Overview

Dataset statistics

Number of variables6
Number of observations98
Missing cells159
Missing cells (%)27.0%
Duplicate rows6
Duplicate rows (%)6.1%
Total size in memory4.7 KiB
Average record size in memory49.3 B

Variable types

DateTime1
Categorical3
Text2

Dataset

Description업종,업소명,소재지,위반사항,행정처분에 대한 데이터로 시민들에게 식품위생법을 위반하여 행정처분을 받은 업소에 대한 정보를 공개
Author부산광역시 영도구
URLhttps://www.data.go.kr/data/3069356/fileData.do

Alerts

Dataset has 6 (6.1%) duplicate rowsDuplicates
처분일자 has 53 (54.1%) missing valuesMissing
업소명 has 53 (54.1%) missing valuesMissing
소재지주소 has 53 (54.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 21:01:56.503098
Analysis finished2023-12-12 21:01:57.160056
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처분일자
Date

MISSING 

Distinct34
Distinct (%)75.6%
Missing53
Missing (%)54.1%
Memory size916.0 B
Minimum2023-01-04 00:00:00
Maximum2023-09-25 00:00:00
2023-12-13T06:01:57.576473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:01:57.723774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

업종
Categorical

Distinct9
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size916.0 B
<NA>
53 
일반음식점
30 
휴게음식점
 
5
숙박업(일반)
 
3
식품제조가공업
 
2
Other values (4)
 
5

Length

Max length7
Median length4
Mean length4.5612245
Min length4

Unique

Unique3 ?
Unique (%)3.1%

Sample

1st row일반음식점
2nd row일반음식점
3rd row숙박업(일반)
4th row일반음식점
5th row일반음식점

Common Values

ValueCountFrequency (%)
<NA> 53
54.1%
일반음식점 30
30.6%
휴게음식점 5
 
5.1%
숙박업(일반) 3
 
3.1%
식품제조가공업 2
 
2.0%
피부미용업 2
 
2.0%
유흥주점영업 1
 
1.0%
제과점영업 1
 
1.0%
목욕장업 1
 
1.0%

Length

2023-12-13T06:01:57.861252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:01:57.980756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
54.1%
일반음식점 30
30.6%
휴게음식점 5
 
5.1%
숙박업(일반 3
 
3.1%
식품제조가공업 2
 
2.0%
피부미용업 2
 
2.0%
유흥주점영업 1
 
1.0%
제과점영업 1
 
1.0%
목욕장업 1
 
1.0%

업소명
Text

MISSING 

Distinct37
Distinct (%)82.2%
Missing53
Missing (%)54.1%
Memory size916.0 B
2023-12-13T06:01:58.186116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.0888889
Min length2

Characters and Unicode

Total characters274
Distinct characters144
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

Unique29 ?
Unique (%)64.4%

Sample

1st row조방낙지
2nd row조방낙지
3rd row아란치모텔
4th row금성 숯불갈비
5th row수 Bar
ValueCountFrequency (%)
영도점 3
 
5.2%
왔다식당 2
 
3.4%
조방낙지 2
 
3.4%
자매보리밥 2
 
3.4%
해녀수산물판매장 2
 
3.4%
잘말린누나들 2
 
3.4%
본전김밥천국 2
 
3.4%
주)국민푸드 2
 
3.4%
숲앤뷰티 2
 
3.4%
칼국수 2
 
3.4%
Other values (37) 37
63.8%
2023-12-13T06:01:58.547609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13
 
4.7%
국 8
 
2.9%
식 6
 
2.2%
수 6
 
2.2%
영 5
 
1.8%
매 5
 
1.8%
리 5
 
1.8%
밥 5
 
1.8%
) 4
 
1.5%
당 4
 
1.5%
Other values (134) 213
77.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 241
88.0%
Space Separator 13
 
4.7%
Lowercase Letter 5
 
1.8%
Close Punctuation 4
 
1.5%
Open Punctuation 4
 
1.5%
Decimal Number 4
 
1.5%
Other Punctuation 2
 
0.7%
Uppercase Letter 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 8
 
3.3%
식 6
 
2.5%
수 6
 
2.5%
영 5
 
2.1%
매 5
 
2.1%
리 5
 
2.1%
밥 5
 
2.1%
당 4
 
1.7%
도 4
 
1.7%
점 4
 
1.7%
Other values (121) 189
78.4%
Lowercase Letter
ValueCountFrequency (%)
r 1
20.0%
a 1
20.0%
t 1
20.0%
c 1
20.0%
e 1
20.0%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
9 1
25.0%
1 1
25.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 241
88.0%
Common 27
 
9.9%
Latin 6
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 8
 
3.3%
식 6
 
2.5%
수 6
 
2.5%
영 5
 
2.1%
매 5
 
2.1%
리 5
 
2.1%
밥 5
 
2.1%
당 4
 
1.7%
도 4
 
1.7%
점 4
 
1.7%
Other values (121) 189
78.4%
Common
ValueCountFrequency (%)
13
48.1%
) 4
 
14.8%
( 4
 
14.8%
. 2
 
7.4%
0 2
 
7.4%
9 1
 
3.7%
1 1
 
3.7%
Latin
ValueCountFrequency (%)
r 1
16.7%
a 1
16.7%
B 1
16.7%
t 1
16.7%
c 1
16.7%
e 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 241
88.0%
ASCII 33
 
12.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13
39.4%
) 4
 
12.1%
( 4
 
12.1%
. 2
 
6.1%
0 2
 
6.1%
r 1
 
3.0%
a 1
 
3.0%
B 1
 
3.0%
t 1
 
3.0%
c 1
 
3.0%
Other values (3) 3
 
9.1%
Hangul
ValueCountFrequency (%)
국 8
 
3.3%
식 6
 
2.5%
수 6
 
2.5%
영 5
 
2.1%
매 5
 
2.1%
리 5
 
2.1%
밥 5
 
2.1%
당 4
 
1.7%
도 4
 
1.7%
점 4
 
1.7%
Other values (121) 189
78.4%

소재지주소
Text

MISSING 

Distinct37
Distinct (%)82.2%
Missing53
Missing (%)54.1%
Memory size916.0 B
2023-12-13T06:01:58.828560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length28.911111
Min length22

Characters and Unicode

Total characters1301
Distinct characters80
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

Unique29 ?
Unique (%)64.4%

Sample

1st row부산광역시 영도구 동삼로 85 (동삼동)
2nd row부산광역시 영도구 동삼로 85 (동삼동)
3rd row부산광역시 영도구 태종로 761 (동삼동)
4th row부산광역시 영도구 남항로10번길 11 (남항동2가)
5th row부산광역시 영도구 남항로 38-2 (남항동2가)
ValueCountFrequency (%)
부산광역시 45
17.6%
영도구 45
17.6%
동삼동 19
 
7.4%
태종로 10
 
3.9%
1층 8
 
3.1%
2층 7
 
2.7%
동삼로 6
 
2.3%
청학동 5
 
2.0%
남항동1가 4
 
1.6%
25 3
 
1.2%
Other values (84) 104
40.6%
2023-12-13T06:01:59.227356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
211
 
16.2%
동 78
 
6.0%
영 56
 
4.3%
도 48
 
3.7%
시 47
 
3.6%
산 46
 
3.5%
부 45
 
3.5%
광 45
 
3.5%
역 45
 
3.5%
구 45
 
3.5%
Other values (70) 635
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 755
58.0%
Space Separator 211
 
16.2%
Decimal Number 204
 
15.7%
Close Punctuation 45
 
3.5%
Open Punctuation 45
 
3.5%
Other Punctuation 28
 
2.2%
Dash Punctuation 9
 
0.7%
Uppercase Letter 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 78
 
10.3%
영 56
 
7.4%
도 48
 
6.4%
시 47
 
6.2%
산 46
 
6.1%
부 45
 
6.0%
광 45
 
6.0%
역 45
 
6.0%
구 45
 
6.0%
로 41
 
5.4%
Other values (53) 259
34.3%
Decimal Number
ValueCountFrequency (%)
1 45
22.1%
2 35
17.2%
5 23
11.3%
3 21
10.3%
9 15
 
7.4%
0 15
 
7.4%
6 14
 
6.9%
4 13
 
6.4%
8 12
 
5.9%
7 11
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%
Space Separator
ValueCountFrequency (%)
211
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Other Punctuation
ValueCountFrequency (%)
, 28
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 755
58.0%
Common 542
41.7%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 78
 
10.3%
영 56
 
7.4%
도 48
 
6.4%
시 47
 
6.2%
산 46
 
6.1%
부 45
 
6.0%
광 45
 
6.0%
역 45
 
6.0%
구 45
 
6.0%
로 41
 
5.4%
Other values (53) 259
34.3%
Common
ValueCountFrequency (%)
211
38.9%
) 45
 
8.3%
1 45
 
8.3%
( 45
 
8.3%
2 35
 
6.5%
, 28
 
5.2%
5 23
 
4.2%
3 21
 
3.9%
9 15
 
2.8%
0 15
 
2.8%
Other values (5) 59
 
10.9%
Latin
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 755
58.0%
ASCII 546
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
211
38.6%
) 45
 
8.2%
1 45
 
8.2%
( 45
 
8.2%
2 35
 
6.4%
, 28
 
5.1%
5 23
 
4.2%
3 21
 
3.8%
9 15
 
2.7%
0 15
 
2.7%
Other values (7) 63
 
11.5%
Hangul
ValueCountFrequency (%)
동 78
 
10.3%
영 56
 
7.4%
도 48
 
6.4%
시 47
 
6.2%
산 46
 
6.1%
부 45
 
6.0%
광 45
 
6.0%
역 45
 
6.0%
구 45
 
6.0%
로 41
 
5.4%
Other values (53) 259
34.3%

위반내용
Categorical

Distinct15
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size916.0 B
<NA>
53 
건강진단등 개인위생 위반
13 
위생교육 위반
 
4
위생관리기준 위반
 
4
시설기준 위반
 
4
Other values (10)
20 

Length

Max length13
Median length4
Mean length6.6326531
Min length4

Unique

Unique4 ?
Unique (%)4.1%

Sample

1st row건강진단등 개인위생 위반
2nd row건강진단등 개인위생 위반
3rd row위생교육 위반
4th row위생교육 위반
5th row위생교육 위반

Common Values

ValueCountFrequency (%)
<NA> 53
54.1%
건강진단등 개인위생 위반 13
 
13.3%
위생교육 위반 4
 
4.1%
위생관리기준 위반 4
 
4.1%
시설기준 위반 4
 
4.1%
보존 및 유통기준 위반 4
 
4.1%
기준 및 규격 3
 
3.1%
재난배상책임보험미가입 3
 
3.1%
영업자 준수사항 위반 2
 
2.0%
무허가ㆍ무신고 2
 
2.0%
Other values (5) 6
 
6.1%

Length

2023-12-13T06:01:59.387090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 53
32.9%
위반 32
19.9%
개인위생 13
 
8.1%
건강진단등 13
 
8.1%
및 7
 
4.3%
위생교육 4
 
2.5%
위생관리기준 4
 
2.5%
시설기준 4
 
2.5%
보존 4
 
2.5%
유통기준 4
 
2.5%
Other values (12) 23
14.3%

처분내용
Categorical

Distinct7
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
<NA>
53 
과태료부과
28 
시정명령
9 
과징금부과
 
5
영업소폐쇄
 
1
Other values (2)
 
2

Length

Max length5
Median length4
Mean length4.3469388
Min length4

Unique

Unique3 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 53
54.1%
과태료부과 28
28.6%
시정명령 9
 
9.2%
과징금부과 5
 
5.1%
영업소폐쇄 1
 
1.0%
영업정지 1
 
1.0%
개선명령 1
 
1.0%

Length

2023-12-13T06:01:59.504374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:01:59.626271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
54.1%
과태료부과 28
28.6%
시정명령 9
 
9.2%
과징금부과 5
 
5.1%
영업소폐쇄 1
 
1.0%
영업정지 1
 
1.0%
개선명령 1
 
1.0%

Correlations

2023-12-13T06:01:59.710147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종업소명소재지주소위반내용처분내용
처분일자1.0000.9840.9950.9950.7460.782
업종0.9841.0001.0001.0000.7340.656
업소명0.9951.0001.0001.0000.9670.764
소재지주소0.9951.0001.0001.0000.9670.764
위반내용0.7460.7340.9670.9671.0000.632
처분내용0.7820.6560.7640.7640.6321.000
2023-12-13T06:01:59.817576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위반내용업종처분내용
위반내용1.0000.3920.324
업종0.3921.0000.425
처분내용0.3240.4251.000
2023-12-13T06:01:59.904393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종위반내용처분내용
업종1.0000.3920.425
위반내용0.3921.0000.324
처분내용0.4250.3241.000

Missing values

2023-12-13T06:01:56.882782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:01:56.976184image/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.
2023-12-13T06:01:57.079509image/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

처분일자업종업소명소재지주소위반내용처분내용
02023-01-04일반음식점조방낙지부산광역시 영도구 동삼로 85 (동삼동)건강진단등 개인위생 위반과태료부과
12023-01-04일반음식점조방낙지부산광역시 영도구 동삼로 85 (동삼동)건강진단등 개인위생 위반과태료부과
22023-01-09숙박업(일반)아란치모텔부산광역시 영도구 태종로 761 (동삼동)위생교육 위반과태료부과
32023-01-11일반음식점금성 숯불갈비부산광역시 영도구 남항로10번길 11 (남항동2가)위생교육 위반과태료부과
42023-01-11일반음식점수 Bar부산광역시 영도구 남항로 38-2 (남항동2가)위생교육 위반과태료부과
52023-01-12일반음식점몽작부산광역시 영도구 남항서로 36 (남항동3가)영업자 준수사항 위반시정명령
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처분일자업종업소명소재지주소위반내용처분내용# duplicates
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12023-03-08일반음식점잘말린누나들부산광역시 영도구 태종로 759, 2층 205호 (동삼동)건강진단등 개인위생 위반과태료부과2
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