Overview

Dataset statistics

Number of variables16
Number of observations201
Missing cells198
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.8 KiB
Average record size in memory136.7 B

Variable types

Categorical5
Numeric6
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,불가일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author용산구
URLhttps://data.seoul.go.kr/dataList/OA-11237/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 8 other fieldsHigh correlation
불가일자 is highly overall correlated with 신청일자 and 2 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
업태명 is highly overall correlated with 불가일자 and 1 other fieldsHigh correlation
지정년도 is highly overall correlated with 지정번호 and 3 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 5 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
취소일자 is highly overall correlated with 신청일자 and 1 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
불가일자 is highly imbalanced (89.7%)Imbalance
지정년도 has 9 (4.5%) missing valuesMissing
지정번호 has 9 (4.5%) missing valuesMissing
지정일자 has 9 (4.5%) missing valuesMissing
취소일자 has 142 (70.6%) missing valuesMissing
주된음식 has 28 (13.9%) missing valuesMissing

Reproduction

Analysis started2024-05-03 23:43:21.935036
Analysis finished2024-05-03 23:43:35.474142
Duration13.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
3020000
201 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000
2nd row3020000
3rd row3020000
4th row3020000
5th row3020000

Common Values

ValueCountFrequency (%)
3020000 201
100.0%

Length

2024-05-03T23:43:35.729912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:43:36.038507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000 201
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)10.9%
Missing9
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean2012.8906
Minimum2001
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-03T23:43:36.356705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12007.75
median2015
Q32019
95-th percentile2022
Maximum2023
Range22
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation7.0572465
Coefficient of variation (CV)0.0035060258
Kurtosis-1.1342225
Mean2012.8906
Median Absolute Deviation (MAD)5
Skewness-0.39858084
Sum386475
Variance49.804728
MonotonicityNot monotonic
2024-05-03T23:43:36.725467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2001 27
13.4%
2015 20
10.0%
2018 19
9.5%
2008 15
 
7.5%
2020 13
 
6.5%
2016 13
 
6.5%
2022 12
 
6.0%
2010 10
 
5.0%
2019 10
 
5.0%
2007 10
 
5.0%
Other values (11) 43
21.4%
(Missing) 9
 
4.5%
ValueCountFrequency (%)
2001 27
13.4%
2002 2
 
1.0%
2003 1
 
0.5%
2005 7
 
3.5%
2006 1
 
0.5%
2007 10
 
5.0%
2008 15
7.5%
2009 4
 
2.0%
2010 10
 
5.0%
2011 2
 
1.0%
ValueCountFrequency (%)
2023 7
 
3.5%
2022 12
6.0%
2021 7
 
3.5%
2020 13
6.5%
2019 10
5.0%
2018 19
9.5%
2017 6
 
3.0%
2016 13
6.5%
2015 20
10.0%
2014 3
 
1.5%

지정번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70
Distinct (%)36.5%
Missing9
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean30.447917
Minimum1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-03T23:43:37.154372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.55
Q15
median10
Q328
95-th percentile145.7
Maximum171
Range170
Interquartile range (IQR)23

Descriptive statistics

Standard deviation45.559781
Coefficient of variation (CV)1.4963185
Kurtosis2.3813476
Mean30.447917
Median Absolute Deviation (MAD)6
Skewness1.9204299
Sum5846
Variance2075.6936
MonotonicityNot monotonic
2024-05-03T23:43:37.737680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 12
 
6.0%
4 11
 
5.5%
2 11
 
5.5%
5 10
 
5.0%
6 10
 
5.0%
1 10
 
5.0%
10 10
 
5.0%
8 10
 
5.0%
13 10
 
5.0%
12 9
 
4.5%
Other values (60) 89
44.3%
ValueCountFrequency (%)
1 10
5.0%
2 11
5.5%
3 9
4.5%
4 11
5.5%
5 10
5.0%
6 10
5.0%
7 12
6.0%
8 10
5.0%
9 6
3.0%
10 10
5.0%
ValueCountFrequency (%)
171 1
0.5%
170 1
0.5%
169 1
0.5%
166 1
0.5%
162 1
0.5%
161 1
0.5%
160 1
0.5%
157 1
0.5%
153 1
0.5%
149 1
0.5%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20136145
Minimum20060630
Maximum20230831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-03T23:43:38.558613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20060630
5-th percentile20060630
Q120060630
median20151012
Q320180830
95-th percentile20220819
Maximum20230831
Range170201
Interquartile range (IQR)120200

Descriptive statistics

Standard deviation59912.713
Coefficient of variation (CV)0.0029753816
Kurtosis-1.5436682
Mean20136145
Median Absolute Deviation (MAD)50394
Skewness-0.072672873
Sum4.047365 × 109
Variance3.5895332 × 109
MonotonicityDecreasing
2024-05-03T23:43:39.074899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
20060630 52
25.9%
20180830 23
11.4%
20200831 13
 
6.5%
20160831 13
 
6.5%
20220819 12
 
6.0%
20151012 12
 
6.0%
20170831 10
 
5.0%
20100618 10
 
5.0%
20070718 9
 
4.5%
20210929 7
 
3.5%
Other values (26) 40
19.9%
ValueCountFrequency (%)
20060630 52
25.9%
20070718 9
 
4.5%
20070904 1
 
0.5%
20071115 1
 
0.5%
20080627 1
 
0.5%
20080728 1
 
0.5%
20080808 1
 
0.5%
20090410 1
 
0.5%
20090609 1
 
0.5%
20090610 1
 
0.5%
ValueCountFrequency (%)
20230831 6
3.0%
20220831 1
 
0.5%
20220819 12
6.0%
20210929 7
3.5%
20200831 13
6.5%
20190829 2
 
1.0%
20190828 1
 
0.5%
20190826 3
 
1.5%
20190823 1
 
0.5%
20190821 1
 
0.5%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)12.5%
Missing9
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean20129833
Minimum20010630
Maximum20231011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-03T23:43:39.597790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010630
5-th percentile20010630
Q120078375
median20151211
Q320191011
95-th percentile20220921
Maximum20231011
Range220381
Interquartile range (IQR)112636.25

Descriptive statistics

Standard deviation70717.837
Coefficient of variation (CV)0.0035130862
Kurtosis-1.1353438
Mean20129833
Median Absolute Deviation (MAD)50490
Skewness-0.40090429
Sum3.8649278 × 109
Variance5.0010124 × 109
MonotonicityNot monotonic
2024-05-03T23:43:40.047372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20010630 27
13.4%
20151211 20
10.0%
20181012 19
9.5%
20080829 15
 
7.5%
20201013 13
 
6.5%
20161219 13
 
6.5%
20220921 12
 
6.0%
20191011 10
 
5.0%
20100721 9
 
4.5%
20070718 9
 
4.5%
Other values (14) 45
22.4%
(Missing) 9
 
4.5%
ValueCountFrequency (%)
20010630 27
13.4%
20021022 2
 
1.0%
20030731 1
 
0.5%
20050725 7
 
3.5%
20060630 1
 
0.5%
20070718 9
 
4.5%
20071012 1
 
0.5%
20080829 15
7.5%
20090225 1
 
0.5%
20090720 3
 
1.5%
ValueCountFrequency (%)
20231011 7
 
3.5%
20220921 12
6.0%
20211022 7
 
3.5%
20201013 13
6.5%
20191011 10
5.0%
20181012 19
9.5%
20171013 6
 
3.0%
20161219 13
6.5%
20151211 20
10.0%
20141201 3
 
1.5%

취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)45.8%
Missing142
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean20140665
Minimum20050725
Maximum20230522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-03T23:43:40.512370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20050725
5-th percentile20080829
Q120095666
median20131231
Q320185560
95-th percentile20221230
Maximum20230522
Range179797
Interquartile range (IQR)89894

Descriptive statistics

Standard deviation50543.787
Coefficient of variation (CV)0.0025095391
Kurtosis-1.261997
Mean20140665
Median Absolute Deviation (MAD)49076
Skewness0.21703833
Sum1.1882992 × 109
Variance2.5546744 × 109
MonotonicityNot monotonic
2024-05-03T23:43:40.924917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
20080829 12
 
6.0%
20111031 8
 
4.0%
20180307 3
 
1.5%
20201104 3
 
1.5%
20161104 3
 
1.5%
20200113 3
 
1.5%
20130718 2
 
1.0%
20131231 2
 
1.0%
20151211 2
 
1.0%
20221021 2
 
1.0%
Other values (17) 19
 
9.5%
(Missing) 142
70.6%
ValueCountFrequency (%)
20050725 1
 
0.5%
20080829 12
6.0%
20090130 1
 
0.5%
20090824 1
 
0.5%
20100507 1
 
0.5%
20100702 1
 
0.5%
20110128 1
 
0.5%
20111031 8
4.0%
20121122 1
 
0.5%
20130718 2
 
1.0%
ValueCountFrequency (%)
20230522 1
 
0.5%
20230131 1
 
0.5%
20221230 2
1.0%
20221021 2
1.0%
20201104 3
1.5%
20200113 3
1.5%
20191022 2
1.0%
20190812 1
 
0.5%
20180307 3
1.5%
20171221 1
 
0.5%

불가일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
196 
20181012
 
2
20171013
 
2
20170912
 
1

Length

Max length8
Median length4
Mean length4.0995025
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 196
97.5%
20181012 2
 
1.0%
20171013 2
 
1.0%
20170912 1
 
0.5%

Length

2024-05-03T23:43:41.389764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:43:41.775415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 196
97.5%
20181012 2
 
1.0%
20171013 2
 
1.0%
20170912 1
 
0.5%

업소명
Text

Distinct185
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-03T23:43:42.312299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length17
Mean length5.5820896
Min length2

Characters and Unicode

Total characters1122
Distinct characters328
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)85.1%

Sample

1st row남도미항 용산아이파크몰점
2nd row큰집닭한마리
3rd row금천문
4th row이춘복참치
5th row풍성집
ValueCountFrequency (%)
섬집 3
 
1.3%
부다스벨리 3
 
1.3%
더멘션 2
 
0.9%
남영점 2
 
0.9%
남영본점 2
 
0.9%
피그야 2
 
0.9%
유진막국수 2
 
0.9%
금홍 2
 
0.9%
동아냉면 2
 
0.9%
돈치앤 2
 
0.9%
Other values (197) 204
90.3%
2024-05-03T23:43:43.451251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 42
 
3.7%
점 28
 
2.5%
25
 
2.2%
집 21
 
1.9%
스 18
 
1.6%
( 16
 
1.4%
) 16
 
1.4%
산 16
 
1.4%
한 16
 
1.4%
대 15
 
1.3%
Other values (318) 909
81.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1006
89.7%
Space Separator 25
 
2.2%
Lowercase Letter 20
 
1.8%
Uppercase Letter 19
 
1.7%
Open Punctuation 16
 
1.4%
Close Punctuation 16
 
1.4%
Decimal Number 16
 
1.4%
Other Punctuation 3
 
0.3%
Letter Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 42
 
4.2%
점 28
 
2.8%
집 21
 
2.1%
스 18
 
1.8%
산 16
 
1.6%
한 16
 
1.6%
대 15
 
1.5%
남 14
 
1.4%
비 13
 
1.3%
용 13
 
1.3%
Other values (283) 810
80.5%
Lowercase Letter
ValueCountFrequency (%)
e 3
15.0%
a 3
15.0%
u 2
10.0%
m 2
10.0%
o 2
10.0%
l 2
10.0%
p 1
 
5.0%
c 1
 
5.0%
s 1
 
5.0%
i 1
 
5.0%
Other values (2) 2
10.0%
Uppercase Letter
ValueCountFrequency (%)
S 5
26.3%
B 3
15.8%
I 3
15.8%
U 2
 
10.5%
D 2
 
10.5%
O 1
 
5.3%
L 1
 
5.3%
J 1
 
5.3%
A 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 4
25.0%
0 4
25.0%
2 2
12.5%
3 2
12.5%
4 1
 
6.2%
5 1
 
6.2%
8 1
 
6.2%
9 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1005
89.6%
Common 76
 
6.8%
Latin 40
 
3.6%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 42
 
4.2%
점 28
 
2.8%
집 21
 
2.1%
스 18
 
1.8%
산 16
 
1.6%
한 16
 
1.6%
대 15
 
1.5%
남 14
 
1.4%
비 13
 
1.3%
용 13
 
1.3%
Other values (282) 809
80.5%
Latin
ValueCountFrequency (%)
S 5
 
12.5%
e 3
 
7.5%
B 3
 
7.5%
I 3
 
7.5%
a 3
 
7.5%
U 2
 
5.0%
u 2
 
5.0%
m 2
 
5.0%
o 2
 
5.0%
D 2
 
5.0%
Other values (12) 13
32.5%
Common
ValueCountFrequency (%)
25
32.9%
( 16
21.1%
) 16
21.1%
1 4
 
5.3%
0 4
 
5.3%
. 2
 
2.6%
2 2
 
2.6%
3 2
 
2.6%
4 1
 
1.3%
& 1
 
1.3%
Other values (3) 3
 
3.9%
Han
ValueCountFrequency (%)
新 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1005
89.6%
ASCII 115
 
10.2%
Number Forms 1
 
0.1%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 42
 
4.2%
점 28
 
2.8%
집 21
 
2.1%
스 18
 
1.8%
산 16
 
1.6%
한 16
 
1.6%
대 15
 
1.5%
남 14
 
1.4%
비 13
 
1.3%
용 13
 
1.3%
Other values (282) 809
80.5%
ASCII
ValueCountFrequency (%)
25
21.7%
( 16
13.9%
) 16
13.9%
S 5
 
4.3%
1 4
 
3.5%
0 4
 
3.5%
e 3
 
2.6%
B 3
 
2.6%
I 3
 
2.6%
a 3
 
2.6%
Other values (24) 33
28.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%
CJK
ValueCountFrequency (%)
新 1
100.0%

소재지도로명
Text

Distinct186
Distinct (%)93.0%
Missing1
Missing (%)0.5%
Memory size1.7 KiB
2024-05-03T23:43:44.313032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length43
Mean length34.435
Min length22

Characters and Unicode

Total characters6887
Distinct characters121
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

Unique174 ?
Unique (%)87.0%

Sample

1st row서울특별시 용산구 한강대로23길 55, 용산역 4층 900-6호 (한강로3가)
2nd row서울특별시 용산구 원효로 259-1, 1층 (원효로1가)
3rd row서울특별시 용산구 한강대로 268, (남영동,(지상1층))
4th row서울특별시 용산구 한강대로 266-2, (남영동)
5th row서울특별시 용산구 한강대로62길 38, (한강로1가,(지상1층))
ValueCountFrequency (%)
서울특별시 200
 
17.3%
용산구 200
 
17.3%
1층 35
 
3.0%
지상1층 23
 
2.0%
한강대로 22
 
1.9%
후암로 15
 
1.3%
남영동 12
 
1.0%
한남동 12
 
1.0%
한강로3가 12
 
1.0%
이태원동 12
 
1.0%
Other values (339) 615
53.1%
2024-05-03T23:43:45.510758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
958
 
13.9%
, 357
 
5.2%
1 346
 
5.0%
) 289
 
4.2%
( 289
 
4.2%
로 248
 
3.6%
서 216
 
3.1%
산 213
 
3.1%
용 211
 
3.1%
울 203
 
2.9%
Other values (111) 3557
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3795
55.1%
Decimal Number 1126
 
16.3%
Space Separator 958
 
13.9%
Other Punctuation 361
 
5.2%
Close Punctuation 289
 
4.2%
Open Punctuation 289
 
4.2%
Dash Punctuation 60
 
0.9%
Uppercase Letter 6
 
0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 248
 
6.5%
서 216
 
5.7%
산 213
 
5.6%
용 211
 
5.6%
울 203
 
5.3%
시 201
 
5.3%
특 200
 
5.3%
구 200
 
5.3%
별 200
 
5.3%
층 182
 
4.8%
Other values (92) 1721
45.3%
Decimal Number
ValueCountFrequency (%)
1 346
30.7%
2 178
15.8%
3 101
 
9.0%
4 100
 
8.9%
0 75
 
6.7%
6 74
 
6.6%
7 72
 
6.4%
5 71
 
6.3%
8 61
 
5.4%
9 48
 
4.3%
Other Punctuation
ValueCountFrequency (%)
, 357
98.9%
. 4
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
B 4
66.7%
A 2
33.3%
Space Separator
ValueCountFrequency (%)
958
100.0%
Close Punctuation
ValueCountFrequency (%)
) 289
100.0%
Open Punctuation
ValueCountFrequency (%)
( 289
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3795
55.1%
Common 3086
44.8%
Latin 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 248
 
6.5%
서 216
 
5.7%
산 213
 
5.6%
용 211
 
5.6%
울 203
 
5.3%
시 201
 
5.3%
특 200
 
5.3%
구 200
 
5.3%
별 200
 
5.3%
층 182
 
4.8%
Other values (92) 1721
45.3%
Common
ValueCountFrequency (%)
958
31.0%
, 357
 
11.6%
1 346
 
11.2%
) 289
 
9.4%
( 289
 
9.4%
2 178
 
5.8%
3 101
 
3.3%
4 100
 
3.2%
0 75
 
2.4%
6 74
 
2.4%
Other values (7) 319
 
10.3%
Latin
ValueCountFrequency (%)
B 4
66.7%
A 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3795
55.1%
ASCII 3092
44.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
958
31.0%
, 357
 
11.5%
1 346
 
11.2%
) 289
 
9.3%
( 289
 
9.3%
2 178
 
5.8%
3 101
 
3.3%
4 100
 
3.2%
0 75
 
2.4%
6 74
 
2.4%
Other values (9) 325
 
10.5%
Hangul
ValueCountFrequency (%)
로 248
 
6.5%
서 216
 
5.7%
산 213
 
5.6%
용 211
 
5.6%
울 203
 
5.3%
시 201
 
5.3%
특 200
 
5.3%
구 200
 
5.3%
별 200
 
5.3%
층 182
 
4.8%
Other values (92) 1721
45.3%

소재지지번
Text

Distinct186
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-03T23:43:46.175158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length39
Mean length30.900498
Min length23

Characters and Unicode

Total characters6211
Distinct characters98
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)86.1%

Sample

1st row서울특별시 용산구 한강로3가 40번지 999호 용산역-900-6
2nd row서울특별시 용산구 원효로1가 39번지 10호
3rd row서울특별시 용산구 남영동 84번지 8호 (지상1층)
4th row서울특별시 용산구 남영동 85번지 1호 (지상1층)
5th row서울특별시 용산구 한강로1가 231번지 21호 (지상1층)
ValueCountFrequency (%)
서울특별시 201
 
17.2%
용산구 201
 
17.2%
지상1층 76
 
6.5%
한남동 27
 
2.3%
이태원동 25
 
2.1%
1호 25
 
2.1%
동자동 23
 
2.0%
남영동 20
 
1.7%
한강로3가 17
 
1.5%
1층 15
 
1.3%
Other values (255) 541
46.2%
2024-05-03T23:43:47.351503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1412
22.7%
지 337
 
5.4%
1 320
 
5.2%
용 212
 
3.4%
산 211
 
3.4%
서 206
 
3.3%
울 204
 
3.3%
시 202
 
3.3%
별 201
 
3.2%
번 201
 
3.2%
Other values (88) 2705
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3487
56.1%
Space Separator 1412
22.7%
Decimal Number 1058
 
17.0%
Open Punctuation 103
 
1.7%
Close Punctuation 103
 
1.7%
Other Punctuation 36
 
0.6%
Uppercase Letter 7
 
0.1%
Dash Punctuation 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 337
 
9.7%
용 212
 
6.1%
산 211
 
6.1%
서 206
 
5.9%
울 204
 
5.9%
시 202
 
5.8%
별 201
 
5.8%
번 201
 
5.8%
특 201
 
5.8%
구 201
 
5.8%
Other values (69) 1311
37.6%
Decimal Number
ValueCountFrequency (%)
1 320
30.2%
2 178
16.8%
3 124
 
11.7%
5 80
 
7.6%
4 79
 
7.5%
6 75
 
7.1%
0 64
 
6.0%
7 52
 
4.9%
9 48
 
4.5%
8 38
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 30
83.3%
. 6
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
B 5
71.4%
A 2
 
28.6%
Space Separator
ValueCountFrequency (%)
1412
100.0%
Open Punctuation
ValueCountFrequency (%)
( 103
100.0%
Close Punctuation
ValueCountFrequency (%)
) 103
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3487
56.1%
Common 2717
43.7%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 337
 
9.7%
용 212
 
6.1%
산 211
 
6.1%
서 206
 
5.9%
울 204
 
5.9%
시 202
 
5.8%
별 201
 
5.8%
번 201
 
5.8%
특 201
 
5.8%
구 201
 
5.8%
Other values (69) 1311
37.6%
Common
ValueCountFrequency (%)
1412
52.0%
1 320
 
11.8%
2 178
 
6.6%
3 124
 
4.6%
( 103
 
3.8%
) 103
 
3.8%
5 80
 
2.9%
4 79
 
2.9%
6 75
 
2.8%
0 64
 
2.4%
Other values (7) 179
 
6.6%
Latin
ValueCountFrequency (%)
B 5
71.4%
A 2
 
28.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3487
56.1%
ASCII 2724
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1412
51.8%
1 320
 
11.7%
2 178
 
6.5%
3 124
 
4.6%
( 103
 
3.8%
) 103
 
3.8%
5 80
 
2.9%
4 79
 
2.9%
6 75
 
2.8%
0 64
 
2.3%
Other values (9) 186
 
6.8%
Hangul
ValueCountFrequency (%)
지 337
 
9.7%
용 212
 
6.1%
산 211
 
6.1%
서 206
 
5.9%
울 204
 
5.9%
시 202
 
5.8%
별 201
 
5.8%
번 201
 
5.8%
특 201
 
5.8%
구 201
 
5.8%
Other values (69) 1311
37.6%

허가(신고)번호
Text

Distinct190
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-03T23:43:48.060986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4422
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

Unique179 ?
Unique (%)89.1%

Sample

1st row3020000-101-2018-00360
2nd row3020000-101-2012-00126
3rd row3020000-101-2000-07490
4th row3020000-101-2008-00164
5th row3020000-101-1986-07037
ValueCountFrequency (%)
3020000-101-2005-00270 2
 
1.0%
3020000-101-2017-00407 2
 
1.0%
3020000-101-1994-05909 2
 
1.0%
3020000-101-1997-05151 2
 
1.0%
3020000-101-1982-06322 2
 
1.0%
3020000-101-1988-06641 2
 
1.0%
3020000-101-2000-07830 2
 
1.0%
3020000-101-1988-06973 2
 
1.0%
3020000-101-2014-00444 2
 
1.0%
3020000-101-2001-07858 2
 
1.0%
Other values (180) 181
90.0%
2024-05-03T23:43:49.567763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1840
41.6%
1 649
 
14.7%
- 603
 
13.6%
2 438
 
9.9%
3 266
 
6.0%
9 171
 
3.9%
7 96
 
2.2%
8 94
 
2.1%
6 90
 
2.0%
4 88
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3819
86.4%
Dash Punctuation 603
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1840
48.2%
1 649
 
17.0%
2 438
 
11.5%
3 266
 
7.0%
9 171
 
4.5%
7 96
 
2.5%
8 94
 
2.5%
6 90
 
2.4%
4 88
 
2.3%
5 87
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 603
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4422
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1840
41.6%
1 649
 
14.7%
- 603
 
13.6%
2 438
 
9.9%
3 266
 
6.0%
9 171
 
3.9%
7 96
 
2.2%
8 94
 
2.1%
6 90
 
2.0%
4 88
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1840
41.6%
1 649
 
14.7%
- 603
 
13.6%
2 438
 
9.9%
3 266
 
6.0%
9 171
 
3.9%
7 96
 
2.2%
8 94
 
2.1%
6 90
 
2.0%
4 88
 
2.0%

업태명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
한식
131 
경양식
21 
일식
16 
중국식
15 
기타
 
7
Other values (7)
 
11

Length

Max length8
Median length2
Mean length2.3283582
Min length2

Unique

Unique4 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
한식 131
65.2%
경양식 21
 
10.4%
일식 16
 
8.0%
중국식 15
 
7.5%
기타 7
 
3.5%
식육(숯불구이) 3
 
1.5%
까페 2
 
1.0%
호프/통닭 2
 
1.0%
복어취급 1
 
0.5%
냉면집 1
 
0.5%
Other values (2) 2
 
1.0%

Length

2024-05-03T23:43:50.138412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 131
65.2%
경양식 21
 
10.4%
일식 16
 
8.0%
중국식 15
 
7.5%
기타 7
 
3.5%
식육(숯불구이 3
 
1.5%
까페 2
 
1.0%
호프/통닭 2
 
1.0%
복어취급 1
 
0.5%
냉면집 1
 
0.5%
Other values (2) 2
 
1.0%

주된음식
Text

MISSING 

Distinct125
Distinct (%)72.3%
Missing28
Missing (%)13.9%
Memory size1.7 KiB
2024-05-03T23:43:50.791692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length4.1965318
Min length2

Characters and Unicode

Total characters726
Distinct characters184
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)59.5%

Sample

1st row비빔밥, 찌게
2nd row닭한마리
3rd row오향족발
4th row참치스페셜
5th row차돌박이
ValueCountFrequency (%)
갈비탕 8
 
4.5%
한정식 7
 
4.0%
돼지갈비 4
 
2.3%
파스타 4
 
2.3%
삼겹살 4
 
2.3%
냉면 3
 
1.7%
보쌈 3
 
1.7%
불고기 3
 
1.7%
순대국 3
 
1.7%
짬뽕 3
 
1.7%
Other values (116) 135
76.3%
2024-05-03T23:43:52.141395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
비 27
 
3.7%
갈 25
 
3.4%
탕 24
 
3.3%
식 23
 
3.2%
국 22
 
3.0%
스 22
 
3.0%
이 17
 
2.3%
기 16
 
2.2%
밥 14
 
1.9%
고 13
 
1.8%
Other values (174) 523
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 708
97.5%
Other Punctuation 12
 
1.7%
Space Separator 4
 
0.6%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
비 27
 
3.8%
갈 25
 
3.5%
탕 24
 
3.4%
식 23
 
3.2%
국 22
 
3.1%
스 22
 
3.1%
이 17
 
2.4%
기 16
 
2.3%
밥 14
 
2.0%
고 13
 
1.8%
Other values (170) 505
71.3%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 708
97.5%
Common 18
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
비 27
 
3.8%
갈 25
 
3.5%
탕 24
 
3.4%
식 23
 
3.2%
국 22
 
3.1%
스 22
 
3.1%
이 17
 
2.4%
기 16
 
2.3%
밥 14
 
2.0%
고 13
 
1.8%
Other values (170) 505
71.3%
Common
ValueCountFrequency (%)
, 12
66.7%
4
 
22.2%
) 1
 
5.6%
( 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 708
97.5%
ASCII 18
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
비 27
 
3.8%
갈 25
 
3.5%
탕 24
 
3.4%
식 23
 
3.2%
국 22
 
3.1%
스 22
 
3.1%
이 17
 
2.4%
기 16
 
2.3%
밥 14
 
2.0%
고 13
 
1.8%
Other values (170) 505
71.3%
ASCII
ValueCountFrequency (%)
, 12
66.7%
4
 
22.2%
) 1
 
5.6%
( 1
 
5.6%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct182
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.61577
Minimum21.5
Maximum1306.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-03T23:43:52.723295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.5
5-th percentile48.15
Q173.03
median113.52
Q3167.6
95-th percentile378.94
Maximum1306.17
Range1284.67
Interquartile range (IQR)94.57

Descriptive statistics

Standard deviation136.59073
Coefficient of variation (CV)0.91294341
Kurtosis26.972597
Mean149.61577
Median Absolute Deviation (MAD)43.8
Skewness4.1189904
Sum30072.77
Variance18657.028
MonotonicityNot monotonic
2024-05-03T23:43:53.309144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132.0 3
 
1.5%
81.0 3
 
1.5%
141.87 2
 
1.0%
105.6 2
 
1.0%
59.94 2
 
1.0%
80.0 2
 
1.0%
165.0 2
 
1.0%
128.7 2
 
1.0%
56.2 2
 
1.0%
113.48 2
 
1.0%
Other values (172) 179
89.1%
ValueCountFrequency (%)
21.5 1
0.5%
31.9 1
0.5%
33.1 1
0.5%
33.59 1
0.5%
41.32 1
0.5%
42.17 1
0.5%
42.5 1
0.5%
42.84 1
0.5%
46.72 1
0.5%
46.86 1
0.5%
ValueCountFrequency (%)
1306.17 1
0.5%
658.51 1
0.5%
629.36 1
0.5%
601.0 1
0.5%
581.64 1
0.5%
431.24 1
0.5%
397.78 1
0.5%
396.77 2
1.0%
380.0 1
0.5%
378.94 1
0.5%

행정동명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
남영동
50 
한강로동
38 
한남동
27 
이태원제1동
24 
원효로제1동
13 
Other values (10)
49 

Length

Max length6
Median length3
Mean length3.9850746
Min length3

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row한강로동
2nd row원효로제1동
3rd row남영동
4th row남영동
5th row한강로동

Common Values

ValueCountFrequency (%)
남영동 50
24.9%
한강로동 38
18.9%
한남동 27
13.4%
이태원제1동 24
11.9%
원효로제1동 13
 
6.5%
청파동 9
 
4.5%
후암동 8
 
4.0%
원효로제2동 7
 
3.5%
이촌제1동 7
 
3.5%
효창동 6
 
3.0%
Other values (5) 12
 
6.0%

Length

2024-05-03T23:43:53.837205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남영동 50
24.9%
한강로동 38
18.9%
한남동 27
13.4%
이태원제1동 24
11.9%
원효로제1동 13
 
6.5%
청파동 9
 
4.5%
후암동 8
 
4.0%
원효로제2동 7
 
3.5%
이촌제1동 7
 
3.5%
효창동 6
 
3.0%
Other values (5) 12
 
6.0%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
상수도전용
129 
<NA>
72 

Length

Max length5
Median length5
Mean length4.641791
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 129
64.2%
<NA> 72
35.8%

Length

2024-05-03T23:43:54.277507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:43:54.825002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 129
64.2%
na 72
35.8%

Interactions

2024-05-03T23:43:32.388932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:24.059287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:25.913964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:27.755197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:29.427011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:30.847784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:32.580829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:24.406163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:26.156109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:28.022920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:29.664480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:31.114943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:32.852242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:24.675430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:26.427694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:28.306928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:29.908075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:31.357085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:33.125572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:24.939632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:26.723671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:28.564180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:30.156238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:31.670518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:33.379053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:25.245797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:27.052833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:28.809463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:30.395037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:31.914190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:33.597858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:25.601007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:27.483784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:29.161638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:30.621713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:43:32.186524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T23:43:55.154787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자불가일자업태명영업장면적(㎡)행정동명
지정년도1.0000.8150.9561.0000.849NaN0.0000.0000.534
지정번호0.8151.0000.8010.8150.489NaN0.0000.0000.568
신청일자0.9560.8011.0000.9630.3541.0000.0000.3010.505
지정일자1.0000.8150.9631.0000.849NaN0.0000.0000.543
취소일자0.8490.4890.3540.8491.000NaN0.5470.2130.471
불가일자NaNNaN1.000NaNNaN1.0001.0000.0000.598
업태명0.0000.0000.0000.0000.5471.0001.0000.0000.316
영업장면적(㎡)0.0000.0000.3010.0000.2130.0000.0001.0000.253
행정동명0.5340.5680.5050.5430.4710.5980.3160.2531.000
2024-05-03T23:43:55.680847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분불가일자행정동명업태명
급수시설구분1.0001.0001.0001.000
불가일자1.0001.0000.0000.816
행정동명1.0000.0001.0000.119
업태명1.0000.8160.1191.000
2024-05-03T23:43:55.969807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)불가일자업태명행정동명급수시설구분
지정년도1.000-0.6000.9701.0000.477-0.1590.0000.0000.2131.000
지정번호-0.6001.000-0.617-0.599-0.3350.1140.0000.0000.2431.000
신청일자0.970-0.6171.0000.9700.625-0.1530.8160.0640.1101.000
지정일자1.000-0.5990.9701.0000.477-0.1590.0000.0000.2181.000
취소일자0.477-0.3350.6250.4771.0000.2540.0000.1500.1801.000
영업장면적(㎡)-0.1590.114-0.153-0.1590.2541.0000.0000.0000.1161.000
불가일자0.0000.0000.8160.0000.0000.0001.0000.8160.0001.000
업태명0.0000.0000.0640.0000.1500.0000.8161.0000.1191.000
행정동명0.2130.2430.1100.2180.1800.1160.0000.1191.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-03T23:43:33.947113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T23:43:34.632860image/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.
2024-05-03T23:43:35.147309image/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

시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
03020000202352023083120231011<NA><NA>남도미항 용산아이파크몰점서울특별시 용산구 한강대로23길 55, 용산역 4층 900-6호 (한강로3가)서울특별시 용산구 한강로3가 40번지 999호 용산역-900-63020000-101-2018-00360한식비빔밥, 찌게180.53한강로동상수도전용
13020000202332023083120231011<NA><NA>큰집닭한마리서울특별시 용산구 원효로 259-1, 1층 (원효로1가)서울특별시 용산구 원효로1가 39번지 10호3020000-101-2012-00126기타닭한마리142.51원효로제1동<NA>
23020000202362023083120231011<NA><NA>금천문서울특별시 용산구 한강대로 268, (남영동,(지상1층))서울특별시 용산구 남영동 84번지 8호 (지상1층)3020000-101-2000-07490한식오향족발73.03남영동상수도전용
33020000202372023083120231011<NA><NA>이춘복참치서울특별시 용산구 한강대로 266-2, (남영동)서울특별시 용산구 남영동 85번지 1호 (지상1층)3020000-101-2008-00164일식참치스페셜126.0남영동<NA>
43020000202312023083120231011<NA><NA>풍성집서울특별시 용산구 한강대로62길 38, (한강로1가,(지상1층))서울특별시 용산구 한강로1가 231번지 21호 (지상1층)3020000-101-1986-07037한식차돌박이33.1한강로동상수도전용
53020000202342023083120231011<NA><NA>산마루돌구이서울특별시 용산구 한강대로62다길 12, 지상1층 (한강로1가)서울특별시 용산구 한강로1가 211번지 1호 지상1층3020000-101-2015-00086까페산낙지돌구이101.73한강로동상수도전용
63020000202322022083120231011<NA><NA>섬집서울특별시 용산구 한강대로14길 18, (한강로3가,(지상1층))서울특별시 용산구 한강로3가 65번지 162호 (지상1층)3020000-101-2011-00087한식간장게장, 참게꽃게매운탕65.52한강로동<NA>
730200002022102022081920220921<NA><NA>선명구이가서울특별시 용산구 한강대로 270, 2층 201호 (남영동)서울특별시 용산구 남영동 83번지 1호3020000-101-2021-00494한식제주산근고기151.45남영동상수도전용
83020000202212022081920220921<NA><NA>마구로참치1980서울특별시 용산구 한강대로 259, 고려에이트리움 1층 110호~113호 (갈월동)서울특별시 용산구 갈월동 101번지 49호 고려에이트리움3020000-101-2019-00195일식참치197.13남영동상수도전용
93020000202282022081920220921<NA><NA>서울추어탕서울특별시 용산구 한강대로104가길 11-2, 1층 (동자동)서울특별시 용산구 동자동 22번지 31호 1층3020000-101-2018-00031한식추어탕69.72남영동상수도전용
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
1913020000201536200606302015121120161104<NA>상하이베이서울특별시 용산구 청파로 74, (한강로3가,전자랜드 신관 6층 A-3호)서울특별시 용산구 한강로3가 16번지 9호 전자랜드 신관 6층 A-3호3020000-101-2003-00038중국식칠리새우220.0한강로동상수도전용
19230200002001143200606302001063020171221<NA>아틀리에서울특별시 용산구 이태원로27가길 36, (이태원동, 지하1층,지상1층)서울특별시 용산구 이태원동 116번지 2호 지하1층,지상1층3020000-101-1985-01147경양식탄두리182.68이태원제1동상수도전용
1933020000200182006063020010630<NA><NA>은성서울특별시 용산구 한강대로84길 11-16, (남영동)서울특별시 용산구 남영동 40번지 4호3020000-101-1999-06589한식등심구이83.13남영동상수도전용
1943020000200272200606302002102220111031<NA>신장터타운서울특별시 용산구 대사관로34길 21, (한남동,(지상1층))서울특별시 용산구 한남동 627번지 0호 (지상1층)3020000-101-1996-05536한식갈비탕99.6한남동상수도전용
1953020000200810200606302008082920080829<NA>스페이스후암23(Space huam23)서울특별시 용산구 후암로 23, (후암동,(지상1층))서울특별시 용산구 후암동 236번지 1호 (지상1층)3020000-101-2001-07903한식<NA>80.5후암동상수도전용
196302000020061492006063020060630<NA><NA>서계마을서울특별시 용산구 효창원로 270, 만리시장 (서계동)서울특별시 용산구 서계동 260번지 1호 만리시장 B동 2층 11호3020000-101-2004-00128한식명태매콤조림128.7청파동<NA>
1973020000200830200606302008082920080829<NA>신토불이서울특별시 용산구 독서당로 11, (한남동,(지상1층))서울특별시 용산구 한남동 557번지 15호 (지상1층)3020000-101-1994-06222한식<NA>97.31한남동상수도전용
198302000020014200606302001063020110128<NA>와라쿠동경술집서울특별시 용산구 한강대로 273, (갈월동,(지하1층))서울특별시 용산구 갈월동 92번지 (지하1층)3020000-101-1997-07745기타스파게티368.07남영동상수도전용
1993020000200846200606302008082920080829<NA>선명구이가서울특별시 용산구 후암로 16, (후암동, 249-7 지상1층)서울특별시 용산구 후암동 249번지 7호 지상1층3020000-101-1996-05520일식<NA>157.0후암동상수도전용
200302000020051052006063020050725<NA><NA>태양서울특별시 용산구 백범로99길 54, (한강로1가,(지상1층))서울특별시 용산구 한강로1가 253번지 (지상1층)3020000-101-2004-00269중국식짬뽕141.9한강로동상수도전용