Overview

Dataset statistics

Number of variables16
Number of observations164
Missing cells13
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.8 KiB
Average record size in memory135.8 B

Variable types

Categorical4
Numeric6
Text6

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,지정취소사유,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author구로구
URLhttps://data.seoul.go.kr/dataList/OA-2329/S/1/datasetView.do

Alerts

시군구코드 has constant value ""Constant
급수시설구분 is highly overall correlated with 지정년도 and 7 other fieldsHigh correlation
행정동명 is highly overall correlated with 급수시설구분High correlation
업태명 is highly overall correlated with 영업장면적(㎡) and 1 other fieldsHigh 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 업태명 and 1 other fieldsHigh correlation
업태명 is highly imbalanced (63.6%)Imbalance
급수시설구분 is highly imbalanced (69.3%)Imbalance
소재지도로명 has 9 (5.5%) missing valuesMissing
주된음식 has 4 (2.4%) missing valuesMissing

Reproduction

Analysis started2024-05-11 03:16:10.109204
Analysis finished2024-05-11 03:16:32.474405
Duration22.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
3160000
164 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3160000
2nd row3160000
3rd row3160000
4th row3160000
5th row3160000

Common Values

ValueCountFrequency (%)
3160000 164
100.0%

Length

2024-05-11T03:16:32.835846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:16:33.196256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3160000 164
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.7134
Minimum1987
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T03:16:33.571223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile2002
Q12002
median2004
Q32007
95-th percentile2014
Maximum2019
Range32
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.4070679
Coefficient of variation (CV)0.0026971775
Kurtosis3.8496126
Mean2004.7134
Median Absolute Deviation (MAD)2
Skewness-1.1091661
Sum328773
Variance29.236383
MonotonicityNot monotonic
2024-05-11T03:16:34.055124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2002 45
27.4%
2006 22
13.4%
2004 21
12.8%
2005 12
 
7.3%
2007 11
 
6.7%
2003 9
 
5.5%
2008 7
 
4.3%
1987 7
 
4.3%
2014 5
 
3.0%
2010 5
 
3.0%
Other values (8) 20
12.2%
ValueCountFrequency (%)
1987 7
 
4.3%
1988 1
 
0.6%
2002 45
27.4%
2003 9
 
5.5%
2004 21
12.8%
2005 12
 
7.3%
2006 22
13.4%
2007 11
 
6.7%
2008 7
 
4.3%
2009 5
 
3.0%
ValueCountFrequency (%)
2019 1
 
0.6%
2016 1
 
0.6%
2015 3
1.8%
2014 5
3.0%
2013 1
 
0.6%
2012 4
2.4%
2011 4
2.4%
2010 5
3.0%
2009 5
3.0%
2008 7
4.3%

지정번호
Real number (ℝ)

HIGH CORRELATION 

Distinct102
Distinct (%)62.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.487805
Minimum1
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T03:16:34.656818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110.75
median37.5
Q3113.25
95-th percentile222.85
Maximum247
Range246
Interquartile range (IQR)102.5

Descriptive statistics

Standard deviation75.729539
Coefficient of variation (CV)1.0898249
Kurtosis-0.38665018
Mean69.487805
Median Absolute Deviation (MAD)29.5
Skewness1.0599658
Sum11396
Variance5734.963
MonotonicityNot monotonic
2024-05-11T03:16:35.263123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 10
 
6.1%
9 6
 
3.7%
6 4
 
2.4%
5 4
 
2.4%
4 4
 
2.4%
3 3
 
1.8%
39 3
 
1.8%
15 3
 
1.8%
8 3
 
1.8%
10 3
 
1.8%
Other values (92) 121
73.8%
ValueCountFrequency (%)
1 2
 
1.2%
2 10
6.1%
3 3
 
1.8%
4 4
 
2.4%
5 4
 
2.4%
6 4
 
2.4%
7 2
 
1.2%
8 3
 
1.8%
9 6
3.7%
10 3
 
1.8%
ValueCountFrequency (%)
247 1
0.6%
240 1
0.6%
238 1
0.6%
233 1
0.6%
228 1
0.6%
226 1
0.6%
225 1
0.6%
224 1
0.6%
223 1
0.6%
222 1
0.6%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20047616
Minimum19870409
Maximum20191010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T03:16:35.861560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870409
5-th percentile20020320
Q120020401
median20040415
Q320070629
95-th percentile20140227
Maximum20191010
Range320601
Interquartile range (IQR)50228

Descriptive statistics

Standard deviation54119.269
Coefficient of variation (CV)0.0026995364
Kurtosis3.8438524
Mean20047616
Median Absolute Deviation (MAD)20014
Skewness-1.1050508
Sum3.287809 × 109
Variance2.9288953 × 109
MonotonicityNot monotonic
2024-05-11T03:16:36.540868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
20020401 39
23.8%
20040415 21
12.8%
20060619 17
10.4%
20070629 11
 
6.7%
20050601 10
 
6.1%
20030401 9
 
5.5%
20080701 7
 
4.3%
19870409 7
 
4.3%
20100630 6
 
3.7%
20090701 5
 
3.0%
Other values (24) 32
19.5%
ValueCountFrequency (%)
19870409 7
 
4.3%
19880525 1
 
0.6%
20020320 1
 
0.6%
20020321 1
 
0.6%
20020330 1
 
0.6%
20020401 39
23.8%
20020402 1
 
0.6%
20020410 1
 
0.6%
20020502 1
 
0.6%
20030401 9
 
5.5%
ValueCountFrequency (%)
20191010 1
0.6%
20161108 1
0.6%
20150917 1
0.6%
20150915 2
1.2%
20141015 1
0.6%
20140507 1
0.6%
20140318 1
0.6%
20140227 1
0.6%
20140226 1
0.6%
20131014 1
0.6%

지정일자
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20047817
Minimum19870409
Maximum20191107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T03:16:37.134054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870409
5-th percentile20020502
Q120020502
median20040702
Q320070705
95-th percentile20140619
Maximum20191107
Range320698
Interquartile range (IQR)50203

Descriptive statistics

Standard deviation54212.32
Coefficient of variation (CV)0.0027041508
Kurtosis3.827595
Mean20047817
Median Absolute Deviation (MAD)20200
Skewness-1.1021441
Sum3.287842 × 109
Variance2.9389757 × 109
MonotonicityNot monotonic
2024-05-11T03:16:37.690434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20020502 45
27.4%
20040702 21
12.8%
20060710 17
 
10.4%
20050720 12
 
7.3%
20070705 11
 
6.7%
20030702 9
 
5.5%
20080717 7
 
4.3%
19870409 7
 
4.3%
20100802 5
 
3.0%
20090803 5
 
3.0%
Other values (10) 25
15.2%
ValueCountFrequency (%)
19870409 7
 
4.3%
19880525 1
 
0.6%
20020502 45
27.4%
20030702 9
 
5.5%
20040702 21
12.8%
20050720 12
 
7.3%
20060702 5
 
3.0%
20060710 17
 
10.4%
20070705 11
 
6.7%
20080717 7
 
4.3%
ValueCountFrequency (%)
20191107 1
 
0.6%
20161205 1
 
0.6%
20151118 3
1.8%
20141218 1
 
0.6%
20140619 4
2.4%
20131210 1
 
0.6%
20121122 4
2.4%
20111222 4
2.4%
20100802 5
3.0%
20090803 5
3.0%

취소일자
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20099418
Minimum20020501
Maximum20231110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T03:16:38.273473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20020501
5-th percentile20020552
Q120051082
median20090803
Q320141218
95-th percentile20191107
Maximum20231110
Range210609
Interquartile range (IQR)90135.5

Descriptive statistics

Standard deviation54963.562
Coefficient of variation (CV)0.0027345847
Kurtosis-0.69094603
Mean20099418
Median Absolute Deviation (MAD)40491
Skewness0.42931526
Sum3.2963046 × 109
Variance3.0209931 × 109
MonotonicityNot monotonic
2024-05-11T03:16:38.813537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20141218 18
 
11.0%
20080717 12
 
7.3%
20100802 11
 
6.7%
20020501 8
 
4.9%
20090803 7
 
4.3%
20121122 6
 
3.7%
20070705 5
 
3.0%
20181228 5
 
3.0%
20191107 5
 
3.0%
20111122 4
 
2.4%
Other values (70) 83
50.6%
ValueCountFrequency (%)
20020501 8
4.9%
20020507 1
 
0.6%
20020806 1
 
0.6%
20020831 1
 
0.6%
20020926 1
 
0.6%
20021029 1
 
0.6%
20030212 1
 
0.6%
20030224 1
 
0.6%
20030324 1
 
0.6%
20030415 1
 
0.6%
ValueCountFrequency (%)
20231110 2
 
1.2%
20221222 1
 
0.6%
20221111 1
 
0.6%
20211109 3
1.8%
20201109 1
 
0.6%
20191107 5
3.0%
20181228 5
3.0%
20171219 2
 
1.2%
20171130 2
 
1.2%
20171121 1
 
0.6%

업소명
Text

Distinct121
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:16:39.692892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length6.6219512
Min length2

Characters and Unicode

Total characters1086
Distinct characters252
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)51.2%

Sample

1st row콩부자 개봉점
2nd row갈비명가
3rd row가빈
4th row황제갈비
5th row황제갈비
ValueCountFrequency (%)
구로디지털단지점 6
 
2.7%
부뚜막 4
 
1.8%
청국장 4
 
1.8%
큰바다수산 3
 
1.4%
서울회마차 3
 
1.4%
왕뼈감자탕 3
 
1.4%
고척점 3
 
1.4%
고흥숯불갈비 3
 
1.4%
개봉점 3
 
1.4%
정육식당 3
 
1.4%
Other values (150) 187
84.2%
2024-05-11T03:16:41.143938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
 
5.3%
점 34
 
3.1%
지 28
 
2.6%
구 23
 
2.1%
로 22
 
2.0%
식 21
 
1.9%
당 20
 
1.8%
비 18
 
1.7%
가 17
 
1.6%
마 16
 
1.5%
Other values (242) 829
76.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1007
92.7%
Space Separator 58
 
5.3%
Uppercase Letter 8
 
0.7%
Other Punctuation 5
 
0.5%
Open Punctuation 3
 
0.3%
Close Punctuation 3
 
0.3%
Decimal Number 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 34
 
3.4%
지 28
 
2.8%
구 23
 
2.3%
로 22
 
2.2%
식 21
 
2.1%
당 20
 
2.0%
비 18
 
1.8%
가 17
 
1.7%
마 16
 
1.6%
갈 16
 
1.6%
Other values (228) 792
78.6%
Uppercase Letter
ValueCountFrequency (%)
I 2
25.0%
E 1
12.5%
N 1
12.5%
W 1
12.5%
O 1
12.5%
S 1
12.5%
M 1
12.5%
Other Punctuation
ValueCountFrequency (%)
？ 2
40.0%
& 2
40.0%
. 1
20.0%
Space Separator
ValueCountFrequency (%)
58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1007
92.7%
Common 71
 
6.5%
Latin 8
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 34
 
3.4%
지 28
 
2.8%
구 23
 
2.3%
로 22
 
2.2%
식 21
 
2.1%
당 20
 
2.0%
비 18
 
1.8%
가 17
 
1.7%
마 16
 
1.6%
갈 16
 
1.6%
Other values (228) 792
78.6%
Common
ValueCountFrequency (%)
58
81.7%
( 3
 
4.2%
) 3
 
4.2%
？ 2
 
2.8%
& 2
 
2.8%
2 2
 
2.8%
. 1
 
1.4%
Latin
ValueCountFrequency (%)
I 2
25.0%
E 1
12.5%
N 1
12.5%
W 1
12.5%
O 1
12.5%
S 1
12.5%
M 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1007
92.7%
ASCII 77
 
7.1%
None 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58
75.3%
( 3
 
3.9%
) 3
 
3.9%
I 2
 
2.6%
& 2
 
2.6%
2 2
 
2.6%
E 1
 
1.3%
N 1
 
1.3%
W 1
 
1.3%
O 1
 
1.3%
Other values (3) 3
 
3.9%
Hangul
ValueCountFrequency (%)
점 34
 
3.4%
지 28
 
2.8%
구 23
 
2.3%
로 22
 
2.2%
식 21
 
2.1%
당 20
 
2.0%
비 18
 
1.8%
가 17
 
1.7%
마 16
 
1.6%
갈 16
 
1.6%
Other values (228) 792
78.6%
None
ValueCountFrequency (%)
？ 2
100.0%

소재지도로명
Text

MISSING 

Distinct114
Distinct (%)73.5%
Missing9
Missing (%)5.5%
Memory size1.4 KiB
2024-05-11T03:16:41.802549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length44
Mean length29.380645
Min length22

Characters and Unicode

Total characters4554
Distinct characters117
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

Unique79 ?
Unique (%)51.0%

Sample

1st row서울특별시 구로구 개봉로 56, (개봉동)
2nd row서울특별시 구로구 가마산로 260, (구로동)
3rd row서울특별시 구로구 구로동로 174-9, (구로동)
4th row서울특별시 구로구 경인로40길 12, (개봉동)
5th row서울특별시 구로구 경인로40길 12, (개봉동)
ValueCountFrequency (%)
서울특별시 155
18.1%
구로구 155
18.1%
구로동 79
 
9.2%
개봉동 27
 
3.2%
고척동 24
 
2.8%
1층 17
 
2.0%
디지털로32길 15
 
1.8%
경인로 13
 
1.5%
디지털로32나길 10
 
1.2%
고척로 10
 
1.2%
Other values (194) 352
41.1%
2024-05-11T03:16:42.973519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
702
 
15.4%
로 419
 
9.2%
구 418
 
9.2%
, 192
 
4.2%
동 165
 
3.6%
시 158
 
3.5%
서 157
 
3.4%
) 157
 
3.4%
( 157
 
3.4%
별 155
 
3.4%
Other values (107) 1874
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2615
57.4%
Space Separator 702
 
15.4%
Decimal Number 688
 
15.1%
Other Punctuation 194
 
4.3%
Close Punctuation 157
 
3.4%
Open Punctuation 157
 
3.4%
Dash Punctuation 33
 
0.7%
Uppercase Letter 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 419
16.0%
구 418
16.0%
동 165
 
6.3%
시 158
 
6.0%
서 157
 
6.0%
별 155
 
5.9%
특 155
 
5.9%
울 155
 
5.9%
길 93
 
3.6%
봉 43
 
1.6%
Other values (90) 697
26.7%
Decimal Number
ValueCountFrequency (%)
1 147
21.4%
2 131
19.0%
3 102
14.8%
7 54
 
7.8%
0 51
 
7.4%
4 48
 
7.0%
5 43
 
6.2%
6 43
 
6.2%
9 36
 
5.2%
8 33
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 192
99.0%
. 2
 
1.0%
Space Separator
ValueCountFrequency (%)
702
100.0%
Close Punctuation
ValueCountFrequency (%)
) 157
100.0%
Open Punctuation
ValueCountFrequency (%)
( 157
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 33
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2615
57.4%
Common 1931
42.4%
Latin 8
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 419
16.0%
구 418
16.0%
동 165
 
6.3%
시 158
 
6.0%
서 157
 
6.0%
별 155
 
5.9%
특 155
 
5.9%
울 155
 
5.9%
길 93
 
3.6%
봉 43
 
1.6%
Other values (90) 697
26.7%
Common
ValueCountFrequency (%)
702
36.4%
, 192
 
9.9%
) 157
 
8.1%
( 157
 
8.1%
1 147
 
7.6%
2 131
 
6.8%
3 102
 
5.3%
7 54
 
2.8%
0 51
 
2.6%
4 48
 
2.5%
Other values (6) 190
 
9.8%
Latin
ValueCountFrequency (%)
B 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2615
57.4%
ASCII 1939
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
702
36.2%
, 192
 
9.9%
) 157
 
8.1%
( 157
 
8.1%
1 147
 
7.6%
2 131
 
6.8%
3 102
 
5.3%
7 54
 
2.8%
0 51
 
2.6%
4 48
 
2.5%
Other values (7) 198
 
10.2%
Hangul
ValueCountFrequency (%)
로 419
16.0%
구 418
16.0%
동 165
 
6.3%
시 158
 
6.0%
서 157
 
6.0%
별 155
 
5.9%
특 155
 
5.9%
울 155
 
5.9%
길 93
 
3.6%
봉 43
 
1.6%
Other values (90) 697
26.7%

소재지지번
Text

Distinct116
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:16:43.571987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length47
Mean length28.097561
Min length22

Characters and Unicode

Total characters4608
Distinct characters106
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)46.3%

Sample

1st row서울특별시 구로구 개봉동 403번지 65호
2nd row서울특별시 구로구 구로동 98번지 1호
3rd row서울특별시 구로구 구로동 416번지 23호
4th row서울특별시 구로구 개봉동 178번지 13호
5th row서울특별시 구로구 개봉동 178번지 13호
ValueCountFrequency (%)
구로구 165
18.6%
서울특별시 164
18.5%
구로동 91
 
10.3%
개봉동 28
 
3.2%
고척동 27
 
3.1%
1124번지 19
 
2.1%
3호 14
 
1.6%
1호 12
 
1.4%
10호 10
 
1.1%
오류동 10
 
1.1%
Other values (192) 345
39.0%
2024-05-11T03:16:44.523475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1171
25.4%
구 428
 
9.3%
로 260
 
5.6%
1 231
 
5.0%
지 173
 
3.8%
동 167
 
3.6%
서 164
 
3.6%
특 164
 
3.6%
별 164
 
3.6%
시 164
 
3.6%
Other values (96) 1522
33.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2538
55.1%
Space Separator 1171
25.4%
Decimal Number 857
 
18.6%
Dash Punctuation 16
 
0.3%
Uppercase Letter 10
 
0.2%
Other Punctuation 10
 
0.2%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 428
16.9%
로 260
10.2%
지 173
 
6.8%
동 167
 
6.6%
서 164
 
6.5%
특 164
 
6.5%
별 164
 
6.5%
시 164
 
6.5%
울 164
 
6.5%
호 164
 
6.5%
Other values (77) 526
20.7%
Decimal Number
ValueCountFrequency (%)
1 231
27.0%
2 109
12.7%
3 100
11.7%
4 85
 
9.9%
0 75
 
8.8%
5 66
 
7.7%
7 61
 
7.1%
8 48
 
5.6%
6 45
 
5.3%
9 37
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
B 8
80.0%
C 1
 
10.0%
D 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
, 8
80.0%
. 2
 
20.0%
Space Separator
ValueCountFrequency (%)
1171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2538
55.1%
Common 2060
44.7%
Latin 10
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 428
16.9%
로 260
10.2%
지 173
 
6.8%
동 167
 
6.6%
서 164
 
6.5%
특 164
 
6.5%
별 164
 
6.5%
시 164
 
6.5%
울 164
 
6.5%
호 164
 
6.5%
Other values (77) 526
20.7%
Common
ValueCountFrequency (%)
1171
56.8%
1 231
 
11.2%
2 109
 
5.3%
3 100
 
4.9%
4 85
 
4.1%
0 75
 
3.6%
5 66
 
3.2%
7 61
 
3.0%
8 48
 
2.3%
6 45
 
2.2%
Other values (6) 69
 
3.3%
Latin
ValueCountFrequency (%)
B 8
80.0%
C 1
 
10.0%
D 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2538
55.1%
ASCII 2070
44.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1171
56.6%
1 231
 
11.2%
2 109
 
5.3%
3 100
 
4.8%
4 85
 
4.1%
0 75
 
3.6%
5 66
 
3.2%
7 61
 
2.9%
8 48
 
2.3%
6 45
 
2.2%
Other values (9) 79
 
3.8%
Hangul
ValueCountFrequency (%)
구 428
16.9%
로 260
10.2%
지 173
 
6.8%
동 167
 
6.6%
서 164
 
6.5%
특 164
 
6.5%
별 164
 
6.5%
시 164
 
6.5%
울 164
 
6.5%
호 164
 
6.5%
Other values (77) 526
20.7%

허가(신고)번호
Text

Distinct123
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:16:45.106906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters3608
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

Unique86 ?
Unique (%)52.4%

Sample

1st row3160000-101-2005-00362
2nd row3160000-101-2006-00197
3rd row3160000-101-2003-00486
4th row3160000-101-1996-01877
5th row3160000-101-1996-01877
ValueCountFrequency (%)
3160000-101-2004-00157 3
 
1.8%
3160000-101-1998-01990 3
 
1.8%
3160000-101-2000-08811 3
 
1.8%
3160000-101-1984-08300 3
 
1.8%
3160000-101-1987-00677 2
 
1.2%
3160000-101-1987-01696 2
 
1.2%
3160000-101-1987-00436 2
 
1.2%
3160000-101-2003-00485 2
 
1.2%
3160000-101-2005-00051 2
 
1.2%
3160000-101-1997-04390 2
 
1.2%
Other values (113) 140
85.4%
2024-05-11T03:16:46.202395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1270
35.2%
1 693
19.2%
- 492
 
13.6%
3 246
 
6.8%
6 224
 
6.2%
9 216
 
6.0%
2 161
 
4.5%
8 94
 
2.6%
7 78
 
2.2%
4 70
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3116
86.4%
Dash Punctuation 492
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1270
40.8%
1 693
22.2%
3 246
 
7.9%
6 224
 
7.2%
9 216
 
6.9%
2 161
 
5.2%
8 94
 
3.0%
7 78
 
2.5%
4 70
 
2.2%
5 64
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1270
35.2%
1 693
19.2%
- 492
 
13.6%
3 246
 
6.8%
6 224
 
6.2%
9 216
 
6.0%
2 161
 
4.5%
8 94
 
2.6%
7 78
 
2.2%
4 70
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1270
35.2%
1 693
19.2%
- 492
 
13.6%
3 246
 
6.8%
6 224
 
6.2%
9 216
 
6.0%
2 161
 
4.5%
8 94
 
2.6%
7 78
 
2.2%
4 70
 
1.9%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
한식
133 
일식
 
8
호프/통닭
 
6
중국식
 
5
복어취급
 
3
Other values (6)
 
9

Length

Max length8
Median length2
Mean length2.3109756
Min length2

Unique

Unique3 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
한식 133
81.1%
일식 8
 
4.9%
호프/통닭 6
 
3.7%
중국식 5
 
3.0%
복어취급 3
 
1.8%
통닭(치킨) 2
 
1.2%
패스트푸드 2
 
1.2%
분식 2
 
1.2%
식육(숯불구이) 1
 
0.6%
경양식 1
 
0.6%

Length

2024-05-11T03:16:46.900981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한식 133
81.1%
일식 8
 
4.9%
호프/통닭 6
 
3.7%
중국식 5
 
3.0%
복어취급 3
 
1.8%
통닭(치킨 2
 
1.2%
패스트푸드 2
 
1.2%
분식 2
 
1.2%
식육(숯불구이 1
 
0.6%
경양식 1
 
0.6%

지정취소사유
Text

Distinct53
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-05-11T03:16:47.537126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length9.0243902
Min length2

Characters and Unicode

Total characters1480
Distinct characters113
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)22.6%

Sample

1st row상호변경(주류전문 호프로변경)
2nd row지위승계,상호변경(2009.4.21)
3rd row지위승계
4th row영업자지위승계
5th row지위승계
ValueCountFrequency (%)
영업자지위승계 44
19.5%
명의변경 18
 
8.0%
지위승계 13
 
5.8%
서울시 12
 
5.3%
위생등급평가 12
 
5.3%
등외 12
 
5.3%
관련 9
 
4.0%
행정처분 8
 
3.5%
폐업 8
 
3.5%
상호변경 7
 
3.1%
Other values (60) 83
36.7%
2024-05-11T03:16:48.688141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
위 101
 
6.8%
지 77
 
5.2%
승 75
 
5.1%
계 75
 
5.1%
62
 
4.2%
업 61
 
4.1%
2 52
 
3.5%
자 49
 
3.3%
영 48
 
3.2%
경 47
 
3.2%
Other values (103) 833
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1073
72.5%
Decimal Number 212
 
14.3%
Other Punctuation 66
 
4.5%
Space Separator 62
 
4.2%
Open Punctuation 28
 
1.9%
Close Punctuation 28
 
1.9%
Dash Punctuation 11
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
위 101
 
9.4%
지 77
 
7.2%
승 75
 
7.0%
계 75
 
7.0%
업 61
 
5.7%
자 49
 
4.6%
영 48
 
4.5%
경 47
 
4.4%
변 47
 
4.4%
호 35
 
3.3%
Other values (86) 458
42.7%
Decimal Number
ValueCountFrequency (%)
2 52
24.5%
0 45
21.2%
1 43
20.3%
3 24
11.3%
8 18
 
8.5%
5 8
 
3.8%
9 8
 
3.8%
7 7
 
3.3%
4 4
 
1.9%
6 3
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 41
62.1%
, 24
36.4%
: 1
 
1.5%
Space Separator
ValueCountFrequency (%)
62
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1073
72.5%
Common 407
 
27.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
위 101
 
9.4%
지 77
 
7.2%
승 75
 
7.0%
계 75
 
7.0%
업 61
 
5.7%
자 49
 
4.6%
영 48
 
4.5%
경 47
 
4.4%
변 47
 
4.4%
호 35
 
3.3%
Other values (86) 458
42.7%
Common
ValueCountFrequency (%)
62
15.2%
2 52
12.8%
0 45
11.1%
1 43
10.6%
. 41
10.1%
( 28
6.9%
) 28
6.9%
, 24
 
5.9%
3 24
 
5.9%
8 18
 
4.4%
Other values (7) 42
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1073
72.5%
ASCII 407
 
27.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
위 101
 
9.4%
지 77
 
7.2%
승 75
 
7.0%
계 75
 
7.0%
업 61
 
5.7%
자 49
 
4.6%
영 48
 
4.5%
경 47
 
4.4%
변 47
 
4.4%
호 35
 
3.3%
Other values (86) 458
42.7%
ASCII
ValueCountFrequency (%)
62
15.2%
2 52
12.8%
0 45
11.1%
1 43
10.6%
. 41
10.1%
( 28
6.9%
) 28
6.9%
, 24
 
5.9%
3 24
 
5.9%
8 18
 
4.4%
Other values (7) 42
10.3%

주된음식
Text

MISSING 

Distinct90
Distinct (%)56.2%
Missing4
Missing (%)2.4%
Memory size1.4 KiB
2024-05-11T03:16:49.414162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.575
Min length1

Characters and Unicode

Total characters572
Distinct characters125
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)41.2%

Sample

1st row묵은지 등갈비
2nd row돼지갈비
3rd row삼겹살
4th row왕소갈비
5th row낙지한마리
ValueCountFrequency (%)
감자탕 14
 
8.6%
돼지갈비 12
 
7.4%
팔보채 7
 
4.3%
냉면 6
 
3.7%
설렁탕 5
 
3.1%
생삼겹살 5
 
3.1%
회덮밥 4
 
2.5%
삼겹살 4
 
2.5%
갈비탕 3
 
1.9%
소갈비살 3
 
1.9%
Other values (82) 99
61.1%
2024-05-11T03:16:50.809840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 34
 
5.9%
비 29
 
5.1%
갈 28
 
4.9%
지 22
 
3.8%
자 17
 
3.0%
생 15
 
2.6%
돼 15
 
2.6%
살 15
 
2.6%
감 14
 
2.4%
구 13
 
2.3%
Other values (115) 370
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 564
98.6%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%
Space Separator 2
 
0.3%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 34
 
6.0%
비 29
 
5.1%
갈 28
 
5.0%
지 22
 
3.9%
자 17
 
3.0%
생 15
 
2.7%
돼 15
 
2.7%
살 15
 
2.7%
감 14
 
2.5%
구 13
 
2.3%
Other values (111) 362
64.2%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 564
98.6%
Common 8
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 34
 
6.0%
비 29
 
5.1%
갈 28
 
5.0%
지 22
 
3.9%
자 17
 
3.0%
생 15
 
2.7%
돼 15
 
2.7%
살 15
 
2.7%
감 14
 
2.5%
구 13
 
2.3%
Other values (111) 362
64.2%
Common
ValueCountFrequency (%)
( 2
25.0%
) 2
25.0%
2
25.0%
, 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 564
98.6%
ASCII 8
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 34
 
6.0%
비 29
 
5.1%
갈 28
 
5.0%
지 22
 
3.9%
자 17
 
3.0%
생 15
 
2.7%
돼 15
 
2.7%
살 15
 
2.7%
감 14
 
2.5%
구 13
 
2.3%
Other values (111) 362
64.2%
ASCII
ValueCountFrequency (%)
( 2
25.0%
) 2
25.0%
2
25.0%
, 2
25.0%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.24098
Minimum0
Maximum22275.8
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-05-11T03:16:51.432942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38.917
Q166.84
median99.42
Q3161.5125
95-th percentile262.395
Maximum22275.8
Range22275.8
Interquartile range (IQR)94.6725

Descriptive statistics

Standard deviation1731.607
Coefficient of variation (CV)6.7577286
Kurtosis163.37593
Mean256.24098
Median Absolute Deviation (MAD)37.96
Skewness12.770048
Sum42023.52
Variance2998462.7
MonotonicityNot monotonic
2024-05-11T03:16:52.297082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.67 3
 
1.8%
45.6 3
 
1.8%
73.16 3
 
1.8%
66.2 3
 
1.8%
278.06 3
 
1.8%
110.0 2
 
1.2%
91.73 2
 
1.2%
86.0 2
 
1.2%
86.25 2
 
1.2%
91.98 2
 
1.2%
Other values (110) 139
84.8%
ValueCountFrequency (%)
0.0 1
0.6%
15.0 1
0.6%
18.75 1
0.6%
22.32 1
0.6%
27.41 1
0.6%
32.52 1
0.6%
34.65 1
0.6%
35.5 1
0.6%
38.71 1
0.6%
40.09 1
0.6%
ValueCountFrequency (%)
22275.8 1
 
0.6%
451.93 1
 
0.6%
423.17 2
1.2%
369.6 1
 
0.6%
278.06 3
1.8%
264.0 1
 
0.6%
253.3 2
1.2%
236.14 1
 
0.6%
221.26 1
 
0.6%
220.6 1
 
0.6%

행정동명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
구로제3동
36 
구로제2동
24 
구로제5동
22 
개봉제1동
15 
고척제2동
15 
Other values (9)
52 

Length

Max length5
Median length5
Mean length4.9329268
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개봉제2동
2nd row구로제4동
3rd row구로제2동
4th row개봉제1동
5th row개봉제1동

Common Values

ValueCountFrequency (%)
구로제3동 36
22.0%
구로제2동 24
14.6%
구로제5동 22
13.4%
개봉제1동 15
9.1%
고척제2동 15
9.1%
고척제1동 12
 
7.3%
구로제4동 9
 
5.5%
개봉제2동 8
 
4.9%
오류제2동 7
 
4.3%
개봉제3동 5
 
3.0%
Other values (4) 11
 
6.7%

Length

2024-05-11T03:16:53.033791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구로제3동 36
22.0%
구로제2동 24
14.6%
구로제5동 22
13.4%
개봉제1동 15
9.1%
고척제2동 15
9.1%
고척제1동 12
 
7.3%
구로제4동 9
 
5.5%
개봉제2동 8
 
4.9%
오류제2동 7
 
4.3%
개봉제3동 5
 
3.0%
Other values (4) 11
 
6.7%

급수시설구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
상수도전용
155 
<NA>
 
9

Length

Max length5
Median length5
Mean length4.945122
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 155
94.5%
<NA> 9
 
5.5%

Length

2024-05-11T03:16:53.659665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T03:16:54.079566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 155
94.5%
na 9
 
5.5%

Interactions

2024-05-11T03:16:29.108697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:18.038210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:20.014074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:21.702056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:23.814465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:26.357883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:29.402022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:18.358546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:20.284740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:22.110585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:24.193797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:26.723779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:29.652602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:18.706688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:20.602909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:22.392307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:24.583627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:27.167027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:29.920284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:19.105207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:20.952225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:22.710787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:24.955998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:27.786792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:30.245274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:19.433705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:21.233106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:23.095839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:25.456243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:28.328233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:30.494123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:19.683669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:21.467265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:23.455053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:25.802972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T03:16:28.842526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T03:16:54.316300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명지정취소사유주된음식영업장면적(㎡)행정동명
지정년도1.0000.5371.0001.0000.6520.0000.9230.8120.0000.000
지정번호0.5371.0000.5370.5370.3880.0000.0000.8340.3020.286
신청일자1.0000.5371.0001.0000.6520.0000.9230.8120.0000.000
지정일자1.0000.5371.0001.0000.6520.0000.9230.8120.0000.000
취소일자0.6520.3880.6520.6521.0000.0000.9540.6520.0000.000
업태명0.0000.0000.0000.0000.0001.0000.0000.9241.0000.408
지정취소사유0.9230.0000.9230.9230.9540.0001.0000.9120.0000.553
주된음식0.8120.8340.8120.8120.6520.9240.9121.0001.0000.734
영업장면적(㎡)0.0000.3020.0000.0000.0001.0000.0001.0001.0000.000
행정동명0.0000.2860.0000.0000.0000.4080.5530.7340.0001.000
2024-05-11T03:16:54.679811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분행정동명업태명
급수시설구분1.0001.0001.000
행정동명1.0001.0000.169
업태명1.0000.1691.000
2024-05-11T03:16:54.976674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.2650.9960.9990.6960.1570.0000.0001.000
지정번호-0.2651.000-0.271-0.261-0.1510.1100.0000.1151.000
신청일자0.996-0.2711.0000.9940.6910.1570.0000.0001.000
지정일자0.999-0.2610.9941.0000.6960.1560.0000.0001.000
취소일자0.696-0.1510.6910.6961.0000.1430.0000.0481.000
영업장면적(㎡)0.1570.1100.1570.1560.1431.0000.9720.0001.000
업태명0.0000.0000.0000.0000.0000.9721.0000.1691.000
행정동명0.0000.1150.0000.0000.0480.0000.1691.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T03:16:30.877894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T03:16:31.656185image/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.
2024-05-11T03:16:32.297553image/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
03160000200619200606192006071020090803콩부자 개봉점서울특별시 구로구 개봉로 56, (개봉동)서울특별시 구로구 개봉동 403번지 65호3160000-101-2005-00362한식상호변경(주류전문 호프로변경)묵은지 등갈비110.0개봉제2동상수도전용
13160000200748200706292007070520090803갈비명가서울특별시 구로구 가마산로 260, (구로동)서울특별시 구로구 구로동 98번지 1호3160000-101-2006-00197한식지위승계,상호변경(2009.4.21)돼지갈비423.17구로제4동상수도전용
23160000200846200807012008071720151118가빈서울특별시 구로구 구로동로 174-9, (구로동)서울특별시 구로구 구로동 416번지 23호3160000-101-2003-00486한식지위승계삼겹살83.0구로제2동상수도전용
331600002002139200203212002050220051102황제갈비서울특별시 구로구 경인로40길 12, (개봉동)서울특별시 구로구 개봉동 178번지 13호3160000-101-1996-01877한식영업자지위승계왕소갈비103.72개봉제1동상수도전용
43160000200712200706292007070520080717황제갈비서울특별시 구로구 경인로40길 12, (개봉동)서울특별시 구로구 개봉동 178번지 13호3160000-101-1996-01877한식지위승계낙지한마리103.72개봉제1동상수도전용
5316000020157201509152015111820191107옛날황소곱창서울특별시 구로구 디지털로32나길 16, (구로동)서울특별시 구로구 구로동 1124번지 37호3160000-101-1985-00599한식기준미달(폐문상태)곱창구이,전골165.0구로제3동상수도전용
63160000200237200204012002050220031010개봉칡냉면서울특별시 구로구 경인로35길 113-17, (고척동)서울특별시 구로구 고척동 273번지 3호3160000-101-1989-05093한식장소이전냉면64.0고척제2동상수도전용
7316000020046200404152004070220080717개봉칡냉면서울특별시 구로구 경인로35길 113-17, (고척동)서울특별시 구로구 고척동 273번지 3호3160000-101-1989-05093한식지위승계냉면64.0고척제2동상수도전용
8316000020025200204012002050220050228해바라기 정육식당서울특별시 구로구 고척로 101, (개봉동)서울특별시 구로구 개봉동 63번지 35호3160000-101-1994-01645한식영업자지위승계칼국수100.5개봉제1동상수도전용
9316000020052200506012005072020221222해바라기 정육식당서울특별시 구로구 고척로 101, (개봉동)서울특별시 구로구 개봉동 63번지 35호3160000-101-1994-01645한식영업자지위승계웰빙샤브샤브100.5개봉제1동상수도전용
시군구코드지정년도지정번호신청일자지정일자취소일자업소명소재지도로명소재지지번허가(신고)번호업태명지정취소사유주된음식영업장면적(㎡)행정동명급수시설구분
1543160000200456200404152004070220181228무한 자금성서울특별시 구로구 디지털로 273, 에이스트윈타워2차 지하1층 B104,B105호 (구로동)서울특별시 구로구 구로동 212번지 30호 에이스트윈타워2차3160000-101-2003-00330중국식위생과-22338(2018.12.28)호 관련자장면191.62구로제3동상수도전용
15531600002009223200907012009080320141218티엠 웨딩시티서울특별시 구로구 새말로 97, 8층 6호외168구좌호 (구로동, 신도림테크노마트)서울특별시 구로구 구로동 3번지 25호 신도림테크노마트 내 8층6호 외 168구좌3160000-101-2008-00264뷔페식명의변경뷔페22275.8구로제5동상수도전용
15631600002009220200907012009080320151123부대찌개대사관 구로디지털본점서울특별시 구로구 디지털로33길 12, 104호 (구로동, 우림이비지센터 2차)서울특별시 구로구 구로동 184번지 1호 우림이비지센터 2차-1043160000-101-2005-00124한식위생과-21551(2015.11.23.)호 관련한우암소191.4구로제3동<NA>
15731600002003238200304012003070220061201속초오징어어시장 구로디지털단지점서울특별시 구로구 디지털로32나길 34, (구로동)서울특별시 구로구 구로동 1124번지 41호 케이제이빌딩1층3160000-101-2002-00395한식영업자지위승계감자탕218.79구로제3동상수도전용
1583160000200617200606252006070220100802속초오징어어시장 구로디지털단지점서울특별시 구로구 디지털로32나길 34, (구로동)서울특별시 구로구 구로동 1124번지 41호 케이제이빌딩1층3160000-101-2002-00395한식지위승계,상호변경감자탕218.79구로제3동상수도전용
1593160000200441200404152004070220181228우림 더이룸푸드서울특별시 구로구 디지털로33길 28, B108호 (구로동, 우림이비즈센터)서울특별시 구로구 구로동 170번지 5호 우림이비지센터-B1083160000-101-2003-00112한식위생과-22338(2018.12.28)호 관련백반369.6구로제3동상수도전용
160316000020078200706292007070520181228고기에미친남자서울특별시 구로구 개봉로 6, (개봉동)서울특별시 구로구 개봉동 290번지 5호3160000-101-1984-02992한식위생과-22338(2018.12.28.)호 관련쭈꾸미볶음141.95개봉제3동상수도전용
161316000020114201110062011122220231110마부식당서울특별시 구로구 오류로8길 60, B동 (오류동)서울특별시 구로구 오류동 148번지 3호 B3160000-101-2005-00051한식지정기준 미달감자탕59.4오류제2동상수도전용
162316000020049200404152004070220100802사천성서울특별시 구로구 디지털로27길 47, (가리봉동)서울특별시 구로구 가리봉동 115번지 109호3160000-101-1991-02335중국식위생수준미흡짜장면50.74가리봉동상수도전용
1633160000200254200204012002050220090803육미제당 구로디지털단지점서울특별시 구로구 디지털로32나길 12, 1층 (구로동)서울특별시 구로구 구로동 1124번지 36호3160000-101-2000-08677한식지위승계,상호변경(2009.3.31)한방닭180.52구로제3동상수도전용