Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells3
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.4 KiB
Average record size in memory127.3 B

Variable types

Categorical5
Text5
Numeric5

Alerts

base_de has constant value ""Constant
ctprvn_eng_nm is highly overall correlated with ctprvn_cd and 5 other fieldsHigh correlation
ctprvn_chnlng_nm is highly overall correlated with ctprvn_cd and 5 other fieldsHigh correlation
ctprvn_klang_nm is highly overall correlated with ctprvn_cd and 5 other fieldsHigh correlation
ctprvn_cd is highly overall correlated with signgu_cd and 3 other fieldsHigh correlation
signgu_cd is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
fclty_la is highly overall correlated with tel_no and 3 other fieldsHigh correlation
fclty_lo is highly overall correlated with ctprvn_klang_nm and 2 other fieldsHigh correlation
tel_no is highly overall correlated with fclty_laHigh correlation
fclty_flag_nm is highly imbalanced (80.6%)Imbalance
fclty_nm has unique valuesUnique
rdnmadr_nm has unique valuesUnique
fclty_la has unique valuesUnique
fclty_lo has unique valuesUnique
tel_no has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:50:02.970284
Analysis finished2023-12-10 09:50:10.637306
Duration7.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

fclty_flag_nm
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
동물병원
97 
동물약국
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동물병원
2nd row동물약국
3rd row동물병원
4th row동물병원
5th row동물병원

Common Values

ValueCountFrequency (%)
동물병원 97
97.0%
동물약국 3
 
3.0%

Length

2023-12-10T18:50:10.747903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:10.929528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동물병원 97
97.0%
동물약국 3
 
3.0%

fclty_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:11.248822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length15
Mean length8.55
Min length4

Characters and Unicode

Total characters855
Distinct characters182
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

Unique100 ?
Unique (%)100.0%

Sample

1st row카라동물병원
2nd row해뜨는약국
3rd row도그스타 동물병원
4th row아산아이윌24시동물메디컬센터
5th row데이동물의료센터
ValueCountFrequency (%)
동물병원 28
 
18.1%
동물의료센터 5
 
3.2%
24시 3
 
1.9%
웰니스동물병원 2
 
1.3%
카라동물병원 1
 
0.6%
모두동물병원 1
 
0.6%
연경동물병원 1
 
0.6%
쿨펫 1
 
0.6%
녹십자동물병원 1
 
0.6%
베테랑 1
 
0.6%
Other values (111) 111
71.6%
2023-12-10T18:50:12.014716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 97
 
11.3%
물 94
 
11.0%
원 81
 
9.5%
병 72
 
8.4%
55
 
6.4%
센 26
 
3.0%
터 25
 
2.9%
의 18
 
2.1%
료 17
 
2.0%
이 12
 
1.4%
Other values (172) 358
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 771
90.2%
Space Separator 55
 
6.4%
Decimal Number 10
 
1.2%
Uppercase Letter 9
 
1.1%
Lowercase Letter 6
 
0.7%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 97
 
12.6%
물 94
 
12.2%
원 81
 
10.5%
병 72
 
9.3%
센 26
 
3.4%
터 25
 
3.2%
의 18
 
2.3%
료 17
 
2.2%
이 12
 
1.6%
디 10
 
1.3%
Other values (155) 319
41.4%
Uppercase Letter
ValueCountFrequency (%)
C 3
33.3%
N 2
22.2%
S 1
 
11.1%
H 1
 
11.1%
M 1
 
11.1%
A 1
 
11.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
16.7%
n 1
16.7%
i 1
16.7%
m 1
16.7%
a 1
16.7%
l 1
16.7%
Decimal Number
ValueCountFrequency (%)
4 5
50.0%
2 5
50.0%
Space Separator
ValueCountFrequency (%)
55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 771
90.2%
Common 69
 
8.1%
Latin 15
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 97
 
12.6%
물 94
 
12.2%
원 81
 
10.5%
병 72
 
9.3%
센 26
 
3.4%
터 25
 
3.2%
의 18
 
2.3%
료 17
 
2.2%
이 12
 
1.6%
디 10
 
1.3%
Other values (155) 319
41.4%
Latin
ValueCountFrequency (%)
C 3
20.0%
N 2
13.3%
e 1
 
6.7%
S 1
 
6.7%
H 1
 
6.7%
M 1
 
6.7%
A 1
 
6.7%
n 1
 
6.7%
i 1
 
6.7%
m 1
 
6.7%
Other values (2) 2
13.3%
Common
ValueCountFrequency (%)
55
79.7%
4 5
 
7.2%
2 5
 
7.2%
( 2
 
2.9%
) 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 771
90.2%
ASCII 84
 
9.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 97
 
12.6%
물 94
 
12.2%
원 81
 
10.5%
병 72
 
9.3%
센 26
 
3.4%
터 25
 
3.2%
의 18
 
2.3%
료 17
 
2.2%
이 12
 
1.6%
디 10
 
1.3%
Other values (155) 319
41.4%
ASCII
ValueCountFrequency (%)
55
65.5%
4 5
 
6.0%
2 5
 
6.0%
C 3
 
3.6%
( 2
 
2.4%
) 2
 
2.4%
N 2
 
2.4%
e 1
 
1.2%
S 1
 
1.2%
H 1
 
1.2%
Other values (7) 7
 
8.3%

rdnmadr_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:50:12.548958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length41.5
Mean length33.48
Min length18

Characters and Unicode

Total characters3348
Distinct characters282
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row경기도 파주시 법원읍 술이홀로 1409
2nd row서울특별시 동작구 양녕로 281 (상도1동)
3rd row경기도 수원시 권선구 호매실로218번길 124, 202호 (호매실동)
4th row충청남도 아산시 온천대로 1676, 1층 (풍기동)
5th row경기도 하남시 미사강변동로 121, 더랜드시티 1층 101,102호 (망월동)
ValueCountFrequency (%)
경기도 31
 
4.6%
1층 29
 
4.3%
2층 15
 
2.2%
부산광역시 12
 
1.8%
서울특별시 12
 
1.8%
인천광역시 8
 
1.2%
제주특별자치도 6
 
0.9%
대구광역시 6
 
0.9%
101호 6
 
0.9%
북구 5
 
0.7%
Other values (462) 549
80.9%
2023-12-10T18:50:13.312237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
579
 
17.3%
1 161
 
4.8%
동 112
 
3.3%
시 104
 
3.1%
2 101
 
3.0%
로 98
 
2.9%
, 97
 
2.9%
) 83
 
2.5%
( 83
 
2.5%
0 78
 
2.3%
Other values (272) 1852
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1921
57.4%
Space Separator 579
 
17.3%
Decimal Number 565
 
16.9%
Other Punctuation 97
 
2.9%
Close Punctuation 83
 
2.5%
Open Punctuation 83
 
2.5%
Dash Punctuation 11
 
0.3%
Uppercase Letter 5
 
0.1%
Math Symbol 3
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 112
 
5.8%
시 104
 
5.4%
로 98
 
5.1%
도 65
 
3.4%
구 64
 
3.3%
층 50
 
2.6%
호 48
 
2.5%
경 43
 
2.2%
기 36
 
1.9%
광 35
 
1.8%
Other values (252) 1266
65.9%
Decimal Number
ValueCountFrequency (%)
1 161
28.5%
2 101
17.9%
0 78
13.8%
3 47
 
8.3%
4 37
 
6.5%
7 33
 
5.8%
5 31
 
5.5%
6 28
 
5.0%
8 25
 
4.4%
9 24
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
S 2
40.0%
M 1
20.0%
Space Separator
ValueCountFrequency (%)
579
100.0%
Other Punctuation
ValueCountFrequency (%)
, 97
100.0%
Close Punctuation
ValueCountFrequency (%)
) 83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 83
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1921
57.4%
Common 1421
42.4%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 112
 
5.8%
시 104
 
5.4%
로 98
 
5.1%
도 65
 
3.4%
구 64
 
3.3%
층 50
 
2.6%
호 48
 
2.5%
경 43
 
2.2%
기 36
 
1.9%
광 35
 
1.8%
Other values (252) 1266
65.9%
Common
ValueCountFrequency (%)
579
40.7%
1 161
 
11.3%
2 101
 
7.1%
, 97
 
6.8%
) 83
 
5.8%
( 83
 
5.8%
0 78
 
5.5%
3 47
 
3.3%
4 37
 
2.6%
7 33
 
2.3%
Other values (6) 122
 
8.6%
Latin
ValueCountFrequency (%)
A 2
33.3%
S 2
33.3%
e 1
16.7%
M 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1921
57.4%
ASCII 1427
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
579
40.6%
1 161
 
11.3%
2 101
 
7.1%
, 97
 
6.8%
) 83
 
5.8%
( 83
 
5.8%
0 78
 
5.5%
3 47
 
3.3%
4 37
 
2.6%
7 33
 
2.3%
Other values (10) 128
 
9.0%
Hangul
ValueCountFrequency (%)
동 112
 
5.8%
시 104
 
5.4%
로 98
 
5.1%
도 65
 
3.4%
구 64
 
3.3%
층 50
 
2.6%
호 48
 
2.5%
경 43
 
2.2%
기 36
 
1.9%
광 35
 
1.8%
Other values (252) 1266
65.9%

ctprvn_klang_nm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
31 
서울특별시
12 
부산광역시
12 
인천광역시
8 
대구광역시
6 
Other values (11)
31 

Length

Max length7
Median length5
Mean length4.3
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row경기도
2nd row서울특별시
3rd row경기도
4th row충청남도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 31
31.0%
서울특별시 12
 
12.0%
부산광역시 12
 
12.0%
인천광역시 8
 
8.0%
대구광역시 6
 
6.0%
제주특별자치도 6
 
6.0%
충청남도 5
 
5.0%
전라남도 4
 
4.0%
경상남도 4
 
4.0%
울산광역시 3
 
3.0%
Other values (6) 9
 
9.0%

Length

2023-12-10T18:50:13.650758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 31
31.0%
서울특별시 12
 
12.0%
부산광역시 12
 
12.0%
인천광역시 8
 
8.0%
대구광역시 6
 
6.0%
제주특별자치도 6
 
6.0%
충청남도 5
 
5.0%
전라남도 4
 
4.0%
경상남도 4
 
4.0%
울산광역시 3
 
3.0%
Other values (6) 9
 
9.0%

signgu_klang_nm
Text

Distinct68
Distinct (%)68.7%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:50:14.154769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5656566
Min length2

Characters and Unicode

Total characters353
Distinct characters76
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

Unique47 ?
Unique (%)47.5%

Sample

1st row파주시
2nd row동작구
3rd row수원시 권선구
4th row아산시
5th row하남시
ValueCountFrequency (%)
북구 5
 
4.5%
남양주시 4
 
3.6%
수원시 4
 
3.6%
연수구 3
 
2.7%
서귀포시 3
 
2.7%
하남시 3
 
2.7%
해운대구 3
 
2.7%
제주시 3
 
2.7%
고양시 3
 
2.7%
동구 2
 
1.8%
Other values (64) 79
70.5%
2023-12-10T18:50:15.015293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 56
 
15.9%
시 53
 
15.0%
13
 
3.7%
양 12
 
3.4%
주 11
 
3.1%
수 11
 
3.1%
서 10
 
2.8%
북 8
 
2.3%
남 8
 
2.3%
군 8
 
2.3%
Other values (66) 163
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 340
96.3%
Space Separator 13
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 56
 
16.5%
시 53
 
15.6%
양 12
 
3.5%
주 11
 
3.2%
수 11
 
3.2%
서 10
 
2.9%
북 8
 
2.4%
남 8
 
2.4%
군 8
 
2.4%
원 8
 
2.4%
Other values (65) 155
45.6%
Space Separator
ValueCountFrequency (%)
13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 340
96.3%
Common 13
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 56
 
16.5%
시 53
 
15.6%
양 12
 
3.5%
주 11
 
3.2%
수 11
 
3.2%
서 10
 
2.9%
북 8
 
2.4%
남 8
 
2.4%
군 8
 
2.4%
원 8
 
2.4%
Other values (65) 155
45.6%
Common
ValueCountFrequency (%)
13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 340
96.3%
ASCII 13
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 56
 
16.5%
시 53
 
15.6%
양 12
 
3.5%
주 11
 
3.2%
수 11
 
3.2%
서 10
 
2.9%
북 8
 
2.4%
남 8
 
2.4%
군 8
 
2.4%
원 8
 
2.4%
Other values (65) 155
45.6%
ASCII
ValueCountFrequency (%)
13
100.0%

ctprvn_eng_nm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Gyeonggi-do
31 
Seoul
12 
Busan
12 
Incheon
8 
Daegu
6 
Other values (11)
31 

Length

Max length17
Median length16
Mean length8.9
Min length4

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st rowGyeonggi-do
2nd rowSeoul
3rd rowGyeonggi-do
4th rowChungcheongnam-do
5th rowGyeonggi-do

Common Values

ValueCountFrequency (%)
Gyeonggi-do 31
31.0%
Seoul 12
 
12.0%
Busan 12
 
12.0%
Incheon 8
 
8.0%
Daegu 6
 
6.0%
Jeju 6
 
6.0%
Chungcheongnam-do 5
 
5.0%
Jeollanam-do 4
 
4.0%
Gyeongsangnam-do 4
 
4.0%
Ulsan 3
 
3.0%
Other values (6) 9
 
9.0%

Length

2023-12-10T18:50:15.367419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gyeonggi-do 31
31.0%
seoul 12
 
12.0%
busan 12
 
12.0%
incheon 8
 
8.0%
daegu 6
 
6.0%
jeju 6
 
6.0%
chungcheongnam-do 5
 
5.0%
jeollanam-do 4
 
4.0%
gyeongsangnam-do 4
 
4.0%
ulsan 3
 
3.0%
Other values (6) 9
 
9.0%

signgu_eng_nm
Text

Distinct68
Distinct (%)68.7%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:50:15.835452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length11.10101
Min length6

Characters and Unicode

Total characters1099
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)47.5%

Sample

1st rowPaju-si
2nd rowDongjak-gu
3rd rowSuwon-si Gwonseon-gu
4th rowAsan-si
5th rowHanam-si
ValueCountFrequency (%)
buk-gu 5
 
4.4%
suwon-si 4
 
3.5%
namyangju-si 4
 
3.5%
seogwipo-si 3
 
2.7%
haeundae-gu 3
 
2.7%
jeju-si 3
 
2.7%
goyang-si 3
 
2.7%
yeonsu-gu 3
 
2.7%
hanam-si 3
 
2.7%
dong-gu 2
 
1.8%
Other values (65) 80
70.8%
2023-12-10T18:50:16.711880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 127
11.6%
n 120
10.9%
u 118
10.7%
- 113
10.3%
o 83
 
7.6%
i 73
 
6.6%
e 71
 
6.5%
s 69
 
6.3%
a 60
 
5.5%
S 23
 
2.1%
Other values (29) 242
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 859
78.2%
Dash Punctuation 113
 
10.3%
Uppercase Letter 113
 
10.3%
Space Separator 14
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 127
14.8%
n 120
14.0%
u 118
13.7%
o 83
9.7%
i 73
8.5%
e 71
8.3%
s 69
8.0%
a 60
7.0%
h 20
 
2.3%
j 19
 
2.2%
Other values (11) 99
11.5%
Uppercase Letter
ValueCountFrequency (%)
S 23
20.4%
G 18
15.9%
B 11
9.7%
Y 10
8.8%
J 10
8.8%
H 9
 
8.0%
D 8
 
7.1%
C 6
 
5.3%
N 5
 
4.4%
P 3
 
2.7%
Other values (6) 10
8.8%
Dash Punctuation
ValueCountFrequency (%)
- 113
100.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 972
88.4%
Common 127
 
11.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 127
13.1%
n 120
12.3%
u 118
12.1%
o 83
 
8.5%
i 73
 
7.5%
e 71
 
7.3%
s 69
 
7.1%
a 60
 
6.2%
S 23
 
2.4%
h 20
 
2.1%
Other values (27) 208
21.4%
Common
ValueCountFrequency (%)
- 113
89.0%
14
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 127
11.6%
n 120
10.9%
u 118
10.7%
- 113
10.3%
o 83
 
7.6%
i 73
 
6.6%
e 71
 
6.5%
s 69
 
6.3%
a 60
 
5.5%
S 23
 
2.1%
Other values (29) 242
22.0%

ctprvn_chnlng_nm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
京畿道
31 
서울特別市
12 
釜山廣域市
12 
仁川廣域市
8 
大邱廣域市
6 
Other values (11)
31 

Length

Max length7
Median length5
Mean length4.3
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row京畿道
2nd row서울特別市
3rd row京畿道
4th row忠淸南道
5th row京畿道

Common Values

ValueCountFrequency (%)
京畿道 31
31.0%
서울特別市 12
 
12.0%
釜山廣域市 12
 
12.0%
仁川廣域市 8
 
8.0%
大邱廣域市 6
 
6.0%
濟州特別自治道 6
 
6.0%
忠淸南道 5
 
5.0%
全羅南道 4
 
4.0%
慶尙南道 4
 
4.0%
蔚山廣域市 3
 
3.0%
Other values (6) 9
 
9.0%

Length

2023-12-10T18:50:17.077076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
京畿道 31
31.0%
서울特別市 12
 
12.0%
釜山廣域市 12
 
12.0%
仁川廣域市 8
 
8.0%
大邱廣域市 6
 
6.0%
濟州特別自治道 6
 
6.0%
忠淸南道 5
 
5.0%
全羅南道 4
 
4.0%
慶尙南道 4
 
4.0%
蔚山廣域市 3
 
3.0%
Other values (6) 9
 
9.0%

signgu_chnlng_nm
Text

Distinct68
Distinct (%)68.7%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:50:17.527052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.6161616
Min length2

Characters and Unicode

Total characters358
Distinct characters99
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)47.5%

Sample

1st row坡州市
2nd row銅雀區
3rd row水原市 勸善區
4th row牙山市
5th row河南市
ValueCountFrequency (%)
北區 5
 
4.4%
水原市 4
 
3.5%
南楊州市 4
 
3.5%
西歸浦市 3
 
2.7%
海雲臺區 3
 
2.7%
濟州市 3
 
2.7%
高陽市 3
 
2.7%
延壽區 3
 
2.7%
河南市 3
 
2.7%
東區 2
 
1.8%
Other values (65) 80
70.8%
2023-12-10T18:50:18.439925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
區 53
 
14.8%
市 51
 
14.2%
14
 
3.9%
州 11
 
3.1%
西 9
 
2.5%
北 8
 
2.2%
南 8
 
2.2%
郡 8
 
2.2%
原 8
 
2.2%
陽 7
 
2.0%
Other values (89) 181
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 344
96.1%
Space Separator 14
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
區 53
 
15.4%
市 51
 
14.8%
州 11
 
3.2%
西 9
 
2.6%
北 8
 
2.3%
南 8
 
2.3%
郡 8
 
2.3%
原 8
 
2.3%
陽 7
 
2.0%
山 7
 
2.0%
Other values (88) 174
50.6%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Han 344
96.1%
Common 14
 
3.9%

Most frequent character per script

Han
ValueCountFrequency (%)
區 53
 
15.4%
市 51
 
14.8%
州 11
 
3.2%
西 9
 
2.6%
北 8
 
2.3%
南 8
 
2.3%
郡 8
 
2.3%
原 8
 
2.3%
陽 7
 
2.0%
山 7
 
2.0%
Other values (88) 174
50.6%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
CJK 335
93.6%
ASCII 14
 
3.9%
CJK Compat Ideographs 9
 
2.5%

Most frequent character per block

CJK
ValueCountFrequency (%)
區 53
 
15.8%
市 51
 
15.2%
州 11
 
3.3%
西 9
 
2.7%
北 8
 
2.4%
南 8
 
2.4%
郡 8
 
2.4%
原 8
 
2.4%
陽 7
 
2.1%
山 7
 
2.1%
Other values (81) 165
49.3%
ASCII
ValueCountFrequency (%)
14
100.0%
CJK Compat Ideographs
ValueCountFrequency (%)
龍 2
22.2%
靈 2
22.2%
梁 1
11.1%
蓮 1
11.1%
寧 1
11.1%
金 1
11.1%
蘆 1
11.1%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.3
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:18.690585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q122
median31
Q332
95-th percentile39
Maximum39
Range28
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1321162
Coefficient of variation (CV)0.29787971
Kurtosis-0.40315241
Mean27.3
Median Absolute Deviation (MAD)5.5
Skewness-0.64954971
Sum2730
Variance66.131313
MonotonicityNot monotonic
2023-12-10T18:50:18.954336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
31 31
31.0%
21 12
 
12.0%
11 12
 
12.0%
23 8
 
8.0%
22 6
 
6.0%
39 6
 
6.0%
34 5
 
5.0%
36 4
 
4.0%
38 4
 
4.0%
26 3
 
3.0%
Other values (6) 9
 
9.0%
ValueCountFrequency (%)
11 12
 
12.0%
21 12
 
12.0%
22 6
 
6.0%
23 8
 
8.0%
25 1
 
1.0%
26 3
 
3.0%
29 1
 
1.0%
31 31
31.0%
32 2
 
2.0%
33 2
 
2.0%
ValueCountFrequency (%)
39 6
 
6.0%
38 4
 
4.0%
37 1
 
1.0%
36 4
 
4.0%
35 2
 
2.0%
34 5
 
5.0%
33 2
 
2.0%
32 2
 
2.0%
31 31
31.0%
29 1
 
1.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27410.22
Minimum11020
Maximum39020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:19.678599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11020
5-th percentile11148.5
Q122050
median31060
Q332105
95-th percentile39010
Maximum39020
Range28000
Interquartile range (IQR)10055

Descriptive statistics

Standard deviation8126.6625
Coefficient of variation (CV)0.29648294
Kurtosis-0.41654175
Mean27410.22
Median Absolute Deviation (MAD)5665
Skewness-0.64733953
Sum2741022
Variance66042644
MonotonicityNot monotonic
2023-12-10T18:50:20.059990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22050 4
 
4.0%
31130 4
 
4.0%
39020 3
 
3.0%
31180 3
 
3.0%
21090 3
 
3.0%
39010 3
 
3.0%
23040 3
 
3.0%
21140 2
 
2.0%
31101 2
 
2.0%
21050 2
 
2.0%
Other values (62) 71
71.0%
ValueCountFrequency (%)
11020 1
1.0%
11060 1
1.0%
11080 1
1.0%
11110 1
1.0%
11120 1
1.0%
11150 1
1.0%
11160 1
1.0%
11170 2
2.0%
11200 1
1.0%
11220 1
1.0%
ValueCountFrequency (%)
39020 3
3.0%
39010 3
3.0%
38380 1
 
1.0%
38115 1
 
1.0%
38100 1
 
1.0%
38030 1
 
1.0%
37100 1
 
1.0%
36350 1
 
1.0%
36320 1
 
1.0%
36030 2
2.0%

fclty_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.494535
Minimum33.22615
Maximum38.150595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:20.456452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.22615
5-th percentile33.48566
Q135.461362
median37.067713
Q337.532773
95-th percentile37.65844
Maximum38.150595
Range4.9244455
Interquartile range (IQR)2.0714107

Descriptive statistics

Standard deviation1.256956
Coefficient of variation (CV)0.034442307
Kurtosis0.01399725
Mean36.494535
Median Absolute Deviation (MAD)0.56955855
Skewness-0.94035269
Sum3649.4535
Variance1.5799383
MonotonicityNot monotonic
2023-12-10T18:50:20.819738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.8933939 1
 
1.0%
37.4932353 1
 
1.0%
37.6624195 1
 
1.0%
35.5530177 1
 
1.0%
35.9426488 1
 
1.0%
37.2386526 1
 
1.0%
36.2824495 1
 
1.0%
37.0202541 1
 
1.0%
37.4186999 1
 
1.0%
37.5153094 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
33.2261496 1
1.0%
33.2539739 1
1.0%
33.2807072 1
1.0%
33.4367891 1
1.0%
33.484494 1
1.0%
33.4857211 1
1.0%
34.6025295 1
1.0%
34.9342233 1
1.0%
34.9511086 1
1.0%
35.1443936 1
1.0%
ValueCountFrequency (%)
38.1505951 1
1.0%
37.8933939 1
1.0%
37.7471504 1
1.0%
37.7412024 1
1.0%
37.6624195 1
1.0%
37.6582307 1
1.0%
37.6531288 1
1.0%
37.6469771 1
1.0%
37.6399381 1
1.0%
37.6346053 1
1.0%

fclty_lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.46859
Minimum126.25822
Maximum129.42884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:21.313189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.25822
5-th percentile126.5067
Q1126.85613
median127.12963
Q3127.97667
95-th percentile129.13437
Maximum129.42884
Range3.1706216
Interquartile range (IQR)1.1205395

Descriptive statistics

Standard deviation0.91022039
Coefficient of variation (CV)0.0071407428
Kurtosis-0.54806958
Mean127.46859
Median Absolute Deviation (MAD)0.3067261
Skewness0.9770726
Sum12746.859
Variance0.82850117
MonotonicityNot monotonic
2023-12-10T18:50:21.632112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8798615 1
 
1.0%
126.8564633 1
 
1.0%
126.750031 1
 
1.0%
129.3028574 1
 
1.0%
128.617379 1
 
1.0%
127.0569064 1
 
1.0%
126.910048 1
 
1.0%
126.9163797 1
 
1.0%
126.6792186 1
 
1.0%
126.8597299 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.2582217 1
1.0%
126.2782597 1
1.0%
126.4659912 1
1.0%
126.4797766 1
1.0%
126.5011609 1
1.0%
126.5069911 1
1.0%
126.5937169 1
1.0%
126.6185611 1
1.0%
126.6269232 1
1.0%
126.6407752 1
1.0%
ValueCountFrequency (%)
129.4288433 1
1.0%
129.3638046 1
1.0%
129.3028574 1
1.0%
129.2136844 1
1.0%
129.1442096 1
1.0%
129.133853 1
1.0%
129.1334088 1
1.0%
129.1233595 1
1.0%
129.119611 1
1.0%
129.087259 1
1.0%

tel_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2296486 × 108
Minimum28163869
Maximum7.0781489 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:50:22.028994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28163869
5-th percentile2.2633328 × 108
Q13.1566176 × 108
median4.1728394 × 108
Q35.3769428 × 108
95-th percentile3.1531588 × 109
Maximum7.0781489 × 109
Range7.049985 × 109
Interquartile range (IQR)2.2203252 × 108

Descriptive statistics

Standard deviation1.250157 × 109
Coefficient of variation (CV)1.7292085
Kurtosis18.783215
Mean7.2296486 × 108
Median Absolute Deviation (MAD)1.0372014 × 108
Skewness4.3084264
Sum7.2296486 × 1010
Variance1.5628925 × 1018
MonotonicityNot monotonic
2023-12-10T18:50:22.426681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
319598600 1
 
1.0%
220607975 1
 
1.0%
319277700 1
 
1.0%
527137575 1
 
1.0%
539857550 1
 
1.0%
312067503 1
 
1.0%
418352879 1
 
1.0%
316848882 1
 
1.0%
328158775 1
 
1.0%
226533335 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
28163869 1
1.0%
220607975 1
1.0%
222178930 1
1.0%
222351533 1
1.0%
222532233 1
1.0%
226533335 1
1.0%
226877975 1
1.0%
234280045 1
1.0%
269252225 1
1.0%
269560040 1
1.0%
ValueCountFrequency (%)
7078148857 1
1.0%
7056794849 1
1.0%
7043513854 1
1.0%
3180921515 1
1.0%
3180278080 1
1.0%
3151731487 1
1.0%
3116667501 1
1.0%
648030038 1
1.0%
647927580 1
1.0%
647482838 1
1.0%

base_de
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210630
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210630
2nd row20210630
3rd row20210630
4th row20210630
5th row20210630

Common Values

ValueCountFrequency (%)
20210630 100
100.0%

Length

2023-12-10T18:50:22.694854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:50:22.891433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210630 100
100.0%

Interactions

2023-12-10T18:50:08.811704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:04.762201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.676958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:06.925130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.854973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.994238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:04.924652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.932422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.133200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.042698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.177044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.099103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:06.183519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.333097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.252146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.351849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.267545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:06.405454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.512817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.466889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:09.548768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:05.457278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:06.659559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:07.688749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:50:08.641815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:50:23.032658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fclty_flag_nmfclty_nmrdnmadr_nmctprvn_klang_nmsigngu_klang_nmctprvn_eng_nmsigngu_eng_nmctprvn_chnlng_nmsigngu_chnlng_nmctprvn_cdsigngu_cdfclty_lafclty_lotel_no
fclty_flag_nm1.0001.0001.0000.0001.0000.0001.0000.0001.0000.0000.0000.0000.0000.227
fclty_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
rdnmadr_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ctprvn_klang_nm0.0001.0001.0001.0000.9881.0000.9881.0000.9881.0001.0000.9060.9060.000
signgu_klang_nm1.0001.0001.0000.9881.0000.9881.0000.9881.0000.9860.9770.9870.9670.837
ctprvn_eng_nm0.0001.0001.0001.0000.9881.0000.9881.0000.9881.0001.0000.9060.9060.000
signgu_eng_nm1.0001.0001.0000.9881.0000.9881.0000.9881.0000.9860.9770.9870.9670.837
ctprvn_chnlng_nm0.0001.0001.0001.0000.9881.0000.9881.0000.9881.0001.0000.9060.9060.000
signgu_chnlng_nm1.0001.0001.0000.9881.0000.9881.0000.9881.0000.9860.9770.9870.9670.837
ctprvn_cd0.0001.0001.0001.0000.9861.0000.9861.0000.9861.0000.9990.7500.7240.000
signgu_cd0.0001.0001.0001.0000.9771.0000.9771.0000.9770.9991.0000.7450.7140.000
fclty_la0.0001.0001.0000.9060.9870.9060.9870.9060.9870.7500.7451.0000.8920.000
fclty_lo0.0001.0001.0000.9060.9670.9060.9670.9060.9670.7240.7140.8921.0000.000
tel_no0.2271.0001.0000.0000.8370.0000.8370.0000.8370.0000.0000.0000.0001.000
2023-12-10T18:50:23.303122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_eng_nmctprvn_chnlng_nmfclty_flag_nmctprvn_klang_nm
ctprvn_eng_nm1.0001.0000.0001.000
ctprvn_chnlng_nm1.0001.0000.0001.000
fclty_flag_nm0.0000.0001.0000.000
ctprvn_klang_nm1.0001.0000.0001.000
2023-12-10T18:50:23.520956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_cdsigngu_cdfclty_lafclty_lotel_nofclty_flag_nmctprvn_klang_nmctprvn_eng_nmctprvn_chnlng_nm
ctprvn_cd1.0000.983-0.264-0.2060.4100.0000.9560.9560.956
signgu_cd0.9831.000-0.253-0.2040.4030.0000.9400.9400.940
fclty_la-0.264-0.2531.000-0.402-0.5800.0000.6530.6530.653
fclty_lo-0.206-0.204-0.4021.0000.2480.0000.6540.6540.654
tel_no0.4100.403-0.5800.2481.0000.2810.0000.0000.000
fclty_flag_nm0.0000.0000.0000.0000.2811.0000.0000.0000.000
ctprvn_klang_nm0.9560.9400.6530.6540.0000.0001.0001.0001.000
ctprvn_eng_nm0.9560.9400.6530.6540.0000.0001.0001.0001.000
ctprvn_chnlng_nm0.9560.9400.6530.6540.0000.0001.0001.0001.000

Missing values

2023-12-10T18:50:09.812355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:50:10.237815image/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.
2023-12-10T18:50:10.480913image/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

fclty_flag_nmfclty_nmrdnmadr_nmctprvn_klang_nmsigngu_klang_nmctprvn_eng_nmsigngu_eng_nmctprvn_chnlng_nmsigngu_chnlng_nmctprvn_cdsigngu_cdfclty_lafclty_lotel_nobase_de
0동물병원카라동물병원경기도 파주시 법원읍 술이홀로 1409경기도파주시Gyeonggi-doPaju-si京畿道坡州市313120037.893394126.87986231959860020210630
1동물약국해뜨는약국서울특별시 동작구 양녕로 281 (상도1동)서울특별시동작구SeoulDongjak-gu서울特別市銅雀區111120037.504585126.9494782816386920210630
2동물병원도그스타 동물병원경기도 수원시 권선구 호매실로218번길 124, 202호 (호매실동)경기도수원시 권선구Gyeonggi-doSuwon-si Gwonseon-gu京畿道水原市 勸善區313101237.266132126.96125131291889620210630
3동물병원아산아이윌24시동물메디컬센터충청남도 아산시 온천대로 1676, 1층 (풍기동)충청남도아산시Chungcheongnam-doAsan-si忠淸南道牙山市343404036.776505127.02304241532333220210630
4동물병원데이동물의료센터경기도 하남시 미사강변동로 121, 더랜드시티 1층 101,102호 (망월동)경기도하남시Gyeonggi-doHanam-si京畿道河南市313118037.565416127.19141131791758320210630
5동물병원클럽펫월드 하루동물병원대구광역시 북구 유통단지로7길 107 (검단동)대구광역시북구DaeguBuk-gu大邱廣域市北區222205035.912605128.61338753382009020210630
6동물병원돌봄 동물병원부산광역시 해운대구 센텀2로 33, 센텀뷰라움 107호 (우동)부산광역시해운대구BusanHaeundae-gu釜山廣域市海雲臺區212109035.166415129.13340951751758520210630
7동물약국약손약국대구광역시 달서구 월배로 164, 스카이M타워 101호 (상인동)대구광역시달서구DaeguDalseo-gu大邱廣域市達西區222207035.816619128.53186353622722520210630
8동물병원연산동물의료센터부산광역시 연제구 연수로 135 (연산동)부산광역시연제구BusanYeonje-gu釜山廣域市蓮堤區212113035.174958129.08556951791017420210630
9동물병원원 애니멀 씨앤씨(원 Animal CNC)경상북도 경산시 하양읍 하양로 183경상북도경산시Gyeongsangbuk-doGyeongsan-si慶尙北道慶山市373710035.917844128.82604253852470920210630
fclty_flag_nmfclty_nmrdnmadr_nmctprvn_klang_nmsigngu_klang_nmctprvn_eng_nmsigngu_eng_nmctprvn_chnlng_nmsigngu_chnlng_nmctprvn_cdsigngu_cdfclty_lafclty_lotel_nobase_de
90동물병원가나다 동물병원부산광역시 사상구 가야대로 325 (주례동)부산광역시사상구BusanSasang-gu釜山廣域市沙上區212115035.151197129.00806551715797920210630
91동물병원두루동물병원강원도 원주시 만대로 166, 102호 (무실동)강원도원주시Gangwon-doWonju-si江原道原州市323202037.333698127.92234433744672220210630
92동물병원마린파크 동물병원부산광역시 해운대구 마린시티2로 2, 마린파크 2층 208호 (우동)부산광역시해운대구BusanHaeundae-gu釜山廣域市海雲臺區212109035.15934129.1442151746411920210630
93동물병원린동물병원부산광역시 동래구 중앙대로1381번길 43, 2층 (온천동)부산광역시동래구BusanDongnae-gu釜山廣域市東萊區212106035.211038129.07552351514247020210630
94동물병원정직한 동물병원제주특별자치도 서귀포시 대정읍 글로벌에듀로 370, 이노에듀파크 117,118호제주특별자치도서귀포시JejuSeogwipo-si濟州特別自治道西歸浦市393902033.280707126.27826705679484920210630
95동물병원라포레동물메디컬센터경기도 의왕시 부곡중앙로 3-3 (삼동)경기도의왕시Gyeonggi-doUiwang-si京畿道義王市313117037.321184126.94939631360758820210630
96동물병원바른마음동물병원경기도 시흥시 장현능곡로 178, 능곡타운 2층 204호 (능곡동)경기도시흥시Gyeonggi-doSiheung-si京畿道始興市313115037.369592126.80995631778751220210630
97동물병원24시 위드힐동물메디컬센터경기도 남양주시 두물로11번길 42, e드림타워 2층 (별내동)경기도남양주시Gyeonggi-doNamyangju-si京畿道南楊州市313113037.658231127.12506831523325620210630
98동물병원탑케어동물병원경기도 고양시 덕양구 화신로272번길 5, 3층 (화정동)경기도고양시 덕양구Gyeonggi-doGoyang-si Deogyang-gu京畿道高陽市 德陽區313110137.631188126.831097311666750120210630
99동물병원비타민동물병원대구광역시 북구 복현로 127 (복현동)대구광역시북구DaeguBuk-gu大邱廣域市北區222205035.900962128.61810353383119920210630