Overview

Dataset statistics

Number of variables14
Number of observations56
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory115.4 B

Variable types

Numeric1
Categorical7
Text5
DateTime1

Alerts

자료출처 has constant value ""Constant
공개여부 has constant value ""Constant
작성일 has constant value ""Constant
갱신주기 has constant value ""Constant
순번 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
향토음식명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
구분 is highly overall correlated with 영업시간High correlation
영업시간 is highly overall correlated with 구분High correlation
순번 has unique valuesUnique
지번주소 has unique valuesUnique
도로명주소 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 02:53:14.682914
Analysis finished2024-03-14 02:53:15.667458
Duration0.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size636.0 B
2024-03-14T11:53:15.739001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.75
Q114.75
median28.5
Q342.25
95-th percentile53.25
Maximum56
Range55
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation16.309506
Coefficient of variation (CV)0.57226338
Kurtosis-1.2
Mean28.5
Median Absolute Deviation (MAD)14
Skewness0
Sum1596
Variance266
MonotonicityStrictly increasing
2024-03-14T11:53:15.854968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
30 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
39 1
 
1.8%
Other values (46) 46
82.1%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
56 1
1.8%
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%
47 1
1.8%

시군명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size580.0 B
전주시
18 
김제시
5 
완주군
5 
고창군
5 
부안군
5 
Other values (8)
18 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.8%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 18
32.1%
김제시 5
 
8.9%
완주군 5
 
8.9%
고창군 5
 
8.9%
부안군 5
 
8.9%
정읍시 3
 
5.4%
진안군 3
 
5.4%
무주군 3
 
5.4%
군산시 2
 
3.6%
익산시 2
 
3.6%
Other values (3) 5
 
8.9%

Length

2024-03-14T11:53:15.952826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 18
32.1%
김제시 5
 
8.9%
완주군 5
 
8.9%
고창군 5
 
8.9%
부안군 5
 
8.9%
정읍시 3
 
5.4%
진안군 3
 
5.4%
무주군 3
 
5.4%
군산시 2
 
3.6%
익산시 2
 
3.6%
Other values (3) 5
 
8.9%

지번주소
Text

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T11:53:16.156730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length16.107143
Min length12

Characters and Unicode

Total characters902
Distinct characters96
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 전동 60-2
2nd row전주시 덕진구 금암동 712-3
3rd row전주시 완산구 전동 2-1
4th row전주시 완산구 중앙동3가 80
5th row전주시 덕진구 덕진동2가 168-
ValueCountFrequency (%)
전주시 18
 
8.3%
완산구 15
 
6.9%
김제시 5
 
2.3%
부안군 5
 
2.3%
완주군 5
 
2.3%
고창군 5
 
2.3%
아산면 5
 
2.3%
삼인리 3
 
1.4%
화평리 3
 
1.4%
무주군 3
 
1.4%
Other values (117) 150
69.1%
2024-03-14T11:53:16.511987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
161
 
17.8%
1 47
 
5.2%
- 44
 
4.9%
산 36
 
4.0%
시 32
 
3.5%
2 32
 
3.5%
리 31
 
3.4%
군 28
 
3.1%
주 27
 
3.0%
면 26
 
2.9%
Other values (86) 438
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 492
54.5%
Decimal Number 205
22.7%
Space Separator 161
 
17.8%
Dash Punctuation 44
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 36
 
7.3%
시 32
 
6.5%
리 31
 
6.3%
군 28
 
5.7%
주 27
 
5.5%
면 26
 
5.3%
동 25
 
5.1%
전 21
 
4.3%
완 20
 
4.1%
구 19
 
3.9%
Other values (74) 227
46.1%
Decimal Number
ValueCountFrequency (%)
1 47
22.9%
2 32
15.6%
4 24
11.7%
3 20
9.8%
7 16
 
7.8%
6 16
 
7.8%
5 14
 
6.8%
0 12
 
5.9%
8 12
 
5.9%
9 12
 
5.9%
Space Separator
ValueCountFrequency (%)
161
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 492
54.5%
Common 410
45.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 36
 
7.3%
시 32
 
6.5%
리 31
 
6.3%
군 28
 
5.7%
주 27
 
5.5%
면 26
 
5.3%
동 25
 
5.1%
전 21
 
4.3%
완 20
 
4.1%
구 19
 
3.9%
Other values (74) 227
46.1%
Common
ValueCountFrequency (%)
161
39.3%
1 47
 
11.5%
- 44
 
10.7%
2 32
 
7.8%
4 24
 
5.9%
3 20
 
4.9%
7 16
 
3.9%
6 16
 
3.9%
5 14
 
3.4%
0 12
 
2.9%
Other values (2) 24
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 492
54.5%
ASCII 410
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
161
39.3%
1 47
 
11.5%
- 44
 
10.7%
2 32
 
7.8%
4 24
 
5.9%
3 20
 
4.9%
7 16
 
3.9%
6 16
 
3.9%
5 14
 
3.4%
0 12
 
2.9%
Other values (2) 24
 
5.9%
Hangul
ValueCountFrequency (%)
산 36
 
7.3%
시 32
 
6.5%
리 31
 
6.3%
군 28
 
5.7%
주 27
 
5.5%
면 26
 
5.3%
동 25
 
5.1%
전 21
 
4.3%
완 20
 
4.1%
구 19
 
3.9%
Other values (74) 227
46.1%

도로명주소
Text

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T11:53:16.756692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length19.160714
Min length12

Characters and Unicode

Total characters1073
Distinct characters109
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)100.0%

Sample

1st row전주시 완산구 전동성당길 88-5 (전동)
2nd row전주시 덕진구 기린대로 425 (금암동)
3rd row전주시 완산구 어진길 119 (전동)
4th row전주시 완산구 전라감영5길 17 (중앙동3가)
5th row전주시 덕진구 호반4길 7 (덕진동2가)
ValueCountFrequency (%)
전주시 18
 
7.5%
완산구 15
 
6.2%
김제시 5
 
2.1%
아산면 5
 
2.1%
부안군 5
 
2.1%
고창군 5
 
2.1%
완주군 5
 
2.1%
화산로 3
 
1.2%
금산면 3
 
1.2%
화산면 3
 
1.2%
Other values (136) 173
72.1%
2024-03-14T11:53:17.211211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184
 
17.1%
( 52
 
4.8%
) 52
 
4.8%
산 45
 
4.2%
1 36
 
3.4%
로 36
 
3.4%
시 32
 
3.0%
동 31
 
2.9%
전 30
 
2.8%
주 30
 
2.8%
Other values (99) 545
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 596
55.5%
Space Separator 184
 
17.1%
Decimal Number 177
 
16.5%
Open Punctuation 52
 
4.8%
Close Punctuation 52
 
4.8%
Dash Punctuation 12
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 45
 
7.6%
로 36
 
6.0%
시 32
 
5.4%
동 31
 
5.2%
전 30
 
5.0%
주 30
 
5.0%
군 26
 
4.4%
면 25
 
4.2%
길 21
 
3.5%
완 20
 
3.4%
Other values (85) 300
50.3%
Decimal Number
ValueCountFrequency (%)
1 36
20.3%
2 26
14.7%
3 18
10.2%
6 18
10.2%
4 17
9.6%
8 15
8.5%
7 15
8.5%
9 13
 
7.3%
5 12
 
6.8%
0 7
 
4.0%
Space Separator
ValueCountFrequency (%)
184
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 596
55.5%
Common 477
44.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 45
 
7.6%
로 36
 
6.0%
시 32
 
5.4%
동 31
 
5.2%
전 30
 
5.0%
주 30
 
5.0%
군 26
 
4.4%
면 25
 
4.2%
길 21
 
3.5%
완 20
 
3.4%
Other values (85) 300
50.3%
Common
ValueCountFrequency (%)
184
38.6%
( 52
 
10.9%
) 52
 
10.9%
1 36
 
7.5%
2 26
 
5.5%
3 18
 
3.8%
6 18
 
3.8%
4 17
 
3.6%
8 15
 
3.1%
7 15
 
3.1%
Other values (4) 44
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 596
55.5%
ASCII 477
44.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
184
38.6%
( 52
 
10.9%
) 52
 
10.9%
1 36
 
7.5%
2 26
 
5.5%
3 18
 
3.8%
6 18
 
3.8%
4 17
 
3.6%
8 15
 
3.1%
7 15
 
3.1%
Other values (4) 44
 
9.2%
Hangul
ValueCountFrequency (%)
산 45
 
7.6%
로 36
 
6.0%
시 32
 
5.4%
동 31
 
5.2%
전 30
 
5.0%
주 30
 
5.0%
군 26
 
4.4%
면 25
 
4.2%
길 21
 
3.5%
완 20
 
3.4%
Other values (85) 300
50.3%

향토음식명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Memory size580.0 B
콩나물국밥
7 
한정식
5 
전주비빔밥
5 
민물장어구이
4 
바지락죽
 
3
Other values (21)
32 

Length

Max length8
Median length7
Mean length4.6071429
Min length2

Unique

Unique12 ?
Unique (%)21.4%

Sample

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

Common Values

ValueCountFrequency (%)
콩나물국밥 7
 
12.5%
한정식 5
 
8.9%
전주비빔밥 5
 
8.9%
민물장어구이 4
 
7.1%
바지락죽 3
 
5.4%
순두부찌개 3
 
5.4%
붕어찜 3
 
5.4%
비빕밥 2
 
3.6%
추어탕(숙회) 2
 
3.6%
산채정식 2
 
3.6%
Other values (16) 20
35.7%

Length

2024-03-14T11:53:17.354960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
콩나물국밥 7
 
12.5%
전주비빔밥 5
 
8.9%
한정식 5
 
8.9%
민물장어구이 4
 
7.1%
바지락죽 3
 
5.4%
순두부찌개 3
 
5.4%
붕어찜 3
 
5.4%
용궁탕 2
 
3.6%
흑돼지삼겹살구이 2
 
3.6%
애저찜 2
 
3.6%
Other values (16) 20
35.7%

업소명
Text

Distinct55
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T11:53:17.558674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length4.9464286
Min length1

Characters and Unicode

Total characters277
Distinct characters120
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)96.4%

Sample

1st row종로회관
2nd row한국관
3rd row한국집
4th row가족회관
5th row고궁
ValueCountFrequency (%)
가시리 2
 
3.6%
내고향산장 1
 
1.8%
전주한국관 1
 
1.8%
원평원조시골집순대 1
 
1.8%
약수가든 1
 
1.8%
화산식당 1
 
1.8%
산수장 1
 
1.8%
원조화심생두부 1
 
1.8%
화심순두부 1
 
1.8%
진안관 1
 
1.8%
Other values (45) 45
80.4%
2024-03-14T11:53:17.871451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 12
 
4.3%
산 10
 
3.6%
가 9
 
3.2%
전 8
 
2.9%
한 8
 
2.9%
식 7
 
2.5%
집 7
 
2.5%
당 7
 
2.5%
회 7
 
2.5%
든 6
 
2.2%
Other values (110) 196
70.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 277
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 12
 
4.3%
산 10
 
3.6%
가 9
 
3.2%
전 8
 
2.9%
한 8
 
2.9%
식 7
 
2.5%
집 7
 
2.5%
당 7
 
2.5%
회 7
 
2.5%
든 6
 
2.2%
Other values (110) 196
70.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 277
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 12
 
4.3%
산 10
 
3.6%
가 9
 
3.2%
전 8
 
2.9%
한 8
 
2.9%
식 7
 
2.5%
집 7
 
2.5%
당 7
 
2.5%
회 7
 
2.5%
든 6
 
2.2%
Other values (110) 196
70.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 277
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 12
 
4.3%
산 10
 
3.6%
가 9
 
3.2%
전 8
 
2.9%
한 8
 
2.9%
식 7
 
2.5%
집 7
 
2.5%
당 7
 
2.5%
회 7
 
2.5%
든 6
 
2.2%
Other values (110) 196
70.8%

위치
Text

Distinct49
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T11:53:18.126699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length15
Mean length12.517857
Min length5

Characters and Unicode

Total characters701
Distinct characters148
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

Unique44 ?
Unique (%)78.6%

Sample

1st row전동성당 앞 골목
2nd row종합경기장 사거리
3rd row예술회관 앞 골목 20m
4th row구 전주우체국 사거리
5th row도립 국악원 맞은편
ValueCountFrequency (%)
옆 7
 
4.0%
앞 7
 
4.0%
내 5
 
2.8%
부근 5
 
2.8%
입구 4
 
2.3%
전주 4
 
2.3%
사거리 3
 
1.7%
우측 3
 
1.7%
약 3
 
1.7%
쪽 3
 
1.7%
Other values (98) 132
75.0%
2024-03-14T11:53:18.475155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120
 
17.1%
0 22
 
3.1%
m 19
 
2.7%
사 18
 
2.6%
산 16
 
2.3%
구 16
 
2.3%
리 13
 
1.9%
서 13
 
1.9%
에 12
 
1.7%
내 11
 
1.6%
Other values (138) 441
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 503
71.8%
Space Separator 120
 
17.1%
Decimal Number 48
 
6.8%
Lowercase Letter 24
 
3.4%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%
Other Punctuation 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 18
 
3.6%
산 16
 
3.2%
구 16
 
3.2%
리 13
 
2.6%
서 13
 
2.6%
에 12
 
2.4%
내 11
 
2.2%
쪽 11
 
2.2%
지 11
 
2.2%
동 11
 
2.2%
Other values (124) 371
73.8%
Decimal Number
ValueCountFrequency (%)
0 22
45.8%
2 9
18.8%
1 6
 
12.5%
3 4
 
8.3%
5 4
 
8.3%
4 2
 
4.2%
9 1
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
m 19
79.2%
k 5
 
20.8%
Space Separator
ValueCountFrequency (%)
120
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 503
71.8%
Common 174
 
24.8%
Latin 24
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 18
 
3.6%
산 16
 
3.2%
구 16
 
3.2%
리 13
 
2.6%
서 13
 
2.6%
에 12
 
2.4%
내 11
 
2.2%
쪽 11
 
2.2%
지 11
 
2.2%
동 11
 
2.2%
Other values (124) 371
73.8%
Common
ValueCountFrequency (%)
120
69.0%
0 22
 
12.6%
2 9
 
5.2%
1 6
 
3.4%
3 4
 
2.3%
5 4
 
2.3%
4 2
 
1.1%
( 2
 
1.1%
) 2
 
1.1%
. 1
 
0.6%
Other values (2) 2
 
1.1%
Latin
ValueCountFrequency (%)
m 19
79.2%
k 5
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 503
71.8%
ASCII 198
 
28.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
60.6%
0 22
 
11.1%
m 19
 
9.6%
2 9
 
4.5%
1 6
 
3.0%
k 5
 
2.5%
3 4
 
2.0%
5 4
 
2.0%
4 2
 
1.0%
( 2
 
1.0%
Other values (4) 5
 
2.5%
Hangul
ValueCountFrequency (%)
사 18
 
3.6%
산 16
 
3.2%
구 16
 
3.2%
리 13
 
2.6%
서 13
 
2.6%
에 12
 
2.4%
내 11
 
2.2%
쪽 11
 
2.2%
지 11
 
2.2%
동 11
 
2.2%
Other values (124) 371
73.8%

전화번호
Text

UNIQUE 

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size580.0 B
2024-03-14T11:53:18.677522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters672
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

Unique56 ?
Unique (%)100.0%

Sample

1st row063-288-4578
2nd row063-272-9229
3rd row063-284-2224
4th row063-284-0982
5th row063-251-3211
ValueCountFrequency (%)
063-288-4578 1
 
1.8%
063-272-9229 1
 
1.8%
063-322-3123 1
 
1.8%
063-548-9595 1
 
1.8%
063-545-0666 1
 
1.8%
063-262-2602 1
 
1.8%
063-263-5109 1
 
1.8%
063-263-5078 1
 
1.8%
063-243-8952 1
 
1.8%
063-243-8268 1
 
1.8%
Other values (46) 46
82.1%
2024-03-14T11:53:18.971983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 112
16.7%
3 104
15.5%
6 94
14.0%
0 85
12.6%
2 67
10.0%
8 49
7.3%
5 48
7.1%
4 43
 
6.4%
1 32
 
4.8%
7 20
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 560
83.3%
Dash Punctuation 112
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 104
18.6%
6 94
16.8%
0 85
15.2%
2 67
12.0%
8 49
8.8%
5 48
8.6%
4 43
7.7%
1 32
 
5.7%
7 20
 
3.6%
9 18
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 112
16.7%
3 104
15.5%
6 94
14.0%
0 85
12.6%
2 67
10.0%
8 49
7.3%
5 48
7.1%
4 43
 
6.4%
1 32
 
4.8%
7 20
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 112
16.7%
3 104
15.5%
6 94
14.0%
0 85
12.6%
2 67
10.0%
8 49
7.3%
5 48
7.1%
4 43
 
6.4%
1 32
 
4.8%
7 20
 
3.0%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size580.0 B
-
30 
모범음식점
24 
향토음식점
 
2

Length

Max length5
Median length1
Mean length2.8571429
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
- 30
53.6%
모범음식점 24
42.9%
향토음식점 2
 
3.6%

Length

2024-03-14T11:53:19.429575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:53:19.527009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
30
53.6%
모범음식점 24
42.9%
향토음식점 2
 
3.6%

영업시간
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
-
28 
09:00~21:00
6 
11:00~22:00
3 
11:00~21:00
 
2
11:00~21:30
 
2
Other values (10)
15 

Length

Max length11
Median length6
Mean length6
Min length1

Unique

Unique5 ?
Unique (%)8.9%

Sample

1st row09:00~21:00
2nd row11:00~21:00
3rd row09:00~21:00
4th row11:00~21:00
5th row11:00~21:30

Common Values

ValueCountFrequency (%)
- 28
50.0%
09:00~21:00 6
 
10.7%
11:00~22:00 3
 
5.4%
11:00~21:00 2
 
3.6%
11:00~21:30 2
 
3.6%
24:00~24:00 2
 
3.6%
11:30~21:30 2
 
3.6%
12:00~21:30 2
 
3.6%
08:00~21:00 2
 
3.6%
11:00~20:00 2
 
3.6%
Other values (5) 5
 
8.9%

Length

2024-03-14T11:53:19.608181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
28
50.0%
09:00~21:00 6
 
10.7%
11:00~22:00 3
 
5.4%
11:00~21:00 2
 
3.6%
11:00~21:30 2
 
3.6%
24:00~24:00 2
 
3.6%
11:30~21:30 2
 
3.6%
12:00~21:30 2
 
3.6%
08:00~21:00 2
 
3.6%
11:00~20:00 2
 
3.6%
Other values (5) 5
 
8.9%

자료출처
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
건강안전과
56 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건강안전과 56
100.0%

Length

2024-03-14T11:53:19.692303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:53:19.762240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건강안전과 56
100.0%

공개여부
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
공개
56 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공개 56
100.0%

Length

2024-03-14T11:53:19.831115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:53:19.898711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공개 56
100.0%

작성일
Date

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
Minimum2016-03-01 00:00:00
Maximum2016-03-01 00:00:00
2024-03-14T11:53:19.957681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T11:53:20.027046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

갱신주기
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size580.0 B
1년
56 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1년
2nd row1년
3rd row1년
4th row1년
5th row1년

Common Values

ValueCountFrequency (%)
1년 56
100.0%

Length

2024-03-14T11:53:20.116738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T11:53:20.238570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1년 56
100.0%

Interactions

2024-03-14T11:53:15.367267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T11:53:20.303053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명지번주소도로명주소향토음식명업소명위치전화번호구분영업시간
순번1.0000.9091.0001.0000.9650.9180.9521.0000.4400.454
시군명0.9091.0001.0001.0000.9980.9251.0001.0000.5300.404
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
향토음식명0.9650.9981.0001.0001.0000.9650.9911.0000.5870.565
업소명0.9180.9251.0001.0000.9651.0000.9941.0001.0001.000
위치0.9521.0001.0001.0000.9910.9941.0001.0000.0000.955
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
구분0.4400.5301.0001.0000.5871.0000.0001.0001.0000.990
영업시간0.4540.4041.0001.0000.5651.0000.9551.0000.9901.000
2024-03-14T11:53:20.425320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명구분향토음식명영업시간
시군명1.0000.3130.7600.135
구분0.3131.0000.2650.775
향토음식명0.7600.2651.0000.144
영업시간0.1350.7750.1441.000
2024-03-14T11:53:20.522683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군명향토음식명구분영업시간
순번1.0000.6440.6790.2430.147
시군명0.6441.0000.7600.3130.135
향토음식명0.6790.7601.0000.2650.144
구분0.2430.3130.2651.0000.775
영업시간0.1470.1350.1440.7751.000

Missing values

2024-03-14T11:53:15.461050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T11:53:15.607926image/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

순번시군명지번주소도로명주소향토음식명업소명위치전화번호구분영업시간자료출처공개여부작성일갱신주기
01전주시전주시 완산구 전동 60-2전주시 완산구 전동성당길 88-5 (전동)전주비빔밥종로회관전동성당 앞 골목063-288-4578모범음식점09:00~21:00건강안전과공개2016. 3.1년
12전주시전주시 덕진구 금암동 712-3전주시 덕진구 기린대로 425 (금암동)전주비빔밥한국관종합경기장 사거리063-272-9229모범음식점11:00~21:00건강안전과공개2016. 3.1년
23전주시전주시 완산구 전동 2-1전주시 완산구 어진길 119 (전동)전주비빔밥한국집예술회관 앞 골목 20m063-284-2224모범음식점09:00~21:00건강안전과공개2016. 3.1년
34전주시전주시 완산구 중앙동3가 80전주시 완산구 전라감영5길 17 (중앙동3가)전주비빔밥가족회관구 전주우체국 사거리063-284-0982모범음식점11:00~21:00건강안전과공개2016. 3.1년
45전주시전주시 덕진구 덕진동2가 168-전주시 덕진구 호반4길 7 (덕진동2가)전주비빔밥고궁도립 국악원 맞은편063-251-3211모범음식점11:00~21:30건강안전과공개2016. 3.1년
56전주시전주시 완산구 고사동 454-1전주시 완산구 전주객사2길 22 (고사동)콩나물국밥삼백집명동사우나 옆063-284-2227모범음식점24:00~24:00건강안전과공개2016. 3.1년
67전주시전주시 완산구 고사동 451전주시 완산구 전주객사2길 20 (고사동)콩나물국밥가시리명동사우나 옆063-284-8964모범음식점11:30~21:30건강안전과공개2016. 3.1년
78전주시전주시 완산구 경원동1가 12-1전주시 완산구 전동성당길 14 (경원동1가)콩나물국밥콩나루전주 홍지서림 뒤 10m063-288-4853--건강안전과공개2016. 3.1년
89전주시전주시 완산구 경원동2가 12-1전주시 완산구 동문길 88 (경원동2가)콩나물국밥전주왱이콩나물국밥전문점동문사거리063-287-6980--건강안전과공개2016. 3.1년
910전주시전주시 완산구 중화산동2가 4-전주시 완산구 어은로 48 (중화산동2가)콩나물국밥한일관어은점어은터널 우석한방병원 옆063-226-1569--건강안전과공개2016. 3.1년
순번시군명지번주소도로명주소향토음식명업소명위치전화번호구분영업시간자료출처공개여부작성일갱신주기
4647고창군고창군 아산면 삼인리 29-34고창군 선운사로 8 (아산면)민물장어구이신덕식당선운사 진입로 오른쪽 부근063-562-1533모범음식점11:00~20:00건강안전과공개2016. 3.1년
4748고창군고창군 아산면 삼인리 37-1고창군 선운사로 25 (아산면)민물장어구이유신식당선운사 진입로 부근 1km 왼쪽063-562-1566모범음식점10:00~21:00건강안전과공개2016. 3.1년
4849고창군고창군 아산면 삼인리 29-29고창군 선운대로 2727 (아산면)민물장어구이연기식당선운사 진입로 부근063-561-3815--건강안전과공개2016. 3.1년
4950고창군고창군 아산면 반암리 430-3고창군 인천강서길 12 (아산면)민물장어구이청림가든정금자할매집강정다리 부근063-564-1406--건강안전과공개2016. 3.1년
5051고창군고창군 아산면 반암리 428-1고창군 인천강서길 6 (아산면)장삼보양죽강촌식당선운산 입구063-563-3471--건강안전과공개2016. 3.1년
5152부안군부안군 행안면 신기리 211-2부안군 변산로 95 (행안면)백합죽계화회관행안면사무소에서 변산 쪽 1km063-584-8100--건강안전과공개2016. 3.1년
5253부안군부안군 변산면 대항리 109-2부안군 묵정길 83-6 (변산면)바지락죽변산온천산장변산온천장 입구 우측 약 300m063-584-4874--건강안전과공개2016. 3.1년
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