Overview

Dataset statistics

Number of variables12
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory107.8 B

Variable types

Categorical1
Text1
Numeric9
DateTime1

Dataset

Description경기도 용인시 반려동물 등록현황입니다.(등록주체)시군구등록, (등록주체)대행업체등록, (등록주체)기타, (RFID종류)내장형, (RFID종류)외장형, (RFID종류)인식표, 등록품종수, 등록소유자수, 동물소유자당등록동물수, 데이터기준일자 데이터를 제공합니다.
Author경기도 용인시
URLhttps://www.data.go.kr/data/15093611/fileData.do

Alerts

시군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
(등록주체)시군구등록 is highly overall correlated with (등록주체)대행업체등록 and 5 other fieldsHigh correlation
(등록주체)대행업체등록 is highly overall correlated with (등록주체)시군구등록 and 5 other fieldsHigh correlation
(RFID종류)내장형 is highly overall correlated with (등록주체)시군구등록 and 5 other fieldsHigh correlation
(RFID종류)외장형 is highly overall correlated with (등록주체)시군구등록 and 5 other fieldsHigh correlation
(RFID종류)인식표 is highly overall correlated with (등록주체)시군구등록 and 5 other fieldsHigh correlation
등록품종수 is highly overall correlated with (등록주체)시군구등록 and 5 other fieldsHigh correlation
등록소유자수 is highly overall correlated with (등록주체)시군구등록 and 5 other fieldsHigh correlation
읍면동명 has unique valuesUnique
(등록주체)대행업체등록 has unique valuesUnique
(RFID종류)내장형 has unique valuesUnique
(RFID종류)외장형 has unique valuesUnique
(등록주체)시군구등록 has 1 (2.2%) zerosZeros
(등록주체)기타 has 20 (44.4%) zerosZeros

Reproduction

Analysis started2024-03-14 15:11:21.576079
Analysis finished2024-03-14 15:11:41.310094
Duration19.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size488.0 B
용인시
45 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
용인시 45
100.0%

Length

2024-03-15T00:11:41.493962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:11:41.801669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
용인시 45
100.0%

읍면동명
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size488.0 B
2024-03-15T00:11:42.693311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9777778
Min length2

Characters and Unicode

Total characters134
Distinct characters58
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

Unique45 ?
Unique (%)100.0%

Sample

1st row중동
2nd row고매동
3rd row공세동
4th row구갈동
5th row농서동
ValueCountFrequency (%)
중동 1
 
2.2%
죽전동 1
 
2.2%
남동 1
 
2.2%
호동 1
 
2.2%
고림동 1
 
2.2%
남사면 1
 
2.2%
남사읍 1
 
2.2%
마평동 1
 
2.2%
모현면 1
 
2.2%
모현읍 1
 
2.2%
Other values (35) 35
77.8%
2024-03-15T00:11:44.092154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 39
29.1%
면 6
 
4.5%
읍 4
 
3.0%
갈 4
 
3.0%
남 4
 
3.0%
상 3
 
2.2%
고 3
 
2.2%
현 3
 
2.2%
곡 3
 
2.2%
덕 3
 
2.2%
Other values (48) 62
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 39
29.1%
면 6
 
4.5%
읍 4
 
3.0%
갈 4
 
3.0%
남 4
 
3.0%
상 3
 
2.2%
고 3
 
2.2%
현 3
 
2.2%
곡 3
 
2.2%
덕 3
 
2.2%
Other values (48) 62
46.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 134
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 39
29.1%
면 6
 
4.5%
읍 4
 
3.0%
갈 4
 
3.0%
남 4
 
3.0%
상 3
 
2.2%
고 3
 
2.2%
현 3
 
2.2%
곡 3
 
2.2%
덕 3
 
2.2%
Other values (48) 62
46.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 134
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 39
29.1%
면 6
 
4.5%
읍 4
 
3.0%
갈 4
 
3.0%
남 4
 
3.0%
상 3
 
2.2%
고 3
 
2.2%
현 3
 
2.2%
곡 3
 
2.2%
덕 3
 
2.2%
Other values (48) 62
46.3%

(등록주체)시군구등록
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.51111
Minimum0
Maximum694
Zeros1
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:44.487391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.6
Q153
median113
Q3273
95-th percentile493.4
Maximum694
Range694
Interquartile range (IQR)220

Descriptive statistics

Standard deviation162.96704
Coefficient of variation (CV)0.95018356
Kurtosis1.513167
Mean171.51111
Median Absolute Deviation (MAD)78
Skewness1.3586906
Sum7718
Variance26558.256
MonotonicityNot monotonic
2024-03-15T00:11:44.924086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
8 2
 
4.4%
504 1
 
2.2%
142 1
 
2.2%
21 1
 
2.2%
108 1
 
2.2%
59 1
 
2.2%
35 1
 
2.2%
47 1
 
2.2%
31 1
 
2.2%
113 1
 
2.2%
Other values (34) 34
75.6%
ValueCountFrequency (%)
0 1
2.2%
8 2
4.4%
21 1
2.2%
23 1
2.2%
27 1
2.2%
31 1
2.2%
35 1
2.2%
47 1
2.2%
48 1
2.2%
49 1
2.2%
ValueCountFrequency (%)
694 1
2.2%
563 1
2.2%
504 1
2.2%
451 1
2.2%
388 1
2.2%
350 1
2.2%
325 1
2.2%
324 1
2.2%
310 1
2.2%
300 1
2.2%

(등록주체)대행업체등록
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1548.3333
Minimum20
Maximum4857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:45.555284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile143.6
Q1587
median1300
Q32182
95-th percentile4181.4
Maximum4857
Range4837
Interquartile range (IQR)1595

Descriptive statistics

Standard deviation1206.7061
Coefficient of variation (CV)0.7793581
Kurtosis0.57868705
Mean1548.3333
Median Absolute Deviation (MAD)824
Skewness1.0064293
Sum69675
Variance1456139.7
MonotonicityNot monotonic
2024-03-15T00:11:45.977792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2914 1
 
2.2%
861 1
 
2.2%
458 1
 
2.2%
53 1
 
2.2%
2975 1
 
2.2%
664 1
 
2.2%
827 1
 
2.2%
709 1
 
2.2%
425 1
 
2.2%
1712 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
20 1
2.2%
53 1
2.2%
128 1
2.2%
206 1
2.2%
324 1
2.2%
365 1
2.2%
408 1
2.2%
412 1
2.2%
425 1
2.2%
458 1
2.2%
ValueCountFrequency (%)
4857 1
2.2%
4362 1
2.2%
4284 1
2.2%
3771 1
2.2%
2975 1
2.2%
2914 1
2.2%
2776 1
2.2%
2647 1
2.2%
2466 1
2.2%
2439 1
2.2%

(등록주체)기타
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2222222
Minimum0
Maximum7
Zeros20
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:46.323492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6080605
Coefficient of variation (CV)1.3156859
Kurtosis3.0321207
Mean1.2222222
Median Absolute Deviation (MAD)1
Skewness1.7139761
Sum55
Variance2.5858586
MonotonicityNot monotonic
2024-03-15T00:11:46.679060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 20
44.4%
1 11
24.4%
2 8
 
17.8%
4 4
 
8.9%
7 1
 
2.2%
5 1
 
2.2%
ValueCountFrequency (%)
0 20
44.4%
1 11
24.4%
2 8
 
17.8%
4 4
 
8.9%
5 1
 
2.2%
7 1
 
2.2%
ValueCountFrequency (%)
7 1
 
2.2%
5 1
 
2.2%
4 4
 
8.9%
2 8
 
17.8%
1 11
24.4%
0 20
44.4%

(RFID종류)내장형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean896.31111
Minimum12
Maximum2782
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:47.054878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile79.6
Q1316
median721
Q31247
95-th percentile2457.2
Maximum2782
Range2770
Interquartile range (IQR)931

Descriptive statistics

Standard deviation721.46186
Coefficient of variation (CV)0.80492348
Kurtosis0.49012894
Mean896.31111
Median Absolute Deviation (MAD)452
Skewness1.0575354
Sum40334
Variance520507.22
MonotonicityNot monotonic
2024-03-15T00:11:47.456595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1831 1
 
2.2%
455 1
 
2.2%
300 1
 
2.2%
32 1
 
2.2%
2097 1
 
2.2%
390 1
 
2.2%
423 1
 
2.2%
352 1
 
2.2%
236 1
 
2.2%
980 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
12 1
2.2%
32 1
2.2%
72 1
2.2%
110 1
2.2%
166 1
2.2%
215 1
2.2%
231 1
2.2%
236 1
2.2%
256 1
2.2%
269 1
2.2%
ValueCountFrequency (%)
2782 1
2.2%
2566 1
2.2%
2479 1
2.2%
2370 1
2.2%
2097 1
2.2%
1831 1
2.2%
1623 1
2.2%
1498 1
2.2%
1312 1
2.2%
1310 1
2.2%

(RFID종류)외장형
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean634.44444
Minimum6
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:47.889858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile60.4
Q1206
median499
Q3854
95-th percentile1563.6
Maximum1996
Range1990
Interquartile range (IQR)648

Descriptive statistics

Standard deviation486.34521
Coefficient of variation (CV)0.76656863
Kurtosis0.39578246
Mean634.44444
Median Absolute Deviation (MAD)334
Skewness0.91588481
Sum28550
Variance236531.66
MonotonicityNot monotonic
2024-03-15T00:11:48.331563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1132 1
 
2.2%
391 1
 
2.2%
161 1
 
2.2%
20 1
 
2.2%
848 1
 
2.2%
248 1
 
2.2%
411 1
 
2.2%
355 1
 
2.2%
189 1
 
2.2%
664 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
6 1
2.2%
20 1
2.2%
53 1
2.2%
90 1
2.2%
144 1
2.2%
155 1
2.2%
161 1
2.2%
165 1
2.2%
184 1
2.2%
189 1
2.2%
ValueCountFrequency (%)
1996 1
2.2%
1767 1
2.2%
1610 1
2.2%
1378 1
2.2%
1327 1
2.2%
1236 1
2.2%
1132 1
2.2%
1097 1
2.2%
1047 1
2.2%
1010 1
2.2%

(RFID종류)인식표
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.31111
Minimum2
Maximum775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:48.748726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile15.6
Q152
median138
Q3302
95-th percentile483.8
Maximum775
Range773
Interquartile range (IQR)250

Descriptive statistics

Standard deviation183.07113
Coefficient of variation (CV)0.96195711
Kurtosis2.4729738
Mean190.31111
Median Absolute Deviation (MAD)98
Skewness1.527051
Sum8564
Variance33515.037
MonotonicityNot monotonic
2024-03-15T00:11:49.082767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
159 3
 
6.7%
459 1
 
2.2%
41 1
 
2.2%
22 1
 
2.2%
9 1
 
2.2%
138 1
 
2.2%
85 1
 
2.2%
29 1
 
2.2%
49 1
 
2.2%
38 1
 
2.2%
Other values (33) 33
73.3%
ValueCountFrequency (%)
2 1
2.2%
9 1
2.2%
14 1
2.2%
22 1
2.2%
24 1
2.2%
26 1
2.2%
29 1
2.2%
38 1
2.2%
41 1
2.2%
46 1
2.2%
ValueCountFrequency (%)
775 1
2.2%
751 1
2.2%
490 1
2.2%
459 1
2.2%
412 1
2.2%
371 1
2.2%
370 1
2.2%
334 1
2.2%
332 1
2.2%
331 1
2.2%

등록품종수
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.088889
Minimum11
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:49.318357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile35.4
Q152
median65
Q374
95-th percentile80
Maximum80
Range69
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.246429
Coefficient of variation (CV)0.26594737
Kurtosis1.4980729
Mean61.088889
Median Absolute Deviation (MAD)11
Skewness-1.1388869
Sum2749
Variance263.94646
MonotonicityNot monotonic
2024-03-15T00:11:49.543542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
80 4
 
8.9%
68 4
 
8.9%
76 3
 
6.7%
55 3
 
6.7%
61 2
 
4.4%
74 2
 
4.4%
52 2
 
4.4%
78 2
 
4.4%
60 2
 
4.4%
73 2
 
4.4%
Other values (17) 19
42.2%
ValueCountFrequency (%)
11 1
2.2%
16 1
2.2%
35 1
2.2%
37 1
2.2%
44 1
2.2%
46 1
2.2%
47 2
4.4%
48 1
2.2%
49 1
2.2%
50 1
2.2%
ValueCountFrequency (%)
80 4
8.9%
79 1
 
2.2%
78 2
4.4%
76 3
6.7%
75 1
 
2.2%
74 2
4.4%
73 2
4.4%
71 1
 
2.2%
68 4
8.9%
66 2
4.4%

등록소유자수
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.1556
Minimum16
Maximum4337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:49.796397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile114.6
Q1452
median1039
Q31776
95-th percentile3659.2
Maximum4337
Range4321
Interquartile range (IQR)1324

Descriptive statistics

Standard deviation1051.9362
Coefficient of variation (CV)0.84077168
Kurtosis1.3716934
Mean1251.1556
Median Absolute Deviation (MAD)710
Skewness1.2525845
Sum56302
Variance1106569.7
MonotonicityNot monotonic
2024-03-15T00:11:50.114556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
888 2
 
4.4%
681 1
 
2.2%
283 1
 
2.2%
40 1
 
2.2%
1377 1
 
2.2%
496 1
 
2.2%
545 1
 
2.2%
572 1
 
2.2%
321 1
 
2.2%
1206 1
 
2.2%
Other values (34) 34
75.6%
ValueCountFrequency (%)
16 1
2.2%
40 1
2.2%
100 1
2.2%
173 1
2.2%
251 1
2.2%
260 1
2.2%
283 1
2.2%
321 1
2.2%
325 1
2.2%
326 1
2.2%
ValueCountFrequency (%)
4337 1
2.2%
3927 1
2.2%
3815 1
2.2%
3036 1
2.2%
2480 1
2.2%
2256 1
2.2%
2102 1
2.2%
2076 1
2.2%
1962 1
2.2%
1950 1
2.2%

동물소유자당등록동물수
Real number (ℝ)

Distinct31
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4324444
Minimum1.23
Maximum2.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-15T00:11:50.342076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.23
5-th percentile1.24
Q11.29
median1.33
Q31.51
95-th percentile2.022
Maximum2.24
Range1.01
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.2322142
Coefficient of variation (CV)0.16211044
Kurtosis4.7238341
Mean1.4324444
Median Absolute Deviation (MAD)0.08
Skewness2.1426893
Sum64.46
Variance0.053923434
MonotonicityNot monotonic
2024-03-15T00:11:50.569988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1.3 4
 
8.9%
1.24 3
 
6.7%
1.33 3
 
6.7%
1.27 3
 
6.7%
1.32 3
 
6.7%
1.25 2
 
4.4%
1.47 2
 
4.4%
1.51 2
 
4.4%
1.38 1
 
2.2%
2.13 1
 
2.2%
Other values (21) 21
46.7%
ValueCountFrequency (%)
1.23 1
 
2.2%
1.24 3
6.7%
1.25 2
4.4%
1.26 1
 
2.2%
1.27 3
6.7%
1.28 1
 
2.2%
1.29 1
 
2.2%
1.3 4
8.9%
1.31 1
 
2.2%
1.32 3
6.7%
ValueCountFrequency (%)
2.24 1
2.2%
2.13 1
2.2%
2.1 1
2.2%
1.71 1
2.2%
1.65 1
2.2%
1.64 1
2.2%
1.59 1
2.2%
1.58 1
2.2%
1.53 1
2.2%
1.52 1
2.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size488.0 B
Minimum2023-12-31 00:00:00
Maximum2023-12-31 00:00:00
2024-03-15T00:11:50.750528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:50.907170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T00:11:38.197541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:22.061253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:24.380564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:25.912363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:27.403282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:29.271810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:31.360275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:33.696662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:36.004526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:38.452974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:22.318789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:24.593455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:26.074838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:27.556739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:29.439608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:31.622806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:33.955905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:36.251714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:38.695002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:22.570015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:24.733068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:26.222004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:27.774146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:29.590046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:31.875628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:34.206856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:36.485486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:38.946894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:22.833395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:24.897088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:26.381366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:27.947298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:29.755070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:32.138582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:34.466184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:36.732373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:39.188062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:23.085972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:25.063610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:26.601429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:28.086833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:29.903545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:32.388441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:34.717292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:36.966498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:39.449835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:23.354651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:25.223649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:26.782343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:28.272632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:30.114321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:32.661956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:34.988064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:37.225057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:39.710861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:23.618372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:25.476046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:26.947726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:28.535858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:30.385640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:32.934090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:35.254400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:37.477618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:39.963448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:23.879645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:25.631216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:27.110444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:28.790426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:30.653500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:33.198748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:35.513542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:37.734598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:40.197623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:24.123410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:25.767504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:27.252662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:29.028533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:30.896563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:33.436951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:35.752198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:37.958552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:11:51.042354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명(등록주체)시군구등록(등록주체)대행업체등록(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수
읍면동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
(등록주체)시군구등록1.0001.0000.9300.4610.9470.8210.9360.1590.8800.000
(등록주체)대행업체등록1.0000.9301.0000.0000.9740.9070.8050.5040.9230.643
(등록주체)기타1.0000.4610.0001.0000.2370.3540.2540.0000.3250.491
(RFID종류)내장형1.0000.9470.9740.2371.0000.7730.8350.5100.9010.644
(RFID종류)외장형1.0000.8210.9070.3540.7731.0000.7860.5220.9510.000
(RFID종류)인식표1.0000.9360.8050.2540.8350.7861.0000.6350.8900.343
등록품종수1.0000.1590.5040.0000.5100.5220.6351.0000.5020.000
등록소유자수1.0000.8800.9230.3250.9010.9510.8900.5021.0000.058
동물소유자당등록동물수1.0000.0000.6430.4910.6440.0000.3430.0000.0581.000
2024-03-15T00:11:51.354863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(등록주체)시군구등록(등록주체)대행업체등록(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수
(등록주체)시군구등록1.0000.8980.3960.8820.9130.9860.7910.937-0.406
(등록주체)대행업체등록0.8981.0000.4250.9890.9760.9050.9240.974-0.252
(등록주체)기타0.3960.4251.0000.3980.4350.3630.4240.444-0.063
(RFID종류)내장형0.8820.9890.3981.0000.9440.8830.9270.949-0.190
(RFID종류)외장형0.9130.9760.4350.9441.0000.9200.8850.988-0.352
(RFID종류)인식표0.9860.9050.3630.8830.9201.0000.8010.944-0.412
등록품종수0.7910.9240.4240.9270.8850.8011.0000.873-0.060
등록소유자수0.9370.9740.4440.9490.9880.9440.8731.000-0.387
동물소유자당등록동물수-0.406-0.252-0.063-0.190-0.352-0.412-0.060-0.3871.000

Missing values

2024-03-15T00:11:40.552899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:11:41.091287image/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

시군명읍면동명(등록주체)시군구등록(등록주체)대행업체등록(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수데이터기준일자
0용인시중동50429144183111324597324801.382023-12-31
1용인시고매동66412123118464463251.472023-12-31
2용인시공세동156130006616361596510491.392023-12-31
3용인시구갈동35021874116210473326819501.32023-12-31
4용인시농서동820601109014351731.242023-12-31
5용인시동백동324188619968443716817761.242023-12-31
6용인시마북동2472000112477652366616931.332023-12-31
7용인시보라동273169818248832656615211.32023-12-31
8용인시보정동2222182012578542937619111.262023-12-31
9용인시상갈동94587031627788475301.272023-12-31
시군명읍면동명(등록주체)시군구등록(등록주체)대행업체등록(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수데이터기준일자
35용인시양지면1422081513127301867813971.592023-12-31
36용인시역북동124178319498141457414351.332023-12-31
37용인시운학동231280725326371001.512023-12-31
38용인시원삼면77912465426871746061.642023-12-31
39용인시유방동621001153645375557501.422023-12-31
40용인시이동면27408025615524493261.332023-12-31
41용인시이동읍166170001233474159738882.12023-12-31
42용인시포곡읍11627761149812361598019621.472023-12-31
43용인시해곡동0200126211161.252023-12-31
44용인시김량장동10413511722635995911451.272023-12-31