Overview

Dataset statistics

Number of variables10
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory94.1 B

Variable types

Text1
Numeric8
Categorical1

Dataset

Description대구광역시 수성구_동별 반려동물 등록현황_20240125
Author대구광역시 수성구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15111239&dataSetDetailId=151112391ec920f865ec0&provdMethod=FILE

Alerts

등록주체_시군구 is highly overall correlated with 등록주체_대행업체 and 6 other fieldsHigh correlation
등록주체_대행업체 is highly overall correlated with 등록주체_시군구 and 6 other fieldsHigh correlation
내장형 전자태그(RFID) is highly overall correlated with 등록주체_시군구 and 6 other fieldsHigh correlation
외장형 전자태그(RFID) is highly overall correlated with 등록주체_시군구 and 6 other fieldsHigh correlation
인식표 전자태그(RFID) is highly overall correlated with 등록주체_시군구 and 6 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 6 other fieldsHigh correlation
등록주체_기타(이벤트등) is highly overall correlated with 등록주체_시군구 and 5 other fieldsHigh correlation
읍면동(법정동) has unique valuesUnique
등록주체_대행업체 has unique valuesUnique
내장형 전자태그(RFID) has unique valuesUnique
동물소유자수 has unique valuesUnique
등록주체_시군구 has 4 (15.4%) zerosZeros

Reproduction

Analysis started2024-03-13 13:33:56.594999
Analysis finished2024-03-13 13:34:03.554080
Duration6.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동(법정동)
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-03-13T22:34:03.684762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1538462
Min length2

Characters and Unicode

Total characters82
Distinct characters39
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

Unique26 ?
Unique (%)100.0%

Sample

1st row상동
2nd row성동
3rd row중동
4th row파동
5th row가천동
ValueCountFrequency (%)
상동 1
 
3.8%
성동 1
 
3.8%
수성동3가 1
 
3.8%
수성동2가 1
 
3.8%
수성동1가 1
 
3.8%
황금동 1
 
3.8%
지산동 1
 
3.8%
이천동 1
 
3.8%
욱수동 1
 
3.8%
연호동 1
 
3.8%
Other values (16) 16
61.5%
2024-03-13T22:34:04.021262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
31.7%
수 5
 
6.1%
성 5
 
6.1%
가 5
 
6.1%
산 2
 
2.4%
호 2
 
2.4%
매 2
 
2.4%
지 2
 
2.4%
범 2
 
2.4%
천 2
 
2.4%
Other values (29) 29
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
95.1%
Decimal Number 4
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
33.3%
수 5
 
6.4%
성 5
 
6.4%
가 5
 
6.4%
산 2
 
2.6%
호 2
 
2.6%
매 2
 
2.6%
지 2
 
2.6%
범 2
 
2.6%
천 2
 
2.6%
Other values (25) 25
32.1%
Decimal Number
ValueCountFrequency (%)
1 1
25.0%
2 1
25.0%
3 1
25.0%
4 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
95.1%
Common 4
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
33.3%
수 5
 
6.4%
성 5
 
6.4%
가 5
 
6.4%
산 2
 
2.6%
호 2
 
2.6%
매 2
 
2.6%
지 2
 
2.6%
범 2
 
2.6%
천 2
 
2.6%
Other values (25) 25
32.1%
Common
ValueCountFrequency (%)
1 1
25.0%
2 1
25.0%
3 1
25.0%
4 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
95.1%
ASCII 4
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
33.3%
수 5
 
6.4%
성 5
 
6.4%
가 5
 
6.4%
산 2
 
2.6%
호 2
 
2.6%
매 2
 
2.6%
지 2
 
2.6%
범 2
 
2.6%
천 2
 
2.6%
Other values (25) 25
32.1%
ASCII
ValueCountFrequency (%)
1 1
25.0%
2 1
25.0%
3 1
25.0%
4 1
25.0%

등록주체_시군구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.115385
Minimum0
Maximum41
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T22:34:04.146191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10.5
Q315.75
95-th percentile32.75
Maximum41
Range41
Interquartile range (IQR)12.75

Descriptive statistics

Standard deviation11.652732
Coefficient of variation (CV)0.96181277
Kurtosis0.17028421
Mean12.115385
Median Absolute Deviation (MAD)7.5
Skewness0.98701169
Sum315
Variance135.78615
MonotonicityNot monotonic
2024-03-13T22:34:04.316351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 4
 
15.4%
12 2
 
7.7%
3 2
 
7.7%
1 2
 
7.7%
11 1
 
3.8%
14 1
 
3.8%
24 1
 
3.8%
32 1
 
3.8%
8 1
 
3.8%
15 1
 
3.8%
Other values (10) 10
38.5%
ValueCountFrequency (%)
0 4
15.4%
1 2
7.7%
3 2
7.7%
4 1
 
3.8%
5 1
 
3.8%
6 1
 
3.8%
8 1
 
3.8%
10 1
 
3.8%
11 1
 
3.8%
12 2
7.7%
ValueCountFrequency (%)
41 1
3.8%
33 1
3.8%
32 1
3.8%
29 1
3.8%
24 1
3.8%
22 1
3.8%
16 1
3.8%
15 1
3.8%
14 1
3.8%
13 1
3.8%

등록주체_대행업체
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean938.65385
Minimum11
Maximum3793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T22:34:04.455050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile16.5
Q178.25
median708.5
Q31341.25
95-th percentile3123.25
Maximum3793
Range3782
Interquartile range (IQR)1263

Descriptive statistics

Standard deviation1038.7454
Coefficient of variation (CV)1.1066331
Kurtosis1.5631644
Mean938.65385
Median Absolute Deviation (MAD)661
Skewness1.4358833
Sum24405
Variance1078992
MonotonicityNot monotonic
2024-03-13T22:34:04.566561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1362 1
 
3.8%
38 1
 
3.8%
827 1
 
3.8%
519 1
 
3.8%
239 1
 
3.8%
876 1
 
3.8%
2191 1
 
3.8%
2659 1
 
3.8%
31 1
 
3.8%
193 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
11 1
3.8%
14 1
3.8%
24 1
3.8%
31 1
3.8%
34 1
3.8%
38 1
3.8%
40 1
3.8%
193 1
3.8%
239 1
3.8%
266 1
3.8%
ValueCountFrequency (%)
3793 1
3.8%
3278 1
3.8%
2659 1
3.8%
2191 1
3.8%
1582 1
3.8%
1502 1
3.8%
1362 1
3.8%
1279 1
3.8%
878 1
3.8%
876 1
3.8%

등록주체_기타(이벤트등)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
0
18 
1
5 
2
 
1
5
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)11.5%

Sample

1st row0
2nd row0
3rd row1
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 18
69.2%
1 5
 
19.2%
2 1
 
3.8%
5 1
 
3.8%
3 1
 
3.8%

Length

2024-03-13T22:34:04.686739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:34:04.797070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18
69.2%
1 5
 
19.2%
2 1
 
3.8%
5 1
 
3.8%
3 1
 
3.8%

내장형 전자태그(RFID)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean334.30769
Minimum4
Maximum1575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T22:34:04.908746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.25
Q135.75
median229
Q3399.25
95-th percentile1175.75
Maximum1575
Range1571
Interquartile range (IQR)363.5

Descriptive statistics

Standard deviation405.10786
Coefficient of variation (CV)1.2117815
Kurtosis3.0457697
Mean334.30769
Median Absolute Deviation (MAD)193.5
Skewness1.8081134
Sum8692
Variance164112.38
MonotonicityNot monotonic
2024-03-13T22:34:05.023757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
414 1
 
3.8%
27 1
 
3.8%
302 1
 
3.8%
217 1
 
3.8%
79 1
 
3.8%
339 1
 
3.8%
870 1
 
3.8%
890 1
 
3.8%
9 1
 
3.8%
62 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
4 1
3.8%
6 1
3.8%
7 1
3.8%
9 1
3.8%
13 1
3.8%
19 1
3.8%
27 1
3.8%
62 1
3.8%
79 1
3.8%
91 1
3.8%
ValueCountFrequency (%)
1575 1
3.8%
1271 1
3.8%
890 1
3.8%
870 1
3.8%
566 1
3.8%
463 1
3.8%
414 1
3.8%
355 1
3.8%
339 1
3.8%
302 1
3.8%

외장형 전자태그(RFID)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean514.46154
Minimum5
Maximum1961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T22:34:05.161845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.5
Q145
median440.5
Q3780.25
95-th percentile1547.75
Maximum1961
Range1956
Interquartile range (IQR)735.25

Descriptive statistics

Standard deviation531.30844
Coefficient of variation (CV)1.0327467
Kurtosis1.1037154
Mean514.46154
Median Absolute Deviation (MAD)381.5
Skewness1.2351322
Sum13376
Variance282288.66
MonotonicityNot monotonic
2024-03-13T22:34:05.323263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20 3
 
11.5%
856 1
 
3.8%
345 1
 
3.8%
479 1
 
3.8%
281 1
 
3.8%
141 1
 
3.8%
491 1
 
3.8%
1157 1
 
3.8%
1373 1
 
3.8%
120 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
5 1
 
3.8%
6 1
 
3.8%
8 1
 
3.8%
17 1
 
3.8%
20 3
11.5%
120 1
 
3.8%
141 1
 
3.8%
156 1
 
3.8%
281 1
 
3.8%
345 1
 
3.8%
ValueCountFrequency (%)
1961 1
3.8%
1606 1
3.8%
1373 1
3.8%
1157 1
3.8%
856 1
3.8%
842 1
3.8%
802 1
3.8%
715 1
3.8%
551 1
3.8%
523 1
3.8%

인식표 전자태그(RFID)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.57692
Minimum1
Maximum439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T22:34:05.464611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.25
Q119.75
median50.5
Q3101.5
95-th percentile426.25
Maximum439
Range438
Interquartile range (IQR)81.75

Descriptive statistics

Standard deviation138.16054
Coefficient of variation (CV)1.3468969
Kurtosis1.7296662
Mean102.57692
Median Absolute Deviation (MAD)46.5
Skewness1.7067135
Sum2667
Variance19088.334
MonotonicityNot monotonic
2024-03-13T22:34:05.580364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 3
 
11.5%
1 2
 
7.7%
103 1
 
3.8%
4 1
 
3.8%
60 1
 
3.8%
24 1
 
3.8%
22 1
 
3.8%
58 1
 
3.8%
191 1
 
3.8%
428 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
1 2
7.7%
2 3
11.5%
4 1
 
3.8%
19 1
 
3.8%
22 1
 
3.8%
23 1
 
3.8%
24 1
 
3.8%
31 1
 
3.8%
35 1
 
3.8%
43 1
 
3.8%
ValueCountFrequency (%)
439 1
3.8%
428 1
3.8%
421 1
3.8%
299 1
3.8%
191 1
3.8%
157 1
3.8%
103 1
3.8%
97 1
3.8%
75 1
3.8%
69 1
3.8%

등록품종수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.807692
Minimum8
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T22:34:06.017467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q123.25
median48.5
Q361
95-th percentile85.75
Maximum90
Range82
Interquartile range (IQR)37.75

Descriptive statistics

Standard deviation25.43072
Coefficient of variation (CV)0.56755256
Kurtosis-0.95122191
Mean44.807692
Median Absolute Deviation (MAD)17
Skewness0.052576982
Sum1165
Variance646.72154
MonotonicityNot monotonic
2024-03-13T22:34:06.131864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
9 3
 
11.5%
53 2
 
7.7%
72 1
 
3.8%
54 1
 
3.8%
40 1
 
3.8%
36 1
 
3.8%
51 1
 
3.8%
77 1
 
3.8%
79 1
 
3.8%
8 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
8 1
 
3.8%
9 3
11.5%
11 1
 
3.8%
17 1
 
3.8%
20 1
 
3.8%
33 1
 
3.8%
35 1
 
3.8%
36 1
 
3.8%
39 1
 
3.8%
40 1
 
3.8%
ValueCountFrequency (%)
90 1
3.8%
88 1
3.8%
79 1
3.8%
77 1
3.8%
72 1
3.8%
67 1
3.8%
63 1
3.8%
55 1
3.8%
54 1
3.8%
53 2
7.7%

동물소유자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean735.5
Minimum10
Maximum2999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T22:34:06.264605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q166.5
median555.5
Q31052.25
95-th percentile2473.75
Maximum2999
Range2989
Interquartile range (IQR)985.75

Descriptive statistics

Standard deviation817.99062
Coefficient of variation (CV)1.1121558
Kurtosis1.7086344
Mean735.5
Median Absolute Deviation (MAD)514
Skewness1.4713707
Sum19123
Variance669108.66
MonotonicityNot monotonic
2024-03-13T22:34:06.390254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1002 1
 
3.8%
20 1
 
3.8%
641 1
 
3.8%
425 1
 
3.8%
181 1
 
3.8%
706 1
 
3.8%
1727 1
 
3.8%
2071 1
 
3.8%
22 1
 
3.8%
167 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
10 1
3.8%
11 1
3.8%
15 1
3.8%
18 1
3.8%
20 1
3.8%
22 1
3.8%
33 1
3.8%
167 1
3.8%
181 1
3.8%
211 1
3.8%
ValueCountFrequency (%)
2999 1
3.8%
2608 1
3.8%
2071 1
3.8%
1727 1
3.8%
1238 1
3.8%
1070 1
3.8%
1069 1
3.8%
1002 1
3.8%
706 1
3.8%
690 1
3.8%

동물소유자당동물등록수
Real number (ℝ)

Distinct20
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4103846
Minimum1
Maximum3.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-03-13T22:34:06.606482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2025
Q11.2375
median1.285
Q31.365
95-th percentile1.88
Maximum3.56
Range2.56
Interquartile range (IQR)0.1275

Descriptive statistics

Standard deviation0.47069719
Coefficient of variation (CV)0.33373676
Kurtosis18.864702
Mean1.4103846
Median Absolute Deviation (MAD)0.065
Skewness4.1442372
Sum36.67
Variance0.22155585
MonotonicityNot monotonic
2024-03-13T22:34:06.735524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1.28 4
 
15.4%
1.21 2
 
7.7%
1.35 2
 
7.7%
1.22 2
 
7.7%
1.37 1
 
3.8%
1.31 1
 
3.8%
1.23 1
 
3.8%
1.34 1
 
3.8%
1.26 1
 
3.8%
1.3 1
 
3.8%
Other values (10) 10
38.5%
ValueCountFrequency (%)
1.0 1
 
3.8%
1.2 1
 
3.8%
1.21 2
7.7%
1.22 2
7.7%
1.23 1
 
3.8%
1.26 1
 
3.8%
1.27 1
 
3.8%
1.28 4
15.4%
1.29 1
 
3.8%
1.3 1
 
3.8%
ValueCountFrequency (%)
3.56 1
3.8%
1.95 1
3.8%
1.67 1
3.8%
1.43 1
3.8%
1.41 1
3.8%
1.4 1
3.8%
1.37 1
3.8%
1.35 2
7.7%
1.34 1
3.8%
1.31 1
3.8%

Interactions

2024-03-13T22:34:02.551668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:56.928968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.751645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.492552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:59.524522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.358438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.098498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.765898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.631304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.028356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.843877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.591860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:59.605955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.442064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.172745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.840415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.716244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.140059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.943407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.686073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:59.733547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.539596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.257604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.944567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.806815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.242330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.049278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.777549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:59.869089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.654057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.354781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.085386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.894561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.350010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.137230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.871450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:59.975871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.757195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.439539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.224714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.970551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.422917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.227612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.961096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.088070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.849757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.519718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.313265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:03.059461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.536734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.308011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:59.038577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.182065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.941897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.587512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.386139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:03.162969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:57.651641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:58.401510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:33:59.131642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:00.270801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.020278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:01.679925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:34:02.467808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:34:06.823028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동(법정동)등록주체_시군구등록주체_대행업체등록주체_기타(이벤트등)내장형 전자태그(RFID)외장형 전자태그(RFID)인식표 전자태그(RFID)등록품종수동물소유자수동물소유자당동물등록수
읍면동(법정동)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
등록주체_시군구1.0001.0000.8680.7700.8620.8510.7990.7670.8680.000
등록주체_대행업체1.0000.8681.0000.8530.9240.9990.9320.9661.0000.000
등록주체_기타(이벤트등)1.0000.7700.8531.0000.7290.8380.7130.6580.8530.000
내장형 전자태그(RFID)1.0000.8620.9240.7291.0000.9260.9770.8430.9240.000
외장형 전자태그(RFID)1.0000.8510.9990.8380.9261.0000.9310.9580.9990.000
인식표 전자태그(RFID)1.0000.7990.9320.7130.9770.9311.0000.7870.9320.000
등록품종수1.0000.7670.9660.6580.8430.9580.7871.0000.9660.000
동물소유자수1.0000.8681.0000.8530.9240.9990.9320.9661.0000.000
동물소유자당동물등록수1.0000.0000.0000.0000.0000.0000.0000.0000.0001.000
2024-03-13T22:34:06.957644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록주체_시군구등록주체_대행업체내장형 전자태그(RFID)외장형 전자태그(RFID)인식표 전자태그(RFID)등록품종수동물소유자수동물소유자당동물등록수등록주체_기타(이벤트등)
등록주체_시군구1.0000.8300.8230.8310.8850.8000.816-0.0200.563
등록주체_대행업체0.8301.0000.9930.9850.9580.9810.992-0.1290.631
내장형 전자태그(RFID)0.8230.9931.0000.9730.9530.9760.988-0.1120.549
외장형 전자태그(RFID)0.8310.9850.9731.0000.9510.9740.986-0.1620.609
인식표 전자태그(RFID)0.8850.9580.9530.9511.0000.9450.950-0.0150.529
등록품종수0.8000.9810.9760.9740.9451.0000.976-0.1740.396
동물소유자수0.8160.9920.9880.9860.9500.9761.000-0.1850.631
동물소유자당동물등록수-0.020-0.129-0.112-0.162-0.015-0.174-0.1851.0000.000
등록주체_기타(이벤트등)0.5630.6310.5490.6090.5290.3960.6310.0001.000

Missing values

2024-03-13T22:34:03.282352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:34:03.478915image/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

읍면동(법정동)등록주체_시군구등록주체_대행업체등록주체_기타(이벤트등)내장형 전자태그(RFID)외장형 전자태그(RFID)인식표 전자태그(RFID)등록품종수동물소유자수동물소유자당동물등록수
0상동11136204148561037210021.37
1성동1240617211151.67
2중동10878127755161536591.35
3파동5756225843669495651.35
4가천동01104619111.0
5고모동01407529101.4
6노변동426609115623332111.28
7대흥동293411320319183.56
8두산동22150215668021576710701.43
9만촌동3332785127116064399026081.27
읍면동(법정동)등록주체_시군구등록주체_대행업체등록주체_기타(이벤트등)내장형 전자태그(RFID)외장형 전자태그(RFID)인식표 전자태그(RFID)등록품종수동물소유자수동물소유자당동물등록수
16신매동1512790355842975510691.21
17연호동04001920120331.21
18욱수동819306212019351671.2
19이천동031092028221.41
20지산동322659089013734287920711.3
21황금동242191387011571917717271.28
22수성동1가12876033949158517061.26
23수성동2가323907914122361811.34
24수성동3가3519021728124404251.23
25수성동4가14827030247960536411.31