Overview

Dataset statistics

Number of variables16
Number of observations228
Missing cells523
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.4 KiB
Average record size in memory136.6 B

Variable types

Categorical4
Numeric6
Unsupported1
Text5

Dataset

Description시군구코드,지정년도,지정번호,신청일자,지정일자,취소일자,불가일자,업소명,소재지도로명,소재지지번,허가(신고)번호,업태명,주된음식,영업장면적(㎡),행정동명,급수시설구분
Author금천구
URLhttps://data.seoul.go.kr/dataList/OA-10159/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 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
업태명 is highly imbalanced (71.4%)Imbalance
급수시설구분 is highly imbalanced (66.7%)Imbalance
지정년도 has 56 (24.6%) missing valuesMissing
지정번호 has 56 (24.6%) missing valuesMissing
지정일자 has 56 (24.6%) missing valuesMissing
취소일자 has 39 (17.1%) missing valuesMissing
불가일자 has 228 (100.0%) missing valuesMissing
주된음식 has 88 (38.6%) missing valuesMissing
불가일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 06:30:05.289139
Analysis finished2024-05-11 06:30:14.059872
Duration8.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
3170000
228 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3170000
2nd row3170000
3rd row3170000
4th row3170000
5th row3170000

Common Values

ValueCountFrequency (%)
3170000 228
100.0%

Length

2024-05-11T15:30:14.173308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:30:14.329768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3170000 228
100.0%

지정년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)8.1%
Missing56
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean2007.436
Minimum1987
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T15:30:14.485071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile2003
Q12004
median2007
Q32010
95-th percentile2015.45
Maximum2016
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.5029479
Coefficient of variation (CV)0.0027412818
Kurtosis4.6277414
Mean2007.436
Median Absolute Deviation (MAD)3
Skewness-1.3782999
Sum345279
Variance30.282436
MonotonicityNot monotonic
2024-05-11T15:30:14.660046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2004 36
15.8%
2006 24
10.5%
2008 21
 
9.2%
2013 14
 
6.1%
2015 13
 
5.7%
2007 12
 
5.3%
2010 11
 
4.8%
2009 11
 
4.8%
2016 9
 
3.9%
1987 6
 
2.6%
Other values (4) 15
 
6.6%
(Missing) 56
24.6%
ValueCountFrequency (%)
1987 6
 
2.6%
2003 6
 
2.6%
2004 36
15.8%
2005 6
 
2.6%
2006 24
10.5%
2007 12
 
5.3%
2008 21
9.2%
2009 11
 
4.8%
2010 11
 
4.8%
2011 1
 
0.4%
ValueCountFrequency (%)
2016 9
 
3.9%
2015 13
5.7%
2014 2
 
0.9%
2013 14
6.1%
2011 1
 
0.4%
2010 11
4.8%
2009 11
4.8%
2008 21
9.2%
2007 12
5.3%
2006 24
10.5%

지정번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct106
Distinct (%)61.6%
Missing56
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean84.651163
Minimum1
Maximum1562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T15:30:14.872450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q114.75
median32.5
Q3165.25
95-th percentile225.45
Maximum1562
Range1561
Interquartile range (IQR)150.5

Descriptive statistics

Standard deviation138.71497
Coefficient of variation (CV)1.6386658
Kurtosis75.084053
Mean84.651163
Median Absolute Deviation (MAD)25.5
Skewness7.2575131
Sum14560
Variance19241.843
MonotonicityNot monotonic
2024-05-11T15:30:15.091907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 5
 
2.2%
1 5
 
2.2%
13 5
 
2.2%
15 4
 
1.8%
5 4
 
1.8%
23 4
 
1.8%
10 4
 
1.8%
17 4
 
1.8%
7 4
 
1.8%
3 3
 
1.3%
Other values (96) 130
57.0%
(Missing) 56
24.6%
ValueCountFrequency (%)
1 5
2.2%
2 2
 
0.9%
3 3
1.3%
4 1
 
0.4%
5 4
1.8%
6 2
 
0.9%
7 4
1.8%
8 1
 
0.4%
9 3
1.3%
10 4
1.8%
ValueCountFrequency (%)
1562 1
0.4%
244 1
0.4%
240 1
0.4%
236 1
0.4%
235 1
0.4%
234 1
0.4%
233 1
0.4%
228 1
0.4%
226 1
0.4%
225 1
0.4%

신청일자
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20075637
Minimum19870409
Maximum20161226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T15:30:15.325570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870409
5-th percentile20030310
Q120040701
median20070706
Q320100813
95-th percentile20151023
Maximum20161226
Range290817
Interquartile range (IQR)60112

Descriptive statistics

Standard deviation48243.402
Coefficient of variation (CV)0.0024030821
Kurtosis5.2228998
Mean20075637
Median Absolute Deviation (MAD)30005
Skewness-1.1902273
Sum4.5772452 × 109
Variance2.3274259 × 109
MonotonicityNot monotonic
2024-05-11T15:30:15.535087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20060510 32
14.0%
20040701 28
12.3%
20080630 26
11.4%
20040422 18
 
7.9%
20100813 15
 
6.6%
20130531 14
 
6.1%
20090703 13
 
5.7%
20151023 13
 
5.7%
20070706 12
 
5.3%
20161226 10
 
4.4%
Other values (18) 47
20.6%
ValueCountFrequency (%)
19870409 4
 
1.8%
19880525 1
 
0.4%
19981210 1
 
0.4%
20030310 8
 
3.5%
20040422 18
7.9%
20040701 28
12.3%
20050607 1
 
0.4%
20050720 7
 
3.1%
20060510 32
14.0%
20070410 1
 
0.4%
ValueCountFrequency (%)
20161226 10
4.4%
20151023 13
5.7%
20141231 2
 
0.9%
20131022 3
 
1.3%
20130531 14
6.1%
20111220 2
 
0.9%
20111130 2
 
0.9%
20100930 2
 
0.9%
20100813 15
6.6%
20091123 1
 
0.4%

지정일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)10.5%
Missing56
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean20075151
Minimum19870409
Maximum20161226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T15:30:15.764190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19870409
5-th percentile20030422
Q120040721
median20070726
Q320100930
95-th percentile20155673
Maximum20161226
Range290817
Interquartile range (IQR)60209

Descriptive statistics

Standard deviation55190.105
Coefficient of variation (CV)0.0027491751
Kurtosis4.6032338
Mean20075151
Median Absolute Deviation (MAD)30006
Skewness-1.3708711
Sum3.452926 × 109
Variance3.0459477 × 109
MonotonicityNot monotonic
2024-05-11T15:30:15.957923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20060710 24
10.5%
20080717 21
 
9.2%
20040701 17
 
7.5%
20151130 13
 
5.7%
20070726 12
 
5.3%
20040721 11
 
4.8%
20130716 11
 
4.8%
20100930 11
 
4.8%
20090804 10
 
4.4%
20161226 9
 
3.9%
Other values (8) 33
14.5%
(Missing) 56
24.6%
ValueCountFrequency (%)
19870409 6
 
2.6%
20030422 6
 
2.6%
20040701 17
7.5%
20040720 8
 
3.5%
20040721 11
4.8%
20050720 6
 
2.6%
20060710 24
10.5%
20070726 12
5.3%
20080717 21
9.2%
20090804 10
4.4%
ValueCountFrequency (%)
20161226 9
3.9%
20151130 13
5.7%
20141231 2
 
0.9%
20131210 3
 
1.3%
20130716 11
4.8%
20111220 1
 
0.4%
20100930 11
4.8%
20091123 1
 
0.4%
20090804 10
4.4%
20080717 21
9.2%

취소일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct53
Distinct (%)28.0%
Missing39
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean20122750
Minimum20030310
Maximum20231108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T15:30:16.218853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20030310
5-th percentile20051048
Q120090804
median20121130
Q320141231
95-th percentile20221213
Maximum20231108
Range200798
Interquartile range (IQR)50427

Descriptive statistics

Standard deviation49150.554
Coefficient of variation (CV)0.0024425367
Kurtosis-0.33799805
Mean20122750
Median Absolute Deviation (MAD)30326
Skewness0.34575124
Sum3.8031997 × 109
Variance2.415777 × 109
MonotonicityNot monotonic
2024-05-11T15:30:16.496840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20141224 31
13.6%
20111220 15
 
6.6%
20090804 14
 
6.1%
20080717 12
 
5.3%
20221213 12
 
5.3%
20100930 10
 
4.4%
20121130 9
 
3.9%
20161226 8
 
3.5%
20141231 8
 
3.5%
20070726 6
 
2.6%
Other values (43) 64
28.1%
(Missing) 39
17.1%
ValueCountFrequency (%)
20030310 6
2.6%
20030702 1
 
0.4%
20050825 1
 
0.4%
20051004 1
 
0.4%
20051012 1
 
0.4%
20051101 1
 
0.4%
20060104 1
 
0.4%
20060126 1
 
0.4%
20060303 1
 
0.4%
20060331 1
 
0.4%
ValueCountFrequency (%)
20231108 4
 
1.8%
20221213 12
5.3%
20201007 1
 
0.4%
20200903 1
 
0.4%
20191118 1
 
0.4%
20191004 1
 
0.4%
20181218 4
 
1.8%
20181217 3
 
1.3%
20171227 3
 
1.3%
20171219 1
 
0.4%

불가일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing228
Missing (%)100.0%
Memory size2.1 KiB

업소명
Text

Distinct184
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-05-11T15:30:17.036153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length6.5570175
Min length1

Characters and Unicode

Total characters1495
Distinct characters313
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

Unique150 ?
Unique (%)65.8%

Sample

1st row한방전주콩나물국밥 시흥점
2nd row한방전주콩나물국밥 시흥점
3rd row김가네
4th row국빈성
5th row금천장어구이
ValueCountFrequency (%)
가산점 8
 
2.6%
독산점 5
 
1.6%
정육식당 4
 
1.3%
한방전주콩나물국밥 3
 
1.0%
시흥점 3
 
1.0%
김명태?굴국밥솥밥 3
 
1.0%
닭갈비 3
 
1.0%
춘천 3
 
1.0%
정든소문난순대국 3
 
1.0%
정통 3
 
1.0%
Other values (222) 273
87.8%
2024-05-11T15:30:17.751756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83
 
5.6%
점 42
 
2.8%
가 36
 
2.4%
산 34
 
2.3%
국 24
 
1.6%
동 24
 
1.6%
비 22
 
1.5%
갈 22
 
1.5%
밥 20
 
1.3%
정 20
 
1.3%
Other values (303) 1168
78.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1365
91.3%
Space Separator 83
 
5.6%
Uppercase Letter 15
 
1.0%
Decimal Number 10
 
0.7%
Other Punctuation 7
 
0.5%
Open Punctuation 6
 
0.4%
Close Punctuation 6
 
0.4%
Lowercase Letter 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 42
 
3.1%
가 36
 
2.6%
산 34
 
2.5%
국 24
 
1.8%
동 24
 
1.8%
비 22
 
1.6%
갈 22
 
1.6%
밥 20
 
1.5%
정 20
 
1.5%
대 19
 
1.4%
Other values (282) 1102
80.7%
Uppercase Letter
ValueCountFrequency (%)
S 3
20.0%
O 2
13.3%
L 2
13.3%
E 2
13.3%
J 2
13.3%
G 1
 
6.7%
N 1
 
6.7%
U 1
 
6.7%
A 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 5
50.0%
2 4
40.0%
3 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
? 3
42.9%
& 2
28.6%
. 2
28.6%
Lowercase Letter
ValueCountFrequency (%)
b 1
33.3%
h 1
33.3%
c 1
33.3%
Space Separator
ValueCountFrequency (%)
83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1365
91.3%
Common 112
 
7.5%
Latin 18
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 42
 
3.1%
가 36
 
2.6%
산 34
 
2.5%
국 24
 
1.8%
동 24
 
1.8%
비 22
 
1.6%
갈 22
 
1.6%
밥 20
 
1.5%
정 20
 
1.5%
대 19
 
1.4%
Other values (282) 1102
80.7%
Latin
ValueCountFrequency (%)
S 3
16.7%
O 2
11.1%
L 2
11.1%
E 2
11.1%
J 2
11.1%
G 1
 
5.6%
N 1
 
5.6%
U 1
 
5.6%
A 1
 
5.6%
b 1
 
5.6%
Other values (2) 2
11.1%
Common
ValueCountFrequency (%)
83
74.1%
( 6
 
5.4%
) 6
 
5.4%
1 5
 
4.5%
2 4
 
3.6%
? 3
 
2.7%
& 2
 
1.8%
. 2
 
1.8%
3 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1365
91.3%
ASCII 130
 
8.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
83
63.8%
( 6
 
4.6%
) 6
 
4.6%
1 5
 
3.8%
2 4
 
3.1%
S 3
 
2.3%
? 3
 
2.3%
O 2
 
1.5%
L 2
 
1.5%
& 2
 
1.5%
Other values (11) 14
 
10.8%
Hangul
ValueCountFrequency (%)
점 42
 
3.1%
가 36
 
2.6%
산 34
 
2.5%
국 24
 
1.8%
동 24
 
1.8%
비 22
 
1.6%
갈 22
 
1.6%
밥 20
 
1.5%
정 20
 
1.5%
대 19
 
1.4%
Other values (282) 1102
80.7%

소재지도로명
Text

Distinct184
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-05-11T15:30:18.261988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length51
Mean length37.013158
Min length24

Characters and Unicode

Total characters8439
Distinct characters161
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)65.4%

Sample

1st row서울특별시 금천구 독산로 49, 지상1층 (시흥동)
2nd row서울특별시 금천구 독산로 49, 지상1층 (시흥동)
3rd row서울특별시 금천구 가산디지털1로 2, 117호 (가산동, 우림라이온스밸리 2차)
4th row서울특별시 금천구 시흥대로52길 7, (시흥동,지상1층 (대명시장길 40))
5th row서울특별시 금천구 시흥대로152길 11-43, 105~107, 109, 110호 (독산동, 삼부르네상스플러스 )
ValueCountFrequency (%)
서울특별시 228
 
15.5%
금천구 228
 
15.5%
지상1층 79
 
5.4%
가산동 59
 
4.0%
독산동 48
 
3.3%
시흥동 46
 
3.1%
시흥대로 39
 
2.6%
독산로 22
 
1.5%
벚꽃로 20
 
1.4%
가산디지털1로 19
 
1.3%
Other values (348) 687
46.6%
2024-05-11T15:30:18.961812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1248
 
14.8%
1 496
 
5.9%
, 405
 
4.8%
시 399
 
4.7%
) 277
 
3.3%
( 277
 
3.3%
산 265
 
3.1%
동 247
 
2.9%
로 240
 
2.8%
금 237
 
2.8%
Other values (151) 4348
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4732
56.1%
Decimal Number 1393
 
16.5%
Space Separator 1248
 
14.8%
Other Punctuation 405
 
4.8%
Close Punctuation 277
 
3.3%
Open Punctuation 277
 
3.3%
Uppercase Letter 68
 
0.8%
Dash Punctuation 32
 
0.4%
Math Symbol 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 399
 
8.4%
산 265
 
5.6%
동 247
 
5.2%
로 240
 
5.1%
금 237
 
5.0%
구 232
 
4.9%
천 230
 
4.9%
서 229
 
4.8%
울 229
 
4.8%
특 228
 
4.8%
Other values (127) 2196
46.4%
Decimal Number
ValueCountFrequency (%)
1 496
35.6%
2 206
14.8%
5 112
 
8.0%
3 104
 
7.5%
0 101
 
7.3%
4 91
 
6.5%
6 82
 
5.9%
8 81
 
5.8%
9 69
 
5.0%
7 51
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 44
64.7%
A 9
 
13.2%
S 6
 
8.8%
J 5
 
7.4%
E 1
 
1.5%
I 1
 
1.5%
T 1
 
1.5%
K 1
 
1.5%
Space Separator
ValueCountFrequency (%)
1248
100.0%
Other Punctuation
ValueCountFrequency (%)
, 405
100.0%
Close Punctuation
ValueCountFrequency (%)
) 277
100.0%
Open Punctuation
ValueCountFrequency (%)
( 277
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4732
56.1%
Common 3639
43.1%
Latin 68
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 399
 
8.4%
산 265
 
5.6%
동 247
 
5.2%
로 240
 
5.1%
금 237
 
5.0%
구 232
 
4.9%
천 230
 
4.9%
서 229
 
4.8%
울 229
 
4.8%
특 228
 
4.8%
Other values (127) 2196
46.4%
Common
ValueCountFrequency (%)
1248
34.3%
1 496
 
13.6%
, 405
 
11.1%
) 277
 
7.6%
( 277
 
7.6%
2 206
 
5.7%
5 112
 
3.1%
3 104
 
2.9%
0 101
 
2.8%
4 91
 
2.5%
Other values (6) 322
 
8.8%
Latin
ValueCountFrequency (%)
B 44
64.7%
A 9
 
13.2%
S 6
 
8.8%
J 5
 
7.4%
E 1
 
1.5%
I 1
 
1.5%
T 1
 
1.5%
K 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4732
56.1%
ASCII 3707
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1248
33.7%
1 496
 
13.4%
, 405
 
10.9%
) 277
 
7.5%
( 277
 
7.5%
2 206
 
5.6%
5 112
 
3.0%
3 104
 
2.8%
0 101
 
2.7%
4 91
 
2.5%
Other values (14) 390
 
10.5%
Hangul
ValueCountFrequency (%)
시 399
 
8.4%
산 265
 
5.6%
동 247
 
5.2%
로 240
 
5.1%
금 237
 
5.0%
구 232
 
4.9%
천 230
 
4.9%
서 229
 
4.8%
울 229
 
4.8%
특 228
 
4.8%
Other values (127) 2196
46.4%

소재지지번
Text

Distinct180
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-05-11T15:30:19.437837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length48
Mean length33.719298
Min length25

Characters and Unicode

Total characters7688
Distinct characters147
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)62.3%

Sample

1st row서울특별시 금천구 시흥동 896번지 15호
2nd row서울특별시 금천구 시흥동 896번지 15호
3rd row서울특별시 금천구 가산동 680번지 117호 우림라이온스밸리 2차
4th row서울특별시 금천구 시흥동 890번지 9호 지상1층 (대명시장길 40)
5th row서울특별시 금천구 독산동 953번지 삼부르네상스플러스
ValueCountFrequency (%)
서울특별시 228
 
15.7%
금천구 228
 
15.7%
가산동 86
 
5.9%
지상1층 82
 
5.7%
시흥동 74
 
5.1%
독산동 68
 
4.7%
60번지 17
 
1.2%
11호 15
 
1.0%
10호 15
 
1.0%
143번지 13
 
0.9%
Other values (300) 624
43.0%
2024-05-11T15:30:20.152308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1708
22.2%
1 435
 
5.7%
지 355
 
4.6%
시 327
 
4.3%
호 247
 
3.2%
동 238
 
3.1%
금 230
 
3.0%
천 230
 
3.0%
구 230
 
3.0%
울 229
 
3.0%
Other values (137) 3459
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4233
55.1%
Space Separator 1708
22.2%
Decimal Number 1531
 
19.9%
Uppercase Letter 56
 
0.7%
Open Punctuation 52
 
0.7%
Close Punctuation 52
 
0.7%
Dash Punctuation 36
 
0.5%
Other Punctuation 18
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 355
 
8.4%
시 327
 
7.7%
호 247
 
5.8%
동 238
 
5.6%
금 230
 
5.4%
천 230
 
5.4%
구 230
 
5.4%
울 229
 
5.4%
서 229
 
5.4%
특 228
 
5.4%
Other values (113) 1690
39.9%
Decimal Number
ValueCountFrequency (%)
1 435
28.4%
2 170
 
11.1%
0 158
 
10.3%
8 134
 
8.8%
3 133
 
8.7%
9 128
 
8.4%
4 119
 
7.8%
5 101
 
6.6%
6 91
 
5.9%
7 62
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 34
60.7%
A 9
 
16.1%
S 5
 
8.9%
J 4
 
7.1%
T 1
 
1.8%
E 1
 
1.8%
I 1
 
1.8%
K 1
 
1.8%
Space Separator
ValueCountFrequency (%)
1708
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4233
55.1%
Common 3399
44.2%
Latin 56
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 355
 
8.4%
시 327
 
7.7%
호 247
 
5.8%
동 238
 
5.6%
금 230
 
5.4%
천 230
 
5.4%
구 230
 
5.4%
울 229
 
5.4%
서 229
 
5.4%
특 228
 
5.4%
Other values (113) 1690
39.9%
Common
ValueCountFrequency (%)
1708
50.3%
1 435
 
12.8%
2 170
 
5.0%
0 158
 
4.6%
8 134
 
3.9%
3 133
 
3.9%
9 128
 
3.8%
4 119
 
3.5%
5 101
 
3.0%
6 91
 
2.7%
Other values (6) 222
 
6.5%
Latin
ValueCountFrequency (%)
B 34
60.7%
A 9
 
16.1%
S 5
 
8.9%
J 4
 
7.1%
T 1
 
1.8%
E 1
 
1.8%
I 1
 
1.8%
K 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4233
55.1%
ASCII 3455
44.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1708
49.4%
1 435
 
12.6%
2 170
 
4.9%
0 158
 
4.6%
8 134
 
3.9%
3 133
 
3.8%
9 128
 
3.7%
4 119
 
3.4%
5 101
 
2.9%
6 91
 
2.6%
Other values (14) 278
 
8.0%
Hangul
ValueCountFrequency (%)
지 355
 
8.4%
시 327
 
7.7%
호 247
 
5.8%
동 238
 
5.6%
금 230
 
5.4%
천 230
 
5.4%
구 230
 
5.4%
울 229
 
5.4%
서 229
 
5.4%
특 228
 
5.4%
Other values (113) 1690
39.9%

허가(신고)번호
Text

Distinct185
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-05-11T15:30:20.502521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters5016
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

Unique151 ?
Unique (%)66.2%

Sample

1st row3170000-101-1991-04478
2nd row3170000-101-1991-04478
3rd row3170000-101-2006-00246
4th row3170000-101-1994-04453
5th row3170000-101-2005-00312
ValueCountFrequency (%)
3170000-101-1991-04478 3
 
1.3%
3170000-101-2001-06951 3
 
1.3%
3170000-101-1989-05196 3
 
1.3%
3170000-101-1999-02223 3
 
1.3%
3170000-101-2003-00320 3
 
1.3%
3170000-101-1993-03555 3
 
1.3%
3170000-101-2001-06732 3
 
1.3%
3170000-101-2002-05603 3
 
1.3%
3170000-101-1989-05225 3
 
1.3%
3170000-101-1994-04524 2
 
0.9%
Other values (175) 199
87.3%
2024-05-11T15:30:21.005786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1865
37.2%
1 914
18.2%
- 684
 
13.6%
3 344
 
6.9%
7 312
 
6.2%
2 267
 
5.3%
9 221
 
4.4%
5 127
 
2.5%
4 108
 
2.2%
6 92
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4332
86.4%
Dash Punctuation 684
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1865
43.1%
1 914
21.1%
3 344
 
7.9%
7 312
 
7.2%
2 267
 
6.2%
9 221
 
5.1%
5 127
 
2.9%
4 108
 
2.5%
6 92
 
2.1%
8 82
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 684
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5016
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1865
37.2%
1 914
18.2%
- 684
 
13.6%
3 344
 
6.9%
7 312
 
6.2%
2 267
 
5.3%
9 221
 
4.4%
5 127
 
2.5%
4 108
 
2.2%
6 92
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1865
37.2%
1 914
18.2%
- 684
 
13.6%
3 344
 
6.9%
7 312
 
6.2%
2 267
 
5.3%
9 221
 
4.4%
5 127
 
2.5%
4 108
 
2.2%
6 92
 
1.8%

업태명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
한식
195 
중국식
 
15
일식
 
6
회집
 
4
호프/통닭
 
2
Other values (5)
 
6

Length

Max length8
Median length2
Mean length2.1447368
Min length2

Unique

Unique4 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
한식 195
85.5%
중국식 15
 
6.6%
일식 6
 
2.6%
회집 4
 
1.8%
호프/통닭 2
 
0.9%
경양식 2
 
0.9%
식육(숯불구이) 1
 
0.4%
분식 1
 
0.4%
통닭(치킨) 1
 
0.4%
기타 1
 
0.4%

Length

2024-05-11T15:30:21.211044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:30:21.805580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 195
85.5%
중국식 15
 
6.6%
일식 6
 
2.6%
회집 4
 
1.8%
호프/통닭 2
 
0.9%
경양식 2
 
0.9%
식육(숯불구이 1
 
0.4%
분식 1
 
0.4%
통닭(치킨 1
 
0.4%
기타 1
 
0.4%

주된음식
Text

MISSING 

Distinct64
Distinct (%)45.7%
Missing88
Missing (%)38.6%
Memory size1.9 KiB
2024-05-11T15:30:22.148740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.1642857
Min length1

Characters and Unicode

Total characters443
Distinct characters99
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)27.1%

Sample

1st row고추장등짝갈비
2nd row등심
3rd row탕수육
4th row닭갈비
5th row아구찜
ValueCountFrequency (%)
돼지갈비 17
 
12.1%
식육 11
 
7.8%
한식 7
 
5.0%
갈비탕 6
 
4.3%
보쌈 5
 
3.5%
삼겹살 5
 
3.5%
백반 4
 
2.8%
소갈비 4
 
2.8%
팔보채 4
 
2.8%
갈비 4
 
2.8%
Other values (54) 74
52.5%
2024-05-11T15:30:22.853538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
갈 35
 
7.9%
비 35
 
7.9%
탕 22
 
5.0%
지 20
 
4.5%
식 20
 
4.5%
돼 17
 
3.8%
육 15
 
3.4%
국 12
 
2.7%
보 11
 
2.5%
장 10
 
2.3%
Other values (89) 246
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 437
98.6%
Other Punctuation 5
 
1.1%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
갈 35
 
8.0%
비 35
 
8.0%
탕 22
 
5.0%
지 20
 
4.6%
식 20
 
4.6%
돼 17
 
3.9%
육 15
 
3.4%
국 12
 
2.7%
보 11
 
2.5%
장 10
 
2.3%
Other values (87) 240
54.9%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 437
98.6%
Common 6
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
갈 35
 
8.0%
비 35
 
8.0%
탕 22
 
5.0%
지 20
 
4.6%
식 20
 
4.6%
돼 17
 
3.9%
육 15
 
3.4%
국 12
 
2.7%
보 11
 
2.5%
장 10
 
2.3%
Other values (87) 240
54.9%
Common
ValueCountFrequency (%)
, 5
83.3%
1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 437
98.6%
ASCII 6
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
갈 35
 
8.0%
비 35
 
8.0%
탕 22
 
5.0%
지 20
 
4.6%
식 20
 
4.6%
돼 17
 
3.9%
육 15
 
3.4%
국 12
 
2.7%
보 11
 
2.5%
장 10
 
2.3%
Other values (87) 240
54.9%
ASCII
ValueCountFrequency (%)
, 5
83.3%
1
 
16.7%

영업장면적(㎡)
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)80.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.72654
Minimum15.26
Maximum1262.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-05-11T15:30:23.090934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.26
5-th percentile49.6035
Q179.315
median114.745
Q3181.4425
95-th percentile664.6455
Maximum1262.78
Range1247.52
Interquartile range (IQR)102.1275

Descriptive statistics

Standard deviation185.957
Coefficient of variation (CV)1.0522302
Kurtosis8.4149836
Mean176.72654
Median Absolute Deviation (MAD)40.595
Skewness2.7769433
Sum40293.65
Variance34580.007
MonotonicityNot monotonic
2024-05-11T15:30:23.354288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131.61 3
 
1.3%
123.55 3
 
1.3%
166.51 3
 
1.3%
115.0 3
 
1.3%
129.9 3
 
1.3%
197.28 3
 
1.3%
63.12 3
 
1.3%
102.48 3
 
1.3%
270.22 3
 
1.3%
90.0 2
 
0.9%
Other values (174) 199
87.3%
ValueCountFrequency (%)
15.26 1
0.4%
22.4 1
0.4%
23.76 1
0.4%
32.4 1
0.4%
39.34 1
0.4%
41.58 1
0.4%
42.16 1
0.4%
43.69 2
0.9%
45.55 1
0.4%
47.48 1
0.4%
ValueCountFrequency (%)
1262.78 1
0.4%
914.22 1
0.4%
826.0 2
0.9%
811.7 1
0.4%
760.0 1
0.4%
735.52 1
0.4%
728.93 1
0.4%
686.4 2
0.9%
677.0 1
0.4%
674.19 1
0.4%

행정동명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
가산동
86 
시흥제1동
46 
독산제1동
29 
독산제3동
15 
독산제4동
14 
Other values (5)
38 

Length

Max length5
Median length5
Mean length4.245614
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시흥제1동
2nd row시흥제1동
3rd row가산동
4th row시흥제1동
5th row독산제3동

Common Values

ValueCountFrequency (%)
가산동 86
37.7%
시흥제1동 46
20.2%
독산제1동 29
 
12.7%
독산제3동 15
 
6.6%
독산제4동 14
 
6.1%
시흥제3동 13
 
5.7%
독산제2동 10
 
4.4%
시흥제4동 6
 
2.6%
시흥제5동 5
 
2.2%
시흥제2동 4
 
1.8%

Length

2024-05-11T15:30:23.598523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:30:23.883025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가산동 86
37.7%
시흥제1동 46
20.2%
독산제1동 29
 
12.7%
독산제3동 15
 
6.6%
독산제4동 14
 
6.1%
시흥제3동 13
 
5.7%
독산제2동 10
 
4.4%
시흥제4동 6
 
2.6%
시흥제5동 5
 
2.2%
시흥제2동 4
 
1.8%

급수시설구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
상수도전용
214 
<NA>
 
14

Length

Max length5
Median length5
Mean length4.9385965
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
상수도전용 214
93.9%
<NA> 14
 
6.1%

Length

2024-05-11T15:30:24.108672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:30:24.272543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상수도전용 214
93.9%
na 14
 
6.1%

Interactions

2024-05-11T15:30:12.376964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:06.645529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:07.689774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:08.909929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:10.293247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:11.344547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:12.542620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:06.807855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:07.872170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:09.058039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:10.443748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:11.499268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:12.701812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:06.959436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:08.080247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:09.279245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:10.623430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:11.657406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:12.860211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:07.122091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:08.272337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:09.808174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:10.785503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:11.829279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:13.003576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:07.322401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:08.480272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:09.978496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:10.992492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:12.009811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:13.158924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:07.526915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:08.740106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:10.143772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:11.187937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:30:12.216512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T15:30:24.409849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자업태명주된음식영업장면적(㎡)행정동명
지정년도1.0001.0001.0001.0000.7640.0000.8950.3890.238
지정번호1.0001.0000.9691.0000.4720.6320.9250.0000.199
신청일자1.0000.9691.0001.0000.7670.7410.9700.2090.176
지정일자1.0001.0001.0001.0000.7640.0000.8950.3890.238
취소일자0.7640.4720.7670.7641.0000.0000.7930.0000.000
업태명0.0000.6320.7410.0000.0001.0000.9830.0000.435
주된음식0.8950.9250.9700.8950.7930.9831.0000.0000.803
영업장면적(㎡)0.3890.0000.2090.3890.0000.0000.0001.0000.259
행정동명0.2380.1990.1760.2380.0000.4350.8030.2591.000
2024-05-11T15:30:24.634823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
급수시설구분행정동명업태명
급수시설구분1.0001.0001.000
행정동명1.0001.0000.146
업태명1.0000.1461.000
2024-05-11T15:30:24.809478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정년도지정번호신청일자지정일자취소일자영업장면적(㎡)업태명행정동명급수시설구분
지정년도1.000-0.1450.9950.9960.7120.1590.1730.1311.000
지정번호-0.1451.000-0.130-0.125-0.049-0.0750.4680.1171.000
신청일자0.995-0.1301.0000.9980.6880.1820.3290.0821.000
지정일자0.996-0.1250.9981.0000.7060.1550.1730.1311.000
취소일자0.712-0.0490.6880.7061.0000.2240.1160.0201.000
영업장면적(㎡)0.159-0.0750.1820.1550.2241.0000.0000.1191.000
업태명0.1730.4680.3290.1730.1160.0001.0000.1461.000
행정동명0.1310.1170.0820.1310.0200.1190.1461.0001.000
급수시설구분1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T15:30:13.386860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T15:30:13.705944image/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.
2024-05-11T15:30:13.932915image/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
03170000200613200605102006071020100930<NA>한방전주콩나물국밥 시흥점서울특별시 금천구 독산로 49, 지상1층 (시흥동)서울특별시 금천구 시흥동 896번지 15호3170000-101-1991-04478한식고추장등짝갈비131.61시흥제1동상수도전용
13170000<NA><NA>20100813<NA>20141231<NA>한방전주콩나물국밥 시흥점서울특별시 금천구 독산로 49, 지상1층 (시흥동)서울특별시 금천구 시흥동 896번지 15호3170000-101-1991-04478한식<NA>131.61시흥제1동상수도전용
23170000<NA><NA>20070510<NA>20080717<NA>김가네서울특별시 금천구 가산디지털1로 2, 117호 (가산동, 우림라이온스밸리 2차)서울특별시 금천구 가산동 680번지 117호 우림라이온스밸리 2차3170000-101-2006-00246한식<NA>66.8가산동상수도전용
33170000200489200407012004072020060303<NA>국빈성서울특별시 금천구 시흥대로52길 7, (시흥동,지상1층 (대명시장길 40))서울특별시 금천구 시흥동 890번지 9호 지상1층 (대명시장길 40)3170000-101-1994-04453중국식등심126.38시흥제1동상수도전용
43170000200659200605102006071020100901<NA>금천장어구이서울특별시 금천구 시흥대로152길 11-43, 105~107, 109, 110호 (독산동, 삼부르네상스플러스 )서울특별시 금천구 독산동 953번지 삼부르네상스플러스3170000-101-2005-00312한식탕수육229.2독산제3동상수도전용
5317000019871562199812101987040920030310<NA>라이코스서울특별시 금천구 시흥대로50길 17, (시흥동, 지상1층)서울특별시 금천구 시흥동 891번지 6호 지상1층3170000-101-1998-01320호프/통닭닭갈비49.32시흥제1동상수도전용
63170000200458200404222004070120051012<NA>청진동해장국서울특별시 금천구 독산로 191-1, (독산동, 지상1층)서울특별시 금천구 독산동 1043번지 10호 지상1층3170000-101-2003-00101한식아구찜112.46독산제2동상수도전용
73170000200627200605102006071020080109<NA>청진동해장국서울특별시 금천구 독산로 191-1, (독산동, 지상1층)서울특별시 금천구 독산동 1043번지 10호 지상1층3170000-101-2003-00101한식선지해장국112.46독산제2동상수도전용
83170000<NA><NA>20060510<NA>20080717<NA>말뚝곱창서울특별시 금천구 가산디지털1로 168, A동 지상2층 224일부호 (가산동, 우림라이온스밸리)서울특별시 금천구 가산동 371번지 28호 A 우림라이온스밸리 지상2층-224일부3170000-101-2005-00355한식<NA>130.0가산동상수도전용
931700002010233201008132010093020141224<NA>메가커피 금천현대시장점서울특별시 금천구 독산로 131, 지상1층 (시흥동)서울특별시 금천구 시흥동 852번지 31호 지상1층3170000-101-1999-05790한식<NA>61.62시흥제1동상수도전용
시군구코드지정년도지정번호신청일자지정일자취소일자불가일자업소명소재지도로명소재지지번허가(신고)번호업태명주된음식영업장면적(㎡)행정동명급수시설구분
21831700002006552006051020060710<NA><NA>내조국국밥서울특별시 금천구 벚꽃로 278, SJ테크노빌 B144,B145호 (가산동)서울특별시 금천구 가산동 60번지 19호 SJ테크노빌3170000-101-2005-00186한식갈비탕194.64가산동상수도전용
21931700002008223200807172008071720160115<NA>전주웰빙 한식뷔페 소고기삼겹살 무한리필서울특별시 금천구 벚꽃로 298, (가산동,대륭포스트타워6차 B114, B115호)서울특별시 금천구 가산동 50번지 3호 대륭포스트타워6차 B114, B115호3170000-101-2010-00300한식<NA>248.92가산동<NA>
2203170000201515201510232015113020181217<NA>전주웰빙 한식뷔페 소고기삼겹살 무한리필서울특별시 금천구 벚꽃로 298, (가산동,대륭포스트타워6차 B114, B115호)서울특별시 금천구 가산동 50번지 3호 대륭포스트타워6차 B114, B115호3170000-101-2010-00300한식김치찌개,식육248.92가산동<NA>
2213170000201632016122620161226<NA><NA>밀면의 법칙 (독산점)서울특별시 금천구 독산로75길 20, 지상1층 (독산동)서울특별시 금천구 독산동 196번지 33호3170000-101-2014-00227일식회79.38독산제1동<NA>
2223170000<NA><NA>20040701<NA>20141224<NA>야시장서울특별시 금천구 벚꽃로 309, 가산디지털단지역 (가산동)서울특별시 금천구 가산동 468번지 4호 가산디지털단지역3170000-101-1991-05212한식<NA>77.7가산동상수도전용
223317000020092122009070320090804<NA><NA>대촌 불쭈꾸미 본점서울특별시 금천구 벚꽃로 312, (가산동, 지상1층)서울특별시 금천구 가산동 41번지 11호 지상1층3170000-101-2008-00266한식<NA>186.21가산동상수도전용
2243170000<NA><NA>20040422<NA>20141224<NA>한우소가 살살서울특별시 금천구 시흥대로 371, (독산동,지상1,2층 (시흥대로314))서울특별시 금천구 독산동 292번지 8호 지상1,2층 (시흥대로314)3170000-101-1987-04999한식<NA>131.65독산제1동상수도전용
22531700002008165200806302008071720130716<NA>SJ 구내식당서울특별시 금천구 벚꽃로 278, SJ테크노빌 지하1층 B156,B156-2호 (가산동)서울특별시 금천구 가산동 60번지 19호 SJ테크노빌 B156,B156-23170000-101-2007-00035한식백반826.0가산동상수도전용
2263170000<NA><NA>20130531<NA>20181218<NA>SJ 구내식당서울특별시 금천구 벚꽃로 278, SJ테크노빌 지하1층 B156,B156-2호 (가산동)서울특별시 금천구 가산동 60번지 19호 SJ테크노빌 B156,B156-23170000-101-2007-00035한식<NA>826.0가산동상수도전용
22731700002004127200407012004072120060821<NA>부뚜막서울특별시 금천구 은행나무로 49, 지상1층 (시흥동)서울특별시 금천구 시흥동 909번지 26호3170000-101-1990-04461한식돼지갈비136.82시흥제5동상수도전용