Overview

Dataset statistics

Number of variables12
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory108.8 B

Variable types

Numeric9
Text1
Categorical1
DateTime1

Dataset

Description인천광역시 중구 관내에 위치한 반려동물 등록 현황에 대한 데이터 입니다.파일명 인천광역시_중구_반려동물 등록 현황파일내용 반려동물 등록 주체, RFID 종류(내장형, 외장형, 인식표) 등
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15040307&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
등록주체 시군구 is highly overall correlated with 등록주체 대행업체 and 5 other fieldsHigh correlation
등록주체 대행업체 is highly overall correlated with 등록주체 시군구 and 5 other fieldsHigh correlation
무선태그 내장형 is highly overall correlated with 등록주체 시군구 and 5 other fieldsHigh correlation
무선태그 외장형 is highly overall correlated with 등록주체 시군구 and 5 other fieldsHigh correlation
무선태그 인식표 is highly overall correlated with 등록주체 시군구 and 5 other fieldsHigh correlation
등록품종수 is highly overall correlated with 등록주체 시군구 and 5 other fieldsHigh correlation
동물소유자수 is highly overall correlated with 등록주체 시군구 and 5 other fieldsHigh correlation
등록주체 기타(이벤트등) is highly imbalanced (65.8%)Imbalance
연번 has unique valuesUnique
읍면동(법정동) has unique valuesUnique
등록주체 시군구 has 3 (6.4%) zerosZeros
등록주체 대행업체 has 3 (6.4%) zerosZeros
무선태그 내장형 has 3 (6.4%) zerosZeros
무선태그 외장형 has 5 (10.6%) zerosZeros
무선태그 인식표 has 2 (4.3%) zerosZeros

Reproduction

Analysis started2024-01-28 11:15:58.183873
Analysis finished2024-01-28 11:16:04.056045
Duration5.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:04.110420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3
Q112.5
median24
Q335.5
95-th percentile44.7
Maximum47
Range46
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.711309
Coefficient of variation (CV)0.57130455
Kurtosis-1.2
Mean24
Median Absolute Deviation (MAD)12
Skewness0
Sum1128
Variance188
MonotonicityStrictly increasing
2024-01-28T20:16:04.208490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 1
 
2.1%
2 1
 
2.1%
27 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
Other values (37) 37
78.7%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%
38 1
2.1%

읍면동(법정동)
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2024-01-28T20:16:04.393415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.787234
Min length2

Characters and Unicode

Total characters178
Distinct characters49
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 (%)100.0%

Sample

1st row경동
2nd row내동
3rd row답동
4th row사동
5th row용동
ValueCountFrequency (%)
경동 1
 
2.1%
관동3가 1
 
2.1%
항동5가 1
 
2.1%
항동7가 1
 
2.1%
북성동1가 1
 
2.1%
북성동2가 1
 
2.1%
북성동3가 1
 
2.1%
송월동1가 1
 
2.1%
송월동2가 1
 
2.1%
송월동3가 1
 
2.1%
Other values (37) 37
78.7%
2024-01-28T20:16:04.691730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 47
26.4%
가 25
14.0%
1 7
 
3.9%
2 7
 
3.9%
3 7
 
3.9%
송 6
 
3.4%
북 5
 
2.8%
중 5
 
2.8%
신 5
 
2.8%
앙 4
 
2.2%
Other values (39) 60
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 153
86.0%
Decimal Number 25
 
14.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 47
30.7%
가 25
16.3%
송 6
 
3.9%
북 5
 
3.3%
중 5
 
3.3%
신 5
 
3.3%
앙 4
 
2.6%
월 3
 
2.0%
성 3
 
2.0%
항 3
 
2.0%
Other values (33) 47
30.7%
Decimal Number
ValueCountFrequency (%)
1 7
28.0%
2 7
28.0%
3 7
28.0%
4 2
 
8.0%
7 1
 
4.0%
5 1
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 153
86.0%
Common 25
 
14.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 47
30.7%
가 25
16.3%
송 6
 
3.9%
북 5
 
3.3%
중 5
 
3.3%
신 5
 
3.3%
앙 4
 
2.6%
월 3
 
2.0%
성 3
 
2.0%
항 3
 
2.0%
Other values (33) 47
30.7%
Common
ValueCountFrequency (%)
1 7
28.0%
2 7
28.0%
3 7
28.0%
4 2
 
8.0%
7 1
 
4.0%
5 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 153
86.0%
ASCII 25
 
14.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 47
30.7%
가 25
16.3%
송 6
 
3.9%
북 5
 
3.3%
중 5
 
3.3%
신 5
 
3.3%
앙 4
 
2.6%
월 3
 
2.0%
성 3
 
2.0%
항 3
 
2.0%
Other values (33) 47
30.7%
ASCII
ValueCountFrequency (%)
1 7
28.0%
2 7
28.0%
3 7
28.0%
4 2
 
8.0%
7 1
 
4.0%
5 1
 
4.0%

등록주체 시군구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.276596
Minimum0
Maximum106
Zeros3
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:04.799506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.5
median7
Q323
95-th percentile47.7
Maximum106
Range106
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation22.630013
Coefficient of variation (CV)1.3903407
Kurtosis5.4889484
Mean16.276596
Median Absolute Deviation (MAD)6
Skewness2.1973363
Sum765
Variance512.11748
MonotonicityNot monotonic
2024-01-28T20:16:04.902681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 9
19.1%
2 3
 
6.4%
0 3
 
6.4%
3 3
 
6.4%
15 2
 
4.3%
10 2
 
4.3%
6 2
 
4.3%
5 2
 
4.3%
41 2
 
4.3%
7 2
 
4.3%
Other values (16) 17
36.2%
ValueCountFrequency (%)
0 3
 
6.4%
1 9
19.1%
2 3
 
6.4%
3 3
 
6.4%
4 1
 
2.1%
5 2
 
4.3%
6 2
 
4.3%
7 2
 
4.3%
8 2
 
4.3%
9 1
 
2.1%
ValueCountFrequency (%)
106 1
2.1%
85 1
2.1%
48 1
2.1%
47 1
2.1%
43 1
2.1%
42 1
2.1%
41 2
4.3%
35 1
2.1%
34 1
2.1%
33 1
2.1%

등록주체 대행업체
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.61702
Minimum0
Maximum2690
Zeros3
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:05.001920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q115.5
median41
Q3154
95-th percentile1032.1
Maximum2690
Range2690
Interquartile range (IQR)138.5

Descriptive statistics

Standard deviation509.83734
Coefficient of variation (CV)2.3645505
Kurtosis15.314576
Mean215.61702
Median Absolute Deviation (MAD)40
Skewness3.8532574
Sum10134
Variance259934.11
MonotonicityNot monotonic
2024-01-28T20:16:05.097211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 3
 
6.4%
29 2
 
4.3%
81 2
 
4.3%
37 2
 
4.3%
1 2
 
4.3%
177 2
 
4.3%
14 2
 
4.3%
20 2
 
4.3%
4 2
 
4.3%
73 1
 
2.1%
Other values (27) 27
57.4%
ValueCountFrequency (%)
0 3
6.4%
1 2
4.3%
4 2
4.3%
5 1
 
2.1%
7 1
 
2.1%
13 1
 
2.1%
14 2
4.3%
17 1
 
2.1%
20 2
4.3%
22 1
 
2.1%
ValueCountFrequency (%)
2690 1
2.1%
2067 1
2.1%
1270 1
2.1%
477 1
2.1%
453 1
2.1%
374 1
2.1%
313 1
2.1%
278 1
2.1%
253 1
2.1%
200 1
2.1%

등록주체 기타(이벤트등)
Categorical

IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
0
44 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 44
93.6%
1 3
 
6.4%

Length

2024-01-28T20:16:05.185863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:16:05.251940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 44
93.6%
1 3
 
6.4%

무선태그 내장형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.14894
Minimum0
Maximum1473
Zeros3
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:05.325773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q111.5
median37
Q3102.5
95-th percentile596.4
Maximum1473
Range1473
Interquartile range (IQR)91

Descriptive statistics

Standard deviation276.61604
Coefficient of variation (CV)2.1927735
Kurtosis14.757318
Mean126.14894
Median Absolute Deviation (MAD)33
Skewness3.7418568
Sum5929
Variance76516.434
MonotonicityNot monotonic
2024-01-28T20:16:05.421826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 3
 
6.4%
52 2
 
4.3%
1 2
 
4.3%
12 2
 
4.3%
4 2
 
4.3%
19 1
 
2.1%
3 1
 
2.1%
329 1
 
2.1%
85 1
 
2.1%
53 1
 
2.1%
Other values (31) 31
66.0%
ValueCountFrequency (%)
0 3
6.4%
1 2
4.3%
2 1
 
2.1%
3 1
 
2.1%
4 2
4.3%
6 1
 
2.1%
8 1
 
2.1%
11 1
 
2.1%
12 2
4.3%
14 1
 
2.1%
ValueCountFrequency (%)
1473 1
2.1%
1081 1
2.1%
711 1
2.1%
329 1
2.1%
287 1
2.1%
247 1
2.1%
175 1
2.1%
169 1
2.1%
147 1
2.1%
124 1
2.1%

무선태그 외장형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.829787
Minimum0
Maximum1118
Zeros5
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:05.516196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median17
Q352
95-th percentile399.5
Maximum1118
Range1118
Interquartile range (IQR)46.5

Descriptive statistics

Standard deviation215.71734
Coefficient of variation (CV)2.5429433
Kurtosis15.738901
Mean84.829787
Median Absolute Deviation (MAD)16
Skewness3.9461846
Sum3987
Variance46533.97
MonotonicityNot monotonic
2024-01-28T20:16:05.607632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 5
 
10.6%
6 3
 
6.4%
7 3
 
6.4%
2 3
 
6.4%
17 2
 
4.3%
24 2
 
4.3%
25 2
 
4.3%
13 2
 
4.3%
1 2
 
4.3%
53 1
 
2.1%
Other values (22) 22
46.8%
ValueCountFrequency (%)
0 5
10.6%
1 2
 
4.3%
2 3
6.4%
3 1
 
2.1%
5 1
 
2.1%
6 3
6.4%
7 3
6.4%
8 1
 
2.1%
12 1
 
2.1%
13 2
 
4.3%
ValueCountFrequency (%)
1118 1
2.1%
919 1
2.1%
506 1
2.1%
151 1
2.1%
142 1
2.1%
139 1
2.1%
127 1
2.1%
111 1
2.1%
104 1
2.1%
78 1
2.1%

무선태그 인식표
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.978723
Minimum0
Maximum205
Zeros2
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:05.704406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q322
95-th percentile89.5
Maximum205
Range205
Interquartile range (IQR)20

Descriptive statistics

Standard deviation37.658932
Coefficient of variation (CV)1.7951012
Kurtosis12.471883
Mean20.978723
Median Absolute Deviation (MAD)4
Skewness3.231078
Sum986
Variance1418.1952
MonotonicityNot monotonic
2024-01-28T20:16:06.015434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2 7
14.9%
1 7
14.9%
4 4
 
8.5%
3 3
 
6.4%
11 2
 
4.3%
0 2
 
4.3%
18 2
 
4.3%
10 2
 
4.3%
8 2
 
4.3%
41 1
 
2.1%
Other values (15) 15
31.9%
ValueCountFrequency (%)
0 2
 
4.3%
1 7
14.9%
2 7
14.9%
3 3
6.4%
4 4
8.5%
5 1
 
2.1%
6 1
 
2.1%
7 1
 
2.1%
8 2
 
4.3%
10 2
 
4.3%
ValueCountFrequency (%)
205 1
2.1%
115 1
2.1%
91 1
2.1%
86 1
2.1%
57 1
2.1%
48 1
2.1%
47 1
2.1%
41 1
2.1%
36 1
2.1%
33 1
2.1%

등록품종수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.212766
Minimum1
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:06.102209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19.5
median19
Q329.5
95-th percentile64.8
Maximum87
Range86
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.598066
Coefficient of variation (CV)0.8822884
Kurtosis2.6126565
Mean22.212766
Median Absolute Deviation (MAD)10
Skewness1.5877512
Sum1044
Variance384.08418
MonotonicityNot monotonic
2024-01-28T20:16:06.192116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 4
 
8.5%
6 4
 
8.5%
19 3
 
6.4%
21 3
 
6.4%
10 3
 
6.4%
14 2
 
4.3%
33 2
 
4.3%
13 2
 
4.3%
18 2
 
4.3%
11 1
 
2.1%
Other values (21) 21
44.7%
ValueCountFrequency (%)
1 4
8.5%
2 1
 
2.1%
4 1
 
2.1%
5 1
 
2.1%
6 4
8.5%
9 1
 
2.1%
10 3
6.4%
11 1
 
2.1%
13 2
4.3%
14 2
4.3%
ValueCountFrequency (%)
87 1
2.1%
76 1
2.1%
66 1
2.1%
62 1
2.1%
51 1
2.1%
39 1
2.1%
37 1
2.1%
35 1
2.1%
34 1
2.1%
33 2
4.3%

동물소유자수
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.6383
Minimum1
Maximum2006
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:06.283263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q112
median32
Q3122
95-th percentile763.5
Maximum2006
Range2005
Interquartile range (IQR)110

Descriptive statistics

Standard deviation386.53137
Coefficient of variation (CV)2.3766319
Kurtosis15.200408
Mean162.6383
Median Absolute Deviation (MAD)28
Skewness3.8612791
Sum7644
Variance149406.5
MonotonicityNot monotonic
2024-01-28T20:16:06.381696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 5
 
10.6%
58 2
 
4.3%
12 2
 
4.3%
70 2
 
4.3%
9 2
 
4.3%
15 2
 
4.3%
27 2
 
4.3%
25 2
 
4.3%
291 1
 
2.1%
6 1
 
2.1%
Other values (26) 26
55.3%
ValueCountFrequency (%)
1 5
10.6%
4 1
 
2.1%
5 1
 
2.1%
6 1
 
2.1%
8 1
 
2.1%
9 2
 
4.3%
12 2
 
4.3%
15 2
 
4.3%
19 1
 
2.1%
20 1
 
2.1%
ValueCountFrequency (%)
2006 1
2.1%
1633 1
2.1%
933 1
2.1%
368 1
2.1%
291 1
2.1%
255 1
2.1%
242 1
2.1%
229 1
2.1%
184 1
2.1%
153 1
2.1%

동물소유자당 동물등록수
Real number (ℝ)

Distinct30
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.457234
Minimum1
Maximum2.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2024-01-28T20:16:06.482184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.315
median1.44
Q31.555
95-th percentile2.028
Maximum2.28
Range1.28
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.28632379
Coefficient of variation (CV)0.19648442
Kurtosis0.95104324
Mean1.457234
Median Absolute Deviation (MAD)0.12
Skewness0.68661726
Sum68.49
Variance0.081981314
MonotonicityNot monotonic
2024-01-28T20:16:06.576556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1.0 5
 
10.6%
1.52 3
 
6.4%
1.67 3
 
6.4%
1.44 3
 
6.4%
1.4 3
 
6.4%
1.25 3
 
6.4%
1.47 2
 
4.3%
1.37 2
 
4.3%
1.6 2
 
4.3%
1.97 1
 
2.1%
Other values (20) 20
42.6%
ValueCountFrequency (%)
1.0 5
10.6%
1.05 1
 
2.1%
1.17 1
 
2.1%
1.25 3
6.4%
1.29 1
 
2.1%
1.3 1
 
2.1%
1.33 1
 
2.1%
1.35 1
 
2.1%
1.36 1
 
2.1%
1.37 2
 
4.3%
ValueCountFrequency (%)
2.28 1
 
2.1%
2.05 1
 
2.1%
2.04 1
 
2.1%
2.0 1
 
2.1%
1.97 1
 
2.1%
1.75 1
 
2.1%
1.67 3
6.4%
1.6 2
4.3%
1.56 1
 
2.1%
1.55 1
 
2.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
Minimum2023-08-18 00:00:00
Maximum2023-08-18 00:00:00
2024-01-28T20:16:06.660332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:06.725092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-28T20:16:03.224417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.481732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.016278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.604726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.176604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.778656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.322032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.089231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.626576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.287152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.537278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.073507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.661476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.236363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.835250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.377992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.145802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.691775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.347665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.593669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.132492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.721111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.300802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.892926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.437439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.202164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.755059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.411218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.651487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.203068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.781060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.366753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.954067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.497474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.259462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.819588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.476993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.713359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.281990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.845047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.435448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.017382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.774785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.324238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.888633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.539153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.775566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.347560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.905801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.498359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.075281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.831885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.382171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.953462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.597841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.831746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.406048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.964693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.560791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.133531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.888777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.439232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.014205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.662506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.890117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.462572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.028064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.632209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.190597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.951997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.491922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.076672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.738837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:58.955698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:15:59.542903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.107457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:00.706775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:01.259232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.027252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:02.560180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:03.152700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:16:06.784800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동(법정동)등록주체 시군구등록주체 대행업체등록주체 기타(이벤트등)무선태그 내장형무선태그 외장형무선태그 인식표등록품종수동물소유자수동물소유자당 동물등록수
연번1.0001.0000.3120.2680.0000.2470.3700.3330.5780.2680.402
읍면동(법정동)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
등록주체 시군구0.3121.0001.0000.7900.0000.8630.7720.8700.8720.7900.000
등록주체 대행업체0.2681.0000.7901.0000.4201.0001.0000.9380.9181.0000.000
등록주체 기타(이벤트등)0.0001.0000.0000.4201.0000.6740.4190.6800.4950.4200.108
무선태그 내장형0.2471.0000.8631.0000.6741.0000.9690.9940.9671.0000.000
무선태그 외장형0.3701.0000.7721.0000.4190.9691.0000.9190.9211.0000.000
무선태그 인식표0.3331.0000.8700.9380.6800.9940.9191.0000.9480.9380.000
등록품종수0.5781.0000.8720.9180.4950.9670.9210.9481.0000.9180.414
동물소유자수0.2681.0000.7901.0000.4201.0001.0000.9380.9181.0000.000
동물소유자당 동물등록수0.4021.0000.0000.0000.1080.0000.0000.0000.4140.0001.000
2024-01-28T20:16:06.893090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번등록주체 시군구등록주체 대행업체무선태그 내장형무선태그 외장형무선태그 인식표등록품종수동물소유자수동물소유자당 동물등록수등록주체 기타(이벤트등)
연번1.000-0.143-0.356-0.344-0.354-0.237-0.369-0.336-0.1950.000
등록주체 시군구-0.1431.0000.8980.8990.9070.9690.8760.9080.2780.000
등록주체 대행업체-0.3560.8981.0000.9950.9790.9360.9790.9910.2680.493
무선태그 내장형-0.3440.8990.9951.0000.9650.9290.9720.9870.2710.470
무선태그 외장형-0.3540.9070.9790.9651.0000.9470.9660.9800.2520.492
무선태그 인식표-0.2370.9690.9360.9290.9471.0000.9210.9420.2970.474
등록품종수-0.3690.8760.9790.9720.9660.9211.0000.9780.2470.453
동물소유자수-0.3360.9080.9910.9870.9800.9420.9781.0000.2040.493
동물소유자당 동물등록수-0.1950.2780.2680.2710.2520.2970.2470.2041.0000.078
등록주체 기타(이벤트등)0.0000.0000.4930.4700.4920.4740.4530.4930.0781.000

Missing values

2024-01-28T20:16:03.834337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:16:03.990263image/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

연번읍면동(법정동)등록주체 시군구등록주체 대행업체등록주체 기타(이벤트등)무선태그 내장형무선태그 외장형무선태그 인식표등록품종수동물소유자수동물소유자당 동물등록수데이터기준일자
01경동1573052251122581.522023-08-18
12내동89505639821701.472023-08-18
23답동1081147341121691.332023-08-18
34사동1370287314251.522023-08-18
45용동13402013215271.32023-08-18
56유동63001614619251.442023-08-18
67전동1720001217818331511.442023-08-18
78남북동15714017227381.552023-08-18
89덕교동014011219121.172023-08-18
910도원동48278017510447332291.422023-08-18
연번읍면동(법정동)등록주체 시군구등록주체 대행업체등록주체 기타(이벤트등)무선태그 내장형무선태그 외장형무선태그 인식표등록품종수동물소유자수동물소유자당 동물등록수데이터기준일자
3738신흥동1가43313016913948392551.42023-08-18
3839신흥동2가2917701245329341531.352023-08-18
3940신흥동3가41374024712741352911.432023-08-18
4041중앙동1가200002212.02023-08-18
4142중앙동2가010100111.02023-08-18
4243중앙동3가4170413410121.752023-08-18
4344중앙동4가150411661.02023-08-18
4445해안동2가340313551.42023-08-18
4546해안동3가100001111.02023-08-18
4647해안동4가100001111.02023-08-18