Overview

Dataset statistics

Number of variables13
Number of observations53
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory116.5 B

Variable types

Categorical3
Text1
Numeric9

Dataset

Description경기도 고양시 반려동물 등록 현황 데이터는 읍면동명, 등록 동물수, (등록주체)시군구등록, (등록주체)대행업체등록, (등록주체)기타, (RFID종류)내장형, (RFID종류)외장형, (RFID종류)인식표, 등록품종수, 등록소유자수, 동물소유자당등록동물수 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15084285/fileData.do

Alerts

시군명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
등록동물수 is highly overall correlated with (등록주체)시군구등록 and 7 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 7 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 7 other fieldsHigh correlation
(RFID종류)인식표 is highly overall correlated with 등록동물수 and 7 other fieldsHigh correlation
등록품종수 is highly overall correlated with 등록동물수 and 7 other fieldsHigh correlation
등록소유자수 is highly overall correlated with 등록동물수 and 8 other fieldsHigh correlation
동물소유자당등록동물수 is highly overall correlated with 등록동물수 and 7 other fieldsHigh correlation
(등록주체)기타 is highly overall correlated with 등록소유자수High correlation
읍면동명 has unique valuesUnique
(등록주체)시군구등록 has 7 (13.2%) zerosZeros

Reproduction

Analysis started2023-12-12 15:54:27.907611
Analysis finished2023-12-12 15:54:38.467872
Duration10.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
고양시
53 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 53
100.0%

Length

2023-12-13T00:54:38.585654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:54:38.720337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시 53
100.0%

읍면동명
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-13T00:54:39.023100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0566038
Min length2

Characters and Unicode

Total characters162
Distinct characters71
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

Unique53 ?
Unique (%)100.0%

Sample

1st row강매동
2nd row고양동
3rd row관산동
4th row내곡동
5th row내유동
ValueCountFrequency (%)
강매동 1
 
1.9%
화전동 1
 
1.9%
효자동 1
 
1.9%
행주내동 1
 
1.9%
행주외동 1
 
1.9%
풍동 1
 
1.9%
마두동 1
 
1.9%
문봉동 1
 
1.9%
백석동 1
 
1.9%
산황동 1
 
1.9%
Other values (43) 43
81.1%
2023-12-13T00:54:39.704698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55
34.0%
7
 
4.3%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (61) 74
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 162
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
55
34.0%
7
 
4.3%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (61) 74
45.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 162
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
55
34.0%
7
 
4.3%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (61) 74
45.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 162
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
55
34.0%
7
 
4.3%
4
 
2.5%
4
 
2.5%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
3
 
1.9%
Other values (61) 74
45.7%

등록동물수
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1556.7547
Minimum12
Maximum6287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:39.940156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile36.8
Q1158
median1221
Q32823
95-th percentile4714
Maximum6287
Range6275
Interquartile range (IQR)2665

Descriptive statistics

Standard deviation1709.2243
Coefficient of variation (CV)1.0979407
Kurtosis0.063828006
Mean1556.7547
Median Absolute Deviation (MAD)1078
Skewness1.0563439
Sum82508
Variance2921447.8
MonotonicityNot monotonic
2023-12-13T00:54:40.145194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 2
 
3.8%
250 1
 
1.9%
38 1
 
1.9%
88 1
 
1.9%
58 1
 
1.9%
2998 1
 
1.9%
3115 1
 
1.9%
167 1
 
1.9%
4774 1
 
1.9%
35 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
12 1
1.9%
19 1
1.9%
35 1
1.9%
38 1
1.9%
58 1
1.9%
62 2
3.8%
87 1
1.9%
88 1
1.9%
90 1
1.9%
122 1
1.9%
ValueCountFrequency (%)
6287 1
1.9%
5376 1
1.9%
4774 1
1.9%
4674 1
1.9%
4560 1
1.9%
4523 1
1.9%
4009 1
1.9%
3954 1
1.9%
3517 1
1.9%
3389 1
1.9%

(등록주체)시군구등록
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.735849
Minimum0
Maximum85
Zeros7
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:40.331680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median11
Q325
95-th percentile55.8
Maximum85
Range85
Interquartile range (IQR)22

Descriptive statistics

Standard deviation19.569551
Coefficient of variation (CV)1.1033896
Kurtosis1.6735426
Mean17.735849
Median Absolute Deviation (MAD)10
Skewness1.3939554
Sum940
Variance382.96734
MonotonicityNot monotonic
2023-12-13T00:54:40.513589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 7
 
13.2%
5 4
 
7.5%
2 3
 
5.7%
4 3
 
5.7%
18 3
 
5.7%
1 3
 
5.7%
13 2
 
3.8%
3 2
 
3.8%
24 2
 
3.8%
11 1
 
1.9%
Other values (23) 23
43.4%
ValueCountFrequency (%)
0 7
13.2%
1 3
5.7%
2 3
5.7%
3 2
 
3.8%
4 3
5.7%
5 4
7.5%
6 1
 
1.9%
8 1
 
1.9%
9 1
 
1.9%
10 1
 
1.9%
ValueCountFrequency (%)
85 1
1.9%
61 1
1.9%
60 1
1.9%
53 1
1.9%
48 1
1.9%
47 1
1.9%
43 1
1.9%
42 1
1.9%
40 1
1.9%
36 1
1.9%

(등록주체)대행업체등록
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1538.434
Minimum12
Maximum6223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:40.736835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile34.2
Q1157
median1209
Q32805
95-th percentile4665.8
Maximum6223
Range6211
Interquartile range (IQR)2648

Descriptive statistics

Standard deviation1692.8983
Coefficient of variation (CV)1.1004036
Kurtosis0.075120582
Mean1538.434
Median Absolute Deviation (MAD)1069
Skewness1.0615594
Sum81537
Variance2865904.6
MonotonicityNot monotonic
2023-12-13T00:54:40.959011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 2
 
3.8%
246 1
 
1.9%
33 1
 
1.9%
84 1
 
1.9%
58 1
 
1.9%
2968 1
 
1.9%
3070 1
 
1.9%
163 1
 
1.9%
4730 1
 
1.9%
35 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
12 1
1.9%
19 1
1.9%
33 1
1.9%
35 1
1.9%
58 1
1.9%
62 2
3.8%
84 1
1.9%
86 1
1.9%
88 1
1.9%
120 1
1.9%
ValueCountFrequency (%)
6223 1
1.9%
5340 1
1.9%
4730 1
1.9%
4623 1
1.9%
4506 1
1.9%
4480 1
1.9%
3973 1
1.9%
3923 1
1.9%
3489 1
1.9%
3363 1
1.9%

(등록주체)기타
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size556.0 B
0
34 
1
13 
3
 
3
2
 
2
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.9%

Sample

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

Common Values

ValueCountFrequency (%)
0 34
64.2%
1 13
 
24.5%
3 3
 
5.7%
2 2
 
3.8%
5 1
 
1.9%

Length

2023-12-13T00:54:41.179922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:54:41.353146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 34
64.2%
1 13
 
24.5%
3 3
 
5.7%
2 2
 
3.8%
5 1
 
1.9%

(RFID종류)내장형
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean949.49057
Minimum1
Maximum4073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:41.514538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.8
Q197
median669
Q31648
95-th percentile2967.6
Maximum4073
Range4072
Interquartile range (IQR)1551

Descriptive statistics

Standard deviation1078.8951
Coefficient of variation (CV)1.1362884
Kurtosis0.26007427
Mean949.49057
Median Absolute Deviation (MAD)603
Skewness1.1349967
Sum50323
Variance1164014.7
MonotonicityNot monotonic
2023-12-13T00:54:41.782802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
57 3
 
5.7%
126 2
 
3.8%
2950 2
 
3.8%
99 2
 
3.8%
1834 1
 
1.9%
27 1
 
1.9%
33 1
 
1.9%
1889 1
 
1.9%
1986 1
 
1.9%
97 1
 
1.9%
Other values (38) 38
71.7%
ValueCountFrequency (%)
1 1
 
1.9%
13 1
 
1.9%
24 1
 
1.9%
27 1
 
1.9%
33 1
 
1.9%
41 1
 
1.9%
57 3
5.7%
59 1
 
1.9%
66 1
 
1.9%
77 1
 
1.9%
ValueCountFrequency (%)
4073 1
1.9%
3281 1
1.9%
2994 1
1.9%
2950 2
3.8%
2750 1
1.9%
2474 1
1.9%
2395 1
1.9%
2215 1
1.9%
2193 1
1.9%
1986 1
1.9%

(RFID종류)외장형
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.86792
Minimum4
Maximum1922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:41.959738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.6
Q150
median397
Q3847
95-th percentile1561
Maximum1922
Range1918
Interquartile range (IQR)797

Descriptive statistics

Standard deviation553.07675
Coefficient of variation (CV)1.0537446
Kurtosis-0.20398201
Mean524.86792
Median Absolute Deviation (MAD)352
Skewness0.94486474
Sum27818
Variance305893.89
MonotonicityNot monotonic
2023-12-13T00:54:42.533018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2
 
3.8%
10 2
 
3.8%
1083 1
 
1.9%
13 1
 
1.9%
914 1
 
1.9%
990 1
 
1.9%
61 1
 
1.9%
1553 1
 
1.9%
9 1
 
1.9%
109 1
 
1.9%
Other values (41) 41
77.4%
ValueCountFrequency (%)
4 2
3.8%
9 1
1.9%
10 2
3.8%
13 1
1.9%
19 1
1.9%
20 1
1.9%
25 1
1.9%
28 1
1.9%
32 1
1.9%
36 1
1.9%
ValueCountFrequency (%)
1922 1
1.9%
1799 1
1.9%
1573 1
1.9%
1553 1
1.9%
1501 1
1.9%
1372 1
1.9%
1361 1
1.9%
1344 1
1.9%
1249 1
1.9%
1083 1
1.9%

(RFID종류)인식표
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.396226
Minimum1
Maximum296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:42.729239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median45
Q3137
95-th percentile277
Maximum296
Range295
Interquartile range (IQR)127

Descriptive statistics

Standard deviation91.11475
Coefficient of variation (CV)1.1058122
Kurtosis-0.061256283
Mean82.396226
Median Absolute Deviation (MAD)40
Skewness1.0766269
Sum4367
Variance8301.8977
MonotonicityNot monotonic
2023-12-13T00:54:42.935683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 4
 
7.5%
2 3
 
5.7%
12 3
 
5.7%
7 2
 
3.8%
78 2
 
3.8%
17 2
 
3.8%
139 1
 
1.9%
9 1
 
1.9%
271 1
 
1.9%
83 1
 
1.9%
Other values (33) 33
62.3%
ValueCountFrequency (%)
1 4
7.5%
2 3
5.7%
4 1
 
1.9%
5 1
 
1.9%
6 1
 
1.9%
7 2
3.8%
9 1
 
1.9%
10 1
 
1.9%
12 3
5.7%
13 1
 
1.9%
ValueCountFrequency (%)
296 1
1.9%
292 1
1.9%
286 1
1.9%
271 1
1.9%
241 1
1.9%
237 1
1.9%
229 1
1.9%
195 1
1.9%
194 1
1.9%
187 1
1.9%

등록품종수
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.641509
Minimum4
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:43.129993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15
Q132
median61
Q384
95-th percentile95.8
Maximum106
Range102
Interquartile range (IQR)52

Descriptive statistics

Standard deviation28.495677
Coefficient of variation (CV)0.50308824
Kurtosis-1.2196523
Mean56.641509
Median Absolute Deviation (MAD)25
Skewness-0.043837895
Sum3002
Variance812.00363
MonotonicityNot monotonic
2023-12-13T00:54:43.302283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
32 3
 
5.7%
74 3
 
5.7%
68 3
 
5.7%
15 2
 
3.8%
85 2
 
3.8%
93 2
 
3.8%
106 2
 
3.8%
18 2
 
3.8%
53 2
 
3.8%
66 2
 
3.8%
Other values (27) 30
56.6%
ValueCountFrequency (%)
4 1
1.9%
9 1
1.9%
15 2
3.8%
18 2
3.8%
19 1
1.9%
22 1
1.9%
25 1
1.9%
27 1
1.9%
29 1
1.9%
31 1
1.9%
ValueCountFrequency (%)
106 2
3.8%
97 1
1.9%
95 1
1.9%
93 2
3.8%
92 1
1.9%
91 1
1.9%
89 2
3.8%
86 1
1.9%
85 2
3.8%
84 1
1.9%

등록소유자수
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1136.4528
Minimum4
Maximum5009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:43.504728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile23.6
Q194
median739
Q31746
95-th percentile3581.8
Maximum5009
Range5005
Interquartile range (IQR)1652

Descriptive statistics

Standard deviation1305.893
Coefficient of variation (CV)1.1490957
Kurtosis0.54899091
Mean1136.4528
Median Absolute Deviation (MAD)667
Skewness1.1963953
Sum60232
Variance1705356.6
MonotonicityNot monotonic
2023-12-13T00:54:43.733065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 2
 
3.8%
29 1
 
1.9%
2545 1
 
1.9%
24 1
 
1.9%
65 1
 
1.9%
37 1
 
1.9%
2299 1
 
1.9%
2453 1
 
1.9%
110 1
 
1.9%
3491 1
 
1.9%
Other values (42) 42
79.2%
ValueCountFrequency (%)
4 1
1.9%
9 1
1.9%
23 1
1.9%
24 1
1.9%
26 1
1.9%
29 1
1.9%
37 1
1.9%
44 1
1.9%
59 1
1.9%
65 1
1.9%
ValueCountFrequency (%)
5009 1
1.9%
4270 1
1.9%
3718 1
1.9%
3491 1
1.9%
3264 1
1.9%
3139 1
1.9%
3102 1
1.9%
2947 1
1.9%
2655 1
1.9%
2545 1
1.9%

동물소유자당등록동물수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5618868
Minimum1.26
Maximum3.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-13T00:54:43.926781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.26
5-th percentile1.26
Q11.35
median1.44
Q31.58
95-th percentile2.122
Maximum3.78
Range2.52
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.4259878
Coefficient of variation (CV)0.27273923
Kurtosis15.692865
Mean1.5618868
Median Absolute Deviation (MAD)0.11
Skewness3.6198199
Sum82.78
Variance0.1814656
MonotonicityNot monotonic
2023-12-13T00:54:44.095013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1.26 4
 
7.5%
1.35 4
 
7.5%
1.52 4
 
7.5%
1.37 4
 
7.5%
1.39 2
 
3.8%
1.41 2
 
3.8%
1.68 2
 
3.8%
1.34 2
 
3.8%
1.32 2
 
3.8%
1.33 2
 
3.8%
Other values (25) 25
47.2%
ValueCountFrequency (%)
1.26 4
7.5%
1.27 1
 
1.9%
1.29 1
 
1.9%
1.3 1
 
1.9%
1.32 2
3.8%
1.33 2
3.8%
1.34 2
3.8%
1.35 4
7.5%
1.37 4
7.5%
1.39 2
3.8%
ValueCountFrequency (%)
3.78 1
1.9%
3.0 1
1.9%
2.14 1
1.9%
2.11 1
1.9%
1.87 1
1.9%
1.83 1
1.9%
1.82 1
1.9%
1.79 1
1.9%
1.78 1
1.9%
1.71 1
1.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-08-31
53 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-31
2nd row2023-08-31
3rd row2023-08-31
4th row2023-08-31
5th row2023-08-31

Common Values

ValueCountFrequency (%)
2023-08-31 53
100.0%

Length

2023-12-13T00:54:44.265342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:54:44.373762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-31 53
100.0%

Interactions

2023-12-13T00:54:36.820736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:28.367506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:29.559606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.601993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:31.615751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.721010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:33.829984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.023876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.943630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.958935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:28.458737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:29.690592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.721394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:31.736679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.841244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:33.946519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.131424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.046973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:37.072626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:28.576687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:29.817109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.833516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:31.878346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.955237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:34.061486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.215587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.141229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:37.300380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:28.683413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:29.936018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.927669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.009099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:33.078161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:34.443331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.311912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.230507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:37.449854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:28.808153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.058774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:31.058768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.123517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:33.208906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:34.552106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.438064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.320380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:37.587676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:28.955204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.180881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:31.188191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.249856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:33.355698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:34.645586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.549933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.419970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:37.704868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:29.120969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.296539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:31.303847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.348717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:33.471676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:34.735793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.663590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.526367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:37.806299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:29.263932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.388300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:31.413614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.474701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:33.587501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:34.842588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.751821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.625970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:37.912479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:29.415370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:30.480795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:31.523371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:32.597501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:33.709563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:34.925052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:35.849340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:54:36.722763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:54:44.447233image/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
등록동물수1.0001.0000.8271.0000.8250.9870.9500.9270.8480.9850.000
(등록주체)시군구등록1.0000.8271.0000.8270.6140.8270.8440.7990.4610.8050.000
(등록주체)대행업체등록1.0001.0000.8271.0000.8250.9870.9500.9270.8480.9850.000
(등록주체)기타1.0000.8250.6140.8251.0000.7760.7940.8300.2850.9090.000
(RFID종류)내장형1.0000.9870.8270.9870.7761.0000.9320.9090.8440.9780.000
(RFID종류)외장형1.0000.9500.8440.9500.7940.9321.0000.9610.8590.9420.000
(RFID종류)인식표1.0000.9270.7990.9270.8300.9090.9611.0000.8410.9150.000
등록품종수1.0000.8480.4610.8480.2850.8440.8590.8411.0000.8300.575
등록소유자수1.0000.9850.8050.9850.9090.9780.9420.9150.8301.0000.000
동물소유자당등록동물수1.0000.0000.0000.0000.0000.0000.0000.0000.5750.0001.000
2023-12-13T00:54:44.621279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록동물수(등록주체)시군구등록(등록주체)대행업체등록(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수(등록주체)기타
등록동물수1.0000.9101.0000.9940.9900.9650.9800.994-0.6060.456
(등록주체)시군구등록0.9101.0000.9080.9060.9100.8790.9160.912-0.5720.396
(등록주체)대행업체등록1.0000.9081.0000.9940.9900.9630.9790.994-0.6050.456
(RFID종류)내장형0.9940.9060.9941.0000.9760.9550.9730.988-0.5880.405
(RFID종류)외장형0.9900.9100.9900.9761.0000.9560.9790.988-0.6190.423
(RFID종류)인식표0.9650.8790.9630.9550.9561.0000.9570.960-0.5900.461
등록품종수0.9800.9160.9790.9730.9790.9571.0000.976-0.5630.100
등록소유자수0.9940.9120.9940.9880.9880.9600.9761.000-0.6570.570
동물소유자당등록동물수-0.606-0.572-0.605-0.588-0.619-0.590-0.563-0.6571.0000.000
(등록주체)기타0.4560.3960.4560.4050.4230.4610.1000.5700.0001.000

Missing values

2023-12-13T00:54:38.086193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:54:38.381468image/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
0고양시강매동620620574115292.142023-08-31
1고양시고양동177018175111084592946813091.352023-08-31
2고양시관산동1369171352076353373689821.392023-08-31
3고양시내곡동9028805728527591.532023-08-31
4고양시내유동1447181429082653982749791.482023-08-31
5고양시대자동197519201087712361321.492023-08-31
6고양시대장동122212007936729911.342023-08-31
7고양시덕은동2583255012612210481811.432023-08-31
8고양시도내동1242121230066952746639421.322023-08-31
9고양시동산동148981481081559678688351.782023-08-31
시군명읍면동명등록동물수(등록주체)시군구등록(등록주체)대행업체등록(등록주체)기타(RFID종류)내장형(RFID종류)외장형(RFID종류)인식표등록품종수등록소유자수동물소유자당등록동물수데이터기준일자
43고양시사리현동59010580030524738533931.52023-08-31
44고양시정발산동2207202187013946931208516151.372023-08-31
45고양시가좌동14979148719364061556610171.472023-08-31
46고양시구산동1841317101264513321011.822023-08-31
47고양시대화동452342448012950134422910632641.392023-08-31
48고양시덕이동28231828050164810381378618531.522023-08-31
49고양시법곳동1433140099321231941.522023-08-31
50고양시일산동53763553401328117992969142701.262023-08-31
51고양시주엽동39543039231239513721879231391.262023-08-31
52고양시탄현동40093639730247412492869331021.292023-08-31