.2 1
 
0.7%
300.09 1
 
0.7%
290.09 1
 
0.7%
256.81 1
 
0.7%
254.6 2
1.5%
251.19 3
2.2%

행정동명
Categorical

Distinct13
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
신촌동
60 
연희동
22 
충현동
16 
홍은제2동
 
6
홍제제1동
 
5
Other values (8)
25 

Length

Max length6
Median length3
Mean length3.5522388
Min length3

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row충현동
2nd row신촌동
3rd row충현동
4th row북가좌제1동
5th row신촌동

Common Values

ValueCountFrequency (%)
신촌동 60
44.8%
연희동 22
 
16.4%
충현동 16
 
11.9%
홍은제2동 6
 
4.5%
홍제제1동 5
 
3.7%
홍은제1동 5
 
3.7%
북가좌제1동 4
 
3.0%
남가좌제2동 4
 
3.0%
천연동 4
 
3.0%
북가좌제2동 3
 
2.2%
Other values (3) 5
 
3.7%

Length

2024-05-11T15:09:13.198353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신촌동 60
44.8%
연희동 22
 
16.4%
충현동 16
 
11.9%
홍은제2동 6
 
4.5%
홍제제1동 5
 
3.7%
홍은제1동 5
 
3.7%
북가좌제1동 4
 
3.0%
남가좌제2동 4
 
3.0%
천연동 4
 
3.0%
북가좌제2동 3
 
2.2%
Other values (3) 5
 
3.7%

급수시설구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
상수도전용
113 
<NA>
17 
상수도(음용)지하수(주방용)겸용
 
4

Length

Max length17
Median length5
Mean length5.2313433
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 113
84.3%
<NA> 17
 
12.7%
상수도(음용)지하수(주방용)겸용 4
 
3.0%

Length

2024-05-11T15:09:13.410017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:09:13.607619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 113
84.3%
na 17
 
12.7%
상수도(음용)지하수(주방용)겸용 4
 
3.0%

Interactions

2024-05-11T15:09:02.444291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:56.946171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:58.115466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:59.015594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:59.969061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:01.173592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:02.607778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:57.085908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:58.254767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:59.177027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:00.146461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:01.384841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:02.795340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:57.249880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:58.428092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:59.332558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:00.326754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:01.614551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:02.974349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:57.696087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:58.581453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:59.516880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:00.510481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:01.846230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:03.174926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:57.838295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:58.736157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:59.680938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:00.741931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:02.054677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:03.336666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:57.990283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:58.880658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:08:59.828932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:00.985836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:09:02.286217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:09:13.778224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
지정년도1.0000.6320.7421.0000.3020.8500.9090.9000.8980.2530.2130.8100.0000.3060.000
지정번호0.6321.0000.6060.6320.0000.9490.9550.9540.9560.2930.0000.6000.4130.2730.246
신청일자0.7420.6061.0000.7420.4580.0000.0000.0000.0000.2140.3740.9380.0000.0000.000
지정일자1.0000.6320.7421.0000.3020.8500.9090.9000.8980.2530.2130.8100.0000.3060.000
취소일자0.3020.0000.4580.3021.0000.9800.9790.9780.9790.0820.9190.0000.3380.3650.133
업소명0.8500.9490.0000.8500.9801.0001.0001.0001.0001.0000.9280.9340.9990.9981.000
소재지도로명0.9090.9550.0000.9090.9791.0001.0001.0001.0001.0000.9120.9521.0001.0001.000
소재지지번0.9000.9540.0000.9000.9781.0001.0001.0001.0000.9960.9340.9381.0001.0001.000
허가(신고)번호0.8980.9560.0000.8980.9791.0001.0001.0001.0001.0000.9330.9481.0001.0001.000
업태명0.2530.2930.2140.2530.0821.0001.0000.9961.0001.0000.0000.8600.3700.0000.000
지정취소사유0.2130.0000.3740.2130.9190.9280.9120.9340.9330.0001.0000.0000.6820.6610.139
주된음식0.8100.6000.9380.8100.0000.9340.9520.9380.9480.8600.0001.0000.8220.4670.774
영업장면적(㎡)0.0000.4130.0000.0000.3380.9991.0001.0001.0000.3700.6820.8221.0000.0000.571
행정동명0.3060.2730.0000.3060.3650.9981.0001.0001.0000.0000.6610.4670.0001.0000.000
급수시설구분0.0000.2460.0000.0000.1331.0001.0001.0001.0000.0000.1390.7740.5710.0001.000
2024-05-11T15:09:14.021116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명지정취소사유행정동명
급수시설구분1.0000.0000.1150.000
업태명0.0001.0000.0000.000
지정취소사유0.1150.0001.0000.304
행정동명0.0000.0000.3041.000
2024-05-11T15:09:14.214239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명지정취소사유행정동명급수시설구분
지정년도1.0000.0320.7841.0000.402-0.0560.0810.3000.1260.000
지정번호0.0321.0000.2150.0340.1170.0060.1260.0000.1110.180
신청일자0.7840.2151.0000.7840.499-0.0090.0380.3650.0000.000
지정일자1.0000.0340.7841.0000.402-0.0540.0810.3000.1260.000
취소일자0.4020.1170.4990.4021.000-0.0520.0390.7010.1190.098
영업장면적(㎡)-0.0560.006-0.009-0.054-0.0521.0000.1650.3230.0000.425
업태명0.0810.1260.0380.0810.0390.1651.0000.0000.0000.000
지정취소사유0.3000.0000.3650.3000.7010.3230.0001.0000.3040.115
행정동명0.1260.1110.0000.1260.1190.0000.0000.3041.0000.000
급수시설구분0.0000.1800.0000.0000.0980.4250.0000.1150.0001.000

Missing values

2024-05-11T15:09:03.563690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:09:03.971645image/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.
2024-05-11T15:09:04.218999image/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

시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
03120000201412201410102014110520221108벙커원서울특별시 서대문구 충정로 20, 1층 (충정로3가)서울특별시 서대문구 충정로3가 270번지 1층3120000-101-2002-00590호프/통닭새올대장정리햄버거256.81충현동상수도전용
131200002014141201410102014110520221108이코노스시서울특별시 서대문구 신촌역로 14, (대현동)서울특별시 서대문구 대현동 101번지 5호3120000-101-1994-04213정종/대포집/소주방새올대장정리회덮밥87.36신촌동상수도전용
231200002014151201410102014110520221108이화수전통육개장(충정로점)서울특별시 서대문구 충정로 18, 1층 (충정로3가)서울특별시 서대문구 충정로3가 360번지 24호 1층3120000-101-2000-03500한식새올대장정리설렁탕132.0충현동상수도전용
3312000020143201410102014110520201103한촌설렁탕 북가좌점서울특별시 서대문구 응암로 67, (북가좌동)서울특별시 서대문구 북가좌동 307번지 1호3120000-101-1994-05535한식행정처분설렁탕109.09북가좌제1동상수도전용
4312000020149201410102014110520221108인도야시장서울특별시 서대문구 연세로 20, (창천동,지상3층)서울특별시 서대문구 창천동 9번지 20호 지상3층3120000-101-2002-00539기타새올대장정리카레덮밥82.5신촌동<NA>
5312000020141201410102014110520221108족발야시장 남가좌점서울특별시 서대문구 증가로 117-1, 1층 (남가좌동)서울특별시 서대문구 남가좌동 338번지 5호 1층3120000-101-1999-09211한식새올대장정리돼지고기구이104.26남가좌제2동상수도전용
631200002014148201410102014110520221108라장 훠궈서울특별시 서대문구 연세로 37, 지하1층 (창천동)서울특별시 서대문구 창천동 33번지 4호3120000-101-2011-00162한식새올대장정리모듬초밥185.0신촌동상수도전용
73120000201419201410102014110520221108등촌 샤브칼국수서울특별시 서대문구 모래내로 273, (홍은동,(지상1,2층))서울특별시 서대문구 홍은동 415번지 6호 (지상1,2층)3120000-101-2003-00373한식새올대장정리샤브샤브141.87홍은제2동상수도전용
831200002014150201410102014110520221108손큰할매순대국 서대문역점서울특별시 서대문구 통일로9안길 38, 1층 (충정로2가)서울특별시 서대문구 충정로2가 26번지 1층 전체3120000-101-2002-00217한식새올대장정리짜장면100.5충현동<NA>
931200002014152201410102014110520221108하남돼지집서울특별시 서대문구 충정로 71-1, (충정로2가)서울특별시 서대문구 충정로2가 89번지3120000-101-2001-05291통닭(치킨)새올대장정리참치정식77.89충현동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
1243120000200446200903272004102020100726한우본가갈비서울특별시 서대문구 홍은중앙로 81, (홍은동,동일아파트 상가1층 2,3호)서울특별시 서대문구 홍은동 457번지 동일아파트 상가1층 2,3호3120000-101-1993-01760한식기준미달<NA>111.6홍은제1동상수도전용
1253120000200490200410182004102020221108연희동 영월서울특별시 서대문구 연희로 81-21, 2층 (연희동)서울특별시 서대문구 연희동 193번지 17호 2층3120000-101-1997-03826한식새올대장정리낙지찜110.67연희동상수도전용
1263120000200487200903122004102020221108명물갈비서울특별시 서대문구 명물길 43-3, (창천동)서울특별시 서대문구 창천동 2번지 28호3120000-101-1997-00731한식새올대장정리한식97.18신촌동상수도전용
1273120000200487200410182004102020221108명물갈비서울특별시 서대문구 명물길 43-3, (창천동)서울특별시 서대문구 창천동 2번지 28호3120000-101-1997-00731한식새올대장정리소갈비97.18신촌동상수도전용
12831200001999146201110201999041020221108장어세상서울특별시 서대문구 수색로10길 11, (북가좌동)서울특별시 서대문구 북가좌동 371번지 11호3120000-101-2000-10447한식새올대장정리등심109.0북가좌제1동<NA>
1293120000199983200903121999041020091217맘스터치 신촌점서울특별시 서대문구 명물길 16, (창천동)서울특별시 서대문구 창천동 13번지 28호3120000-101-1983-01540한식행정처분일식92.44신촌동상수도전용
130312000019870198705131987051320030605철길부산집 신촌점서울특별시 서대문구 연세로7안길 10-3, 1층 (창천동)서울특별시 서대문구 창천동 52번지 43호3120000-101-1986-10345한식영업자지위승계갈비탕61.2신촌동상수도전용
131312000019870198705131987051319981020여향서울특별시 서대문구 연희로 353, (홍은동)서울특별시 서대문구 홍은동 201번지 1호3120000-101-1987-09032중국식자진폐업갈비탕401.31홍은제1동상수도전용
132312000019870198705131987051319961021너스레서울특별시 서대문구 이화여대1안길 8-3, 2층 (대현동)서울특별시 서대문구 대현동 90번지 3호3120000-101-1986-01539경양식자진폐업갈비탕68.0신촌동상수도전용
133312000019870198705131987051320091217맘스터치 신촌점서울특별시 서대문구 명물길 16, (창천동)서울특별시 서대문구 창천동 13번지 28호3120000-101-1983-01540한식행정처분갈비탕92.44신촌동상수도전용