Overview

Dataset statistics

Number of variables7
Number of observations53
Missing cells5
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory60.5 B

Variable types

Text4
Numeric2
DateTime1

Dataset

Description전라북도 전주시 내 동물병원을 제공하며 동물병원명, 인허가일자, 영업상태명 등을 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=3&menuCd=DOM_000000103007001000&pListTypeStr=&pId=15053269

Alerts

데이터기준일자 has constant value ""Constant
지번주소 has 1 (1.9%) missing valuesMissing
전화번호 has 4 (7.5%) missing valuesMissing
동물병원명 has unique valuesUnique
도로명주소 has unique valuesUnique

Reproduction

Analysis started2024-03-14 00:11:43.637340
Analysis finished2024-03-14 00:11:44.574430
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동물병원명
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2024-03-14T09:11:44.703808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.3773585
Min length5

Characters and Unicode

Total characters391
Distinct characters101
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st row홍 동물병원
2nd row초원 동물병원
3rd row푸른 동물병원
4th row박영재 동물병원
5th row아리랑 동물병원
ValueCountFrequency (%)
동물병원 24
29.3%
코끼리 2
 
2.4%
동물의료센터 2
 
2.4%
혁신동물병원 1
 
1.2%
아프리카 1
 
1.2%
힐링동물병원 1
 
1.2%
전주동물병원 1
 
1.2%
유앤아이동물병원 1
 
1.2%
데이지동물병원 1
 
1.2%
하트동물병원 1
 
1.2%
Other values (47) 47
57.3%
2024-03-14T09:11:45.018604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 52
 
13.3%
물 51
 
13.0%
원 48
 
12.3%
병 47
 
12.0%
31
 
7.9%
리 7
 
1.8%
이 6
 
1.5%
터 5
 
1.3%
스 5
 
1.3%
하 4
 
1.0%
Other values (91) 135
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 351
89.8%
Space Separator 31
 
7.9%
Decimal Number 6
 
1.5%
Uppercase Letter 3
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 52
14.8%
물 51
14.5%
원 48
 
13.7%
병 47
 
13.4%
리 7
 
2.0%
이 6
 
1.7%
터 5
 
1.4%
스 5
 
1.4%
하 4
 
1.1%
펫 4
 
1.1%
Other values (84) 122
34.8%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
4 2
33.3%
1 1
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
V 1
33.3%
M 1
33.3%
Space Separator
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 351
89.8%
Common 37
 
9.5%
Latin 3
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 52
14.8%
물 51
14.5%
원 48
 
13.7%
병 47
 
13.4%
리 7
 
2.0%
이 6
 
1.7%
터 5
 
1.4%
스 5
 
1.4%
하 4
 
1.1%
펫 4
 
1.1%
Other values (84) 122
34.8%
Common
ValueCountFrequency (%)
31
83.8%
2 3
 
8.1%
4 2
 
5.4%
1 1
 
2.7%
Latin
ValueCountFrequency (%)
P 1
33.3%
V 1
33.3%
M 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 351
89.8%
ASCII 40
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 52
14.8%
물 51
14.5%
원 48
 
13.7%
병 47
 
13.4%
리 7
 
2.0%
이 6
 
1.7%
터 5
 
1.4%
스 5
 
1.4%
하 4
 
1.1%
펫 4
 
1.1%
Other values (84) 122
34.8%
ASCII
ValueCountFrequency (%)
31
77.5%
2 3
 
7.5%
4 2
 
5.0%
P 1
 
2.5%
V 1
 
2.5%
M 1
 
2.5%
1 1
 
2.5%

도로명주소
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2024-03-14T09:11:45.321929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length36
Mean length28.584906
Min length19

Characters and Unicode

Total characters1515
Distinct characters103
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st row전라북도 전주시 완산구 팔달로 99-2 (전동)
2nd row전라북도 전주시 완산구 팔달로 286-11 (서노송동)
3rd row전라북도 전주시 덕진구 쪽구름로 71-2 (반월동)
4th row전라북도 전주시 완산구 전주천서로 111 (서서학동)
5th row전라북도 전주시 완산구 서원로 294 (중화산동2가)
ValueCountFrequency (%)
전라북도 53
 
16.5%
전주시 53
 
16.5%
덕진구 27
 
8.4%
완산구 26
 
8.1%
인후동1가 5
 
1.6%
송천동1가 4
 
1.2%
모악로 4
 
1.2%
서신동 4
 
1.2%
백제대로 4
 
1.2%
서원로 3
 
0.9%
Other values (112) 138
43.0%
2024-03-14T09:11:45.660476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
268
 
17.7%
전 109
 
7.2%
1 61
 
4.0%
구 56
 
3.7%
북 55
 
3.6%
주 55
 
3.6%
동 55
 
3.6%
시 54
 
3.6%
도 53
 
3.5%
라 53
 
3.5%
Other values (93) 696
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 931
61.5%
Space Separator 268
 
17.7%
Decimal Number 202
 
13.3%
Close Punctuation 51
 
3.4%
Open Punctuation 51
 
3.4%
Other Punctuation 7
 
0.5%
Dash Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 109
 
11.7%
구 56
 
6.0%
북 55
 
5.9%
주 55
 
5.9%
동 55
 
5.9%
시 54
 
5.8%
도 53
 
5.7%
라 53
 
5.7%
로 49
 
5.3%
가 35
 
3.8%
Other values (78) 357
38.3%
Decimal Number
ValueCountFrequency (%)
1 61
30.2%
2 40
19.8%
6 16
 
7.9%
4 14
 
6.9%
7 14
 
6.9%
3 14
 
6.9%
0 13
 
6.4%
8 12
 
5.9%
9 11
 
5.4%
5 7
 
3.5%
Space Separator
ValueCountFrequency (%)
268
100.0%
Close Punctuation
ValueCountFrequency (%)
) 51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 51
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 931
61.5%
Common 584
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 109
 
11.7%
구 56
 
6.0%
북 55
 
5.9%
주 55
 
5.9%
동 55
 
5.9%
시 54
 
5.8%
도 53
 
5.7%
라 53
 
5.7%
로 49
 
5.3%
가 35
 
3.8%
Other values (78) 357
38.3%
Common
ValueCountFrequency (%)
268
45.9%
1 61
 
10.4%
) 51
 
8.7%
( 51
 
8.7%
2 40
 
6.8%
6 16
 
2.7%
4 14
 
2.4%
7 14
 
2.4%
3 14
 
2.4%
0 13
 
2.2%
Other values (5) 42
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 931
61.5%
ASCII 584
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
268
45.9%
1 61
 
10.4%
) 51
 
8.7%
( 51
 
8.7%
2 40
 
6.8%
6 16
 
2.7%
4 14
 
2.4%
7 14
 
2.4%
3 14
 
2.4%
0 13
 
2.2%
Other values (5) 42
 
7.2%
Hangul
ValueCountFrequency (%)
전 109
 
11.7%
구 56
 
6.0%
북 55
 
5.9%
주 55
 
5.9%
동 55
 
5.9%
시 54
 
5.8%
도 53
 
5.7%
라 53
 
5.7%
로 49
 
5.3%
가 35
 
3.8%
Other values (78) 357
38.3%

지번주소
Text

MISSING 

Distinct51
Distinct (%)98.1%
Missing1
Missing (%)1.9%
Memory size556.0 B
2024-03-14T09:11:45.922817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length23.730769
Min length19

Characters and Unicode

Total characters1234
Distinct characters50
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

Unique50 ?
Unique (%)96.2%

Sample

1st row전라북도 전주시 완산구 전동 260
2nd row전라북도 전주시 완산구 서노송동 660-60
3rd row전라북도 전주시 덕진구 반월동 248-7
4th row전라북도 전주시 완산구 서서학동 942-8
5th row전라북도 전주시 완산구 중화산동2가 535-4
ValueCountFrequency (%)
전라북도 52
19.8%
전주시 52
19.8%
완산구 26
 
9.9%
덕진구 26
 
9.9%
인후동1가 5
 
1.9%
송천동1가 4
 
1.5%
서신동 4
 
1.5%
효자동1가 4
 
1.5%
송천동2가 3
 
1.1%
중화산동2가 3
 
1.1%
Other values (74) 83
31.7%
2024-03-14T09:11:46.239851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
210
17.0%
전 105
 
8.5%
1 56
 
4.5%
북 54
 
4.4%
동 53
 
4.3%
라 52
 
4.2%
도 52
 
4.2%
주 52
 
4.2%
시 52
 
4.2%
구 52
 
4.2%
Other values (40) 496
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 718
58.2%
Decimal Number 258
 
20.9%
Space Separator 210
 
17.0%
Dash Punctuation 48
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 105
14.6%
북 54
 
7.5%
동 53
 
7.4%
라 52
 
7.2%
도 52
 
7.2%
주 52
 
7.2%
시 52
 
7.2%
구 52
 
7.2%
가 37
 
5.2%
산 33
 
4.6%
Other values (28) 176
24.5%
Decimal Number
ValueCountFrequency (%)
1 56
21.7%
2 40
15.5%
3 33
12.8%
6 25
9.7%
8 24
9.3%
4 21
 
8.1%
7 17
 
6.6%
0 15
 
5.8%
9 15
 
5.8%
5 12
 
4.7%
Space Separator
ValueCountFrequency (%)
210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 718
58.2%
Common 516
41.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 105
14.6%
북 54
 
7.5%
동 53
 
7.4%
라 52
 
7.2%
도 52
 
7.2%
주 52
 
7.2%
시 52
 
7.2%
구 52
 
7.2%
가 37
 
5.2%
산 33
 
4.6%
Other values (28) 176
24.5%
Common
ValueCountFrequency (%)
210
40.7%
1 56
 
10.9%
- 48
 
9.3%
2 40
 
7.8%
3 33
 
6.4%
6 25
 
4.8%
8 24
 
4.7%
4 21
 
4.1%
7 17
 
3.3%
0 15
 
2.9%
Other values (2) 27
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 718
58.2%
ASCII 516
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
210
40.7%
1 56
 
10.9%
- 48
 
9.3%
2 40
 
7.8%
3 33
 
6.4%
6 25
 
4.8%
8 24
 
4.7%
4 21
 
4.1%
7 17
 
3.3%
0 15
 
2.9%
Other values (2) 27
 
5.2%
Hangul
ValueCountFrequency (%)
전 105
14.6%
북 54
 
7.5%
동 53
 
7.4%
라 52
 
7.2%
도 52
 
7.2%
주 52
 
7.2%
시 52
 
7.2%
구 52
 
7.2%
가 37
 
5.2%
산 33
 
4.6%
Other values (28) 176
24.5%

위도
Real number (ℝ)

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.831476
Minimum35.788308
Maximum35.874402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-03-14T09:11:46.363117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.788308
5-th percentile35.795077
Q135.81238
median35.828885
Q335.848854
95-th percentile35.872392
Maximum35.874402
Range0.086093616
Interquartile range (IQR)0.036474883

Descriptive statistics

Standard deviation0.024203638
Coefficient of variation (CV)0.00067548538
Kurtosis-0.88659314
Mean35.831476
Median Absolute Deviation (MAD)0.017283013
Skewness0.21809656
Sum1899.0682
Variance0.00058581609
MonotonicityNot monotonic
2024-03-14T09:11:46.465526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.7950769974 2
 
3.8%
35.8115052812 1
 
1.9%
35.8071131529 1
 
1.9%
35.8627914698 1
 
1.9%
35.8321511969 1
 
1.9%
35.8341431577 1
 
1.9%
35.8077493799 1
 
1.9%
35.8648252698 1
 
1.9%
35.8123795376 1
 
1.9%
35.874132253 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
35.7883084277 1
1.9%
35.7891967896 1
1.9%
35.7950769974 2
3.8%
35.8000210475 1
1.9%
35.8014014415 1
1.9%
35.8037905428 1
1.9%
35.8061680315 1
1.9%
35.8071131529 1
1.9%
35.8077493799 1
1.9%
35.8100952093 1
1.9%
ValueCountFrequency (%)
35.8744020441 1
1.9%
35.874132253 1
1.9%
35.8732864221 1
1.9%
35.8717963216 1
1.9%
35.8710292115 1
1.9%
35.8660237386 1
1.9%
35.8649147272 1
1.9%
35.8648252698 1
1.9%
35.8627914698 1
1.9%
35.8615454467 1
1.9%

경도
Real number (ℝ)

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.12775
Minimum127.05998
Maximum127.16877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-03-14T09:11:46.570199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.05998
5-th percentile127.07589
Q1127.11671
median127.12648
Q3127.14593
95-th percentile127.16608
Maximum127.16877
Range0.10879195
Interquartile range (IQR)0.029220319

Descriptive statistics

Standard deviation0.024973312
Coefficient of variation (CV)0.00019644266
Kurtosis0.52929359
Mean127.12775
Median Absolute Deviation (MAD)0.014245971
Skewness-0.63100974
Sum6737.7706
Variance0.00062366633
MonotonicityNot monotonic
2024-03-14T09:11:46.678302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1321811493 2
 
3.8%
127.1492174041 1
 
1.9%
127.1167129671 1
 
1.9%
127.1213141528 1
 
1.9%
127.1028547834 1
 
1.9%
127.1117391567 1
 
1.9%
127.1164072 1
 
1.9%
127.1212833716 1
 
1.9%
127.1236140324 1
 
1.9%
127.0761982875 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
127.0599762115 1
1.9%
127.074657517 1
1.9%
127.0754931082 1
1.9%
127.0761584025 1
1.9%
127.0761982875 1
1.9%
127.1023800241 1
1.9%
127.1028547834 1
1.9%
127.1114225246 1
1.9%
127.1117391567 1
1.9%
127.1129001666 1
1.9%
ValueCountFrequency (%)
127.1687681628 1
1.9%
127.167891946 1
1.9%
127.1672352644 1
1.9%
127.1653042855 1
1.9%
127.1636050396 1
1.9%
127.1575322302 1
1.9%
127.1563597524 1
1.9%
127.1550939989 1
1.9%
127.1530501307 1
1.9%
127.1520961391 1
1.9%

전화번호
Text

MISSING 

Distinct48
Distinct (%)98.0%
Missing4
Missing (%)7.5%
Memory size556.0 B
2024-03-14T09:11:46.877763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters588
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

Unique47 ?
Unique (%)95.9%

Sample

1st row063-284-6520
2nd row063-254-3266
3rd row063-284-9491
4th row063-284-7774
5th row063-223-5311
ValueCountFrequency (%)
063-902-7975 2
 
4.1%
063-276-7500 1
 
2.0%
063-285-7975 1
 
2.0%
063-284-6520 1
 
2.0%
063-211-6677 1
 
2.0%
063-221-7582 1
 
2.0%
063-247-8874 1
 
2.0%
063-272-8575 1
 
2.0%
063-255-0630 1
 
2.0%
063-275-7553 1
 
2.0%
Other values (38) 38
77.6%
2024-03-14T09:11:47.157088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 98
16.7%
2 77
13.1%
3 74
12.6%
0 73
12.4%
6 73
12.4%
7 64
10.9%
5 48
8.2%
4 27
 
4.6%
8 20
 
3.4%
9 18
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 490
83.3%
Dash Punctuation 98
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 77
15.7%
3 74
15.1%
0 73
14.9%
6 73
14.9%
7 64
13.1%
5 48
9.8%
4 27
 
5.5%
8 20
 
4.1%
9 18
 
3.7%
1 16
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 588
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 98
16.7%
2 77
13.1%
3 74
12.6%
0 73
12.4%
6 73
12.4%
7 64
10.9%
5 48
8.2%
4 27
 
4.6%
8 20
 
3.4%
9 18
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 98
16.7%
2 77
13.1%
3 74
12.6%
0 73
12.4%
6 73
12.4%
7 64
10.9%
5 48
8.2%
4 27
 
4.6%
8 20
 
3.4%
9 18
 
3.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
Minimum2021-07-22 00:00:00
Maximum2021-07-22 00:00:00
2024-03-14T09:11:47.305711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:11:47.395250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-14T09:11:44.247513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:11:44.134377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:11:44.306394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:11:44.189399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:11:47.455703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동물병원명도로명주소지번주소위도경도전화번호
동물병원명1.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0000.4650.825
경도1.0001.0001.0000.4651.0000.974
전화번호1.0001.0001.0000.8250.9741.000
2024-03-14T09:11:47.535255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.000-0.104
경도-0.1041.000

Missing values

2024-03-14T09:11:44.389518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:11:44.471931image/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.
2024-03-14T09:11:44.540877image/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

동물병원명도로명주소지번주소위도경도전화번호데이터기준일자
0홍 동물병원전라북도 전주시 완산구 팔달로 99-2 (전동)전라북도 전주시 완산구 전동 26035.811505127.149217063-284-65202021-07-22
1초원 동물병원전라북도 전주시 완산구 팔달로 286-11 (서노송동)전라북도 전주시 완산구 서노송동 660-6035.828057127.143012063-254-32662021-07-22
2푸른 동물병원전라북도 전주시 덕진구 쪽구름로 71-2 (반월동)전라북도 전주시 덕진구 반월동 248-735.871796127.076158063-284-94912021-07-22
3박영재 동물병원전라북도 전주시 완산구 전주천서로 111 (서서학동)전라북도 전주시 완산구 서서학동 942-835.810095127.149184063-284-77742021-07-22
4아리랑 동물병원전라북도 전주시 완산구 서원로 294 (중화산동2가)전라북도 전주시 완산구 중화산동2가 535-435.811602127.126482063-223-53112021-07-22
5효자 동물병원전라북도 전주시 완산구 백제대로 121 (효자동1가)전라북도 전주시 완산구 효자동1가 563-835.803791127.123758063-224-66722021-07-22
6우리동물병원전라북도 전주시 완산구 용머리로 86 (효자동1가)전라북도 전주시 완산구 효자동1가 276-735.806168127.119433063-226-33332021-07-22
7하나 동물병원전라북도 전주시 덕진구 송천중앙로 17, 1층 (덕진동2가)전라북도 전주시 덕진구 덕진동2가 167-15635.848854127.118559063-253-56772021-07-22
8서신 동물병원전라북도 전주시 완산구 서신로 33 (서신동)전라북도 전주시 완산구 서신동 831-435.828369127.115595063-253-27472021-07-22
9동부 동물병원전라북도 전주시 완산구 기린대로 139 (경원동3가)전라북도 전주시 완산구 경원동3가 3-1035.820985127.152096063-231-24562021-07-22
동물병원명도로명주소지번주소위도경도전화번호데이터기준일자
43하루동물병원전라북도 전주시 완산구 모악로 4692 (평화동2가)전라북도 전주시 완산구 평화동2가 302-335.788308127.130931063-232-59002021-07-22
44사랑플러스 동물병원전라북도 전주시 덕진구 쪽구름2길 3 (반월동)전라북도 전주시 덕진구 반월동 236-1135.873286127.075493<NA>2021-07-22
45사라 양한방 동물병원전라북도 전주시 덕진구 벚꽃로 21 (진북동)전라북도 전주시 덕진구 진북동 434-9135.828885127.132097<NA>2021-07-22
46MVP동물병원전라북도 전주시 완산구 세내로 285 (송천1동)전라북도 전주시 완산구 효자동3가 1542-135.815549127.111423063-227-85752021-07-22
47더펫 동물병원전라북도 전주시 덕진구 한배미1길 61 (인후동1가)전라북도 전주시 덕진구 인후동1가 948-335.822781127.167892063-247-75662021-07-22
48에코동물병원전라북도 전주시 덕진구 세병로 41 (송천동2가)전라북도 전주시 덕진구 송천동2가 131135.874402127.129846063-251-79752021-07-22
49자스민동물병원전라북도 전주시 덕진구 백석로 18전라북도 전주시 덕진구 송천동2가 1358-335.871029127.136262063-276-39392021-07-22
50코끼리 동물의료센터전라북도 전주시 완산구 모악로 4769, 2층 1호 (평화동1가)전라북도 전주시 완산구 평화동1가 708-435.795077127.132181<NA>2021-07-22
51아이들 동물병원전라북도 전주시 덕진구 만성중앙로 25, 미네르바빌딩 1층 101,102,103호 (만성동)전라북도 전주시 덕진구 만성동 1366-335.844127.074658063-273-55752021-07-22
52더펫동물병원전라북도 전주시 완산구 서원로 168전라북도 전주시 완산구 효자동3가 37-7135.813276127.1129<NA>2021-07-22