Overview

Dataset statistics

Number of variables14
Number of observations186
Missing cells661
Missing cells (%)25.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 KiB
Average record size in memory118.7 B

Variable types

Categorical3
Text3
DateTime2
Unsupported3
Numeric3

Dataset

Description일반음식점(출장조리) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=N98U1LYAANEO373T8NLO13518996&infSeq=1

Alerts

위생업태명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
폐업일자 has 80 (43.0%) missing valuesMissing
다중이용업소여부 has 186 (100.0%) missing valuesMissing
총시설규모(㎡) has 186 (100.0%) missing valuesMissing
위생업종명 has 186 (100.0%) missing valuesMissing
소재지도로명주소 has 9 (4.8%) missing valuesMissing
소재지우편번호 has 8 (4.3%) missing valuesMissing
WGS84위도 has 3 (1.6%) missing valuesMissing
WGS84경도 has 3 (1.6%) missing valuesMissing
다중이용업소여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported
총시설규모(㎡) is an unsupported type, check if it needs cleaning or further analysisUnsupported
위생업종명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 23:11:55.053789
Analysis finished2023-12-10 23:11:56.709354
Duration1.66 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
하남시
23 
부천시
13 
평택시
 
11
광명시
 
10
고양시
 
9
Other values (36)
120 

Length

Max length4
Median length4
Mean length3.8763441
Min length3

Unique

Unique14 ?
Unique (%)7.5%

Sample

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

Common Values

ValueCountFrequency (%)
하남시 23
 
12.4%
부천시 13
 
7.0%
평택시 11
 
5.9%
광명시 10
 
5.4%
고양시 9
 
4.8%
남양주시 8
 
4.3%
의정부시 8
 
4.3%
성남시 8
 
4.3%
시흥시 7
 
3.8%
구리시 7
 
3.8%
Other values (31) 82
44.1%

Length

2023-12-11T08:11:56.767793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
하남시 25
 
13.4%
부천시 18
 
9.7%
고양시 12
 
6.5%
평택시 11
 
5.9%
광명시 11
 
5.9%
성남시 9
 
4.8%
남양주시 8
 
4.3%
의정부시 8
 
4.3%
구리시 8
 
4.3%
안산시 8
 
4.3%
Other values (18) 68
36.6%

사업장명
Text

Distinct179
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T08:11:56.982858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length6.7365591
Min length2

Characters and Unicode

Total characters1253
Distinct characters311
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

Unique173 ?
Unique (%)93.0%

Sample

1st row엘수아레
2nd row오수테이블
3rd row호가호가외식(경기북부지점)
4th row그래머시 킨텍스(제2전시장지하)
5th row생어거스틴 케이터링
ValueCountFrequency (%)
주식회사 4
 
1.8%
궁중파티부페 3
 
1.4%
케이터링 3
 
1.4%
파티코리아 2
 
0.9%
모심푸드 2
 
0.9%
장수마을 2
 
0.9%
푸드스토리 2
 
0.9%
요리조리 2
 
0.9%
홀리데이출장부페 1
 
0.5%
쿡프랜즈 1
 
0.5%
Other values (199) 199
90.0%
2023-12-11T08:11:57.309096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
페 41
 
3.3%
드 40
 
3.2%
35
 
2.8%
장 35
 
2.8%
푸 34
 
2.7%
부 31
 
2.5%
주 31
 
2.5%
출 30
 
2.4%
식 26
 
2.1%
) 25
 
2.0%
Other values (301) 925
73.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1131
90.3%
Space Separator 35
 
2.8%
Close Punctuation 25
 
2.0%
Open Punctuation 25
 
2.0%
Lowercase Letter 17
 
1.4%
Uppercase Letter 14
 
1.1%
Other Punctuation 4
 
0.3%
Decimal Number 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
페 41
 
3.6%
드 40
 
3.5%
장 35
 
3.1%
푸 34
 
3.0%
부 31
 
2.7%
주 31
 
2.7%
출 30
 
2.7%
식 26
 
2.3%
이 25
 
2.2%
스 25
 
2.2%
Other values (268) 813
71.9%
Lowercase Letter
ValueCountFrequency (%)
a 3
17.6%
r 2
11.8%
n 2
11.8%
u 1
 
5.9%
o 1
 
5.9%
e 1
 
5.9%
s 1
 
5.9%
t 1
 
5.9%
i 1
 
5.9%
m 1
 
5.9%
Other values (3) 3
17.6%
Uppercase Letter
ValueCountFrequency (%)
B 2
14.3%
O 2
14.3%
Y 1
7.1%
C 1
7.1%
A 1
7.1%
N 1
7.1%
F 1
7.1%
T 1
7.1%
P 1
7.1%
I 1
7.1%
Other values (2) 2
14.3%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
, 1
25.0%
/ 1
25.0%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 25
100.0%
Open Punctuation
ValueCountFrequency (%)
( 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1131
90.3%
Common 91
 
7.3%
Latin 31
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
페 41
 
3.6%
드 40
 
3.5%
장 35
 
3.1%
푸 34
 
3.0%
부 31
 
2.7%
주 31
 
2.7%
출 30
 
2.7%
식 26
 
2.3%
이 25
 
2.2%
스 25
 
2.2%
Other values (268) 813
71.9%
Latin
ValueCountFrequency (%)
a 3
 
9.7%
B 2
 
6.5%
r 2
 
6.5%
O 2
 
6.5%
n 2
 
6.5%
Y 1
 
3.2%
u 1
 
3.2%
C 1
 
3.2%
o 1
 
3.2%
e 1
 
3.2%
Other values (15) 15
48.4%
Common
ValueCountFrequency (%)
35
38.5%
) 25
27.5%
( 25
27.5%
& 2
 
2.2%
2 1
 
1.1%
, 1
 
1.1%
4 1
 
1.1%
/ 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1131
90.3%
ASCII 122
 
9.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
페 41
 
3.6%
드 40
 
3.5%
장 35
 
3.1%
푸 34
 
3.0%
부 31
 
2.7%
주 31
 
2.7%
출 30
 
2.7%
식 26
 
2.3%
이 25
 
2.2%
스 25
 
2.2%
Other values (268) 813
71.9%
ASCII
ValueCountFrequency (%)
35
28.7%
) 25
20.5%
( 25
20.5%
a 3
 
2.5%
B 2
 
1.6%
r 2
 
1.6%
O 2
 
1.6%
& 2
 
1.6%
n 2
 
1.6%
Y 1
 
0.8%
Other values (23) 23
18.9%

인허가일자
Date

Distinct177
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum1989-06-10 00:00:00
Maximum2023-11-17 00:00:00
2023-12-11T08:11:57.437155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:57.583903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
폐업
106 
영업
80 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 106
57.0%
영업 80
43.0%

Length

2023-12-11T08:11:57.716952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:57.801235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 106
57.0%
영업 80
43.0%

폐업일자
Date

MISSING 

Distinct104
Distinct (%)98.1%
Missing80
Missing (%)43.0%
Memory size1.6 KiB
Minimum1999-02-22 00:00:00
Maximum2023-12-05 00:00:00
2023-12-11T08:11:57.886761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:58.191128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

다중이용업소여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing186
Missing (%)100.0%
Memory size1.8 KiB

총시설규모(㎡)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing186
Missing (%)100.0%
Memory size1.8 KiB

위생업종명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing186
Missing (%)100.0%
Memory size1.8 KiB

위생업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
출장조리
186 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
출장조리 186
100.0%

Length

2023-12-11T08:11:58.301777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:11:58.375256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
출장조리 186
100.0%

소재지도로명주소
Text

MISSING 

Distinct175
Distinct (%)98.9%
Missing9
Missing (%)4.8%
Memory size1.6 KiB
2023-12-11T08:11:58.551073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length47
Mean length30.903955
Min length14

Characters and Unicode

Total characters5470
Distinct characters278
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

Unique173 ?
Unique (%)97.7%

Sample

1st row경기도 고양시 일산서구 덕이로 20, 양우씨네플렉스 가동 1층 D-116호 (덕이동)
2nd row경기도 고양시 일산서구 현중로36번길 75, 2층 일부(202호)호 (탄현동)
3rd row경기도 고양시 일산서구 탄중로 111, 양우로데오메인타운 4동 204,205,206호 (덕이동)
4th row경기도 고양시 일산서구 킨텍스로 217-59, 지하1층 일부호 (대화동, 킨텍스 제2전시장 전시동)
5th row경기도 고양시 덕양구 강매로 242, 1층 (강매동)
ValueCountFrequency (%)
경기도 177
 
15.4%
1층 81
 
7.1%
하남시 23
 
2.0%
부천시 17
 
1.5%
일부호 13
 
1.1%
고양시 12
 
1.0%
광명시 11
 
1.0%
일부 10
 
0.9%
평택시 10
 
0.9%
안산시 8
 
0.7%
Other values (557) 785
68.4%
2023-12-11T08:11:58.881769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
970
 
17.7%
1 298
 
5.4%
시 187
 
3.4%
도 183
 
3.3%
동 183
 
3.3%
기 182
 
3.3%
경 178
 
3.3%
로 162
 
3.0%
, 159
 
2.9%
) 146
 
2.7%
Other values (268) 2822
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3013
55.1%
Space Separator 970
 
17.7%
Decimal Number 953
 
17.4%
Other Punctuation 159
 
2.9%
Close Punctuation 146
 
2.7%
Open Punctuation 146
 
2.7%
Dash Punctuation 58
 
1.1%
Uppercase Letter 23
 
0.4%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 187
 
6.2%
도 183
 
6.1%
동 183
 
6.1%
기 182
 
6.0%
경 178
 
5.9%
로 162
 
5.4%
층 125
 
4.1%
길 106
 
3.5%
번 84
 
2.8%
부 65
 
2.2%
Other values (242) 1558
51.7%
Decimal Number
ValueCountFrequency (%)
1 298
31.3%
2 133
14.0%
4 81
 
8.5%
0 79
 
8.3%
3 70
 
7.3%
6 70
 
7.3%
5 66
 
6.9%
9 59
 
6.2%
7 59
 
6.2%
8 38
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 5
21.7%
A 3
13.0%
I 3
13.0%
C 3
13.0%
D 3
13.0%
E 2
 
8.7%
U 1
 
4.3%
N 1
 
4.3%
T 1
 
4.3%
R 1
 
4.3%
Space Separator
ValueCountFrequency (%)
970
100.0%
Other Punctuation
ValueCountFrequency (%)
, 159
100.0%
Close Punctuation
ValueCountFrequency (%)
) 146
100.0%
Open Punctuation
ValueCountFrequency (%)
( 146
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3013
55.1%
Common 2434
44.5%
Latin 23
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 187
 
6.2%
도 183
 
6.1%
동 183
 
6.1%
기 182
 
6.0%
경 178
 
5.9%
로 162
 
5.4%
층 125
 
4.1%
길 106
 
3.5%
번 84
 
2.8%
부 65
 
2.2%
Other values (242) 1558
51.7%
Common
ValueCountFrequency (%)
970
39.9%
1 298
 
12.2%
, 159
 
6.5%
) 146
 
6.0%
( 146
 
6.0%
2 133
 
5.5%
4 81
 
3.3%
0 79
 
3.2%
3 70
 
2.9%
6 70
 
2.9%
Other values (6) 282
 
11.6%
Latin
ValueCountFrequency (%)
B 5
21.7%
A 3
13.0%
I 3
13.0%
C 3
13.0%
D 3
13.0%
E 2
 
8.7%
U 1
 
4.3%
N 1
 
4.3%
T 1
 
4.3%
R 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3013
55.1%
ASCII 2457
44.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
970
39.5%
1 298
 
12.1%
, 159
 
6.5%
) 146
 
5.9%
( 146
 
5.9%
2 133
 
5.4%
4 81
 
3.3%
0 79
 
3.2%
3 70
 
2.8%
6 70
 
2.8%
Other values (16) 305
 
12.4%
Hangul
ValueCountFrequency (%)
시 187
 
6.2%
도 183
 
6.1%
동 183
 
6.1%
기 182
 
6.0%
경 178
 
5.9%
로 162
 
5.4%
층 125
 
4.1%
길 106
 
3.5%
번 84
 
2.8%
부 65
 
2.2%
Other values (242) 1558
51.7%

소재지지번주소
Text

Distinct183
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T08:11:59.124923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length42
Mean length25.188172
Min length15

Characters and Unicode

Total characters4685
Distinct characters248
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

Unique180 ?
Unique (%)96.8%

Sample

1st row경기도 고양시 일산서구 덕이동 219-8 양우씨네플렉스 가동 1층 D-116호
2nd row경기도 고양시 일산서구 탄현동 1491-6
3rd row경기도 고양시 일산서구 덕이동 263-19 양우로데오메인타운 4동 204,205,206호
4th row경기도 고양시 일산서구 대화동 2700 킨텍스제2전시동 지하1층일부
5th row경기도 고양시 덕양구 강매동 98-7
ValueCountFrequency (%)
경기도 186
 
18.2%
1층 54
 
5.3%
하남시 25
 
2.4%
부천시 18
 
1.8%
고양시 12
 
1.2%
평택시 11
 
1.1%
광명시 11
 
1.1%
성남시 9
 
0.9%
일부 9
 
0.9%
구리시 8
 
0.8%
Other values (474) 679
66.4%
2023-12-11T08:11:59.491728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
859
 
18.3%
1 273
 
5.8%
시 193
 
4.1%
동 193
 
4.1%
기 190
 
4.1%
도 189
 
4.0%
경 187
 
4.0%
- 163
 
3.5%
2 125
 
2.7%
지 121
 
2.6%
Other values (238) 2192
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2609
55.7%
Decimal Number 967
 
20.6%
Space Separator 859
 
18.3%
Dash Punctuation 163
 
3.5%
Other Punctuation 23
 
0.5%
Open Punctuation 21
 
0.4%
Close Punctuation 21
 
0.4%
Uppercase Letter 20
 
0.4%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 193
 
7.4%
동 193
 
7.4%
기 190
 
7.3%
도 189
 
7.2%
경 187
 
7.2%
지 121
 
4.6%
번 89
 
3.4%
층 86
 
3.3%
구 57
 
2.2%
부 55
 
2.1%
Other values (211) 1249
47.9%
Uppercase Letter
ValueCountFrequency (%)
I 3
15.0%
B 3
15.0%
A 3
15.0%
T 2
10.0%
E 2
10.0%
D 2
10.0%
R 1
 
5.0%
N 1
 
5.0%
C 1
 
5.0%
U 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
1 273
28.2%
2 125
12.9%
3 103
 
10.7%
4 82
 
8.5%
5 81
 
8.4%
0 67
 
6.9%
7 66
 
6.8%
6 59
 
6.1%
9 57
 
5.9%
8 54
 
5.6%
Space Separator
ValueCountFrequency (%)
859
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 163
100.0%
Other Punctuation
ValueCountFrequency (%)
, 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2609
55.7%
Common 2056
43.9%
Latin 20
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 193
 
7.4%
동 193
 
7.4%
기 190
 
7.3%
도 189
 
7.2%
경 187
 
7.2%
지 121
 
4.6%
번 89
 
3.4%
층 86
 
3.3%
구 57
 
2.2%
부 55
 
2.1%
Other values (211) 1249
47.9%
Common
ValueCountFrequency (%)
859
41.8%
1 273
 
13.3%
- 163
 
7.9%
2 125
 
6.1%
3 103
 
5.0%
4 82
 
4.0%
5 81
 
3.9%
0 67
 
3.3%
7 66
 
3.2%
6 59
 
2.9%
Other values (6) 178
 
8.7%
Latin
ValueCountFrequency (%)
I 3
15.0%
B 3
15.0%
A 3
15.0%
T 2
10.0%
E 2
10.0%
D 2
10.0%
R 1
 
5.0%
N 1
 
5.0%
C 1
 
5.0%
U 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2609
55.7%
ASCII 2076
44.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
859
41.4%
1 273
 
13.2%
- 163
 
7.9%
2 125
 
6.0%
3 103
 
5.0%
4 82
 
3.9%
5 81
 
3.9%
0 67
 
3.2%
7 66
 
3.2%
6 59
 
2.8%
Other values (17) 198
 
9.5%
Hangul
ValueCountFrequency (%)
시 193
 
7.4%
동 193
 
7.4%
기 190
 
7.3%
도 189
 
7.2%
경 187
 
7.2%
지 121
 
4.6%
번 89
 
3.4%
층 86
 
3.3%
구 57
 
2.2%
부 55
 
2.1%
Other values (211) 1249
47.9%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct158
Distinct (%)88.8%
Missing8
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean14155.596
Minimum10079
Maximum18626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-11T08:11:59.615976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10079
5-th percentile10444.4
Q112186.25
median14009.5
Q316005.5
95-th percentile18114.9
Maximum18626
Range8547
Interquartile range (IQR)3819.25

Descriptive statistics

Standard deviation2377.5933
Coefficient of variation (CV)0.16796138
Kurtosis-0.94094893
Mean14155.596
Median Absolute Deviation (MAD)1852
Skewness0.24135638
Sum2519696
Variance5652950
MonotonicityNot monotonic
2023-12-11T08:11:59.719887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12986 6
 
3.2%
12991 4
 
2.2%
11322 2
 
1.1%
11419 2
 
1.1%
11906 2
 
1.1%
10135 2
 
1.1%
17823 2
 
1.1%
14354 2
 
1.1%
16610 2
 
1.1%
16066 2
 
1.1%
Other values (148) 152
81.7%
(Missing) 8
 
4.3%
ValueCountFrequency (%)
10079 1
0.5%
10135 2
1.1%
10233 1
0.5%
10237 1
0.5%
10244 1
0.5%
10279 1
0.5%
10390 1
0.5%
10441 1
0.5%
10445 1
0.5%
10507 1
0.5%
ValueCountFrequency (%)
18626 1
0.5%
18533 1
0.5%
18497 1
0.5%
18486 1
0.5%
18474 1
0.5%
18401 1
0.5%
18374 1
0.5%
18144 1
0.5%
18137 1
0.5%
18111 1
0.5%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct179
Distinct (%)97.8%
Missing3
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean37.448046
Minimum36.959833
Maximum38.050129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-11T08:11:59.828067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.959833
5-th percentile37.030523
Q137.318516
median37.477516
Q337.587711
95-th percentile37.756746
Maximum38.050129
Range1.0902955
Interquartile range (IQR)0.26919545

Descriptive statistics

Standard deviation0.21151554
Coefficient of variation (CV)0.0056482398
Kurtosis-0.028310423
Mean37.448046
Median Absolute Deviation (MAD)0.13013319
Skewness-0.19650297
Sum6852.9925
Variance0.044738826
MonotonicityNot monotonic
2023-12-11T08:11:59.945806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4899906433 2
 
1.1%
37.6376694562 2
 
1.1%
37.8264482309 2
 
1.1%
37.8892393747 2
 
1.1%
37.2574844576 1
 
0.5%
37.7448856391 1
 
0.5%
37.2438345426 1
 
0.5%
37.3515788817 1
 
0.5%
37.345506978 1
 
0.5%
37.3473830986 1
 
0.5%
Other values (169) 169
90.9%
(Missing) 3
 
1.6%
ValueCountFrequency (%)
36.9598334221 1
0.5%
36.983019934 1
0.5%
36.9864111696 1
0.5%
36.9903335166 1
0.5%
37.0005936113 1
0.5%
37.0064332761 1
0.5%
37.013227019 1
0.5%
37.02220374 1
0.5%
37.0236831291 1
0.5%
37.0292741371 1
0.5%
ValueCountFrequency (%)
38.0501289265 1
0.5%
37.91660394 1
0.5%
37.8920694468 1
0.5%
37.8892393747 2
1.1%
37.8264482309 2
1.1%
37.8046974002 1
0.5%
37.7865028843 1
0.5%
37.7572781683 1
0.5%
37.7519537733 1
0.5%
37.7448856391 1
0.5%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct179
Distinct (%)97.8%
Missing3
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean127.02913
Minimum126.67143
Maximum127.64783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-11T08:12:00.071609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.67143
5-th percentile126.77024
Q1126.86401
median127.04592
Q3127.14937
95-th percentile127.28836
Maximum127.64783
Range0.9763981
Interquartile range (IQR)0.28536427

Descriptive statistics

Standard deviation0.18829143
Coefficient of variation (CV)0.0014822696
Kurtosis0.68742324
Mean127.02913
Median Absolute Deviation (MAD)0.13575957
Skewness0.58204175
Sum23246.331
Variance0.035453661
MonotonicityNot monotonic
2023-12-11T08:12:00.180058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7429064391 2
 
1.1%
127.1425272107 2
 
1.1%
126.9835129255 2
 
1.1%
127.1372770443 2
 
1.1%
127.4695067057 1
 
0.5%
127.034805882 1
 
0.5%
127.1032645387 1
 
0.5%
127.0700105125 1
 
0.5%
126.9850979383 1
 
0.5%
126.9751269723 1
 
0.5%
Other values (169) 169
90.9%
(Missing) 3
 
1.6%
ValueCountFrequency (%)
126.6714282398 1
0.5%
126.6915648518 1
0.5%
126.7422467741 1
0.5%
126.7429064391 2
1.1%
126.7494033011 1
0.5%
126.7570111697 1
0.5%
126.7570987892 1
0.5%
126.7604013995 1
0.5%
126.7702088026 1
0.5%
126.7704857204 1
0.5%
ValueCountFrequency (%)
127.6478263427 1
0.5%
127.6418414917 1
0.5%
127.63179653 1
0.5%
127.5458088098 1
0.5%
127.4779898813 1
0.5%
127.4695067057 1
0.5%
127.4425351446 1
0.5%
127.3947118836 1
0.5%
127.3003036309 1
0.5%
127.2885777758 1
0.5%

Interactions

2023-12-11T08:11:56.072925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:55.633641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:55.851804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:56.135397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:55.703032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:55.925426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:56.223811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:55.778512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:11:56.000488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:12:00.251980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명소재지우편번호WGS84위도WGS84경도
시군명1.0000.5040.9940.9780.913
영업상태명0.5041.0000.0760.1640.249
소재지우편번호0.9940.0761.0000.9300.858
WGS84위도0.9780.1640.9301.0000.649
WGS84경도0.9130.2490.8580.6491.000
2023-12-11T08:12:00.330632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.376
시군명0.3761.000
2023-12-11T08:12:00.396122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명영업상태명
소재지우편번호1.000-0.915-0.0690.8560.047
WGS84위도-0.9151.000-0.0090.7680.122
WGS84경도-0.069-0.0091.0000.5620.186
시군명0.8560.7680.5621.0000.376
영업상태명0.0470.1220.1860.3761.000

Missing values

2023-12-11T08:11:56.349686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:11:56.543076image/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.
2023-12-11T08:11:56.653573image/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

시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0고양시엘수아레2023-08-10영업<NA><NA><NA><NA>출장조리경기도 고양시 일산서구 덕이로 20, 양우씨네플렉스 가동 1층 D-116호 (덕이동)경기도 고양시 일산서구 덕이동 219-8 양우씨네플렉스 가동 1층 D-116호1023737.689482126.757011
1고양시오수테이블2023-04-18영업<NA><NA><NA><NA>출장조리경기도 고양시 일산서구 현중로36번길 75, 2층 일부(202호)호 (탄현동)경기도 고양시 일산서구 탄현동 1491-61024437.694559126.771797
2고양시호가호가외식(경기북부지점)2022-07-28영업<NA><NA><NA><NA>출장조리경기도 고양시 일산서구 탄중로 111, 양우로데오메인타운 4동 204,205,206호 (덕이동)경기도 고양시 일산서구 덕이동 263-19 양우로데오메인타운 4동 204,205,206호1023337.693205126.757099
3고양시그래머시 킨텍스(제2전시장지하)20131206영업<NA><NA><NA><NA>출장조리경기도 고양시 일산서구 킨텍스로 217-59, 지하1층 일부호 (대화동, 킨텍스 제2전시장 전시동)경기도 고양시 일산서구 대화동 2700 킨텍스제2전시동 지하1층일부1039037.664674126.742247
4고양시생어거스틴 케이터링20220610영업<NA><NA><NA><NA>출장조리경기도 고양시 덕양구 강매로 242, 1층 (강매동)경기도 고양시 덕양구 강매동 98-71044137.608273126.845239
5고양시스타일링랩20200911영업<NA><NA><NA><NA>출장조리경기도 고양시 덕양구 꽃내음1길 71, 1층 (향동동)경기도 고양시 덕양구 향동동 491-15 1층호1054537.598485126.889143
6고양시시루20180718영업<NA><NA><NA><NA>출장조리경기도 고양시 덕양구 덕은로 18, 1(전체)층 (덕은동)경기도 고양시 덕양구 덕은동 209-20 1층전체1054137.588494126.876233
7고양시마띠에르20040408폐업20051102<NA><NA><NA>출장조리경기도 고양시 덕양구 지도로125번길 20경기도 고양시 덕양구 토당동 833-11번지 1층1050737.628097126.822003
8고양시전주철판해물등갈비찜20100916폐업20150518<NA><NA><NA>출장조리경기도 고양시 덕양구 동헌로209번길 10 (대자동, 1층전체)경기도 고양시 덕양구 대자동 834-22번지 1층전체1027937.709893126.879425
9고양시별밥20140613폐업20200617<NA><NA><NA>출장조리경기도 고양시 덕양구 덕은로 39, 1(일부)층 (덕은동, 정우길씨주택)경기도 고양시 덕양구 덕은동 201-1번지 정우길씨주택 1(일부)층1054137.58645126.876555
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
176하남시블레스출장뷔페20100713폐업20180814<NA><NA><NA>출장조리경기도 하남시 초이로44번길 22, 1층 (초이동)경기도 하남시 초이동 322-66번지1298637.534506127.172336
177하남시(주)주아나아이앤씨20100820폐업20180711<NA><NA><NA>출장조리경기도 하남시 감이남로106번길 41, 1층 (감이동)경기도 하남시 감이동 311-2번지1300637.498269127.164368
178하남시푸드스토리20091113폐업20110627<NA><NA><NA>출장조리<NA>경기도 하남시 덕풍동 산 23-9번지 (1층)<NA>37.543482127.20451
179화성시유하경푸드2023-11-17영업<NA><NA><NA><NA>출장조리경기도 화성시 동탄대로14길 5-24, 1층 일부(101호)호 (오산동)경기도 화성시 오산동 1031-51848637.185322127.10241
180화성시로엔드케이터링20220908영업<NA><NA><NA><NA>출장조리경기도 화성시 동탄순환대로3길 27-32, 1층 일부호 (송동)경기도 화성시 송동 714-10 1층 일부호1849737.165919127.095934
181화성시대성출장부페20101104영업<NA><NA><NA><NA>출장조리경기도 화성시 향남읍 안요골길 4-1경기도 화성시 향남읍 요리 364 A동1862637.107986126.968264
182화성시나봄20200114영업<NA><NA><NA><NA>출장조리경기도 화성시 동탄치동천로2길 3-26, 1층 102 일부호 (영천동)경기도 화성시 영천동 749-61847437.203408127.125744
183화성시썬희푸드20190705영업<NA><NA><NA><NA>출장조리경기도 화성시 효행로 972, 1층 104호 (진안동)경기도 화성시 진안동 485-51840137.210602127.034455
184화성시몬스출장부페20060904영업<NA><NA><NA><NA>출장조리경기도 화성시 반정로154번길 20 (반정동, B동 일부)경기도 화성시 반정동 118-21837437.23399127.040511
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