Overview

Dataset statistics

Number of variables15
Number of observations30
Missing cells6
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory130.4 B

Variable types

Numeric6
Categorical1
Text7
DateTime1

Dataset

Description파주시 착한가격업소에 대한 데이터로, 한식, 이미용업, 중식 등의 업종, 업소명, 대표자, 주소, 연락처, 품목, 가격, 위경도 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3043695/fileData.do

Alerts

기준일자 has constant value ""Constant
가격1 is highly overall correlated with 가격2 and 1 other fieldsHigh correlation
가격2 is highly overall correlated with 가격1 and 1 other fieldsHigh correlation
가격3 is highly overall correlated with 가격1 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 업종High correlation
업종 is highly overall correlated with 경도High correlation
전화번호 has 2 (6.7%) missing valuesMissing
품목3 has 2 (6.7%) missing valuesMissing
가격3 has 2 (6.7%) missing valuesMissing
연번 has unique valuesUnique
업소명 has unique valuesUnique
대표자 has unique valuesUnique
주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:12:00.468091
Analysis finished2023-12-11 23:12:04.267202
Duration3.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:12:04.341100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-12T08:12:04.465369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

업종
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
한식
19 
미용업
9 
중식
 
1
숙박업
 
1

Length

Max length3
Median length2
Mean length2.3333333
Min length2

Unique

Unique2 ?
Unique (%)6.7%

Sample

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

Common Values

ValueCountFrequency (%)
한식 19
63.3%
미용업 9
30.0%
중식 1
 
3.3%
숙박업 1
 
3.3%

Length

2023-12-12T08:12:04.568039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:12:04.659482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 19
63.3%
미용업 9
30.0%
중식 1
 
3.3%
숙박업 1
 
3.3%

업소명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T08:12:04.847779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length5.2666667
Min length1

Characters and Unicode

Total characters158
Distinct characters99
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

Unique30 ?
Unique (%)100.0%

Sample

1st row은혜미용실
2nd row팜스헤어클럽
3rd row로이미용실
4th row헤어짱
5th row시온미용실
ValueCountFrequency (%)
은혜미용실 1
 
3.1%
팜스헤어클럽 1
 
3.1%
네일엔젤 1
 
3.1%
진말생면국수 1
 
3.1%
전주콩나물국밥 1
 
3.1%
전주웰빙새알팥죽 1
 
3.1%
참숯한우천국 1
 
3.1%
아리몽 1
 
3.1%
옛날집 1
 
3.1%
김밥레스토랑 1
 
3.1%
Other values (22) 22
68.8%
2023-12-12T08:12:05.186625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 8
 
5.1%
실 7
 
4.4%
미 6
 
3.8%
용 6
 
3.8%
대 4
 
2.5%
주 4
 
2.5%
식 3
 
1.9%
시 3
 
1.9%
네 3
 
1.9%
이 3
 
1.9%
Other values (89) 111
70.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
97.5%
Space Separator 2
 
1.3%
Close Punctuation 1
 
0.6%
Open Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 8
 
5.2%
실 7
 
4.5%
미 6
 
3.9%
용 6
 
3.9%
대 4
 
2.6%
주 4
 
2.6%
식 3
 
1.9%
시 3
 
1.9%
네 3
 
1.9%
이 3
 
1.9%
Other values (86) 107
69.5%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 154
97.5%
Common 4
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 8
 
5.2%
실 7
 
4.5%
미 6
 
3.9%
용 6
 
3.9%
대 4
 
2.6%
주 4
 
2.6%
식 3
 
1.9%
시 3
 
1.9%
네 3
 
1.9%
이 3
 
1.9%
Other values (86) 107
69.5%
Common
ValueCountFrequency (%)
2
50.0%
) 1
25.0%
( 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 154
97.5%
ASCII 4
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 8
 
5.2%
실 7
 
4.5%
미 6
 
3.9%
용 6
 
3.9%
대 4
 
2.6%
주 4
 
2.6%
식 3
 
1.9%
시 3
 
1.9%
네 3
 
1.9%
이 3
 
1.9%
Other values (86) 107
69.5%
ASCII
ValueCountFrequency (%)
2
50.0%
) 1
25.0%
( 1
25.0%

대표자
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T08:12:05.386461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9666667
Min length2

Characters and Unicode

Total characters89
Distinct characters54
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

Unique30 ?
Unique (%)100.0%

Sample

1st row정영신
2nd row신순자
3rd row김혜란
4th row최수남
5th row송도자
ValueCountFrequency (%)
정영신 1
 
3.3%
신순자 1
 
3.3%
조금례 1
 
3.3%
안종섭 1
 
3.3%
김희정 1
 
3.3%
장경자 1
 
3.3%
안승모 1
 
3.3%
박미수 1
 
3.3%
김혜영 1
 
3.3%
유순월 1
 
3.3%
Other values (20) 20
66.7%
2023-12-12T08:12:05.704158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 5
 
5.6%
이 4
 
4.5%
자 4
 
4.5%
미 4
 
4.5%
영 4
 
4.5%
정 3
 
3.4%
신 3
 
3.4%
순 3
 
3.4%
박 3
 
3.4%
유 3
 
3.4%
Other values (44) 53
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 5
 
5.6%
이 4
 
4.5%
자 4
 
4.5%
미 4
 
4.5%
영 4
 
4.5%
정 3
 
3.4%
신 3
 
3.4%
순 3
 
3.4%
박 3
 
3.4%
유 3
 
3.4%
Other values (44) 53
59.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 5
 
5.6%
이 4
 
4.5%
자 4
 
4.5%
미 4
 
4.5%
영 4
 
4.5%
정 3
 
3.4%
신 3
 
3.4%
순 3
 
3.4%
박 3
 
3.4%
유 3
 
3.4%
Other values (44) 53
59.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 5
 
5.6%
이 4
 
4.5%
자 4
 
4.5%
미 4
 
4.5%
영 4
 
4.5%
정 3
 
3.4%
신 3
 
3.4%
순 3
 
3.4%
박 3
 
3.4%
유 3
 
3.4%
Other values (44) 53
59.6%

주소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T08:12:05.967041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length25
Mean length21.7
Min length14

Characters and Unicode

Total characters651
Distinct characters89
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

Unique30 ?
Unique (%)100.0%

Sample

1st row경기도 파주시 파평면 장마루로 246-1
2nd row경기도 파주시 송화로 11 팜스상가 169동 2층 210
3rd row경기도 파주시 광탄면 혜음로 1127-2
4th row경기도 파주시 탄현면 여치길 81
5th row경기도 파주시 월롱면 쉰우물길 13-16
ValueCountFrequency (%)
경기도 31
20.0%
파주시 31
20.0%
금정24길 4
 
2.6%
월롱면 3
 
1.9%
파주읍 3
 
1.9%
금촌동 3
 
1.9%
문산읍 3
 
1.9%
광탄면 2
 
1.3%
19 2
 
1.3%
탄현면 2
 
1.3%
Other values (68) 71
45.8%
2023-12-12T08:12:06.372057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
126
19.4%
파 36
 
5.5%
1 36
 
5.5%
주 34
 
5.2%
시 33
 
5.1%
경 31
 
4.8%
도 31
 
4.8%
기 31
 
4.8%
2 24
 
3.7%
로 20
 
3.1%
Other values (79) 249
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 372
57.1%
Decimal Number 127
 
19.5%
Space Separator 126
 
19.4%
Dash Punctuation 9
 
1.4%
Uppercase Letter 5
 
0.8%
Close Punctuation 4
 
0.6%
Open Punctuation 4
 
0.6%
Other Punctuation 3
 
0.5%
Math Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 36
 
9.7%
주 34
 
9.1%
시 33
 
8.9%
경 31
 
8.3%
도 31
 
8.3%
기 31
 
8.3%
로 20
 
5.4%
길 15
 
4.0%
금 9
 
2.4%
면 8
 
2.2%
Other values (58) 124
33.3%
Decimal Number
ValueCountFrequency (%)
1 36
28.3%
2 24
18.9%
4 12
 
9.4%
6 10
 
7.9%
0 9
 
7.1%
7 8
 
6.3%
9 8
 
6.3%
5 7
 
5.5%
3 7
 
5.5%
8 6
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
L 1
20.0%
C 1
20.0%
D 1
20.0%
A 1
20.0%
E 1
20.0%
Space Separator
ValueCountFrequency (%)
126
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 372
57.1%
Common 274
42.1%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 36
 
9.7%
주 34
 
9.1%
시 33
 
8.9%
경 31
 
8.3%
도 31
 
8.3%
기 31
 
8.3%
로 20
 
5.4%
길 15
 
4.0%
금 9
 
2.4%
면 8
 
2.2%
Other values (58) 124
33.3%
Common
ValueCountFrequency (%)
126
46.0%
1 36
 
13.1%
2 24
 
8.8%
4 12
 
4.4%
6 10
 
3.6%
0 9
 
3.3%
- 9
 
3.3%
7 8
 
2.9%
9 8
 
2.9%
5 7
 
2.6%
Other values (6) 25
 
9.1%
Latin
ValueCountFrequency (%)
L 1
20.0%
C 1
20.0%
D 1
20.0%
A 1
20.0%
E 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 372
57.1%
ASCII 279
42.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
126
45.2%
1 36
 
12.9%
2 24
 
8.6%
4 12
 
4.3%
6 10
 
3.6%
0 9
 
3.2%
- 9
 
3.2%
7 8
 
2.9%
9 8
 
2.9%
5 7
 
2.5%
Other values (11) 30
 
10.8%
Hangul
ValueCountFrequency (%)
파 36
 
9.7%
주 34
 
9.1%
시 33
 
8.9%
경 31
 
8.3%
도 31
 
8.3%
기 31
 
8.3%
로 20
 
5.4%
길 15
 
4.0%
금 9
 
2.4%
면 8
 
2.2%
Other values (58) 124
33.3%

전화번호
Text

MISSING 

Distinct28
Distinct (%)100.0%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-12T08:12:06.585574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters336
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

Unique28 ?
Unique (%)100.0%

Sample

1st row031-958-9761
2nd row031-947-8908
3rd row031-957-5007
4th row031-944-0810
5th row031-941-5285
ValueCountFrequency (%)
031-947-8908 1
 
3.6%
031-957-5007 1
 
3.6%
031-948-9080 1
 
3.6%
031-953-1888 1
 
3.6%
031-944-1592 1
 
3.6%
031-957-7100 1
 
3.6%
031-945-6677 1
 
3.6%
031-941-2904 1
 
3.6%
031-952-5558 1
 
3.6%
031-953-0068 1
 
3.6%
Other values (18) 18
64.3%
2023-12-12T08:12:06.932229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 56
16.7%
0 47
14.0%
1 47
14.0%
3 38
11.3%
9 35
10.4%
5 27
8.0%
4 26
7.7%
8 23
6.8%
2 15
 
4.5%
7 13
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 280
83.3%
Dash Punctuation 56
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47
16.8%
1 47
16.8%
3 38
13.6%
9 35
12.5%
5 27
9.6%
4 26
9.3%
8 23
8.2%
2 15
 
5.4%
7 13
 
4.6%
6 9
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 56
16.7%
0 47
14.0%
1 47
14.0%
3 38
11.3%
9 35
10.4%
5 27
8.0%
4 26
7.7%
8 23
6.8%
2 15
 
4.5%
7 13
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 56
16.7%
0 47
14.0%
1 47
14.0%
3 38
11.3%
9 35
10.4%
5 27
8.0%
4 26
7.7%
8 23
6.8%
2 15
 
4.5%
7 13
 
3.9%

품목1
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T08:12:07.069034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.6
Min length2

Characters and Unicode

Total characters108
Distinct characters56
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)56.7%

Sample

1st row커트
2nd row커트
3rd row커트
4th row커트
5th row커트
ValueCountFrequency (%)
커트 8
26.7%
순대국 3
 
10.0%
김밥 2
 
6.7%
육개장 1
 
3.3%
대패삼겹살(180g 1
 
3.3%
손젤 1
 
3.3%
콩나물국밥 1
 
3.3%
새알팥죽 1
 
3.3%
한우우거지탕 1
 
3.3%
국산돼지갈비 1
 
3.3%
Other values (10) 10
33.3%
2023-12-12T08:12:07.313566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 9
 
8.3%
커 8
 
7.4%
트 8
 
7.4%
대 5
 
4.6%
밥 5
 
4.6%
지 4
 
3.7%
순 3
 
2.8%
0 3
 
2.8%
장 3
 
2.8%
( 2
 
1.9%
Other values (46) 58
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
88.9%
Decimal Number 6
 
5.6%
Open Punctuation 2
 
1.9%
Close Punctuation 2
 
1.9%
Lowercase Letter 2
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 9
 
9.4%
커 8
 
8.3%
트 8
 
8.3%
대 5
 
5.2%
밥 5
 
5.2%
지 4
 
4.2%
순 3
 
3.1%
장 3
 
3.1%
비 2
 
2.1%
갈 2
 
2.1%
Other values (39) 47
49.0%
Decimal Number
ValueCountFrequency (%)
0 3
50.0%
1 1
 
16.7%
8 1
 
16.7%
2 1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
88.9%
Common 10
 
9.3%
Latin 2
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 9
 
9.4%
커 8
 
8.3%
트 8
 
8.3%
대 5
 
5.2%
밥 5
 
5.2%
지 4
 
4.2%
순 3
 
3.1%
장 3
 
3.1%
비 2
 
2.1%
갈 2
 
2.1%
Other values (39) 47
49.0%
Common
ValueCountFrequency (%)
0 3
30.0%
( 2
20.0%
) 2
20.0%
1 1
 
10.0%
8 1
 
10.0%
2 1
 
10.0%
Latin
ValueCountFrequency (%)
g 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
88.9%
ASCII 12
 
11.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 9
 
9.4%
커 8
 
8.3%
트 8
 
8.3%
대 5
 
5.2%
밥 5
 
5.2%
지 4
 
4.2%
순 3
 
3.1%
장 3
 
3.1%
비 2
 
2.1%
갈 2
 
2.1%
Other values (39) 47
49.0%
ASCII
ValueCountFrequency (%)
0 3
25.0%
( 2
16.7%
) 2
16.7%
g 2
16.7%
1 1
 
8.3%
8 1
 
8.3%
2 1
 
8.3%

가격1
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9333.3333
Minimum2500
Maximum29000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:12:07.422963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2500
5-th percentile3625
Q16250
median8000
Q310000
95-th percentile21100
Maximum29000
Range26500
Interquartile range (IQR)3750

Descriptive statistics

Standard deviation5831.4447
Coefficient of variation (CV)0.62479764
Kurtosis4.0169218
Mean9333.3333
Median Absolute Deviation (MAD)2000
Skewness1.9403229
Sum280000
Variance34005747
MonotonicityNot monotonic
2023-12-12T08:12:07.525411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8000 10
33.3%
5000 5
16.7%
10000 3
 
10.0%
7000 3
 
10.0%
2500 2
 
6.7%
22000 1
 
3.3%
13000 1
 
3.3%
6000 1
 
3.3%
11000 1
 
3.3%
18000 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
2500 2
 
6.7%
5000 5
16.7%
6000 1
 
3.3%
7000 3
 
10.0%
8000 10
33.3%
10000 3
 
10.0%
11000 1
 
3.3%
13000 1
 
3.3%
18000 1
 
3.3%
20000 1
 
3.3%
ValueCountFrequency (%)
29000 1
 
3.3%
22000 1
 
3.3%
20000 1
 
3.3%
18000 1
 
3.3%
13000 1
 
3.3%
11000 1
 
3.3%
10000 3
 
10.0%
8000 10
33.3%
7000 3
 
10.0%
6000 1
 
3.3%

품목2
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T08:12:07.653271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length3.3
Min length1

Characters and Unicode

Total characters99
Distinct characters58
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)60.0%

Sample

1st row펌
2nd row펌
3rd row펌
4th row펌
5th row펌
ValueCountFrequency (%)
펌 8
26.7%
삼겹살 2
 
6.7%
돼지국밥 2
 
6.7%
숯불고기정식 1
 
3.3%
냉동삼겹살 1
 
3.3%
발젤 1
 
3.3%
얼큰콩나물국밥 1
 
3.3%
보리비빔밥 1
 
3.3%
갈비탕 1
 
3.3%
닭곰탕 1
 
3.3%
Other values (11) 11
36.7%
2023-12-12T08:12:07.928485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
펌 8
 
8.1%
밥 8
 
8.1%
국 7
 
7.1%
4
 
4.0%
비 4
 
4.0%
지 3
 
3.0%
삼 3
 
3.0%
겹 3
 
3.0%
살 3
 
3.0%
돼 3
 
3.0%
Other values (48) 53
53.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 95
96.0%
Space Separator 4
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
펌 8
 
8.4%
밥 8
 
8.4%
국 7
 
7.4%
비 4
 
4.2%
지 3
 
3.2%
삼 3
 
3.2%
겹 3
 
3.2%
살 3
 
3.2%
돼 3
 
3.2%
탕 2
 
2.1%
Other values (47) 51
53.7%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 95
96.0%
Common 4
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
펌 8
 
8.4%
밥 8
 
8.4%
국 7
 
7.4%
비 4
 
4.2%
지 3
 
3.2%
삼 3
 
3.2%
겹 3
 
3.2%
살 3
 
3.2%
돼 3
 
3.2%
탕 2
 
2.1%
Other values (47) 51
53.7%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 95
96.0%
ASCII 4
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
펌 8
 
8.4%
밥 8
 
8.4%
국 7
 
7.4%
비 4
 
4.2%
지 3
 
3.2%
삼 3
 
3.2%
겹 3
 
3.2%
살 3
 
3.2%
돼 3
 
3.2%
탕 2
 
2.1%
Other values (47) 51
53.7%
ASCII
ValueCountFrequency (%)
4
100.0%

가격2
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14450
Minimum3000
Maximum39000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:12:08.065777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile4175
Q17000
median8500
Q320000
95-th percentile35000
Maximum39000
Range36000
Interquartile range (IQR)13000

Descriptive statistics

Standard deviation10272.452
Coefficient of variation (CV)0.71089635
Kurtosis0.025393743
Mean14450
Median Absolute Deviation (MAD)4250
Skewness1.0356036
Sum433500
Variance1.0552328 × 108
MonotonicityNot monotonic
2023-12-12T08:12:08.168171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8000 6
20.0%
20000 4
13.3%
7000 3
10.0%
6000 3
10.0%
25000 2
 
6.7%
35000 2
 
6.7%
16000 2
 
6.7%
30000 1
 
3.3%
3000 1
 
3.3%
10000 1
 
3.3%
Other values (5) 5
16.7%
ValueCountFrequency (%)
3000 1
 
3.3%
3500 1
 
3.3%
5000 1
 
3.3%
6000 3
10.0%
7000 3
10.0%
8000 6
20.0%
9000 1
 
3.3%
10000 1
 
3.3%
15000 1
 
3.3%
16000 2
 
6.7%
ValueCountFrequency (%)
39000 1
 
3.3%
35000 2
 
6.7%
30000 1
 
3.3%
25000 2
 
6.7%
20000 4
13.3%
16000 2
 
6.7%
15000 1
 
3.3%
10000 1
 
3.3%
9000 1
 
3.3%
8000 6
20.0%

품목3
Text

MISSING 

Distinct22
Distinct (%)78.6%
Missing2
Missing (%)6.7%
Memory size372.0 B
2023-12-12T08:12:08.321576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length3.4285714
Min length2

Characters and Unicode

Total characters96
Distinct characters60
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)75.0%

Sample

1st row염색
2nd row염색
3rd row염색
4th row염색
5th row염색
ValueCountFrequency (%)
염색 7
25.0%
순대 1
 
3.6%
육개장 1
 
3.6%
콩나물비빔밥 1
 
3.6%
팥칼국수 1
 
3.6%
한우(500g 1
 
3.6%
닭개장 1
 
3.6%
참치김밥 1
 
3.6%
주물럭 1
 
3.6%
생삼겹살 1
 
3.6%
Other values (12) 12
42.9%
2023-12-12T08:12:08.613131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
염 7
 
7.3%
색 7
 
7.3%
4
 
4.2%
장 3
 
3.1%
개 3
 
3.1%
국 3
 
3.1%
비 2
 
2.1%
밥 2
 
2.1%
0 2
 
2.1%
살 2
 
2.1%
Other values (50) 61
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 86
89.6%
Space Separator 4
 
4.2%
Decimal Number 3
 
3.1%
Lowercase Letter 1
 
1.0%
Open Punctuation 1
 
1.0%
Close Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
염 7
 
8.1%
색 7
 
8.1%
장 3
 
3.5%
개 3
 
3.5%
국 3
 
3.5%
비 2
 
2.3%
밥 2
 
2.3%
살 2
 
2.3%
겹 2
 
2.3%
생 2
 
2.3%
Other values (44) 53
61.6%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
5 1
33.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 86
89.6%
Common 9
 
9.4%
Latin 1
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
염 7
 
8.1%
색 7
 
8.1%
장 3
 
3.5%
개 3
 
3.5%
국 3
 
3.5%
비 2
 
2.3%
밥 2
 
2.3%
살 2
 
2.3%
겹 2
 
2.3%
생 2
 
2.3%
Other values (44) 53
61.6%
Common
ValueCountFrequency (%)
4
44.4%
0 2
22.2%
( 1
 
11.1%
5 1
 
11.1%
) 1
 
11.1%
Latin
ValueCountFrequency (%)
g 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 86
89.6%
ASCII 10
 
10.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
염 7
 
8.1%
색 7
 
8.1%
장 3
 
3.5%
개 3
 
3.5%
국 3
 
3.5%
비 2
 
2.3%
밥 2
 
2.3%
살 2
 
2.3%
겹 2
 
2.3%
생 2
 
2.3%
Other values (44) 53
61.6%
ASCII
ValueCountFrequency (%)
4
40.0%
0 2
20.0%
g 1
 
10.0%
( 1
 
10.0%
5 1
 
10.0%
) 1
 
10.0%

가격3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)50.0%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean13321.429
Minimum3500
Maximum49000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:12:08.741426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3500
5-th percentile4025
Q17000
median9500
Q316250
95-th percentile28250
Maximum49000
Range45500
Interquartile range (IQR)9250

Descriptive statistics

Standard deviation9947.2817
Coefficient of variation (CV)0.74671283
Kurtosis5.0648276
Mean13321.429
Median Absolute Deviation (MAD)4000
Skewness1.9777984
Sum373000
Variance98948413
MonotonicityNot monotonic
2023-12-12T08:12:08.828649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
7000 4
13.3%
20000 3
10.0%
15000 3
10.0%
25000 2
6.7%
13000 2
6.7%
5000 2
6.7%
3500 2
6.7%
8000 2
6.7%
9000 2
6.7%
6000 2
6.7%
Other values (4) 4
13.3%
(Missing) 2
6.7%
ValueCountFrequency (%)
3500 2
6.7%
5000 2
6.7%
6000 2
6.7%
7000 4
13.3%
8000 2
6.7%
9000 2
6.7%
10000 1
 
3.3%
12000 1
 
3.3%
13000 2
6.7%
15000 3
10.0%
ValueCountFrequency (%)
49000 1
 
3.3%
30000 1
 
3.3%
25000 2
6.7%
20000 3
10.0%
15000 3
10.0%
13000 2
6.7%
12000 1
 
3.3%
10000 1
 
3.3%
9000 2
6.7%
8000 2
6.7%

위도
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.791854
Minimum37.714958
Maximum37.943733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:12:08.929766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.714958
5-th percentile37.729757
Q137.761379
median37.768013
Q337.830575
95-th percentile37.86845
Maximum37.943733
Range0.2287752
Interquartile range (IQR)0.069195725

Descriptive statistics

Standard deviation0.051824465
Coefficient of variation (CV)0.0013713131
Kurtosis1.006312
Mean37.791854
Median Absolute Deviation (MAD)0.0122509
Skewness1.0889136
Sum1133.7556
Variance0.0026857752
MonotonicityNot monotonic
2023-12-12T08:12:09.103866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
37.9437332 1
 
3.3%
37.764482 1
 
3.3%
37.758217 1
 
3.3%
37.714958 1
 
3.3%
37.75897 1
 
3.3%
37.8536339 1
 
3.3%
37.7574348 1
 
3.3%
37.7632866 1
 
3.3%
37.7609673 1
 
3.3%
37.7247543 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
37.714958 1
3.3%
37.7247543 1
3.3%
37.7358704 1
3.3%
37.7574348 1
3.3%
37.7581596 1
3.3%
37.758217 1
3.3%
37.75897 1
3.3%
37.7609673 1
3.3%
37.7626152 1
3.3%
37.7627563 1
3.3%
ValueCountFrequency (%)
37.9437332 1
3.3%
37.8763033 1
3.3%
37.8588524 1
3.3%
37.8584862 1
3.3%
37.85724 1
3.3%
37.8536339 1
3.3%
37.8313103 1
3.3%
37.8309517 1
3.3%
37.8294449 1
3.3%
37.8103714 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.78213
Minimum126.68738
Maximum126.90807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T08:12:09.283498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68738
5-th percentile126.70932
Q1126.7726
median126.77617
Q3126.78571
95-th percentile126.84293
Maximum126.90807
Range0.2206941
Interquartile range (IQR)0.013107325

Descriptive statistics

Standard deviation0.041461032
Coefficient of variation (CV)0.00032702583
Kurtosis2.8144144
Mean126.78213
Median Absolute Deviation (MAD)0.0084687
Skewness0.53701867
Sum3803.464
Variance0.0017190171
MonotonicityNot monotonic
2023-12-12T08:12:09.421145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
126.8376846 1
 
3.3%
126.7764053 1
 
3.3%
126.908072 1
 
3.3%
126.76573 1
 
3.3%
126.7769 1
 
3.3%
126.7848969 1
 
3.3%
126.7720337 1
 
3.3%
126.7724609 1
 
3.3%
126.7759857 1
 
3.3%
126.7175751 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
126.6873779 1
3.3%
126.7025681 1
3.3%
126.7175751 1
3.3%
126.7621841 1
3.3%
126.7636108 1
3.3%
126.76573 1
3.3%
126.7720337 1
3.3%
126.7724609 1
3.3%
126.7730255 1
3.3%
126.7734985 1
3.3%
ValueCountFrequency (%)
126.908072 1
3.3%
126.847229 1
3.3%
126.8376846 1
3.3%
126.8348312 1
3.3%
126.8189774 1
3.3%
126.8170319 1
3.3%
126.7895126 1
3.3%
126.7859802 1
3.3%
126.7848969 1
3.3%
126.7843781 1
3.3%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2023-05-09 00:00:00
Maximum2023-05-09 00:00:00
2023-12-12T08:12:09.517474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:09.611857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T08:12:03.501798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.084686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.595311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.016319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.664156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.073095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.579611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.166497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.669060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.089476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.728040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.160521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.650267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.265234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.739673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.159408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.788166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.230272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.719752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.377202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.819383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.226349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.856974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.302156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.788376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.445627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.886410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.304424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.916981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.369472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.852528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.521594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:01.952822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.597726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:02.978932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:12:03.430699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:12:09.973862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종업소명대표자주소전화번호품목1가격1품목2가격2품목3가격3위도경도
연번1.0000.4671.0001.0001.0001.0000.8090.4070.6870.4500.8530.3100.4770.265
업종0.4671.0001.0001.0001.0001.0001.0000.8431.0000.8291.0000.6000.0000.941
업소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대표자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
품목10.8091.0001.0001.0001.0001.0001.0000.9300.9930.0001.0000.0000.8830.000
가격10.4070.8431.0001.0001.0001.0000.9301.0000.8560.9040.9140.1400.6960.517
품목20.6871.0001.0001.0001.0001.0000.9930.8561.0000.0001.0000.0000.9060.178
가격20.4500.8291.0001.0001.0001.0000.0000.9040.0001.0000.0000.7920.0000.755
품목30.8531.0001.0001.0001.0001.0001.0000.9141.0000.0001.0000.3620.8830.000
가격30.3100.6001.0001.0001.0001.0000.0000.1400.0000.7920.3621.0000.0000.158
위도0.4770.0001.0001.0001.0001.0000.8830.6960.9060.0000.8830.0001.0000.680
경도0.2650.9411.0001.0001.0001.0000.0000.5170.1780.7550.0000.1580.6801.000
2023-12-12T08:12:10.103182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번가격1가격2가격3위도경도업종
연번1.0000.038-0.277-0.348-0.337-0.0520.240
가격10.0381.0000.7140.5710.000-0.2950.480
가격2-0.2770.7141.0000.8280.019-0.0300.461
가격3-0.3480.5710.8281.0000.1130.0350.458
위도-0.3370.0000.0190.1131.0000.4670.000
경도-0.052-0.295-0.0300.0350.4671.0000.624
업종0.2400.4800.4610.4580.0000.6241.000

Missing values

2023-12-12T08:12:03.954337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:12:04.115697image/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.
2023-12-12T08:12:04.220229image/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

연번업종업소명대표자주소전화번호품목1가격1품목2가격2품목3가격3위도경도기준일자
01미용업은혜미용실정영신경기도 파주시 파평면 장마루로 246-1031-958-9761커트10000펌20000염색2000037.943733126.8376852023-05-09
12미용업팜스헤어클럽신순자경기도 파주시 송화로 11 팜스상가 169동 2층 210031-947-8908커트7000펌20000염색2000037.771984126.7770312023-05-09
23미용업로이미용실김혜란경기도 파주시 광탄면 혜음로 1127-2031-957-5007커트8000펌25000염색2500037.781937126.8472292023-05-09
34미용업헤어짱최수남경기도 파주시 탄현면 여치길 81031-944-0810커트10000펌30000염색2500037.770416126.7025682023-05-09
45미용업시온미용실송도자경기도 파주시 월롱면 쉰우물길 13-16031-941-5285커트5000펌20000염색1300037.777874126.7895132023-05-09
56한식털보순대이유림경기도 파주시 금정24길 15031-941-5558순대국8000돼지국밥8000순대500037.762756126.7734992023-05-09
67미용업선미용실민명심경기도 파주시 금정24길 5-1031-941-2882커트8000펌20000염색2000037.762615126.7730262023-05-09
78한식소망순대국한미경기도 파주시 금정24길 19031-944-3929순대국8000돼지국밥8000김치찌개700037.762844126.7737812023-05-09
89한식중앙스낵(중앙순대국)엄남이경기도 파주시 금정24길 21031-943-1252순대국8000돼비지7000제육볶음1200037.762909126.7739182023-05-09
910한식암소식당임규태경기도 파주시 월롱면 엘지로360번길 19031-945-1255차돌박이22000꽃등심25000생오겹살1500037.876303126.7621842023-05-09
연번업종업소명대표자주소전화번호품목1가격1품목2가격2품목3가격3위도경도기준일자
2021한식삼겹살에 소주한잔김창구경기도 파주시 문산읍 문향로 84번길 2, 2층031-953-0068대패삼겹살(180g)8000냉동삼겹살8000생삼겹살1300037.858852126.785982023-05-09
2122한식원주네식당유순월경기도 파주시 파주읍 시장거리길 34031-952-5558육개장5000소머리국밥5000주물럭500037.830952126.8170322023-05-09
2223한식김밥레스토랑김혜영경기도 파주시 청석로 256 교하1번가 106호031-941-2904김밥2500치즈김밥3500참치김밥350037.724754126.7175752023-05-09
2324한식옛날집박미수경기도 파주시 문화로 74-3031-945-6677닭칼국수8000닭곰탕8000닭개장900037.760967126.7759862023-05-09
2425한식아리몽안승모경기도 파주시 금정로 98031-957-7100국산돼지갈비18000삼겹살16000<NA><NA>37.763287126.7724612023-05-09
2526한식참숯한우천국장경자경기도 파주시 한마음2길 47031-944-1592한우우거지탕8000갈비탕8000한우(500g)4900037.757435126.7720342023-05-09
2627한식전주웰빙새알팥죽김희정경기도 파주시 문향로 17번길 21 1층 102호031-953-1888새알팥죽8000보리비빔밥6000팥칼국수700037.853634126.7848972023-05-09
2728한식전주콩나물국밥 진말생면국수안종섭경기도 파주시 시청로 25, 101~102호031-948-9080콩나물국밥5000얼큰콩나물국밥6000콩나물비빔밥600037.75897126.77692023-05-09
2829미용업네일엔젤조금례경기도 파주시 송학 1길, 158-27<NA>손젤29000발젤39000손케어1500037.714958126.765732023-05-09
2930숙박업시실리이일승경기도 파주시 광탄면 보광로 569<NA>대실20000숙박35000<NA><NA>37.758217126.9080722023-05-09