Overview

Dataset statistics

Number of variables13
Number of observations76
Missing cells235
Missing cells (%)23.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory108.7 B

Variable types

Categorical2
Text8
Numeric3

Dataset

Description농산물 직거래 사업장 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=QZTWVHWN1UE4ZHV8RI7Y28182844&infSeq=1

Alerts

우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 우편번호 and 2 other fieldsHigh correlation
전화번호 has 2 (2.6%) missing valuesMissing
도로명주소 has 15 (19.7%) missing valuesMissing
사업장상세주소 has 64 (84.2%) missing valuesMissing
우편번호 has 7 (9.2%) missing valuesMissing
위치정보 has 70 (92.1%) missing valuesMissing
운영주기/URL has 63 (82.9%) missing valuesMissing
WGS84위도 has 7 (9.2%) missing valuesMissing
WGS84경도 has 7 (9.2%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:49:01.693624
Analysis finished2023-12-10 22:49:03.489796
Duration1.8 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size740.0 B
고양시
15 
성남시
6 
안성시
6 
김포시
5 
과천시
 
4
Other values (20)
40 

Length

Max length4
Median length3
Mean length3.0526316
Min length3

Unique

Unique7 ?
Unique (%)9.2%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 15
19.7%
성남시 6
 
7.9%
안성시 6
 
7.9%
김포시 5
 
6.6%
과천시 4
 
5.3%
시흥시 4
 
5.3%
수원시 3
 
3.9%
포천시 3
 
3.9%
파주시 3
 
3.9%
여주시 3
 
3.9%
Other values (15) 24
31.6%

Length

2023-12-11T07:49:03.568874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 15
19.7%
안성시 6
 
7.9%
성남시 6
 
7.9%
김포시 5
 
6.6%
과천시 4
 
5.3%
시흥시 4
 
5.3%
수원시 3
 
3.9%
포천시 3
 
3.9%
파주시 3
 
3.9%
여주시 3
 
3.9%
Other values (15) 24
31.6%

운영유형
Categorical

Distinct3
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size740.0 B
직매장
27 
직거래장터
26 
로컬푸드직매장
23 

Length

Max length7
Median length5
Mean length4.8947368
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row직거래장터
2nd row직거래장터
3rd row직거래장터
4th row로컬푸드직매장
5th row직거래장터

Common Values

ValueCountFrequency (%)
직매장 27
35.5%
직거래장터 26
34.2%
로컬푸드직매장 23
30.3%

Length

2023-12-11T07:49:03.671985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:49:03.759012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직매장 27
35.5%
직거래장터 26
34.2%
로컬푸드직매장 23
30.3%

사업장명
Text

Distinct72
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-11T07:49:03.965748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.7236842
Min length4

Characters and Unicode

Total characters739
Distinct characters146
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

Unique69 ?
Unique (%)90.8%

Sample

1st row2016 고양 라페장터
2nd row벽제농협
3rd row벽제농협
4th row벽제농협 로컬푸드직매장
5th row부안조합공동법인
ValueCountFrequency (%)
로컬푸드직매장 13
 
11.2%
송포농협 4
 
3.4%
직거래장터 3
 
2.6%
벽제농협 3
 
2.6%
성남농협 3
 
2.6%
낙생농협 3
 
2.6%
김포로컬푸드 2
 
1.7%
화성 2
 
1.7%
로컬푸드직매장(1호점 2
 
1.7%
일산농협 2
 
1.7%
Other values (76) 79
68.1%
2023-12-11T07:49:04.324499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 51
 
6.9%
40
 
5.4%
협 38
 
5.1%
장 34
 
4.6%
로 28
 
3.8%
드 28
 
3.8%
컬 27
 
3.7%
푸 27
 
3.7%
직 25
 
3.4%
매 24
 
3.2%
Other values (136) 417
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 658
89.0%
Space Separator 40
 
5.4%
Decimal Number 17
 
2.3%
Close Punctuation 11
 
1.5%
Open Punctuation 11
 
1.5%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 51
 
7.8%
협 38
 
5.8%
장 34
 
5.2%
로 28
 
4.3%
드 28
 
4.3%
컬 27
 
4.1%
푸 27
 
4.1%
직 25
 
3.8%
매 24
 
3.6%
점 15
 
2.3%
Other values (127) 361
54.9%
Decimal Number
ValueCountFrequency (%)
2 6
35.3%
1 6
35.3%
6 2
 
11.8%
0 2
 
11.8%
3 1
 
5.9%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 658
89.0%
Common 81
 
11.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 51
 
7.8%
협 38
 
5.8%
장 34
 
5.2%
로 28
 
4.3%
드 28
 
4.3%
컬 27
 
4.1%
푸 27
 
4.1%
직 25
 
3.8%
매 24
 
3.6%
점 15
 
2.3%
Other values (127) 361
54.9%
Common
ValueCountFrequency (%)
40
49.4%
) 11
 
13.6%
( 11
 
13.6%
2 6
 
7.4%
1 6
 
7.4%
6 2
 
2.5%
0 2
 
2.5%
/ 2
 
2.5%
3 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 658
89.0%
ASCII 81
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 51
 
7.8%
협 38
 
5.8%
장 34
 
5.2%
로 28
 
4.3%
드 28
 
4.3%
컬 27
 
4.1%
푸 27
 
4.1%
직 25
 
3.8%
매 24
 
3.6%
점 15
 
2.3%
Other values (127) 361
54.9%
ASCII
ValueCountFrequency (%)
40
49.4%
) 11
 
13.6%
( 11
 
13.6%
2 6
 
7.4%
1 6
 
7.4%
6 2
 
2.5%
0 2
 
2.5%
/ 2
 
2.5%
3 1
 
1.2%

전화번호
Text

MISSING 

Distinct58
Distinct (%)78.4%
Missing2
Missing (%)2.6%
Memory size740.0 B
2023-12-11T07:49:04.564404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.027027
Min length9

Characters and Unicode

Total characters890
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)68.9%

Sample

1st row070-4735-1275
2nd row031-220-8673
3rd row031-220-8673
4th row031-962-1873
5th row063-240-3144
ValueCountFrequency (%)
031-220-8673 11
 
14.9%
2
 
2.7%
031-678-2543 2
 
2.7%
031-944-6556 2
 
2.7%
031-962-1873 2
 
2.7%
031-8025-4666 2
 
2.7%
031-975-8322 2
 
2.7%
031-671-7823 1
 
1.4%
031-671-6969 1
 
1.4%
031-656-0747 1
 
1.4%
Other values (48) 48
64.9%
2023-12-11T07:49:04.957567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 148
16.6%
0 131
14.7%
3 125
14.0%
1 87
9.8%
2 72
8.1%
6 68
7.6%
7 65
7.3%
8 58
 
6.5%
5 50
 
5.6%
4 36
 
4.0%
Other values (2) 50
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 720
80.9%
Dash Punctuation 148
 
16.6%
Other Punctuation 22
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 131
18.2%
3 125
17.4%
1 87
12.1%
2 72
10.0%
6 68
9.4%
7 65
9.0%
8 58
8.1%
5 50
 
6.9%
4 36
 
5.0%
9 28
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%
Other Punctuation
ValueCountFrequency (%)
* 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 890
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 148
16.6%
0 131
14.7%
3 125
14.0%
1 87
9.8%
2 72
8.1%
6 68
7.6%
7 65
7.3%
8 58
 
6.5%
5 50
 
5.6%
4 36
 
4.0%
Other values (2) 50
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 148
16.6%
0 131
14.7%
3 125
14.0%
1 87
9.8%
2 72
8.1%
6 68
7.6%
7 65
7.3%
8 58
 
6.5%
5 50
 
5.6%
4 36
 
4.0%
Other values (2) 50
 
5.6%

도로명주소
Text

MISSING 

Distinct53
Distinct (%)86.9%
Missing15
Missing (%)19.7%
Memory size740.0 B
2023-12-11T07:49:05.188582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length18.918033
Min length13

Characters and Unicode

Total characters1154
Distinct characters135
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

Unique45 ?
Unique (%)73.8%

Sample

1st row경기도 고양시 덕양구 통일로 775
2nd row경기도 고양시 덕양구 혜음로 44
3rd row경기도 고양시 덕양구 통일로 775
4th row경기도 고양시 일산서구 경의로 856
5th row경기도 고양시 일산서구 일산로 640
ValueCountFrequency (%)
경기도 61
 
21.5%
고양시 12
 
4.2%
일산서구 6
 
2.1%
분당구 5
 
1.8%
성남시 5
 
1.8%
안성시 4
 
1.4%
덕양구 4
 
1.4%
30 4
 
1.4%
시흥시 4
 
1.4%
여주시 3
 
1.1%
Other values (138) 176
62.0%
2023-12-11T07:49:05.561097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
223
19.3%
경 65
 
5.6%
도 64
 
5.5%
시 62
 
5.4%
기 61
 
5.3%
로 55
 
4.8%
1 38
 
3.3%
양 27
 
2.3%
구 25
 
2.2%
4 21
 
1.8%
Other values (125) 513
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 732
63.4%
Space Separator 223
 
19.3%
Decimal Number 192
 
16.6%
Dash Punctuation 7
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 65
 
8.9%
도 64
 
8.7%
시 62
 
8.5%
기 61
 
8.3%
로 55
 
7.5%
양 27
 
3.7%
구 25
 
3.4%
고 18
 
2.5%
길 16
 
2.2%
일 14
 
1.9%
Other values (113) 325
44.4%
Decimal Number
ValueCountFrequency (%)
1 38
19.8%
4 21
10.9%
0 21
10.9%
2 21
10.9%
3 20
10.4%
7 16
8.3%
9 16
8.3%
8 14
 
7.3%
6 13
 
6.8%
5 12
 
6.2%
Space Separator
ValueCountFrequency (%)
223
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 732
63.4%
Common 422
36.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 65
 
8.9%
도 64
 
8.7%
시 62
 
8.5%
기 61
 
8.3%
로 55
 
7.5%
양 27
 
3.7%
구 25
 
3.4%
고 18
 
2.5%
길 16
 
2.2%
일 14
 
1.9%
Other values (113) 325
44.4%
Common
ValueCountFrequency (%)
223
52.8%
1 38
 
9.0%
4 21
 
5.0%
0 21
 
5.0%
2 21
 
5.0%
3 20
 
4.7%
7 16
 
3.8%
9 16
 
3.8%
8 14
 
3.3%
6 13
 
3.1%
Other values (2) 19
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 732
63.4%
ASCII 422
36.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223
52.8%
1 38
 
9.0%
4 21
 
5.0%
0 21
 
5.0%
2 21
 
5.0%
3 20
 
4.7%
7 16
 
3.8%
9 16
 
3.8%
8 14
 
3.3%
6 13
 
3.1%
Other values (2) 19
 
4.5%
Hangul
ValueCountFrequency (%)
경 65
 
8.9%
도 64
 
8.7%
시 62
 
8.5%
기 61
 
8.3%
로 55
 
7.5%
양 27
 
3.7%
구 25
 
3.4%
고 18
 
2.5%
길 16
 
2.2%
일 14
 
1.9%
Other values (113) 325
44.4%

지번주소
Text

Distinct68
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-11T07:49:05.858332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length25
Mean length21.710526
Min length13

Characters and Unicode

Total characters1650
Distinct characters151
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

Unique60 ?
Unique (%)78.9%

Sample

1st row경기도 고양시 일산동구 장항동 1479번지
2nd row경기도 고양시 덕양구 관산동 226-2번지
3rd row경기도 고양시 덕양구 고양동 144번지
4th row경기도 고양시 덕양구 관산동 226-2번지
5th row경기도 고양시 일산서구 덕이동 238-12번지
ValueCountFrequency (%)
경기도 73
 
20.1%
고양시 15
 
4.1%
일산서구 7
 
1.9%
안성시 6
 
1.6%
분당구 6
 
1.6%
성남시 6
 
1.6%
덕양구 5
 
1.4%
김포시 5
 
1.4%
과천시 4
 
1.1%
시흥시 4
 
1.1%
Other values (186) 233
64.0%
2023-12-11T07:49:06.268265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
288
 
17.5%
시 81
 
4.9%
도 79
 
4.8%
경 78
 
4.7%
기 78
 
4.7%
번 64
 
3.9%
지 62
 
3.8%
동 61
 
3.7%
1 59
 
3.6%
- 49
 
3.0%
Other values (141) 751
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1024
62.1%
Decimal Number 289
 
17.5%
Space Separator 288
 
17.5%
Dash Punctuation 49
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 81
 
7.9%
도 79
 
7.7%
경 78
 
7.6%
기 78
 
7.6%
번 64
 
6.2%
지 62
 
6.1%
동 61
 
6.0%
양 37
 
3.6%
구 31
 
3.0%
리 22
 
2.1%
Other values (129) 431
42.1%
Decimal Number
ValueCountFrequency (%)
1 59
20.4%
2 46
15.9%
4 35
12.1%
5 34
11.8%
3 27
9.3%
6 22
 
7.6%
8 19
 
6.6%
0 18
 
6.2%
9 16
 
5.5%
7 13
 
4.5%
Space Separator
ValueCountFrequency (%)
288
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1024
62.1%
Common 626
37.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 81
 
7.9%
도 79
 
7.7%
경 78
 
7.6%
기 78
 
7.6%
번 64
 
6.2%
지 62
 
6.1%
동 61
 
6.0%
양 37
 
3.6%
구 31
 
3.0%
리 22
 
2.1%
Other values (129) 431
42.1%
Common
ValueCountFrequency (%)
288
46.0%
1 59
 
9.4%
- 49
 
7.8%
2 46
 
7.3%
4 35
 
5.6%
5 34
 
5.4%
3 27
 
4.3%
6 22
 
3.5%
8 19
 
3.0%
0 18
 
2.9%
Other values (2) 29
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1024
62.1%
ASCII 626
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288
46.0%
1 59
 
9.4%
- 49
 
7.8%
2 46
 
7.3%
4 35
 
5.6%
5 34
 
5.4%
3 27
 
4.3%
6 22
 
3.5%
8 19
 
3.0%
0 18
 
2.9%
Other values (2) 29
 
4.6%
Hangul
ValueCountFrequency (%)
시 81
 
7.9%
도 79
 
7.7%
경 78
 
7.6%
기 78
 
7.6%
번 64
 
6.2%
지 62
 
6.1%
동 61
 
6.0%
양 37
 
3.6%
구 31
 
3.0%
리 22
 
2.1%
Other values (129) 431
42.1%

사업장상세주소
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing64
Missing (%)84.2%
Memory size740.0 B
2023-12-11T07:49:06.433529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length9.3333333
Min length4

Characters and Unicode

Total characters112
Distinct characters44
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

Unique12 ?
Unique (%)100.0%

Sample

1st row벽제농협 본점
2nd row벽제농협 고양지점
3rd row고양시 탄현역
4th row송포농협(가좌지점)마트앞
5th row송포농협(대화지점)마트앞
ValueCountFrequency (%)
벽제농협 2
 
9.5%
본점 2
 
9.5%
낙생농협 2
 
9.5%
일산농협 2
 
9.5%
탄현역 1
 
4.8%
송포농협(가좌지점)마트앞 1
 
4.8%
송포농협(대화지점)마트앞 1
 
4.8%
송포농협(백송지점)마트앞 1
 
4.8%
고양시 1
 
4.8%
풍산지점 1
 
4.8%
Other values (7) 7
33.3%
2023-12-11T07:49:06.702595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 9
 
8.0%
협 9
 
8.0%
9
 
8.0%
점 9
 
8.0%
지 7
 
6.2%
송 4
 
3.6%
포 4
 
3.6%
앞 4
 
3.6%
마 4
 
3.6%
) 3
 
2.7%
Other values (34) 50
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 97
86.6%
Space Separator 9
 
8.0%
Close Punctuation 3
 
2.7%
Open Punctuation 3
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 9
 
9.3%
협 9
 
9.3%
점 9
 
9.3%
지 7
 
7.2%
송 4
 
4.1%
포 4
 
4.1%
앞 4
 
4.1%
마 4
 
4.1%
산 3
 
3.1%
트 3
 
3.1%
Other values (31) 41
42.3%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 97
86.6%
Common 15
 
13.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 9
 
9.3%
협 9
 
9.3%
점 9
 
9.3%
지 7
 
7.2%
송 4
 
4.1%
포 4
 
4.1%
앞 4
 
4.1%
마 4
 
4.1%
산 3
 
3.1%
트 3
 
3.1%
Other values (31) 41
42.3%
Common
ValueCountFrequency (%)
9
60.0%
) 3
 
20.0%
( 3
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 97
86.6%
ASCII 15
 
13.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 9
 
9.3%
협 9
 
9.3%
점 9
 
9.3%
지 7
 
7.2%
송 4
 
4.1%
포 4
 
4.1%
앞 4
 
4.1%
마 4
 
4.1%
산 3
 
3.1%
트 3
 
3.1%
Other values (31) 41
42.3%
ASCII
ValueCountFrequency (%)
9
60.0%
) 3
 
20.0%
( 3
 
20.0%

우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct60
Distinct (%)87.0%
Missing7
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean13257.116
Minimum10073
Maximum18531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-11T07:49:06.822856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10073
5-th percentile10135
Q110369
median12662
Q314971
95-th percentile17730.6
Maximum18531
Range8458
Interquartile range (IQR)4602

Descriptive statistics

Standard deviation2735.1721
Coefficient of variation (CV)0.20631728
Kurtosis-1.0639433
Mean13257.116
Median Absolute Deviation (MAD)2301
Skewness0.49143247
Sum914741
Variance7481166.3
MonotonicityNot monotonic
2023-12-11T07:49:06.937628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10342 2
 
2.6%
10937 2
 
2.6%
10274 2
 
2.6%
16488 2
 
2.6%
13627 2
 
2.6%
10135 2
 
2.6%
10287 2
 
2.6%
10308 2
 
2.6%
10369 2
 
2.6%
13831 1
 
1.3%
Other values (50) 50
65.8%
(Missing) 7
 
9.2%
ValueCountFrequency (%)
10073 1
1.3%
10098 1
1.3%
10100 1
1.3%
10135 2
2.6%
10214 1
1.3%
10228 1
1.3%
10232 1
1.3%
10274 2
2.6%
10287 2
2.6%
10308 2
2.6%
ValueCountFrequency (%)
18531 1
1.3%
18421 1
1.3%
18336 1
1.3%
17829 1
1.3%
17583 1
1.3%
17572 1
1.3%
17561 1
1.3%
17560 1
1.3%
17514 1
1.3%
17505 1
1.3%

위치정보
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing70
Missing (%)92.1%
Memory size740.0 B
2023-12-11T07:49:07.086137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length8.5
Mean length8.5
Min length4

Characters and Unicode

Total characters51
Distinct characters38
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

Unique6 ?
Unique (%)100.0%

Sample

1st row라페스타 내
2nd row벽제농협 고양지점
3rd row운양동 롯데캐슬
4th row농협 주차장
5th row공도공원 금호3단지 아파트 사잇길
ValueCountFrequency (%)
라페스타 1
 
7.7%
내 1
 
7.7%
벽제농협 1
 
7.7%
고양지점 1
 
7.7%
운양동 1
 
7.7%
롯데캐슬 1
 
7.7%
농협 1
 
7.7%
주차장 1
 
7.7%
공도공원 1
 
7.7%
금호3단지 1
 
7.7%
Other values (3) 3
23.1%
2023-12-11T07:49:07.345540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
 
13.7%
양 3
 
5.9%
공 2
 
3.9%
아 2
 
3.9%
농 2
 
3.9%
협 2
 
3.9%
지 2
 
3.9%
도 1
 
2.0%
원 1
 
2.0%
금 1
 
2.0%
Other values (28) 28
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43
84.3%
Space Separator 7
 
13.7%
Decimal Number 1
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 3
 
7.0%
공 2
 
4.7%
아 2
 
4.7%
농 2
 
4.7%
협 2
 
4.7%
지 2
 
4.7%
도 1
 
2.3%
원 1
 
2.3%
금 1
 
2.3%
호 1
 
2.3%
Other values (26) 26
60.5%
Space Separator
ValueCountFrequency (%)
7
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43
84.3%
Common 8
 
15.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 3
 
7.0%
공 2
 
4.7%
아 2
 
4.7%
농 2
 
4.7%
협 2
 
4.7%
지 2
 
4.7%
도 1
 
2.3%
원 1
 
2.3%
금 1
 
2.3%
호 1
 
2.3%
Other values (26) 26
60.5%
Common
ValueCountFrequency (%)
7
87.5%
3 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43
84.3%
ASCII 8
 
15.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7
87.5%
3 1
 
12.5%
Hangul
ValueCountFrequency (%)
양 3
 
7.0%
공 2
 
4.7%
아 2
 
4.7%
농 2
 
4.7%
협 2
 
4.7%
지 2
 
4.7%
도 1
 
2.3%
원 1
 
2.3%
금 1
 
2.3%
호 1
 
2.3%
Other values (26) 26
60.5%

운영주기/URL
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing63
Missing (%)82.9%
Memory size740.0 B
2023-12-11T07:49:07.490966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length11.615385
Min length4

Characters and Unicode

Total characters151
Distinct characters39
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row매주 수, 목
2nd row2회/주(월, 금)
3rd row3회/년
4th rowwww.ohjung.co.kr
5th rowwww.snnh.com
ValueCountFrequency (%)
금 3
14.3%
1회/주 2
 
9.5%
토 2
 
9.5%
3회/주 1
 
4.8%
www.gosam.co.kr 1
 
4.8%
4회/년 1
 
4.8%
banking.nonghyup.com 1
 
4.8%
shcity.atc.go.kr 1
 
4.8%
일 1
 
4.8%
매주 1
 
4.8%
Other values (7) 7
33.3%
2023-12-11T07:49:07.975042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 16
 
10.6%
w 12
 
7.9%
o 10
 
6.6%
8
 
5.3%
n 7
 
4.6%
c 7
 
4.6%
회 6
 
4.0%
/ 6
 
4.0%
k 5
 
3.3%
g 5
 
3.3%
Other values (29) 69
45.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80
53.0%
Other Punctuation 26
 
17.2%
Other Letter 23
 
15.2%
Space Separator 8
 
5.3%
Decimal Number 6
 
4.0%
Close Punctuation 4
 
2.6%
Open Punctuation 4
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 12
15.0%
o 10
12.5%
n 7
 
8.8%
c 7
 
8.8%
k 5
 
6.2%
g 5
 
6.2%
m 4
 
5.0%
h 4
 
5.0%
r 4
 
5.0%
u 3
 
3.8%
Other values (9) 19
23.8%
Other Letter
ValueCountFrequency (%)
회 6
26.1%
주 5
21.7%
금 3
13.0%
년 2
 
8.7%
토 2
 
8.7%
매 1
 
4.3%
일 1
 
4.3%
월 1
 
4.3%
목 1
 
4.3%
수 1
 
4.3%
Decimal Number
ValueCountFrequency (%)
3 2
33.3%
1 2
33.3%
4 1
16.7%
2 1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 16
61.5%
/ 6
 
23.1%
, 4
 
15.4%
Space Separator
ValueCountFrequency (%)
8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 80
53.0%
Common 48
31.8%
Hangul 23
 
15.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 12
15.0%
o 10
12.5%
n 7
 
8.8%
c 7
 
8.8%
k 5
 
6.2%
g 5
 
6.2%
m 4
 
5.0%
h 4
 
5.0%
r 4
 
5.0%
u 3
 
3.8%
Other values (9) 19
23.8%
Common
ValueCountFrequency (%)
. 16
33.3%
8
16.7%
/ 6
 
12.5%
) 4
 
8.3%
( 4
 
8.3%
, 4
 
8.3%
3 2
 
4.2%
1 2
 
4.2%
4 1
 
2.1%
2 1
 
2.1%
Hangul
ValueCountFrequency (%)
회 6
26.1%
주 5
21.7%
금 3
13.0%
년 2
 
8.7%
토 2
 
8.7%
매 1
 
4.3%
일 1
 
4.3%
월 1
 
4.3%
목 1
 
4.3%
수 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128
84.8%
Hangul 23
 
15.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 16
 
12.5%
w 12
 
9.4%
o 10
 
7.8%
8
 
6.2%
n 7
 
5.5%
c 7
 
5.5%
/ 6
 
4.7%
k 5
 
3.9%
g 5
 
3.9%
) 4
 
3.1%
Other values (19) 48
37.5%
Hangul
ValueCountFrequency (%)
회 6
26.1%
주 5
21.7%
금 3
13.0%
년 2
 
8.7%
토 2
 
8.7%
매 1
 
4.3%
일 1
 
4.3%
월 1
 
4.3%
목 1
 
4.3%
수 1
 
4.3%

판매품목
Text

Distinct48
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-11T07:49:08.220299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length87
Median length83
Mean length23.934211
Min length1

Characters and Unicode

Total characters1819
Distinct characters149
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

Unique45 ?
Unique (%)59.2%

Sample

1st row옥수수, 복숭아, 포도 등 계절상품
2nd row농,축,제,선
3rd row농,축,수,제,선
4th row지역 원예농산물, 축산물, 가공품
5th row농,축,특
ValueCountFrequency (%)
가공품 25
 
5.9%
지역 23
 
5.4%
원예농산물 23
 
5.4%
축산물 23
 
5.4%
쌀 14
 
3.3%
찹쌀 13
 
3.1%
검정콩 10
 
2.3%
포도 8
 
1.9%
양파 8
 
1.9%
땅콩 8
 
1.9%
Other values (111) 271
63.6%
2023-12-11T07:49:08.588230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
350
19.2%
, 330
18.1%
물 58
 
3.2%
산 56
 
3.1%
쌀 40
 
2.2%
축 38
 
2.1%
농 38
 
2.1%
고 31
 
1.7%
품 28
 
1.5%
지 28
 
1.5%
Other values (139) 822
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1127
62.0%
Space Separator 350
 
19.2%
Other Punctuation 330
 
18.1%
Open Punctuation 6
 
0.3%
Close Punctuation 6
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
물 58
 
5.1%
산 56
 
5.0%
쌀 40
 
3.5%
축 38
 
3.4%
농 38
 
3.4%
고 31
 
2.8%
품 28
 
2.5%
지 28
 
2.5%
가 28
 
2.5%
공 26
 
2.3%
Other values (135) 756
67.1%
Space Separator
ValueCountFrequency (%)
350
100.0%
Other Punctuation
ValueCountFrequency (%)
, 330
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1127
62.0%
Common 692
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
물 58
 
5.1%
산 56
 
5.0%
쌀 40
 
3.5%
축 38
 
3.4%
농 38
 
3.4%
고 31
 
2.8%
품 28
 
2.5%
지 28
 
2.5%
가 28
 
2.5%
공 26
 
2.3%
Other values (135) 756
67.1%
Common
ValueCountFrequency (%)
350
50.6%
, 330
47.7%
( 6
 
0.9%
) 6
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1127
62.0%
ASCII 692
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
350
50.6%
, 330
47.7%
( 6
 
0.9%
) 6
 
0.9%
Hangul
ValueCountFrequency (%)
물 58
 
5.1%
산 56
 
5.0%
쌀 40
 
3.5%
축 38
 
3.4%
농 38
 
3.4%
고 31
 
2.8%
품 28
 
2.5%
지 28
 
2.5%
가 28
 
2.5%
공 26
 
2.3%
Other values (135) 756
67.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)88.4%
Missing7
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean37.480646
Minimum36.999158
Maximum38.041124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-11T07:49:08.721338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.999158
5-th percentile37.007746
Q137.298216
median37.46669
Q337.686616
95-th percentile37.873016
Maximum38.041124
Range1.0419661
Interquartile range (IQR)0.38839961

Descriptive statistics

Standard deviation0.25260304
Coefficient of variation (CV)0.0067395594
Kurtosis-0.61435877
Mean37.480646
Median Absolute Deviation (MAD)0.20467051
Skewness-0.14915095
Sum2586.1646
Variance0.063808297
MonotonicityNot monotonic
2023-12-11T07:49:08.854552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.7428753591 2
 
2.6%
37.7043785302 2
 
2.6%
37.6821804988 2
 
2.6%
37.6872226851 2
 
2.6%
37.6639559951 2
 
2.6%
37.6866155418 2
 
2.6%
37.3497116373 2
 
2.6%
37.2620194717 2
 
2.6%
37.7964116425 1
 
1.3%
37.794707959 1
 
1.3%
Other values (51) 51
67.1%
(Missing) 7
 
9.2%
ValueCountFrequency (%)
36.9991575017 1
1.3%
37.0034397403 1
1.3%
37.00359127 1
1.3%
37.0036364195 1
1.3%
37.0139109782 1
1.3%
37.0653747712 1
1.3%
37.0817058518 1
1.3%
37.1435662717 1
1.3%
37.1737405241 1
1.3%
37.2076867031 1
1.3%
ValueCountFrequency (%)
38.0411236356 1
1.3%
37.9457922787 1
1.3%
37.8949001275 1
1.3%
37.8914474615 1
1.3%
37.8453680722 1
1.3%
37.7964116425 1
1.3%
37.794707959 1
1.3%
37.7428753591 2
2.6%
37.7097713849 1
1.3%
37.7043785302 2
2.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)88.4%
Missing7
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean127.01722
Minimum126.67869
Maximum127.65393
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-11T07:49:08.987727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.67869
5-th percentile126.73813
Q1126.7996
median126.99821
Q3127.16607
95-th percentile127.4731
Maximum127.65393
Range0.97523862
Interquartile range (IQR)0.36646961

Descriptive statistics

Standard deviation0.24219694
Coefficient of variation (CV)0.001906804
Kurtosis-0.082168979
Mean127.01722
Median Absolute Deviation (MAD)0.19290162
Skewness0.72653304
Sum8764.188
Variance0.058659356
MonotonicityNot monotonic
2023-12-11T07:49:09.130302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8102887077 2
 
2.6%
126.9024549999 2
 
2.6%
126.7582861371 2
 
2.6%
126.8638494304 2
 
2.6%
126.7996021339 2
 
2.6%
126.7713868104 2
 
2.6%
127.11128911 2
 
2.6%
127.0326496721 2
 
2.6%
127.0797577568 1
 
1.3%
127.0899329992 1
 
1.3%
Other values (51) 51
67.1%
(Missing) 7
 
9.2%
ValueCountFrequency (%)
126.6786903019 1
1.3%
126.7010761713 1
1.3%
126.7108552242 1
1.3%
126.7373037111 1
1.3%
126.7393575183 1
1.3%
126.74026949 1
1.3%
126.7582861371 2
2.6%
126.7610276057 1
1.3%
126.7653515249 1
1.3%
126.7684890241 1
1.3%
ValueCountFrequency (%)
127.6539289238 1
1.3%
127.6366282067 1
1.3%
127.5433433064 1
1.3%
127.4925566554 1
1.3%
127.4439208535 1
1.3%
127.4138282787 1
1.3%
127.3964699581 1
1.3%
127.3584474992 1
1.3%
127.2627328516 1
1.3%
127.25763248 1
1.3%

Interactions

2023-12-11T07:49:02.899256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:02.524734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:02.713284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:02.963493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:02.580896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:02.777473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:03.030747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:02.651563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:49:02.839194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:49:09.218663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명운영유형사업장명전화번호도로명주소지번주소사업장상세주소우편번호위치정보운영주기/URL판매품목WGS84위도WGS84경도
시군명1.0000.6841.0000.9931.0001.0001.0000.9981.0001.0000.8160.9740.882
운영유형0.6841.0000.9730.9790.0000.000NaN0.643NaN1.0001.0000.5090.000
사업장명1.0000.9731.0000.9990.9960.9951.0001.0001.0001.0000.9931.0000.989
전화번호0.9930.9790.9991.0000.9940.9961.0000.9781.0001.0000.9680.9160.924
도로명주소1.0000.0000.9960.9941.0001.0001.0001.0000.0001.0000.9471.0001.000
지번주소1.0000.0000.9950.9961.0001.0001.0001.0001.0001.0000.9771.0001.000
사업장상세주소1.000NaN1.0001.0001.0001.0001.0001.000NaNNaN1.0001.0001.000
우편번호0.9980.6431.0000.9781.0001.0001.0001.0001.0001.0000.7140.9330.851
위치정보1.000NaN1.0001.0000.0001.000NaN1.0001.000NaN1.0001.0001.000
운영주기/URL1.0001.0001.0001.0001.0001.000NaN1.000NaN1.0001.0001.0001.000
판매품목0.8161.0000.9930.9680.9470.9771.0000.7141.0001.0001.0000.6750.787
WGS84위도0.9740.5091.0000.9161.0001.0001.0000.9331.0001.0000.6751.0000.751
WGS84경도0.8820.0000.9890.9241.0001.0001.0000.8511.0001.0000.7870.7511.000
2023-12-11T07:49:09.381744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명운영유형
시군명1.0000.385
운영유형0.3851.000
2023-12-11T07:49:09.477251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호WGS84위도WGS84경도시군명운영유형
우편번호1.000-0.8250.4650.8650.468
WGS84위도-0.8251.000-0.4410.7420.333
WGS84경도0.465-0.4411.0000.5010.000
시군명0.8650.7420.5011.0000.385
운영유형0.4680.3330.0000.3851.000

Missing values

2023-12-11T07:49:03.126128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:49:03.269281image/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.
2023-12-11T07:49:03.386765image/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

시군명운영유형사업장명전화번호도로명주소지번주소사업장상세주소우편번호위치정보운영주기/URL판매품목WGS84위도WGS84경도
0고양시직거래장터2016 고양 라페장터070-4735-1275<NA>경기도 고양시 일산동구 장항동 1479번지<NA><NA>라페스타 내<NA>옥수수, 복숭아, 포도 등 계절상품37.661098126.768489
1고양시직거래장터벽제농협031-220-8673경기도 고양시 덕양구 통일로 775경기도 고양시 덕양구 관산동 226-2번지벽제농협 본점10287<NA><NA>농,축,제,선37.687223126.863849
2고양시직거래장터벽제농협031-220-8673경기도 고양시 덕양구 혜음로 44경기도 고양시 덕양구 고양동 144번지벽제농협 고양지점10274<NA><NA>농,축,수,제,선37.704379126.902455
3고양시로컬푸드직매장벽제농협 로컬푸드직매장031-962-1873경기도 고양시 덕양구 통일로 775경기도 고양시 덕양구 관산동 226-2번지<NA>10287<NA><NA>지역 원예농산물, 축산물, 가공품37.687223126.863849
4고양시직거래장터부안조합공동법인063-240-3144경기도 고양시 일산서구 경의로 856경기도 고양시 일산서구 덕이동 238-12번지고양시 탄현역10232<NA><NA>농,축,특37.693915126.761028
5고양시직거래장터송포농협031-220-8673<NA>경기도 고양시 일산서구 가좌동 310-3송포농협(가좌지점)마트앞10214<NA><NA>농,축,제,선<NA><NA>
6고양시직거래장터송포농협031-220-8673경기도 고양시 일산서구 일산로 640경기도 고양시 일산서구 대화동 2032-2번지송포농협(대화지점)마트앞10369<NA><NA>농,축,제,선37.68218126.758286
7고양시직거래장터송포농협031-220-8673경기도 고양시 일산서구 덕이로 212경기도 고양시 일산서구 덕이동 1047-1번지송포농협(백송지점)마트앞10228<NA><NA>농,축,제,선37.696688126.739358
8고양시로컬푸드직매장송포농협 로컬푸드직매장031-929-3022경기도 고양시 일산서구 일산로 640경기도 고양시 일산서구 대화동 2032-2번지<NA>10369<NA><NA>지역 원예농산물, 축산물, 가공품37.68218126.758286
9고양시직거래장터수요/목요 직거래장터031-962-1873경기도 고양시 덕양구 혜음로 44경기도 고양시 덕양구 고양동 144번지<NA>10274벽제농협 고양지점<NA>신선농축산물37.704379126.902455
시군명운영유형사업장명전화번호도로명주소지번주소사업장상세주소우편번호위치정보운영주기/URL판매품목WGS84위도WGS84경도
66파주시로컬푸드직매장조리농협 로컬푸드직매장031-944-6556경기도 파주시 조리읍 봉천로 12경기도 파주시 조리읍 봉일천리 125-8번지<NA>10937<NA><NA>지역 원예농산물, 축산물, 가공품37.742875126.810289
67파주시직거래장터조리농협로컬푸드직거래장터031-944-6556경기도 파주시 조리읍 봉천로 12경기도 파주시 조리읍 봉일천리 125-8번지<NA>10937<NA><NA>딸기, 토마토 등 제철과일 및 농산물37.742875126.810289
68파주시직매장파주장단유통사업단031-953-9500경기도 파주시 문산읍 임진각로 148-73경기도 파주시 문산읍 마정리 1360-44번지<NA>10808<NA><NA>검정현미, 쌀, 찹쌀, 강낭콩, 검정콩, 일반콩37.891447126.740269
69평택시로컬푸드직매장평택로컬푸드직매장070-4800-5869경기도 평택시 은실고가길 54경기도 평택시 신대동 280-6번지<NA>17829<NA><NA>지역 원예농산물, 축산물, 가공품37.003591127.065426
70포천시로컬푸드직매장포천로컬푸드 파머스마켓070-8848-6478경기도 포천시 호국로 886경기도 포천시 설운동 72-2번지<NA>11162<NA><NA>지역 원예농산물, 축산물, 가공품37.845368127.159849
71포천시직거래장터포천시지부031-220-8673경기도 포천시 중앙로 96경기도 포천시 신읍동 33-4번지포천시 자원봉사센터 앞11147<NA><NA>농,축,선37.8949127.201681
72포천시직매장허브아일랜드031-535-6494<NA>경기 포천시 신북면 청신로347번길 35<NA><NA><NA><NA>은행 호두 밤<NA><NA>
73화성시로컬푸드직매장화성 로컬푸드직매장(1호점)031-8025-4666경기도 화성시 봉담읍 서봉산길 10경기도 화성시 봉담읍 덕리 7번지<NA>18336<NA><NA>지역 원예농산물, 축산물, 가공품37.173741126.938166
74화성시로컬푸드직매장화성 로컬푸드직매장(3호점)<NA>경기도 화성시 팔탄면 서해안고속도로 302경기도 화성시 팔탄면 덕천리 산101-2번지 화성휴게소 서울방향<NA>18531<NA><NA>지역 원예농산물, 축산물, 가공품37.143566126.881222
75화성시로컬푸드직매장화성로컬푸드직매장(2호점)031-8025-4666경기도 화성시 동탄숲속로35번길 10경기도 화성시 능동 535-1번지<NA>18421<NA><NA>지역 원예농산물, 축산물, 가공품37.209579127.052053