Overview

Dataset statistics

Number of variables13
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory117.8 B

Variable types

Categorical3
Text1
Numeric9

Dataset

Description파주시 반려동물 등록현황 데이터로, 읍면동별 등록동물수, 등록주체, RFID 종류, 등록품종수, 등록소유자수 등에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15043171/fileData.do

Alerts

시군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
등록동물수 is highly overall correlated with (등록주체)시군구등록 and 6 other fieldsHigh correlation
(등록주체)시군구등록 is highly overall correlated with 등록동물수 and 7 other fieldsHigh correlation
(등록주체)대행업체등록 is highly overall correlated with 등록동물수 and 6 other fieldsHigh correlation
(RFID종류)내장형 is highly overall correlated with 등록동물수 and 7 other fieldsHigh correlation
(RFID종류)외장형 is highly overall correlated with 등록동물수 and 6 other fieldsHigh correlation
(RFID종류)인식표 is highly overall correlated with 등록동물수 and 7 other fieldsHigh correlation
등록품종수 is highly overall correlated with 등록동물수 and 6 other fieldsHigh correlation
등록소유자수 is highly overall correlated with 등록동물수 and 6 other fieldsHigh correlation
(등록주체)기타 is highly overall correlated with (등록주체)시군구등록 and 2 other fieldsHigh correlation
읍면동명 has unique valuesUnique
(등록주체)대행업체등록 has unique valuesUnique
(등록주체)시군구등록 has 5 (14.3%) zerosZeros
(RFID종류)외장형 has 1 (2.9%) zerosZeros
(RFID종류)인식표 has 7 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-12 13:37:52.828983
Analysis finished2023-12-12 13:38:00.249198
Duration7.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
파주시
35 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
파주시 35
100.0%

Length

2023-12-12T22:38:00.314796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:38:00.413766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
파주시 35
100.0%

읍면동명
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T22:38:00.594205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0857143
Min length3

Characters and Unicode

Total characters108
Distinct characters49
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

Unique35 ?
Unique (%)100.0%

Sample

1st row검산동
2nd row광탄면
3rd row교하동
4th row교하읍
5th row군내면
ValueCountFrequency (%)
검산동 1
 
2.9%
송촌동 1
 
2.9%
아동동 1
 
2.9%
야당동 1
 
2.9%
야동동 1
 
2.9%
오도동 1
 
2.9%
와동동 1
 
2.9%
월롱면 1
 
2.9%
신촌동 1
 
2.9%
적성면 1
 
2.9%
Other values (25) 25
71.4%
2023-12-12T22:38:00.958950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 29
26.9%
면 7
 
6.5%
읍 5
 
4.6%
산 4
 
3.7%
하 4
 
3.7%
금 4
 
3.7%
촌 3
 
2.8%
다 2
 
1.9%
문 2
 
1.9%
패 2
 
1.9%
Other values (39) 46
42.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 29
26.9%
면 7
 
6.5%
읍 5
 
4.6%
산 4
 
3.7%
하 4
 
3.7%
금 4
 
3.7%
촌 3
 
2.8%
다 2
 
1.9%
문 2
 
1.9%
패 2
 
1.9%
Other values (39) 46
42.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 29
26.9%
면 7
 
6.5%
읍 5
 
4.6%
산 4
 
3.7%
하 4
 
3.7%
금 4
 
3.7%
촌 3
 
2.8%
다 2
 
1.9%
문 2
 
1.9%
패 2
 
1.9%
Other values (39) 46
42.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 29
26.9%
면 7
 
6.5%
읍 5
 
4.6%
산 4
 
3.7%
하 4
 
3.7%
금 4
 
3.7%
촌 3
 
2.8%
다 2
 
1.9%
문 2
 
1.9%
패 2
 
1.9%
Other values (39) 46
42.6%

등록동물수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1065.2857
Minimum10
Maximum4365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:01.099014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile32.8
Q176.5
median397
Q31210
95-th percentile4015.2
Maximum4365
Range4355
Interquartile range (IQR)1133.5

Descriptive statistics

Standard deviation1378.7517
Coefficient of variation (CV)1.2942553
Kurtosis0.76090633
Mean1065.2857
Median Absolute Deviation (MAD)357
Skewness1.4696165
Sum37285
Variance1900956.3
MonotonicityNot monotonic
2023-12-12T22:38:01.253733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
40 2
 
5.7%
358 1
 
2.9%
1999 1
 
2.9%
4365 1
 
2.9%
397 1
 
2.9%
75 1
 
2.9%
3927 1
 
2.9%
587 1
 
2.9%
390 1
 
2.9%
10 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
10 1
2.9%
16 1
2.9%
40 2
5.7%
46 1
2.9%
57 1
2.9%
65 1
2.9%
68 1
2.9%
75 1
2.9%
78 1
2.9%
184 1
2.9%
ValueCountFrequency (%)
4365 1
2.9%
4221 1
2.9%
3927 1
2.9%
3669 1
2.9%
3640 1
2.9%
3347 1
2.9%
1999 1
2.9%
1289 1
2.9%
1237 1
2.9%
1183 1
2.9%

(등록주체)시군구등록
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.885714
Minimum0
Maximum122
Zeros5
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:01.362582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q351.5
95-th percentile94.3
Maximum122
Range122
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation33.2935
Coefficient of variation (CV)1.152594
Kurtosis0.59010755
Mean28.885714
Median Absolute Deviation (MAD)11
Skewness1.2262806
Sum1011
Variance1108.4571
MonotonicityNot monotonic
2023-12-12T22:38:01.473040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 5
 
14.3%
11 3
 
8.6%
1 2
 
5.7%
6 2
 
5.7%
4 2
 
5.7%
9 1
 
2.9%
40 1
 
2.9%
31 1
 
2.9%
53 1
 
2.9%
55 1
 
2.9%
Other values (16) 16
45.7%
ValueCountFrequency (%)
0 5
14.3%
1 2
 
5.7%
2 1
 
2.9%
4 2
 
5.7%
6 2
 
5.7%
7 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
11 3
8.6%
12 1
 
2.9%
ValueCountFrequency (%)
122 1
2.9%
95 1
2.9%
94 1
2.9%
81 1
2.9%
74 1
2.9%
70 1
2.9%
56 1
2.9%
55 1
2.9%
53 1
2.9%
50 1
2.9%

(등록주체)대행업체등록
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1036
Minimum10
Maximum4289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:01.618383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile27.9
Q174.5
median385
Q31147.5
95-th percentile3946.1
Maximum4289
Range4279
Interquartile range (IQR)1073

Descriptive statistics

Standard deviation1349.8978
Coefficient of variation (CV)1.3029902
Kurtosis0.81255942
Mean1036
Median Absolute Deviation (MAD)346
Skewness1.4871682
Sum36260
Variance1822224.1
MonotonicityNot monotonic
2023-12-12T22:38:01.766024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
349 1
 
2.9%
1166 1
 
2.9%
1101 1
 
2.9%
4289 1
 
2.9%
385 1
 
2.9%
75 1
 
2.9%
3869 1
 
2.9%
569 1
 
2.9%
363 1
 
2.9%
1905 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
10 1
2.9%
16 1
2.9%
33 1
2.9%
39 1
2.9%
46 1
2.9%
55 1
2.9%
61 1
2.9%
67 1
2.9%
74 1
2.9%
75 1
2.9%
ValueCountFrequency (%)
4289 1
2.9%
4126 1
2.9%
3869 1
2.9%
3590 1
2.9%
3547 1
2.9%
3264 1
2.9%
1905 1
2.9%
1233 1
2.9%
1166 1
2.9%
1129 1
2.9%

(등록주체)기타
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size412.0 B
0
26 
1
5 
2
3 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
0 26
74.3%
1 5
 
14.3%
2 3
 
8.6%
3 1
 
2.9%

Length

2023-12-12T22:38:01.903718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:38:02.023128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 26
74.3%
1 5
 
14.3%
2 3
 
8.6%
3 1
 
2.9%

(RFID종류)내장형
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean597.2
Minimum6
Maximum2509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:02.158041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile18.4
Q148.5
median210
Q3684.5
95-th percentile2318.8
Maximum2509
Range2503
Interquartile range (IQR)636

Descriptive statistics

Standard deviation768.97571
Coefficient of variation (CV)1.2876351
Kurtosis1.031062
Mean597.2
Median Absolute Deviation (MAD)200
Skewness1.5133008
Sum20902
Variance591323.64
MonotonicityNot monotonic
2023-12-12T22:38:02.330252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
26 2
 
5.7%
198 1
 
2.9%
577 1
 
2.9%
2509 1
 
2.9%
210 1
 
2.9%
44 1
 
2.9%
2449 1
 
2.9%
328 1
 
2.9%
188 1
 
2.9%
50 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
6 1
2.9%
10 1
2.9%
22 1
2.9%
26 2
5.7%
27 1
2.9%
32 1
2.9%
44 1
2.9%
47 1
2.9%
50 1
2.9%
110 1
2.9%
ValueCountFrequency (%)
2509 1
2.9%
2449 1
2.9%
2263 1
2.9%
2061 1
2.9%
1889 1
2.9%
1681 1
2.9%
1056 1
2.9%
827 1
2.9%
758 1
2.9%
611 1
2.9%

(RFID종류)외장형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.11429
Minimum0
Maximum1753
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:02.499596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.9
Q129
median162
Q3464.5
95-th percentile1617.3
Maximum1753
Range1753
Interquartile range (IQR)435.5

Descriptive statistics

Standard deviation554.03811
Coefficient of variation (CV)1.3314566
Kurtosis0.82061752
Mean416.11429
Median Absolute Deviation (MAD)148
Skewness1.4952253
Sum14564
Variance306958.22
MonotonicityNot monotonic
2023-12-12T22:38:02.641891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
14 2
 
5.7%
154 1
 
2.9%
27 1
 
2.9%
1632 1
 
2.9%
172 1
 
2.9%
23 1
 
2.9%
1308 1
 
2.9%
233 1
 
2.9%
162 1
 
2.9%
795 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
0 1
2.9%
3 1
2.9%
10 1
2.9%
13 1
2.9%
14 2
5.7%
18 1
2.9%
23 1
2.9%
27 1
2.9%
31 1
2.9%
36 1
2.9%
ValueCountFrequency (%)
1753 1
2.9%
1632 1
2.9%
1611 1
2.9%
1548 1
2.9%
1406 1
2.9%
1308 1
2.9%
795 1
2.9%
479 1
2.9%
472 1
2.9%
457 1
2.9%

(RFID종류)인식표
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.971429
Minimum0
Maximum224
Zeros7
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:02.767465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median16
Q379.5
95-th percentile202.9
Maximum224
Range224
Interquartile range (IQR)75.5

Descriptive statistics

Standard deviation67.952616
Coefficient of variation (CV)1.3074995
Kurtosis0.64921549
Mean51.971429
Median Absolute Deviation (MAD)16
Skewness1.3657167
Sum1819
Variance4617.558
MonotonicityNot monotonic
2023-12-12T22:38:02.890033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 7
20.0%
8 2
 
5.7%
4 2
 
5.7%
16 1
 
2.9%
224 1
 
2.9%
3 1
 
2.9%
28 1
 
2.9%
11 1
 
2.9%
100 1
 
2.9%
74 1
 
2.9%
Other values (17) 17
48.6%
ValueCountFrequency (%)
0 7
20.0%
3 1
 
2.9%
4 2
 
5.7%
5 1
 
2.9%
8 2
 
5.7%
9 1
 
2.9%
10 1
 
2.9%
11 1
 
2.9%
15 1
 
2.9%
16 1
 
2.9%
ValueCountFrequency (%)
224 1
2.9%
205 1
2.9%
202 1
2.9%
170 1
2.9%
148 1
2.9%
140 1
2.9%
118 1
2.9%
100 1
2.9%
85 1
2.9%
74 1
2.9%

등록품종수
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.942857
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:03.024217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile11.9
Q125
median49
Q370.5
95-th percentile95.3
Maximum99
Range92
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation27.594696
Coefficient of variation (CV)0.55252537
Kurtosis-1.1308342
Mean49.942857
Median Absolute Deviation (MAD)24
Skewness0.18768171
Sum1748
Variance761.46723
MonotonicityNot monotonic
2023-12-12T22:38:03.154733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
25 3
 
8.6%
33 2
 
5.7%
7 2
 
5.7%
14 2
 
5.7%
87 2
 
5.7%
81 1
 
2.9%
54 1
 
2.9%
82 1
 
2.9%
64 1
 
2.9%
49 1
 
2.9%
Other values (19) 19
54.3%
ValueCountFrequency (%)
7 2
5.7%
14 2
5.7%
17 1
 
2.9%
20 1
 
2.9%
23 1
 
2.9%
25 3
8.6%
33 2
5.7%
35 1
 
2.9%
36 1
 
2.9%
37 1
 
2.9%
ValueCountFrequency (%)
99 1
2.9%
96 1
2.9%
95 1
2.9%
87 2
5.7%
82 1
2.9%
81 1
2.9%
80 1
2.9%
73 1
2.9%
68 1
2.9%
64 1
2.9%

등록소유자수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.2
Minimum5
Maximum3181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:03.266615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile21.5
Q139.5
median297
Q3743
95-th percentile2806.1
Maximum3181
Range3176
Interquartile range (IQR)703.5

Descriptive statistics

Standard deviation966.87373
Coefficient of variation (CV)1.3614105
Kurtosis1.1061853
Mean710.2
Median Absolute Deviation (MAD)264
Skewness1.5700727
Sum24857
Variance934844.81
MonotonicityNot monotonic
2023-12-12T22:38:03.406272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
37 2
 
5.7%
248 1
 
2.9%
269 1
 
2.9%
831 1
 
2.9%
3000 1
 
2.9%
297 1
 
2.9%
2220 1
 
2.9%
370 1
 
2.9%
1384 1
 
2.9%
26 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
5 1
2.9%
11 1
2.9%
26 1
2.9%
27 1
2.9%
28 1
2.9%
33 1
2.9%
37 2
5.7%
39 1
2.9%
40 1
2.9%
74 1
2.9%
ValueCountFrequency (%)
3181 1
2.9%
3000 1
2.9%
2723 1
2.9%
2602 1
2.9%
2338 1
2.9%
2220 1
2.9%
1384 1
2.9%
831 1
2.9%
784 1
2.9%
702 1
2.9%

동물소유자당등록동물수
Real number (ℝ)

Distinct30
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6794286
Minimum1.21
Maximum3.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T22:38:03.536638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.303
Q11.435
median1.55
Q31.765
95-th percentile2.48
Maximum3.47
Range2.26
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.4416476
Coefficient of variation (CV)0.26297493
Kurtosis7.3996559
Mean1.6794286
Median Absolute Deviation (MAD)0.19
Skewness2.3979634
Sum58.78
Variance0.19505261
MonotonicityNot monotonic
2023-12-12T22:38:03.683875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1.44 2
 
5.7%
1.45 2
 
5.7%
1.46 2
 
5.7%
1.64 2
 
5.7%
1.75 2
 
5.7%
1.21 1
 
2.9%
1.58 1
 
2.9%
1.95 1
 
2.9%
1.76 1
 
2.9%
2.01 1
 
2.9%
Other values (20) 20
57.1%
ValueCountFrequency (%)
1.21 1
2.9%
1.24 1
2.9%
1.33 1
2.9%
1.34 1
2.9%
1.35 1
2.9%
1.36 1
2.9%
1.37 1
2.9%
1.4 1
2.9%
1.43 1
2.9%
1.44 2
5.7%
ValueCountFrequency (%)
3.47 1
2.9%
2.62 1
2.9%
2.42 1
2.9%
2.03 1
2.9%
2.01 1
2.9%
2.0 1
2.9%
1.95 1
2.9%
1.86 1
2.9%
1.77 1
2.9%
1.76 1
2.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-05-10
35 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-10
2nd row2023-05-10
3rd row2023-05-10
4th row2023-05-10
5th row2023-05-10

Common Values

ValueCountFrequency (%)
2023-05-10 35
100.0%

Length

2023-12-12T22:38:03.828506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:38:03.965362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-10 35
100.0%

Interactions

2023-12-12T22:37:59.070059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.177151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.822461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.512188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.163515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.914906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.729543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.388633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.037283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.137926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.239276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.894279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.577578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.225090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.046132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.805004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.450102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.119293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.224370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.314989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.974767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.655477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.290464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.145504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.891269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.520546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.208870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.311224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.388028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.044009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.718010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.355823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.229000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.959563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.589110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.275378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.394280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.464200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.126344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.785029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.423864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.325706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.039087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.659708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.344696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.538385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.538252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.214251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.869296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.515080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.414463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.122193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.736230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.743030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.658083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.608792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.286967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.953578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.586573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.488803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.188510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.809587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.812763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.736917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.683745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.361094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.029031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.689074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.568918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.251523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.887077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.897352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.818330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:53.755450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:54.437977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.105158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:55.792080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:56.649410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.324208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:57.960068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:58.992244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:38:04.070612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명등록동물수(등록주체)시군구등록(등록주체)대행업체등록(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수
읍면동명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
등록동물수1.0001.0000.7910.9990.5710.9550.9890.9090.8040.9740.000
(등록주체)시군구등록1.0000.7911.0000.7730.7440.9450.7680.9190.6590.8100.303
(등록주체)대행업체등록1.0000.9990.7731.0000.6500.9790.9880.9040.7440.9740.000
(등록주체)기타1.0000.5710.7440.6501.0000.7520.5810.7200.6890.8270.000
(RFID종류)내장형1.0000.9550.9450.9790.7521.0000.9250.9590.7550.9370.000
(RFID종류)외장형1.0000.9890.7680.9880.5810.9251.0000.9230.7060.9480.000
(RFID종류)인식표1.0000.9090.9190.9040.7200.9590.9231.0000.7190.9260.000
등록품종수1.0000.8040.6590.7440.6890.7550.7060.7191.0000.7090.337
등록소유자수1.0000.9740.8100.9740.8270.9370.9480.9260.7091.0000.000
동물소유자당등록동물수1.0000.0000.3030.0000.0000.0000.0000.0000.3370.0001.000
2023-12-12T22:38:04.225729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록동물수(등록주체)시군구등록(등록주체)대행업체등록(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수(등록주체)기타
등록동물수1.0000.9140.9990.9910.9460.9350.9770.984-0.2760.405
(등록주체)시군구등록0.9141.0000.9080.8880.9490.9520.9290.898-0.2030.533
(등록주체)대행업체등록0.9990.9081.0000.9900.9460.9360.9770.983-0.2760.481
(RFID종류)내장형0.9910.8880.9901.0000.9090.9040.9620.969-0.2790.542
(RFID종류)외장형0.9460.9490.9460.9091.0000.9710.9510.945-0.2570.413
(RFID종류)인식표0.9350.9520.9360.9040.9711.0000.9430.934-0.2630.505
등록품종수0.9770.9290.9770.9620.9510.9431.0000.965-0.2530.434
등록소유자수0.9840.8980.9830.9690.9450.9340.9651.000-0.4000.459
동물소유자당등록동물수-0.276-0.203-0.276-0.279-0.257-0.263-0.253-0.4001.0000.000
(등록주체)기타0.4050.5330.4810.5420.4130.5050.4340.4590.0001.000

Missing values

2023-12-12T22:37:59.956548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:38:00.184129image/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

시군명읍면동명등록동물수(등록주체)시군구등록(등록주체)대행업체등록(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수데이터기준일자
0파주시검산동3589349018815416402481.442023-05-10
1파주시광탄면1237701166182736149735122.422023-05-10
2파주시교하동25711246015498533743.472023-05-10
3파주시교하읍4580458045530363681.242023-05-10
4파주시군내면16016061007111.452023-05-10
5파주시금능동4013902713014331.212023-05-10
6파주시금릉동279627301451268371771.582023-05-10
7파주시금촌동366912235470206114062028727231.352023-05-10
8파주시다율동91323890044542246586721.362023-05-10
9파주시당하동220621401159510331421.552023-05-10
시군명읍면동명등록동물수(등록주체)시군구등록(등록주체)대행업체등록(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수데이터기준일자
25파주시월롱면58717569132823326643701.592023-05-10
26파주시적성면39027363019816230492691.452023-05-10
27파주시조리읍1999941905010567951488113841.442023-05-10
28파주시진동면1001001000752.02023-05-10
29파주시탄현면1289551233175845774807841.642023-05-10
30파주시파주읍11835311291611472100687021.692023-05-10
31파주시파평면22911218011010811351231.862023-05-10
32파주시상지석동73231701044725728633642.012023-05-10
33파주시연다산동6546102636323371.762023-05-10
34파주시하지석동7847404731025401.952023-05-10