Overview

Dataset statistics

Number of variables7
Number of observations201
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.5%
Total size in memory11.1 KiB
Average record size in memory56.7 B

Variable types

DateTime2
Categorical3
Text2

Dataset

Description부산광역시 사상구 식품위생업소의 행정처분 현황 데이터 제공(처분일자,업종명, 업소명,도로명주소,위반내용 등)
URLhttps://www.data.go.kr/data/3070019/fileData.do

Alerts

Dataset has 1 (0.5%) duplicate rowsDuplicates
처분명 is highly overall correlated with 위반유형분류High correlation
위반유형분류 is highly overall correlated with 처분명High correlation
처분명 is highly imbalanced (60.6%)Imbalance

Reproduction

Analysis started2023-12-12 19:53:34.226406
Analysis finished2023-12-12 19:53:35.206790
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct80
Distinct (%)39.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2022-06-23 00:00:00
Maximum2023-06-02 00:00:00
2023-12-13T04:53:35.281178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:53:35.432663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업종명
Categorical

Distinct18
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
일반음식점
65 
즉석판매제조가공업
35 
휴게음식점
19 
식품제조가공업
18 
유흥주점영업
17 
Other values (13)
47 

Length

Max length11
Median length9
Mean length6.5771144
Min length4

Unique

Unique3 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
일반음식점 65
32.3%
즉석판매제조가공업 35
17.4%
휴게음식점 19
 
9.5%
식품제조가공업 18
 
9.0%
유흥주점영업 17
 
8.5%
건강기능식품일반판매업 9
 
4.5%
집단급식소 식품판매업 7
 
3.5%
단란주점 6
 
3.0%
식품소분업 6
 
3.0%
위탁급식영업 4
 
2.0%
Other values (8) 15
 
7.5%

Length

2023-12-13T04:53:35.589810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일반음식점 65
31.2%
즉석판매제조가공업 35
16.8%
휴게음식점 19
 
9.1%
식품제조가공업 18
 
8.7%
유흥주점영업 17
 
8.2%
건강기능식품일반판매업 9
 
4.3%
집단급식소 8
 
3.8%
식품판매업 7
 
3.4%
식품소분업 6
 
2.9%
단란주점 6
 
2.9%
Other values (8) 18
 
8.7%
Distinct185
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T04:53:35.945901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length15
Mean length6.5572139
Min length2

Characters and Unicode

Total characters1318
Distinct characters340
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

Unique170 ?
Unique (%)84.6%

Sample

1st row국자야
2nd row발렌타인
3rd row발렌타인
4th row돈돈촌
5th row냉정 종가 김치찜
ValueCountFrequency (%)
주)도투락 3
 
1.2%
노래주점 3
 
1.2%
식품 3
 
1.2%
사상점 3
 
1.2%
천하왕소금구이 2
 
0.8%
대현식품((현)global 2
 
0.8%
글로벌 2
 
0.8%
food 2
 
0.8%
주식회사 2
 
0.8%
hyun 2
 
0.8%
Other values (220) 235
90.7%
2023-12-13T04:53:36.434543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58
 
4.4%
31
 
2.4%
29
 
2.2%
( 28
 
2.1%
) 28
 
2.1%
26
 
2.0%
21
 
1.6%
21
 
1.6%
16
 
1.2%
16
 
1.2%
Other values (330) 1044
79.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1106
83.9%
Space Separator 58
 
4.4%
Lowercase Letter 44
 
3.3%
Uppercase Letter 38
 
2.9%
Open Punctuation 28
 
2.1%
Close Punctuation 28
 
2.1%
Other Punctuation 10
 
0.8%
Decimal Number 6
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
31
 
2.8%
29
 
2.6%
26
 
2.4%
21
 
1.9%
21
 
1.9%
16
 
1.4%
16
 
1.4%
16
 
1.4%
15
 
1.4%
14
 
1.3%
Other values (289) 901
81.5%
Uppercase Letter
ValueCountFrequency (%)
F 6
15.8%
C 5
13.2%
P 3
 
7.9%
L 3
 
7.9%
D 2
 
5.3%
A 2
 
5.3%
S 2
 
5.3%
M 2
 
5.3%
T 2
 
5.3%
E 2
 
5.3%
Other values (7) 9
23.7%
Lowercase Letter
ValueCountFrequency (%)
o 10
22.7%
d 6
13.6%
e 4
 
9.1%
u 4
 
9.1%
a 4
 
9.1%
l 4
 
9.1%
t 3
 
6.8%
n 2
 
4.5%
y 2
 
4.5%
b 2
 
4.5%
Other values (3) 3
 
6.8%
Decimal Number
ValueCountFrequency (%)
0 2
33.3%
2 1
16.7%
3 1
16.7%
1 1
16.7%
9 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 4
40.0%
& 4
40.0%
, 2
20.0%
Space Separator
ValueCountFrequency (%)
58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1106
83.9%
Common 130
 
9.9%
Latin 82
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
31
 
2.8%
29
 
2.6%
26
 
2.4%
21
 
1.9%
21
 
1.9%
16
 
1.4%
16
 
1.4%
16
 
1.4%
15
 
1.4%
14
 
1.3%
Other values (289) 901
81.5%
Latin
ValueCountFrequency (%)
o 10
 
12.2%
F 6
 
7.3%
d 6
 
7.3%
C 5
 
6.1%
e 4
 
4.9%
u 4
 
4.9%
a 4
 
4.9%
l 4
 
4.9%
P 3
 
3.7%
L 3
 
3.7%
Other values (20) 33
40.2%
Common
ValueCountFrequency (%)
58
44.6%
( 28
21.5%
) 28
21.5%
. 4
 
3.1%
& 4
 
3.1%
, 2
 
1.5%
0 2
 
1.5%
2 1
 
0.8%
3 1
 
0.8%
1 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1106
83.9%
ASCII 212
 
16.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58
27.4%
( 28
13.2%
) 28
13.2%
o 10
 
4.7%
F 6
 
2.8%
d 6
 
2.8%
C 5
 
2.4%
. 4
 
1.9%
e 4
 
1.9%
u 4
 
1.9%
Other values (31) 59
27.8%
Hangul
ValueCountFrequency (%)
31
 
2.8%
29
 
2.6%
26
 
2.4%
21
 
1.9%
21
 
1.9%
16
 
1.4%
16
 
1.4%
16
 
1.4%
15
 
1.4%
14
 
1.3%
Other values (289) 901
81.5%
Distinct184
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-13T04:53:36.704738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length47
Mean length30.502488
Min length21

Characters and Unicode

Total characters6131
Distinct characters144
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

Unique169 ?
Unique (%)84.1%

Sample

1st row부산광역시 사상구 주례로9번길 12 (주례동)
2nd row부산광역시 사상구 대동로 154 (학장동)
3rd row부산광역시 사상구 대동로 154 (학장동)
4th row부산광역시 사상구 백양대로 887-26 (모라동)
5th row부산광역시 사상구 가야대로344번길 74 (주례동)
ValueCountFrequency (%)
부산광역시 201
 
16.8%
사상구 201
 
16.8%
괘법동 51
 
4.2%
1층 50
 
4.2%
주례동 42
 
3.5%
감전동 29
 
2.4%
학장동 20
 
1.7%
엄궁동 20
 
1.7%
사상로 14
 
1.2%
덕포동 13
 
1.1%
Other values (298) 559
46.6%
2023-12-13T04:53:37.117026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
999
 
16.3%
258
 
4.2%
257
 
4.2%
245
 
4.0%
1 234
 
3.8%
232
 
3.8%
222
 
3.6%
221
 
3.6%
207
 
3.4%
( 203
 
3.3%
Other values (134) 3053
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3569
58.2%
Space Separator 999
 
16.3%
Decimal Number 976
 
15.9%
Open Punctuation 206
 
3.4%
Close Punctuation 206
 
3.4%
Other Punctuation 142
 
2.3%
Dash Punctuation 24
 
0.4%
Uppercase Letter 8
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
258
 
7.2%
257
 
7.2%
245
 
6.9%
232
 
6.5%
222
 
6.2%
221
 
6.2%
207
 
5.8%
201
 
5.6%
201
 
5.6%
201
 
5.6%
Other values (112) 1324
37.1%
Decimal Number
ValueCountFrequency (%)
1 234
24.0%
2 164
16.8%
3 123
12.6%
4 88
 
9.0%
6 74
 
7.6%
0 68
 
7.0%
7 58
 
5.9%
9 58
 
5.9%
5 56
 
5.7%
8 53
 
5.4%
Other Punctuation
ValueCountFrequency (%)
, 138
97.2%
* 3
 
2.1%
. 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 203
98.5%
[ 3
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 203
98.5%
] 3
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
A 5
62.5%
B 3
37.5%
Space Separator
ValueCountFrequency (%)
999
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3569
58.2%
Common 2554
41.7%
Latin 8
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
258
 
7.2%
257
 
7.2%
245
 
6.9%
232
 
6.5%
222
 
6.2%
221
 
6.2%
207
 
5.8%
201
 
5.6%
201
 
5.6%
201
 
5.6%
Other values (112) 1324
37.1%
Common
ValueCountFrequency (%)
999
39.1%
1 234
 
9.2%
( 203
 
7.9%
) 203
 
7.9%
2 164
 
6.4%
, 138
 
5.4%
3 123
 
4.8%
4 88
 
3.4%
6 74
 
2.9%
0 68
 
2.7%
Other values (10) 260
 
10.2%
Latin
ValueCountFrequency (%)
A 5
62.5%
B 3
37.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3569
58.2%
ASCII 2562
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
999
39.0%
1 234
 
9.1%
( 203
 
7.9%
) 203
 
7.9%
2 164
 
6.4%
, 138
 
5.4%
3 123
 
4.8%
4 88
 
3.4%
6 74
 
2.9%
0 68
 
2.7%
Other values (12) 268
 
10.5%
Hangul
ValueCountFrequency (%)
258
 
7.2%
257
 
7.2%
245
 
6.9%
232
 
6.5%
222
 
6.2%
221
 
6.2%
207
 
5.8%
201
 
5.6%
201
 
5.6%
201
 
5.6%
Other values (112) 1324
37.1%

처분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
과태료부과
163 
시정명령
18 
영업정지
 
14
품목제조정지
 
3
과징금부과
 
2

Length

Max length6
Median length5
Mean length4.8606965
Min length4

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row영업정지
2nd row시설개수명령
3rd row영업정지
4th row과태료부과
5th row시정명령

Common Values

ValueCountFrequency (%)
과태료부과 163
81.1%
시정명령 18
 
9.0%
영업정지 14
 
7.0%
품목제조정지 3
 
1.5%
과징금부과 2
 
1.0%
시설개수명령 1
 
0.5%

Length

2023-12-13T04:53:37.296184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:53:37.432184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
과태료부과 163
81.1%
시정명령 18
 
9.0%
영업정지 14
 
7.0%
품목제조정지 3
 
1.5%
과징금부과 2
 
1.0%
시설개수명령 1
 
0.5%

위반유형분류
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
위생교육 위반
115 
건강진단등 개인위생 위반
22 
보존 및 유통기준 위반
13 
기타
 
10
위해우려식품
 
10
Other values (7)
31 

Length

Max length15
Median length7
Mean length8.0547264
Min length2

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row청소년불법고용·출입 등 위반
2nd row시설기준 위반
3rd row영업자 준수사항 위반
4th row기타
5th row위해우려식품

Common Values

ValueCountFrequency (%)
위생교육 위반 115
57.2%
건강진단등 개인위생 위반 22
 
10.9%
보존 및 유통기준 위반 13
 
6.5%
기타 10
 
5.0%
위해우려식품 10
 
5.0%
시설기준 위반 8
 
4.0%
영업자 준수사항 위반 7
 
3.5%
청소년불법고용·출입 등 위반 6
 
3.0%
표시기준 위반 5
 
2.5%
기준 및 규격 3
 
1.5%
Other values (2) 2
 
1.0%

Length

2023-12-13T04:53:37.572937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
위반 176
39.6%
위생교육 115
25.9%
건강진단등 22
 
5.0%
개인위생 22
 
5.0%
16
 
3.6%
보존 13
 
2.9%
유통기준 13
 
2.9%
기타 10
 
2.3%
위해우려식품 10
 
2.3%
시설기준 8
 
1.8%
Other values (9) 39
 
8.8%
Distinct79
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2022-06-07 00:00:00
Maximum2023-05-12 00:00:00
2023-12-13T04:53:37.709458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:53:37.880592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Correlations

2023-12-13T04:53:38.003101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처분일자업종명처분명위반유형분류위반일자
처분일자1.0000.8530.9250.9550.998
업종명0.8531.0000.6390.6830.680
처분명0.9250.6391.0000.9150.971
위반유형분류0.9550.6830.9151.0000.992
위반일자0.9980.6800.9710.9921.000
2023-12-13T04:53:38.116343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위반유형분류처분명업종명
위반유형분류1.0000.5970.298
처분명0.5971.0000.301
업종명0.2980.3011.000
2023-12-13T04:53:38.215815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명처분명위반유형분류
업종명1.0000.3010.298
처분명0.3011.0000.597
위반유형분류0.2980.5971.000

Missing values

2023-12-13T04:53:35.019606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:53:35.154630image/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.

Sample

처분일자업종명업소명소재지(도로명)처분명위반유형분류위반일자
02022-06-23일반음식점국자야부산광역시 사상구 주례로9번길 12 (주례동)영업정지청소년불법고용·출입 등 위반2022-06-10
12022-07-06일반음식점발렌타인부산광역시 사상구 대동로 154 (학장동)시설개수명령시설기준 위반2022-06-08
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