Overview

Dataset statistics

Number of variables16
Number of observations112
Missing cells21
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory136.2 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
Author관악구
URLhttps://data.seoul.go.kr/dataList/OA-11544/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 신청일자 and 3 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 2 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
지정취소사유 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
급수시설구분 is highly overall correlated with 지정취소사유High correlation
업태명 is highly imbalanced (69.0%)Imbalance
급수시설구분 is highly imbalanced (54.1%)Imbalance
주된음식 has 21 (18.8%) missing valuesMissing

Reproduction

Analysis started2024-05-11 08:39:16.638497
Analysis finished2024-05-11 08:39:33.741517
Duration17.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
3200000
112 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3200000
2nd row3200000
3rd row3200000
4th row3200000
5th row3200000

Common Values

ValueCountFrequency (%)
3200000 112
100.0%

Length

2024-05-11T08:39:33.951295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:39:34.249915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3200000 112
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.4018
Minimum2001
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T08:39:34.528939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12001
median2005
Q32008.25
95-th percentile2018
Maximum2021
Range20
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation6.2937976
Coefficient of variation (CV)0.0031368581
Kurtosis-0.38284935
Mean2006.4018
Median Absolute Deviation (MAD)4
Skewness1.0203362
Sum224717
Variance39.611889
MonotonicityNot monotonic
2024-05-11T08:39:34.959730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2001 44
39.3%
2005 28
25.0%
2018 19
17.0%
2007 5
 
4.5%
2009 3
 
2.7%
2008 3
 
2.7%
2006 3
 
2.7%
2010 2
 
1.8%
2014 1
 
0.9%
2002 1
 
0.9%
Other values (3) 3
 
2.7%
ValueCountFrequency (%)
2001 44
39.3%
2002 1
 
0.9%
2005 28
25.0%
2006 3
 
2.7%
2007 5
 
4.5%
2008 3
 
2.7%
2009 3
 
2.7%
2010 2
 
1.8%
2013 1
 
0.9%
2014 1
 
0.9%
ValueCountFrequency (%)
2021 1
 
0.9%
2018 19
17.0%
2017 1
 
0.9%
2014 1
 
0.9%
2013 1
 
0.9%
2010 2
 
1.8%
2009 3
 
2.7%
2008 3
 
2.7%
2007 5
 
4.5%
2006 3
 
2.7%

지정번호
Real number (ℝ)

Distinct100
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.71429
Minimum1
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T08:39:35.373413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.55
Q167.75
median126.5
Q3309
95-th percentile476.5
Maximum588
Range587
Interquartile range (IQR)241.25

Descriptive statistics

Standard deviation152.37362
Coefficient of variation (CV)0.8117316
Kurtosis-0.49755591
Mean187.71429
Median Absolute Deviation (MAD)92.5
Skewness0.7841311
Sum21024
Variance23217.719
MonotonicityNot monotonic
2024-05-11T08:39:35.817293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 3
 
2.7%
76 3
 
2.7%
113 2
 
1.8%
71 2
 
1.8%
358 2
 
1.8%
60 2
 
1.8%
118 2
 
1.8%
117 2
 
1.8%
11 2
 
1.8%
22 2
 
1.8%
Other values (90) 90
80.4%
ValueCountFrequency (%)
1 1
 
0.9%
4 1
 
0.9%
5 1
 
0.9%
7 1
 
0.9%
11 2
1.8%
12 1
 
0.9%
15 1
 
0.9%
22 2
1.8%
27 3
2.7%
30 1
 
0.9%
ValueCountFrequency (%)
588 1
0.9%
543 1
0.9%
532 1
0.9%
514 1
0.9%
508 1
0.9%
482 1
0.9%
472 1
0.9%
466 1
0.9%
453 1
0.9%
442 1
0.9%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20054433
Minimum20010630
Maximum20211031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T08:39:36.289655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010630
5-th percentile20010630
Q120010630
median20050614
Q320073171
95-th percentile20165420
Maximum20211031
Range200401
Interquartile range (IQR)62541.25

Descriptive statistics

Standard deviation48925.704
Coefficient of variation (CV)0.0024396454
Kurtosis0.89627939
Mean20054433
Median Absolute Deviation (MAD)39699
Skewness1.208458
Sum2.2460965 × 109
Variance2.3937245 × 109
MonotonicityNot monotonic
2024-05-11T08:39:36.779246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20010630 36
32.1%
20050614 30
26.8%
20011230 8
 
7.1%
20090313 5
 
4.5%
20070720 3
 
2.7%
20080710 3
 
2.7%
20170920 3
 
2.7%
20160920 2
 
1.8%
20060703 2
 
1.8%
20100510 2
 
1.8%
Other values (16) 18
16.1%
ValueCountFrequency (%)
20010630 36
32.1%
20011230 8
 
7.1%
20021018 1
 
0.9%
20050614 30
26.8%
20060510 1
 
0.9%
20060703 2
 
1.8%
20070720 3
 
2.7%
20070723 1
 
0.9%
20070725 2
 
1.8%
20080510 2
 
1.8%
ValueCountFrequency (%)
20211031 1
 
0.9%
20181109 1
 
0.9%
20180831 1
 
0.9%
20170920 3
2.7%
20160920 2
1.8%
20150605 1
 
0.9%
20140708 1
 
0.9%
20140704 1
 
0.9%
20130709 1
 
0.9%
20130704 1
 
0.9%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20064822
Minimum20010701
Maximum20211115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T08:39:37.264360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010701
5-th percentile20010701
Q120010701
median20050630
Q320083169
95-th percentile20181109
Maximum20211115
Range200414
Interquartile range (IQR)72468

Descriptive statistics

Standard deviation63054.564
Coefficient of variation (CV)0.003142543
Kurtosis-0.37717573
Mean20064822
Median Absolute Deviation (MAD)39929
Skewness1.024461
Sum2.24726 × 109
Variance3.9758781 × 109
MonotonicityNot monotonic
2024-05-11T08:39:37.708761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20010701 36
32.1%
20050630 27
24.1%
20181109 19
17.0%
20011231 8
 
7.1%
20070727 4
 
3.6%
20060711 3
 
2.7%
20090501 3
 
2.7%
20080725 2
 
1.8%
20070711 1
 
0.9%
20141110 1
 
0.9%
Other values (8) 8
 
7.1%
ValueCountFrequency (%)
20010701 36
32.1%
20011231 8
 
7.1%
20021231 1
 
0.9%
20050614 1
 
0.9%
20050630 27
24.1%
20060711 3
 
2.7%
20070711 1
 
0.9%
20070727 4
 
3.6%
20080721 1
 
0.9%
20080725 2
 
1.8%
ValueCountFrequency (%)
20211115 1
 
0.9%
20181109 19
17.0%
20171106 1
 
0.9%
20141110 1
 
0.9%
20131105 1
 
0.9%
20100629 1
 
0.9%
20100510 1
 
0.9%
20090501 3
 
2.7%
20080725 2
 
1.8%
20080721 1
 
0.9%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20102679
Minimum20020103
Maximum20240206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T08:39:38.185966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020103
5-th percentile20026185
Q120060709
median20070727
Q320163488
95-th percentile20221124
Maximum20240206
Range220103
Interquartile range (IQR)102778.5

Descriptive statistics

Standard deviation64860.82
Coefficient of variation (CV)0.0032264764
Kurtosis-0.91514104
Mean20102679
Median Absolute Deviation (MAD)29516.5
Skewness0.72633727
Sum2.2515001 × 109
Variance4.2069259 × 109
MonotonicityNot monotonic
2024-05-11T08:39:38.660993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20070727 25
22.3%
20211115 8
 
7.1%
20080725 7
 
6.2%
20221124 6
 
5.4%
20070725 4
 
3.6%
20131204 3
 
2.7%
20191111 3
 
2.7%
20060711 3
 
2.7%
20151201 3
 
2.7%
20171106 2
 
1.8%
Other values (47) 48
42.9%
ValueCountFrequency (%)
20020103 1
0.9%
20020207 1
0.9%
20020618 1
0.9%
20020723 1
0.9%
20021119 1
0.9%
20021130 1
0.9%
20030321 1
0.9%
20030412 1
0.9%
20030530 1
0.9%
20030908 1
0.9%
ValueCountFrequency (%)
20240206 1
 
0.9%
20230725 1
 
0.9%
20221124 6
5.4%
20211115 8
7.1%
20191111 3
 
2.7%
20191018 1
 
0.9%
20180412 1
 
0.9%
20171117 1
 
0.9%
20171106 2
 
1.8%
20170926 1
 
0.9%

업소명
Text

Distinct92
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T08:39:39.433314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length6.4196429
Min length2

Characters and Unicode

Total characters719
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

Unique75 ?
Unique (%)67.0%

Sample

1st row로향
2nd row영진식당
3rd row영진식당
4th row복땡이
5th row복땡이
ValueCountFrequency (%)
삼미옥 3
 
2.1%
남원추어탕 3
 
2.1%
황토방 3
 
2.1%
강남숯불갈비 2
 
1.4%
칼국수 2
 
1.4%
난곡점 2
 
1.4%
옥돌집 2
 
1.4%
하나감자탕 2
 
1.4%
영진식당 2
 
1.4%
킹크랩대게한상 2
 
1.4%
Other values (112) 123
84.2%
2024-05-11T08:39:40.672169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34
 
4.7%
탕 16
 
2.2%
대 15
 
2.1%
점 14
 
1.9%
비 13
 
1.8%
어 12
 
1.7%
수 12
 
1.7%
장 12
 
1.7%
추 11
 
1.5%
집 11
 
1.5%
Other values (232) 569
79.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 660
91.8%
Space Separator 34
 
4.7%
Lowercase Letter 15
 
2.1%
Uppercase Letter 3
 
0.4%
Close Punctuation 2
 
0.3%
Other Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 16
 
2.4%
대 15
 
2.3%
점 14
 
2.1%
비 13
 
2.0%
어 12
 
1.8%
수 12
 
1.8%
장 12
 
1.8%
추 11
 
1.7%
집 11
 
1.7%
국 11
 
1.7%
Other values (212) 533
80.8%
Lowercase Letter
ValueCountFrequency (%)
e 3
20.0%
n 2
13.3%
g 1
 
6.7%
u 1
 
6.7%
h 1
 
6.7%
t 1
 
6.7%
c 1
 
6.7%
l 1
 
6.7%
b 1
 
6.7%
a 1
 
6.7%
Other values (2) 2
13.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
33.3%
H 1
33.3%
K 1
33.3%
Space Separator
ValueCountFrequency (%)
34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 660
91.8%
Common 41
 
5.7%
Latin 18
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 16
 
2.4%
대 15
 
2.3%
점 14
 
2.1%
비 13
 
2.0%
어 12
 
1.8%
수 12
 
1.8%
장 12
 
1.8%
추 11
 
1.7%
집 11
 
1.7%
국 11
 
1.7%
Other values (212) 533
80.8%
Latin
ValueCountFrequency (%)
e 3
16.7%
n 2
 
11.1%
g 1
 
5.6%
u 1
 
5.6%
h 1
 
5.6%
t 1
 
5.6%
c 1
 
5.6%
l 1
 
5.6%
B 1
 
5.6%
H 1
 
5.6%
Other values (5) 5
27.8%
Common
ValueCountFrequency (%)
34
82.9%
) 2
 
4.9%
& 2
 
4.9%
( 2
 
4.9%
- 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 660
91.8%
ASCII 59
 
8.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
57.6%
e 3
 
5.1%
) 2
 
3.4%
& 2
 
3.4%
( 2
 
3.4%
n 2
 
3.4%
g 1
 
1.7%
u 1
 
1.7%
h 1
 
1.7%
t 1
 
1.7%
Other values (10) 10
 
16.9%
Hangul
ValueCountFrequency (%)
탕 16
 
2.4%
대 15
 
2.3%
점 14
 
2.1%
비 13
 
2.0%
어 12
 
1.8%
수 12
 
1.8%
장 12
 
1.8%
추 11
 
1.7%
집 11
 
1.7%
국 11
 
1.7%
Other values (212) 533
80.8%

소재지도로명
Text

Distinct93
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T08:39:41.399506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length37
Mean length28.910714
Min length23

Characters and Unicode

Total characters3238
Distinct characters72
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)67.9%

Sample

1st row서울특별시 관악구 낙성대로 12, 1층 (봉천동)
2nd row서울특별시 관악구 조원로2길 7, 1층 (신림동)
3rd row서울특별시 관악구 조원로2길 7, 1층 (신림동)
4th row서울특별시 관악구 봉천로 333, (봉천동)
5th row서울특별시 관악구 봉천로 333, (봉천동)
ValueCountFrequency (%)
서울특별시 112
17.3%
관악구 112
17.3%
봉천동 54
 
8.3%
1층 52
 
8.0%
신림동 46
 
7.1%
봉천로 16
 
2.5%
남부순환로 15
 
2.3%
지상1층 10
 
1.5%
관악로 8
 
1.2%
남현동 6
 
0.9%
Other values (135) 218
33.6%
2024-05-11T08:39:43.031261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
537
 
16.6%
1 159
 
4.9%
관 135
 
4.2%
악 133
 
4.1%
, 127
 
3.9%
동 115
 
3.6%
) 114
 
3.5%
( 114
 
3.5%
울 112
 
3.5%
서 112
 
3.5%
Other values (62) 1580
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1828
56.5%
Space Separator 537
 
16.6%
Decimal Number 495
 
15.3%
Other Punctuation 127
 
3.9%
Close Punctuation 114
 
3.5%
Open Punctuation 114
 
3.5%
Dash Punctuation 22
 
0.7%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 135
 
7.4%
악 133
 
7.3%
동 115
 
6.3%
울 112
 
6.1%
서 112
 
6.1%
구 112
 
6.1%
시 112
 
6.1%
별 112
 
6.1%
특 112
 
6.1%
로 101
 
5.5%
Other values (46) 672
36.8%
Decimal Number
ValueCountFrequency (%)
1 159
32.1%
2 77
15.6%
6 47
 
9.5%
3 41
 
8.3%
4 37
 
7.5%
8 34
 
6.9%
0 30
 
6.1%
5 30
 
6.1%
7 23
 
4.6%
9 17
 
3.4%
Space Separator
ValueCountFrequency (%)
537
100.0%
Other Punctuation
ValueCountFrequency (%)
, 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 114
100.0%
Open Punctuation
ValueCountFrequency (%)
( 114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1828
56.5%
Common 1410
43.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 135
 
7.4%
악 133
 
7.3%
동 115
 
6.3%
울 112
 
6.1%
서 112
 
6.1%
구 112
 
6.1%
시 112
 
6.1%
별 112
 
6.1%
특 112
 
6.1%
로 101
 
5.5%
Other values (46) 672
36.8%
Common
ValueCountFrequency (%)
537
38.1%
1 159
 
11.3%
, 127
 
9.0%
) 114
 
8.1%
( 114
 
8.1%
2 77
 
5.5%
6 47
 
3.3%
3 41
 
2.9%
4 37
 
2.6%
8 34
 
2.4%
Other values (6) 123
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1828
56.5%
ASCII 1410
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
537
38.1%
1 159
 
11.3%
, 127
 
9.0%
) 114
 
8.1%
( 114
 
8.1%
2 77
 
5.5%
6 47
 
3.3%
3 41
 
2.9%
4 37
 
2.6%
8 34
 
2.4%
Other values (6) 123
 
8.7%
Hangul
ValueCountFrequency (%)
관 135
 
7.4%
악 133
 
7.3%
동 115
 
6.3%
울 112
 
6.1%
서 112
 
6.1%
구 112
 
6.1%
시 112
 
6.1%
별 112
 
6.1%
특 112
 
6.1%
로 101
 
5.5%
Other values (46) 672
36.8%

소재지지번
Text

Distinct93
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T08:39:43.799360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length33
Mean length27.392857
Min length23

Characters and Unicode

Total characters3068
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)67.9%

Sample

1st row서울특별시 관악구 봉천동 1627번지 13호
2nd row서울특별시 관악구 신림동 1656번지
3rd row서울특별시 관악구 신림동 1656번지
4th row서울특별시 관악구 봉천동 957번지 33호
5th row서울특별시 관악구 봉천동 957번지 33호
ValueCountFrequency (%)
서울특별시 112
19.0%
관악구 112
19.0%
봉천동 58
 
9.8%
신림동 48
 
8.1%
지상1층 22
 
3.7%
1호 8
 
1.4%
4호 7
 
1.2%
2호 7
 
1.2%
3호 7
 
1.2%
7호 7
 
1.2%
Other values (126) 203
34.3%
2024-05-11T08:39:45.208377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
782
25.5%
1 155
 
5.1%
지 140
 
4.6%
동 113
 
3.7%
호 113
 
3.7%
구 112
 
3.7%
번 112
 
3.7%
서 112
 
3.7%
악 112
 
3.7%
관 112
 
3.7%
Other values (41) 1205
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1678
54.7%
Space Separator 782
25.5%
Decimal Number 601
 
19.6%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Dash Punctuation 2
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 140
 
8.3%
동 113
 
6.7%
호 113
 
6.7%
구 112
 
6.7%
번 112
 
6.7%
서 112
 
6.7%
악 112
 
6.7%
관 112
 
6.7%
시 112
 
6.7%
별 112
 
6.7%
Other values (26) 528
31.5%
Decimal Number
ValueCountFrequency (%)
1 155
25.8%
2 75
12.5%
5 62
 
10.3%
6 56
 
9.3%
3 54
 
9.0%
4 49
 
8.2%
8 40
 
6.7%
0 39
 
6.5%
9 36
 
6.0%
7 35
 
5.8%
Space Separator
ValueCountFrequency (%)
782
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1678
54.7%
Common 1390
45.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 140
 
8.3%
동 113
 
6.7%
호 113
 
6.7%
구 112
 
6.7%
번 112
 
6.7%
서 112
 
6.7%
악 112
 
6.7%
관 112
 
6.7%
시 112
 
6.7%
별 112
 
6.7%
Other values (26) 528
31.5%
Common
ValueCountFrequency (%)
782
56.3%
1 155
 
11.2%
2 75
 
5.4%
5 62
 
4.5%
6 56
 
4.0%
3 54
 
3.9%
4 49
 
3.5%
8 40
 
2.9%
0 39
 
2.8%
9 36
 
2.6%
Other values (5) 42
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1678
54.7%
ASCII 1390
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
782
56.3%
1 155
 
11.2%
2 75
 
5.4%
5 62
 
4.5%
6 56
 
4.0%
3 54
 
3.9%
4 49
 
3.5%
8 40
 
2.9%
0 39
 
2.8%
9 36
 
2.6%
Other values (5) 42
 
3.0%
Hangul
ValueCountFrequency (%)
지 140
 
8.3%
동 113
 
6.7%
호 113
 
6.7%
구 112
 
6.7%
번 112
 
6.7%
서 112
 
6.7%
악 112
 
6.7%
관 112
 
6.7%
시 112
 
6.7%
별 112
 
6.7%
Other values (26) 528
31.5%

허가(신고)번호
Text

Distinct93
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-05-11T08:39:46.188878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2464
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 (%)67.9%

Sample

1st row3200000-101-1997-09385
2nd row3200000-101-2003-00462
3rd row3200000-101-2003-00462
4th row3200000-101-1988-00084
5th row3200000-101-1988-00084
ValueCountFrequency (%)
3200000-101-2000-00008 3
 
2.7%
3200000-101-1997-05894 3
 
2.7%
3200000-101-2005-00466 2
 
1.8%
3200000-101-1991-00179 2
 
1.8%
3200000-101-1991-00474 2
 
1.8%
3200000-101-2003-00462 2
 
1.8%
3200000-101-1996-04547 2
 
1.8%
3200000-101-1997-05900 2
 
1.8%
3200000-101-2001-00847 2
 
1.8%
3200000-101-1992-00257 2
 
1.8%
Other values (83) 90
80.4%
2024-05-11T08:39:47.442785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 999
40.5%
1 351
 
14.2%
- 336
 
13.6%
2 209
 
8.5%
3 158
 
6.4%
9 152
 
6.2%
6 55
 
2.2%
5 54
 
2.2%
4 54
 
2.2%
8 48
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2128
86.4%
Dash Punctuation 336
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 999
46.9%
1 351
 
16.5%
2 209
 
9.8%
3 158
 
7.4%
9 152
 
7.1%
6 55
 
2.6%
5 54
 
2.5%
4 54
 
2.5%
8 48
 
2.3%
7 48
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2464
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 999
40.5%
1 351
 
14.2%
- 336
 
13.6%
2 209
 
8.5%
3 158
 
6.4%
9 152
 
6.2%
6 55
 
2.2%
5 54
 
2.2%
4 54
 
2.2%
8 48
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 999
40.5%
1 351
 
14.2%
- 336
 
13.6%
2 209
 
8.5%
3 158
 
6.4%
9 152
 
6.2%
6 55
 
2.2%
5 54
 
2.2%
4 54
 
2.2%
8 48
 
1.9%

업태명
Categorical

IMBALANCE 

Distinct9
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
한식
96 
회집
 
5
기타
 
3
호프/통닭
 
2
중국식
 
2
Other values (4)
 
4

Length

Max length15
Median length2
Mean length2.1964286
Min length2

Unique

Unique4 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
한식 96
85.7%
회집 5
 
4.5%
기타 3
 
2.7%
호프/통닭 2
 
1.8%
중국식 2
 
1.8%
분식 1
 
0.9%
경양식 1
 
0.9%
일식 1
 
0.9%
외국음식전문점(인도,태국등) 1
 
0.9%

Length

2024-05-11T08:39:47.988929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:39:48.420106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 96
85.7%
회집 5
 
4.5%
기타 3
 
2.7%
호프/통닭 2
 
1.8%
중국식 2
 
1.8%
분식 1
 
0.9%
경양식 1
 
0.9%
일식 1
 
0.9%
외국음식전문점(인도,태국등 1
 
0.9%

지정취소사유
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
영업자지위승계
27 
무단확장
15 
행정처분
11 
기준미달
7 
지위승계 및 주취급음식변경
7 
Other values (33)
45 

Length

Max length27
Median length25
Mean length8.0357143
Min length4

Unique

Unique27 ?
Unique (%)24.1%

Sample

1st row영업자지위승계
2nd row영업자승계
3rd row영업자 지위승계
4th row무단확장
5th row영업자지위승계

Common Values

ValueCountFrequency (%)
영업자지위승계 27
24.1%
무단확장 15
13.4%
행정처분 11
 
9.8%
기준미달 7
 
6.2%
지위승계 및 주취급음식변경 7
 
6.2%
영업자 지위승계 6
 
5.4%
위생불량 3
 
2.7%
영업자승계 3
 
2.7%
위생등급 평가결과 60점미만 2
 
1.8%
<NA> 2
 
1.8%
Other values (28) 29
25.9%

Length

2024-05-11T08:39:48.990181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영업자지위승계 27
 
15.2%
무단확장 15
 
8.4%
지위승계 13
 
7.3%
및 12
 
6.7%
행정처분 11
 
6.2%
기준미달 7
 
3.9%
주취급음식변경 7
 
3.9%
영업자 7
 
3.9%
미입력사항 4
 
2.2%
주취급음식 4
 
2.2%
Other values (50) 71
39.9%

주된음식
Text

MISSING 

Distinct52
Distinct (%)57.1%
Missing21
Missing (%)18.8%
Memory size1.0 KiB
2024-05-11T08:39:49.451315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.7252747
Min length2

Characters and Unicode

Total characters339
Distinct characters93
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

Unique39 ?
Unique (%)42.9%

Sample

1st row갈비
2nd row감자탕
3rd row냉면
4th row돼지갈비
5th row숯불갈비
ValueCountFrequency (%)
돼지갈비 17
 
18.3%
활어회 5
 
5.4%
삼겹살 5
 
5.4%
갈비탕 4
 
4.3%
추어탕 4
 
4.3%
불고기 4
 
4.3%
브로이정식 2
 
2.2%
설렁탕 2
 
2.2%
소갈비 2
 
2.2%
영원정식 2
 
2.2%
Other values (42) 46
49.5%
2024-05-11T08:39:50.534325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
갈 29
 
8.6%
비 27
 
8.0%
지 19
 
5.6%
탕 17
 
5.0%
돼 17
 
5.0%
어 12
 
3.5%
정 9
 
2.7%
식 9
 
2.7%
고 8
 
2.4%
기 8
 
2.4%
Other values (83) 184
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 333
98.2%
Other Punctuation 4
 
1.2%
Space Separator 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
갈 29
 
8.7%
비 27
 
8.1%
지 19
 
5.7%
탕 17
 
5.1%
돼 17
 
5.1%
어 12
 
3.6%
정 9
 
2.7%
식 9
 
2.7%
고 8
 
2.4%
기 8
 
2.4%
Other values (81) 178
53.5%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 333
98.2%
Common 6
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
갈 29
 
8.7%
비 27
 
8.1%
지 19
 
5.7%
탕 17
 
5.1%
돼 17
 
5.1%
어 12
 
3.6%
정 9
 
2.7%
식 9
 
2.7%
고 8
 
2.4%
기 8
 
2.4%
Other values (81) 178
53.5%
Common
ValueCountFrequency (%)
, 4
66.7%
2
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 333
98.2%
ASCII 6
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
갈 29
 
8.7%
비 27
 
8.1%
지 19
 
5.7%
탕 17
 
5.1%
돼 17
 
5.1%
어 12
 
3.6%
정 9
 
2.7%
식 9
 
2.7%
고 8
 
2.4%
기 8
 
2.4%
Other values (81) 178
53.5%
ASCII
ValueCountFrequency (%)
, 4
66.7%
2
33.3%

영업장면적(㎡)
Real number (ℝ)

Distinct92
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.55813
Minimum24.52
Maximum900.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-05-11T08:39:51.087031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.52
5-th percentile57.0725
Q183.69
median101
Q3141.62
95-th percentile283.722
Maximum900.36
Range875.84
Interquartile range (IQR)57.93

Descriptive statistics

Standard deviation105.48976
Coefficient of variation (CV)0.78984155
Kurtosis26.400673
Mean133.55813
Median Absolute Deviation (MAD)26.745
Skewness4.3448227
Sum14958.51
Variance11128.089
MonotonicityNot monotonic
2024-05-11T08:39:51.568773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141.62 3
 
2.7%
96.16 3
 
2.7%
79.2 3
 
2.7%
230.46 2
 
1.8%
68.2 2
 
1.8%
170.34 2
 
1.8%
92.0 2
 
1.8%
95.71 2
 
1.8%
234.0 2
 
1.8%
109.63 2
 
1.8%
Other values (82) 89
79.5%
ValueCountFrequency (%)
24.52 1
0.9%
37.91 1
0.9%
49.5 1
0.9%
50.98 1
0.9%
52.03 1
0.9%
56.0 1
0.9%
57.95 1
0.9%
60.48 1
0.9%
61.38 1
0.9%
64.21 2
1.8%
ValueCountFrequency (%)
900.36 1
0.9%
540.68 1
0.9%
406.8 1
0.9%
322.0 1
0.9%
307.57 2
1.8%
264.21 1
0.9%
247.0 1
0.9%
239.51 1
0.9%
234.0 2
1.8%
230.46 2
1.8%

행정동명
Categorical

Distinct16
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
낙성대동
21 
신림동
19 
은천동
9 
행운동
9 
서원동
8 
Other values (11)
46 

Length

Max length4
Median length3
Mean length3.2321429
Min length3

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row낙성대동
2nd row조원동
3rd row조원동
4th row은천동
5th row은천동

Common Values

ValueCountFrequency (%)
낙성대동 21
18.8%
신림동 19
17.0%
은천동 9
8.0%
행운동 9
8.0%
서원동 8
 
7.1%
청룡동 7
 
6.2%
조원동 6
 
5.4%
남현동 6
 
5.4%
중앙동 6
 
5.4%
보라매동 5
 
4.5%
Other values (6) 16
14.3%

Length

2024-05-11T08:39:52.197617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙성대동 21
18.8%
신림동 19
17.0%
은천동 9
8.0%
행운동 9
8.0%
서원동 8
 
7.1%
청룡동 7
 
6.2%
조원동 6
 
5.4%
남현동 6
 
5.4%
중앙동 6
 
5.4%
보라매동 5
 
4.5%
Other values (6) 16
14.3%

급수시설구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
상수도전용
92 
<NA>
19 
간이상수도
 
1

Length

Max length5
Median length5
Mean length4.8303571
Min length4

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 92
82.1%
<NA> 19
 
17.0%
간이상수도 1
 
0.9%

Length

2024-05-11T08:39:52.706965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T08:39:53.100090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 92
82.1%
na 19
 
17.0%
간이상수도 1
 
0.9%

Interactions

2024-05-11T08:39:30.970177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:22.217412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:23.865399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:25.466393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:27.337265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:29.131546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:31.239749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:22.504630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:24.115898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:25.748770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:27.671412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:29.662484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:31.557604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:22.771142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:24.442218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:26.109911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:27.950467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:29.910444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:31.820579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:23.058476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:24.712034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:26.403943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:28.293885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:30.170925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:32.097429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:23.353863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:24.979888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:26.788660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:28.602495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:30.401403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:32.351543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:23.608974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:25.235341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:27.065124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:28.887109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T08:39:30.713459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T08:39:53.407782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
지정년도1.0000.9390.9761.0000.7060.9200.9650.9650.9650.7130.8830.9090.0000.466NaN
지정번호0.9391.0000.8850.8880.7280.7980.8610.8610.8610.3000.8480.7460.0400.2630.000
신청일자0.9760.8851.0000.9360.7630.8490.8810.8810.8810.6800.7890.9710.0000.186NaN
지정일자1.0000.8880.9361.0000.7910.6230.8820.8820.8820.5610.9080.9570.0000.292NaN
취소일자0.7060.7280.7630.7911.0000.7790.7860.7860.7860.4470.9600.8850.0000.0000.182
업소명0.9200.7980.8490.6230.7791.0001.0001.0001.0001.0000.9650.9910.9990.9991.000
소재지도로명0.9650.8610.8810.8820.7861.0001.0001.0001.0001.0000.9780.9911.0001.0001.000
소재지지번0.9650.8610.8810.8820.7861.0001.0001.0001.0001.0000.9780.9911.0001.0001.000
허가(신고)번호0.9650.8610.8810.8820.7861.0001.0001.0001.0001.0000.9780.9911.0001.0001.000
업태명0.7130.3000.6800.5610.4471.0001.0001.0001.0001.0000.7470.8920.0000.0000.000
지정취소사유0.8830.8480.7890.9080.9600.9650.9780.9780.9780.7471.0000.8430.0000.6641.000
주된음식0.9090.7460.9710.9570.8850.9910.9910.9910.9910.8920.8431.0000.7840.9140.000
영업장면적(㎡)0.0000.0400.0000.0000.0000.9991.0001.0001.0000.0000.0000.7841.0000.4260.000
행정동명0.4660.2630.1860.2920.0000.9991.0001.0001.0000.0000.6640.9140.4261.0000.394
급수시설구분NaN0.000NaNNaN0.1821.0001.0001.0001.0000.0001.0000.0000.0000.3941.000
2024-05-11T08:39:54.172416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명행정동명지정취소사유급수시설구분
업태명1.0000.0000.3220.000
행정동명0.0001.0000.2100.282
지정취소사유0.3220.2101.0000.841
급수시설구분0.0000.2820.8411.000
2024-05-11T08:39:54.619589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명지정취소사유행정동명급수시설구분
지정년도1.0000.2430.9600.9850.864-0.0260.4640.6060.1400.000
지정번호0.2431.0000.3590.2820.104-0.0290.1370.4230.0960.000
신청일자0.9600.3591.0000.9740.816-0.0820.4190.4350.0000.000
지정일자0.9850.2820.9741.0000.845-0.0400.3450.5730.0760.000
취소일자0.8640.1040.8160.8451.000-0.0010.2200.6580.0000.134
영업장면적(㎡)-0.026-0.029-0.082-0.040-0.0011.0000.0000.0000.1980.000
업태명0.4640.1370.4190.3450.2200.0001.0000.3220.0000.000
지정취소사유0.6060.4230.4350.5730.6580.0000.3221.0000.2100.841
행정동명0.1400.0960.0000.0760.0000.1980.0000.2101.0000.282
급수시설구분0.0000.0000.0000.0000.1340.0000.0000.8410.2821.000

Missing values

2024-05-11T08:39:32.723445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T08:39:33.394313image/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.

Sample

시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
03200000200164200106302001070120060405로향서울특별시 관악구 낙성대로 12, 1층 (봉천동)서울특별시 관악구 봉천동 1627번지 13호3200000-101-1997-09385한식영업자지위승계갈비125.4낙성대동상수도전용
132000002005299200506142005063020070727영진식당서울특별시 관악구 조원로2길 7, 1층 (신림동)서울특별시 관악구 신림동 1656번지3200000-101-2003-00462기타영업자승계감자탕224.79조원동<NA>
232000002007400200707202007072720080725영진식당서울특별시 관악구 조원로2길 7, 1층 (신림동)서울특별시 관악구 신림동 1656번지3200000-101-2003-00462기타영업자 지위승계냉면224.79조원동<NA>
332000002005339200506142005063020070727복땡이서울특별시 관악구 봉천로 333, (봉천동)서울특별시 관악구 봉천동 957번지 33호3200000-101-1988-00084한식무단확장돼지갈비64.21은천동상수도전용
432000002001135200106302001070120031010복땡이서울특별시 관악구 봉천로 333, (봉천동)서울특별시 관악구 봉천동 957번지 33호3200000-101-1988-00084한식영업자지위승계숯불갈비64.21은천동상수도전용
532000002014543201407042014111020161109진&정서울특별시 관악구 난곡로63길 56, (신림동, 지상1층)서울특별시 관악구 신림동 1484번지 3호3200000-101-2005-00157한식평가점수미달청국장,보쌈정식96.41미성동상수도전용
632000002009472200903132009050120161215옛날농장서울특별시 관악구 봉천로 262, 1층 (신림동)서울특별시 관악구 신림동 1427번지 4호 지상1층3200000-101-1979-07457한식위생등급 등외돼지갈비264.21신림동상수도전용
732000002001118200106302001070120070727장추추어탕서울특별시 관악구 보라매로 15-2, 1층 (봉천동)서울특별시 관악구 봉천동 735번지 5호3200000-101-1996-06224한식기준미달매운탕87.77보라매동상수도전용
83200000200132200106302001070120070727놀부부대찌개앤놀부족발보쌈 난곡점서울특별시 관악구 남부순환로 1487, 지하1층, 지상1층 (신림동)서울특별시 관악구 신림동 527번지 6호3200000-101-1989-07488한식영업자승계보쌈406.8신사동상수도전용
932000002005298200506142005063020070727인쌩맥주 신림사거리점서울특별시 관악구 신림로65길 40, (신림동)서울특별시 관악구 신림동 1434번지 8호3200000-101-2003-00653기타무단확장활어회125.02신림동상수도전용
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
10232000002008421200807102008072520131204장군식당서울특별시 관악구 남부순환로 1597-7, 지하1층-1층 (신림동)서울특별시 관악구 신림동 1433번지 78호3200000-101-2008-00163호프/통닭영업주 희망갈비65.9신림동<NA>
10332000002005218200506142005063020170926힘찬정육주식회사서울특별시 관악구 남현1길 58, (남현동)서울특별시 관악구 남현동 1062번지 15호3200000-101-2001-11486한식지위승계및주취급음식변경보쌈182.2남현동상수도전용
10432000002005306200506142005063020060208돼지한마당서울특별시 관악구 봉천로 378, (봉천동)서울특별시 관악구 봉천동 931번지 22호3200000-101-1995-06440한식영업자지위승계뽕잎만두정식83.69은천동상수도전용
10532000002018113200805102018110920211115돼지한마당서울특별시 관악구 봉천로 378, (봉천동)서울특별시 관악구 봉천동 931번지 22호3200000-101-1995-06440한식지위승계 및 주취급음식변경<NA>83.69은천동상수도전용
10632000002005226200506142005061420060816청담 닭 칼국수 그리고 포차서울특별시 관악구 봉천로 465, 1층 (봉천동)서울특별시 관악구 봉천동 871번지 65호3200000-101-1994-00424한식영업자지위승계와인삼겹살52.03중앙동상수도전용
10732000002009466200903132009050120151201일점사서울특별시 관악구 관악로16길 25, 1층 (봉천동)서울특별시 관악구 봉천동 1601번지 3호3200000-101-1983-07449한식위생및시설불량쭈꾸미71.5낙성대동상수도전용
10832000002001125200106302001070120060803단토리 서울대입구역점서울특별시 관악구 관악로16길 13, 1층 (봉천동)서울특별시 관악구 봉천동 853번지 2호 지상1층3200000-101-2001-00387한식영업자지위승계갈비탕117.9낙성대동<NA>
1093200000200144200106302001070120040621대호아구집서울특별시 관악구 남부순환로 1829-9, (봉천동)서울특별시 관악구 봉천동 858번지 4호3200000-101-1983-00400한식행정처분대구탕110.34행운동상수도전용
11032000002005281200506142005063020070727채쉐프 초장집서울특별시 관악구 신림로 371, 1층 (신림동)서울특별시 관악구 신림동 1431번지 2호3200000-101-2003-00512회집무단확장활어회81.82신림동상수도전용
1113200000201893201307042018110920191018채쉐프 초장집서울특별시 관악구 신림로 371, 1층 (신림동)서울특별시 관악구 신림동 1431번지 2호3200000-101-2003-00512회집행정처분<NA>81.82신림동상수도전용