Overview

Dataset statistics

Number of variables16
Number of observations82
Missing cells23
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory136.6 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
Author종로구
URLhttps://data.seoul.go.kr/dataList/OA-9850/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
지정년도 is highly overall correlated with 신청일자 and 1 other fieldsHigh correlation
지정번호 is highly overall correlated with 취소일자High correlation
신청일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 1 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정번호 and 1 other fieldsHigh correlation
지정취소사유 is highly overall correlated with 취소일자High correlation
소재지도로명 has 3 (3.7%) missing valuesMissing
주된음식 has 20 (24.4%) missing valuesMissing

Reproduction

Analysis started2024-05-11 00:15:30.611189
Analysis finished2024-05-11 00:15:50.349377
Duration19.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
3000000
82 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000
2nd row3000000
3rd row3000000
4th row3000000
5th row3000000

Common Values

ValueCountFrequency (%)
3000000 82
100.0%

Length

2024-05-11T00:15:50.638528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:15:51.024649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000 82
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.7317
Minimum2007
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-11T00:15:51.451810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12007
median2009
Q32011.75
95-th percentile2016
Maximum2018
Range11
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation3.0104705
Coefficient of variation (CV)0.0014979465
Kurtosis0.098589848
Mean2009.7317
Median Absolute Deviation (MAD)2
Skewness1.0576814
Sum164798
Variance9.0629329
MonotonicityNot monotonic
2024-05-11T00:15:51.841542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2007 26
31.7%
2008 14
17.1%
2010 10
 
12.2%
2012 8
 
9.8%
2009 7
 
8.5%
2016 5
 
6.1%
2011 4
 
4.9%
2014 4
 
4.9%
2015 2
 
2.4%
2017 1
 
1.2%
ValueCountFrequency (%)
2007 26
31.7%
2008 14
17.1%
2009 7
 
8.5%
2010 10
 
12.2%
2011 4
 
4.9%
2012 8
 
9.8%
2014 4
 
4.9%
2015 2
 
2.4%
2016 5
 
6.1%
2017 1
 
1.2%
ValueCountFrequency (%)
2018 1
 
1.2%
2017 1
 
1.2%
2016 5
 
6.1%
2015 2
 
2.4%
2014 4
 
4.9%
2012 8
9.8%
2011 4
 
4.9%
2010 10
12.2%
2009 7
8.5%
2008 14
17.1%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.73171
Minimum1
Maximum321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-11T00:15:52.249262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.1
Q146
median148
Q3234.25
95-th percentile313.95
Maximum321
Range320
Interquartile range (IQR)188.25

Descriptive statistics

Standard deviation107.75292
Coefficient of variation (CV)0.71963999
Kurtosis-1.4545911
Mean149.73171
Median Absolute Deviation (MAD)100.5
Skewness0.12931436
Sum12278
Variance11610.693
MonotonicityNot monotonic
2024-05-11T00:15:52.796619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213 2
 
2.4%
275 2
 
2.4%
13 2
 
2.4%
4 2
 
2.4%
226 2
 
2.4%
86 1
 
1.2%
40 1
 
1.2%
320 1
 
1.2%
284 1
 
1.2%
60 1
 
1.2%
Other values (67) 67
81.7%
ValueCountFrequency (%)
1 1
1.2%
4 2
2.4%
5 1
1.2%
8 1
1.2%
10 1
1.2%
13 2
2.4%
14 1
1.2%
15 1
1.2%
16 1
1.2%
17 1
1.2%
ValueCountFrequency (%)
321 1
1.2%
320 1
1.2%
316 1
1.2%
315 1
1.2%
314 1
1.2%
313 1
1.2%
312 1
1.2%
301 1
1.2%
298 1
1.2%
297 1
1.2%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20098140
Minimum20071015
Maximum20171031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-11T00:15:53.424514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071015
5-th percentile20071109
Q120071121
median20090831
Q320118411
95-th percentile20161010
Maximum20171031
Range100016
Interquartile range (IQR)47289.75

Descriptive statistics

Standard deviation29774.893
Coefficient of variation (CV)0.0014814751
Kurtosis-0.053268185
Mean20098140
Median Absolute Deviation (MAD)19711
Skewness1.0249584
Sum1.6480475 × 109
Variance8.8654427 × 108
MonotonicityNot monotonic
2024-05-11T00:15:53.947790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20080701 14
17.1%
20100901 10
12.2%
20120914 8
9.8%
20090831 7
8.5%
20071109 7
8.5%
20071121 6
7.3%
20071112 6
7.3%
20110901 4
 
4.9%
20071120 4
 
4.9%
20141117 4
 
4.9%
Other values (9) 12
14.6%
ValueCountFrequency (%)
20071015 1
 
1.2%
20071109 7
8.5%
20071112 6
7.3%
20071120 4
 
4.9%
20071121 6
7.3%
20071122 2
 
2.4%
20080701 14
17.1%
20090831 7
8.5%
20100901 10
12.2%
20110901 4
 
4.9%
ValueCountFrequency (%)
20171031 2
 
2.4%
20161031 1
 
1.2%
20161010 2
 
2.4%
20161005 1
 
1.2%
20161004 1
 
1.2%
20150810 1
 
1.2%
20150626 1
 
1.2%
20141117 4
4.9%
20120914 8
9.8%
20110901 4
4.9%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20098417
Minimum20071120
Maximum20181122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-11T00:15:54.313924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20071120
5-th percentile20071120
Q120071120
median20091028
Q320118626
95-th percentile20161205
Maximum20181122
Range110002
Interquartile range (IQR)47506.5

Descriptive statistics

Standard deviation30128.821
Coefficient of variation (CV)0.0014990644
Kurtosis0.097393777
Mean20098417
Median Absolute Deviation (MAD)19908
Skewness1.0585139
Sum1.6480702 × 109
Variance9.0774585 × 108
MonotonicityNot monotonic
2024-05-11T00:15:54.687507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20071120 26
31.7%
20081006 14
17.1%
20101122 10
 
12.2%
20121127 8
 
9.8%
20091028 7
 
8.5%
20161205 5
 
6.1%
20111125 4
 
4.9%
20141230 4
 
4.9%
20150915 2
 
2.4%
20171130 1
 
1.2%
ValueCountFrequency (%)
20071120 26
31.7%
20081006 14
17.1%
20091028 7
 
8.5%
20101122 10
 
12.2%
20111125 4
 
4.9%
20121127 8
 
9.8%
20141230 4
 
4.9%
20150915 2
 
2.4%
20161205 5
 
6.1%
20171130 1
 
1.2%
ValueCountFrequency (%)
20181122 1
 
1.2%
20171130 1
 
1.2%
20161205 5
 
6.1%
20150915 2
 
2.4%
20141230 4
 
4.9%
20121127 8
9.8%
20111125 4
 
4.9%
20101122 10
12.2%
20091028 7
8.5%
20081006 14
17.1%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20158340
Minimum20080704
Maximum20240215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-11T00:15:55.163111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080704
5-th percentile20090610
Q120110628
median20161205
Q320191231
95-th percentile20231129
Maximum20240215
Range159511
Interquartile range (IQR)80603

Descriptive statistics

Standard deviation48514.443
Coefficient of variation (CV)0.0024066685
Kurtosis-1.0678913
Mean20158340
Median Absolute Deviation (MAD)40691
Skewness0.18049001
Sum1.6529839 × 109
Variance2.3536512 × 109
MonotonicityDecreasing
2024-05-11T00:15:56.052656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20161205 16
19.5%
20231129 11
13.4%
20160909 9
11.0%
20191231 7
 
8.5%
20110628 7
 
8.5%
20121127 4
 
4.9%
20091028 4
 
4.9%
20170120 3
 
3.7%
20090504 1
 
1.2%
20090521 1
 
1.2%
Other values (19) 19
23.2%
ValueCountFrequency (%)
20080704 1
 
1.2%
20090323 1
 
1.2%
20090504 1
 
1.2%
20090521 1
 
1.2%
20090605 1
 
1.2%
20090706 1
 
1.2%
20091026 1
 
1.2%
20091028 4
4.9%
20100120 1
 
1.2%
20100422 1
 
1.2%
ValueCountFrequency (%)
20240215 1
 
1.2%
20240207 1
 
1.2%
20240119 1
 
1.2%
20231229 1
 
1.2%
20231129 11
13.4%
20231023 1
 
1.2%
20211216 1
 
1.2%
20191231 7
8.5%
20170120 3
 
3.7%
20161205 16
19.5%

업소명
Text

Distinct81
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size788.0 B
2024-05-11T00:15:56.704742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length12
Mean length6.0121951
Min length2

Characters and Unicode

Total characters493
Distinct characters217
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)97.6%

Sample

1st row뉴믹스커피 북촌
2nd row종로백부장집 닭한마리
3rd row도화
4th row주식회사 해목경복궁
5th row백제고기나라
ValueCountFrequency (%)
할매 2
 
1.7%
순대국 2
 
1.7%
풍천장어 1
 
0.9%
종로술집 1
 
0.9%
가르텐 1
 
0.9%
신만보성 1
 
0.9%
종각점 1
 
0.9%
모토이시 1
 
0.9%
허그(hug 1
 
0.9%
예원 1
 
0.9%
Other values (103) 103
89.6%
2024-05-11T00:15:57.975907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34
 
6.9%
국 8
 
1.6%
점 8
 
1.6%
비 7
 
1.4%
이 7
 
1.4%
대 7
 
1.4%
종 7
 
1.4%
리 7
 
1.4%
집 6
 
1.2%
( 6
 
1.2%
Other values (207) 396
80.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 413
83.8%
Space Separator 34
 
6.9%
Uppercase Letter 32
 
6.5%
Open Punctuation 6
 
1.2%
Close Punctuation 6
 
1.2%
Other Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 8
 
1.9%
점 8
 
1.9%
비 7
 
1.7%
이 7
 
1.7%
대 7
 
1.7%
종 7
 
1.7%
리 7
 
1.7%
집 6
 
1.5%
도 6
 
1.5%
로 6
 
1.5%
Other values (184) 344
83.3%
Uppercase Letter
ValueCountFrequency (%)
I 4
12.5%
E 4
12.5%
S 3
 
9.4%
F 2
 
6.2%
A 2
 
6.2%
T 2
 
6.2%
O 2
 
6.2%
C 2
 
6.2%
U 2
 
6.2%
H 1
 
3.1%
Other values (8) 8
25.0%
Other Punctuation
ValueCountFrequency (%)
？ 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 411
83.4%
Common 48
 
9.7%
Latin 32
 
6.5%
Han 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 8
 
1.9%
점 8
 
1.9%
비 7
 
1.7%
이 7
 
1.7%
대 7
 
1.7%
종 7
 
1.7%
리 7
 
1.7%
집 6
 
1.5%
도 6
 
1.5%
로 6
 
1.5%
Other values (182) 342
83.2%
Latin
ValueCountFrequency (%)
I 4
12.5%
E 4
12.5%
S 3
 
9.4%
F 2
 
6.2%
A 2
 
6.2%
T 2
 
6.2%
O 2
 
6.2%
C 2
 
6.2%
U 2
 
6.2%
H 1
 
3.1%
Other values (8) 8
25.0%
Common
ValueCountFrequency (%)
34
70.8%
( 6
 
12.5%
) 6
 
12.5%
？ 1
 
2.1%
. 1
 
2.1%
Han
ValueCountFrequency (%)
心 1
50.0%
羊 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 411
83.4%
ASCII 79
 
16.0%
CJK 2
 
0.4%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34
43.0%
( 6
 
7.6%
) 6
 
7.6%
I 4
 
5.1%
E 4
 
5.1%
S 3
 
3.8%
F 2
 
2.5%
A 2
 
2.5%
T 2
 
2.5%
O 2
 
2.5%
Other values (12) 14
17.7%
Hangul
ValueCountFrequency (%)
국 8
 
1.9%
점 8
 
1.9%
비 7
 
1.7%
이 7
 
1.7%
대 7
 
1.7%
종 7
 
1.7%
리 7
 
1.7%
집 6
 
1.5%
도 6
 
1.5%
로 6
 
1.5%
Other values (182) 342
83.2%
None
ValueCountFrequency (%)
？ 1
100.0%
CJK
ValueCountFrequency (%)
心 1
50.0%
羊 1
50.0%

소재지도로명
Text

MISSING 

Distinct76
Distinct (%)96.2%
Missing3
Missing (%)3.7%
Memory size788.0 B
2024-05-11T00:15:58.824860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length37
Mean length29.037975
Min length23

Characters and Unicode

Total characters2294
Distinct characters123
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

Unique73 ?
Unique (%)92.4%

Sample

1st row서울특별시 종로구 창덕궁1길 40, (계동)
2nd row서울특별시 종로구 종로5길 42-5, 1-2층 (청진동)
3rd row서울특별시 종로구 사직로 130, (적선동,적선현대 지하1층 6호)
4th row서울특별시 종로구 자하문로 26-1, 지상1~지상3층 (통의동)
5th row서울특별시 종로구 돈화문로11길 41, (낙원동,2층)
ValueCountFrequency (%)
서울특별시 79
 
17.9%
종로구 79
 
17.9%
1층 12
 
2.7%
지하1층 8
 
1.8%
종로 7
 
1.6%
수송동 6
 
1.4%
낙원동 5
 
1.1%
삼봉로 4
 
0.9%
평창동 4
 
0.9%
대학로 3
 
0.7%
Other values (186) 234
53.1%
2024-05-11T00:16:00.145338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
362
 
15.8%
로 155
 
6.8%
종 108
 
4.7%
1 104
 
4.5%
, 99
 
4.3%
) 83
 
3.6%
( 83
 
3.6%
서 81
 
3.5%
울 79
 
3.4%
구 79
 
3.4%
Other values (113) 1061
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1287
56.1%
Space Separator 362
 
15.8%
Decimal Number 343
 
15.0%
Other Punctuation 102
 
4.4%
Close Punctuation 83
 
3.6%
Open Punctuation 83
 
3.6%
Dash Punctuation 29
 
1.3%
Math Symbol 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 155
 
12.0%
종 108
 
8.4%
서 81
 
6.3%
울 79
 
6.1%
구 79
 
6.1%
시 79
 
6.1%
별 79
 
6.1%
특 79
 
6.1%
동 72
 
5.6%
층 43
 
3.3%
Other values (95) 433
33.6%
Decimal Number
ValueCountFrequency (%)
1 104
30.3%
2 54
15.7%
3 39
 
11.4%
4 36
 
10.5%
5 23
 
6.7%
8 23
 
6.7%
0 18
 
5.2%
7 17
 
5.0%
6 15
 
4.4%
9 14
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 99
97.1%
. 2
 
2.0%
? 1
 
1.0%
Space Separator
ValueCountFrequency (%)
362
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1287
56.1%
Common 1007
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 155
 
12.0%
종 108
 
8.4%
서 81
 
6.3%
울 79
 
6.1%
구 79
 
6.1%
시 79
 
6.1%
별 79
 
6.1%
특 79
 
6.1%
동 72
 
5.6%
층 43
 
3.3%
Other values (95) 433
33.6%
Common
ValueCountFrequency (%)
362
35.9%
1 104
 
10.3%
, 99
 
9.8%
) 83
 
8.2%
( 83
 
8.2%
2 54
 
5.4%
3 39
 
3.9%
4 36
 
3.6%
- 29
 
2.9%
5 23
 
2.3%
Other values (8) 95
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1287
56.1%
ASCII 1007
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
362
35.9%
1 104
 
10.3%
, 99
 
9.8%
) 83
 
8.2%
( 83
 
8.2%
2 54
 
5.4%
3 39
 
3.9%
4 36
 
3.6%
- 29
 
2.9%
5 23
 
2.3%
Other values (8) 95
 
9.4%
Hangul
ValueCountFrequency (%)
로 155
 
12.0%
종 108
 
8.4%
서 81
 
6.3%
울 79
 
6.1%
구 79
 
6.1%
시 79
 
6.1%
별 79
 
6.1%
특 79
 
6.1%
동 72
 
5.6%
층 43
 
3.3%
Other values (95) 433
33.6%

소재지지번
Text

Distinct80
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Memory size788.0 B
2024-05-11T00:16:00.949340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length34
Mean length25.914634
Min length20

Characters and Unicode

Total characters2125
Distinct characters103
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

Unique78 ?
Unique (%)95.1%

Sample

1st row서울특별시 종로구 계동 140번지 43호
2nd row서울특별시 종로구 청진동 3번지 1호
3rd row서울특별시 종로구 적선동 80번지 적선현대 지하1층 6호
4th row서울특별시 종로구 통의동 72번지
5th row서울특별시 종로구 낙원동 136번지 0호 2층
ValueCountFrequency (%)
서울특별시 82
19.6%
종로구 82
19.6%
1호 16
 
3.8%
낙원동 6
 
1.4%
수송동 6
 
1.4%
5호 6
 
1.4%
평창동 5
 
1.2%
1층 5
 
1.2%
지하1층 5
 
1.2%
0호 5
 
1.2%
Other values (140) 200
47.8%
2024-05-11T00:16:02.246736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
557
26.2%
지 108
 
5.1%
1 91
 
4.3%
종 91
 
4.3%
로 91
 
4.3%
서 84
 
4.0%
번 84
 
4.0%
구 82
 
3.9%
울 82
 
3.9%
특 82
 
3.9%
Other values (93) 773
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1221
57.5%
Space Separator 557
26.2%
Decimal Number 326
 
15.3%
Other Punctuation 7
 
0.3%
Close Punctuation 4
 
0.2%
Open Punctuation 4
 
0.2%
Dash Punctuation 3
 
0.1%
Uppercase Letter 2
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 108
 
8.8%
종 91
 
7.5%
로 91
 
7.5%
서 84
 
6.9%
번 84
 
6.9%
구 82
 
6.7%
울 82
 
6.7%
특 82
 
6.7%
시 82
 
6.7%
별 82
 
6.7%
Other values (72) 353
28.9%
Decimal Number
ValueCountFrequency (%)
1 91
27.9%
2 54
16.6%
3 41
12.6%
5 26
 
8.0%
6 25
 
7.7%
0 25
 
7.7%
4 21
 
6.4%
8 19
 
5.8%
7 13
 
4.0%
9 11
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 4
57.1%
/ 1
 
14.3%
? 1
 
14.3%
. 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
D 1
50.0%
Space Separator
ValueCountFrequency (%)
557
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1221
57.5%
Common 902
42.4%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 108
 
8.8%
종 91
 
7.5%
로 91
 
7.5%
서 84
 
6.9%
번 84
 
6.9%
구 82
 
6.7%
울 82
 
6.7%
특 82
 
6.7%
시 82
 
6.7%
별 82
 
6.7%
Other values (72) 353
28.9%
Common
ValueCountFrequency (%)
557
61.8%
1 91
 
10.1%
2 54
 
6.0%
3 41
 
4.5%
5 26
 
2.9%
6 25
 
2.8%
0 25
 
2.8%
4 21
 
2.3%
8 19
 
2.1%
7 13
 
1.4%
Other values (9) 30
 
3.3%
Latin
ValueCountFrequency (%)
B 1
50.0%
D 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1221
57.5%
ASCII 904
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
557
61.6%
1 91
 
10.1%
2 54
 
6.0%
3 41
 
4.5%
5 26
 
2.9%
6 25
 
2.8%
0 25
 
2.8%
4 21
 
2.3%
8 19
 
2.1%
7 13
 
1.4%
Other values (11) 32
 
3.5%
Hangul
ValueCountFrequency (%)
지 108
 
8.8%
종 91
 
7.5%
로 91
 
7.5%
서 84
 
6.9%
번 84
 
6.9%
구 82
 
6.7%
울 82
 
6.7%
특 82
 
6.7%
시 82
 
6.7%
별 82
 
6.7%
Other values (72) 353
28.9%

허가(신고)번호
Text

Distinct81
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size788.0 B
2024-05-11T00:16:03.036947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1804
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

Unique80 ?
Unique (%)97.6%

Sample

1st row3000000-101-2010-00073
2nd row3000000-101-2002-11856
3rd row3000000-101-2008-00248
4th row3000000-101-2006-00091
5th row3000000-101-1974-05722
ValueCountFrequency (%)
3000000-101-1995-02514 2
 
2.4%
3000000-101-2010-00073 1
 
1.2%
3000000-101-2009-00118 1
 
1.2%
3000000-101-1991-04203 1
 
1.2%
3000000-101-1988-03191 1
 
1.2%
3000000-101-1983-08011 1
 
1.2%
3000000-101-2008-00307 1
 
1.2%
3000000-101-1998-07764 1
 
1.2%
3000000-101-1976-04026 1
 
1.2%
3000000-101-2003-00494 1
 
1.2%
Other values (71) 71
86.6%
2024-05-11T00:16:04.530208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 779
43.2%
1 281
 
15.6%
- 246
 
13.6%
3 122
 
6.8%
9 101
 
5.6%
2 86
 
4.8%
8 42
 
2.3%
4 41
 
2.3%
6 37
 
2.1%
7 35
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1558
86.4%
Dash Punctuation 246
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 779
50.0%
1 281
 
18.0%
3 122
 
7.8%
9 101
 
6.5%
2 86
 
5.5%
8 42
 
2.7%
4 41
 
2.6%
6 37
 
2.4%
7 35
 
2.2%
5 34
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 779
43.2%
1 281
 
15.6%
- 246
 
13.6%
3 122
 
6.8%
9 101
 
5.6%
2 86
 
4.8%
8 42
 
2.3%
4 41
 
2.3%
6 37
 
2.1%
7 35
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 779
43.2%
1 281
 
15.6%
- 246
 
13.6%
3 122
 
6.8%
9 101
 
5.6%
2 86
 
4.8%
8 42
 
2.3%
4 41
 
2.3%
6 37
 
2.1%
7 35
 
1.9%

업태명
Categorical

Distinct8
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size788.0 B
한식
52 
일식
8 
경양식
8 
중국식
7 
분식
 
3
Other values (3)
 
4

Length

Max length5
Median length2
Mean length2.2195122
Min length2

Unique

Unique2 ?
Unique (%)2.4%

Sample

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

Common Values

ValueCountFrequency (%)
한식 52
63.4%
일식 8
 
9.8%
경양식 8
 
9.8%
중국식 7
 
8.5%
분식 3
 
3.7%
기타 2
 
2.4%
회집 1
 
1.2%
호프/통닭 1
 
1.2%

Length

2024-05-11T00:16:05.096469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:16:05.756024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 52
63.4%
일식 8
 
9.8%
경양식 8
 
9.8%
중국식 7
 
8.5%
분식 3
 
3.7%
기타 2
 
2.4%
회집 1
 
1.2%
호프/통닭 1
 
1.2%

지정취소사유
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
점수미달
22 
지위승계
12 
행정처분
11 
부적합(기준)
4 
자진취소요청
 
3
Other values (18)
30 

Length

Max length31
Median length4
Mean length6.3902439
Min length2

Unique

Unique10 ?
Unique (%)12.2%

Sample

1st row지위승계
2nd row지위승계
3rd row지위승계
4th row지위승계
5th row모범 대장 정리(2019년 이후 재지정 없음)

Common Values

ValueCountFrequency (%)
점수미달 22
26.8%
지위승계 12
14.6%
행정처분 11
13.4%
부적합(기준) 4
 
4.9%
자진취소요청 3
 
3.7%
부적합 3
 
3.7%
폐업 3
 
3.7%
위생부적정 3
 
3.7%
점검거부 3
 
3.7%
모범 대장 정리(2019년 이후 재지정 없음) 2
 
2.4%
Other values (13) 16
19.5%

Length

2024-05-11T00:16:06.230384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
점수미달 22
19.6%
지위승계 12
 
10.7%
행정처분 11
 
9.8%
대장 4
 
3.6%
없음 4
 
3.6%
재지정 4
 
3.6%
이후 4
 
3.6%
모범 4
 
3.6%
부적합(기준 4
 
3.6%
점검거부 3
 
2.7%
Other values (23) 40
35.7%

주된음식
Text

MISSING 

Distinct45
Distinct (%)72.6%
Missing20
Missing (%)24.4%
Memory size788.0 B
2024-05-11T00:16:06.894715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3387097
Min length2

Characters and Unicode

Total characters207
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)59.7%

Sample

1st row설렁탕
2nd row간장게장
3rd row생선회
4th row참치회
5th row초밥
ValueCountFrequency (%)
한정식 7
 
11.3%
자장면 6
 
9.7%
된장찌개 2
 
3.2%
스테이크 2
 
3.2%
삼겹살 2
 
3.2%
냉면 2
 
3.2%
초밥 2
 
3.2%
생선회 2
 
3.2%
칼국수 1
 
1.6%
낙지전골 1
 
1.6%
Other values (35) 35
56.5%
2024-05-11T00:16:08.312681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 11
 
5.3%
한 10
 
4.8%
밥 9
 
4.3%
식 9
 
4.3%
정 9
 
4.3%
면 8
 
3.9%
자 7
 
3.4%
탕 6
 
2.9%
회 6
 
2.9%
전 5
 
2.4%
Other values (75) 127
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 207
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 11
 
5.3%
한 10
 
4.8%
밥 9
 
4.3%
식 9
 
4.3%
정 9
 
4.3%
면 8
 
3.9%
자 7
 
3.4%
탕 6
 
2.9%
회 6
 
2.9%
전 5
 
2.4%
Other values (75) 127
61.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 207
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 11
 
5.3%
한 10
 
4.8%
밥 9
 
4.3%
식 9
 
4.3%
정 9
 
4.3%
면 8
 
3.9%
자 7
 
3.4%
탕 6
 
2.9%
회 6
 
2.9%
전 5
 
2.4%
Other values (75) 127
61.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 207
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 11
 
5.3%
한 10
 
4.8%
밥 9
 
4.3%
식 9
 
4.3%
정 9
 
4.3%
면 8
 
3.9%
자 7
 
3.4%
탕 6
 
2.9%
회 6
 
2.9%
전 5
 
2.4%
Other values (75) 127
61.4%

영업장면적(㎡)
Real number (ℝ)

Distinct80
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.09817
Minimum40.5
Maximum793.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-05-11T00:16:09.211219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.5
5-th percentile47.595
Q177.0775
median127.095
Q3191.1475
95-th percentile275.926
Maximum793.39
Range752.89
Interquartile range (IQR)114.07

Descriptive statistics

Standard deviation110.74924
Coefficient of variation (CV)0.74279409
Kurtosis15.043854
Mean149.09817
Median Absolute Deviation (MAD)55.49
Skewness3.1582745
Sum12226.05
Variance12265.394
MonotonicityNot monotonic
2024-05-11T00:16:10.114743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.0 2
 
2.4%
193.92 2
 
2.4%
220.06 1
 
1.2%
191.11 1
 
1.2%
114.24 1
 
1.2%
138.19 1
 
1.2%
276.0 1
 
1.2%
151.61 1
 
1.2%
54.9 1
 
1.2%
115.72 1
 
1.2%
Other values (70) 70
85.4%
ValueCountFrequency (%)
40.5 1
1.2%
41.7 1
1.2%
43.55 1
1.2%
46.75 1
1.2%
47.46 1
1.2%
50.16 1
1.2%
52.83 1
1.2%
53.05 1
1.2%
53.1 1
1.2%
54.9 1
1.2%
ValueCountFrequency (%)
793.39 1
1.2%
558.55 1
1.2%
353.0 1
1.2%
288.81 1
1.2%
276.0 1
1.2%
274.52 1
1.2%
272.34 1
1.2%
251.52 1
1.2%
238.46 1
1.2%
237.0 1
1.2%

행정동명
Categorical

Distinct13
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size788.0 B
종로1.2.3.4가동
37 
사직동
8 
혜화동
8 
종로5.6가동
8 
평창동
5 
Other values (8)
16 

Length

Max length11
Median length7
Mean length7.1219512
Min length3

Unique

Unique5 ?
Unique (%)6.1%

Sample

1st row가회동
2nd row종로1.2.3.4가동
3rd row사직동
4th row사직동
5th row종로1.2.3.4가동

Common Values

ValueCountFrequency (%)
종로1.2.3.4가동 37
45.1%
사직동 8
 
9.8%
혜화동 8
 
9.8%
종로5.6가동 8
 
9.8%
평창동 5
 
6.1%
부암동 4
 
4.9%
이화동 4
 
4.9%
숭인제1동 3
 
3.7%
가회동 1
 
1.2%
창신제2동 1
 
1.2%
Other values (3) 3
 
3.7%

Length

2024-05-11T00:16:10.762370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로1.2.3.4가동 37
45.1%
사직동 8
 
9.8%
혜화동 8
 
9.8%
종로5.6가동 8
 
9.8%
평창동 5
 
6.1%
부암동 4
 
4.9%
이화동 4
 
4.9%
숭인제1동 3
 
3.7%
가회동 1
 
1.2%
창신제2동 1
 
1.2%
Other values (3) 3
 
3.7%

급수시설구분
Categorical

Distinct3
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size788.0 B
상수도전용
64 
<NA>
17 
상수도(음용)지하수(주방용)겸용
 
1

Length

Max length17
Median length5
Mean length4.9390244
Min length4

Unique

Unique1 ?
Unique (%)1.2%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 64
78.0%
<NA> 17
 
20.7%
상수도(음용)지하수(주방용)겸용 1
 
1.2%

Length

2024-05-11T00:16:11.345385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:16:11.993021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 64
78.0%
na 17
 
20.7%
상수도(음용)지하수(주방용)겸용 1
 
1.2%

Interactions

2024-05-11T00:15:46.287751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:35.779959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:38.205370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:40.163850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:42.086479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:44.163816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:46.624636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:36.300292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:38.604425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:40.445625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:42.426619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:44.523275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:46.976795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:36.704540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:38.872171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:40.770319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:42.716785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:44.895246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:47.310475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:36.981867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:39.099099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:41.072218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:43.021750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:45.167411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:47.669221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:37.417228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:39.417971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:41.405552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:43.440919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:45.541207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:48.068841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:37.821670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:39.829640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:41.746745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:43.809856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:15:45.913133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T00:16:12.409109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
지정년도1.0000.7970.9851.0000.6871.0001.0001.0001.0000.0000.8650.3000.0000.560NaN
지정번호0.7971.0000.6580.7970.6960.9100.9480.9130.9100.0000.6760.5630.0000.4190.000
신청일자0.9850.6581.0000.9850.5990.9760.9900.9850.9760.0000.7430.0000.0000.3580.000
지정일자1.0000.7970.9851.0000.6871.0001.0001.0001.0000.0000.8650.3000.0000.560NaN
취소일자0.6870.6960.5990.6871.0001.0001.0001.0001.0000.2090.9610.4220.0000.4660.000
업소명1.0000.9100.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명1.0000.9480.9901.0001.0001.0001.0001.0001.0000.9621.0000.9880.9701.0001.000
소재지지번1.0000.9130.9851.0001.0001.0001.0001.0001.0000.9451.0000.9970.9831.0001.000
허가(신고)번호1.0000.9100.9761.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
업태명0.0000.0000.0000.0000.2091.0000.9620.9451.0001.0000.0000.8520.0000.0000.000
지정취소사유0.8650.6760.7430.8650.9611.0001.0001.0001.0000.0001.0000.0000.3740.7510.000
주된음식0.3000.5630.0000.3000.4221.0000.9880.9971.0000.8520.0001.0000.8210.5451.000
영업장면적(㎡)0.0000.0000.0000.0000.0001.0000.9700.9831.0000.0000.3740.8211.0000.0000.000
행정동명0.5600.4190.3580.5600.4661.0001.0001.0001.0000.0000.7510.5450.0001.0000.000
급수시설구분NaN0.0000.000NaN0.0001.0001.0001.0001.0000.0000.0001.0000.0000.0001.000
2024-05-11T00:16:13.121958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명급수시설구분업태명지정취소사유
행정동명1.0000.0000.0000.330
급수시설구분0.0001.0000.0000.000
업태명0.0000.0001.0000.000
지정취소사유0.3300.0000.0001.000
2024-05-11T00:16:13.575799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명지정취소사유행정동명급수시설구분
지정년도1.0000.0860.9841.0000.323-0.1590.0000.3890.1990.000
지정번호0.0861.0000.0550.086-0.5060.0490.0000.2850.1750.000
신청일자0.9840.0551.0000.9840.322-0.1390.0000.3710.1120.000
지정일자1.0000.0860.9841.0000.323-0.1590.0000.3890.1990.000
취소일자0.323-0.5060.3220.3231.000-0.1610.0940.7350.2120.000
영업장면적(㎡)-0.1590.049-0.139-0.159-0.1611.0000.0000.1390.0000.000
업태명0.0000.0000.0000.0000.0940.0001.0000.0000.0000.000
지정취소사유0.3890.2850.3710.3890.7350.1390.0001.0000.3300.000
행정동명0.1990.1750.1120.1990.2120.0000.0000.3301.0000.000
급수시설구분0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-05-11T00:15:48.626648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T00:15:49.536673image/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.
2024-05-11T00:15:50.072229image/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
03000000201586201508102015091520240215뉴믹스커피 북촌서울특별시 종로구 창덕궁1길 40, (계동)서울특별시 종로구 계동 140번지 43호3000000-101-2010-00073한식지위승계설렁탕220.06가회동<NA>
13000000201633201610052016120520240207종로백부장집 닭한마리서울특별시 종로구 종로5길 42-5, 1-2층 (청진동)서울특별시 종로구 청진동 3번지 1호3000000-101-2002-11856한식지위승계간장게장86.0종로1.2.3.4가동상수도전용
23000000201561201506262015091520240119도화서울특별시 종로구 사직로 130, (적선동,적선현대 지하1층 6호)서울특별시 종로구 적선동 80번지 적선현대 지하1층 6호3000000-101-2008-00248한식지위승계생선회60.0사직동상수도전용
33000000200832200807012008100620231229주식회사 해목경복궁서울특별시 종로구 자하문로 26-1, 지상1~지상3층 (통의동)서울특별시 종로구 통의동 72번지3000000-101-2006-00091일식지위승계참치회274.52사직동<NA>
430000002016144201610042016120520231129백제고기나라서울특별시 종로구 돈화문로11길 41, (낙원동,2층)서울특별시 종로구 낙원동 136번지 0호 2층3000000-101-1974-05722일식모범 대장 정리(2019년 이후 재지정 없음)초밥167.05종로1.2.3.4가동상수도전용
53000000201613201610102016120520231129양심(羊心)서울특별시 종로구 인사동8길 16-1, (관훈동)서울특별시 종로구 관훈동 29번지3000000-101-1999-10882한식모범 대장 정리(2021년 이후 재지정 없음)한정식53.05종로1.2.3.4가동상수도전용
63000000201138201109012011112520231129본가왕뼈감자탕(종로송해길점)서울특별시 종로구 수표로 118, 1층 (낙원동)서울특별시 종로구 낙원동 146번지3000000-101-2006-00354경양식모범 대장 정리(2021년 이후 재지정 없음)냉면81.82종로1.2.3.4가동상수도전용
73000000200767200710152007112020231129허서방네서울특별시 종로구 종로 332, (창신동)서울특별시 종로구 창신동 328번지 3호3000000-101-1988-00882한식자진취소요청된장찌개158.34창신제2동상수도전용
83000000200793200711092007112020231129우스블랑 베이커리카페서울특별시 종로구 대명1길 24, (명륜4가)서울특별시 종로구 명륜4가 71번지 1호3000000-101-1996-02636한식모범 대장 정리(2019년 이후 재지정 없음)한정식73.04혜화동상수도전용
93000000201225201209142012112720231129마실서울특별시 종로구 종로 384, (숭인동)서울특별시 종로구 숭인동 200번지 25호3000000-101-2012-00063한식부적합불고기288.81숭인제1동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
7230000002007153200711122007112020091028목란서울특별시 종로구 송월길 47-8, (평동,지상1층)서울특별시 종로구 평동 26번지 10호 지상1층3000000-101-2002-11858분식위생부적정자장면43.55교남동상수도전용
7330000002008126200807012008100620091028팔선생서울특별시 종로구 홍지문길 1, (홍지동)서울특별시 종로구 홍지동 45번지 1호3000000-101-1996-05374경양식위생부적정<NA>125.93부암동상수도전용
7430000002008101200807012008100620091028락궁<NA>서울특별시 종로구 평창동 339번지 0호3000000-101-1996-03290중국식위생부적정<NA>89.76평창동상수도전용
753000000200775200711092007112020091026원조할머니 낙지센타<NA>서울특별시 종로구 청진동 265번지 1호3000000-101-1967-04691경양식행정처분낙지전골131.2종로1.2.3.4가동상수도전용
7630000002008267200807012008100620090706이대감 고깃집서울특별시 종로구 수표로 96, (관수동)서울특별시 종로구 관수동 20번지3000000-101-2006-00326한식행정처분<NA>793.39종로1.2.3.4가동<NA>
7730000002007109200711092007112020090605겔러리 카페서울특별시 종로구 대학로10길 15-7, 1~2층 (동숭동)서울특별시 종로구 동숭동 1번지 62호3000000-101-1997-02827한식영업자지위승계민원처리: 200930000340002235스테이크191.16이화동상수도전용
7830000002007146200711202007112020090521대평갈비서울특별시 종로구 평창문화로 83, 지상2,3층 (평창동)서울특별시 종로구 평창동 153번지 1호 지상2,3층3000000-101-2000-11611한식영업자지위승계민원처리: 200930000340001982한방갈비탕558.55평창동상수도전용
7930000002008226200807012008100620090504풍천장어서울특별시 종로구 종로35가길 7-22, (효제동,(지상1층))서울특별시 종로구 효제동 171번지 5호 (지상1층)3000000-101-2007-00187한식영업자지위승계민원처리: 200930000340001742<NA>119.7종로5.6가동상수도전용
8030000002008213200807012008100620090323제주도집서울특별시 종로구 종로 19, (종로1가,(르메이에르타운 지상1층 110-2호))서울특별시 종로구 종로1가 24번지 (르메이에르타운 지상1층 110-2호)3000000-101-1969-01465한식행정처분<NA>60.13종로1.2.3.4가동상수도전용
813000000200789200711212007112020080704할매 순대국서울특별시 종로구 난계로 243, 1층 (숭인동)서울특별시 종로구 숭인동 1395번지3000000-101-1995-02514일식행정처분삼겹살193.92숭인제1동상수도전용