Overview

Dataset statistics

Number of variables16
Number of observations95
Missing cells8
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.7 KiB
Average record size in memory136.4 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
Author성북구
URLhttps://data.seoul.go.kr/dataList/OA-11159/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정취소사유 is highly overall correlated with 지정년도 and 5 other fieldsHigh correlation
급수시설구분 is highly overall correlated with 지정년도 and 8 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
업태명 is highly overall correlated with 급수시설구분High correlation
지정년도 is highly overall correlated with 신청일자 and 4 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정취소사유 and 1 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 4 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 4 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정년도 and 4 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
소재지도로명 has 2 (2.1%) missing valuesMissing
주된음식 has 6 (6.3%) missing valuesMissing

Reproduction

Analysis started2024-05-04 01:29:57.024987
Analysis finished2024-05-04 01:30:12.710762
Duration15.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
3070000
95 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3070000
2nd row3070000
3rd row3070000
4th row3070000
5th row3070000

Common Values

ValueCountFrequency (%)
3070000 95
100.0%

Length

2024-05-04T01:30:13.006488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T01:30:13.341051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3070000 95
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5474
Minimum2001
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2024-05-04T01:30:13.738504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12001
median2003
Q32008.5
95-th percentile2018
Maximum2021
Range20
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.6375799
Coefficient of variation (CV)0.0028109931
Kurtosis0.19450936
Mean2005.5474
Median Absolute Deviation (MAD)2
Skewness1.1610072
Sum190527
Variance31.782307
MonotonicityNot monotonic
2024-05-04T01:30:14.316025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2001 37
38.9%
2004 12
 
12.6%
2003 8
 
8.4%
2008 6
 
6.3%
2014 5
 
5.3%
2018 4
 
4.2%
2002 4
 
4.2%
2006 3
 
3.2%
2009 3
 
3.2%
2012 3
 
3.2%
Other values (6) 10
 
10.5%
ValueCountFrequency (%)
2001 37
38.9%
2002 4
 
4.2%
2003 8
 
8.4%
2004 12
 
12.6%
2005 1
 
1.1%
2006 3
 
3.2%
2008 6
 
6.3%
2009 3
 
3.2%
2010 2
 
2.1%
2012 3
 
3.2%
ValueCountFrequency (%)
2021 2
 
2.1%
2018 4
4.2%
2016 1
 
1.1%
2015 2
 
2.1%
2014 5
5.3%
2013 2
 
2.1%
2012 3
3.2%
2010 2
 
2.1%
2009 3
3.2%
2008 6
6.3%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.73684
Minimum2
Maximum7130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2024-05-04T01:30:14.729669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.7
Q19
median33
Q3103.5
95-th percentile907.3
Maximum7130
Range7128
Interquartile range (IQR)94.5

Descriptive statistics

Standard deviation763.81282
Coefficient of variation (CV)3.6244864
Kurtosis73.34189
Mean210.73684
Median Absolute Deviation (MAD)29
Skewness8.1447083
Sum20020
Variance583410.03
MonotonicityNot monotonic
2024-05-04T01:30:15.160758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 4
 
4.2%
9 4
 
4.2%
5 3
 
3.2%
3 3
 
3.2%
91 3
 
3.2%
4 3
 
3.2%
6 3
 
3.2%
7 3
 
3.2%
17 2
 
2.1%
18 2
 
2.1%
Other values (56) 65
68.4%
ValueCountFrequency (%)
2 2
2.1%
3 3
3.2%
4 3
3.2%
5 3
3.2%
6 3
3.2%
7 3
3.2%
8 4
4.2%
9 4
4.2%
10 1
 
1.1%
11 2
2.1%
ValueCountFrequency (%)
7130 1
1.1%
1015 1
1.1%
1006 1
1.1%
916 1
1.1%
908 1
1.1%
907 1
1.1%
824 1
1.1%
819 1
1.1%
816 1
1.1%
813 1
1.1%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20056505
Minimum20010601
Maximum20211001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2024-05-04T01:30:15.564724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010601
5-th percentile20010601
Q120010601
median20030825
Q320090605
95-th percentile20180914
Maximum20211001
Range200400
Interquartile range (IQR)80004

Descriptive statistics

Standard deviation56754.685
Coefficient of variation (CV)0.0028297396
Kurtosis0.12581252
Mean20056505
Median Absolute Deviation (MAD)20224
Skewness1.141065
Sum1.9053679 × 109
Variance3.2210943 × 109
MonotonicityNot monotonic
2024-05-04T01:30:15.958971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20010601 37
38.9%
20040707 12
 
12.6%
20030825 7
 
7.4%
20141029 5
 
5.3%
20180914 4
 
4.2%
20080701 4
 
4.2%
20020607 3
 
3.2%
20100610 3
 
3.2%
20060614 3
 
3.2%
20090605 3
 
3.2%
Other values (11) 14
 
14.7%
ValueCountFrequency (%)
20010601 37
38.9%
20020607 3
 
3.2%
20021119 1
 
1.1%
20030208 1
 
1.1%
20030825 7
 
7.4%
20040707 12
 
12.6%
20050714 1
 
1.1%
20060614 3
 
3.2%
20080701 4
 
4.2%
20080829 1
 
1.1%
ValueCountFrequency (%)
20211001 2
 
2.1%
20180914 4
4.2%
20160926 1
 
1.1%
20151005 1
 
1.1%
20151002 1
 
1.1%
20141029 5
5.3%
20131104 2
 
2.1%
20130121 1
 
1.1%
20121203 2
 
2.1%
20100610 3
3.2%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20056263
Minimum20010630
Maximum20211130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2024-05-04T01:30:16.331758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010630
5-th percentile20010630
Q120010630
median20030825
Q320085778
95-th percentile20181116
Maximum20211130
Range200500
Interquartile range (IQR)75148

Descriptive statistics

Standard deviation56564.299
Coefficient of variation (CV)0.0028202811
Kurtosis0.18945368
Mean20056263
Median Absolute Deviation (MAD)20195
Skewness1.1602286
Sum1.905345 × 109
Variance3.1995199 × 109
MonotonicityNot monotonic
2024-05-04T01:30:16.729527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20010630 37
38.9%
20040707 12
 
12.6%
20030825 7
 
7.4%
20141218 5
 
5.3%
20080829 5
 
5.3%
20181116 4
 
4.2%
20020607 3
 
3.2%
20090727 3
 
3.2%
20060818 3
 
3.2%
20121206 3
 
3.2%
Other values (9) 13
 
13.7%
ValueCountFrequency (%)
20010630 37
38.9%
20020607 3
 
3.2%
20021119 1
 
1.1%
20030208 1
 
1.1%
20030825 7
 
7.4%
20040707 12
 
12.6%
20050914 1
 
1.1%
20060818 3
 
3.2%
20080701 1
 
1.1%
20080829 5
 
5.3%
ValueCountFrequency (%)
20211130 2
 
2.1%
20181116 4
4.2%
20161122 1
 
1.1%
20151208 2
 
2.1%
20141218 5
5.3%
20131223 2
 
2.1%
20121206 3
3.2%
20100727 2
 
2.1%
20090727 3
3.2%
20080829 5
5.3%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20095365
Minimum20021016
Maximum20240223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2024-05-04T01:30:17.164431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20021016
5-th percentile20030428
Q120050327
median20061218
Q320150963
95-th percentile20214153
Maximum20240223
Range219207
Interquartile range (IQR)100636

Descriptive statistics

Standard deviation64083.653
Coefficient of variation (CV)0.0031889768
Kurtosis-0.7202427
Mean20095365
Median Absolute Deviation (MAD)20398
Skewness0.87765279
Sum1.9090597 × 109
Variance4.1067145 × 109
MonotonicityDecreasing
2024-05-04T01:30:17.817957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20071112 9
 
9.5%
20200711 8
 
8.4%
20060818 7
 
7.4%
20050914 6
 
6.3%
20171121 4
 
4.2%
20221206 4
 
4.2%
20111114 3
 
3.2%
20100727 3
 
3.2%
20090727 2
 
2.1%
20211130 2
 
2.1%
Other values (47) 47
49.5%
ValueCountFrequency (%)
20021016 1
1.1%
20021114 1
1.1%
20021119 1
1.1%
20021217 1
1.1%
20030208 1
1.1%
20030522 1
1.1%
20030526 1
1.1%
20031001 1
1.1%
20031015 1
1.1%
20040109 1
1.1%
ValueCountFrequency (%)
20240223 1
 
1.1%
20221206 4
4.2%
20211130 2
 
2.1%
20201124 1
 
1.1%
20200711 8
8.4%
20171121 4
4.2%
20170502 1
 
1.1%
20170228 1
 
1.1%
20161122 1
 
1.1%
20160708 1
 
1.1%

업소명
Text

Distinct85
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size892.0 B
2024-05-04T01:30:18.424167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length11
Mean length7.0842105
Min length2

Characters and Unicode

Total characters673
Distinct characters242
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)80.0%

Sample

1st row맥주정원
2nd row감나무
3rd row방배동할매쭈꾸미
4th row완이네해물감자탕
5th row삼대족발(석관점)
ValueCountFrequency (%)
콜렉티보 3
 
2.4%
안암설성점 3
 
2.4%
성신여대점 3
 
2.4%
원조나드리장터순대국 2
 
1.6%
2
 
1.6%
정릉점 2
 
1.6%
커피 2
 
1.6%
무교동낙지.해초나라 2
 
1.6%
육수당 2
 
1.6%
키웨스트 2
 
1.6%
Other values (94) 100
81.3%
2024-05-04T01:30:19.796352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
4.2%
점 23
 
3.4%
대 15
 
2.2%
한 13
 
1.9%
성 13
 
1.9%
) 12
 
1.8%
마 12
 
1.8%
( 12
 
1.8%
당 11
 
1.6%
리 10
 
1.5%
Other values (232) 524
77.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 579
86.0%
Space Separator 28
 
4.2%
Uppercase Letter 19
 
2.8%
Close Punctuation 12
 
1.8%
Open Punctuation 12
 
1.8%
Decimal Number 9
 
1.3%
Lowercase Letter 9
 
1.3%
Other Punctuation 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 23
 
4.0%
대 15
 
2.6%
한 13
 
2.2%
성 13
 
2.2%
마 12
 
2.1%
당 11
 
1.9%
리 10
 
1.7%
이 9
 
1.6%
동 9
 
1.6%
식 9
 
1.6%
Other values (201) 455
78.6%
Uppercase Letter
ValueCountFrequency (%)
B 3
15.8%
O 3
15.8%
A 3
15.8%
Y 2
10.5%
E 1
 
5.3%
N 1
 
5.3%
S 1
 
5.3%
L 1
 
5.3%
M 1
 
5.3%
U 1
 
5.3%
Other values (2) 2
10.5%
Decimal Number
ValueCountFrequency (%)
0 2
22.2%
1 2
22.2%
9 1
11.1%
4 1
11.1%
3 1
11.1%
5 1
11.1%
8 1
11.1%
Lowercase Letter
ValueCountFrequency (%)
p 2
22.2%
t 2
22.2%
o 1
11.1%
n 1
11.1%
a 1
11.1%
e 1
11.1%
i 1
11.1%
Other Punctuation
ValueCountFrequency (%)
. 3
60.0%
& 2
40.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 579
86.0%
Common 66
 
9.8%
Latin 28
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 23
 
4.0%
대 15
 
2.6%
한 13
 
2.2%
성 13
 
2.2%
마 12
 
2.1%
당 11
 
1.9%
리 10
 
1.7%
이 9
 
1.6%
동 9
 
1.6%
식 9
 
1.6%
Other values (201) 455
78.6%
Latin
ValueCountFrequency (%)
B 3
 
10.7%
O 3
 
10.7%
A 3
 
10.7%
p 2
 
7.1%
t 2
 
7.1%
Y 2
 
7.1%
o 1
 
3.6%
n 1
 
3.6%
a 1
 
3.6%
e 1
 
3.6%
Other values (9) 9
32.1%
Common
ValueCountFrequency (%)
28
42.4%
) 12
18.2%
( 12
18.2%
. 3
 
4.5%
0 2
 
3.0%
& 2
 
3.0%
1 2
 
3.0%
9 1
 
1.5%
4 1
 
1.5%
3 1
 
1.5%
Other values (2) 2
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 579
86.0%
ASCII 94
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
29.8%
) 12
12.8%
( 12
12.8%
B 3
 
3.2%
O 3
 
3.2%
A 3
 
3.2%
. 3
 
3.2%
0 2
 
2.1%
& 2
 
2.1%
p 2
 
2.1%
Other values (21) 24
25.5%
Hangul
ValueCountFrequency (%)
점 23
 
4.0%
대 15
 
2.6%
한 13
 
2.2%
성 13
 
2.2%
마 12
 
2.1%
당 11
 
1.9%
리 10
 
1.7%
이 9
 
1.6%
동 9
 
1.6%
식 9
 
1.6%
Other values (201) 455
78.6%

소재지도로명
Text

MISSING 

Distinct83
Distinct (%)89.2%
Missing2
Missing (%)2.1%
Memory size892.0 B
2024-05-04T01:30:20.798295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length45
Mean length29.225806
Min length22

Characters and Unicode

Total characters2718
Distinct characters97
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)79.6%

Sample

1st row서울특별시 성북구 솔샘로 73-8, (정릉동)
2nd row서울특별시 성북구 장위로 87, 1층 (장위동)
3rd row서울특별시 성북구 삼선교로 75-1, (삼선동4가,(1층))
4th row서울특별시 성북구 보국문로20길 18, 대선빌딩 1층 (정릉동)
5th row서울특별시 성북구 한천로 595, (석관동,(지하178.08㎡,지상1층 184.33㎡,지상2층 199.45㎡))
ValueCountFrequency (%)
서울특별시 93
 
18.7%
성북구 93
 
18.7%
1층 13
 
2.6%
안암동5가 10
 
2.0%
정릉동 8
 
1.6%
장위동 8
 
1.6%
동선동1가 7
 
1.4%
화랑로 7
 
1.4%
석관동 7
 
1.4%
삼선교로 6
 
1.2%
Other values (162) 245
49.3%
2024-05-04T01:30:21.835205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
404
 
14.9%
, 118
 
4.3%
동 116
 
4.3%
1 104
 
3.8%
) 100
 
3.7%
( 100
 
3.7%
북 98
 
3.6%
성 97
 
3.6%
서 93
 
3.4%
특 93
 
3.4%
Other values (87) 1395
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1532
56.4%
Decimal Number 437
 
16.1%
Space Separator 404
 
14.9%
Other Punctuation 121
 
4.5%
Close Punctuation 100
 
3.7%
Open Punctuation 100
 
3.7%
Dash Punctuation 19
 
0.7%
Other Symbol 4
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 116
 
7.6%
북 98
 
6.4%
성 97
 
6.3%
서 93
 
6.1%
특 93
 
6.1%
별 93
 
6.1%
시 93
 
6.1%
구 93
 
6.1%
울 93
 
6.1%
로 93
 
6.1%
Other values (69) 570
37.2%
Decimal Number
ValueCountFrequency (%)
1 104
23.8%
2 70
16.0%
5 49
11.2%
3 46
10.5%
4 42
9.6%
7 39
 
8.9%
0 27
 
6.2%
6 26
 
5.9%
8 18
 
4.1%
9 16
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 118
97.5%
. 3
 
2.5%
Space Separator
ValueCountFrequency (%)
404
100.0%
Close Punctuation
ValueCountFrequency (%)
) 100
100.0%
Open Punctuation
ValueCountFrequency (%)
( 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1532
56.4%
Common 1186
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 116
 
7.6%
북 98
 
6.4%
성 97
 
6.3%
서 93
 
6.1%
특 93
 
6.1%
별 93
 
6.1%
시 93
 
6.1%
구 93
 
6.1%
울 93
 
6.1%
로 93
 
6.1%
Other values (69) 570
37.2%
Common
ValueCountFrequency (%)
404
34.1%
, 118
 
9.9%
1 104
 
8.8%
) 100
 
8.4%
( 100
 
8.4%
2 70
 
5.9%
5 49
 
4.1%
3 46
 
3.9%
4 42
 
3.5%
7 39
 
3.3%
Other values (8) 114
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1532
56.4%
ASCII 1182
43.5%
CJK Compat 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
404
34.2%
, 118
 
10.0%
1 104
 
8.8%
) 100
 
8.5%
( 100
 
8.5%
2 70
 
5.9%
5 49
 
4.1%
3 46
 
3.9%
4 42
 
3.6%
7 39
 
3.3%
Other values (7) 110
 
9.3%
Hangul
ValueCountFrequency (%)
동 116
 
7.6%
북 98
 
6.4%
성 97
 
6.3%
서 93
 
6.1%
특 93
 
6.1%
별 93
 
6.1%
시 93
 
6.1%
구 93
 
6.1%
울 93
 
6.1%
로 93
 
6.1%
Other values (69) 570
37.2%
CJK Compat
ValueCountFrequency (%)
㎡ 4
100.0%

소재지지번
Text

Distinct85
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size892.0 B
2024-05-04T01:30:22.412186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length37
Mean length27.515789
Min length23

Characters and Unicode

Total characters2614
Distinct characters76
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)80.0%

Sample

1st row서울특별시 성북구 정릉동 292번지 12호
2nd row서울특별시 성북구 장위동 225번지 45호
3rd row서울특별시 성북구 삼선동4가 312번지 (1층)
4th row서울특별시 성북구 정릉동 260번지 29호 대선빌딩 1층
5th row서울특별시 성북구 석관동 189번지 6호 (지하178.08㎡,지상1층 184.33㎡,지상2층 199.45㎡)
ValueCountFrequency (%)
서울특별시 95
19.5%
성북구 95
19.5%
정릉동 12
 
2.5%
안암동5가 11
 
2.3%
1호 11
 
2.3%
석관동 10
 
2.1%
1층 10
 
2.1%
동선동1가 8
 
1.6%
장위동 8
 
1.6%
종암동 7
 
1.4%
Other values (155) 219
45.1%
2024-05-04T01:30:23.394854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
648
24.8%
1 114
 
4.4%
동 110
 
4.2%
지 105
 
4.0%
성 97
 
3.7%
북 97
 
3.7%
서 95
 
3.6%
번 95
 
3.6%
특 95
 
3.6%
별 95
 
3.6%
Other values (66) 1063
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1440
55.1%
Space Separator 648
24.8%
Decimal Number 489
 
18.7%
Other Punctuation 13
 
0.5%
Open Punctuation 7
 
0.3%
Close Punctuation 7
 
0.3%
Dash Punctuation 5
 
0.2%
Other Symbol 4
 
0.2%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 110
 
7.6%
지 105
 
7.3%
성 97
 
6.7%
북 97
 
6.7%
서 95
 
6.6%
번 95
 
6.6%
특 95
 
6.6%
별 95
 
6.6%
시 95
 
6.6%
구 95
 
6.6%
Other values (48) 461
32.0%
Decimal Number
ValueCountFrequency (%)
1 114
23.3%
2 72
14.7%
3 62
12.7%
5 42
 
8.6%
4 42
 
8.6%
0 39
 
8.0%
6 32
 
6.5%
7 32
 
6.5%
8 27
 
5.5%
9 27
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 10
76.9%
. 3
 
23.1%
Space Separator
ValueCountFrequency (%)
648
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1440
55.1%
Common 1174
44.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 110
 
7.6%
지 105
 
7.3%
성 97
 
6.7%
북 97
 
6.7%
서 95
 
6.6%
번 95
 
6.6%
특 95
 
6.6%
별 95
 
6.6%
시 95
 
6.6%
구 95
 
6.6%
Other values (48) 461
32.0%
Common
ValueCountFrequency (%)
648
55.2%
1 114
 
9.7%
2 72
 
6.1%
3 62
 
5.3%
5 42
 
3.6%
4 42
 
3.6%
0 39
 
3.3%
6 32
 
2.7%
7 32
 
2.7%
8 27
 
2.3%
Other values (8) 64
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1440
55.1%
ASCII 1170
44.8%
CJK Compat 4
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
648
55.4%
1 114
 
9.7%
2 72
 
6.2%
3 62
 
5.3%
5 42
 
3.6%
4 42
 
3.6%
0 39
 
3.3%
6 32
 
2.7%
7 32
 
2.7%
8 27
 
2.3%
Other values (7) 60
 
5.1%
Hangul
ValueCountFrequency (%)
동 110
 
7.6%
지 105
 
7.3%
성 97
 
6.7%
북 97
 
6.7%
서 95
 
6.6%
번 95
 
6.6%
특 95
 
6.6%
별 95
 
6.6%
시 95
 
6.6%
구 95
 
6.6%
Other values (48) 461
32.0%
CJK Compat
ValueCountFrequency (%)
㎡ 4
100.0%

허가(신고)번호
Text

Distinct85
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size892.0 B
2024-05-04T01:30:23.999458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2090
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

Unique76 ?
Unique (%)80.0%

Sample

1st row3070000-101-2012-00315
2nd row3070000-101-2021-00184
3rd row3070000-101-2010-00302
4th row3070000-101-2021-00077
5th row3070000-101-2011-00133
ValueCountFrequency (%)
3070000-101-1996-02112 3
 
3.2%
3070000-101-1993-00125 2
 
2.1%
3070000-101-1996-02108 2
 
2.1%
3070000-101-1994-00383 2
 
2.1%
3070000-101-2007-00346 2
 
2.1%
3070000-101-1991-01581 2
 
2.1%
3070000-101-1990-02999 2
 
2.1%
3070000-101-2006-00124 2
 
2.1%
3070000-101-2003-00060 2
 
2.1%
3070000-101-2003-00010 1
 
1.1%
Other values (75) 75
78.9%
2024-05-04T01:30:24.975987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 816
39.0%
1 320
 
15.3%
- 285
 
13.6%
3 140
 
6.7%
9 138
 
6.6%
7 127
 
6.1%
2 95
 
4.5%
4 46
 
2.2%
8 45
 
2.2%
6 43
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1805
86.4%
Dash Punctuation 285
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 816
45.2%
1 320
 
17.7%
3 140
 
7.8%
9 138
 
7.6%
7 127
 
7.0%
2 95
 
5.3%
4 46
 
2.5%
8 45
 
2.5%
6 43
 
2.4%
5 35
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 285
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2090
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 816
39.0%
1 320
 
15.3%
- 285
 
13.6%
3 140
 
6.7%
9 138
 
6.6%
7 127
 
6.1%
2 95
 
4.5%
4 46
 
2.2%
8 45
 
2.2%
6 43
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2090
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 816
39.0%
1 320
 
15.3%
- 285
 
13.6%
3 140
 
6.7%
9 138
 
6.6%
7 127
 
6.1%
2 95
 
4.5%
4 46
 
2.2%
8 45
 
2.2%
6 43
 
2.1%

업태명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size892.0 B
한식
59 
중국식
7 
기타
7 
탕류(보신용)
6 
경양식
 
4
Other values (6)
12 

Length

Max length15
Median length2
Mean length2.7052632
Min length2

Unique

Unique4 ?
Unique (%)4.2%

Sample

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

Common Values

ValueCountFrequency (%)
한식 59
62.1%
중국식 7
 
7.4%
기타 7
 
7.4%
탕류(보신용) 6
 
6.3%
경양식 4
 
4.2%
일식 4
 
4.2%
회집 4
 
4.2%
호프/통닭 1
 
1.1%
식육(숯불구이) 1
 
1.1%
통닭(치킨) 1
 
1.1%

Length

2024-05-04T01:30:25.422766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 59
62.1%
중국식 7
 
7.4%
기타 7
 
7.4%
탕류(보신용 6
 
6.3%
경양식 4
 
4.2%
일식 4
 
4.2%
회집 4
 
4.2%
호프/통닭 1
 
1.1%
식육(숯불구이 1
 
1.1%
통닭(치킨 1
 
1.1%

지정취소사유
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size892.0 B
영업자지위승계
33 
기준미흡
20 
재지정 안됨
8 
행정처분
7 
기준미흡(서울시위생등급평가)
 
3
Other values (17)
24 

Length

Max length15
Median length12
Mean length6.1684211
Min length3

Unique

Unique11 ?
Unique (%)11.6%

Sample

1st row행정처분
2nd row위생 미흡
3rd row위생 미흡
4th row점검 포기
5th row부적합(점검불가-휴업)

Common Values

ValueCountFrequency (%)
영업자지위승계 33
34.7%
기준미흡 20
21.1%
재지정 안됨 8
 
8.4%
행정처분 7
 
7.4%
기준미흡(서울시위생등급평가) 3
 
3.2%
모범음식점 기준 미흡 3
 
3.2%
지위승계 2
 
2.1%
재지정 신청포기 2
 
2.1%
재지정 포기 2
 
2.1%
<NA> 2
 
2.1%
Other values (12) 13
 
13.7%

Length

2024-05-04T01:30:25.842823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영업자지위승계 33
28.4%
기준미흡 20
17.2%
재지정 12
 
10.3%
안됨 8
 
6.9%
행정처분 7
 
6.0%
미흡 5
 
4.3%
포기 3
 
2.6%
기준미흡(서울시위생등급평가 3
 
2.6%
모범음식점 3
 
2.6%
기준 3
 
2.6%
Other values (15) 19
16.4%

주된음식
Text

MISSING 

Distinct51
Distinct (%)57.3%
Missing6
Missing (%)6.3%
Memory size892.0 B
2024-05-04T01:30:26.274743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.3370787
Min length1

Characters and Unicode

Total characters297
Distinct characters93
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

Unique36 ?
Unique (%)40.4%

Sample

1st row찜류
2nd row해물탕
3rd row감자탕
4th row대게
5th row감자탕
ValueCountFrequency (%)
돼지갈비 9
 
10.1%
삼겹살 7
 
7.9%
자장면 6
 
6.7%
돈까스 4
 
4.5%
활어회 4
 
4.5%
정식 3
 
3.4%
감자탕 3
 
3.4%
추어탕 3
 
3.4%
만두국 2
 
2.2%
갈비 2
 
2.2%
Other values (41) 46
51.7%
2024-05-04T01:30:27.154923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 18
 
6.1%
갈 16
 
5.4%
비 16
 
5.4%
지 12
 
4.0%
삼 10
 
3.4%
돼 9
 
3.0%
자 9
 
3.0%
어 9
 
3.0%
살 8
 
2.7%
겹 8
 
2.7%
Other values (83) 182
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 295
99.3%
Other Punctuation 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 18
 
6.1%
갈 16
 
5.4%
비 16
 
5.4%
지 12
 
4.1%
삼 10
 
3.4%
돼 9
 
3.1%
자 9
 
3.1%
어 9
 
3.1%
살 8
 
2.7%
겹 8
 
2.7%
Other values (82) 180
61.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 295
99.3%
Common 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 18
 
6.1%
갈 16
 
5.4%
비 16
 
5.4%
지 12
 
4.1%
삼 10
 
3.4%
돼 9
 
3.1%
자 9
 
3.1%
어 9
 
3.1%
살 8
 
2.7%
겹 8
 
2.7%
Other values (82) 180
61.0%
Common
ValueCountFrequency (%)
, 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 295
99.3%
ASCII 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 18
 
6.1%
갈 16
 
5.4%
비 16
 
5.4%
지 12
 
4.1%
삼 10
 
3.4%
돼 9
 
3.1%
자 9
 
3.1%
어 9
 
3.1%
살 8
 
2.7%
겹 8
 
2.7%
Other values (82) 180
61.0%
ASCII
ValueCountFrequency (%)
, 2
100.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.30158
Minimum46.28
Maximum1390.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size987.0 B
2024-05-04T01:30:27.580884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46.28
5-th percentile50.592
Q178.005
median97.15
Q3131.35
95-th percentile275.6
Maximum1390.09
Range1343.81
Interquartile range (IQR)53.345

Descriptive statistics

Standard deviation151.95584
Coefficient of variation (CV)1.1485565
Kurtosis51.119935
Mean132.30158
Median Absolute Deviation (MAD)29.55
Skewness6.5021187
Sum12568.65
Variance23090.577
MonotonicityNot monotonic
2024-05-04T01:30:28.045540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.45 3
 
3.2%
109.59 2
 
2.1%
49.5 2
 
2.1%
87.53 2
 
2.1%
89.6 2
 
2.1%
120.46 2
 
2.1%
85.1 2
 
2.1%
275.6 2
 
2.1%
64.0 2
 
2.1%
128.7 2
 
2.1%
Other values (73) 74
77.9%
ValueCountFrequency (%)
46.28 1
1.1%
46.8 1
1.1%
47.6 1
1.1%
49.5 2
2.1%
51.06 1
1.1%
51.65 1
1.1%
52.84 1
1.1%
54.45 1
1.1%
56.77 1
1.1%
56.94 1
1.1%
ValueCountFrequency (%)
1390.09 1
1.1%
576.98 1
1.1%
386.0 1
1.1%
294.67 1
1.1%
275.6 2
2.1%
269.92 1
1.1%
260.0 1
1.1%
233.69 1
1.1%
218.68 1
1.1%
202.34 1
1.1%

행정동명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Memory size892.0 B
동선동
13 
삼선동
12 
안암동
12 
석관동
10 
정릉제4동
7 
Other values (13)
41 

Length

Max length5
Median length3
Mean length3.7578947
Min length3

Unique

Unique3 ?
Unique (%)3.2%

Sample

1st row정릉제4동
2nd row장위제1동
3rd row삼선동
4th row정릉제1동
5th row석관동

Common Values

ValueCountFrequency (%)
동선동 13
13.7%
삼선동 12
12.6%
안암동 12
12.6%
석관동 10
10.5%
정릉제4동 7
7.4%
종암동 7
7.4%
장위제1동 5
 
5.3%
월곡제1동 5
 
5.3%
월곡제2동 5
 
5.3%
길음제2동 4
 
4.2%
Other values (8) 15
15.8%

Length

2024-05-04T01:30:28.498053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동선동 13
13.7%
삼선동 12
12.6%
안암동 12
12.6%
석관동 10
10.5%
정릉제4동 7
7.4%
종암동 7
7.4%
장위제1동 5
 
5.3%
월곡제1동 5
 
5.3%
월곡제2동 5
 
5.3%
길음제2동 4
 
4.2%
Other values (8) 15
15.8%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size892.0 B
상수도전용
83 
<NA>
12 

Length

Max length5
Median length5
Mean length4.8736842
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 83
87.4%
<NA> 12
 
12.6%

Length

2024-05-04T01:30:28.908744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T01:30:29.232959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 83
87.4%
na 12
 
12.6%

Interactions

2024-05-04T01:30:09.235827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:00.919030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:02.801214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:04.412445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:06.038867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:07.695705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:09.512933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:01.194577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:03.087816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:04.691680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:06.326996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:07.958772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:09.777435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:01.471091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:03.348606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:04.972194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:06.597711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:08.216249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:10.169333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:01.760266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:03.615527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:05.261275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:06.868418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:08.481596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:10.628606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:02.049249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:03.893668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:05.507360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:07.141099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:08.736548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:10.918028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:02.306414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:04.142639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:05.766737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:07.425192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T01:30:08.978706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T01:30:29.449307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명
지정년도1.0000.7710.9880.9920.7440.6150.6150.6150.6150.0000.8750.5340.8410.000
지정번호0.7711.0000.7760.7710.7940.0000.0000.0000.0000.0000.9550.3250.1380.000
신청일자0.9880.7761.0001.0000.7350.0000.0000.0000.0000.0000.8490.0000.4400.506
지정일자0.9920.7711.0001.0000.7210.6670.6670.6670.6670.0000.8440.0000.4190.518
취소일자0.7440.7940.7350.7211.0000.0000.0000.0000.0000.0000.9450.6830.3550.000
업소명0.6150.0000.0000.6670.0001.0001.0001.0001.0001.0000.9700.9941.0001.000
소재지도로명0.6150.0000.0000.6670.0001.0001.0001.0001.0001.0000.9710.9941.0001.000
소재지지번0.6150.0000.0000.6670.0001.0001.0001.0001.0001.0000.9700.9941.0001.000
허가(신고)번호0.6150.0000.0000.6670.0001.0001.0001.0001.0001.0000.9700.9941.0001.000
업태명0.0000.0000.0000.0000.0001.0001.0001.0001.0001.0000.0000.7460.0000.191
지정취소사유0.8750.9550.8490.8440.9450.9700.9710.9700.9700.0001.0000.8580.7890.436
주된음식0.5340.3250.0000.0000.6830.9940.9940.9940.9940.7460.8581.0000.9790.000
영업장면적(㎡)0.8410.1380.4400.4190.3551.0001.0001.0001.0000.0000.7890.9791.0000.487
행정동명0.0000.0000.5060.5180.0001.0001.0001.0001.0000.1910.4360.0000.4871.000
2024-05-04T01:30:29.812156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정취소사유급수시설구분행정동명업태명
지정취소사유1.0001.0000.1280.000
급수시설구분1.0001.0001.0001.000
행정동명0.1281.0001.0000.051
업태명0.0001.0000.0511.000
2024-05-04T01:30:30.099273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명지정취소사유행정동명급수시설구분
지정년도1.000-0.2720.9991.0000.7620.0160.0000.5820.1111.000
지정번호-0.2721.000-0.272-0.272-0.1400.0900.0000.6960.0001.000
신청일자0.999-0.2721.0001.0000.7620.0140.0000.5450.1611.000
지정일자1.000-0.2721.0001.0000.7620.0170.0000.5400.1691.000
취소일자0.762-0.1400.7620.7621.0000.0330.0000.7100.0001.000
영업장면적(㎡)0.0160.0900.0140.0170.0331.0000.0000.4820.2491.000
업태명0.0000.0000.0000.0000.0000.0001.0000.0000.0511.000
지정취소사유0.5820.6960.5450.5400.7100.4820.0001.0000.1281.000
행정동명0.1110.0000.1610.1690.0000.2490.0510.1281.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-04T01:30:11.332019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T01:30:12.027354image/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.
2024-05-04T01:30:12.526187image/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
03070000201410201410292014121820240223맥주정원서울특별시 성북구 솔샘로 73-8, (정릉동)서울특별시 성북구 정릉동 292번지 12호3070000-101-2012-00315경양식행정처분<NA>163.57정릉제4동<NA>
1307000020216202110012021113020221206감나무서울특별시 성북구 장위로 87, 1층 (장위동)서울특별시 성북구 장위동 225번지 45호3070000-101-2021-00184한식위생 미흡찜류116.96장위제1동<NA>
2307000020123201212032012120620221206방배동할매쭈꾸미서울특별시 성북구 삼선교로 75-1, (삼선동4가,(1층))서울특별시 성북구 삼선동4가 312번지 (1층)3070000-101-2010-00302한식위생 미흡해물탕65.73삼선동<NA>
3307000020218202110012021113020221206완이네해물감자탕서울특별시 성북구 보국문로20길 18, 대선빌딩 1층 (정릉동)서울특별시 성북구 정릉동 260번지 29호 대선빌딩 1층3070000-101-2021-00077한식점검 포기감자탕108.53정릉제1동<NA>
43070000201691201609262016112220221206삼대족발(석관점)서울특별시 성북구 한천로 595, (석관동,(지하178.08㎡,지상1층 184.33㎡,지상2층 199.45㎡))서울특별시 성북구 석관동 189번지 6호 (지하178.08㎡,지상1층 184.33㎡,지상2층 199.45㎡)3070000-101-2011-00133한식부적합(점검불가-휴업)대게576.98석관동<NA>
5307000020158201510022015120820211130참이맛감자탕(정릉점)서울특별시 성북구 솔샘로25길 20, 정릉풍림상가 상가1동 108-1호 (정릉동)서울특별시 성북구 정릉동 239번지 정릉풍림상가3070000-101-2014-00026탕류(보신용)시설물멸실감자탕46.8정릉제4동상수도전용
63070000201411201410292014121820211130성가네낙지마을서울특별시 성북구 화랑로 236, (석관동)서울특별시 성북구 석관동 343번지 19호3070000-101-2013-00145한식지정취소희망<NA>269.92석관동<NA>
73070000201894201809142018111620201124넘버팔오(NO.85)서울특별시 성북구 고려대로28길 17, 2층 (안암동5가)서울특별시 성북구 안암동5가 86번지 185호3070000-101-1998-06628중국식부적합중국식96.0안암동상수도전용
8307000020139201311042013122320200711담소면옥서울특별시 성북구 솔샘로 71, (정릉동,1층)서울특별시 성북구 정릉동 290번지 54호 1층3070000-101-2010-00141한식재지정 안됨순대국75.01정릉제4동상수도전용
93070000201891201809142018111620200711명동칼국수서울특별시 성북구 고려대로5길 87, 1층 (삼선동5가)서울특별시 성북구 삼선동5가 399번지3070000-101-2017-00129한식재지정 안됨칼국수56.77삼선동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
8530700002001101200106012001063020040109콜렉티보 안암설성점서울특별시 성북구 고려대로27길 6, 2층 (안암동5가)서울특별시 성북구 안암동5가 100번지 5호3070000-101-1996-02112중국식영업자지위승계자장면85.45안암동상수도전용
863070000200127200106012001063020031015전통만두국서울특별시 성북구 고려대로8길 67, (안암동3가)서울특별시 성북구 안암동3가 99번지3070000-101-1996-02198한식영업자지위승계만두국98.09안암동상수도전용
873070000200179200106012001063020031001무교동낙지.해초나라서울특별시 성북구 화랑로 255-3, (장위동)서울특별시 성북구 장위동 63번지 107호3070000-101-1991-01581한식영업자지위승계돼지갈비87.53장위제2동상수도전용
88307000020011232001060120010630200305261943 성신여대점서울특별시 성북구 동소문로20가길 24, (동선동1가)서울특별시 성북구 동선동1가 99번지 0호 지상2층3070000-101-1999-09364기타영업자지위승계돈까스154.44동선동상수도전용
893070000200137200106012001063020030522한방닭한마리서울특별시 성북구 한천로78길 63, (석관동)서울특별시 성북구 석관동 133번지 74호3070000-101-1998-09445한식영업자지위승계수제비66.0석관동상수도전용
903070000200189200106012001063020030208키웨스트 커피서울특별시 성북구 보국문로16길 2, (정릉동)서울특별시 성북구 정릉동 281번지 3호3070000-101-1996-02108한식영업자지위승계추어탕85.1정릉제4동상수도전용
913070000200117200106012001063020021217메밀촌서울특별시 성북구 화랑로25길 17, (상월곡동)서울특별시 성북구 상월곡동 7번지 153호3070000-101-1993-00289한식영업자지위승계만두국97.15월곡제2동상수도전용
9230700002001120200106012001063020021119내마음의 풍차서울특별시 성북구 종암로 107, (종암동)서울특별시 성북구 종암동 87번지 3호3070000-101-1990-02999경양식영업자지위승계정식101.97종암동상수도전용
933070000200126200106012001063020021114두부촌맷돌할매숨두부서울특별시 성북구 안암로 43, (안암동5가)서울특별시 성북구 안암동5가 110번지 135호3070000-101-1993-00125한식영업자지위승계갈비275.6안암동상수도전용
943070000200136200106012001063020021016조마루뼈다귀전문점서울특별시 성북구 화랑로 262, (석관동)서울특별시 성북구 석관동 238번지 4호3070000-101-1991-04063한식행정처분(영업정지)탕류95.3석관동상수도전용