Overview

Dataset statistics

Number of variables6
Number of observations158
Missing cells13
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory48.8 B

Variable types

Text4
Categorical2

Dataset

Description식품위생법 제47조(위생등급), 식품위생법 시행규칙 제61조(우수업소·모범업소 지정 등)에 따른 전주시 내 모범음식점 현황을 제공합니다
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=1&menuCd=DOM_000000103007001000&pListTypeStr=&pId=3079790

Alerts

데이터기준일 has constant value ""Constant
소재지전화번호 has 13 (8.2%) missing valuesMissing
업소명 has unique valuesUnique
소재지(도로명) has unique valuesUnique

Reproduction

Analysis started2024-03-13 23:48:57.543407
Analysis finished2024-03-13 23:48:57.944893
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업소명
Text

UNIQUE 

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-14T08:48:58.090127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length6.1708861
Min length1

Characters and Unicode

Total characters975
Distinct characters263
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

Unique158 ?
Unique (%)100.0%

Sample

1st row예우랑중산점
2nd row청학동버섯전골
3rd row꽃마름
4th row어랑일식
5th row다산돼지와낙지
ValueCountFrequency (%)
풍남정 2
 
1.2%
예우랑중산점 1
 
0.6%
아중도토리묵촌 1
 
0.6%
홍익궁중전통육개장전주송천점 1
 
0.6%
고궁 1
 
0.6%
해궁 1
 
0.6%
현대옥송천농수산시장점 1
 
0.6%
오박사동태탕찜 1
 
0.6%
자금성 1
 
0.6%
동창갈비본점 1
 
0.6%
Other values (152) 152
93.3%
2024-03-14T08:48:58.416320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45
 
4.6%
23
 
2.4%
22
 
2.3%
19
 
1.9%
18
 
1.8%
15
 
1.5%
14
 
1.4%
14
 
1.4%
12
 
1.2%
12
 
1.2%
Other values (253) 781
80.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 948
97.2%
Open Punctuation 8
 
0.8%
Close Punctuation 8
 
0.8%
Decimal Number 6
 
0.6%
Space Separator 5
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
45
 
4.7%
23
 
2.4%
22
 
2.3%
19
 
2.0%
18
 
1.9%
15
 
1.6%
14
 
1.5%
14
 
1.5%
12
 
1.3%
12
 
1.3%
Other values (246) 754
79.5%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
9 2
33.3%
2 1
16.7%
0 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 948
97.2%
Common 27
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
45
 
4.7%
23
 
2.4%
22
 
2.3%
19
 
2.0%
18
 
1.9%
15
 
1.6%
14
 
1.5%
14
 
1.5%
12
 
1.3%
12
 
1.3%
Other values (246) 754
79.5%
Common
ValueCountFrequency (%)
( 8
29.6%
) 8
29.6%
5
18.5%
1 2
 
7.4%
9 2
 
7.4%
2 1
 
3.7%
0 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 948
97.2%
ASCII 27
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
45
 
4.7%
23
 
2.4%
22
 
2.3%
19
 
2.0%
18
 
1.9%
15
 
1.6%
14
 
1.5%
14
 
1.5%
12
 
1.3%
12
 
1.3%
Other values (246) 754
79.5%
ASCII
ValueCountFrequency (%)
( 8
29.6%
) 8
29.6%
5
18.5%
1 2
 
7.4%
9 2
 
7.4%
2 1
 
3.7%
0 1
 
3.7%
Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-14T08:48:58.746074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length59
Median length43
Mean length31.113924
Min length21

Characters and Unicode

Total characters4916
Distinct characters175
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)100.0%

Sample

1st row전라북도 전주시 완산구 효자로 333 (중화산동2가)
2nd row전라북도 전주시 완산구 효자로 331 (중화산동2가)
3rd row전라북도 전주시 완산구 효자로 178 (효자동2가,2층)
4th row전라북도 전주시 완산구 홍산중앙로 20 (효자동3가)
5th row전라북도 전주시 완산구 홍산북로 83-9 (효자동3가)
ValueCountFrequency (%)
전라북도 158
 
15.5%
전주시 158
 
15.5%
덕진구 81
 
7.9%
완산구 77
 
7.5%
1층 28
 
2.7%
송천동2가 16
 
1.6%
인후동1가 12
 
1.2%
효자동3가 12
 
1.2%
중화산동2가 10
 
1.0%
우아동2가 8
 
0.8%
Other values (314) 461
45.2%
2024-03-14T08:48:59.262119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
864
 
17.6%
334
 
6.8%
1 217
 
4.4%
181
 
3.7%
2 172
 
3.5%
162
 
3.3%
161
 
3.3%
160
 
3.3%
159
 
3.2%
159
 
3.2%
Other values (165) 2347
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2867
58.3%
Space Separator 864
 
17.6%
Decimal Number 745
 
15.2%
Close Punctuation 157
 
3.2%
Open Punctuation 157
 
3.2%
Other Punctuation 74
 
1.5%
Dash Punctuation 49
 
1.0%
Math Symbol 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
334
 
11.6%
181
 
6.3%
162
 
5.7%
161
 
5.6%
160
 
5.6%
159
 
5.5%
159
 
5.5%
158
 
5.5%
129
 
4.5%
108
 
3.8%
Other values (149) 1156
40.3%
Decimal Number
ValueCountFrequency (%)
1 217
29.1%
2 172
23.1%
3 109
14.6%
0 54
 
7.2%
4 42
 
5.6%
5 42
 
5.6%
6 31
 
4.2%
9 30
 
4.0%
7 27
 
3.6%
8 21
 
2.8%
Space Separator
ValueCountFrequency (%)
864
100.0%
Close Punctuation
ValueCountFrequency (%)
) 157
100.0%
Open Punctuation
ValueCountFrequency (%)
( 157
100.0%
Other Punctuation
ValueCountFrequency (%)
, 74
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2867
58.3%
Common 2049
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
334
 
11.6%
181
 
6.3%
162
 
5.7%
161
 
5.6%
160
 
5.6%
159
 
5.5%
159
 
5.5%
158
 
5.5%
129
 
4.5%
108
 
3.8%
Other values (149) 1156
40.3%
Common
ValueCountFrequency (%)
864
42.2%
1 217
 
10.6%
2 172
 
8.4%
) 157
 
7.7%
( 157
 
7.7%
3 109
 
5.3%
, 74
 
3.6%
0 54
 
2.6%
- 49
 
2.4%
4 42
 
2.0%
Other values (6) 154
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2867
58.3%
ASCII 2049
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
864
42.2%
1 217
 
10.6%
2 172
 
8.4%
) 157
 
7.7%
( 157
 
7.7%
3 109
 
5.3%
, 74
 
3.6%
0 54
 
2.6%
- 49
 
2.4%
4 42
 
2.0%
Other values (6) 154
 
7.5%
Hangul
ValueCountFrequency (%)
334
 
11.6%
181
 
6.3%
162
 
5.7%
161
 
5.6%
160
 
5.6%
159
 
5.5%
159
 
5.5%
158
 
5.5%
129
 
4.5%
108
 
3.8%
Other values (149) 1156
40.3%

재지정일자
Categorical

Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2020-12-10
81 
2020-12-22
76 
2021-07-21
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row2020-12-22
2nd row2020-12-22
3rd row2020-12-22
4th row2020-12-22
5th row2020-12-22

Common Values

ValueCountFrequency (%)
2020-12-10 81
51.3%
2020-12-22 76
48.1%
2021-07-21 1
 
0.6%

Length

2024-03-14T08:48:59.369730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:48:59.455893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-10 81
51.3%
2020-12-22 76
48.1%
2021-07-21 1
 
0.6%
Distinct104
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-14T08:48:59.686330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.1962025
Min length1

Characters and Unicode

Total characters663
Distinct characters131
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

Unique81 ?
Unique (%)51.3%

Sample

1st row삼계탕
2nd row버섯전골
3rd row샤브샤브
4th row생선회, 초밥
5th row양푼돼지갈비
ValueCountFrequency (%)
비빔밥 12
 
6.7%
콩나물국밥 7
 
3.9%
삼겹살 6
 
3.4%
냉면 6
 
3.4%
갈비탕 6
 
3.4%
소고기 5
 
2.8%
소고기구이 4
 
2.2%
추어탕 4
 
2.2%
한정식 4
 
2.2%
짜장면 4
 
2.2%
Other values (94) 121
67.6%
2024-03-14T08:49:00.026745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/