Overview

Dataset statistics

Number of variables16
Number of observations81
Missing cells6
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.8 KiB
Average record size in memory136.6 B

Variable types

Categorical4
Numeric6
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,지정취소사유,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author광진구
URLhttps://data.seoul.go.kr/dataList/OA-9927/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 7 other fieldsHigh correlation
업태명 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
지정취소사유 has 5 (6.2%) missing valuesMissing
주된음식 has 1 (1.2%) missing valuesMissing

Reproduction

Analysis started2024-05-11 06:50:32.456085
Analysis finished2024-05-11 06:50:42.066407
Duration9.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
3040000
81 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3040000
2nd row3040000
3rd row3040000
4th row3040000
5th row3040000

Common Values

ValueCountFrequency (%)
3040000 81
100.0%

Length

2024-05-11T15:50:42.195061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:50:42.364537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3040000 81
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5926
Minimum1998
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-05-11T15:50:42.533110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile1998
Q12002
median2005
Q32008
95-th percentile2016
Maximum2019
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9466599
Coefficient of variation (CV)0.0024664331
Kurtosis0.68024383
Mean2005.5926
Median Absolute Deviation (MAD)3
Skewness0.87111534
Sum162453
Variance24.469444
MonotonicityNot monotonic
2024-05-11T15:50:42.734101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2005 12
14.8%
2006 9
11.1%
2008 9
11.1%
2001 8
9.9%
2002 8
9.9%
2007 5
 
6.2%
1998 5
 
6.2%
2004 4
 
4.9%
2016 4
 
4.9%
2003 3
 
3.7%
Other values (9) 14
17.3%
ValueCountFrequency (%)
1998 5
6.2%
1999 2
 
2.5%
2000 2
 
2.5%
2001 8
9.9%
2002 8
9.9%
2003 3
 
3.7%
2004 4
 
4.9%
2005 12
14.8%
2006 9
11.1%
2007 5
6.2%
ValueCountFrequency (%)
2019 2
 
2.5%
2017 1
 
1.2%
2016 4
4.9%
2015 1
 
1.2%
2013 1
 
1.2%
2011 1
 
1.2%
2010 1
 
1.2%
2009 3
 
3.7%
2008 9
11.1%
2007 5
6.2%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.6667
Minimum2
Maximum5655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-05-11T15:50:42.960338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q15244
median5416
Q35503
95-th percentile5601
Maximum5655
Range5653
Interquartile range (IQR)259

Descriptive statistics

Standard deviation1712.2224
Coefficient of variation (CV)0.35673777
Kurtosis4.3525064
Mean4799.6667
Median Absolute Deviation (MAD)139
Skewness-2.4830024
Sum388773
Variance2931705.4
MonotonicityNot monotonic
2024-05-11T15:50:43.206780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2
 
2.5%
5397 2
 
2.5%
9 2
 
2.5%
6 2
 
2.5%
2 2
 
2.5%
5252 1
 
1.2%
5526 1
 
1.2%
5384 1
 
1.2%
5474 1
 
1.2%
5012 1
 
1.2%
Other values (66) 66
81.5%
ValueCountFrequency (%)
2 2
2.5%
3 1
1.2%
6 2
2.5%
9 2
2.5%
10 2
2.5%
5012 1
1.2%
5026 1
1.2%
5031 1
1.2%
5042 1
1.2%
5059 1
1.2%
ValueCountFrequency (%)
5655 1
1.2%
5638 1
1.2%
5614 1
1.2%
5603 1
1.2%
5601 1
1.2%
5592 1
1.2%
5588 1
1.2%
5584 1
1.2%
5581 1
1.2%
5579 1
1.2%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20056781
Minimum19980220
Maximum20190910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-05-11T15:50:43.434847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19980220
5-th percentile19980220
Q120021230
median20051111
Q320080430
95-th percentile20161021
Maximum20190910
Range210690
Interquartile range (IQR)59200

Descriptive statistics

Standard deviation49415.434
Coefficient of variation (CV)0.0024637769
Kurtosis0.71236087
Mean20056781
Median Absolute Deviation (MAD)29881
Skewness0.87797738
Sum1.6245993 × 109
Variance2.4418851 × 109
MonotonicityNot monotonic
2024-05-11T15:50:43.679962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20051111 9
 
11.1%
20011218 6
 
7.4%
19980220 5
 
6.2%
20021230 5
 
6.2%
20061023 5
 
6.2%
20081103 5
 
6.2%
20070427 4
 
4.9%
20080430 4
 
4.9%
20050705 4
 
4.9%
20161021 4
 
4.9%
Other values (20) 30
37.0%
ValueCountFrequency (%)
19980220 5
6.2%
19990730 2
 
2.5%
20000630 2
 
2.5%
20010517 1
 
1.2%
20011218 6
7.4%
20020523 1
 
1.2%
20020917 1
 
1.2%
20021010 2
 
2.5%
20021230 5
6.2%
20030530 1
 
1.2%
ValueCountFrequency (%)
20190910 2
 
2.5%
20170915 1
 
1.2%
20161021 4
4.9%
20151013 1
 
1.2%
20130730 1
 
1.2%
20111201 1
 
1.2%
20100618 1
 
1.2%
20090910 1
 
1.2%
20090907 2
 
2.5%
20081103 5
6.2%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20056819
Minimum19980220
Maximum20191029
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-05-11T15:50:43.954341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19980220
5-th percentile19980220
Q120021030
median20051111
Q320080620
95-th percentile20161021
Maximum20191029
Range210809
Interquartile range (IQR)59590

Descriptive statistics

Standard deviation49550.803
Coefficient of variation (CV)0.0024705215
Kurtosis0.68214211
Mean20056819
Median Absolute Deviation (MAD)29881
Skewness0.86551806
Sum1.6246023 × 109
Variance2.4552821 × 109
MonotonicityNot monotonic
2024-05-11T15:50:44.206235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20051111 8
 
9.9%
20011218 6
 
7.4%
20061128 5
 
6.2%
19980220 5
 
6.2%
20080620 4
 
4.9%
20060510 4
 
4.9%
20021230 4
 
4.9%
20081201 4
 
4.9%
20050705 4
 
4.9%
20161021 4
 
4.9%
Other values (20) 33
40.7%
ValueCountFrequency (%)
19980220 5
6.2%
19990730 2
 
2.5%
20000630 2
 
2.5%
20010517 2
 
2.5%
20011218 6
7.4%
20020523 1
 
1.2%
20021010 2
 
2.5%
20021030 1
 
1.2%
20021230 4
4.9%
20030630 1
 
1.2%
ValueCountFrequency (%)
20191029 2
2.5%
20171018 1
 
1.2%
20161021 4
4.9%
20151013 1
 
1.2%
20130912 1
 
1.2%
20111201 1
 
1.2%
20100720 1
 
1.2%
20091105 3
3.7%
20081201 4
4.9%
20081103 1
 
1.2%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20125266
Minimum20040108
Maximum20221031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-05-11T15:50:44.564926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040108
5-th percentile20060622
Q120071130
median20110906
Q320171207
95-th percentile20220217
Maximum20221031
Range180923
Interquartile range (IQR)100077

Descriptive statistics

Standard deviation54721.311
Coefficient of variation (CV)0.0027190354
Kurtosis-1.1629989
Mean20125266
Median Absolute Deviation (MAD)40277
Skewness0.43240647
Sum1.6301466 × 109
Variance2.9944219 × 109
MonotonicityNot monotonic
2024-05-11T15:50:44.856283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
20070629 7
 
8.6%
20110906 5
 
6.2%
20071130 4
 
4.9%
20201111 4
 
4.9%
20211129 4
 
4.9%
20121108 4
 
4.9%
20100427 3
 
3.7%
20191029 3
 
3.7%
20101130 3
 
3.7%
20221031 3
 
3.7%
Other values (39) 41
50.6%
ValueCountFrequency (%)
20040108 1
1.2%
20060307 1
1.2%
20060317 1
1.2%
20060613 1
1.2%
20060622 1
1.2%
20060627 1
1.2%
20060725 1
1.2%
20060803 1
1.2%
20060811 1
1.2%
20060831 1
1.2%
ValueCountFrequency (%)
20221031 3
3.7%
20220325 1
 
1.2%
20220217 1
 
1.2%
20220207 1
 
1.2%
20211129 4
4.9%
20201111 4
4.9%
20191029 3
3.7%
20190418 1
 
1.2%
20180710 1
 
1.2%
20180322 1
 
1.2%

업소명
Text

Distinct75
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size780.0 B
2024-05-11T15:50:45.295579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length6.4197531
Min length2

Characters and Unicode

Total characters520
Distinct characters194
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

Unique69 ?
Unique (%)85.2%

Sample

1st row할매시골청국장
2nd row장원닭한마리
3rd row중국성
4th row늘봄참숯갈비
5th row청도양꼬치2호점
ValueCountFrequency (%)
원조두부촌 2
 
2.3%
깐부치킨광장점 2
 
2.3%
계진상 2
 
2.3%
한마음정육식당 2
 
2.3%
구의강변점 2
 
2.3%
마포돼지갈비 2
 
2.3%
장수마을정육식당 2
 
2.3%
건대점 2
 
2.3%
채선당플러스강변역점 1
 
1.1%
함흥본가면옥 1
 
1.1%
Other values (70) 70
79.5%
2024-05-11T15:50:46.004400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 20
 
3.8%
장 12
 
2.3%
마 11
 
2.1%
갈 10
 
1.9%
비 9
 
1.7%
이 8
 
1.5%
국 8
 
1.5%
당 8
 
1.5%
자 7
 
1.3%
대 7
 
1.3%
Other values (184) 420
80.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 507
97.5%
Space Separator 7
 
1.3%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Other Punctuation 1
 
0.2%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 20
 
3.9%
장 12
 
2.4%
마 11
 
2.2%
갈 10
 
2.0%
비 9
 
1.8%
이 8
 
1.6%
국 8
 
1.6%
당 8
 
1.6%
자 7
 
1.4%
대 7
 
1.4%
Other values (179) 407
80.3%
Space Separator
ValueCountFrequency (%)
7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 507
97.5%
Common 13
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 20
 
3.9%
장 12
 
2.4%
마 11
 
2.2%
갈 10
 
2.0%
비 9
 
1.8%
이 8
 
1.6%
국 8
 
1.6%
당 8
 
1.6%
자 7
 
1.4%
대 7
 
1.4%
Other values (179) 407
80.3%
Common
ValueCountFrequency (%)
7
53.8%
) 2
 
15.4%
( 2
 
15.4%
& 1
 
7.7%
2 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 507
97.5%
ASCII 13
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 20
 
3.9%
장 12
 
2.4%
마 11
 
2.2%
갈 10
 
2.0%
비 9
 
1.8%
이 8
 
1.6%
국 8
 
1.6%
당 8
 
1.6%
자 7
 
1.4%
대 7
 
1.4%
Other values (179) 407
80.3%
ASCII
ValueCountFrequency (%)
7
53.8%
) 2
 
15.4%
( 2
 
15.4%
& 1
 
7.7%
2 1
 
7.7%

소재지도로명
Text

Distinct74
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size780.0 B
2024-05-11T15:50:46.628350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length37
Mean length27.271605
Min length23

Characters and Unicode

Total characters2209
Distinct characters79
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

Unique67 ?
Unique (%)82.7%

Sample

1st row서울특별시 광진구 아차산로 327, 1층 (자양동)
2nd row서울특별시 광진구 뚝섬로52길 8, (자양동,(101호))
3rd row서울특별시 광진구 뚝섬로 741, (구의동,,22)
4th row서울특별시 광진구 자양번영로 20, (자양동)
5th row서울특별시 광진구 동일로18길 69, (자양동,(1층))
ValueCountFrequency (%)
서울특별시 81
18.8%
광진구 81
18.8%
구의동 15
 
3.5%
자양동 14
 
3.3%
화양동 13
 
3.0%
군자동 12
 
2.8%
중곡동 11
 
2.6%
아차산로 10
 
2.3%
능동로 7
 
1.6%
1층 7
 
1.6%
Other values (123) 179
41.6%
2024-05-11T15:50:47.421593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
349
 
15.8%
구 106
 
4.8%
동 105
 
4.8%
, 93
 
4.2%
광 90
 
4.1%
) 85
 
3.8%
( 85
 
3.8%
진 82
 
3.7%
서 81
 
3.7%
로 81
 
3.7%
Other values (69) 1052
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1273
57.6%
Space Separator 349
 
15.8%
Decimal Number 315
 
14.3%
Other Punctuation 93
 
4.2%
Close Punctuation 85
 
3.8%
Open Punctuation 85
 
3.8%
Math Symbol 5
 
0.2%
Dash Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 106
 
8.3%
동 105
 
8.2%
광 90
 
7.1%
진 82
 
6.4%
서 81
 
6.4%
로 81
 
6.4%
울 81
 
6.4%
시 81
 
6.4%
별 81
 
6.4%
특 81
 
6.4%
Other values (53) 404
31.7%
Decimal Number
ValueCountFrequency (%)
1 67
21.3%
2 51
16.2%
3 40
12.7%
5 27
8.6%
0 27
8.6%
8 26
 
8.3%
4 24
 
7.6%
6 23
 
7.3%
7 16
 
5.1%
9 14
 
4.4%
Space Separator
ValueCountFrequency (%)
349
100.0%
Other Punctuation
ValueCountFrequency (%)
, 93
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1273
57.6%
Common 936
42.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 106
 
8.3%
동 105
 
8.2%
광 90
 
7.1%
진 82
 
6.4%
서 81
 
6.4%
로 81
 
6.4%
울 81
 
6.4%
시 81
 
6.4%
별 81
 
6.4%
특 81
 
6.4%
Other values (53) 404
31.7%
Common
ValueCountFrequency (%)
349
37.3%
, 93
 
9.9%
) 85
 
9.1%
( 85
 
9.1%
1 67
 
7.2%
2 51
 
5.4%
3 40
 
4.3%
5 27
 
2.9%
0 27
 
2.9%
8 26
 
2.8%
Other values (6) 86
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1273
57.6%
ASCII 936
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
349
37.3%
, 93
 
9.9%
) 85
 
9.1%
( 85
 
9.1%
1 67
 
7.2%
2 51
 
5.4%
3 40
 
4.3%
5 27
 
2.9%
0 27
 
2.9%
8 26
 
2.8%
Other values (6) 86
 
9.2%
Hangul
ValueCountFrequency (%)
구 106
 
8.3%
동 105
 
8.2%
광 90
 
7.1%
진 82
 
6.4%
서 81
 
6.4%
로 81
 
6.4%
울 81
 
6.4%
시 81
 
6.4%
별 81
 
6.4%
특 81
 
6.4%
Other values (53) 404
31.7%

소재지지번
Text

Distinct74
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size780.0 B
2024-05-11T15:50:47.936608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length33
Mean length25.975309
Min length22

Characters and Unicode

Total characters2104
Distinct characters55
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

Unique67 ?
Unique (%)82.7%

Sample

1st row서울특별시 광진구 자양동 774번지 28호 1층
2nd row서울특별시 광진구 자양동 606번지 28호 (101호)
3rd row서울특별시 광진구 구의동 593번지 19호 ,22
4th row서울특별시 광진구 자양동 600번지
5th row서울특별시 광진구 자양동 9번지 6호 (1층)
ValueCountFrequency (%)
서울특별시 81
19.3%
광진구 81
19.3%
자양동 19
 
4.5%
구의동 19
 
4.5%
화양동 13
 
3.1%
군자동 12
 
2.9%
중곡동 11
 
2.6%
2호 8
 
1.9%
1층 6
 
1.4%
1호 6
 
1.4%
Other values (110) 163
38.9%
2024-05-11T15:50:48.915006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
565
26.9%
구 100
 
4.8%
광 85
 
4.0%
지 82
 
3.9%
서 81
 
3.8%
특 81
 
3.8%
별 81
 
3.8%
시 81
 
3.8%
진 81
 
3.8%
울 81
 
3.8%
Other values (45) 786
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1161
55.2%
Space Separator 565
26.9%
Decimal Number 367
 
17.4%
Close Punctuation 4
 
0.2%
Open Punctuation 4
 
0.2%
Other Punctuation 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 100
 
8.6%
광 85
 
7.3%
지 82
 
7.1%
서 81
 
7.0%
특 81
 
7.0%
별 81
 
7.0%
시 81
 
7.0%
진 81
 
7.0%
울 81
 
7.0%
동 81
 
7.0%
Other values (30) 327
28.2%
Decimal Number
ValueCountFrequency (%)
1 77
21.0%
2 55
15.0%
5 40
10.9%
3 35
9.5%
0 32
8.7%
4 32
8.7%
9 31
8.4%
6 25
 
6.8%
7 24
 
6.5%
8 16
 
4.4%
Space Separator
ValueCountFrequency (%)
565
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1161
55.2%
Common 943
44.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 100
 
8.6%
광 85
 
7.3%
지 82
 
7.1%
서 81
 
7.0%
특 81
 
7.0%
별 81
 
7.0%
시 81
 
7.0%
진 81
 
7.0%
울 81
 
7.0%
동 81
 
7.0%
Other values (30) 327
28.2%
Common
ValueCountFrequency (%)
565
59.9%
1 77
 
8.2%
2 55
 
5.8%
5 40
 
4.2%
3 35
 
3.7%
0 32
 
3.4%
4 32
 
3.4%
9 31
 
3.3%
6 25
 
2.7%
7 24
 
2.5%
Other values (5) 27
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1161
55.2%
ASCII 943
44.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
565
59.9%
1 77
 
8.2%
2 55
 
5.8%
5 40
 
4.2%
3 35
 
3.7%
0 32
 
3.4%
4 32
 
3.4%
9 31
 
3.3%
6 25
 
2.7%
7 24
 
2.5%
Other values (5) 27
 
2.9%
Hangul
ValueCountFrequency (%)
구 100
 
8.6%
광 85
 
7.3%
지 82
 
7.1%
서 81
 
7.0%
특 81
 
7.0%
별 81
 
7.0%
시 81
 
7.0%
진 81
 
7.0%
울 81
 
7.0%
동 81
 
7.0%
Other values (30) 327
28.2%

허가(신고)번호
Text

Distinct75
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size780.0 B
2024-05-11T15:50:49.335556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1782
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

Unique69 ?
Unique (%)85.2%

Sample

1st row3040000-101-2002-00501
2nd row3040000-101-2007-00301
3rd row3040000-101-1998-02593
4th row3040000-101-1995-00267
5th row3040000-101-2011-00271
ValueCountFrequency (%)
3040000-101-1995-01516 2
 
2.5%
3040000-101-1994-00474 2
 
2.5%
3040000-101-1995-05250 2
 
2.5%
3040000-101-1996-01769 2
 
2.5%
3040000-101-1991-01236 2
 
2.5%
3040000-101-2004-00099 2
 
2.5%
3040000-101-2002-00501 1
 
1.2%
3040000-101-1992-00892 1
 
1.2%
3040000-101-2005-00281 1
 
1.2%
3040000-101-1997-06090 1
 
1.2%
Other values (65) 65
80.2%
2024-05-11T15:50:49.870961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 727
40.8%
1 259
 
14.5%
- 243
 
13.6%
4 120
 
6.7%
3 113
 
6.3%
9 108
 
6.1%
2 75
 
4.2%
5 39
 
2.2%
8 39
 
2.2%
6 35
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1539
86.4%
Dash Punctuation 243
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 727
47.2%
1 259
 
16.8%
4 120
 
7.8%
3 113
 
7.3%
9 108
 
7.0%
2 75
 
4.9%
5 39
 
2.5%
8 39
 
2.5%
6 35
 
2.3%
7 24
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1782
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 727
40.8%
1 259
 
14.5%
- 243
 
13.6%
4 120
 
6.7%
3 113
 
6.3%
9 108
 
6.1%
2 75
 
4.2%
5 39
 
2.2%
8 39
 
2.2%
6 35
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1782
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 727
40.8%
1 259
 
14.5%
- 243
 
13.6%
4 120
 
6.7%
3 113
 
6.3%
9 108
 
6.1%
2 75
 
4.2%
5 39
 
2.2%
8 39
 
2.2%
6 35
 
2.0%

업태명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size780.0 B
한식
46 
일식
9 
식육(숯불구이)
8 
중국식
5 
호프/통닭
5 
Other values (3)
8 

Length

Max length15
Median length2
Mean length3
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row한식
2nd row한식
3rd row중국식
4th row식육(숯불구이)
5th row중국식

Common Values

ValueCountFrequency (%)
한식 46
56.8%
일식 9
 
11.1%
식육(숯불구이) 8
 
9.9%
중국식 5
 
6.2%
호프/통닭 5
 
6.2%
분식 4
 
4.9%
기타 3
 
3.7%
외국음식전문점(인도,태국등) 1
 
1.2%

Length

2024-05-11T15:50:50.154238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:50:50.386888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 46
56.8%
일식 9
 
11.1%
식육(숯불구이 8
 
9.9%
중국식 5
 
6.2%
호프/통닭 5
 
6.2%
분식 4
 
4.9%
기타 3
 
3.7%
외국음식전문점(인도,태국등 1
 
1.2%

지정취소사유
Text

MISSING 

Distinct40
Distinct (%)52.6%
Missing5
Missing (%)6.2%
Memory size780.0 B
2024-05-11T15:50:50.864362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length9.8026316
Min length3

Characters and Unicode

Total characters745
Distinct characters104
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

Unique30 ?
Unique (%)39.5%

Sample

1st row시설 및 위생미흡
2nd row기준 미달
3rd row2008.7.14 업태 및 명의 변경
4th row행정처분
5th row주취급메뉴 및 영업주변경
ValueCountFrequency (%)
및 17
 
9.9%
영업자지위승계 15
 
8.7%
업태 12
 
7.0%
행정처분 8
 
4.7%
영업자변경 7
 
4.1%
변경 7
 
4.1%
자진취소 5
 
2.9%
영업주 5
 
2.9%
따른 5
 
2.9%
명의 4
 
2.3%
Other values (65) 87
50.6%
2024-05-11T15:50:51.572612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
97
 
13.0%
업 59
 
7.9%
영 37
 
5.0%
자 30
 
4.0%
경 24
 
3.2%
변 24
 
3.2%
주 21
 
2.8%
위 20
 
2.7%
지 19
 
2.6%
및 18
 
2.4%
Other values (94) 396
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 582
78.1%
Space Separator 97
 
13.0%
Decimal Number 30
 
4.0%
Other Punctuation 13
 
1.7%
Close Punctuation 9
 
1.2%
Open Punctuation 9
 
1.2%
Uppercase Letter 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 59
 
10.1%
영 37
 
6.4%
자 30
 
5.2%
경 24
 
4.1%
변 24
 
4.1%
주 21
 
3.6%
위 20
 
3.4%
지 19
 
3.3%
및 18
 
3.1%
계 17
 
2.9%
Other values (78) 313
53.8%
Decimal Number
ValueCountFrequency (%)
2 10
33.3%
0 6
20.0%
1 5
16.7%
4 3
 
10.0%
8 3
 
10.0%
6 1
 
3.3%
5 1
 
3.3%
7 1
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 9
69.2%
, 3
 
23.1%
: 1
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
C 4
80.0%
B 1
 
20.0%
Space Separator
ValueCountFrequency (%)
97
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 582
78.1%
Common 158
 
21.2%
Latin 5
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 59
 
10.1%
영 37
 
6.4%
자 30
 
5.2%
경 24
 
4.1%
변 24
 
4.1%
주 21
 
3.6%
위 20
 
3.4%
지 19
 
3.3%
및 18
 
3.1%
계 17
 
2.9%
Other values (78) 313
53.8%
Common
ValueCountFrequency (%)
97
61.4%
2 10
 
6.3%
) 9
 
5.7%
. 9
 
5.7%
( 9
 
5.7%
0 6
 
3.8%
1 5
 
3.2%
4 3
 
1.9%
8 3
 
1.9%
, 3
 
1.9%
Other values (4) 4
 
2.5%
Latin
ValueCountFrequency (%)
C 4
80.0%
B 1
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 582
78.1%
ASCII 163
 
21.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
97
59.5%
2 10
 
6.1%
) 9
 
5.5%
. 9
 
5.5%
( 9
 
5.5%
0 6
 
3.7%
1 5
 
3.1%
C 4
 
2.5%
4 3
 
1.8%
8 3
 
1.8%
Other values (6) 8
 
4.9%
Hangul
ValueCountFrequency (%)
업 59
 
10.1%
영 37
 
6.4%
자 30
 
5.2%
경 24
 
4.1%
변 24
 
4.1%
주 21
 
3.6%
위 20
 
3.4%
지 19
 
3.3%
및 18
 
3.1%
계 17
 
2.9%
Other values (78) 313
53.8%

주된음식
Text

MISSING 

Distinct57
Distinct (%)71.2%
Missing1
Missing (%)1.2%
Memory size780.0 B
2024-05-11T15:50:51.947448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.3875
Min length2

Characters and Unicode

Total characters271
Distinct characters102
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)53.8%

Sample

1st row곱창
2nd row갈비찜
3rd row자장면
4th row버섯전골
5th row양꼬치
ValueCountFrequency (%)
삼겹살 6
 
7.5%
감자탕 4
 
5.0%
생선회 3
 
3.8%
돼지갈비 3
 
3.8%
활어회 3
 
3.8%
설렁탕 2
 
2.5%
보쌈정식 2
 
2.5%
칼국수 2
 
2.5%
냉면 2
 
2.5%
족발 2
 
2.5%
Other values (47) 51
63.7%
2024-05-11T15:50:52.605624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 15
 
5.5%
비 11
 
4.1%
갈 9
 
3.3%
삼 8
 
3.0%
회 8
 
3.0%
살 7
 
2.6%
겹 7
 
2.6%
지 7
 
2.6%
국 5
 
1.8%
정 5
 
1.8%
Other values (92) 189
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 269
99.3%
Decimal Number 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 15
 
5.6%
비 11
 
4.1%
갈 9
 
3.3%
삼 8
 
3.0%
회 8
 
3.0%
살 7
 
2.6%
겹 7
 
2.6%
지 7
 
2.6%
국 5
 
1.9%
정 5
 
1.9%
Other values (91) 187
69.5%
Decimal Number
ValueCountFrequency (%)
0 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 269
99.3%
Common 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 15
 
5.6%
비 11
 
4.1%
갈 9
 
3.3%
삼 8
 
3.0%
회 8
 
3.0%
살 7
 
2.6%
겹 7
 
2.6%
지 7
 
2.6%
국 5
 
1.9%
정 5
 
1.9%
Other values (91) 187
69.5%
Common
ValueCountFrequency (%)
0 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 269
99.3%
ASCII 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 15
 
5.6%
비 11
 
4.1%
갈 9
 
3.3%
삼 8
 
3.0%
회 8
 
3.0%
살 7
 
2.6%
겹 7
 
2.6%
지 7
 
2.6%
국 5
 
1.9%
정 5
 
1.9%
Other values (91) 187
69.5%
ASCII
ValueCountFrequency (%)
0 2
100.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.06951
Minimum33.6
Maximum420.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-05-11T15:50:52.891306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.6
5-th percentile50.5
Q170.75
median110
Q3149.3
95-th percentile337.87
Maximum420.99
Range387.39
Interquartile range (IQR)78.55

Descriptive statistics

Standard deviation85.011347
Coefficient of variation (CV)0.65358399
Kurtosis3.2807192
Mean130.06951
Median Absolute Deviation (MAD)39.3
Skewness1.848216
Sum10535.63
Variance7226.9292
MonotonicityNot monotonic
2024-05-11T15:50:53.205861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337.87 2
 
2.5%
209.0 2
 
2.5%
140.24 2
 
2.5%
110.0 2
 
2.5%
156.02 2
 
2.5%
129.36 2
 
2.5%
67.07 1
 
1.2%
105.0 1
 
1.2%
167.48 1
 
1.2%
420.99 1
 
1.2%
Other values (65) 65
80.2%
ValueCountFrequency (%)
33.6 1
1.2%
37.91 1
1.2%
41.22 1
1.2%
50.45 1
1.2%
50.5 1
1.2%
51.8 1
1.2%
53.96 1
1.2%
54.52 1
1.2%
55.04 1
1.2%
56.2 1
1.2%
ValueCountFrequency (%)
420.99 1
1.2%
400.52 1
1.2%
386.25 1
1.2%
337.87 2
2.5%
336.72 1
1.2%
324.52 1
1.2%
238.8 1
1.2%
209.0 2
2.5%
194.7 1
1.2%
187.61 1
1.2%

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size780.0 B
화양동
13 
구의제3동
12 
군자동
12 
자양제1동
8 
자양제2동
6 
Other values (10)
30 

Length

Max length5
Median length5
Mean length4.1728395
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row자양제1동
2nd row자양제2동
3rd row구의제3동
4th row자양제2동
5th row자양제1동

Common Values

ValueCountFrequency (%)
화양동 13
16.0%
구의제3동 12
14.8%
군자동 12
14.8%
자양제1동 8
9.9%
자양제2동 6
7.4%
구의제1동 5
 
6.2%
중곡제1동 4
 
4.9%
광장동 4
 
4.9%
능동 3
 
3.7%
중곡제3동 3
 
3.7%
Other values (5) 11
13.6%

Length

2024-05-11T15:50:53.464559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
화양동 13
16.0%
구의제3동 12
14.8%
군자동 12
14.8%
자양제1동 8
9.9%
자양제2동 6
7.4%
구의제1동 5
 
6.2%
중곡제1동 4
 
4.9%
광장동 4
 
4.9%
능동 3
 
3.7%
중곡제3동 3
 
3.7%
Other values (5) 11
13.6%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size780.0 B
상수도전용
72 
<NA>
9 

Length

Max length5
Median length5
Mean length4.8888889
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 72
88.9%
<NA> 9
 
11.1%

Length

2024-05-11T15:50:53.706047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:50:53.872161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 72
88.9%
na 9
 
11.1%

Interactions

2024-05-11T15:50:39.854229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:34.448452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:35.526620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:36.695382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:37.813124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:38.744423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:40.027246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:34.596546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:35.719652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:36.862985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:37.976190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:38.904215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:40.175709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:34.746887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:35.896774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:37.066469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:38.135908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:39.053134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:40.342726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:34.914156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:36.083524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:37.279872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:38.289074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:39.226741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:40.502708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:35.111038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:36.280466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:37.460600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:38.439807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:39.438854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:40.681648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:35.319750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:36.487996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:37.650098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:38.589871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:50:39.619475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:50:54.004522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명
지정년도1.0000.7551.0001.0000.5300.0000.0000.0000.0000.3820.7690.7050.0000.481
지정번호0.7551.0000.7550.7550.6210.5780.7150.7150.5780.3950.0000.9910.1000.000
신청일자1.0000.7551.0001.0000.5530.0000.0000.0000.0000.4210.7630.5680.0000.530
지정일자1.0000.7551.0001.0000.5300.0000.0000.0000.0000.3820.7690.7050.0000.481
취소일자0.5300.6210.5530.5301.0000.7160.2620.2620.7160.0000.9630.0000.0000.473
업소명0.0000.5780.0000.0000.7161.0001.0001.0001.0001.0000.9720.9511.0001.000
소재지도로명0.0000.7150.0000.0000.2621.0001.0001.0001.0001.0000.8610.9330.9971.000
소재지지번0.0000.7150.0000.0000.2621.0001.0001.0001.0001.0000.8610.9330.9971.000
허가(신고)번호0.0000.5780.0000.0000.7161.0001.0001.0001.0001.0000.9720.9511.0001.000
업태명0.3820.3950.4210.3820.0001.0001.0001.0001.0001.0000.0000.9120.0000.429
지정취소사유0.7690.0000.7630.7690.9630.9720.8610.8610.9720.0001.0000.0000.0000.667
주된음식0.7050.9910.5680.7050.0000.9510.9330.9330.9510.9120.0001.0000.8300.000
영업장면적(㎡)0.0000.1000.0000.0000.0001.0000.9970.9971.0000.0000.0000.8301.0000.000
행정동명0.4810.0000.5300.4810.4731.0001.0001.0001.0000.4290.6670.0000.0001.000
2024-05-11T15:50:54.222568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명행정동명
급수시설구분1.0001.0001.000
업태명1.0001.0000.184
행정동명1.0000.1841.000
2024-05-11T15:50:54.373724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.0000.4580.9880.9970.4890.0600.1790.1461.000
지정번호0.4581.0000.4570.4630.096-0.0130.2630.0001.000
신청일자0.9880.4571.0000.9900.4960.0560.1960.1791.000
지정일자0.9970.4630.9901.0000.4960.0630.1790.1461.000
취소일자0.4890.0960.4960.4961.000-0.1270.0000.2341.000
영업장면적(㎡)0.060-0.0130.0560.063-0.1271.0000.0000.0001.000
업태명0.1790.2630.1960.1790.0000.0001.0000.1841.000
행정동명0.1460.0000.1790.1460.2340.0000.1841.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T15:50:41.282552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:50:41.675794image/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.
2024-05-11T15:50:41.944697image/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
0304000020015252200507052001051720071130할매시골청국장서울특별시 광진구 아차산로 327, 1층 (자양동)서울특별시 광진구 자양동 774번지 28호 1층3040000-101-2002-00501한식<NA>곱창41.22자양제1동상수도전용
1304000020085560200804302008062020101130장원닭한마리서울특별시 광진구 뚝섬로52길 8, (자양동,(101호))서울특별시 광진구 자양동 606번지 28호 (101호)3040000-101-2007-00301한식시설 및 위생미흡갈비찜59.84자양제2동상수도전용
2304000020025157200210102002101020080630중국성서울특별시 광진구 뚝섬로 741, (구의동,,22)서울특별시 광진구 구의동 593번지 19호 ,223040000-101-1998-02593중국식기준 미달자장면148.6구의제3동상수도전용
3304000020015268200112182001121820080930늘봄참숯갈비서울특별시 광진구 자양번영로 20, (자양동)서울특별시 광진구 자양동 600번지3040000-101-1995-00267식육(숯불구이)2008.7.14 업태 및 명의 변경버섯전골101.45자양제2동상수도전용
4304000020199201909102019102920220217청도양꼬치2호점서울특별시 광진구 동일로18길 69, (자양동,(1층))서울특별시 광진구 자양동 9번지 6호 (1층)3040000-101-2011-00271중국식행정처분양꼬치96.6자양제1동상수도전용
5304000020095603200909072009110520100427베트남쌍둥이커피서울특별시 광진구 능동로13길 28, (화양동)서울특별시 광진구 화양동 12번지 33호3040000-101-2008-00163한식주취급메뉴 및 영업주변경복국70.6화양동상수도전용
6304000020135655201307302013091220171207독도수산서울특별시 광진구 뚝섬로23길 48, 1층 (자양동)서울특별시 광진구 자양동 553번지 303호3040000-101-2005-00256일식영업주 변경에 따른 재심사시 등급 미달모듬회70.4자양제3동상수도전용
7304000020015276200112182001121820221031쭈요일서울특별시 광진구 능동로 389, (중곡동)서울특별시 광진구 중곡동 165번지 2호3040000-101-1998-02120한식영업주 변경에 의한 자진취소(영업주와 유선연락함)추어탕72.32중곡제1동상수도전용
8304000020085581200811032008120120201111명륜진사갈비아차산역점서울특별시 광진구 능동로36길 187, 공원갈비 1~2층 (능동)서울특별시 광진구 능동 256번지 14호 공원갈비3040000-101-2001-09594식육(숯불구이)업소 변경 확인오리훈제400.52능동상수도전용
9304000020055416200507052005070520130912황금돈삼겹살서울특별시 광진구 뚝섬로45길 15, (자양동)서울특별시 광진구 자양동 614번지 21호3040000-101-2003-00259한식등급미달제주뒷고기68.49자양제1동상수도전용
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
71304000020085584200811032008120120110906계진상서울특별시 광진구 아차산로 209, (화양동)서울특별시 광진구 화양동 48번지 4호3040000-101-1995-05250일식영업자지위승계<NA>129.36화양동상수도전용
72304000020115638201112012011120120211129계진상서울특별시 광진구 아차산로 209, (화양동)서울특별시 광진구 화양동 48번지 4호3040000-101-1995-05250일식자진취소생선회129.36화양동상수도전용
73304000020025322200212302002123020060725포베이강변테크노마트점서울특별시 광진구 광나루로56길 85, (구의동,테크노마트21 9층042호)서울특별시 광진구 구의동 546번지 4호 테크노마트21 9층042호3040000-101-1998-06833외국음식전문점(인도,태국등)영업자지위승계후라이드치킨98.4구의제3동상수도전용
74304000020035381200305302003063020191029세광양대창(군자점)서울특별시 광진구 능동로36길 16, (능동)서울특별시 광진구 능동 223번지 2호3040000-101-2002-00120한식연속2회 C등급오리고기117.05능동상수도전용
75304000020065470200604042006051020070629아차성서울특별시 광진구 천호대로129길 48, (구의동)서울특별시 광진구 구의동 29번지 12호3040000-101-2004-00201한식업태 및 영업자변경삼겹살51.8구의제2동상수도전용
76304000020085567200804302008062020101130오늘와인한잔서울특별시 광진구 동일로24길 102, 1층 (화양동)서울특별시 광진구 화양동 8번지 8호3040000-101-2005-00384기타폐업예정(모범 의사 없음)우삼겹92.25화양동상수도전용
77304000020065473200604042006051020160225조마루뼈다귀감자탕서울특별시 광진구 아차산로 208, (자양동)서울특별시 광진구 자양동 8번지 3호3040000-101-2005-00324한식행정처분감자탕127.07자양제4동상수도전용
78304000020169201610212016102120221031매운향솥서울특별시 광진구 동일로18길 61, (자양동)서울특별시 광진구 자양동 9번지 33호3040000-101-2007-00304중국식영업주 자진취소(영업주와 유선으로 연락함)샤브샤브60.3자양제4동상수도전용
79304000020035367200312032003120320070629베트남쌀국수 미스사이공 건대점서울특별시 광진구 능동로11길 8-13, 지1층 (화양동)서울특별시 광진구 화양동 5번지 108호3040000-101-1999-08431분식업태 및 영업자변경부대찌개55.04화양동상수도전용
80304000020045397200212302004092320080620기와집서울특별시 광진구 면목로 32, (군자동)서울특별시 광진구 군자동 470번지 1호3040000-101-1997-01982한식업태 및 명의 변경보쌈정식75.86군자동상수도전용