Overview

Dataset statistics

Number of variables16
Number of observations203
Missing cells196
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.1 KiB
Average record size in memory136.7 B

Variable types

Categorical5
Numeric6
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,불가일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author성동구
URLhttps://data.seoul.go.kr/dataList/OA-10775/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 7 other fieldsHigh correlation
업태명 is highly overall correlated with 급수시설구분High correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
지정년도 is highly overall correlated with 신청일자 and 3 other fieldsHigh correlation
지정번호 is highly overall correlated with 급수시설구분High correlation
신청일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
취소일자 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
영업장면적(㎡) is highly overall correlated with 급수시설구분High correlation
불가일자 is highly imbalanced (95.5%)Imbalance
업태명 is highly imbalanced (54.5%)Imbalance
지정년도 has 12 (5.9%) missing valuesMissing
지정번호 has 12 (5.9%) missing valuesMissing
지정일자 has 12 (5.9%) missing valuesMissing
취소일자 has 151 (74.4%) missing valuesMissing
주된음식 has 9 (4.4%) missing valuesMissing

Reproduction

Analysis started2024-05-11 06:20:21.567687
Analysis finished2024-05-11 06:20:30.435204
Duration8.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
3030000
203 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3030000
2nd row3030000
3rd row3030000
4th row3030000
5th row3030000

Common Values

ValueCountFrequency (%)
3030000 203
100.0%

Length

2024-05-11T15:20:30.567758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:20:30.752309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3030000 203
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)8.4%
Missing12
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean2020.3351
Minimum2004
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-11T15:20:30.931678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2004
Q12023
median2024
Q32024
95-th percentile2024
Maximum2024
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.0234775
Coefficient of variation (CV)0.0034763924
Kurtosis0.98671981
Mean2020.3351
Median Absolute Deviation (MAD)0
Skewness-1.6358773
Sum385884
Variance49.329237
MonotonicityDecreasing
2024-05-11T15:20:31.164280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2024 141
69.5%
2004 21
 
10.3%
2017 7
 
3.4%
2015 5
 
2.5%
2023 3
 
1.5%
2010 2
 
1.0%
2006 2
 
1.0%
2005 2
 
1.0%
2021 1
 
0.5%
2016 1
 
0.5%
Other values (6) 6
 
3.0%
(Missing) 12
 
5.9%
ValueCountFrequency (%)
2004 21
10.3%
2005 2
 
1.0%
2006 2
 
1.0%
2007 1
 
0.5%
2009 1
 
0.5%
2010 2
 
1.0%
2011 1
 
0.5%
2012 1
 
0.5%
2013 1
 
0.5%
2015 5
 
2.5%
ValueCountFrequency (%)
2024 141
69.5%
2023 3
 
1.5%
2022 1
 
0.5%
2021 1
 
0.5%
2017 7
 
3.4%
2016 1
 
0.5%
2015 5
 
2.5%
2013 1
 
0.5%
2012 1
 
0.5%
2011 1
 
0.5%

지정번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct96
Distinct (%)50.3%
Missing12
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean94.387435
Minimum1
Maximum423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-11T15:20:31.448631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q111
median25
Q3134.5
95-th percentile417.5
Maximum423
Range422
Interquartile range (IQR)123.5

Descriptive statistics

Standard deviation133.76498
Coefficient of variation (CV)1.4171906
Kurtosis1.5964352
Mean94.387435
Median Absolute Deviation (MAD)21
Skewness1.7262456
Sum18028
Variance17893.07
MonotonicityNot monotonic
2024-05-11T15:20:31.701860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 6
 
3.0%
7 6
 
3.0%
2 6
 
3.0%
12 6
 
3.0%
4 6
 
3.0%
8 5
 
2.5%
22 5
 
2.5%
20 5
 
2.5%
9 5
 
2.5%
10 5
 
2.5%
Other values (86) 136
67.0%
(Missing) 12
 
5.9%
ValueCountFrequency (%)
1 4
2.0%
2 6
3.0%
3 4
2.0%
4 6
3.0%
5 2
 
1.0%
6 4
2.0%
7 6
3.0%
8 5
2.5%
9 5
2.5%
10 5
2.5%
ValueCountFrequency (%)
423 1
0.5%
422 1
0.5%
421 2
1.0%
420 2
1.0%
419 2
1.0%
418 2
1.0%
417 2
1.0%
416 2
1.0%
415 2
1.0%
414 2
1.0%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20144772
Minimum20040501
Maximum20231115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-11T15:20:31.935281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040501
5-th percentile20040501
Q120100623
median20161030
Q320191016
95-th percentile20230115
Maximum20231115
Range190614
Interquartile range (IQR)90393

Descriptive statistics

Standard deviation63521.663
Coefficient of variation (CV)0.0031532581
Kurtosis-1.0850408
Mean20144772
Median Absolute Deviation (MAD)40071
Skewness-0.52197035
Sum4.0893886 × 109
Variance4.0350017 × 109
MonotonicityNot monotonic
2024-05-11T15:20:32.205794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
20040501 33
16.3%
20171020 22
 
10.8%
20191016 19
 
9.4%
20201101 13
 
6.4%
20211101 12
 
5.9%
20231115 11
 
5.4%
20221116 9
 
4.4%
20111116 7
 
3.4%
20131028 6
 
3.0%
20060519 5
 
2.5%
Other values (32) 66
32.5%
ValueCountFrequency (%)
20040501 33
16.3%
20050601 4
 
2.0%
20060519 5
 
2.5%
20060619 1
 
0.5%
20060930 1
 
0.5%
20061120 1
 
0.5%
20070522 2
 
1.0%
20071123 1
 
0.5%
20081127 1
 
0.5%
20090612 1
 
0.5%
ValueCountFrequency (%)
20231115 11
5.4%
20221116 9
4.4%
20221115 3
 
1.5%
20211101 12
5.9%
20201101 13
6.4%
20191016 19
9.4%
20181224 3
 
1.5%
20181029 1
 
0.5%
20181026 1
 
0.5%
20181025 3
 
1.5%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)8.4%
Missing12
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean20203644
Minimum20040707
Maximum20240118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-11T15:20:32.477354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040707
5-th percentile20040707
Q120230118
median20240118
Q320240118
95-th percentile20240118
Maximum20240118
Range199411
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation69979.422
Coefficient of variation (CV)0.003463703
Kurtosis1.0020226
Mean20203644
Median Absolute Deviation (MAD)0
Skewness-1.6404606
Sum3.858896 × 109
Variance4.8971195 × 109
MonotonicityDecreasing
2024-05-11T15:20:32.732844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20240118 141
69.5%
20040707 21
 
10.3%
20171220 7
 
3.4%
20151110 5
 
2.5%
20230118 3
 
1.5%
20100701 2
 
1.0%
20060701 2
 
1.0%
20050630 2
 
1.0%
20210118 1
 
0.5%
20161130 1
 
0.5%
Other values (6) 6
 
3.0%
(Missing) 12
 
5.9%
ValueCountFrequency (%)
20040707 21
10.3%
20050630 2
 
1.0%
20060701 2
 
1.0%
20070712 1
 
0.5%
20090630 1
 
0.5%
20100701 2
 
1.0%
20111130 1
 
0.5%
20121030 1
 
0.5%
20131118 1
 
0.5%
20151110 5
 
2.5%
ValueCountFrequency (%)
20240118 141
69.5%
20230118 3
 
1.5%
20220118 1
 
0.5%
20210118 1
 
0.5%
20171220 7
 
3.4%
20161130 1
 
0.5%
20151110 5
 
2.5%
20131118 1
 
0.5%
20121030 1
 
0.5%
20111130 1
 
0.5%

취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)80.8%
Missing151
Missing (%)74.4%
Infinite0
Infinite (%)0.0%
Mean20136780
Minimum20040802
Maximum20240112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-11T15:20:33.037673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20040802
5-th percentile20050403
Q120057958
median20151110
Q320203055
95-th percentile20230575
Maximum20240112
Range199310
Interquartile range (IQR)145097.75

Descriptive statistics

Standard deviation70250.026
Coefficient of variation (CV)0.0034886425
Kurtosis-1.6486944
Mean20136780
Median Absolute Deviation (MAD)69007
Skewness-0.060742489
Sum1.0471126 × 109
Variance4.9350661 × 109
MonotonicityNot monotonic
2024-05-11T15:20:33.372594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
20050630 4
 
2.0%
20191223 3
 
1.5%
20221116 2
 
1.0%
20141110 2
 
1.0%
20161130 2
 
1.0%
20220117 2
 
1.0%
20151110 2
 
1.0%
20181224 1
 
0.5%
20100512 1
 
0.5%
20231130 1
 
0.5%
Other values (32) 32
 
15.8%
(Missing) 151
74.4%
ValueCountFrequency (%)
20040802 1
 
0.5%
20050125 1
 
0.5%
20050126 1
 
0.5%
20050630 4
2.0%
20050728 1
 
0.5%
20050819 1
 
0.5%
20051007 1
 
0.5%
20051017 1
 
0.5%
20051212 1
 
0.5%
20051227 1
 
0.5%
ValueCountFrequency (%)
20240112 1
0.5%
20231130 1
0.5%
20231124 1
0.5%
20230125 1
0.5%
20221116 2
1.0%
20220714 1
0.5%
20220707 1
0.5%
20220117 2
1.0%
20211202 1
0.5%
20210603 1
0.5%

불가일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
<NA>
202 
20060630
 
1

Length

Max length8
Median length4
Mean length4.0197044
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row<NA>
2nd row20060630
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 202
99.5%
20060630 1
 
0.5%

Length

2024-05-11T15:20:33.681063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:20:33.857985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 202
99.5%
20060630 1
 
0.5%

업소명
Text

Distinct186
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-11T15:20:34.179483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length5.8374384
Min length2

Characters and Unicode

Total characters1185
Distinct characters285
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

Unique170 ?
Unique (%)83.7%

Sample

1st row해물명가
2nd row동호일식
3rd row블랙&압구정
4th row미성 숯불갈비
5th row노바
ValueCountFrequency (%)
한양대점 5
 
2.0%
유래회관 3
 
1.2%
주식회사 3
 
1.2%
숯불갈비 3
 
1.2%
왕십리 3
 
1.2%
먹깨비식당 2
 
0.8%
성수점 2
 
0.8%
육대장 2
 
0.8%
주)에스푸드 2
 
0.8%
동수원 2
 
0.8%
Other values (213) 226
89.3%
2024-05-11T15:20:34.849808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
 
4.2%
점 31
 
2.6%
한 25
 
2.1%
수 21
 
1.8%
식 20
 
1.7%
대 17
 
1.4%
리 17
 
1.4%
이 16
 
1.4%
집 16
 
1.4%
비 15
 
1.3%
Other values (275) 957
80.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1093
92.2%
Space Separator 50
 
4.2%
Close Punctuation 12
 
1.0%
Open Punctuation 11
 
0.9%
Decimal Number 7
 
0.6%
Uppercase Letter 6
 
0.5%
Other Punctuation 5
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 31
 
2.8%
한 25
 
2.3%
수 21
 
1.9%
식 20
 
1.8%
대 17
 
1.6%
리 17
 
1.6%
이 16
 
1.5%
집 16
 
1.5%
비 15
 
1.4%
마 15
 
1.4%
Other values (259) 900
82.3%
Uppercase Letter
ValueCountFrequency (%)
E 2
33.3%
N 1
16.7%
O 1
16.7%
A 1
16.7%
S 1
16.7%
Decimal Number
ValueCountFrequency (%)
2 4
57.1%
8 1
 
14.3%
1 1
 
14.3%
4 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 2
40.0%
& 2
40.0%
, 1
20.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1093
92.2%
Common 86
 
7.3%
Latin 6
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 31
 
2.8%
한 25
 
2.3%
수 21
 
1.9%
식 20
 
1.8%
대 17
 
1.6%
리 17
 
1.6%
이 16
 
1.5%
집 16
 
1.5%
비 15
 
1.4%
마 15
 
1.4%
Other values (259) 900
82.3%
Common
ValueCountFrequency (%)
50
58.1%
) 12
 
14.0%
( 11
 
12.8%
2 4
 
4.7%
. 2
 
2.3%
& 2
 
2.3%
8 1
 
1.2%
1 1
 
1.2%
- 1
 
1.2%
4 1
 
1.2%
Latin
ValueCountFrequency (%)
E 2
33.3%
N 1
16.7%
O 1
16.7%
A 1
16.7%
S 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1093
92.2%
ASCII 92
 
7.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50
54.3%
) 12
 
13.0%
( 11
 
12.0%
2 4
 
4.3%
E 2
 
2.2%
. 2
 
2.2%
& 2
 
2.2%
8 1
 
1.1%
1 1
 
1.1%
- 1
 
1.1%
Other values (6) 6
 
6.5%
Hangul
ValueCountFrequency (%)
점 31
 
2.8%
한 25
 
2.3%
수 21
 
1.9%
식 20
 
1.8%
대 17
 
1.6%
리 17
 
1.6%
이 16
 
1.5%
집 16
 
1.5%
비 15
 
1.4%
마 15
 
1.4%
Other values (259) 900
82.3%

소재지도로명
Text

Distinct187
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-11T15:20:35.347206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length65
Median length50
Mean length32.679803
Min length23

Characters and Unicode

Total characters6634
Distinct characters138
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)84.7%

Sample

1st row서울특별시 성동구 금호로11길 4, (금호동2가)
2nd row서울특별시 성동구 천호대로 328, (용답동)
3rd row서울특별시 성동구 행당로 75, 목련상가동 1층 103-106호 (행당동, 347 )
4th row서울특별시 성동구 고산자로 346, (마장동)
5th row서울특별시 성동구 금호산길 58, (금호동2가,(지상2층))
ValueCountFrequency (%)
서울특별시 203
 
16.7%
성동구 203
 
16.7%
1층 49
 
4.0%
성수동2가 34
 
2.8%
성수동1가 24
 
2.0%
지상1층 21
 
1.7%
행당동 20
 
1.6%
마장동 19
 
1.6%
용답동 16
 
1.3%
2층 15
 
1.2%
Other values (333) 609
50.2%
2024-05-11T15:20:36.113645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1012
 
15.3%
동 432
 
6.5%
1 383
 
5.8%
, 309
 
4.7%
성 306
 
4.6%
) 226
 
3.4%
( 226
 
3.4%
2 220
 
3.3%
서 219
 
3.3%
울 207
 
3.1%
Other values (128) 3094
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3640
54.9%
Decimal Number 1144
 
17.2%
Space Separator 1012
 
15.3%
Other Punctuation 309
 
4.7%
Close Punctuation 226
 
3.4%
Open Punctuation 226
 
3.4%
Dash Punctuation 56
 
0.8%
Lowercase Letter 9
 
0.1%
Uppercase Letter 8
 
0.1%
Math Symbol 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 432
 
11.9%
성 306
 
8.4%
서 219
 
6.0%
울 207
 
5.7%
시 205
 
5.6%
구 203
 
5.6%
별 203
 
5.6%
특 203
 
5.6%
로 152
 
4.2%
층 134
 
3.7%
Other values (100) 1376
37.8%
Decimal Number
ValueCountFrequency (%)
1 383
33.5%
2 220
19.2%
3 104
 
9.1%
4 86
 
7.5%
0 85
 
7.4%
7 62
 
5.4%
5 54
 
4.7%
9 53
 
4.6%
6 52
 
4.5%
8 45
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
o 2
22.2%
h 1
11.1%
e 1
11.1%
t 1
11.1%
a 1
11.1%
m 1
11.1%
r 1
11.1%
p 1
11.1%
Uppercase Letter
ValueCountFrequency (%)
I 3
37.5%
T 3
37.5%
L 1
 
12.5%
M 1
 
12.5%
Space Separator
ValueCountFrequency (%)
1012
100.0%
Other Punctuation
ValueCountFrequency (%)
, 309
100.0%
Close Punctuation
ValueCountFrequency (%)
) 226
100.0%
Open Punctuation
ValueCountFrequency (%)
( 226
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3640
54.9%
Common 2977
44.9%
Latin 17
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 432
 
11.9%
성 306
 
8.4%
서 219
 
6.0%
울 207
 
5.7%
시 205
 
5.6%
구 203
 
5.6%
별 203
 
5.6%
특 203
 
5.6%
로 152
 
4.2%
층 134
 
3.7%
Other values (100) 1376
37.8%
Common
ValueCountFrequency (%)
1012
34.0%
1 383
 
12.9%
, 309
 
10.4%
) 226
 
7.6%
( 226
 
7.6%
2 220
 
7.4%
3 104
 
3.5%
4 86
 
2.9%
0 85
 
2.9%
7 62
 
2.1%
Other values (6) 264
 
8.9%
Latin
ValueCountFrequency (%)
I 3
17.6%
T 3
17.6%
o 2
11.8%
L 1
 
5.9%
h 1
 
5.9%
M 1
 
5.9%
e 1
 
5.9%
t 1
 
5.9%
a 1
 
5.9%
m 1
 
5.9%
Other values (2) 2
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3640
54.9%
ASCII 2994
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1012
33.8%
1 383
 
12.8%
, 309
 
10.3%
) 226
 
7.5%
( 226
 
7.5%
2 220
 
7.3%
3 104
 
3.5%
4 86
 
2.9%
0 85
 
2.8%
7 62
 
2.1%
Other values (18) 281
 
9.4%
Hangul
ValueCountFrequency (%)
동 432
 
11.9%
성 306
 
8.4%
서 219
 
6.0%
울 207
 
5.7%
시 205
 
5.6%
구 203
 
5.6%
별 203
 
5.6%
특 203
 
5.6%
로 152
 
4.2%
층 134
 
3.7%
Other values (100) 1376
37.8%

소재지지번
Text

Distinct185
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-11T15:20:36.539414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length43
Mean length28.91133
Min length22

Characters and Unicode

Total characters5869
Distinct characters122
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)82.8%

Sample

1st row서울특별시 성동구 금호동2가 225번지
2nd row서울특별시 성동구 용답동 5번지 3호
3rd row서울특별시 성동구 행당동 347번지
4th row서울특별시 성동구 마장동 522번지 4호
5th row서울특별시 성동구 금호동2가 485번지 1호 (지상2층)
ValueCountFrequency (%)
서울특별시 203
18.5%
성동구 203
18.5%
성수동2가 43
 
3.9%
지상1층 37
 
3.4%
행당동 30
 
2.7%
성수동1가 27
 
2.5%
1호 22
 
2.0%
마장동 19
 
1.7%
용답동 18
 
1.6%
홍익동 15
 
1.4%
Other values (253) 478
43.7%
2024-05-11T15:20:37.302176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1409
24.0%
동 413
 
7.0%
성 281
 
4.8%
1 279
 
4.8%
지 263
 
4.5%
서 207
 
3.5%
울 206
 
3.5%
시 205
 
3.5%
특 203
 
3.5%
별 203
 
3.5%
Other values (112) 2200
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3249
55.4%
Space Separator 1409
24.0%
Decimal Number 1101
 
18.8%
Open Punctuation 33
 
0.6%
Close Punctuation 33
 
0.6%
Other Punctuation 17
 
0.3%
Dash Punctuation 9
 
0.2%
Lowercase Letter 9
 
0.2%
Uppercase Letter 7
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 413
12.7%
성 281
 
8.6%
지 263
 
8.1%
서 207
 
6.4%
울 206
 
6.3%
시 205
 
6.3%
특 203
 
6.2%
별 203
 
6.2%
구 203
 
6.2%
번 203
 
6.2%
Other values (84) 862
26.5%
Decimal Number
ValueCountFrequency (%)
1 279
25.3%
2 184
16.7%
3 108
 
9.8%
6 91
 
8.3%
4 83
 
7.5%
7 81
 
7.4%
5 78
 
7.1%
9 69
 
6.3%
0 67
 
6.1%
8 61
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
o 2
22.2%
a 1
11.1%
e 1
11.1%
t 1
11.1%
m 1
11.1%
r 1
11.1%
p 1
11.1%
h 1
11.1%
Uppercase Letter
ValueCountFrequency (%)
I 3
42.9%
T 3
42.9%
M 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
, 16
94.1%
@ 1
 
5.9%
Space Separator
ValueCountFrequency (%)
1409
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3249
55.4%
Common 2604
44.4%
Latin 16
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 413
12.7%
성 281
 
8.6%
지 263
 
8.1%
서 207
 
6.4%
울 206
 
6.3%
시 205
 
6.3%
특 203
 
6.2%
별 203
 
6.2%
구 203
 
6.2%
번 203
 
6.2%
Other values (84) 862
26.5%
Common
ValueCountFrequency (%)
1409
54.1%
1 279
 
10.7%
2 184
 
7.1%
3 108
 
4.1%
6 91
 
3.5%
4 83
 
3.2%
7 81
 
3.1%
5 78
 
3.0%
9 69
 
2.6%
0 67
 
2.6%
Other values (7) 155
 
6.0%
Latin
ValueCountFrequency (%)
I 3
18.8%
T 3
18.8%
o 2
12.5%
a 1
 
6.2%
M 1
 
6.2%
e 1
 
6.2%
t 1
 
6.2%
m 1
 
6.2%
r 1
 
6.2%
p 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3249
55.4%
ASCII 2620
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1409
53.8%
1 279
 
10.6%
2 184
 
7.0%
3 108
 
4.1%
6 91
 
3.5%
4 83
 
3.2%
7 81
 
3.1%
5 78
 
3.0%
9 69
 
2.6%
0 67
 
2.6%
Other values (18) 171
 
6.5%
Hangul
ValueCountFrequency (%)
동 413
12.7%
성 281
 
8.6%
지 263
 
8.1%
서 207
 
6.4%
울 206
 
6.3%
시 205
 
6.3%
특 203
 
6.2%
별 203
 
6.2%
구 203
 
6.2%
번 203
 
6.2%
Other values (84) 862
26.5%

허가(신고)번호
Text

Distinct187
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-05-11T15:20:37.653170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters4466
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

Unique172 ?
Unique (%)84.7%

Sample

1st row3030000-101-1997-04617
2nd row3030000-101-1987-03200
3rd row3030000-101-1999-00087
4th row3030000-101-1996-04868
5th row3030000-101-2003-00047
ValueCountFrequency (%)
3030000-101-1980-04896 3
 
1.5%
3030000-101-1999-00087 2
 
1.0%
3030000-101-2006-00173 2
 
1.0%
3030000-101-1995-05722 2
 
1.0%
3030000-101-2004-00279 2
 
1.0%
3030000-101-1997-03133 2
 
1.0%
3030000-101-2003-00130 2
 
1.0%
3030000-101-1994-06043 2
 
1.0%
3030000-101-1993-05381 2
 
1.0%
3030000-101-1995-06023 2
 
1.0%
Other values (177) 182
89.7%
2024-05-11T15:20:38.261061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1826
40.9%
1 648
 
14.5%
- 609
 
13.6%
3 512
 
11.5%
2 227
 
5.1%
9 203
 
4.5%
4 100
 
2.2%
8 93
 
2.1%
5 86
 
1.9%
6 82
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3857
86.4%
Dash Punctuation 609
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1826
47.3%
1 648
 
16.8%
3 512
 
13.3%
2 227
 
5.9%
9 203
 
5.3%
4 100
 
2.6%
8 93
 
2.4%
5 86
 
2.2%
6 82
 
2.1%
7 80
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 609
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4466
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1826
40.9%
1 648
 
14.5%
- 609
 
13.6%
3 512
 
11.5%
2 227
 
5.1%
9 203
 
4.5%
4 100
 
2.2%
8 93
 
2.1%
5 86
 
1.9%
6 82
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1826
40.9%
1 648
 
14.5%
- 609
 
13.6%
3 512
 
11.5%
2 227
 
5.1%
9 203
 
4.5%
4 100
 
2.2%
8 93
 
2.1%
5 86
 
1.9%
6 82
 
1.8%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
한식
146 
중국식
15 
일식
 
13
경양식
 
9
기타
 
5
Other values (8)
15 

Length

Max length10
Median length2
Mean length2.2906404
Min length2

Unique

Unique5 ?
Unique (%)2.5%

Sample

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

Common Values

ValueCountFrequency (%)
한식 146
71.9%
중국식 15
 
7.4%
일식 13
 
6.4%
경양식 9
 
4.4%
기타 5
 
2.5%
분식 4
 
2.0%
호프/통닭 3
 
1.5%
뷔페식 3
 
1.5%
김밥(도시락) 1
 
0.5%
회집 1
 
0.5%
Other values (3) 3
 
1.5%

Length

2024-05-11T15:20:38.498598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 146
71.9%
중국식 15
 
7.4%
일식 13
 
6.4%
경양식 9
 
4.4%
기타 5
 
2.5%
분식 4
 
2.0%
호프/통닭 3
 
1.5%
뷔페식 3
 
1.5%
김밥(도시락 1
 
0.5%
회집 1
 
0.5%
Other values (3) 3
 
1.5%

주된음식
Text

MISSING 

Distinct132
Distinct (%)68.0%
Missing9
Missing (%)4.4%
Memory size1.7 KiB
2024-05-11T15:20:38.943254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length4.2938144
Min length2

Characters and Unicode

Total characters833
Distinct characters156
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)51.5%

Sample

1st row생선회
2nd row자장면
3rd row돼지갈비
4th row생갈비
5th row돼지갈비
ValueCountFrequency (%)
삼겹살 14
 
6.2%
돼지갈비 8
 
3.5%
등심 8
 
3.5%
생선회 6
 
2.6%
탕수육 5
 
2.2%
돼지고기 5
 
2.2%
냉면 5
 
2.2%
짜장면 5
 
2.2%
설렁탕 5
 
2.2%
소고기 5
 
2.2%
Other values (122) 161
70.9%
2024-05-11T15:20:40.037578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 42
 
5.0%
33
 
4.0%
탕 32
 
3.8%
갈 28
 
3.4%
비 28
 
3.4%
지 22
 
2.6%
살 19
 
2.3%
삼 18
 
2.2%
돼 18
 
2.2%
기 18
 
2.2%
Other values (146) 575
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 755
90.6%
Other Punctuation 43
 
5.2%
Space Separator 33
 
4.0%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 32
 
4.2%
갈 28
 
3.7%
비 28
 
3.7%
지 22
 
2.9%
살 19
 
2.5%
삼 18
 
2.4%
돼 18
 
2.4%
기 18
 
2.4%
고 18
 
2.4%
겹 17
 
2.3%
Other values (141) 537
71.1%
Other Punctuation
ValueCountFrequency (%)
, 42
97.7%
？ 1
 
2.3%
Space Separator
ValueCountFrequency (%)
33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 755
90.6%
Common 78
 
9.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 32
 
4.2%
갈 28
 
3.7%
비 28
 
3.7%
지 22
 
2.9%
살 19
 
2.5%
삼 18
 
2.4%
돼 18
 
2.4%
기 18
 
2.4%
고 18
 
2.4%
겹 17
 
2.3%
Other values (141) 537
71.1%
Common
ValueCountFrequency (%)
, 42
53.8%
33
42.3%
) 1
 
1.3%
( 1
 
1.3%
？ 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 755
90.6%
ASCII 77
 
9.2%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 42
54.5%
33
42.9%
) 1
 
1.3%
( 1
 
1.3%
Hangul
ValueCountFrequency (%)
탕 32
 
4.2%
갈 28
 
3.7%
비 28
 
3.7%
지 22
 
2.9%
살 19
 
2.5%
삼 18
 
2.4%
돼 18
 
2.4%
기 18
 
2.4%
고 18
 
2.4%
겹 17
 
2.3%
Other values (141) 537
71.1%
None
ValueCountFrequency (%)
？ 1
100.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct181
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.8668
Minimum20
Maximum1063.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-05-11T15:20:40.321630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34.754
Q168.485
median91.2
Q3142.6
95-th percentile295.603
Maximum1063.55
Range1043.55
Interquartile range (IQR)74.115

Descriptive statistics

Standard deviation145.08768
Coefficient of variation (CV)1.1172038
Kurtosis26.517619
Mean129.8668
Median Absolute Deviation (MAD)34.9
Skewness4.7327018
Sum26362.96
Variance21050.434
MonotonicityNot monotonic
2024-05-11T15:20:40.560559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1063.55 3
 
1.5%
150.94 3
 
1.5%
120.28 2
 
1.0%
69.4 2
 
1.0%
120.63 2
 
1.0%
89.79 2
 
1.0%
134.28 2
 
1.0%
89.82 2
 
1.0%
395.0 2
 
1.0%
162.0 2
 
1.0%
Other values (171) 181
89.2%
ValueCountFrequency (%)
20.0 1
0.5%
20.5 1
0.5%
22.44 1
0.5%
24.0 1
0.5%
27.0 1
0.5%
28.7 1
0.5%
28.8 1
0.5%
31.66 1
0.5%
33.0 1
0.5%
33.2 1
0.5%
ValueCountFrequency (%)
1063.55 3
1.5%
840.85 1
 
0.5%
396.0 1
 
0.5%
395.0 2
1.0%
389.82 2
1.0%
361.21 1
 
0.5%
297.0 1
 
0.5%
283.03 1
 
0.5%
270.45 1
 
0.5%
248.04 1
 
0.5%

행정동명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
왕십리도선동
29 
성수2가제1동
28 
마장동
19 
용답동
18 
사근동
17 
Other values (11)
92 

Length

Max length7
Median length6
Mean length5.2906404
Min length3

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row금호2.3가동
2nd row용답동
3rd row행당제2동
4th row마장동
5th row금호2.3가동

Common Values

ValueCountFrequency (%)
왕십리도선동 29
14.3%
성수2가제1동 28
13.8%
마장동 19
9.4%
용답동 18
8.9%
사근동 17
8.4%
성수1가제1동 16
7.9%
성수2가제3동 15
7.4%
행당제1동 13
6.4%
금호2.3가동 11
 
5.4%
성수1가제2동 11
 
5.4%
Other values (6) 26
12.8%

Length

2024-05-11T15:20:40.792581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
왕십리도선동 29
14.3%
성수2가제1동 28
13.8%
마장동 19
9.4%
용답동 18
8.9%
사근동 17
8.4%
성수1가제1동 16
7.9%
성수2가제3동 15
7.4%
행당제1동 13
6.4%
금호2.3가동 11
 
5.4%
성수1가제2동 11
 
5.4%
Other values (6) 26
12.8%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
상수도전용
116 
<NA>
87 

Length

Max length5
Median length5
Mean length4.5714286
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 116
57.1%
<NA> 87
42.9%

Length

2024-05-11T15:20:41.009565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:20:41.212989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 116
57.1%
na 87
42.9%

Interactions

2024-05-11T15:20:28.567272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:22.901894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:23.944073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:24.818197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:25.933446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:27.524264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:28.733625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:23.076054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:24.093570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:25.050472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:26.113043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:27.689404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:28.882903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:23.230263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:24.214787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:25.232328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:26.242634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:27.845521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:29.042484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:23.422278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:24.371434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:25.394816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:26.542488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:28.013426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:29.233812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:23.621948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:24.533689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:25.571630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:26.741390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:28.200919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:29.410353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:23.816087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:24.669948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:25.757208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:26.926230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:20:28.395906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:20:41.346695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명영업장면적(㎡)행정동명
지정년도1.0000.3510.6730.9900.7550.2700.6550.000
지정번호0.3511.0000.9610.2770.0000.2760.0000.445
신청일자0.6730.9611.0000.8170.6560.0000.4300.363
지정일자0.9900.2770.8171.0000.8260.0000.5810.000
취소일자0.7550.0000.6560.8261.0000.0000.1920.121
업태명0.2700.2760.0000.0000.0001.0000.4550.073
영업장면적(㎡)0.6550.0000.4300.5810.1920.4551.0000.305
행정동명0.0000.4450.3630.0000.1210.0730.3051.000
2024-05-11T15:20:41.559911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분업태명불가일자행정동명
급수시설구분1.0001.000NaN1.000
업태명1.0001.000NaN0.017
불가일자NaNNaN1.000NaN
행정동명1.0000.017NaN1.000
2024-05-11T15:20:41.748942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)불가일자업태명행정동명급수시설구분
지정년도1.0000.0270.5321.0000.887-0.1580.0000.1200.0001.000
지정번호0.0271.0000.3830.027-0.212-0.2110.0000.1490.2371.000
신청일자0.5320.3831.0000.5320.866-0.409NaN0.0000.1261.000
지정일자1.0000.0270.5321.0000.887-0.1580.0000.0340.0001.000
취소일자0.887-0.2120.8660.8871.000-0.3100.0000.0000.0001.000
영업장면적(㎡)-0.158-0.211-0.409-0.158-0.3101.000NaN0.2400.1461.000
불가일자0.0000.000NaN0.0000.000NaN1.000NaNNaN0.000
업태명0.1200.1490.0000.0340.0000.240NaN1.0000.0171.000
행정동명0.0000.2370.1260.0000.0000.146NaN0.0171.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0000.0001.0001.0001.000

Missing values

2024-05-11T15:20:29.616936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:20:30.013205image/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.
2024-05-11T15:20:30.285769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
03030000<NA><NA>20050601<NA><NA><NA>해물명가서울특별시 성동구 금호로11길 4, (금호동2가)서울특별시 성동구 금호동2가 225번지3030000-101-1997-04617한식<NA>120.28금호2.3가동상수도전용
13030000<NA><NA>20060519<NA><NA>20060630동호일식서울특별시 성동구 천호대로 328, (용답동)서울특별시 성동구 용답동 5번지 3호3030000-101-1987-03200일식생선회127.16용답동<NA>
23030000<NA><NA>20060619<NA><NA><NA>블랙&압구정서울특별시 성동구 행당로 75, 목련상가동 1층 103-106호 (행당동, 347 )서울특별시 성동구 행당동 347번지3030000-101-1999-00087중국식자장면91.2행당제2동상수도전용
33030000<NA><NA>20040501<NA><NA><NA>미성 숯불갈비서울특별시 성동구 고산자로 346, (마장동)서울특별시 성동구 마장동 522번지 4호3030000-101-1996-04868한식<NA>73.5마장동상수도전용
43030000<NA><NA>20100624<NA>20100701<NA>노바서울특별시 성동구 금호산길 58, (금호동2가,(지상2층))서울특별시 성동구 금호동2가 485번지 1호 (지상2층)3030000-101-2003-00047일식<NA>111.25금호2.3가동상수도전용
53030000<NA><NA>20050601<NA><NA><NA>이가네곱창서울특별시 성동구 장터5길 2, (금호동3가)서울특별시 성동구 금호동3가 423번지3030000-101-1993-05865한식<NA>43.6금호2.3가동상수도전용
63030000<NA><NA>20040501<NA><NA><NA>마장갈비서울특별시 성동구 마장로 292-1, (마장동, 마장동 792 지상1층)서울특별시 성동구 마장동 792번지 (지상1층)3030000-101-1986-04834한식<NA>147.94마장동상수도전용
73030000<NA><NA>20040501<NA><NA><NA>성수감자국전문식당서울특별시 성동구 연무장길 45, (성수동2가)서울특별시 성동구 성수동2가 315번지 100호3030000-101-1995-01913한식<NA>148.0성수2가제1동상수도전용
83030000<NA><NA>20040501<NA><NA><NA>왕십리 통골뱅이서울특별시 성동구 왕십리로24길 7-1, 지상1층 (도선동)서울특별시 성동구 도선동 209번지3030000-101-1997-04544한식돼지갈비64.65왕십리도선동상수도전용
93030000<NA><NA>20040501<NA>20171114<NA>명문의집서울특별시 성동구 마장로 360, (용답동, 121 명문예식장)서울특별시 성동구 용답동 121번지3030000-101-1984-06024한식<NA>396.0용답동<NA>
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
1933030000200429200405012004070720151110<NA>삼겹살앤낙지서울특별시 성동구 마장로27길 2, (마장동)서울특별시 성동구 마장동 566번지 31호 (지상1층)3030000-101-1993-04297한식돼지갈비81.97마장동상수도전용
1943030000200458200405012004070720051227<NA>블랙&압구정서울특별시 성동구 행당로 75, 목련상가동 1층 103-106호 (행당동, 347 )서울특별시 성동구 행당동 347번지3030000-101-1999-00087중국식자장면91.2행당제2동상수도전용
1953030000200435200405012004070720040802<NA>교촌치킨 한양대점서울특별시 성동구 마조로 31, 1층 (행당동)서울특별시 성동구 행당동 3번지 29호 두성빌딩3030000-101-1996-04784한식삼겹살113.37사근동상수도전용
19630300002004124200405012004070720141110<NA>원산면옥서울특별시 성동구 성수이로 118, (성수동2가)서울특별시 성동구 성수동2가 277번지 17호3030000-101-1999-00137한식냉면144.54성수2가제1동상수도전용
19730300002004145200405012004070720060630<NA>동수원 숯불갈비서울특별시 성동구 용답29길 21, (용답동)서울특별시 성동구 용답동 14번지 2호3030000-101-2000-06430한식갈비탕90.0용답동<NA>
1983030000200437200405012004070720100511<NA>마장갈비서울특별시 성동구 마조로3가길 11, 1층 (행당동)서울특별시 성동구 행당동 19번지 89호 19-883030000-101-1998-04142한식돼지갈비147.0행당제1동상수도전용
19930300002004146200405012004070720061128<NA>만게츠 답십리점서울특별시 성동구 용답19길 12, (용답동)서울특별시 성동구 용답동 48번지 8호3030000-101-2001-07128한식삼겹살62.53용답동상수도전용
2003030000200423200405012004070720050819<NA>다현목포낙지서울특별시 성동구 왕십리로24길 9, (도선동,지상1층)서울특별시 성동구 도선동 201번지 6호 지상1층3030000-101-1995-05722한식냉면89.82왕십리도선동상수도전용
2013030000200468200405012004070720051017<NA>노바서울특별시 성동구 금호산길 58, (금호동2가,(지상2층))서울특별시 성동구 금호동2가 485번지 1호 (지상2층)3030000-101-2003-00047일식생선회111.25금호2.3가동상수도전용
20230300002004108200405012004070720050630<NA>다모아뷔페서울특별시 성동구 광나루로8길 11, 2층 (성수동2가)서울특별시 성동구 성수동2가 281번지 2호3030000-101-1993-03333한식육개장240.62성수2가제1동상수도전용