Overview

Dataset statistics

Number of variables9
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory82.3 B

Variable types

Text1
Numeric6
Categorical2

Dataset

Description광주광역시 북구 내 등록된 반려동물 현황 데이터로, 법정동별로 등록된 반려동물 등록 현황 정보를 제공합니다. 법정동, 등록형태(내장형, 외장형, 인식표), 등록동물수, 등록 품종수, 동물소유자수, 동물소유자당 동물등록수 등을 확인하실 수 있습니다.
URLhttps://www.data.go.kr/data/15114273/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
등록형태(내장형 RFID) is highly overall correlated with 등록형태(외장형RFID) and 4 other fieldsHigh correlation
등록형태(외장형RFID) is highly overall correlated with 등록형태(내장형 RFID) and 4 other fieldsHigh correlation
등록형태(인식표) is highly overall correlated with 등록형태(내장형 RFID) and 4 other fieldsHigh correlation
등록동물수 is highly overall correlated with 등록형태(내장형 RFID) and 4 other fieldsHigh correlation
동물소유자수 is highly overall correlated with 등록형태(내장형 RFID) and 4 other fieldsHigh correlation
등록 품종수 is highly overall correlated with 등록형태(내장형 RFID) and 4 other fieldsHigh correlation
동명 has unique valuesUnique
등록형태(인식표) has 7 (17.5%) zerosZeros

Reproduction

Analysis started2023-12-12 00:25:50.730248
Analysis finished2023-12-12 00:25:54.481360
Duration3.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동명
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T09:25:54.658747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.925
Min length2

Characters and Unicode

Total characters117
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row각화동
2nd row금곡동
3rd row누문동
4th row대촌동
5th row동림동
ValueCountFrequency (%)
각화동 1
 
2.5%
금곡동 1
 
2.5%
일곡동 1
 
2.5%
용봉동 1
 
2.5%
용전동 1
 
2.5%
우산동 1
 
2.5%
운암동 1
 
2.5%
운정동 1
 
2.5%
월출동 1
 
2.5%
유동 1
 
2.5%
Other values (30) 30
75.0%
2023-12-12T09:25:55.091741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 41
35.0%
용 6
 
5.1%
곡 4
 
3.4%
암 3
 
2.6%
각 2
 
1.7%
운 2
 
1.7%
산 2
 
1.7%
화 2
 
1.7%
신 2
 
1.7%
오 2
 
1.7%
Other values (43) 51
43.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 117
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 41
35.0%
용 6
 
5.1%
곡 4
 
3.4%
암 3
 
2.6%
각 2
 
1.7%
운 2
 
1.7%
산 2
 
1.7%
화 2
 
1.7%
신 2
 
1.7%
오 2
 
1.7%
Other values (43) 51
43.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 117
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 41
35.0%
용 6
 
5.1%
곡 4
 
3.4%
암 3
 
2.6%
각 2
 
1.7%
운 2
 
1.7%
산 2
 
1.7%
화 2
 
1.7%
신 2
 
1.7%
오 2
 
1.7%
Other values (43) 51
43.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 117
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 41
35.0%
용 6
 
5.1%
곡 4
 
3.4%
암 3
 
2.6%
각 2
 
1.7%
운 2
 
1.7%
산 2
 
1.7%
화 2
 
1.7%
신 2
 
1.7%
오 2
 
1.7%
Other values (43) 51
43.6%

등록형태(내장형 RFID)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.175
Minimum1
Maximum1270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:25:55.234512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median91.5
Q3368.75
95-th percentile875.2
Maximum1270
Range1269
Interquartile range (IQR)358.75

Descriptive statistics

Standard deviation312.08759
Coefficient of variation (CV)1.2781308
Kurtosis1.8387907
Mean244.175
Median Absolute Deviation (MAD)90
Skewness1.4649975
Sum9767
Variance97398.661
MonotonicityNot monotonic
2023-12-12T09:25:55.374863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 3
 
7.5%
10 3
 
7.5%
226 2
 
5.0%
12 2
 
5.0%
15 2
 
5.0%
2 2
 
5.0%
7 2
 
5.0%
49 1
 
2.5%
875 1
 
2.5%
1270 1
 
2.5%
Other values (21) 21
52.5%
ValueCountFrequency (%)
1 1
 
2.5%
2 2
5.0%
5 3
7.5%
7 2
5.0%
8 1
 
2.5%
10 3
7.5%
11 1
 
2.5%
12 2
5.0%
15 2
5.0%
18 1
 
2.5%
ValueCountFrequency (%)
1270 1
2.5%
879 1
2.5%
875 1
2.5%
727 1
2.5%
635 1
2.5%
605 1
2.5%
603 1
2.5%
536 1
2.5%
527 1
2.5%
371 1
2.5%

등록형태(외장형RFID)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256
Minimum1
Maximum980
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:25:55.521656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q110.5
median95.5
Q3392.5
95-th percentile909
Maximum980
Range979
Interquartile range (IQR)382

Descriptive statistics

Standard deviation310.06219
Coefficient of variation (CV)1.2111804
Kurtosis-0.051287723
Mean256
Median Absolute Deviation (MAD)92.5
Skewness1.0947969
Sum10240
Variance96138.564
MonotonicityNot monotonic
2023-12-12T09:25:55.952743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
8 4
 
10.0%
3 3
 
7.5%
909 2
 
5.0%
11 2
 
5.0%
292 1
 
2.5%
695 1
 
2.5%
288 1
 
2.5%
980 1
 
2.5%
18 1
 
2.5%
48 1
 
2.5%
Other values (23) 23
57.5%
ValueCountFrequency (%)
1 1
 
2.5%
3 3
7.5%
4 1
 
2.5%
8 4
10.0%
9 1
 
2.5%
11 2
5.0%
13 1
 
2.5%
18 1
 
2.5%
19 1
 
2.5%
28 1
 
2.5%
ValueCountFrequency (%)
980 1
2.5%
909 2
5.0%
886 1
2.5%
705 1
2.5%
695 1
2.5%
679 1
2.5%
575 1
2.5%
474 1
2.5%
424 1
2.5%
382 1
2.5%

등록형태(인식표)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.975
Minimum0
Maximum240
Zeros7
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:25:56.072914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median18
Q376.25
95-th percentile164.2
Maximum240
Range240
Interquartile range (IQR)74.5

Descriptive statistics

Standard deviation61.655157
Coefficient of variation (CV)1.2851518
Kurtosis1.790687
Mean47.975
Median Absolute Deviation (MAD)18
Skewness1.4784854
Sum1919
Variance3801.3583
MonotonicityNot monotonic
2023-12-12T09:25:56.188231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 7
17.5%
2 3
 
7.5%
1 3
 
7.5%
83 2
 
5.0%
3 2
 
5.0%
69 2
 
5.0%
162 1
 
2.5%
26 1
 
2.5%
5 1
 
2.5%
6 1
 
2.5%
Other values (17) 17
42.5%
ValueCountFrequency (%)
0 7
17.5%
1 3
7.5%
2 3
7.5%
3 2
 
5.0%
4 1
 
2.5%
5 1
 
2.5%
6 1
 
2.5%
7 1
 
2.5%
14 1
 
2.5%
22 1
 
2.5%
ValueCountFrequency (%)
240 1
2.5%
206 1
2.5%
162 1
2.5%
148 1
2.5%
139 1
2.5%
106 1
2.5%
97 1
2.5%
87 1
2.5%
83 2
5.0%
74 1
2.5%

등록동물수
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.625
Minimum2
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:25:56.305025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.95
Q112
median27.5
Q347.25
95-th percentile69.1
Maximum71
Range69
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation21.707481
Coefficient of variation (CV)0.70881572
Kurtosis-1.2026661
Mean30.625
Median Absolute Deviation (MAD)18.5
Skewness0.35774866
Sum1225
Variance471.21474
MonotonicityNot monotonic
2023-12-12T09:25:56.421706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
13 3
 
7.5%
8 3
 
7.5%
15 2
 
5.0%
45 2
 
5.0%
48 2
 
5.0%
12 2
 
5.0%
44 2
 
5.0%
5 2
 
5.0%
21 2
 
5.0%
71 2
 
5.0%
Other values (18) 18
45.0%
ValueCountFrequency (%)
2 1
 
2.5%
4 1
 
2.5%
5 2
5.0%
6 1
 
2.5%
7 1
 
2.5%
8 3
7.5%
12 2
5.0%
13 3
7.5%
14 1
 
2.5%
15 2
5.0%
ValueCountFrequency (%)
71 2
5.0%
69 1
2.5%
66 1
2.5%
58 1
2.5%
57 1
2.5%
53 1
2.5%
50 1
2.5%
48 2
5.0%
47 1
2.5%
45 2
5.0%

등록 품종수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
1
21 
2
19 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 21
52.5%
2 19
47.5%

Length

2023-12-12T09:25:56.534139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:56.621630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 21
52.5%
2 19
47.5%

동물소유자수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.6
Minimum4
Maximum1864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:25:56.716469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.95
Q115
median142
Q3616.75
95-th percentile1495.45
Maximum1864
Range1860
Interquartile range (IQR)601.75

Descriptive statistics

Standard deviation518.9848
Coefficient of variation (CV)1.2517723
Kurtosis0.60186518
Mean414.6
Median Absolute Deviation (MAD)137.5
Skewness1.2432351
Sum16584
Variance269345.22
MonotonicityNot monotonic
2023-12-12T09:25:56.844164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
16 2
 
5.0%
7 2
 
5.0%
892 2
 
5.0%
6 2
 
5.0%
10 2
 
5.0%
30 1
 
2.5%
405 1
 
2.5%
1864 1
 
2.5%
81 1
 
2.5%
1101 1
 
2.5%
Other values (25) 25
62.5%
ValueCountFrequency (%)
4 1
2.5%
5 1
2.5%
6 2
5.0%
7 2
5.0%
10 2
5.0%
11 1
2.5%
12 1
2.5%
16 2
5.0%
17 1
2.5%
21 1
2.5%
ValueCountFrequency (%)
1864 1
2.5%
1504 1
2.5%
1495 1
2.5%
1413 1
2.5%
1101 1
2.5%
1078 1
2.5%
1025 1
2.5%
892 2
5.0%
667 1
2.5%
600 1
2.5%

동물소유자당 동물등록수
Real number (ℝ)

Distinct23
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.46075
Minimum1
Maximum3.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:25:57.008525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.17
Q11.2875
median1.33
Q31.3925
95-th percentile2.72
Maximum3.18
Range2.18
Interquartile range (IQR)0.105

Descriptive statistics

Standard deviation0.44573988
Coefficient of variation (CV)0.30514453
Kurtosis8.6251354
Mean1.46075
Median Absolute Deviation (MAD)0.05
Skewness2.9989153
Sum58.43
Variance0.19868404
MonotonicityNot monotonic
2023-12-12T09:25:57.166909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1.37 4
 
10.0%
1.33 4
 
10.0%
1.28 4
 
10.0%
1.3 3
 
7.5%
1.29 2
 
5.0%
1.25 2
 
5.0%
1.17 2
 
5.0%
1.34 2
 
5.0%
1.31 2
 
5.0%
1.57 2
 
5.0%
Other values (13) 13
32.5%
ValueCountFrequency (%)
1.0 1
 
2.5%
1.17 2
5.0%
1.2 1
 
2.5%
1.25 2
5.0%
1.28 4
10.0%
1.29 2
5.0%
1.3 3
7.5%
1.31 2
5.0%
1.32 1
 
2.5%
1.33 4
10.0%
ValueCountFrequency (%)
3.18 1
2.5%
2.91 1
2.5%
2.71 1
2.5%
1.7 1
2.5%
1.63 1
2.5%
1.57 2
5.0%
1.5 1
2.5%
1.44 1
2.5%
1.43 1
2.5%
1.38 1
2.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-05-31
40 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-31
2nd row2023-05-31
3rd row2023-05-31
4th row2023-05-31
5th row2023-05-31

Common Values

ValueCountFrequency (%)
2023-05-31 40
100.0%

Length

2023-12-12T09:25:57.281437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:25:57.363525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-31 40
100.0%

Interactions

2023-12-12T09:25:53.696295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:50.973888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.420500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.955674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.522942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.149738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.781960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.044880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.499141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.040252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.623943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.244715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.876782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.114209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.575113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.130063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.714955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.326678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.955719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.185697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.660086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.221512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.806514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.414984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:54.049545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.270129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.762222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.324890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.940399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.505195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:54.141371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.350539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:51.869321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:52.427332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.055167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:25:53.616291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:25:57.423937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명등록형태(내장형 RFID)등록형태(외장형RFID)등록형태(인식표)등록동물수등록 품종수동물소유자수동물소유자당 동물등록수
동명1.0001.0001.0001.0001.0001.0001.0001.000
등록형태(내장형 RFID)1.0001.0000.8830.8850.8870.5530.9760.000
등록형태(외장형RFID)1.0000.8831.0000.9380.8100.6250.9730.000
등록형태(인식표)1.0000.8850.9381.0000.8260.6310.9670.000
등록동물수1.0000.8870.8100.8261.0000.7590.8880.000
등록 품종수1.0000.5530.6250.6310.7591.0000.5790.032
동물소유자수1.0000.9760.9730.9670.8880.5791.0000.000
동물소유자당 동물등록수1.0000.0000.0000.0000.0000.0320.0001.000
2023-12-12T09:25:57.533232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록형태(내장형 RFID)등록형태(외장형RFID)등록형태(인식표)등록동물수동물소유자수동물소유자당 동물등록수등록 품종수
등록형태(내장형 RFID)1.0000.9520.9340.9630.972-0.0770.552
등록형태(외장형RFID)0.9521.0000.9400.9660.984-0.0250.567
등록형태(인식표)0.9340.9401.0000.9370.942-0.0970.573
등록동물수0.9630.9660.9371.0000.973-0.0380.527
동물소유자수0.9720.9840.9420.9731.000-0.1110.523
동물소유자당 동물등록수-0.077-0.025-0.097-0.038-0.1111.0000.000
등록 품종수0.5520.5670.5730.5270.5230.0001.000

Missing values

2023-12-12T09:25:54.274288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:25:54.413558image/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

동명등록형태(내장형 RFID)등록형태(외장형RFID)등록형태(인식표)등록동물수등록 품종수동물소유자수동물소유자당 동물등록수데이터기준일자
0각화동253292834124801.312023-05-31
1금곡동59081121.172023-05-31
2누문동12133141211.332023-05-31
3대촌동11807172.712023-05-31
4동림동605474655328921.282023-05-31
5두암동87990920671115041.332023-05-31
6망월동15730211561.572023-05-31
7매곡동272318744525181.282023-05-31
8문흥동72788624058214131.312023-05-31
9본촌동206187463523311.332023-05-31
동명등록형태(내장형 RFID)등록형태(외장형RFID)등록형태(인식표)등록동물수등록 품종수동물소유자수동물소유자당 동물등록수데이터기준일자
30임동215194224223331.292023-05-31
31장등동10334151301.572023-05-31
32중흥동368424874726671.322023-05-31
33지야동15116151112.912023-05-31
34청풍동10193131251.282023-05-31
35충효동12375131173.182023-05-31
36태령동88151101.72023-05-31
37풍향동134118263112031.372023-05-31
38화암동1404141.252023-05-31
39효령동2312251.22023-05-31