Overview

Dataset statistics

Number of variables7
Number of observations27
Missing cells12
Missing cells (%)6.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory62.7 B

Variable types

Text4
Numeric2
Categorical1

Dataset

Description전북특별자치도 전주시 동물판매업소 현황입니다.항목 : 사업장명칭, 소재지주소(도로명), 소재지 지번주소, 위도, 경도, 소재지전화번호제공부서 : 동물복지과
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/15053266/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
소재지전화번호 has 12 (44.4%) missing valuesMissing
사업장명칭 has unique valuesUnique
소재지주소(도로명) has unique valuesUnique
소재지 지번주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-03-15 02:28:10.376927
Analysis finished2024-03-15 02:28:12.703566
Duration2.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명칭
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2024-03-15T11:28:13.314682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length18
Mean length10.407407
Min length2

Characters and Unicode

Total characters281
Distinct characters98
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

Unique27 ?
Unique (%)100.0%

Sample

1st row펫365 강아지분양 고양이분양 전주백제로점
2nd row야옹 멍멍
3rd row콩피에르(confier)
4th row묘해묘해
5th row온퍼피 강아지분양 고양이분양 전주본점
ValueCountFrequency (%)
고양이분양 5
 
9.1%
강아지분양 5
 
9.1%
전주점 3
 
5.5%
펫365 2
 
3.6%
전주본점 2
 
3.6%
에버플러스 2
 
3.6%
홈플러스 2
 
3.6%
효자점 2
 
3.6%
야옹아 2
 
3.6%
수조관 2
 
3.6%
Other values (28) 28
50.9%
2024-03-15T11:28:14.485000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
10.0%
양 17
 
6.0%
점 11
 
3.9%
아 11
 
3.9%
분 11
 
3.9%
이 9
 
3.2%
주 9
 
3.2%
멍 8
 
2.8%
전 8
 
2.8%
고 6
 
2.1%
Other values (88) 163
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 226
80.4%
Space Separator 28
 
10.0%
Lowercase Letter 7
 
2.5%
Uppercase Letter 6
 
2.1%
Decimal Number 6
 
2.1%
Open Punctuation 4
 
1.4%
Close Punctuation 4
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
양 17
 
7.5%
점 11
 
4.9%
아 11
 
4.9%
분 11
 
4.9%
이 9
 
4.0%
주 9
 
4.0%
멍 8
 
3.5%
전 8
 
3.5%
고 6
 
2.7%
강 6
 
2.7%
Other values (70) 130
57.5%
Lowercase Letter
ValueCountFrequency (%)
c 1
14.3%
r 1
14.3%
e 1
14.3%
i 1
14.3%
f 1
14.3%
n 1
14.3%
o 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
L 2
33.3%
I 1
16.7%
D 1
16.7%
O 1
16.7%
S 1
16.7%
Decimal Number
ValueCountFrequency (%)
3 2
33.3%
6 2
33.3%
5 2
33.3%
Space Separator
ValueCountFrequency (%)
28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 226
80.4%
Common 42
 
14.9%
Latin 13
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
양 17
 
7.5%
점 11
 
4.9%
아 11
 
4.9%
분 11
 
4.9%
이 9
 
4.0%
주 9
 
4.0%
멍 8
 
3.5%
전 8
 
3.5%
고 6
 
2.7%
강 6
 
2.7%
Other values (70) 130
57.5%
Latin
ValueCountFrequency (%)
L 2
15.4%
c 1
7.7%
I 1
7.7%
D 1
7.7%
O 1
7.7%
r 1
7.7%
S 1
7.7%
e 1
7.7%
i 1
7.7%
f 1
7.7%
Other values (2) 2
15.4%
Common
ValueCountFrequency (%)
28
66.7%
( 4
 
9.5%
) 4
 
9.5%
3 2
 
4.8%
6 2
 
4.8%
5 2
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 226
80.4%
ASCII 55
 
19.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
50.9%
( 4
 
7.3%
) 4
 
7.3%
L 2
 
3.6%
3 2
 
3.6%
6 2
 
3.6%
5 2
 
3.6%
c 1
 
1.8%
I 1
 
1.8%
D 1
 
1.8%
Other values (8) 8
 
14.5%
Hangul
ValueCountFrequency (%)
양 17
 
7.5%
점 11
 
4.9%
아 11
 
4.9%
분 11
 
4.9%
이 9
 
4.0%
주 9
 
4.0%
멍 8
 
3.5%
전 8
 
3.5%
고 6
 
2.7%
강 6
 
2.7%
Other values (70) 130
57.5%

소재지주소(도로명)
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2024-03-15T11:28:15.175880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length23.518519
Min length21

Characters and Unicode

Total characters635
Distinct characters62
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

Unique27 ?
Unique (%)100.0%

Sample

1st row전북특별자치도 전주시 완산구 백제대로 237
2nd row전북특별자치도 전주시 덕진구 견훤로 375
3rd row전북특별자치도 전주시 완산구 우전2길 72
4th row전북특별자치도 전주시 완산구 구이로 2077
5th row전북특별자치도 전주시 덕진구 숲정이5길 69
ValueCountFrequency (%)
전북특별자치도 27
20.0%
전주시 27
20.0%
완산구 18
13.3%
덕진구 9
 
6.7%
기린대로 3
 
2.2%
거마평로 3
 
2.2%
견훤로 2
 
1.5%
서원로 2
 
1.5%
백제대로 2
 
1.5%
용머리로 2
 
1.5%
Other values (40) 40
29.6%
2024-03-15T11:28:16.197570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
108
17.0%
전 56
 
8.8%
구 28
 
4.4%
특 27
 
4.3%
별 27
 
4.3%
자 27
 
4.3%
치 27
 
4.3%
도 27
 
4.3%
주 27
 
4.3%
시 27
 
4.3%
Other values (52) 254
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 446
70.2%
Space Separator 108
 
17.0%
Decimal Number 77
 
12.1%
Dash Punctuation 4
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 56
 
12.6%
구 28
 
6.3%
특 27
 
6.1%
별 27
 
6.1%
자 27
 
6.1%
치 27
 
6.1%
도 27
 
6.1%
주 27
 
6.1%
시 27
 
6.1%
북 27
 
6.1%
Other values (40) 146
32.7%
Decimal Number
ValueCountFrequency (%)
1 14
18.2%
2 13
16.9%
7 11
14.3%
3 10
13.0%
5 9
11.7%
4 5
 
6.5%
6 5
 
6.5%
8 4
 
5.2%
0 4
 
5.2%
9 2
 
2.6%
Space Separator
ValueCountFrequency (%)
108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 446
70.2%
Common 189
29.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 56
 
12.6%
구 28
 
6.3%
특 27
 
6.1%
별 27
 
6.1%
자 27
 
6.1%
치 27
 
6.1%
도 27
 
6.1%
주 27
 
6.1%
시 27
 
6.1%
북 27
 
6.1%
Other values (40) 146
32.7%
Common
ValueCountFrequency (%)
108
57.1%
1 14
 
7.4%
2 13
 
6.9%
7 11
 
5.8%
3 10
 
5.3%
5 9
 
4.8%
4 5
 
2.6%
6 5
 
2.6%
8 4
 
2.1%
0 4
 
2.1%
Other values (2) 6
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 446
70.2%
ASCII 189
29.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
108
57.1%
1 14
 
7.4%
2 13
 
6.9%
7 11
 
5.8%
3 10
 
5.3%
5 9
 
4.8%
4 5
 
2.6%
6 5
 
2.6%
8 4
 
2.1%
0 4
 
2.1%
Other values (2) 6
 
3.2%
Hangul
ValueCountFrequency (%)
전 56
 
12.6%
구 28
 
6.3%
특 27
 
6.1%
별 27
 
6.1%
자 27
 
6.1%
치 27
 
6.1%
도 27
 
6.1%
주 27
 
6.1%
시 27
 
6.1%
북 27
 
6.1%
Other values (40) 146
32.7%

소재지 지번주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size344.0 B
2024-03-15T11:28:16.865961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length28
Mean length27
Min length25

Characters and Unicode

Total characters729
Distinct characters43
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

Unique27 ?
Unique (%)100.0%

Sample

1st row전북특별자치도 전주시 완산구 중화산동2가 631-6
2nd row전북특별자치도 전주시 덕진구 인후동2가 1578-15
3rd row전북특별자치도 전주시 완산구 효자동2가 93-1
4th row전북특별자치도 전주시 완산구 평화동2가 955
5th row전북특별자치도 전주시 덕진구 진북동 1166-16
ValueCountFrequency (%)
전북특별자치도 27
20.0%
전주시 27
20.0%
완산구 18
13.3%
덕진구 9
 
6.7%
효자동1가 5
 
3.7%
효자동2가 4
 
3.0%
효자동3가 3
 
2.2%
인후동1가 2
 
1.5%
중화산동2가 2
 
1.5%
우아동3가 2
 
1.5%
Other values (36) 36
26.7%
2024-03-15T11:28:17.882619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
111
 
15.2%
전 54
 
7.4%
자 39
 
5.3%
1 33
 
4.5%
북 28
 
3.8%
동 27
 
3.7%
특 27
 
3.7%
별 27
 
3.7%
치 27
 
3.7%
도 27
 
3.7%
Other values (33) 329
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 459
63.0%
Decimal Number 137
 
18.8%
Space Separator 111
 
15.2%
Dash Punctuation 22
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 54
 
11.8%
자 39
 
8.5%
북 28
 
6.1%
동 27
 
5.9%
특 27
 
5.9%
별 27
 
5.9%
치 27
 
5.9%
도 27
 
5.9%
주 27
 
5.9%
시 27
 
5.9%
Other values (21) 149
32.5%
Decimal Number
ValueCountFrequency (%)
1 33
24.1%
3 21
15.3%
2 19
13.9%
5 14
10.2%
7 12
 
8.8%
4 9
 
6.6%
8 9
 
6.6%
6 8
 
5.8%
9 7
 
5.1%
0 5
 
3.6%
Space Separator
ValueCountFrequency (%)
111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 459
63.0%
Common 270
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 54
 
11.8%
자 39
 
8.5%
북 28
 
6.1%
동 27
 
5.9%
특 27
 
5.9%
별 27
 
5.9%
치 27
 
5.9%
도 27
 
5.9%
주 27
 
5.9%
시 27
 
5.9%
Other values (21) 149
32.5%
Common
ValueCountFrequency (%)
111
41.1%
1 33
 
12.2%
- 22
 
8.1%
3 21
 
7.8%
2 19
 
7.0%
5 14
 
5.2%
7 12
 
4.4%
4 9
 
3.3%
8 9
 
3.3%
6 8
 
3.0%
Other values (2) 12
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 459
63.0%
ASCII 270
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111
41.1%
1 33
 
12.2%
- 22
 
8.1%
3 21
 
7.8%
2 19
 
7.0%
5 14
 
5.2%
7 12
 
4.4%
4 9
 
3.3%
8 9
 
3.3%
6 8
 
3.0%
Other values (2) 12
 
4.4%
Hangul
ValueCountFrequency (%)
전 54
 
11.8%
자 39
 
8.5%
북 28
 
6.1%
동 27
 
5.9%
특 27
 
5.9%
별 27
 
5.9%
치 27
 
5.9%
도 27
 
5.9%
주 27
 
5.9%
시 27
 
5.9%
Other values (21) 149
32.5%

위도
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.822125
Minimum35.783992
Maximum35.873907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-15T11:28:18.251490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.783992
5-th percentile35.802124
Q135.807956
median35.816332
Q335.836068
95-th percentile35.847085
Maximum35.873907
Range0.08991475
Interquartile range (IQR)0.02811109

Descriptive statistics

Standard deviation0.019111329
Coefficient of variation (CV)0.0005335063
Kurtosis0.72072337
Mean35.822125
Median Absolute Deviation (MAD)0.00960252
Skewness0.67176834
Sum967.19738
Variance0.00036524291
MonotonicityNot monotonic
2024-03-15T11:28:18.698510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
35.81428605 1
 
3.7%
35.84462783 1
 
3.7%
35.80657657 1
 
3.7%
35.84751071 1
 
3.7%
35.80021596 1
 
3.7%
35.82240755 1
 
3.7%
35.81680564 1
 
3.7%
35.80672988 1
 
3.7%
35.81327638 1
 
3.7%
35.80711315 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
35.78399198 1
3.7%
35.80021596 1
3.7%
35.80657657 1
3.7%
35.80672988 1
3.7%
35.80711315 1
3.7%
35.80774938 1
3.7%
35.80780598 1
3.7%
35.80810687 1
3.7%
35.81038959 1
3.7%
35.81193964 1
3.7%
ValueCountFrequency (%)
35.87390673 1
3.7%
35.84751071 1
3.7%
35.84609317 1
3.7%
35.84462783 1
3.7%
35.84411799 1
3.7%
35.83913095 1
3.7%
35.8374358 1
3.7%
35.83469923 1
3.7%
35.83369089 1
3.7%
35.82710136 1
3.7%

경도
Real number (ℝ)

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.12461
Minimum127.09407
Maximum127.16829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size371.0 B
2024-03-15T11:28:19.073254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.09407
5-th percentile127.09495
Q1127.11357
median127.12024
Q3127.13756
95-th percentile127.15566
Maximum127.16829
Range0.0742188
Interquartile range (IQR)0.02399045

Descriptive statistics

Standard deviation0.020603786
Coefficient of variation (CV)0.00016207551
Kurtosis-0.60473663
Mean127.12461
Median Absolute Deviation (MAD)0.0132059
Skewness0.44506882
Sum3432.3645
Variance0.00042451602
MonotonicityNot monotonic
2024-03-15T11:28:19.489382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
127.1225664 1
 
3.7%
127.1531333 1
 
3.7%
127.1142487 1
 
3.7%
127.1528945 1
 
3.7%
127.1207353 1
 
3.7%
127.1503786 1
 
3.7%
127.140198 1
 
3.7%
127.1202424 1
 
3.7%
127.1129002 1
 
3.7%
127.116713 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
127.0940695 1
3.7%
127.0948342 1
3.7%
127.095229 1
3.7%
127.1007469 1
3.7%
127.1025105 1
3.7%
127.1070365 1
3.7%
127.1129002 1
3.7%
127.1142487 1
3.7%
127.1162647 1
3.7%
127.1164072 1
3.7%
ValueCountFrequency (%)
127.1682883 1
3.7%
127.1563762 1
3.7%
127.1539756 1
3.7%
127.1531333 1
3.7%
127.1528945 1
3.7%
127.1503786 1
3.7%
127.140198 1
3.7%
127.1349318 1
3.7%
127.1291127 1
3.7%
127.128696 1
3.7%

소재지전화번호
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing12
Missing (%)44.4%
Memory size344.0 B
2024-03-15T11:28:20.174961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters180
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

Unique15 ?
Unique (%)100.0%

Sample

1st row063-247-9990
2nd row063-249-2500
3rd row063-221-1284
4th row063-271-0046
5th row063-283-9500
ValueCountFrequency (%)
063-247-9990 1
 
6.7%
063-249-2500 1
 
6.7%
063-221-1284 1
 
6.7%
063-271-0046 1
 
6.7%
063-283-9500 1
 
6.7%
063-275-0720 1
 
6.7%
063-236-6355 1
 
6.7%
063-254-5709 1
 
6.7%
063-221-7582 1
 
6.7%
063-232-9484 1
 
6.7%
Other values (5) 5
33.3%
2024-03-15T11:28:21.173061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
18.3%
- 30
16.7%
2 28
15.6%
3 21
11.7%
6 18
10.0%
8 11
 
6.1%
9 10
 
5.6%
4 8
 
4.4%
7 8
 
4.4%
5 8
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150
83.3%
Dash Punctuation 30
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33
22.0%
2 28
18.7%
3 21
14.0%
6 18
12.0%
8 11
 
7.3%
9 10
 
6.7%
4 8
 
5.3%
7 8
 
5.3%
5 8
 
5.3%
1 5
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33
18.3%
- 30
16.7%
2 28
15.6%
3 21
11.7%
6 18
10.0%
8 11
 
6.1%
9 10
 
5.6%
4 8
 
4.4%
7 8
 
4.4%
5 8
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
18.3%
- 30
16.7%
2 28
15.6%
3 21
11.7%
6 18
10.0%
8 11
 
6.1%
9 10
 
5.6%
4 8
 
4.4%
7 8
 
4.4%
5 8
 
4.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size344.0 B
2024-01-12
27 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-12
2nd row2024-01-12
3rd row2024-01-12
4th row2024-01-12
5th row2024-01-12

Common Values

ValueCountFrequency (%)
2024-01-12 27
100.0%

Length

2024-03-15T11:28:21.495316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T11:28:21.782742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-12 27
100.0%

Interactions

2024-03-15T11:28:11.291984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:10.796496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:11.544635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T11:28:11.028322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T11:28:21.965720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장명칭소재지주소(도로명)소재지 지번주소위도경도소재지전화번호
사업장명칭1.0001.0001.0001.0001.0001.000
소재지주소(도로명)1.0001.0001.0001.0001.0001.000
소재지 지번주소1.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0000.2811.000
경도1.0001.0001.0000.2811.0001.000
소재지전화번호1.0001.0001.0001.0001.0001.000
2024-03-15T11:28:22.142926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.485
경도0.4851.000

Missing values

2024-03-15T11:28:12.143258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T11:28:12.543071image/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

사업장명칭소재지주소(도로명)소재지 지번주소위도경도소재지전화번호데이터기준일자
0펫365 강아지분양 고양이분양 전주백제로점전북특별자치도 전주시 완산구 백제대로 237전북특별자치도 전주시 완산구 중화산동2가 631-635.814286127.122566<NA>2024-01-12
1야옹 멍멍전북특별자치도 전주시 덕진구 견훤로 375전북특별자치도 전주시 덕진구 인후동2가 1578-1535.844628127.153133063-247-99902024-01-12
2콩피에르(confier)전북특별자치도 전주시 완산구 우전2길 72전북특별자치도 전주시 완산구 효자동2가 93-135.81039127.107037<NA>2024-01-12
3묘해묘해전북특별자치도 전주시 완산구 구이로 2077전북특별자치도 전주시 완산구 평화동2가 95535.783992127.129113<NA>2024-01-12
4온퍼피 강아지분양 고양이분양 전주본점전북특별자치도 전주시 덕진구 숲정이5길 69전북특별자치도 전주시 덕진구 진북동 1166-1635.833691127.128696<NA>2024-01-12
5(주)롯데마트(전주점)전북특별자치도 전주시 완산구 우전로 240전북특별자치도 전주시 완산구 효자동2가 1234-335.816332127.10251063-249-25002024-01-12
6챔피언켄넬전북특별자치도 전주시 완산구 천잠로 227-14전북특별자치도 전주시 완산구 효자동2가 105835.807806127.09407<NA>2024-01-12
7도그똑똑전북특별자치도 전주시 완산구 중화산로 128전북특별자치도 전주시 완산구 중화산동2가 701-935.821942127.118278063-221-12842024-01-12
8미야옹전북특별자치도 전주시 덕진구 아중로 173전북특별자치도 전주시 덕진구 인후동1가 920-535.827101127.168288<NA>2024-01-12
9IDOLLS전북특별자치도 전주시 덕진구 견훤로 368전북특별자치도 전주시 덕진구 우아동3가 749-5735.844118127.153976<NA>2024-01-12
사업장명칭소재지주소(도로명)소재지 지번주소위도경도소재지전화번호데이터기준일자
17펫365 강아지분양 고양이분양 전주본점전북특별자치도 전주시 완산구 서원로 8전북특별자치도 전주시 완산구 효자동3가 1724-235.813402127.095229063-236-63552024-01-12
18쌍방울애견할인마트전북특별자치도 전주시 덕진구 기린대로 353전북특별자치도 전주시 덕진구 금암동 461-935.834699127.134932063-254-57092024-01-12
19펫샵전북특별자치도 전주시 완산구 거마평로 162전북특별자치도 전주시 완산구 효자동1가 287-1335.807113127.116713063-221-75822024-01-12
20개린이집 강아지분양 고양이분양 전주점전북특별자치도 전주시 완산구 서원로 168전북특별자치도 전주시 완산구 효자동2가 1-235.813276127.1129<NA>2024-01-12
21완산애견전북특별자치도 전주시 완산구 용머리로 101전북특별자치도 전주시 완산구 효자동1가 81035.80673127.120242063-232-94842024-01-12
22짱구와동팔이전북특별자치도 전주시 완산구 충경로 5-2전북특별자치도 전주시 완산구 다가동3가 64-1835.816806127.140198063-287-08222024-01-12
23화이트애견전북특별자치도 전주시 완산구 기린대로 163전북특별자치도 전주시 완산구 중노송동 498-2535.822408127.150379063-232-98982024-01-12
24아리아리 애견전북특별자치도 전주시 완산구 송정중앙로 2전북특별자치도 전주시 완산구 삼천동1가 575-535.800216127.120735063-222-20732024-01-12
25홈플러스 전주점 에버플러스 수조관전북특별자치도 전주시 덕진구 백제대로 771전북특별자치도 전주시 덕진구 우아동3가 733-135.847511127.152895063-249-80002024-01-12
26홈플러스 효자점 에버플러스 수조관전북특별자치도 전주시 완산구 용머리로 31전북특별자치도 전주시 완산구 효자동1가 431-535.806577127.114249063-281-80002024-01-12