Overview

Dataset statistics

Number of variables10
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory92.0 B

Variable types

Text1
Numeric7
Categorical2

Dataset

Description대구광역시 북구의 동별 반려동물 등록현황(법정동별 반려동물 등록현황, RFID 종류, 등록 품종수, 동물소유자수 등) 정보를 제공합니다.
Author대구광역시 북구
URLhttps://www.data.go.kr/data/15103381/fileData.do

Alerts

기준일자 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 6 other fieldsHigh correlation
(RFID종류)내장형 is highly overall correlated with (등록주체)시군구 and 6 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 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 is highly overall correlated with (등록주체)대행업체 and 4 other fieldsHigh correlation
법정동명 has unique valuesUnique
동물소유자수 has unique valuesUnique
(등록주체)시군구 has 6 (18.8%) zerosZeros
(RFID종류)인식표 has 1 (3.1%) zerosZeros

Reproduction

Analysis started2024-03-14 09:13:32.805597
Analysis finished2024-03-14 09:13:45.867896
Duration13.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

법정동명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size384.0 B
2024-03-14T18:13:46.481235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.5
Min length3

Characters and Unicode

Total characters112
Distinct characters48
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

Unique32 ?
Unique (%)100.0%

Sample

1st row검단동
2nd row관음동
3rd row구암동
4th row국우동
5th row금호동
ValueCountFrequency (%)
검단동 1
 
3.1%
관음동 1
 
3.1%
칠성동2가 1
 
3.1%
칠성동1가 1
 
3.1%
노원동3가 1
 
3.1%
노원동2가 1
 
3.1%
노원동1가 1
 
3.1%
고성동3가 1
 
3.1%
고성동2가 1
 
3.1%
고성동1가 1
 
3.1%
Other values (22) 22
68.8%
2024-03-14T18:13:47.755570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 35
31.2%
가 8
 
7.1%
성 5
 
4.5%
노 4
 
3.6%
고 3
 
2.7%
원 3
 
2.7%
2 3
 
2.7%
1 3
 
2.7%
현 2
 
1.8%
산 2
 
1.8%
Other values (38) 44
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
92.9%
Decimal Number 8
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 35
33.7%
가 8
 
7.7%
성 5
 
4.8%
노 4
 
3.8%
고 3
 
2.9%
원 3
 
2.9%
현 2
 
1.9%
산 2
 
1.9%
천 2
 
1.9%
변 2
 
1.9%
Other values (35) 38
36.5%
Decimal Number
ValueCountFrequency (%)
2 3
37.5%
1 3
37.5%
3 2
25.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
92.9%
Common 8
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 35
33.7%
가 8
 
7.7%
성 5
 
4.8%
노 4
 
3.8%
고 3
 
2.9%
원 3
 
2.9%
현 2
 
1.9%
산 2
 
1.9%
천 2
 
1.9%
변 2
 
1.9%
Other values (35) 38
36.5%
Common
ValueCountFrequency (%)
2 3
37.5%
1 3
37.5%
3 2
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
92.9%
ASCII 8
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 35
33.7%
가 8
 
7.7%
성 5
 
4.8%
노 4
 
3.8%
고 3
 
2.9%
원 3
 
2.9%
현 2
 
1.9%
산 2
 
1.9%
천 2
 
1.9%
변 2
 
1.9%
Other values (35) 38
36.5%
ASCII
ValueCountFrequency (%)
2 3
37.5%
1 3
37.5%
3 2
25.0%

(등록주체)시군구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.84375
Minimum0
Maximum36
Zeros6
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-14T18:13:48.113558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q312.25
95-th percentile23.9
Maximum36
Range36
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation8.7182026
Coefficient of variation (CV)1.111484
Kurtosis2.2385547
Mean7.84375
Median Absolute Deviation (MAD)4
Skewness1.4394761
Sum251
Variance76.007056
MonotonicityNot monotonic
2024-03-14T18:13:48.484371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 6
18.8%
1 4
12.5%
2 3
9.4%
12 3
9.4%
3 3
9.4%
13 2
 
6.2%
15 2
 
6.2%
7 2
 
6.2%
10 2
 
6.2%
5 1
 
3.1%
Other values (4) 4
12.5%
ValueCountFrequency (%)
0 6
18.8%
1 4
12.5%
2 3
9.4%
3 3
9.4%
5 1
 
3.1%
7 2
 
6.2%
10 2
 
6.2%
12 3
9.4%
13 2
 
6.2%
15 2
 
6.2%
ValueCountFrequency (%)
36 1
 
3.1%
25 1
 
3.1%
23 1
 
3.1%
17 1
 
3.1%
15 2
6.2%
13 2
6.2%
12 3
9.4%
10 2
6.2%
7 2
6.2%
5 1
 
3.1%

(등록주체)대행업체
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean747.21875
Minimum2
Maximum2828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-14T18:13:48.847981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile20.65
Q195.75
median479.5
Q31049.25
95-th percentile2164.2
Maximum2828
Range2826
Interquartile range (IQR)953.5

Descriptive statistics

Standard deviation779.29835
Coefficient of variation (CV)1.042932
Kurtosis0.27515102
Mean747.21875
Median Absolute Deviation (MAD)410
Skewness1.0763414
Sum23911
Variance607305.92
MonotonicityNot monotonic
2024-03-14T18:13:49.144862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
59 2
 
6.2%
31 2
 
6.2%
304 1
 
3.1%
1796 1
 
3.1%
2 1
 
3.1%
1164 1
 
3.1%
229 1
 
3.1%
298 1
 
3.1%
147 1
 
3.1%
117 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
2 1
3.1%
8 1
3.1%
31 2
6.2%
59 2
6.2%
85 1
3.1%
86 1
3.1%
99 1
3.1%
117 1
3.1%
147 1
3.1%
229 1
3.1%
ValueCountFrequency (%)
2828 1
3.1%
2206 1
3.1%
2130 1
3.1%
1931 1
3.1%
1796 1
3.1%
1643 1
3.1%
1515 1
3.1%
1164 1
3.1%
1011 1
3.1%
944 1
3.1%

(등록주체)기타
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size384.0 B
0
27 
1
5 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 27
84.4%
1 5
 
15.6%

Length

2024-03-14T18:13:49.360907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:13:49.527565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 27
84.4%
1 5
 
15.6%

(RFID종류)내장형
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.5625
Minimum1
Maximum925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-14T18:13:49.704221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.85
Q141.25
median133
Q3392.75
95-th percentile746.2
Maximum925
Range924
Interquartile range (IQR)351.5

Descriptive statistics

Standard deviation269.73594
Coefficient of variation (CV)1.0432137
Kurtosis-0.19014918
Mean258.5625
Median Absolute Deviation (MAD)123
Skewness0.97494845
Sum8274
Variance72757.48
MonotonicityNot monotonic
2024-03-14T18:13:50.052748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
10 2
 
6.2%
94 1
 
3.1%
371 1
 
3.1%
1 1
 
3.1%
458 1
 
3.1%
89 1
 
3.1%
105 1
 
3.1%
47 1
 
3.1%
43 1
 
3.1%
42 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
1 1
3.1%
3 1
3.1%
10 2
6.2%
11 1
3.1%
16 1
3.1%
27 1
3.1%
39 1
3.1%
42 1
3.1%
43 1
3.1%
47 1
3.1%
ValueCountFrequency (%)
925 1
3.1%
777 1
3.1%
721 1
3.1%
672 1
3.1%
608 1
3.1%
606 1
3.1%
555 1
3.1%
458 1
3.1%
371 1
3.1%
340 1
3.1%

(RFID종류)외장형
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean431.46875
Minimum1
Maximum1717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-14T18:13:50.430903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.15
Q153.75
median282.5
Q3589.5
95-th percentile1246.9
Maximum1717
Range1716
Interquartile range (IQR)535.75

Descriptive statistics

Standard deviation450.93237
Coefficient of variation (CV)1.0451101
Kurtosis0.77114063
Mean431.46875
Median Absolute Deviation (MAD)240
Skewness1.1663893
Sum13807
Variance203340
MonotonicityNot monotonic
2024-03-14T18:13:50.831376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
45 2
 
6.2%
186 1
 
3.1%
1029 1
 
3.1%
1 1
 
3.1%
618 1
 
3.1%
20 1
 
3.1%
110 1
 
3.1%
175 1
 
3.1%
89 1
 
3.1%
66 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
1 1
3.1%
4 1
3.1%
17 1
3.1%
20 1
3.1%
40 1
3.1%
45 2
6.2%
50 1
3.1%
55 1
3.1%
66 1
3.1%
89 1
3.1%
ValueCountFrequency (%)
1717 1
3.1%
1303 1
3.1%
1201 1
3.1%
1057 1
3.1%
1029 1
3.1%
920 1
3.1%
805 1
3.1%
618 1
3.1%
580 1
3.1%
566 1
3.1%

(RFID종류)인식표
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.1875
Minimum0
Maximum268
Zeros1
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-14T18:13:51.222631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median37.5
Q382.25
95-th percentile195.8
Maximum268
Range268
Interquartile range (IQR)74.25

Descriptive statistics

Standard deviation72.485566
Coefficient of variation (CV)1.111955
Kurtosis0.74133658
Mean65.1875
Median Absolute Deviation (MAD)33
Skewness1.2656639
Sum2086
Variance5254.1573
MonotonicityNot monotonic
2024-03-14T18:13:51.612586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
70 3
 
9.4%
8 2
 
6.2%
4 2
 
6.2%
47 2
 
6.2%
1 2
 
6.2%
27 1
 
3.1%
10 1
 
3.1%
0 1
 
3.1%
101 1
 
3.1%
32 1
 
3.1%
Other values (16) 16
50.0%
ValueCountFrequency (%)
0 1
3.1%
1 2
6.2%
2 1
3.1%
4 2
6.2%
5 1
3.1%
8 2
6.2%
10 1
3.1%
12 1
3.1%
21 1
3.1%
25 1
3.1%
ValueCountFrequency (%)
268 1
3.1%
209 1
3.1%
185 1
3.1%
174 1
3.1%
169 1
3.1%
165 1
3.1%
129 1
3.1%
101 1
3.1%
76 1
3.1%
73 1
3.1%

등록품종수
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)84.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41
Minimum2
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-14T18:13:51.986074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10.5
Q123.75
median39.5
Q355.5
95-th percentile72.8
Maximum82
Range80
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation21.267877
Coefficient of variation (CV)0.5187287
Kurtosis-0.92188627
Mean41
Median Absolute Deviation (MAD)16
Skewness0.057064263
Sum1312
Variance452.32258
MonotonicityNot monotonic
2024-03-14T18:13:52.398112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
54 2
 
6.2%
23 2
 
6.2%
15 2
 
6.2%
50 2
 
6.2%
24 2
 
6.2%
39 1
 
3.1%
67 1
 
3.1%
2 1
 
3.1%
34 1
 
3.1%
29 1
 
3.1%
Other values (17) 17
53.1%
ValueCountFrequency (%)
2 1
3.1%
5 1
3.1%
15 2
6.2%
17 1
3.1%
20 1
3.1%
23 2
6.2%
24 2
6.2%
26 1
3.1%
29 1
3.1%
34 1
3.1%
ValueCountFrequency (%)
82 1
3.1%
75 1
3.1%
71 1
3.1%
68 1
3.1%
67 1
3.1%
63 1
3.1%
62 1
3.1%
60 1
3.1%
54 2
6.2%
53 1
3.1%

동물소유자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean577.65625
Minimum1
Maximum2171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size416.0 B
2024-03-14T18:13:52.654938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.8
Q172.5
median375.5
Q3812.75
95-th percentile1677.1
Maximum2171
Range2170
Interquartile range (IQR)740.25

Descriptive statistics

Standard deviation606.08413
Coefficient of variation (CV)1.0492125
Kurtosis0.19466275
Mean577.65625
Median Absolute Deviation (MAD)318.5
Skewness1.0739417
Sum18485
Variance367337.97
MonotonicityNot monotonic
2024-03-14T18:13:53.020895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
244 1
 
3.1%
1348 1
 
3.1%
1 1
 
3.1%
902 1
 
3.1%
28 1
 
3.1%
170 1
 
3.1%
220 1
 
3.1%
121 1
 
3.1%
95 1
 
3.1%
75 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1 1
3.1%
7 1
3.1%
23 1
3.1%
28 1
3.1%
42 1
3.1%
52 1
3.1%
59 1
3.1%
65 1
3.1%
75 1
3.1%
95 1
3.1%
ValueCountFrequency (%)
2171 1
3.1%
1687 1
3.1%
1669 1
3.1%
1557 1
3.1%
1348 1
3.1%
1331 1
3.1%
1199 1
3.1%
902 1
3.1%
783 1
3.1%
696 1
3.1%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size384.0 B
2023-12-22
32 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-22
2nd row2023-12-22
3rd row2023-12-22
4th row2023-12-22
5th row2023-12-22

Common Values

ValueCountFrequency (%)
2023-12-22 32
100.0%

Length

2024-03-14T18:13:53.409930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:13:53.718281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-22 32
100.0%

Interactions

2024-03-14T18:13:43.474490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:33.188054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:34.955217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:36.672102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:38.087324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:39.785595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:41.491845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:43.723688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:33.447391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:35.209609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:36.924587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:38.285746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:40.039260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:41.752996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:43.961924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:33.699677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:35.445945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:37.164608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:38.534711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:40.277355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:42.203395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:44.193297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:33.938331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:35.680003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:37.391877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:38.774892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:40.510211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:42.444496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:44.410572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:34.201704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:35.933759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:37.642303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:39.032411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:40.764692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:42.712071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:44.557508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:34.448766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:36.178660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:37.805135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:39.277872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:41.002149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:42.964304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:44.813150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:34.714349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:36.436605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:37.957814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:39.547641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:41.259125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:13:43.230723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:13:53.906535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동명(등록주체)시군구(등록주체)대행업체(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수동물소유자수
법정동명1.0001.0001.0001.0001.0001.0001.0001.0001.000
(등록주체)시군구1.0001.0000.7850.3980.8080.8590.8630.6160.816
(등록주체)대행업체1.0000.7851.0000.7750.9410.9770.9210.7260.990
(등록주체)기타1.0000.3980.7751.0000.9220.7200.8460.0000.856
(RFID종류)내장형1.0000.8080.9410.9221.0000.9210.8750.8180.923
(RFID종류)외장형1.0000.8590.9770.7200.9211.0000.9200.7650.992
(RFID종류)인식표1.0000.8630.9210.8460.8750.9201.0000.8040.924
등록품종수1.0000.6160.7260.0000.8180.7650.8041.0000.775
동물소유자수1.0000.8160.9900.8560.9230.9920.9240.7751.000
2024-03-14T18:13:54.224145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(등록주체)시군구(등록주체)대행업체(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수동물소유자수(등록주체)기타
(등록주체)시군구1.0000.8920.9030.8760.9170.8810.8900.383
(등록주체)대행업체0.8921.0000.9940.9960.9790.9890.9980.693
(RFID종류)내장형0.9030.9941.0000.9850.9740.9780.9930.654
(RFID종류)외장형0.8760.9960.9851.0000.9730.9850.9950.638
(RFID종류)인식표0.9170.9790.9740.9731.0000.9820.9770.592
등록품종수0.8810.9890.9780.9850.9821.0000.9860.000
동물소유자수0.8900.9980.9930.9950.9770.9861.0000.777
(등록주체)기타0.3830.6930.6540.6380.5920.0000.7771.000

Missing values

2024-03-14T18:13:45.171238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:13:45.656866image/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

법정동명(등록주체)시군구(등록주체)대행업체(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수동물소유자수기준일자
0검단동230419418627392442023-12-22
1관음동131011037158073607832023-12-22
2구암동151931172110571696215572023-12-22
3국우동5944032754676546962023-12-22
4금호동78503945823592023-12-22
5노곡동15901640417422023-12-22
6대현동10865034046570536672023-12-22
7도남동23101117515232023-12-22
8동변동1663014846947505072023-12-22
9동천동25151506068051296311992023-12-22
법정동명(등록주체)시군구(등록주체)대행업체(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수동물소유자수기준일자
22학정동13722026440170465252023-12-22
23고성동1가05901045420522023-12-22
24고성동2가09904255223752023-12-22
25고성동3가011704366824952023-12-22
26노원동1가11470478912261212023-12-22
27노원동2가3298010517521292202023-12-22
28노원동3가222908911032341702023-12-22
29칠성동1가03101020115282023-12-22
30칠성동2가1211641458618101549022023-12-22
31율곡동020110212023-12-22