Overview

Dataset statistics

Number of variables16
Number of observations286
Missing cells243
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.1 KiB
Average record size in memory136.5 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
Author영등포구
URLhttps://data.seoul.go.kr/dataList/OA-10467/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
업태명 is highly overall correlated with 불가일자 and 1 other fieldsHigh correlation
급수시설구분 is highly overall correlated with 지정년도 and 8 other fieldsHigh correlation
불가일자 is highly overall correlated with 신청일자 and 2 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
지정년도 is highly overall correlated with 지정번호 and 4 other fieldsHigh correlation
지정번호 is highly overall correlated with 지정년도 and 3 other fieldsHigh correlation
신청일자 is highly overall correlated with 지정년도 and 5 other fieldsHigh correlation
지정일자 is highly overall correlated with 지정년도 and 4 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 (87.7%)Imbalance
업태명 is highly imbalanced (52.7%)Imbalance
지정년도 has 35 (12.2%) missing valuesMissing
지정번호 has 35 (12.2%) missing valuesMissing
지정일자 has 35 (12.2%) missing valuesMissing
취소일자 has 108 (37.8%) missing valuesMissing
소재지도로명 has 6 (2.1%) missing valuesMissing
주된음식 has 24 (8.4%) missing valuesMissing

Reproduction

Analysis started2024-05-11 00:38:44.168918
Analysis finished2024-05-11 00:38:59.700189
Duration15.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
3180000
286 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3180000
2nd row3180000
3rd row3180000
4th row3180000
5th row3180000

Common Values

ValueCountFrequency (%)
3180000 286
100.0%

Length

2024-05-11T00:38:59.909046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:39:00.208783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3180000 286
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)6.0%
Missing35
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean2006.1833
Minimum1997
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-05-11T00:39:00.456741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2003
Q12004
median2004
Q32008
95-th percentile2013
Maximum2016
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3820525
Coefficient of variation (CV)0.0016858143
Kurtosis0.47123186
Mean2006.1833
Median Absolute Deviation (MAD)1
Skewness1.0408695
Sum503552
Variance11.438279
MonotonicityNot monotonic
2024-05-11T00:39:00.842564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2004 112
39.2%
2006 23
 
8.0%
2003 18
 
6.3%
2007 15
 
5.2%
2009 14
 
4.9%
2008 13
 
4.5%
2010 12
 
4.2%
2012 9
 
3.1%
2011 9
 
3.1%
2002 7
 
2.4%
Other values (5) 19
 
6.6%
(Missing) 35
 
12.2%
ValueCountFrequency (%)
1997 1
 
0.3%
2002 7
 
2.4%
2003 18
 
6.3%
2004 112
39.2%
2005 2
 
0.7%
2006 23
 
8.0%
2007 15
 
5.2%
2008 13
 
4.5%
2009 14
 
4.9%
2010 12
 
4.2%
ValueCountFrequency (%)
2016 5
 
1.7%
2014 6
 
2.1%
2013 5
 
1.7%
2012 9
 
3.1%
2011 9
 
3.1%
2010 12
4.2%
2009 14
4.9%
2008 13
4.5%
2007 15
5.2%
2006 23
8.0%

지정번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct153
Distinct (%)61.0%
Missing35
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean94.350598
Minimum1
Maximum357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-05-11T00:39:01.291457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q112.5
median43
Q3171.5
95-th percentile283
Maximum357
Range356
Interquartile range (IQR)159

Descriptive statistics

Standard deviation97.485858
Coefficient of variation (CV)1.0332299
Kurtosis-0.65841255
Mean94.350598
Median Absolute Deviation (MAD)39
Skewness0.8335243
Sum23682
Variance9503.4926
MonotonicityNot monotonic
2024-05-11T00:39:01.736498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 7
 
2.4%
5 7
 
2.4%
1 7
 
2.4%
3 6
 
2.1%
2 6
 
2.1%
8 5
 
1.7%
7 5
 
1.7%
16 5
 
1.7%
12 5
 
1.7%
4 4
 
1.4%
Other values (143) 194
67.8%
(Missing) 35
 
12.2%
ValueCountFrequency (%)
1 7
2.4%
2 6
2.1%
3 6
2.1%
4 4
1.4%
5 7
2.4%
6 3
1.0%
7 5
1.7%
8 5
1.7%
9 4
1.4%
10 7
2.4%
ValueCountFrequency (%)
357 1
0.3%
351 1
0.3%
307 1
0.3%
300 1
0.3%
298 1
0.3%
297 1
0.3%
294 1
0.3%
293 1
0.3%
292 1
0.3%
291 1
0.3%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20057740
Minimum19970513
Maximum20160610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-05-11T00:39:02.435736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19970513
5-th percentile20020601
Q120020601
median20045828
Q320090948
95-th percentile20128275
Maximum20160610
Range190097
Interquartile range (IQR)70347.25

Descriptive statistics

Standard deviation40046.72
Coefficient of variation (CV)0.0019965719
Kurtosis-0.71899943
Mean20057740
Median Absolute Deviation (MAD)25227.5
Skewness0.65077386
Sum5.7365136 × 109
Variance1.6037398 × 109
MonotonicityDecreasing
2024-05-11T00:39:03.035018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
20020601 107
37.4%
20100621 14
 
4.9%
20060612 13
 
4.5%
20071008 12
 
4.2%
20061204 11
 
3.8%
20121226 11
 
3.8%
20031016 10
 
3.5%
20090715 8
 
2.8%
20030401 7
 
2.4%
20091026 7
 
2.4%
Other values (33) 86
30.1%
ValueCountFrequency (%)
19970513 1
 
0.3%
20020601 107
37.4%
20030120 1
 
0.3%
20030401 7
 
2.4%
20030601 3
 
1.0%
20030602 5
 
1.7%
20031016 10
 
3.5%
20031017 2
 
0.7%
20040420 2
 
0.7%
20040608 2
 
0.7%
ValueCountFrequency (%)
20160610 5
1.7%
20141120 1
 
0.3%
20141103 3
 
1.0%
20131203 2
 
0.7%
20131111 1
 
0.3%
20130625 3
 
1.0%
20121226 11
3.8%
20120702 6
2.1%
20111201 1
 
0.3%
20111116 3
 
1.0%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)11.6%
Missing35
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean20062641
Minimum19970513
Maximum20160826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-05-11T00:39:03.705471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19970513
5-th percentile20030716
Q120040712
median20040712
Q320081127
95-th percentile20130923
Maximum20160826
Range190313
Interquartile range (IQR)40415

Descriptive statistics

Standard deviation33914.107
Coefficient of variation (CV)0.0016904109
Kurtosis0.46019781
Mean20062641
Median Absolute Deviation (MAD)9996
Skewness1.0373967
Sum5.035723 × 109
Variance1.1501666 × 109
MonotonicityNot monotonic
2024-05-11T00:39:04.345775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
20040712 110
38.5%
20030716 18
 
6.3%
20061204 11
 
3.8%
20060630 11
 
3.8%
20071204 11
 
3.8%
20100713 9
 
3.1%
20121226 7
 
2.4%
20091026 7
 
2.4%
20090715 7
 
2.4%
20141125 6
 
2.1%
Other values (19) 54
18.9%
(Missing) 35
 
12.2%
ValueCountFrequency (%)
19970513 1
 
0.3%
20020722 6
 
2.1%
20020731 1
 
0.3%
20030716 18
 
6.3%
20040524 1
 
0.3%
20040712 110
38.5%
20041230 1
 
0.3%
20050427 1
 
0.3%
20050822 1
 
0.3%
20060428 1
 
0.3%
ValueCountFrequency (%)
20160826 5
1.7%
20141125 6
2.1%
20131129 2
 
0.7%
20130717 3
 
1.0%
20121226 7
2.4%
20120718 2
 
0.7%
20111206 3
 
1.0%
20110728 6
2.1%
20101206 3
 
1.0%
20100713 9
3.1%

취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct97
Distinct (%)54.5%
Missing108
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean20099639
Minimum20010327
Maximum20231207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-05-11T00:39:04.840603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010327
5-th percentile20030888
Q120050946
median20101119
Q320128164
95-th percentile20191128
Maximum20231207
Range220880
Interquartile range (IQR)77218.25

Descriptive statistics

Standard deviation51323.23
Coefficient of variation (CV)0.0025534404
Kurtosis-0.52977015
Mean20099639
Median Absolute Deviation (MAD)40801.5
Skewness0.44485849
Sum3.5777358 × 109
Variance2.6340739 × 109
MonotonicityNot monotonic
2024-05-11T00:39:05.517538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111021 20
 
7.0%
20171218 16
 
5.6%
20080910 9
 
3.1%
20141125 8
 
2.8%
20100713 5
 
1.7%
20191219 5
 
1.7%
20101122 4
 
1.4%
20121113 4
 
1.4%
20161229 4
 
1.4%
20231207 4
 
1.4%
Other values (87) 99
34.6%
(Missing) 108
37.8%
ValueCountFrequency (%)
20010327 1
0.3%
20020816 1
0.3%
20020916 1
0.3%
20030210 1
0.3%
20030222 1
0.3%
20030410 1
0.3%
20030425 1
0.3%
20030630 1
0.3%
20030813 1
0.3%
20030901 1
0.3%
ValueCountFrequency (%)
20231207 4
 
1.4%
20191219 5
 
1.7%
20191112 1
 
0.3%
20181220 1
 
0.3%
20181101 1
 
0.3%
20171218 16
5.6%
20161229 4
 
1.4%
20151229 2
 
0.7%
20141125 8
2.8%
20140903 1
 
0.3%

불가일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
275 
20121226
 
4
20101206
 
3
20100713
 
2
20131129
 
1

Length

Max length8
Median length4
Mean length4.1538462
Min length4

Unique

Unique2 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 275
96.2%
20121226 4
 
1.4%
20101206 3
 
1.0%
20100713 2
 
0.7%
20131129 1
 
0.3%
20120718 1
 
0.3%

Length

2024-05-11T00:39:06.180511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:39:06.707561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 275
96.2%
20121226 4
 
1.4%
20101206 3
 
1.0%
20100713 2
 
0.7%
20131129 1
 
0.3%
20120718 1
 
0.3%

업소명
Text

Distinct254
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-05-11T00:39:07.526487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length21
Mean length6.2342657
Min length2

Characters and Unicode

Total characters1783
Distinct characters365
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)79.0%

Sample

1st row돈떼목장
2nd row국가대표
3rd row뉴타운갈비탕
4th row평가옥(여의도점)
5th row돌배기집(영등포역점)
ValueCountFrequency (%)
명륜진사갈비 5
 
1.5%
여의도점 5
 
1.5%
나주곰탕 4
 
1.2%
예당 3
 
0.9%
여의도 3
 
0.9%
외백 3
 
0.9%
서여의도점 2
 
0.6%
york 2
 
0.6%
영등포역점 2
 
0.6%
도림점 2
 
0.6%
Other values (270) 295
90.5%
2024-05-11T00:39:08.751711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 54
 
3.0%
도 44
 
2.5%
40
 
2.2%
이 39
 
2.2%
( 32
 
1.8%
여 32
 
1.8%
) 32
 
1.8%
의 31
 
1.7%
탕 29
 
1.6%
집 23
 
1.3%
Other values (355) 1427
80.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1582
88.7%
Uppercase Letter 55
 
3.1%
Space Separator 40
 
2.2%
Open Punctuation 32
 
1.8%
Close Punctuation 32
 
1.8%
Decimal Number 22
 
1.2%
Lowercase Letter 16
 
0.9%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 54
 
3.4%
도 44
 
2.8%
이 39
 
2.5%
여 32
 
2.0%
의 31
 
2.0%
탕 29
 
1.8%
집 23
 
1.5%
대 22
 
1.4%
포 22
 
1.4%
한 22
 
1.4%
Other values (309) 1264
79.9%
Uppercase Letter
ValueCountFrequency (%)
N 7
12.7%
I 5
 
9.1%
U 4
 
7.3%
A 4
 
7.3%
H 4
 
7.3%
Y 3
 
5.5%
E 3
 
5.5%
L 3
 
5.5%
O 3
 
5.5%
K 3
 
5.5%
Other values (11) 16
29.1%
Lowercase Letter
ValueCountFrequency (%)
k 2
12.5%
r 2
12.5%
e 2
12.5%
o 2
12.5%
w 2
12.5%
s 1
6.2%
b 1
6.2%
y 1
6.2%
i 1
6.2%
n 1
6.2%
Decimal Number
ValueCountFrequency (%)
3 4
18.2%
6 4
18.2%
0 4
18.2%
4 4
18.2%
1 3
13.6%
2 1
 
4.5%
9 1
 
4.5%
5 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
& 1
25.0%
' 1
25.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1574
88.3%
Common 130
 
7.3%
Latin 71
 
4.0%
Han 8
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 54
 
3.4%
도 44
 
2.8%
이 39
 
2.5%
여 32
 
2.0%
의 31
 
2.0%
탕 29
 
1.8%
집 23
 
1.5%
대 22
 
1.4%
포 22
 
1.4%
한 22
 
1.4%
Other values (304) 1256
79.8%
Latin
ValueCountFrequency (%)
N 7
 
9.9%
I 5
 
7.0%
U 4
 
5.6%
A 4
 
5.6%
H 4
 
5.6%
Y 3
 
4.2%
E 3
 
4.2%
L 3
 
4.2%
O 3
 
4.2%
K 3
 
4.2%
Other values (22) 32
45.1%
Common
ValueCountFrequency (%)
40
30.8%
( 32
24.6%
) 32
24.6%
3 4
 
3.1%
6 4
 
3.1%
0 4
 
3.1%
4 4
 
3.1%
1 3
 
2.3%
, 2
 
1.5%
& 1
 
0.8%
Other values (4) 4
 
3.1%
Han
ValueCountFrequency (%)
比 2
25.0%
惠 2
25.0%
壽 2
25.0%
士 1
12.5%
紳 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1574
88.3%
ASCII 201
 
11.3%
CJK 8
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 54
 
3.4%
도 44
 
2.8%
이 39
 
2.5%
여 32
 
2.0%
의 31
 
2.0%
탕 29
 
1.8%
집 23
 
1.5%
대 22
 
1.4%
포 22
 
1.4%
한 22
 
1.4%
Other values (304) 1256
79.8%
ASCII
ValueCountFrequency (%)
40
19.9%
( 32
15.9%
) 32
15.9%
N 7
 
3.5%
I 5
 
2.5%
3 4
 
2.0%
6 4
 
2.0%
U 4
 
2.0%
A 4
 
2.0%
H 4
 
2.0%
Other values (36) 65
32.3%
CJK
ValueCountFrequency (%)
比 2
25.0%
惠 2
25.0%
壽 2
25.0%
士 1
12.5%
紳 1
12.5%

소재지도로명
Text

MISSING 

Distinct246
Distinct (%)87.9%
Missing6
Missing (%)2.1%
Memory size2.4 KiB
2024-05-11T00:39:09.629573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length50
Mean length35.778571
Min length24

Characters and Unicode

Total characters10018
Distinct characters195
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)76.8%

Sample

1st row서울특별시 영등포구 영등포로 384-1, 1층 (신길동)
2nd row서울특별시 영등포구 63로 36, (여의도동,리버타워 204호)
3rd row서울특별시 영등포구 도림로 282, 1층 (신길동)
4th row서울특별시 영등포구 여의대방로 379, (여의도동, 제일빌딩 지하1층 10,11,12호)
5th row서울특별시 영등포구 영중로4길 6-1, (영등포동3가,1층)
ValueCountFrequency (%)
서울특별시 280
 
16.6%
영등포구 280
 
16.6%
여의도동 76
 
4.5%
1층 48
 
2.8%
지하1층 29
 
1.7%
신길동 25
 
1.5%
대림동 18
 
1.1%
17 17
 
1.0%
여의나루로 16
 
0.9%
당산동3가 14
 
0.8%
Other values (451) 887
52.5%
2024-05-11T00:39:11.075482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1410
 
14.1%
, 483
 
4.8%
1 472
 
4.7%
영 362
 
3.6%
등 333
 
3.3%
포 333
 
3.3%
동 308
 
3.1%
시 286
 
2.9%
( 286
 
2.9%
) 286
 
2.9%
Other values (185) 5459
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5834
58.2%
Decimal Number 1617
 
16.1%
Space Separator 1410
 
14.1%
Other Punctuation 492
 
4.9%
Open Punctuation 286
 
2.9%
Close Punctuation 286
 
2.9%
Uppercase Letter 36
 
0.4%
Dash Punctuation 34
 
0.3%
Math Symbol 17
 
0.2%
Lowercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 362
 
6.2%
등 333
 
5.7%
포 333
 
5.7%
동 308
 
5.3%
시 286
 
4.9%
별 283
 
4.9%
서 283
 
4.9%
구 281
 
4.8%
로 281
 
4.8%
특 280
 
4.8%
Other values (153) 2804
48.1%
Decimal Number
ValueCountFrequency (%)
1 472
29.2%
2 218
13.5%
3 187
 
11.6%
0 141
 
8.7%
4 127
 
7.9%
6 121
 
7.5%
7 117
 
7.2%
5 93
 
5.8%
8 83
 
5.1%
9 58
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C 13
36.1%
B 8
22.2%
K 6
16.7%
M 4
 
11.1%
A 2
 
5.6%
D 1
 
2.8%
S 1
 
2.8%
V 1
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
c 1
16.7%
n 1
16.7%
t 1
16.7%
r 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 483
98.2%
. 7
 
1.4%
/ 1
 
0.2%
？ 1
 
0.2%
Space Separator
ValueCountFrequency (%)
1410
100.0%
Open Punctuation
ValueCountFrequency (%)
( 286
100.0%
Close Punctuation
ValueCountFrequency (%)
) 286
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 34
100.0%
Math Symbol
ValueCountFrequency (%)
~ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5834
58.2%
Common 4142
41.3%
Latin 42
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 362
 
6.2%
등 333
 
5.7%
포 333
 
5.7%
동 308
 
5.3%
시 286
 
4.9%
별 283
 
4.9%
서 283
 
4.9%
구 281
 
4.8%
로 281
 
4.8%
특 280
 
4.8%
Other values (153) 2804
48.1%
Common
ValueCountFrequency (%)
1410
34.0%
, 483
 
11.7%
1 472
 
11.4%
( 286
 
6.9%
) 286
 
6.9%
2 218
 
5.3%
3 187
 
4.5%
0 141
 
3.4%
4 127
 
3.1%
6 121
 
2.9%
Other values (9) 411
 
9.9%
Latin
ValueCountFrequency (%)
C 13
31.0%
B 8
19.0%
K 6
14.3%
M 4
 
9.5%
A 2
 
4.8%
e 2
 
4.8%
D 1
 
2.4%
S 1
 
2.4%
V 1
 
2.4%
c 1
 
2.4%
Other values (3) 3
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5834
58.2%
ASCII 4183
41.8%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1410
33.7%
, 483
 
11.5%
1 472
 
11.3%
( 286
 
6.8%
) 286
 
6.8%
2 218
 
5.2%
3 187
 
4.5%
0 141
 
3.4%
4 127
 
3.0%
6 121
 
2.9%
Other values (21) 452
 
10.8%
Hangul
ValueCountFrequency (%)
영 362
 
6.2%
등 333
 
5.7%
포 333
 
5.7%
동 308
 
5.3%
시 286
 
4.9%
별 283
 
4.9%
서 283
 
4.9%
구 281
 
4.8%
로 281
 
4.8%
특 280
 
4.8%
Other values (153) 2804
48.1%
None
ValueCountFrequency (%)
？ 1
100.0%

소재지지번
Text

Distinct248
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-05-11T00:39:12.082460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length44
Mean length31.975524
Min length23

Characters and Unicode

Total characters9145
Distinct characters187
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)74.8%

Sample

1st row서울특별시 영등포구 신길동 65번지 40호 1층
2nd row서울특별시 영등포구 여의도동 61번지 5호 리버타워 204호
3rd row서울특별시 영등포구 신길동 342번지 134호
4th row서울특별시 영등포구 여의도동 44번지 35호 제일빌딩 지하1층 10,11,12호
5th row서울특별시 영등포구 영등포동3가 10번지 27호 1층
ValueCountFrequency (%)
서울특별시 286
 
16.9%
영등포구 286
 
16.9%
여의도동 130
 
7.7%
1층 44
 
2.6%
2호 33
 
2.0%
1호 29
 
1.7%
신길동 28
 
1.7%
지하1층 20
 
1.2%
대림동 20
 
1.2%
13번지 20
 
1.2%
Other values (373) 796
47.0%
2024-05-11T00:39:13.388643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2066
22.6%
1 400
 
4.4%
지 353
 
3.9%
영 332
 
3.6%
등 324
 
3.5%
포 324
 
3.5%
동 302
 
3.3%
호 299
 
3.3%
시 291
 
3.2%
서 289
 
3.2%
Other values (177) 4165
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5413
59.2%
Space Separator 2066
 
22.6%
Decimal Number 1538
 
16.8%
Other Punctuation 48
 
0.5%
Uppercase Letter 30
 
0.3%
Dash Punctuation 22
 
0.2%
Math Symbol 10
 
0.1%
Close Punctuation 6
 
0.1%
Open Punctuation 6
 
0.1%
Lowercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 353
 
6.5%
영 332
 
6.1%
등 324
 
6.0%
포 324
 
6.0%
동 302
 
5.6%
호 299
 
5.5%
시 291
 
5.4%
서 289
 
5.3%
구 287
 
5.3%
울 287
 
5.3%
Other values (145) 2325
43.0%
Decimal Number
ValueCountFrequency (%)
1 400
26.0%
2 244
15.9%
3 198
12.9%
4 179
11.6%
0 121
 
7.9%
5 110
 
7.2%
6 92
 
6.0%
7 78
 
5.1%
9 62
 
4.0%
8 54
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
C 12
40.0%
K 6
20.0%
M 4
 
13.3%
B 3
 
10.0%
A 2
 
6.7%
D 1
 
3.3%
S 1
 
3.3%
V 1
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
c 1
16.7%
n 1
16.7%
t 1
16.7%
r 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 39
81.2%
. 7
 
14.6%
/ 1
 
2.1%
？ 1
 
2.1%
Space Separator
ValueCountFrequency (%)
2066
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5413
59.2%
Common 3696
40.4%
Latin 36
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 353
 
6.5%
영 332
 
6.1%
등 324
 
6.0%
포 324
 
6.0%
동 302
 
5.6%
호 299
 
5.5%
시 291
 
5.4%
서 289
 
5.3%
구 287
 
5.3%
울 287
 
5.3%
Other values (145) 2325
43.0%
Common
ValueCountFrequency (%)
2066
55.9%
1 400
 
10.8%
2 244
 
6.6%
3 198
 
5.4%
4 179
 
4.8%
0 121
 
3.3%
5 110
 
3.0%
6 92
 
2.5%
7 78
 
2.1%
9 62
 
1.7%
Other values (9) 146
 
4.0%
Latin
ValueCountFrequency (%)
C 12
33.3%
K 6
16.7%
M 4
 
11.1%
B 3
 
8.3%
e 2
 
5.6%
A 2
 
5.6%
D 1
 
2.8%
S 1
 
2.8%
V 1
 
2.8%
c 1
 
2.8%
Other values (3) 3
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5413
59.2%
ASCII 3731
40.8%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2066
55.4%
1 400
 
10.7%
2 244
 
6.5%
3 198
 
5.3%
4 179
 
4.8%
0 121
 
3.2%
5 110
 
2.9%
6 92
 
2.5%
7 78
 
2.1%
9 62
 
1.7%
Other values (21) 181
 
4.9%
Hangul
ValueCountFrequency (%)
지 353
 
6.5%
영 332
 
6.1%
등 324
 
6.0%
포 324
 
6.0%
동 302
 
5.6%
호 299
 
5.5%
시 291
 
5.4%
서 289
 
5.3%
구 287
 
5.3%
울 287
 
5.3%
Other values (145) 2325
43.0%
None
ValueCountFrequency (%)
？ 1
100.0%

허가(신고)번호
Text

Distinct257
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-05-11T00:39:14.088459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters6292
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

Unique231 ?
Unique (%)80.8%

Sample

1st row3180000-101-2015-00336
2nd row3180000-101-2003-00504
3rd row3180000-101-2000-13108
4th row3180000-101-2014-00317
5th row3180000-101-1986-10338
ValueCountFrequency (%)
3180000-101-1988-07968 3
 
1.0%
3180000-101-1990-11080 3
 
1.0%
3180000-101-1993-05958 3
 
1.0%
3180000-101-2003-00684 2
 
0.7%
3180000-101-1992-11315 2
 
0.7%
3180000-101-2004-00100 2
 
0.7%
3180000-101-1994-06281 2
 
0.7%
3180000-101-1996-10623 2
 
0.7%
3180000-101-1976-07893 2
 
0.7%
3180000-101-1988-10757 2
 
0.7%
Other values (247) 263
92.0%
2024-05-11T00:39:15.088599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2126
33.8%
1 1289
20.5%
- 858
13.6%
8 468
 
7.4%
3 429
 
6.8%
9 356
 
5.7%
2 266
 
4.2%
6 136
 
2.2%
4 132
 
2.1%
7 119
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5434
86.4%
Dash Punctuation 858
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2126
39.1%
1 1289
23.7%
8 468
 
8.6%
3 429
 
7.9%
9 356
 
6.6%
2 266
 
4.9%
6 136
 
2.5%
4 132
 
2.4%
7 119
 
2.2%
5 113
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 858
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2126
33.8%
1 1289
20.5%
- 858
13.6%
8 468
 
7.4%
3 429
 
6.8%
9 356
 
5.7%
2 266
 
4.2%
6 136
 
2.2%
4 132
 
2.1%
7 119
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2126
33.8%
1 1289
20.5%
- 858
13.6%
8 468
 
7.4%
3 429
 
6.8%
9 356
 
5.7%
2 266
 
4.2%
6 136
 
2.2%
4 132
 
2.1%
7 119
 
1.9%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
한식
201 
일식
40 
중국식
22 
경양식
 
11
뷔페식
 
4
Other values (4)
 
8

Length

Max length10
Median length2
Mean length2.1713287
Min length2

Unique

Unique2 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
한식 201
70.3%
일식 40
 
14.0%
중국식 22
 
7.7%
경양식 11
 
3.8%
뷔페식 4
 
1.4%
분식 4
 
1.4%
복어취급 2
 
0.7%
회집 1
 
0.3%
정종/대포집/소주방 1
 
0.3%

Length

2024-05-11T00:39:15.652144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:39:16.163195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 201
70.3%
일식 40
 
14.0%
중국식 22
 
7.7%
경양식 11
 
3.8%
뷔페식 4
 
1.4%
분식 4
 
1.4%
복어취급 2
 
0.7%
회집 1
 
0.3%
정종/대포집/소주방 1
 
0.3%

주된음식
Text

MISSING 

Distinct138
Distinct (%)52.7%
Missing24
Missing (%)8.4%
Memory size2.4 KiB
2024-05-11T00:39:16.870746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.5916031
Min length2

Characters and Unicode

Total characters941
Distinct characters158
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)37.0%

Sample

1st row돼지갈비
2nd row쌈밥
3rd row삼계탕
4th row어복쟁반
5th row차돌박이
ValueCountFrequency (%)
자장면 12
 
4.5%
돼지갈비 12
 
4.5%
한정식 10
 
3.8%
갈비탕 8
 
3.0%
설렁탕 8
 
3.0%
생선회 7
 
2.7%
갈비 7
 
2.7%
추어탕 6
 
2.3%
불고기 6
 
2.3%
알탕 5
 
1.9%
Other values (129) 183
69.3%
2024-05-11T00:39:18.032306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 61
 
6.5%
갈 45
 
4.8%
비 45
 
4.8%
식 25
 
2.7%
회 25
 
2.7%
지 25
 
2.7%
, 22
 
2.3%
한 19
 
2.0%
자 18
 
1.9%
면 18
 
1.9%
Other values (148) 638
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 915
97.2%
Other Punctuation 22
 
2.3%
Space Separator 2
 
0.2%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 61
 
6.7%
갈 45
 
4.9%
비 45
 
4.9%
식 25
 
2.7%
회 25
 
2.7%
지 25
 
2.7%
한 19
 
2.1%
자 18
 
2.0%
면 18
 
2.0%
정 17
 
1.9%
Other values (144) 617
67.4%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
A 1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 915
97.2%
Common 24
 
2.6%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 61
 
6.7%
갈 45
 
4.9%
비 45
 
4.9%
식 25
 
2.7%
회 25
 
2.7%
지 25
 
2.7%
한 19
 
2.1%
자 18
 
2.0%
면 18
 
2.0%
정 17
 
1.9%
Other values (144) 617
67.4%
Common
ValueCountFrequency (%)
, 22
91.7%
2
 
8.3%
Latin
ValueCountFrequency (%)
L 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 915
97.2%
ASCII 26
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 61
 
6.7%
갈 45
 
4.9%
비 45
 
4.9%
식 25
 
2.7%
회 25
 
2.7%
지 25
 
2.7%
한 19
 
2.1%
자 18
 
2.0%
면 18
 
2.0%
정 17
 
1.9%
Other values (144) 617
67.4%
ASCII
ValueCountFrequency (%)
, 22
84.6%
2
 
7.7%
L 1
 
3.8%
A 1
 
3.8%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct252
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.79696
Minimum0
Maximum1840.8
Zeros2
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-05-11T00:39:18.458780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56.4825
Q195.68
median137.42
Q3224.28
95-th percentile557.5575
Maximum1840.8
Range1840.8
Interquartile range (IQR)128.6

Descriptive statistics

Standard deviation233.46363
Coefficient of variation (CV)1.1128075
Kurtosis17.972722
Mean209.79696
Median Absolute Deviation (MAD)51.44
Skewness3.7787974
Sum60001.93
Variance54505.265
MonotonicityNot monotonic
2024-05-11T00:39:18.894873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.61 3
 
1.0%
534.87 3
 
1.0%
106.34 3
 
1.0%
95.68 2
 
0.7%
160.0 2
 
0.7%
109.16 2
 
0.7%
95.78 2
 
0.7%
237.7 2
 
0.7%
95.85 2
 
0.7%
450.0 2
 
0.7%
Other values (242) 263
92.0%
ValueCountFrequency (%)
0.0 2
0.7%
23.4 1
0.3%
27.5 1
0.3%
29.04 1
0.3%
29.7 1
0.3%
31.45 1
0.3%
37.22 1
0.3%
44.1 1
0.3%
45.85 1
0.3%
48.84 1
0.3%
ValueCountFrequency (%)
1840.8 1
0.3%
1550.0 1
0.3%
1461.0 1
0.3%
1333.25 2
0.7%
1047.51 1
0.3%
974.67 1
0.3%
896.66 1
0.3%
744.64 1
0.3%
704.16 1
0.3%
638.85 1
0.3%

행정동명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
여의동
130 
영등포동
35 
당산제1동
20 
당산제2동
18 
문래동
14 
Other values (13)
69 

Length

Max length6
Median length3
Mean length3.8601399
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row신길제1동
2nd row여의동
3rd row신길제5동
4th row여의동
5th row영등포동

Common Values

ValueCountFrequency (%)
여의동 130
45.5%
영등포동 35
 
12.2%
당산제1동 20
 
7.0%
당산제2동 18
 
6.3%
문래동 14
 
4.9%
신길제1동 11
 
3.8%
대림제1동 11
 
3.8%
양평제2동 10
 
3.5%
신길제5동 8
 
2.8%
양평제1동 7
 
2.4%
Other values (8) 22
 
7.7%

Length

2024-05-11T00:39:19.330985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여의동 130
45.5%
영등포동 35
 
12.2%
당산제1동 20
 
7.0%
당산제2동 18
 
6.3%
문래동 14
 
4.9%
신길제1동 11
 
3.8%
대림제1동 11
 
3.8%
양평제2동 10
 
3.5%
신길제5동 8
 
2.8%
양평제1동 7
 
2.4%
Other values (8) 22
 
7.7%

급수시설구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
상수도전용
184 
<NA>
102 

Length

Max length5
Median length5
Mean length4.6433566
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 184
64.3%
<NA> 102
35.7%

Length

2024-05-11T00:39:19.806397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T00:39:20.262794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 184
64.3%
na 102
35.7%

Interactions

2024-05-11T00:38:56.531306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:46.618409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:49.004096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:51.403764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:53.224757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:54.948815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:56.804540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:46.982405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:49.400148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:51.710331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:53.454184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:55.198154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:57.077591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:47.367326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:49.758629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:52.046886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:53.742584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:55.452327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:57.371159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:47.757357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:50.230186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:52.351934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:54.086989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:55.716538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:57.651680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:48.239355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:50.635226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:52.615982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:54.402988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:55.992931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:57.909742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:48.646053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:50.948566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:52.854836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:54.656433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:38:56.262450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T00:39:20.529904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자불가일자업태명영업장면적(㎡)행정동명
지정년도1.0000.5510.9961.0000.585NaN0.0000.1650.231
지정번호0.5511.0000.6300.5510.410NaN0.1770.1130.597
신청일자0.9960.6301.0000.9960.4591.0000.1120.1200.282
지정일자1.0000.5510.9961.0000.585NaN0.0000.1650.231
취소일자0.5850.4100.4590.5851.000NaN0.2040.3940.000
불가일자NaNNaN1.000NaNNaN1.0001.0000.6280.000
업태명0.0000.1770.1120.0000.2041.0001.0000.6890.254
영업장면적(㎡)0.1650.1130.1200.1650.3940.6280.6891.0000.000
행정동명0.2310.5970.2820.2310.0000.0000.2540.0001.000
2024-05-11T00:39:20.893139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업태명급수시설구분불가일자행정동명
업태명1.0001.0000.8160.084
급수시설구분1.0001.0001.0001.000
불가일자0.8161.0001.0000.000
행정동명0.0841.0000.0001.000
2024-05-11T00:39:21.247208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)불가일자업태명행정동명급수시설구분
지정년도1.000-0.7360.9280.9970.7110.1270.0000.0000.0091.000
지정번호-0.7361.000-0.718-0.715-0.370-0.0490.0000.0800.2711.000
신청일자0.928-0.7181.0000.9270.5190.1110.8660.0000.0741.000
지정일자0.997-0.7150.9271.0000.7040.1320.0000.0000.0091.000
취소일자0.711-0.3700.5190.7041.000-0.0310.0000.0950.0001.000
영업장면적(㎡)0.127-0.0490.1110.132-0.0311.0000.4790.2840.0001.000
불가일자0.0000.0000.8660.0000.0000.4791.0000.8160.0001.000
업태명0.0000.0800.0000.0000.0950.2840.8161.0000.0841.000
행정동명0.0090.2710.0740.0090.0000.0000.0000.0841.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T00:38:58.310899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T00:38:59.001702image/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.
2024-05-11T00:38:59.453828image/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
03180000201632016061020160826<NA><NA>돈떼목장서울특별시 영등포구 영등포로 384-1, 1층 (신길동)서울특별시 영등포구 신길동 65번지 40호 1층3180000-101-2015-00336한식돼지갈비92.0신길제1동<NA>
1318000020165201606102016082620171218<NA>국가대표서울특별시 영등포구 63로 36, (여의도동,리버타워 204호)서울특별시 영등포구 여의도동 61번지 5호 리버타워 204호3180000-101-2003-00504한식쌈밥117.81여의동상수도전용
23180000201642016061020160826<NA><NA>뉴타운갈비탕서울특별시 영등포구 도림로 282, 1층 (신길동)서울특별시 영등포구 신길동 342번지 134호3180000-101-2000-13108한식삼계탕66.15신길제5동상수도전용
33180000201612016061020160826<NA><NA>평가옥(여의도점)서울특별시 영등포구 여의대방로 379, (여의도동, 제일빌딩 지하1층 10,11,12호)서울특별시 영등포구 여의도동 44번지 35호 제일빌딩 지하1층 10,11,12호3180000-101-2014-00317한식어복쟁반401.95여의동<NA>
43180000201622016061020160826<NA><NA>돌배기집(영등포역점)서울특별시 영등포구 영중로4길 6-1, (영등포동3가,1층)서울특별시 영등포구 영등포동3가 10번지 27호 1층3180000-101-1986-10338한식차돌박이164.3영등포동상수도전용
53180000201432014112020141125<NA><NA>값진식육서울특별시 영등포구 선유로 58-4, 1층 (문래동3가)서울특별시 영등포구 문래동3가 77번지 43호 1층3180000-101-2012-00480한식식육160.9문래동상수도전용
6318000020145201411032014112520161229<NA>샐러데이즈 여의도롯데캐슬엠파이어점서울특별시 영등포구 의사당대로 127, (여의도동, 롯데캐슬엠파이어 102호일부)서울특별시 영등포구 여의도동 36번지 롯데캐슬엠파이어 102호일부3180000-101-2004-00107일식도시락37.22여의동상수도전용
73180000201412014110320141125<NA><NA>선유참치서울특별시 영등포구 영신로17길 3, (영등포동,외1필지 지하1층(전체))서울특별시 영등포구 영등포동 618번지 55호 외1필지 지하1층(전체)3180000-101-2011-00425한식한정식322.75영등포제2동상수도전용
83180000201422014110320141125<NA><NA>온화정서울특별시 영등포구 국제금융로6길 30, 백상빌딩 1층 117, 118, 119, 120호 (여의도동)서울특별시 영등포구 여의도동 35번지 2호 백상빌딩3180000-101-1998-06410일식생선회104.79여의동상수도전용
93180000<NA><NA>20131203<NA><NA>20131129이태원천상동여의도점서울특별시 영등포구 국제금융로6길 17, (여의도동,부국증권지하1층)서울특별시 영등포구 여의도동 34번지 2호 부국증권지하1층3180000-101-1991-06436일식<NA>224.48여의동상수도전용
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
27631800002004143200206012004071220060327<NA>삼원정서울특별시 영등포구 국회대로70길 22, 1 2호 (여의도동,금강)서울특별시 영등포구 여의도동 14번지 35호 금강- 1 23180000-101-1987-10914한식부대찌개124.95여의동상수도전용
27731800002004243200206012004071220090608<NA>마케집서울특별시 영등포구 영등포로50길 7, (영등포동3가, 1층 전체,2층 전체)서울특별시 영등포구 영등포동3가 13번지 14호 1층 전체,2층 전체3180000-101-2000-13608한식삼겹살77.12영등포동<NA>
2783180000200428200206012004071220060308<NA>준이네당산양꼬치(영등포구청점)서울특별시 영등포구 국회대로36길 13, 1층 (당산동3가)서울특별시 영등포구 당산동3가 86번지 1호3180000-101-1990-10394한식장어탕124.98당산제1동상수도전용
27931800002003176200206012003071620040414<NA>뉴욕뉴욕(New York New York)서울특별시 영등포구 국회대로76길 16, (여의도동)서울특별시 영등포구 여의도동 13번지 3호3180000-101-2000-13995경양식새우볶음밥405.6여의동상수도전용
28031800002004254200206012004071220111021<NA>삼화정서울특별시 영등포구 영신로34길 42, (영등포동4가,1~2층)서울특별시 영등포구 영등포동4가 98번지 1호 1~2층3180000-101-1986-10872한식갈비탕77.73영등포동상수도전용
28131800002004202200206012004071220120523<NA>고릴라쉐프서울특별시 영등포구 여의대방로65길 13, (여의도동, 2층)서울특별시 영등포구 여의도동 46번지 1호 2층3180000-101-1985-10206한식불고기쌈밥187.25여의동상수도전용
2823180000200442200206012004071220060512<NA>늑대식당서울특별시 영등포구 당산로47길 8, (당산동6가, 1층 2호)서울특별시 영등포구 당산동6가 314번지 0호 1층 2호3180000-101-1991-10671한식갈비86.46당산제2동상수도전용
2833180000<NA><NA>20020601<NA>20080910<NA>수석서울특별시 영등포구 국회대로74길 9, 1 6호 (여의도동,삼보)서울특별시 영등포구 여의도동 13번지 2호 삼보- 1 63180000-101-1987-11018한식<NA>75.92여의동상수도전용
28431800002004110200206012004071220101119<NA>원조호수삼계탕서울특별시 영등포구 도림로 274-1, (신길동)서울특별시 영등포구 신길동 342번지 325호3180000-101-1990-11030한식삼계탕29.04신길제5동상수도전용
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