Overview

Dataset statistics

Number of variables15
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 KiB
Average record size in memory134.4 B

Variable types

DateTime2
Categorical4
Numeric9

Dataset

Description서울특별시 노원구에 등록된 반려동물의 년도, 읍면동, 등록주체, RFID종류, 등록건수등에 대한 현황입니다.
Author서울특별시 노원구
URLhttps://www.data.go.kr/data/15105620/fileData.do

Alerts

등록주체(기타(이벤트등)) has constant value ""Constant
미승인 has constant value ""Constant
기준일자 has constant value ""Constant
등록주체(시군구) is highly overall correlated with 등록주체(대행업체) and 7 other fieldsHigh correlation
등록주체(대행업체) is highly overall correlated with 등록주체(시군구) and 8 other fieldsHigh correlation
RFID종류(내장형) is highly overall correlated with 등록주체(시군구) and 7 other fieldsHigh correlation
RFID종류(외장형) is highly overall correlated with 등록주체(시군구) and 6 other fieldsHigh correlation
RFID종류(인식표) is highly overall correlated with 등록주체(시군구) and 7 other fieldsHigh correlation
등록 품종수 is highly overall correlated with 등록주체(시군구) and 6 other fieldsHigh correlation
동물소유자수 is highly overall correlated with 등록주체(시군구) and 7 other fieldsHigh correlation
동물소유자당동물등록수 is highly overall correlated with 등록주체(대행업체) and 1 other fieldsHigh correlation
합계 is highly overall correlated with 등록주체(시군구) and 8 other fieldsHigh correlation
반려 is highly overall correlated with 등록주체(시군구) and 5 other fieldsHigh correlation
반려 is highly imbalanced (62.9%)Imbalance
등록주체(시군구) has 12 (21.8%) zerosZeros
RFID종류(외장형) has 5 (9.1%) zerosZeros
RFID종류(인식표) has 10 (18.2%) zerosZeros

Reproduction

Analysis started2023-12-12 00:16:55.948896
Analysis finished2023-12-12 00:17:04.268939
Duration8.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Date

Distinct11
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
Minimum2012-01-01 00:00:00
Maximum2022-07-01 00:00:00
2023-12-12T09:17:04.305681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:04.383279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

읍면동(법정동)
Categorical

Distinct5
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size572.0 B
공릉동
11 
상계동
11 
월계동
11 
중계동
11 
하계동
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공릉동
2nd row상계동
3rd row월계동
4th row중계동
5th row하계동

Common Values

ValueCountFrequency (%)
공릉동 11
20.0%
상계동 11
20.0%
월계동 11
20.0%
중계동 11
20.0%
하계동 11
20.0%

Length

2023-12-12T09:17:04.480223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:17:04.562840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공릉동 11
20.0%
상계동 11
20.0%
월계동 11
20.0%
중계동 11
20.0%
하계동 11
20.0%

등록주체(시군구)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.563636
Minimum0
Maximum261
Zeros12
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:04.649641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q38
95-th percentile61.3
Maximum261
Range261
Interquartile range (IQR)7

Descriptive statistics

Standard deviation39.71881
Coefficient of variation (CV)2.9283305
Kurtosis29.15226
Mean13.563636
Median Absolute Deviation (MAD)2
Skewness5.1002351
Sum746
Variance1577.5838
MonotonicityNot monotonic
2023-12-12T09:17:04.743314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 12
21.8%
2 10
18.2%
1 9
16.4%
5 4
 
7.3%
11 2
 
3.6%
6 2
 
3.6%
12 2
 
3.6%
4 2
 
3.6%
105 1
 
1.8%
20 1
 
1.8%
Other values (10) 10
18.2%
ValueCountFrequency (%)
0 12
21.8%
1 9
16.4%
2 10
18.2%
3 1
 
1.8%
4 2
 
3.6%
5 4
 
7.3%
6 2
 
3.6%
7 1
 
1.8%
9 1
 
1.8%
10 1
 
1.8%
ValueCountFrequency (%)
261 1
1.8%
105 1
1.8%
104 1
1.8%
43 1
1.8%
41 1
1.8%
20 1
1.8%
15 1
1.8%
13 1
1.8%
12 2
3.6%
11 2
3.6%

등록주체(대행업체)
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean497.87273
Minimum1
Maximum3277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:04.849852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.1
Q1131
median298
Q3578.5
95-th percentile1673.2
Maximum3277
Range3276
Interquartile range (IQR)447.5

Descriptive statistics

Standard deviation632.2356
Coefficient of variation (CV)1.2698739
Kurtosis7.3415555
Mean497.87273
Median Absolute Deviation (MAD)181
Skewness2.5170524
Sum27383
Variance399721.85
MonotonicityNot monotonic
2023-12-12T09:17:04.956754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
3.6%
67 2
 
3.6%
8 1
 
1.8%
316 1
 
1.8%
166 1
 
1.8%
227 1
 
1.8%
126 1
 
1.8%
902 1
 
1.8%
2459 1
 
1.8%
950 1
 
1.8%
Other values (43) 43
78.2%
ValueCountFrequency (%)
1 2
3.6%
4 1
1.8%
7 1
1.8%
8 1
1.8%
67 2
3.6%
74 1
1.8%
90 1
1.8%
108 1
1.8%
117 1
1.8%
118 1
1.8%
ValueCountFrequency (%)
3277 1
1.8%
2459 1
1.8%
1690 1
1.8%
1666 1
1.8%
1556 1
1.8%
1367 1
1.8%
1221 1
1.8%
950 1
1.8%
902 1
1.8%
818 1
1.8%

등록주체(기타(이벤트등))
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
0
55 

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 55
100.0%

Length

2023-12-12T09:17:05.066659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:17:05.144630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 55
100.0%

RFID종류(내장형)
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.49091
Minimum1
Maximum1173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:05.248727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.1
Q168
median124
Q3285.5
95-th percentile757.6
Maximum1173
Range1172
Interquartile range (IQR)217.5

Descriptive statistics

Standard deviation245.47315
Coefficient of variation (CV)1.118375
Kurtosis4.3516999
Mean219.49091
Median Absolute Deviation (MAD)83
Skewness2.0214671
Sum12072
Variance60257.069
MonotonicityNot monotonic
2023-12-12T09:17:05.360603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
3.6%
75 2
 
3.6%
70 2
 
3.6%
41 2
 
3.6%
36 2
 
3.6%
8 1
 
1.8%
530 1
 
1.8%
87 1
 
1.8%
150 1
 
1.8%
450 1
 
1.8%
Other values (40) 40
72.7%
ValueCountFrequency (%)
1 2
3.6%
4 1
1.8%
7 1
1.8%
8 1
1.8%
36 2
3.6%
41 2
3.6%
48 1
1.8%
59 1
1.8%
60 1
1.8%
65 1
1.8%
ValueCountFrequency (%)
1173 1
1.8%
925 1
1.8%
794 1
1.8%
742 1
1.8%
581 1
1.8%
530 1
1.8%
483 1
1.8%
450 1
1.8%
448 1
1.8%
446 1
1.8%

RFID종류(외장형)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.87273
Minimum0
Maximum2246
Zeros5
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:05.470324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q151
median109
Q3274.5
95-th percentile1055.1
Maximum2246
Range2246
Interquartile range (IQR)223.5

Descriptive statistics

Standard deviation387.89904
Coefficient of variation (CV)1.5339695
Kurtosis12.873277
Mean252.87273
Median Absolute Deviation (MAD)74
Skewness3.2285686
Sum13908
Variance150465.67
MonotonicityNot monotonic
2023-12-12T09:17:05.843516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 5
 
9.1%
52 2
 
3.6%
97 2
 
3.6%
62 2
 
3.6%
1044 1
 
1.8%
159 1
 
1.8%
68 1
 
1.8%
372 1
 
1.8%
1081 1
 
1.8%
370 1
 
1.8%
Other values (38) 38
69.1%
ValueCountFrequency (%)
0 5
9.1%
23 1
 
1.8%
24 1
 
1.8%
28 1
 
1.8%
35 1
 
1.8%
43 1
 
1.8%
44 1
 
1.8%
45 1
 
1.8%
46 1
 
1.8%
50 1
 
1.8%
ValueCountFrequency (%)
2246 1
1.8%
1166 1
1.8%
1081 1
1.8%
1044 1
1.8%
797 1
1.8%
757 1
1.8%
532 1
1.8%
437 1
1.8%
413 1
1.8%
372 1
1.8%

RFID종류(인식표)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.072727
Minimum0
Maximum466
Zeros10
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:05.953711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median10
Q337
95-th percentile148.3
Maximum466
Range466
Interquartile range (IQR)33

Descriptive statistics

Standard deviation75.595624
Coefficient of variation (CV)1.9347414
Kurtosis18.873098
Mean39.072727
Median Absolute Deviation (MAD)9
Skewness3.8773612
Sum2149
Variance5714.6983
MonotonicityNot monotonic
2023-12-12T09:17:06.062135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 10
18.2%
10 4
 
7.3%
6 3
 
5.5%
3 3
 
5.5%
9 3
 
5.5%
7 2
 
3.6%
8 2
 
3.6%
13 2
 
3.6%
5 2
 
3.6%
19 2
 
3.6%
Other values (22) 22
40.0%
ValueCountFrequency (%)
0 10
18.2%
2 1
 
1.8%
3 3
 
5.5%
5 2
 
3.6%
6 3
 
5.5%
7 2
 
3.6%
8 2
 
3.6%
9 3
 
5.5%
10 4
 
7.3%
11 1
 
1.8%
ValueCountFrequency (%)
466 1
1.8%
201 1
1.8%
198 1
1.8%
127 1
1.8%
123 1
1.8%
119 1
1.8%
116 1
1.8%
85 1
1.8%
82 1
1.8%
74 1
1.8%

등록 품종수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.254545
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:06.175487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.4
Q125
median33
Q343.5
95-th percentile56.2
Maximum65
Range64
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation14.673254
Coefficient of variation (CV)0.44124055
Kurtosis0.1295696
Mean33.254545
Median Absolute Deviation (MAD)9
Skewness-0.25689046
Sum1829
Variance215.30438
MonotonicityNot monotonic
2023-12-12T09:17:06.286924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
26 3
 
5.5%
30 3
 
5.5%
25 3
 
5.5%
33 3
 
5.5%
45 3
 
5.5%
24 2
 
3.6%
28 2
 
3.6%
36 2
 
3.6%
21 2
 
3.6%
37 2
 
3.6%
Other values (24) 30
54.5%
ValueCountFrequency (%)
1 2
3.6%
2 1
 
1.8%
4 1
 
1.8%
7 1
 
1.8%
16 1
 
1.8%
19 1
 
1.8%
21 2
3.6%
22 1
 
1.8%
24 2
3.6%
25 3
5.5%
ValueCountFrequency (%)
65 1
 
1.8%
62 1
 
1.8%
59 1
 
1.8%
55 1
 
1.8%
52 1
 
1.8%
49 2
3.6%
48 2
3.6%
47 1
 
1.8%
45 3
5.5%
44 1
 
1.8%

동물소유자수
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean447.10909
Minimum1
Maximum2784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:06.414213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.4
Q1121.5
median271
Q3507.5
95-th percentile1457.7
Maximum2784
Range2783
Interquartile range (IQR)386

Descriptive statistics

Standard deviation560.14379
Coefficient of variation (CV)1.2528123
Kurtosis6.7209652
Mean447.10909
Median Absolute Deviation (MAD)163
Skewness2.4504765
Sum24591
Variance313761.06
MonotonicityNot monotonic
2023-12-12T09:17:06.541152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 3
 
5.5%
1 2
 
3.6%
7 1
 
1.8%
401 1
 
1.8%
211 1
 
1.8%
122 1
 
1.8%
844 1
 
1.8%
2357 1
 
1.8%
888 1
 
1.8%
1282 1
 
1.8%
Other values (42) 42
76.4%
ValueCountFrequency (%)
1 2
3.6%
3 1
 
1.8%
5 1
 
1.8%
7 1
 
1.8%
65 3
5.5%
89 1
 
1.8%
98 1
 
1.8%
108 1
 
1.8%
114 1
 
1.8%
119 1
 
1.8%
ValueCountFrequency (%)
2784 1
1.8%
2357 1
1.8%
1499 1
1.8%
1440 1
1.8%
1340 1
1.8%
1282 1
1.8%
1033 1
1.8%
888 1
1.8%
844 1
1.8%
765 1
1.8%

동물소유자당동물등록수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1243636
Minimum1
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:06.670687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.027
Q11.085
median1.12
Q31.15
95-th percentile1.196
Maximum1.4
Range0.4
Interquartile range (IQR)0.065

Descriptive statistics

Standard deviation0.066854281
Coefficient of variation (CV)0.059459662
Kurtosis5.6249832
Mean1.1243636
Median Absolute Deviation (MAD)0.03
Skewness1.5179323
Sum61.84
Variance0.0044694949
MonotonicityNot monotonic
2023-12-12T09:17:06.807778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.11 6
10.9%
1.13 6
10.9%
1.08 5
 
9.1%
1.15 5
 
9.1%
1.16 4
 
7.3%
1.1 3
 
5.5%
1.12 3
 
5.5%
1.06 3
 
5.5%
1.14 3
 
5.5%
1.17 3
 
5.5%
Other values (11) 14
25.5%
ValueCountFrequency (%)
1.0 2
 
3.6%
1.02 1
 
1.8%
1.03 1
 
1.8%
1.04 1
 
1.8%
1.06 3
5.5%
1.07 1
 
1.8%
1.08 5
9.1%
1.09 2
 
3.6%
1.1 3
5.5%
1.11 6
10.9%
ValueCountFrequency (%)
1.4 1
 
1.8%
1.33 1
 
1.8%
1.21 1
 
1.8%
1.19 2
 
3.6%
1.18 1
 
1.8%
1.17 3
5.5%
1.16 4
7.3%
1.15 5
9.1%
1.14 3
5.5%
1.13 6
10.9%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean511.43636
Minimum1
Maximum3290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-12T09:17:06.957074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.1
Q1132.5
median299
Q3589
95-th percentile1677.6
Maximum3290
Range3289
Interquartile range (IQR)456.5

Descriptive statistics

Standard deviation654.30573
Coefficient of variation (CV)1.2793493
Kurtosis7.0357713
Mean511.43636
Median Absolute Deviation (MAD)181
Skewness2.4959207
Sum28129
Variance428115.99
MonotonicityNot monotonic
2023-12-12T09:17:07.090913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
3.6%
164 2
 
3.6%
8 1
 
1.8%
467 1
 
1.8%
171 1
 
1.8%
238 1
 
1.8%
130 1
 
1.8%
945 1
 
1.8%
2720 1
 
1.8%
1054 1
 
1.8%
Other values (43) 43
78.2%
ValueCountFrequency (%)
1 2
3.6%
4 1
1.8%
7 1
1.8%
8 1
1.8%
67 1
1.8%
69 1
1.8%
74 1
1.8%
91 1
1.8%
109 1
1.8%
118 1
1.8%
ValueCountFrequency (%)
3290 1
1.8%
2720 1
1.8%
1693 1
1.8%
1671 1
1.8%
1568 1
1.8%
1472 1
1.8%
1232 1
1.8%
1054 1
1.8%
945 1
1.8%
833 1
1.8%

미승인
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
0
55 

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 55
100.0%

Length

2023-12-12T09:17:07.221830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:17:07.329722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 55
100.0%

반려
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size572.0 B
0
47 
1
 
3
2
 
3
8
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
0 47
85.5%
1 3
 
5.5%
2 3
 
5.5%
8 1
 
1.8%
3 1
 
1.8%

Length

2023-12-12T09:17:07.434231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:17:07.530615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 47
85.5%
1 3
 
5.5%
2 3
 
5.5%
8 1
 
1.8%
3 1
 
1.8%

기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size572.0 B
Minimum2022-08-28 00:00:00
Maximum2022-08-28 00:00:00
2023-12-12T09:17:07.606270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:07.681305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T09:17:03.305046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.516502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.436557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.311285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.084430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.855834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.670374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.766364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.500972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.381941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.620954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.532289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.412844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.161508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.944671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.781238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.847654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.597104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.466239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.730291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.613312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.509776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.237682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.027103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.103535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.923814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.680740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.538581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.854075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.716880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.589138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.311439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.114329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.189216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.997774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.767013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.609621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:56.935140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.804137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.661554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.376873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.186782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.284197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.075762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.850617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.682957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.038388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.895954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.744658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.447233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.299299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.363484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.157766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.934973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.752788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.150201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.000973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.833399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.547781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.389157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.489020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.243228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.021193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.818212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.252654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.111444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.916336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.659779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.478489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.588645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.321309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.107518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.912698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:57.347685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:58.210315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.001938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:59.760950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:00.575896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:01.679021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:02.423864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:03.214401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:17:07.759720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도읍면동(법정동)등록주체(시군구)등록주체(대행업체)RFID종류(내장형)RFID종류(외장형)RFID종류(인식표)등록 품종수동물소유자수동물소유자당동물등록수합계반려
년도1.0000.0000.4800.4890.3330.4950.6490.6600.5240.5150.4430.628
읍면동(법정동)0.0001.0000.0000.0000.0990.0000.0000.3290.1840.2650.1810.000
등록주체(시군구)0.4800.0001.0000.7620.8010.7420.9590.2570.8090.0000.8120.935
등록주체(대행업체)0.4890.0000.7621.0000.9790.9140.8470.6390.9960.0000.9990.747
RFID종류(내장형)0.3330.0990.8010.9791.0000.8530.8040.7390.9750.0000.9780.763
RFID종류(외장형)0.4950.0000.7420.9140.8531.0000.7200.5340.8890.0610.8970.608
RFID종류(인식표)0.6490.0000.9590.8470.8040.7201.0000.5770.8550.0000.8560.928
등록 품종수0.6600.3290.2570.6390.7390.5340.5771.0000.6810.4840.6580.427
동물소유자수0.5240.1840.8090.9960.9750.8890.8550.6811.0000.0000.9980.779
동물소유자당동물등록수0.5150.2650.0000.0000.0000.0610.0000.4840.0001.0000.0000.000
합계0.4430.1810.8120.9990.9780.8970.8560.6580.9980.0001.0000.746
반려0.6280.0000.9350.7470.7630.6080.9280.4270.7790.0000.7461.000
2023-12-12T09:17:07.894277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
반려읍면동(법정동)
반려1.0000.000
읍면동(법정동)0.0001.000
2023-12-12T09:17:08.003145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록주체(시군구)등록주체(대행업체)RFID종류(내장형)RFID종류(외장형)RFID종류(인식표)등록 품종수동물소유자수동물소유자당동물등록수합계읍면동(법정동)반려
등록주체(시군구)1.0000.7780.7820.7220.6040.7690.7800.4230.7810.0000.642
등록주체(대행업체)0.7781.0000.9600.9490.7310.9240.9990.5051.0000.0000.567
RFID종류(내장형)0.7820.9601.0000.8470.7420.9370.9640.4420.9610.0300.587
RFID종류(외장형)0.7220.9490.8471.0000.5900.8460.9470.4830.9480.0000.438
RFID종류(인식표)0.6040.7310.7420.5901.0000.6140.7260.2590.7330.0000.627
등록 품종수0.7690.9240.9370.8460.6141.0000.9250.4680.9240.1810.247
동물소유자수0.7800.9990.9640.9470.7260.9251.0000.4820.9990.0980.608
동물소유자당동물등록수0.4230.5050.4420.4830.2590.4680.4821.0000.5020.1260.000
합계0.7811.0000.9610.9480.7330.9240.9990.5021.0000.0960.565
읍면동(법정동)0.0000.0000.0300.0000.0000.1810.0980.1260.0961.0000.000
반려0.6420.5670.5870.4380.6270.2470.6080.0000.5650.0001.000

Missing values

2023-12-12T09:17:04.041777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:17:04.201926image/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

년도읍면동(법정동)등록주체(시군구)등록주체(대행업체)등록주체(기타(이벤트등))RFID종류(내장형)RFID종류(외장형)RFID종류(인식표)등록 품종수동물소유자수동물소유자당동물등록수합계미승인반려기준일자
02012공릉동080800771.148002022-08-28
12012상계동070700451.47002022-08-28
22012월계동040400231.334002022-08-28
32012중계동010100111.01002022-08-28
42012하계동010100111.01002022-08-28
52013공릉동5166604481166574814401.161671002022-08-28
62013상계동133277092522461195927841.183290002022-08-28
72013월계동11122103197971164310331.191232002022-08-28
82013중계동3169005811044684214991.131693002022-08-28
92013하계동2601018041310335141.17603002022-08-28
년도읍면동(법정동)등록주체(시군구)등록주체(대행업체)등록주체(기타(이벤트등))RFID종류(내장형)RFID종류(외장형)RFID종류(인식표)등록 품종수동물소유자수동물소유자당동물등록수합계미승인반려기준일자
452021공릉동956602792915475011.15575002022-08-28
462021상계동1215560794757176213401.171568012022-08-28
472021월계동452502942269484541.17529032022-08-28
482021중계동680104463556527211.12807012022-08-28
492021하계동234201591823393191.08344002022-08-28
502022-07공릉동2161066970331541.06163002022-08-28
512022-07상계동656102752920494681.21567002022-08-28
522022-07월계동11840701150261721.08185002022-08-28
532022-07중계동2024101111500342251.16261002022-08-28
542022-07하계동19004150021891.0291002022-08-28