Overview

Dataset statistics

Number of variables16
Number of observations184
Missing cells8
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.4 KiB
Average record size in memory135.7 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
Author서초구
URLhttps://data.seoul.go.kr/dataList/OA-11082/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 지정번호 and 3 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
업태명 is highly imbalanced (53.4%)Imbalance
급수시설구분 is highly imbalanced (53.5%)Imbalance
소재지도로명 has 2 (1.1%) missing valuesMissing
주된음식 has 6 (3.3%) missing valuesMissing

Reproduction

Analysis started2024-05-11 07:12:45.648749
Analysis finished2024-05-11 07:12:51.174595
Duration5.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3210000
184 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3210000
2nd row3210000
3rd row3210000
4th row3210000
5th row3210000

Common Values

ValueCountFrequency (%)
3210000 184
100.0%

Length

2024-05-11T16:12:51.237071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:12:51.321846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3210000 184
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.5054
Minimum2003
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T16:12:51.413136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2003
Q12003
median2005
Q32009
95-th percentile2016
Maximum2022
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.5867441
Coefficient of variation (CV)0.0022859366
Kurtosis1.9061424
Mean2006.5054
Median Absolute Deviation (MAD)2
Skewness1.5463151
Sum369197
Variance21.038222
MonotonicityDecreasing
2024-05-11T16:12:51.523955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2003 78
42.4%
2009 16
 
8.7%
2006 16
 
8.7%
2004 12
 
6.5%
2007 11
 
6.0%
2005 10
 
5.4%
2008 8
 
4.3%
2010 7
 
3.8%
2014 5
 
2.7%
2013 5
 
2.7%
Other values (8) 16
 
8.7%
ValueCountFrequency (%)
2003 78
42.4%
2004 12
 
6.5%
2005 10
 
5.4%
2006 16
 
8.7%
2007 11
 
6.0%
2008 8
 
4.3%
2009 16
 
8.7%
2010 7
 
3.8%
2012 3
 
1.6%
2013 5
 
2.7%
ValueCountFrequency (%)
2022 1
 
0.5%
2021 2
 
1.1%
2020 4
2.2%
2019 1
 
0.5%
2018 1
 
0.5%
2016 2
 
1.1%
2015 2
 
1.1%
2014 5
2.7%
2013 5
2.7%
2012 3
1.6%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.46196
Minimum1
Maximum1320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T16:12:51.652602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.15
Q119
median46.5
Q3165
95-th percentile303.85
Maximum1320
Range1319
Interquartile range (IQR)146

Descriptive statistics

Standard deviation221.49381
Coefficient of variation (CV)1.7377248
Kurtosis19.835512
Mean127.46196
Median Absolute Deviation (MAD)37
Skewness4.1872962
Sum23453
Variance49059.507
MonotonicityNot monotonic
2024-05-11T16:12:51.795285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 7
 
3.8%
9 5
 
2.7%
20 5
 
2.7%
3 4
 
2.2%
17 4
 
2.2%
15 4
 
2.2%
30 4
 
2.2%
27 4
 
2.2%
4 3
 
1.6%
31 3
 
1.6%
Other values (115) 141
76.6%
ValueCountFrequency (%)
1 1
 
0.5%
2 2
 
1.1%
3 4
2.2%
4 3
1.6%
5 1
 
0.5%
6 3
1.6%
7 2
 
1.1%
9 5
2.7%
10 2
 
1.1%
11 3
1.6%
ValueCountFrequency (%)
1320 1
0.5%
1312 1
0.5%
1305 1
0.5%
1303 1
0.5%
1301 1
0.5%
338 1
0.5%
337 1
0.5%
318 1
0.5%
311 1
0.5%
304 1
0.5%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20064642
Minimum20030618
Maximum20221001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T16:12:51.915836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030618
5-th percentile20030618
Q120030618
median20050728
Q320090318
95-th percentile20151011
Maximum20221001
Range190383
Interquartile range (IQR)59700

Descriptive statistics

Standard deviation44524.437
Coefficient of variation (CV)0.0022190497
Kurtosis2.4361948
Mean20064642
Median Absolute Deviation (MAD)20110
Skewness1.6333739
Sum3.6918942 × 109
Variance1.9824255 × 109
MonotonicityNot monotonic
2024-05-11T16:12:52.309787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
20030618 78
42.4%
20060630 16
 
8.7%
20040719 12
 
6.5%
20070531 11
 
6.0%
20050728 10
 
5.4%
20090406 9
 
4.9%
20080530 8
 
4.3%
20131230 4
 
2.2%
20201001 3
 
1.6%
20090318 3
 
1.6%
Other values (25) 30
 
16.3%
ValueCountFrequency (%)
20030618 78
42.4%
20040719 12
 
6.5%
20050728 10
 
5.4%
20060630 16
 
8.7%
20070531 11
 
6.0%
20080530 8
 
4.3%
20090316 1
 
0.5%
20090318 3
 
1.6%
20090319 1
 
0.5%
20090326 1
 
0.5%
ValueCountFrequency (%)
20221001 1
 
0.5%
20211001 2
1.1%
20201202 1
 
0.5%
20201001 3
1.6%
20191001 1
 
0.5%
20181001 1
 
0.5%
20151012 1
 
0.5%
20151008 1
 
0.5%
20141231 2
1.1%
20140102 1
 
0.5%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20065760
Minimum20030718
Maximum20221129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T16:12:52.438985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030718
5-th percentile20030718
Q120030718
median20050728
Q320090724
95-th percentile20160720
Maximum20221129
Range190411
Interquartile range (IQR)60006

Descriptive statistics

Standard deviation45865.512
Coefficient of variation (CV)0.00228576
Kurtosis1.9507684
Mean20065760
Median Absolute Deviation (MAD)20010
Skewness1.5544228
Sum3.6920999 × 109
Variance2.1036452 × 109
MonotonicityDecreasing
2024-05-11T16:12:52.562287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20030718 78
42.4%
20090724 16
 
8.7%
20060727 16
 
8.7%
20040816 12
 
6.5%
20070726 11
 
6.0%
20050728 10
 
5.4%
20080805 8
 
4.3%
20100630 7
 
3.8%
20140121 5
 
2.7%
20130218 5
 
2.7%
Other values (8) 16
 
8.7%
ValueCountFrequency (%)
20030718 78
42.4%
20040816 12
 
6.5%
20050728 10
 
5.4%
20060727 16
 
8.7%
20070726 11
 
6.0%
20080805 8
 
4.3%
20090724 16
 
8.7%
20100630 7
 
3.8%
20120119 3
 
1.6%
20130218 5
 
2.7%
ValueCountFrequency (%)
20221129 1
 
0.5%
20211214 2
 
1.1%
20201202 4
2.2%
20191217 1
 
0.5%
20181218 1
 
0.5%
20160720 2
 
1.1%
20150225 2
 
1.1%
20140121 5
2.7%
20130218 5
2.7%
20120119 3
1.6%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20137216
Minimum20030719
Maximum20231114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T16:12:52.708145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030719
5-th percentile20040637
Q120080317
median20111014
Q320221231
95-th percentile20230960
Maximum20231114
Range200395
Interquartile range (IQR)140914

Descriptive statistics

Standard deviation73239.843
Coefficient of variation (CV)0.0036370391
Kurtosis-1.6454297
Mean20137216
Median Absolute Deviation (MAD)69905.5
Skewness0.10740703
Sum3.7052478 × 109
Variance5.3640746 × 109
MonotonicityNot monotonic
2024-05-11T16:12:52.848438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20221231 57
31.0%
20111014 14
 
7.6%
20080317 11
 
6.0%
20231114 9
 
4.9%
20140109 5
 
2.7%
20100107 3
 
1.6%
20091228 3
 
1.6%
20130130 3
 
1.6%
20050728 3
 
1.6%
20100113 3
 
1.6%
Other values (66) 73
39.7%
ValueCountFrequency (%)
20030719 1
0.5%
20030822 1
0.5%
20031008 1
0.5%
20031030 1
0.5%
20031208 1
0.5%
20040106 1
0.5%
20040309 1
0.5%
20040323 1
0.5%
20040617 1
0.5%
20040625 1
0.5%
ValueCountFrequency (%)
20231114 9
 
4.9%
20231109 1
 
0.5%
20230113 1
 
0.5%
20221231 57
31.0%
20221230 2
 
1.1%
20220616 1
 
0.5%
20200722 1
 
0.5%
20190208 1
 
0.5%
20151105 1
 
0.5%
20140109 5
 
2.7%

업소명
Text

Distinct172
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-11T16:12:53.115519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length6.3097826
Min length2

Characters and Unicode

Total characters1161
Distinct characters304
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)87.0%

Sample

1st row켈리포니아
2nd row신의주왕족발순대국
3rd row우나기야 주식회사
4th row금강바베큐
5th row신사정육식당 반포서래점
ValueCountFrequency (%)
교대점 4
 
1.6%
서초점 4
 
1.6%
주식회사 3
 
1.2%
이가네 2
 
0.8%
취향 2
 
0.8%
교대역점 2
 
0.8%
하남돼지집 2
 
0.8%
양재점 2
 
0.8%
양꼬치 2
 
0.8%
교촌치킨 2
 
0.8%
Other values (218) 233
90.3%
2024-05-11T16:12:53.504356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
74
 
6.4%
점 46
 
4.0%
대 22
 
1.9%
서 19
 
1.6%
이 17
 
1.5%
사 16
 
1.4%
가 16
 
1.4%
장 16
 
1.4%
동 15
 
1.3%
주 14
 
1.2%
Other values (294) 906
78.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1048
90.3%
Space Separator 74
 
6.4%
Close Punctuation 10
 
0.9%
Open Punctuation 10
 
0.9%
Uppercase Letter 7
 
0.6%
Decimal Number 6
 
0.5%
Lowercase Letter 5
 
0.4%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 46
 
4.4%
대 22
 
2.1%
서 19
 
1.8%
이 17
 
1.6%
사 16
 
1.5%
가 16
 
1.5%
장 16
 
1.5%
동 15
 
1.4%
주 14
 
1.3%
집 14
 
1.3%
Other values (274) 853
81.4%
Uppercase Letter
ValueCountFrequency (%)
E 1
14.3%
R 1
14.3%
A 1
14.3%
L 1
14.3%
V 1
14.3%
O 1
14.3%
P 1
14.3%
Lowercase Letter
ValueCountFrequency (%)
b 1
20.0%
y 1
20.0%
s 1
20.0%
u 1
20.0%
l 1
20.0%
Decimal Number
ValueCountFrequency (%)
9 2
33.3%
5 2
33.3%
1 1
16.7%
2 1
16.7%
Space Separator
ValueCountFrequency (%)
74
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1046
90.1%
Common 101
 
8.7%
Latin 12
 
1.0%
Han 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 46
 
4.4%
대 22
 
2.1%
서 19
 
1.8%
이 17
 
1.6%
사 16
 
1.5%
가 16
 
1.5%
장 16
 
1.5%
동 15
 
1.4%
주 14
 
1.3%
집 14
 
1.3%
Other values (272) 851
81.4%
Latin
ValueCountFrequency (%)
b 1
8.3%
y 1
8.3%
E 1
8.3%
R 1
8.3%
A 1
8.3%
L 1
8.3%
V 1
8.3%
O 1
8.3%
s 1
8.3%
u 1
8.3%
Other values (2) 2
16.7%
Common
ValueCountFrequency (%)
74
73.3%
) 10
 
9.9%
( 10
 
9.9%
9 2
 
2.0%
5 2
 
2.0%
1 1
 
1.0%
& 1
 
1.0%
2 1
 
1.0%
Han
ValueCountFrequency (%)
味 1
50.0%
鮮 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1046
90.1%
ASCII 113
 
9.7%
CJK 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
74
65.5%
) 10
 
8.8%
( 10
 
8.8%
9 2
 
1.8%
5 2
 
1.8%
b 1
 
0.9%
y 1
 
0.9%
E 1
 
0.9%
R 1
 
0.9%
A 1
 
0.9%
Other values (10) 10
 
8.8%
Hangul
ValueCountFrequency (%)
점 46
 
4.4%
대 22
 
2.1%
서 19
 
1.8%
이 17
 
1.6%
사 16
 
1.5%
가 16
 
1.5%
장 16
 
1.5%
동 15
 
1.4%
주 14
 
1.3%
집 14
 
1.3%
Other values (272) 851
81.4%
CJK
ValueCountFrequency (%)
味 1
50.0%
鮮 1
50.0%

소재지도로명
Text

MISSING 

Distinct169
Distinct (%)92.9%
Missing2
Missing (%)1.1%
Memory size1.6 KiB
2024-05-11T16:12:53.801054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length44
Mean length30.983516
Min length23

Characters and Unicode

Total characters5639
Distinct characters137
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)85.7%

Sample

1st row서울특별시 서초구 사평대로 349, 서초빌딩 109,110호 (반포동)
2nd row서울특별시 서초구 반포대로30길 34, 1층 (서초동)
3rd row서울특별시 서초구 서래로8길 6, 4층 (반포동)
4th row서울특별시 서초구 사평대로 349, (반포동)
5th row서울특별시 서초구 서래로 50, 한영빌딩 2층 (반포동)
ValueCountFrequency (%)
서울특별시 182
 
16.8%
서초구 182
 
16.8%
1층 76
 
7.0%
서초동 75
 
6.9%
방배동 30
 
2.8%
양재동 23
 
2.1%
효령로 12
 
1.1%
지하1층 11
 
1.0%
반포동 11
 
1.0%
8 11
 
1.0%
Other values (287) 471
43.5%
2024-05-11T16:12:54.261079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
902
 
16.0%
서 507
 
9.0%
초 319
 
5.7%
1 257
 
4.6%
, 251
 
4.5%
동 195
 
3.5%
( 189
 
3.4%
) 189
 
3.4%
시 183
 
3.2%
특 182
 
3.2%
Other values (127) 2465
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3185
56.5%
Space Separator 902
 
16.0%
Decimal Number 890
 
15.8%
Other Punctuation 252
 
4.5%
Open Punctuation 189
 
3.4%
Close Punctuation 189
 
3.4%
Dash Punctuation 17
 
0.3%
Uppercase Letter 8
 
0.1%
Lowercase Letter 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 507
15.9%
초 319
 
10.0%
동 195
 
6.1%
시 183
 
5.7%
특 182
 
5.7%
별 182
 
5.7%
구 182
 
5.7%
울 182
 
5.7%
로 180
 
5.7%
층 135
 
4.2%
Other values (100) 938
29.5%
Decimal Number
ValueCountFrequency (%)
1 257
28.9%
2 147
16.5%
0 88
 
9.9%
3 88
 
9.9%
4 82
 
9.2%
5 51
 
5.7%
8 50
 
5.6%
7 45
 
5.1%
6 45
 
5.1%
9 37
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
i 2
28.6%
l 1
14.3%
g 1
14.3%
n 1
14.3%
d 1
14.3%
u 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
B 3
37.5%
H 2
25.0%
U 1
 
12.5%
L 1
 
12.5%
G 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
, 251
99.6%
? 1
 
0.4%
Space Separator
ValueCountFrequency (%)
902
100.0%
Open Punctuation
ValueCountFrequency (%)
( 189
100.0%
Close Punctuation
ValueCountFrequency (%)
) 189
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3185
56.5%
Common 2439
43.3%
Latin 15
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 507
15.9%
초 319
 
10.0%
동 195
 
6.1%
시 183
 
5.7%
특 182
 
5.7%
별 182
 
5.7%
구 182
 
5.7%
울 182
 
5.7%
로 180
 
5.7%
층 135
 
4.2%
Other values (100) 938
29.5%
Common
ValueCountFrequency (%)
902
37.0%
1 257
 
10.5%
, 251
 
10.3%
( 189
 
7.7%
) 189
 
7.7%
2 147
 
6.0%
0 88
 
3.6%
3 88
 
3.6%
4 82
 
3.4%
5 51
 
2.1%
Other values (6) 195
 
8.0%
Latin
ValueCountFrequency (%)
B 3
20.0%
H 2
13.3%
i 2
13.3%
U 1
 
6.7%
l 1
 
6.7%
g 1
 
6.7%
n 1
 
6.7%
d 1
 
6.7%
u 1
 
6.7%
L 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3185
56.5%
ASCII 2454
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
902
36.8%
1 257
 
10.5%
, 251
 
10.2%
( 189
 
7.7%
) 189
 
7.7%
2 147
 
6.0%
0 88
 
3.6%
3 88
 
3.6%
4 82
 
3.3%
5 51
 
2.1%
Other values (17) 210
 
8.6%
Hangul
ValueCountFrequency (%)
서 507
15.9%
초 319
 
10.0%
동 195
 
6.1%
시 183
 
5.7%
특 182
 
5.7%
별 182
 
5.7%
구 182
 
5.7%
울 182
 
5.7%
로 180
 
5.7%
층 135
 
4.2%
Other values (100) 938
29.5%

소재지지번
Text

Distinct172
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-11T16:12:54.547534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length41
Mean length29.706522
Min length23

Characters and Unicode

Total characters5466
Distinct characters117
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)87.0%

Sample

1st row서울특별시 서초구 반포동 746번지 9호 서초빌딩
2nd row서울특별시 서초구 서초동 1555번지 3호 1층
3rd row서울특별시 서초구 반포동 91번지 1호 4층
4th row서울특별시 서초구 반포동 746번지 9호 (104호?)
5th row서울특별시 서초구 반포동 93번지 7호 한영빌딩
ValueCountFrequency (%)
서울특별시 184
16.7%
서초구 184
16.7%
서초동 91
 
8.2%
1층 80
 
7.2%
방배동 36
 
3.3%
양재동 26
 
2.4%
1호 19
 
1.7%
2호 14
 
1.3%
반포동 14
 
1.3%
3호 12
 
1.1%
Other values (247) 445
40.3%
2024-05-11T16:12:54.911144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1325
24.2%
서 464
 
8.5%
1 363
 
6.6%
초 280
 
5.1%
지 211
 
3.9%
호 195
 
3.6%
동 187
 
3.4%
시 185
 
3.4%
별 184
 
3.4%
번 184
 
3.4%
Other values (107) 1888
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2934
53.7%
Space Separator 1325
24.2%
Decimal Number 1111
 
20.3%
Other Punctuation 32
 
0.6%
Close Punctuation 18
 
0.3%
Open Punctuation 18
 
0.3%
Dash Punctuation 13
 
0.2%
Uppercase Letter 7
 
0.1%
Lowercase Letter 7
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 464
15.8%
초 280
9.5%
지 211
 
7.2%
호 195
 
6.6%
동 187
 
6.4%
시 185
 
6.3%
별 184
 
6.3%
번 184
 
6.3%
특 184
 
6.3%
구 184
 
6.3%
Other values (79) 676
23.0%
Decimal Number
ValueCountFrequency (%)
1 363
32.7%
5 119
 
10.7%
2 118
 
10.6%
3 96
 
8.6%
0 85
 
7.7%
4 82
 
7.4%
7 66
 
5.9%
9 64
 
5.8%
6 61
 
5.5%
8 57
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
i 2
28.6%
l 1
14.3%
u 1
14.3%
g 1
14.3%
n 1
14.3%
d 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
B 2
28.6%
H 2
28.6%
U 1
14.3%
L 1
14.3%
G 1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 23
71.9%
? 9
 
28.1%
Space Separator
ValueCountFrequency (%)
1325
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2934
53.7%
Common 2518
46.1%
Latin 14
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 464
15.8%
초 280
9.5%
지 211
 
7.2%
호 195
 
6.6%
동 187
 
6.4%
시 185
 
6.3%
별 184
 
6.3%
번 184
 
6.3%
특 184
 
6.3%
구 184
 
6.3%
Other values (79) 676
23.0%
Common
ValueCountFrequency (%)
1325
52.6%
1 363
 
14.4%
5 119
 
4.7%
2 118
 
4.7%
3 96
 
3.8%
0 85
 
3.4%
4 82
 
3.3%
7 66
 
2.6%
9 64
 
2.5%
6 61
 
2.4%
Other values (7) 139
 
5.5%
Latin
ValueCountFrequency (%)
B 2
14.3%
i 2
14.3%
H 2
14.3%
l 1
7.1%
u 1
7.1%
g 1
7.1%
n 1
7.1%
d 1
7.1%
U 1
7.1%
L 1
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2934
53.7%
ASCII 2532
46.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1325
52.3%
1 363
 
14.3%
5 119
 
4.7%
2 118
 
4.7%
3 96
 
3.8%
0 85
 
3.4%
4 82
 
3.2%
7 66
 
2.6%
9 64
 
2.5%
6 61
 
2.4%
Other values (18) 153
 
6.0%
Hangul
ValueCountFrequency (%)
서 464
15.8%
초 280
9.5%
지 211
 
7.2%
호 195
 
6.6%
동 187
 
6.4%
시 185
 
6.3%
별 184
 
6.3%
번 184
 
6.3%
특 184
 
6.3%
구 184
 
6.3%
Other values (79) 676
23.0%

허가(신고)번호
Text

Distinct172
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-05-11T16:12:55.095387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4048
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

Unique160 ?
Unique (%)87.0%

Sample

1st row3210000-101-2022-00271
2nd row3210000-101-1991-05420
3rd row3210000-101-2021-00242
4th row3210000-101-1994-11711
5th row3210000-101-2017-00371
ValueCountFrequency (%)
3210000-101-1991-07748 2
 
1.1%
3210000-101-1994-09133 2
 
1.1%
3210000-101-1989-03872 2
 
1.1%
3210000-101-1991-03230 2
 
1.1%
3210000-101-1995-08818 2
 
1.1%
3210000-101-1994-09093 2
 
1.1%
3210000-101-1999-13994 2
 
1.1%
3210000-101-1990-09033 2
 
1.1%
3210000-101-2001-15585 2
 
1.1%
3210000-101-1998-08648 2
 
1.1%
Other values (162) 164
89.1%
2024-05-11T16:12:55.409742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1354
33.4%
1 803
19.8%
- 552
13.6%
2 351
 
8.7%
3 297
 
7.3%
9 273
 
6.7%
8 105
 
2.6%
5 104
 
2.6%
4 85
 
2.1%
7 65
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3496
86.4%
Dash Punctuation 552
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1354
38.7%
1 803
23.0%
2 351
 
10.0%
3 297
 
8.5%
9 273
 
7.8%
8 105
 
3.0%
5 104
 
3.0%
4 85
 
2.4%
7 65
 
1.9%
6 59
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1354
33.4%
1 803
19.8%
- 552
13.6%
2 351
 
8.7%
3 297
 
7.3%
9 273
 
6.7%
8 105
 
2.6%
5 104
 
2.6%
4 85
 
2.1%
7 65
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1354
33.4%
1 803
19.8%
- 552
13.6%
2 351
 
8.7%
3 297
 
7.3%
9 273
 
6.7%
8 105
 
2.6%
5 104
 
2.6%
4 85
 
2.1%
7 65
 
1.6%

업태명
Categorical

IMBALANCE 

Distinct13
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
한식
129 
일식
15 
중국식
15 
경양식
 
8
호프/통닭
 
5
Other values (8)
 
12

Length

Max length15
Median length2
Mean length2.3695652
Min length2

Unique

Unique6 ?
Unique (%)3.3%

Sample

1st row한식
2nd row한식
3rd row일식
4th row경양식
5th row한식

Common Values

ValueCountFrequency (%)
한식 129
70.1%
일식 15
 
8.2%
중국식 15
 
8.2%
경양식 8
 
4.3%
호프/통닭 5
 
2.7%
복어취급 3
 
1.6%
분식 3
 
1.6%
외국음식전문점(인도,태국등) 1
 
0.5%
회집 1
 
0.5%
패스트푸드 1
 
0.5%
Other values (3) 3
 
1.6%

Length

2024-05-11T16:12:55.562742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 129
70.1%
일식 15
 
8.2%
중국식 15
 
8.2%
경양식 8
 
4.3%
호프/통닭 5
 
2.7%
복어취급 3
 
1.6%
분식 3
 
1.6%
외국음식전문점(인도,태국등 1
 
0.5%
회집 1
 
0.5%
패스트푸드 1
 
0.5%
Other values (3) 3
 
1.6%

지정취소사유
Categorical

Distinct48
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
<NA>
58 
영업자지위승계
39 
행정처분
16 
서울시 위생등급 평가결과 부적합
14 
지위승계
6 
Other values (43)
51 

Length

Max length44
Median length34
Mean length9.0380435
Min length2

Unique

Unique37 ?
Unique (%)20.1%

Sample

1st row현재 영업안함(폐업예정)
2nd row내림손삼계탕,신의주찹쌀순대에서 지위승계(자진철회)
3rd row폐업(23.1월 사무실로변경)
4th row행정처분
5th row휴락담, 소진상에서 지위승계(자진철회)

Common Values

ValueCountFrequency (%)
<NA> 58
31.5%
영업자지위승계 39
21.2%
행정처분 16
 
8.7%
서울시 위생등급 평가결과 부적합 14
 
7.6%
지위승계 6
 
3.3%
양도양수 3
 
1.6%
재심사 결과 지정기준 부적합 3
 
1.6%
불법건축물 증축 2
 
1.1%
주점 2
 
1.1%
재심사결과 지정기준 부적합 2
 
1.1%
Other values (38) 39
21.2%

Length

2024-05-11T16:12:55.689305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 58
17.6%
영업자지위승계 39
 
11.8%
부적합 20
 
6.1%
행정처분 16
 
4.8%
지정기준 16
 
4.8%
서울시 14
 
4.2%
위생등급 14
 
4.2%
평가결과 14
 
4.2%
재심사결과 9
 
2.7%
지위승계 7
 
2.1%
Other values (87) 123
37.3%

주된음식
Text

MISSING 

Distinct100
Distinct (%)56.2%
Missing6
Missing (%)3.3%
Memory size1.6 KiB
2024-05-11T16:12:55.925950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.3707865
Min length2

Characters and Unicode

Total characters600
Distinct characters140
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

Unique69 ?
Unique (%)38.8%

Sample

1st row한식
2nd row한식
3rd row한식
4th row한식
5th row호프/통닭
ValueCountFrequency (%)
삼겹살 8
 
4.4%
한식 7
 
3.9%
회덮밥 7
 
3.9%
설렁탕 7
 
3.9%
자장면 6
 
3.3%
돼지갈비 6
 
3.3%
회정식 4
 
2.2%
갈비탕 4
 
2.2%
한정식 4
 
2.2%
참치회 4
 
2.2%
Other values (91) 124
68.5%
2024-05-11T16:12:56.297496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 24
 
4.0%
회 23
 
3.8%
비 21
 
3.5%
식 20
 
3.3%
갈 19
 
3.2%
리 18
 
3.0%
밥 16
 
2.7%
국 13
 
2.2%
한 13
 
2.2%
장 12
 
2.0%
Other values (130) 421
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 592
98.7%
Other Punctuation 5
 
0.8%
Space Separator 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 24
 
4.1%
회 23
 
3.9%
비 21
 
3.5%
식 20
 
3.4%
갈 19
 
3.2%
리 18
 
3.0%
밥 16
 
2.7%
국 13
 
2.2%
한 13
 
2.2%
장 12
 
2.0%
Other values (127) 413
69.8%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
/ 1
 
20.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 592
98.7%
Common 8
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 24
 
4.1%
회 23
 
3.9%
비 21
 
3.5%
식 20
 
3.4%
갈 19
 
3.2%
리 18
 
3.0%
밥 16
 
2.7%
국 13
 
2.2%
한 13
 
2.2%
장 12
 
2.0%
Other values (127) 413
69.8%
Common
ValueCountFrequency (%)
, 4
50.0%
3
37.5%
/ 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 592
98.7%
ASCII 8
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 24
 
4.1%
회 23
 
3.9%
비 21
 
3.5%
식 20
 
3.4%
갈 19
 
3.2%
리 18
 
3.0%
밥 16
 
2.7%
국 13
 
2.2%
한 13
 
2.2%
장 12
 
2.0%
Other values (127) 413
69.8%
ASCII
ValueCountFrequency (%)
, 4
50.0%
3
37.5%
/ 1
 
12.5%

영업장면적(㎡)
Real number (ℝ)

Distinct162
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.59103
Minimum32.01
Maximum708.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-05-11T16:12:56.426610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.01
5-th percentile62.7705
Q199.46
median148.16
Q3196.7325
95-th percentile373.9195
Maximum708.6
Range676.59
Interquartile range (IQR)97.2725

Descriptive statistics

Standard deviation107.88506
Coefficient of variation (CV)0.63241931
Kurtosis6.0707085
Mean170.59103
Median Absolute Deviation (MAD)48.82
Skewness2.1376482
Sum31388.75
Variance11639.187
MonotonicityNot monotonic
2024-05-11T16:12:56.562706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148.5 4
 
2.2%
90.0 3
 
1.6%
93.0 3
 
1.6%
142.0 3
 
1.6%
99.0 2
 
1.1%
114.54 2
 
1.1%
348.2 2
 
1.1%
33.0 2
 
1.1%
337.0 2
 
1.1%
66.0 2
 
1.1%
Other values (152) 159
86.4%
ValueCountFrequency (%)
32.01 1
0.5%
33.0 2
1.1%
36.0 1
0.5%
45.33 1
0.5%
53.2 1
0.5%
59.3 1
0.5%
59.4 1
0.5%
59.74 1
0.5%
62.7 1
0.5%
63.17 1
0.5%
ValueCountFrequency (%)
708.6 1
0.5%
635.48 1
0.5%
581.92 1
0.5%
547.13 1
0.5%
477.02 1
0.5%
458.74 1
0.5%
404.68 1
0.5%
385.35 1
0.5%
383.28 1
0.5%
375.19 1
0.5%

행정동명
Categorical

Distinct17
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
서초제3동
49 
서초제2동
20 
양재제2동
15 
양재제1동
15 
서초제1동
14 
Other values (12)
71 

Length

Max length5
Median length5
Mean length4.8369565
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row반포제2동
2nd row서초제3동
3rd row반포제4동
4th row반포제1동
5th row반포제4동

Common Values

ValueCountFrequency (%)
서초제3동 49
26.6%
서초제2동 20
10.9%
양재제2동 15
 
8.2%
양재제1동 15
 
8.2%
서초제1동 14
 
7.6%
방배제2동 11
 
6.0%
서초제4동 10
 
5.4%
잠원동 10
 
5.4%
방배제4동 9
 
4.9%
반포제4동 9
 
4.9%
Other values (7) 22
12.0%

Length

2024-05-11T16:12:56.741629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서초제3동 49
26.6%
서초제2동 20
10.9%
양재제2동 15
 
8.2%
양재제1동 15
 
8.2%
서초제1동 14
 
7.6%
방배제2동 11
 
6.0%
서초제4동 10
 
5.4%
잠원동 10
 
5.4%
반포제4동 9
 
4.9%
방배제4동 9
 
4.9%
Other values (7) 22
12.0%

급수시설구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
상수도전용
149 
<NA>
34 
지하수전용
 
1

Length

Max length5
Median length5
Mean length4.8152174
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 149
81.0%
<NA> 34
 
18.5%
지하수전용 1
 
0.5%

Length

2024-05-11T16:12:56.864682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:12:56.967216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 149
81.0%
na 34
 
18.5%
지하수전용 1
 
0.5%

Interactions

2024-05-11T16:12:50.051906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:46.603201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.446966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.121594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.747593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.399836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:50.178169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:46.698057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.560931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.247005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.864084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.508102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:50.300250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.059320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.666406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.351629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.952054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.617763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:50.420133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.163503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.779922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.445933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.050572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.726267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:50.525120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.264554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.899543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.547310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.154846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.829863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:50.624771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:47.357484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.015809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:48.651740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.281441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:12:49.938526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T16:12:57.040788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
지정년도1.0000.8200.9981.0000.4890.5890.7720.5860.0820.566NaN
지정번호0.8201.0000.9140.8200.2250.0000.4030.5400.0000.6560.210
신청일자0.9980.9141.0000.9980.4920.6170.6460.7480.0000.609NaN
지정일자1.0000.8200.9981.0000.4890.5890.7720.5860.0820.566NaN
취소일자0.4890.2250.4920.4891.0000.6100.7840.8810.0000.0000.000
업태명0.5890.0000.6170.5890.6101.0000.0000.9660.2290.0790.000
지정취소사유0.7720.4030.6460.7720.7840.0001.0000.0000.4360.7470.578
주된음식0.5860.5400.7480.5860.8810.9660.0001.0000.8290.6861.000
영업장면적(㎡)0.0820.0000.0000.0820.0000.2290.4360.8291.0000.0000.286
행정동명0.5660.6560.6090.5660.0000.0790.7470.6860.0001.0000.634
급수시설구분NaN0.210NaNNaN0.0000.0000.5781.0000.2860.6341.000
2024-05-11T16:12:57.164014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정취소사유급수시설구분업태명행정동명
지정취소사유1.0000.4000.0000.243
급수시설구분0.4001.0000.0000.479
업태명0.0000.0001.0000.017
행정동명0.2430.4790.0171.000
2024-05-11T16:12:57.262771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명지정취소사유행정동명급수시설구분
지정년도1.000-0.6581.0001.0000.5990.0040.2090.4570.2310.000
지정번호-0.6581.000-0.657-0.658-0.257-0.0280.0000.1630.4130.138
신청일자1.000-0.6571.0001.0000.5970.0010.2410.4050.2490.000
지정일자1.000-0.6581.0001.0000.5990.0040.2090.4570.2310.000
취소일자0.599-0.2570.5970.5991.0000.1210.3080.3350.0000.000
영업장면적(㎡)0.004-0.0280.0010.0040.1211.0000.0960.1300.0000.212
업태명0.2090.0000.2410.2090.3080.0961.0000.0000.0170.000
지정취소사유0.4570.1630.4050.4570.3350.1300.0001.0000.2430.400
행정동명0.2310.4130.2490.2310.0000.0000.0170.2431.0000.479
급수시설구분0.0000.1380.0000.0000.0000.2120.0000.4000.4791.000

Missing values

2024-05-11T16:12:50.755426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T16:12:50.960129image/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.
2024-05-11T16:12:51.110430image/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
0321000020229202210012022112920231114켈리포니아서울특별시 서초구 사평대로 349, 서초빌딩 109,110호 (반포동)서울특별시 서초구 반포동 746번지 9호 서초빌딩3210000-101-2022-00271한식현재 영업안함(폐업예정)한식99.58반포제2동<NA>
1321000020219202110012021121420231114신의주왕족발순대국서울특별시 서초구 반포대로30길 34, 1층 (서초동)서울특별시 서초구 서초동 1555번지 3호 1층3210000-101-1991-05420한식내림손삼계탕,신의주찹쌀순대에서 지위승계(자진철회)한식197.43서초제3동상수도전용
2321000020213202110012021121420231114우나기야 주식회사서울특별시 서초구 서래로8길 6, 4층 (반포동)서울특별시 서초구 반포동 91번지 1호 4층3210000-101-2021-00242일식폐업(23.1월 사무실로변경)<NA>266.44반포제4동<NA>
33210000202030202010012020120220231114금강바베큐서울특별시 서초구 사평대로 349, (반포동)서울특별시 서초구 반포동 746번지 9호 (104호?)3210000-101-1994-11711경양식행정처분<NA>32.01반포제1동상수도전용
43210000202027202010012020120220231114신사정육식당 반포서래점서울특별시 서초구 서래로 50, 한영빌딩 2층 (반포동)서울특별시 서초구 반포동 93번지 7호 한영빌딩3210000-101-2017-00371한식휴락담, 소진상에서 지위승계(자진철회)한식240.85반포제4동<NA>
5321000020206202012022020120220231114방이옥 사당역서울특별시 서초구 방배천로2안길 7, 1층 (방배동)서울특별시 서초구 방배동 450번지 26호3210000-101-1994-05400한식굴뚝집에서 지위승계건(자진철회)한식158.18방배제2동상수도전용
63210000202021202010012020120220231114공리특허짬뽕서울특별시 서초구 방배로26길 8, (방배동)서울특별시 서초구 방배동 875번지 22호 (1층?)3210000-101-2009-00557중국식폐업(지위승계 공실)<NA>114.68방배제4동<NA>
7321000020194201910012019121720231114교촌치킨 방배1호점서울특별시 서초구 방배중앙로 176, 1층 (방배동)서울특별시 서초구 방배동 769번지 9호 1층3210000-101-2019-00357호프/통닭용이네 정육식당에서 지위승계(자진철회)호프/통닭148.5방배제2동<NA>
83210000201853201810012018121820231114조선면옥서울특별시 서초구 원터2길 2, (원지동,2층)서울특별시 서초구 원지동 379번지 4호 2층3210000-101-2005-00282한식행정처분<NA>266.0양재제2동상수도전용
9321000020161201510082016072020221231반포식스 교대역점서울특별시 서초구 서초대로50길 18, 1층 (서초동)서울특별시 서초구 서초동 1571번지 17호 1층3210000-101-2015-00192외국음식전문점(인도,태국등)<NA>육개장191.3서초제3동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
17432100002003233200306182003071820060316돈데이서울특별시 서초구 동광로 65, 1층 (방배동)서울특별시 서초구 방배동 795번지 9호 외 1필지 1층3210000-101-1995-13882한식영업자지위승계삼겹살116.6방배본동상수도전용
17532100002003257200306182003071820050506송강서울특별시 서초구 명달로9길 5, (방배동,1층)서울특별시 서초구 방배동 1002번지 1호 1층3210000-101-1998-12903한식영업자지위승계버섯전골151.38서초제3동상수도전용
1763210000200366200306182003071820040831육목원서울특별시 서초구 서초대로78길 46, 1층 (서초동)서울특별시 서초구 서초동 1330번지 15호 1층3210000-101-1992-05103한식영업자지위승계꽃게장158.33서초제2동상수도전용
17732100002003229200306182003071820030822초동집서울특별시 서초구 방배천로 22, (방배동)서울특별시 서초구 방배동 452번지 4호3210000-101-1990-09033한식양도양수바다장어탕128.8방배제2동상수도전용
17832100002003132200306182003071820060131강나루서울특별시 서초구 법원로 15, 정곡빌딩 지하1층 (서초동)서울특별시 서초구 서초동 1705번지 정곡빌딩 지하1층3210000-101-1990-05139한식영업자지위승계생태탕102.45서초제3동상수도전용
1793210000200324200306182003071820041206서초간장게장서울특별시 서초구 서초중앙로26길 8, 1층 (서초동)서울특별시 서초구 서초동 1694번지 34호 1층3210000-101-1991-03903한식영업자지위승계설렁탕100.0서초제4동상수도전용
18032100002003137200306182003071820111014수하동 서초점서울특별시 서초구 법원로2길 6, 1층 (서초동)서울특별시 서초구 서초동 1716번지 5호 1층3210000-101-1992-05136한식서울시 위생등급 평가결과 부적합김치전골139.0서초제3동상수도전용
18132100002003115200306182003071820190208일점사 수제돼지갈비서울특별시 서초구 반포대로22길 91, (서초동)서울특별시 서초구 서초동 1569번지 15호3210000-101-1989-11032한식행정처분갈비살171.6서초제3동상수도전용
18232100002003239200306182003071820151105차돌풍(방배점)서울특별시 서초구 효령로31길 19, 1층 (방배동)서울특별시 서초구 방배동 911번지 18호 1층3210000-101-1984-03693식육(숯불구이)행정처분돼지고기102.14방배제1동상수도전용
1833210000200351200306182003071820041025장꼬방김치찌개전문서울특별시 서초구 효령로 364, (서초동)서울특별시 서초구 서초동 1438번지 8호3210000-101-1993-05914한식영업자지위승계참치회224.48서초제1동상수도전용