Overview

Dataset statistics

Number of variables14
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory127.9 B

Variable types

Categorical5
Text1
Numeric8

Dataset

Description경기도 광주시 반려동물등록현황에 대한 데이터로 읍면동, 등록돌물 수, 대행업체 등록건수, 기타 등록건수, RFID종류 등을 제공합니다.
Author경기도 광주시
URLhttps://www.data.go.kr/data/15060586/fileData.do

Alerts

시군명 has constant value ""Constant
(등록주체)기타 has constant value ""Constant
인식표 has constant value ""Constant
데이터기준일자 has constant value ""Constant
등록 동물 수(마리) is highly overall correlated with (등록주체)시군구 등록 and 5 other fieldsHigh correlation
(등록주체)시군구 등록 is highly overall correlated with 등록 동물 수(마리) and 5 other fieldsHigh correlation
(등록주체)대행업체 등록 is highly overall correlated with 등록 동물 수(마리) and 5 other fieldsHigh correlation
내장형 is highly overall correlated with 등록 동물 수(마리) and 5 other fieldsHigh correlation
외장형 is highly overall correlated with 등록 동물 수(마리) and 5 other fieldsHigh correlation
등록 품종 수 is highly overall correlated with 등록 동물 수(마리) and 6 other fieldsHigh correlation
등록 소유자 수 is highly overall correlated with 등록 동물 수(마리) and 5 other fieldsHigh correlation
동물 소유자당 등록 동물 수 is highly overall correlated with 등록 품종 수High correlation
읍면동명 has unique valuesUnique
(등록주체)시군구 등록 has 12 (44.4%) zerosZeros

Reproduction

Analysis started2023-12-12 14:08:40.004518
Analysis finished2023-12-12 14:08:47.312914
Duration7.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
광주시
27 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광주시 27
100.0%

Length

2023-12-12T23:08:47.375280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:47.456058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주시 27
100.0%

읍면동명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T23:08:47.619313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.962963
Min length2

Characters and Unicode

Total characters80
Distinct characters48
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

Unique27 ?
Unique (%)100.0%

Sample

1st row목동
2nd row삼동
3rd row역동
4th row직동
5th row경안동
ValueCountFrequency (%)
목동 1
 
3.7%
쌍령동 1
 
3.7%
곤지암읍 1
 
3.7%
회덕동 1
 
3.7%
퇴촌면 1
 
3.7%
태전동 1
 
3.7%
탄벌동 1
 
3.7%
초월읍 1
 
3.7%
중부면 1
 
3.7%
중대동 1
 
3.7%
Other values (17) 17
63.0%
2023-12-12T23:08:47.952324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 19
23.8%
면 5
 
6.2%
읍 3
 
3.8%
산 3
 
3.8%
목 2
 
2.5%
남 2
 
2.5%
지 2
 
2.5%
중 2
 
2.5%
현 2
 
2.5%
벌 2
 
2.5%
Other values (38) 38
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 19
23.8%
면 5
 
6.2%
읍 3
 
3.8%
산 3
 
3.8%
목 2
 
2.5%
남 2
 
2.5%
지 2
 
2.5%
중 2
 
2.5%
현 2
 
2.5%
벌 2
 
2.5%
Other values (38) 38
47.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 19
23.8%
면 5
 
6.2%
읍 3
 
3.8%
산 3
 
3.8%
목 2
 
2.5%
남 2
 
2.5%
지 2
 
2.5%
중 2
 
2.5%
현 2
 
2.5%
벌 2
 
2.5%
Other values (38) 38
47.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 19
23.8%
면 5
 
6.2%
읍 3
 
3.8%
산 3
 
3.8%
목 2
 
2.5%
남 2
 
2.5%
지 2
 
2.5%
중 2
 
2.5%
현 2
 
2.5%
벌 2
 
2.5%
Other values (38) 38
47.5%

등록 동물 수(마리)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.59259
Minimum8
Maximum619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:08:48.065735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9.6
Q122
median94
Q3121.5
95-th percentile470.8
Maximum619
Range611
Interquartile range (IQR)99.5

Descriptive statistics

Standard deviation149.91363
Coefficient of variation (CV)1.2431413
Kurtosis6.6169372
Mean120.59259
Median Absolute Deviation (MAD)65
Skewness2.5511784
Sum3256
Variance22474.097
MonotonicityNot monotonic
2023-12-12T23:08:48.183556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
98 2
 
7.4%
22 2
 
7.4%
9 1
 
3.7%
81 1
 
3.7%
203 1
 
3.7%
95 1
 
3.7%
183 1
 
3.7%
237 1
 
3.7%
119 1
 
3.7%
11 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
8 1
3.7%
9 1
3.7%
11 1
3.7%
16 1
3.7%
18 1
3.7%
19 1
3.7%
22 2
7.4%
29 1
3.7%
59 1
3.7%
66 1
3.7%
ValueCountFrequency (%)
619 1
3.7%
571 1
3.7%
237 1
3.7%
203 1
3.7%
183 1
3.7%
136 1
3.7%
122 1
3.7%
121 1
3.7%
119 1
3.7%
111 1
3.7%

(등록주체)시군구 등록
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2
Minimum0
Maximum13
Zeros12
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:08:48.294885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6.7
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3
Coefficient of variation (CV)1.5
Kurtosis6.0933333
Mean2
Median Absolute Deviation (MAD)1
Skewness2.2523077
Sum54
Variance9
MonotonicityNot monotonic
2023-12-12T23:08:48.412880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12
44.4%
1 5
18.5%
4 2
 
7.4%
2 2
 
7.4%
3 2
 
7.4%
7 1
 
3.7%
5 1
 
3.7%
6 1
 
3.7%
13 1
 
3.7%
ValueCountFrequency (%)
0 12
44.4%
1 5
18.5%
2 2
 
7.4%
3 2
 
7.4%
4 2
 
7.4%
5 1
 
3.7%
6 1
 
3.7%
7 1
 
3.7%
13 1
 
3.7%
ValueCountFrequency (%)
13 1
 
3.7%
7 1
 
3.7%
6 1
 
3.7%
5 1
 
3.7%
4 2
 
7.4%
3 2
 
7.4%
2 2
 
7.4%
1 5
18.5%
0 12
44.4%

(등록주체)대행업체 등록
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.59259
Minimum8
Maximum613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:08:48.517365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9.6
Q122
median91
Q3118.5
95-th percentile461.4
Maximum613
Range605
Interquartile range (IQR)96.5

Descriptive statistics

Standard deviation147.64361
Coefficient of variation (CV)1.2449648
Kurtosis6.6436529
Mean118.59259
Median Absolute Deviation (MAD)62
Skewness2.5551466
Sum3202
Variance21798.635
MonotonicityNot monotonic
2023-12-12T23:08:48.628342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
94 2
 
7.4%
118 2
 
7.4%
18 2
 
7.4%
22 2
 
7.4%
76 1
 
3.7%
199 1
 
3.7%
180 1
 
3.7%
236 1
 
3.7%
119 1
 
3.7%
11 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
8 1
3.7%
9 1
3.7%
11 1
3.7%
16 1
3.7%
18 2
7.4%
22 2
7.4%
29 1
3.7%
58 1
3.7%
65 1
3.7%
76 1
3.7%
ValueCountFrequency (%)
613 1
3.7%
558 1
3.7%
236 1
3.7%
199 1
3.7%
180 1
3.7%
134 1
3.7%
119 1
3.7%
118 2
7.4%
109 1
3.7%
98 1
3.7%

(등록주체)기타
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
27 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27
100.0%

Length

2023-12-12T23:08:48.737321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:48.824091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
100.0%

내장형
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70
Minimum6
Maximum422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:08:48.903572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q116.5
median51
Q370.5
95-th percentile266
Maximum422
Range416
Interquartile range (IQR)54

Descriptive statistics

Standard deviation94.590209
Coefficient of variation (CV)1.3512887
Kurtosis8.5948675
Mean70
Median Absolute Deviation (MAD)34
Skewness2.8732664
Sum1890
Variance8947.3077
MonotonicityNot monotonic
2023-12-12T23:08:49.020751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6 4
 
14.8%
41 2
 
7.4%
54 2
 
7.4%
17 2
 
7.4%
70 2
 
7.4%
326 1
 
3.7%
126 1
 
3.7%
103 1
 
3.7%
104 1
 
3.7%
422 1
 
3.7%
Other values (10) 10
37.0%
ValueCountFrequency (%)
6 4
14.8%
9 1
 
3.7%
14 1
 
3.7%
16 1
 
3.7%
17 2
7.4%
29 1
 
3.7%
31 1
 
3.7%
41 2
7.4%
51 1
 
3.7%
54 2
7.4%
ValueCountFrequency (%)
422 1
3.7%
326 1
3.7%
126 1
3.7%
104 1
3.7%
103 1
3.7%
78 1
3.7%
71 1
3.7%
70 2
7.4%
66 1
3.7%
56 1
3.7%

외장형
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.592593
Minimum2
Maximum293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:08:49.142782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q110
median42
Q352
95-th percentile144.2
Maximum293
Range291
Interquartile range (IQR)42

Descriptive statistics

Standard deviation60.755031
Coefficient of variation (CV)1.2008681
Kurtosis9.5326526
Mean50.592593
Median Absolute Deviation (MAD)30
Skewness2.7749436
Sum1366
Variance3691.1738
MonotonicityNot monotonic
2023-12-12T23:08:49.326199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3 2
 
7.4%
51 2
 
7.4%
12 2
 
7.4%
5 2
 
7.4%
293 1
 
3.7%
77 1
 
3.7%
41 1
 
3.7%
80 1
 
3.7%
133 1
 
3.7%
49 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
2 1
3.7%
3 2
7.4%
5 2
7.4%
7 1
3.7%
8 1
3.7%
12 2
7.4%
28 1
3.7%
30 1
3.7%
37 1
3.7%
41 1
3.7%
ValueCountFrequency (%)
293 1
3.7%
149 1
3.7%
133 1
3.7%
80 1
3.7%
77 1
3.7%
58 1
3.7%
53 1
3.7%
51 2
7.4%
49 1
3.7%
48 1
3.7%

인식표
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
27 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27
100.0%

Length

2023-12-12T23:08:49.481202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:49.588075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
100.0%

등록 품종 수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21
Minimum5
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:08:49.674486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5.3
Q111
median21
Q324.5
95-th percentile51.2
Maximum57
Range52
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation13.402066
Coefficient of variation (CV)0.63819364
Kurtosis2.0334177
Mean21
Median Absolute Deviation (MAD)9
Skewness1.3409828
Sum567
Variance179.61538
MonotonicityNot monotonic
2023-12-12T23:08:49.800064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5 2
 
7.4%
11 2
 
7.4%
24 2
 
7.4%
23 2
 
7.4%
12 2
 
7.4%
22 2
 
7.4%
10 1
 
3.7%
40 1
 
3.7%
17 1
 
3.7%
28 1
 
3.7%
Other values (11) 11
40.7%
ValueCountFrequency (%)
5 2
7.4%
6 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
11 2
7.4%
12 2
7.4%
17 1
3.7%
18 1
3.7%
20 1
3.7%
ValueCountFrequency (%)
57 1
3.7%
56 1
3.7%
40 1
3.7%
31 1
3.7%
28 1
3.7%
27 1
3.7%
25 1
3.7%
24 2
7.4%
23 2
7.4%
22 2
7.4%

등록 소유자 수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.592593
Minimum8
Maximum542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:08:49.952451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9.6
Q118
median78
Q3104.5
95-th percentile266.4
Maximum542
Range534
Interquartile range (IQR)86.5

Descriptive statistics

Standard deviation111.19119
Coefficient of variation (CV)1.1631779
Kurtosis9.8429716
Mean95.592593
Median Absolute Deviation (MAD)51
Skewness2.8175532
Sum2581
Variance12363.481
MonotonicityNot monotonic
2023-12-12T23:08:50.101614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
15 3
 
11.1%
9 1
 
3.7%
65 1
 
3.7%
160 1
 
3.7%
80 1
 
3.7%
136 1
 
3.7%
216 1
 
3.7%
104 1
 
3.7%
11 1
 
3.7%
288 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
8 1
 
3.7%
9 1
 
3.7%
11 1
 
3.7%
15 3
11.1%
16 1
 
3.7%
20 1
 
3.7%
27 1
 
3.7%
54 1
 
3.7%
58 1
 
3.7%
65 1
 
3.7%
ValueCountFrequency (%)
542 1
3.7%
288 1
3.7%
216 1
3.7%
160 1
3.7%
136 1
3.7%
121 1
3.7%
105 1
3.7%
104 1
3.7%
102 1
3.7%
98 1
3.7%

동물 소유자당 등록 동물 수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1911111
Minimum1
Maximum1.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:08:50.241759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.1
median1.15
Q31.205
95-th percentile1.434
Maximum1.98
Range0.98
Interquartile range (IQR)0.105

Descriptive statistics

Standard deviation0.18872718
Coefficient of variation (CV)0.15844633
Kurtosis11.854583
Mean1.1911111
Median Absolute Deviation (MAD)0.05
Skewness3.024995
Sum32.16
Variance0.035617949
MonotonicityNot monotonic
2023-12-12T23:08:50.398468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1.0 3
11.1%
1.14 3
11.1%
1.19 3
11.1%
1.1 2
 
7.4%
1.15 2
 
7.4%
1.07 2
 
7.4%
1.2 2
 
7.4%
1.26 1
 
3.7%
1.21 1
 
3.7%
1.12 1
 
3.7%
Other values (7) 7
25.9%
ValueCountFrequency (%)
1.0 3
11.1%
1.07 2
7.4%
1.09 1
 
3.7%
1.1 2
7.4%
1.12 1
 
3.7%
1.13 1
 
3.7%
1.14 3
11.1%
1.15 2
7.4%
1.19 3
11.1%
1.2 2
7.4%
ValueCountFrequency (%)
1.98 1
 
3.7%
1.47 1
 
3.7%
1.35 1
 
3.7%
1.27 1
 
3.7%
1.26 1
 
3.7%
1.25 1
 
3.7%
1.21 1
 
3.7%
1.2 2
7.4%
1.19 3
11.1%
1.15 2
7.4%

해당 동의 등록대행업체 수
Categorical

Distinct5
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size348.0 B
0
12 
1
7 
4
4 
2
3 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row4

Common Values

ValueCountFrequency (%)
0 12
44.4%
1 7
25.9%
4 4
 
14.8%
2 3
 
11.1%
3 1
 
3.7%

Length

2023-12-12T23:08:50.559039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:50.682630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12
44.4%
1 7
25.9%
4 4
 
14.8%
2 3
 
11.1%
3 1
 
3.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-11-21
27 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-11-21
2nd row2023-11-21
3rd row2023-11-21
4th row2023-11-21
5th row2023-11-21

Common Values

ValueCountFrequency (%)
2023-11-21 27
100.0%

Length

2023-12-12T23:08:50.819266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:08:50.920872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-11-21 27
100.0%

Interactions

2023-12-12T23:08:46.225456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.383628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.220687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.984737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.862627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.562120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.369302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.402464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.327432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.494936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.323776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.079346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.944599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.654009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.774254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.489487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.439512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.603030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.421278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.192592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.039536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.746738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.886657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.598409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.550018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.719838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.533588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.292963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.128317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.891949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.979003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.692690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.631886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.822689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.611154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.382897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.215709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.981652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.057345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.808599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.720508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:40.928576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.706292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.532804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.307527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.087989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.143814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.924829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.813905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.024008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.805481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.630813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.394036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.175106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.221240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.023575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.909701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.126083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:41.896343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:42.753718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:43.470242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:44.261197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:45.314189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:08:46.119745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:08:50.994218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명등록 동물 수(마리)(등록주체)시군구 등록(등록주체)대행업체 등록내장형외장형등록 품종 수등록 소유자 수동물 소유자당 등록 동물 수해당 동의 등록대행업체 수
읍면동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
등록 동물 수(마리)1.0001.0000.7001.0000.9680.8500.8980.9270.6210.738
(등록주체)시군구 등록1.0000.7001.0000.7000.8470.7850.7450.7930.7200.456
(등록주체)대행업체 등록1.0001.0000.7001.0000.9680.8500.8980.9270.6210.738
내장형1.0000.9680.8470.9681.0000.9350.8270.9330.6900.151
외장형1.0000.8500.7850.8500.9351.0000.9020.9960.8910.487
등록 품종 수1.0000.8980.7450.8980.8270.9021.0000.8760.6540.659
등록 소유자 수1.0000.9270.7930.9270.9330.9960.8761.0000.8570.470
동물 소유자당 등록 동물 수1.0000.6210.7200.6210.6900.8910.6540.8571.0000.585
해당 동의 등록대행업체 수1.0000.7380.4560.7380.1510.4870.6590.4700.5851.000
2023-12-12T23:08:51.437336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록 동물 수(마리)(등록주체)시군구 등록(등록주체)대행업체 등록내장형외장형등록 품종 수등록 소유자 수동물 소유자당 등록 동물 수해당 동의 등록대행업체 수
등록 동물 수(마리)1.0000.7060.9950.9910.9600.9760.9910.4650.354
(등록주체)시군구 등록0.7061.0000.6550.7460.6030.7420.6690.4870.283
(등록주체)대행업체 등록0.9950.6551.0000.9840.9670.9650.9950.4480.354
내장형0.9910.7460.9841.0000.9330.9750.9780.4820.000
외장형0.9600.6030.9670.9331.0000.9320.9730.4080.337
등록 품종 수0.9760.7420.9650.9750.9321.0000.9640.5240.415
등록 소유자 수0.9910.6690.9950.9780.9730.9641.0000.4190.323
동물 소유자당 등록 동물 수0.4650.4870.4480.4820.4080.5240.4191.0000.427
해당 동의 등록대행업체 수0.3540.2830.3540.0000.3370.4150.3230.4271.000

Missing values

2023-12-12T23:08:47.061228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:08:47.249093image/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

시군명읍면동명등록 동물 수(마리)(등록주체)시군구 등록(등록주체)대행업체 등록(등록주체)기타내장형외장형인식표등록 품종 수등록 소유자 수동물 소유자당 등록 동물 수해당 동의 등록대행업체 수데이터기준일자
0광주시목동9090630591.002023-11-21
1광주시삼동5915803128012541.0902023-11-21
2광주시역동8908904148020751.1912023-11-21
3광주시직동8080620581.002023-11-21
4광주시경안동1224118071510251021.242023-11-21
5광주시고산동1601609709151.0712023-11-21
6광주시남종면191180163011161.1902023-11-21
7광주시능평동6616502937018581.1412023-11-21
8광주시도척면9879105642024781.2602023-11-21
9광주시매산동18018061208151.202023-11-21
시군명읍면동명등록 동물 수(마리)(등록주체)시군구 등록(등록주체)대행업체 등록(등록주체)기타내장형외장형인식표등록 품종 수등록 소유자 수동물 소유자당 등록 동물 수해당 동의 등록대행업체 수데이터기준일자
17광주시장지동111210906645022981.1312023-11-21
18광주시중대동220220148010201.122023-11-21
19광주시중부면5711355804221490562881.9802023-11-21
20광주시초월읍1101106506111.012023-11-21
21광주시탄벌동1190119070490241041.1412023-11-21
22광주시태전동237123601041330312161.122023-11-21
23광주시퇴촌면18331800103800281361.3532023-11-21
24광주시회덕동9519405441017801.1902023-11-21
25광주시곤지암읍20341990126770401601.2742023-11-21
26광주시남한산성면220220175011151.4702023-11-21