Overview

Dataset statistics

Number of variables14
Number of observations47
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory124.8 B

Variable types

Text1
Numeric9
Categorical4

Dataset

Description강릉시 반려동물 등록 현황에 대한 데이터로 기관별 등록건수, 유형별 등록건수, 품종수, 동물 소유자수, 등록두수 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15090044/fileData.do

Alerts

관리기관명 has constant value ""Constant
전화번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
시군구 등록건수 is highly overall correlated with 위탁대행업체 등록건수 and 6 other fieldsHigh correlation
위탁대행업체 등록건수 is highly overall correlated with 시군구 등록건수 and 7 other fieldsHigh correlation
내장형 is highly overall correlated with 시군구 등록건수 and 7 other fieldsHigh correlation
외장형 is highly overall correlated with 시군구 등록건수 and 7 other fieldsHigh correlation
인식표 is highly overall correlated with 시군구 등록건수 and 7 other fieldsHigh correlation
등록품종수 is highly overall correlated with 시군구 등록건수 and 7 other fieldsHigh correlation
동물소유자수 is highly overall correlated with 시군구 등록건수 and 7 other fieldsHigh correlation
총 등록두수 is highly overall correlated with 시군구 등록건수 and 7 other fieldsHigh correlation
기타 등록건수 is highly overall correlated with 위탁대행업체 등록건수 and 6 other fieldsHigh correlation
기타 등록건수 is highly imbalanced (57.2%)Imbalance
읍면동 구분 has unique valuesUnique
시군구 등록건수 has 16 (34.0%) zerosZeros

Reproduction

Analysis started2023-12-12 07:23:21.897320
Analysis finished2023-12-12 07:23:30.807740
Duration8.91 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동 구분
Text

UNIQUE 

Distinct47
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-12T16:23:30.992490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters141
Distinct characters63
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

Unique47 ?
Unique (%)100.0%

Sample

1st row교동
2nd row저동
3rd row학동
4th row강동면
5th row강문동
ValueCountFrequency (%)
교동 1
 
2.1%
안현동 1
 
2.1%
옥계면 1
 
2.1%
옥천동 1
 
2.1%
왕산면 1
 
2.1%
용강동 1
 
2.1%
운산동 1
 
2.1%
운정동 1
 
2.1%
유산동 1
 
2.1%
유천동 1
 
2.1%
Other values (37) 37
78.7%
2023-12-12T16:23:31.471004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 40
28.4%
산 8
 
5.7%
면 7
 
5.0%
남 4
 
2.8%
정 3
 
2.1%
곡 3
 
2.1%
강 3
 
2.1%
문 3
 
2.1%
천 3
 
2.1%
성 3
 
2.1%
Other values (53) 64
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 40
28.4%
산 8
 
5.7%
면 7
 
5.0%
남 4
 
2.8%
정 3
 
2.1%
곡 3
 
2.1%
강 3
 
2.1%
문 3
 
2.1%
천 3
 
2.1%
성 3
 
2.1%
Other values (53) 64
45.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 40
28.4%
산 8
 
5.7%
면 7
 
5.0%
남 4
 
2.8%
정 3
 
2.1%
곡 3
 
2.1%
강 3
 
2.1%
문 3
 
2.1%
천 3
 
2.1%
성 3
 
2.1%
Other values (53) 64
45.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 40
28.4%
산 8
 
5.7%
면 7
 
5.0%
남 4
 
2.8%
정 3
 
2.1%
곡 3
 
2.1%
강 3
 
2.1%
문 3
 
2.1%
천 3
 
2.1%
성 3
 
2.1%
Other values (53) 64
45.4%

시군구 등록건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.553191
Minimum0
Maximum364
Zeros16
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:31.634842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q317
95-th percentile94.1
Maximum364
Range364
Interquartile range (IQR)17

Descriptive statistics

Standard deviation58.195625
Coefficient of variation (CV)2.5803721
Kurtosis26.405576
Mean22.553191
Median Absolute Deviation (MAD)2
Skewness4.7376387
Sum1060
Variance3386.7308
MonotonicityNot monotonic
2023-12-12T16:23:31.787462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 16
34.0%
2 5
 
10.6%
1 5
 
10.6%
3 3
 
6.4%
45 1
 
2.1%
96 1
 
2.1%
19 1
 
2.1%
18 1
 
2.1%
25 1
 
2.1%
20 1
 
2.1%
Other values (12) 12
25.5%
ValueCountFrequency (%)
0 16
34.0%
1 5
 
10.6%
2 5
 
10.6%
3 3
 
6.4%
4 1
 
2.1%
5 1
 
2.1%
6 1
 
2.1%
9 1
 
2.1%
13 1
 
2.1%
16 1
 
2.1%
ValueCountFrequency (%)
364 1
2.1%
96 1
2.1%
95 1
2.1%
92 1
2.1%
91 1
2.1%
80 1
2.1%
45 1
2.1%
38 1
2.1%
25 1
2.1%
20 1
2.1%

위탁대행업체 등록건수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.57447
Minimum7
Maximum2088
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:31.930975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13.6
Q138.5
median82
Q3357.5
95-th percentile1431.2
Maximum2088
Range2081
Interquartile range (IQR)319

Descriptive statistics

Standard deviation472.04093
Coefficient of variation (CV)1.5549428
Kurtosis5.5989621
Mean303.57447
Median Absolute Deviation (MAD)63
Skewness2.407512
Sum14268
Variance222822.64
MonotonicityNot monotonic
2023-12-12T16:23:32.064186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
45 2
 
4.3%
408 2
 
4.3%
73 2
 
4.3%
20 2
 
4.3%
30 2
 
4.3%
13 2
 
4.3%
2088 1
 
2.1%
41 1
 
2.1%
7 1
 
2.1%
76 1
 
2.1%
Other values (31) 31
66.0%
ValueCountFrequency (%)
7 1
2.1%
13 2
4.3%
15 1
2.1%
19 1
2.1%
20 2
4.3%
25 1
2.1%
29 1
2.1%
30 2
4.3%
38 1
2.1%
39 1
2.1%
ValueCountFrequency (%)
2088 1
2.1%
1722 1
2.1%
1568 1
2.1%
1112 1
2.1%
1025 1
2.1%
825 1
2.1%
588 1
2.1%
473 1
2.1%
408 2
4.3%
403 1
2.1%

기타 등록건수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size508.0 B
0
41 
1
 
3
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 41
87.2%
1 3
 
6.4%
2 3
 
6.4%

Length

2023-12-12T16:23:32.205517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:32.350661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 41
87.2%
1 3
 
6.4%
2 3
 
6.4%

내장형
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.80851
Minimum4
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:32.497093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.3
Q115
median41
Q3218.5
95-th percentile709.2
Maximum1042
Range1038
Interquartile range (IQR)203.5

Descriptive statistics

Standard deviation244.49012
Coefficient of variation (CV)1.5017036
Kurtosis4.0963929
Mean162.80851
Median Absolute Deviation (MAD)32
Skewness2.1033736
Sum7652
Variance59775.419
MonotonicityNot monotonic
2023-12-12T16:23:32.660668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
15 3
 
6.4%
12 3
 
6.4%
25 2
 
4.3%
20 2
 
4.3%
9 2
 
4.3%
1042 1
 
2.1%
10 1
 
2.1%
5 1
 
2.1%
22 1
 
2.1%
79 1
 
2.1%
Other values (30) 30
63.8%
ValueCountFrequency (%)
4 1
 
2.1%
5 1
 
2.1%
6 1
 
2.1%
7 1
 
2.1%
9 2
4.3%
10 1
 
2.1%
12 3
6.4%
15 3
6.4%
18 1
 
2.1%
20 2
4.3%
ValueCountFrequency (%)
1042 1
2.1%
859 1
2.1%
780 1
2.1%
544 1
2.1%
541 1
2.1%
531 1
2.1%
406 1
2.1%
316 1
2.1%
291 1
2.1%
273 1
2.1%

외장형
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.70213
Minimum2
Maximum734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:32.824971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.3
Q113
median35
Q3116.5
95-th percentile512.8
Maximum734
Range732
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation166.5833
Coefficient of variation (CV)1.5467039
Kurtosis5.5458178
Mean107.70213
Median Absolute Deviation (MAD)27
Skewness2.4058029
Sum5062
Variance27749.996
MonotonicityNot monotonic
2023-12-12T16:23:32.968375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
12 3
 
6.4%
14 2
 
4.3%
8 2
 
4.3%
16 2
 
4.3%
5 2
 
4.3%
734 1
 
2.1%
9 1
 
2.1%
2 1
 
2.1%
38 1
 
2.1%
50 1
 
2.1%
Other values (31) 31
66.0%
ValueCountFrequency (%)
2 1
 
2.1%
3 1
 
2.1%
4 1
 
2.1%
5 2
4.3%
8 2
4.3%
9 1
 
2.1%
11 1
 
2.1%
12 3
6.4%
14 2
4.3%
16 2
4.3%
ValueCountFrequency (%)
734 1
2.1%
606 1
2.1%
562 1
2.1%
398 1
2.1%
354 1
2.1%
284 1
2.1%
233 1
2.1%
188 1
2.1%
146 1
2.1%
141 1
2.1%

인식표
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.808511
Minimum1
Maximum358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:33.121676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median16
Q365
95-th percentile232.2
Maximum358
Range357
Interquartile range (IQR)58

Descriptive statistics

Standard deviation81.388397
Coefficient of variation (CV)1.458351
Kurtosis4.2599393
Mean55.808511
Median Absolute Deviation (MAD)15
Skewness2.0944668
Sum2623
Variance6624.0712
MonotonicityNot monotonic
2023-12-12T16:23:33.289400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 8
 
17.0%
8 3
 
6.4%
56 2
 
4.3%
87 2
 
4.3%
31 2
 
4.3%
11 2
 
4.3%
12 2
 
4.3%
16 2
 
4.3%
10 2
 
4.3%
2 2
 
4.3%
Other values (20) 20
42.6%
ValueCountFrequency (%)
1 8
17.0%
2 2
 
4.3%
4 1
 
2.1%
6 1
 
2.1%
8 3
 
6.4%
9 1
 
2.1%
10 2
 
4.3%
11 2
 
4.3%
12 2
 
4.3%
16 2
 
4.3%
ValueCountFrequency (%)
358 1
2.1%
279 1
2.1%
252 1
2.1%
186 1
2.1%
184 1
2.1%
153 1
2.1%
149 1
2.1%
136 1
2.1%
87 2
4.3%
77 1
2.1%

등록품종수
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.914894
Minimum5
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:33.448811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.3
Q114
median20
Q340
95-th percentile62.1
Maximum71
Range66
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.521233
Coefficient of variation (CV)0.6634893
Kurtosis-0.5611083
Mean27.914894
Median Absolute Deviation (MAD)11
Skewness0.78115628
Sum1312
Variance343.03608
MonotonicityNot monotonic
2023-12-12T16:23:33.585516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
15 4
 
8.5%
14 3
 
6.4%
8 2
 
4.3%
37 2
 
4.3%
35 2
 
4.3%
49 2
 
4.3%
20 2
 
4.3%
60 2
 
4.3%
23 2
 
4.3%
7 2
 
4.3%
Other values (22) 24
51.1%
ValueCountFrequency (%)
5 1
 
2.1%
7 2
4.3%
8 2
4.3%
9 1
 
2.1%
10 1
 
2.1%
11 1
 
2.1%
12 2
4.3%
13 1
 
2.1%
14 3
6.4%
15 4
8.5%
ValueCountFrequency (%)
71 1
2.1%
67 1
2.1%
63 1
2.1%
60 2
4.3%
52 1
2.1%
51 1
2.1%
50 1
2.1%
49 2
4.3%
45 1
2.1%
42 1
2.1%

동물소유자수
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.78723
Minimum6
Maximum1597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:33.749964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile9.2
Q123.5
median67
Q3281
95-th percentile1076.8
Maximum1597
Range1591
Interquartile range (IQR)257.5

Descriptive statistics

Standard deviation360.25015
Coefficient of variation (CV)1.5542277
Kurtosis5.6052908
Mean231.78723
Median Absolute Deviation (MAD)55
Skewness2.3835781
Sum10894
Variance129780.17
MonotonicityNot monotonic
2023-12-12T16:23:33.934267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
6 2
 
4.3%
26 2
 
4.3%
18 2
 
4.3%
12 2
 
4.3%
1597 1
 
2.1%
16 1
 
2.1%
182 1
 
2.1%
80 1
 
2.1%
39 1
 
2.1%
122 1
 
2.1%
Other values (33) 33
70.2%
ValueCountFrequency (%)
6 2
4.3%
8 1
2.1%
12 2
4.3%
15 1
2.1%
16 1
2.1%
18 2
4.3%
19 1
2.1%
21 1
2.1%
23 1
2.1%
24 1
2.1%
ValueCountFrequency (%)
1597 1
2.1%
1325 1
2.1%
1189 1
2.1%
815 1
2.1%
714 1
2.1%
647 1
2.1%
455 1
2.1%
427 1
2.1%
386 1
2.1%
301 1
2.1%

1인당 등록두수
Real number (ℝ)

Distinct37
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4951064
Minimum1.11
Maximum2.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:34.418319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.11
5-th percentile1.223
Q11.325
median1.41
Q31.66
95-th percentile1.929
Maximum2.17
Range1.06
Interquartile range (IQR)0.335

Descriptive statistics

Standard deviation0.23248069
Coefficient of variation (CV)0.15549441
Kurtosis0.38221502
Mean1.4951064
Median Absolute Deviation (MAD)0.13
Skewness0.84990274
Sum70.27
Variance0.054047271
MonotonicityNot monotonic
2023-12-12T16:23:34.558829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1.34 3
 
6.4%
1.33 3
 
6.4%
1.69 2
 
4.3%
1.4 2
 
4.3%
1.52 2
 
4.3%
1.39 2
 
4.3%
1.32 2
 
4.3%
1.31 2
 
4.3%
1.71 1
 
2.1%
1.95 1
 
2.1%
Other values (27) 27
57.4%
ValueCountFrequency (%)
1.11 1
2.1%
1.18 1
2.1%
1.22 1
2.1%
1.23 1
2.1%
1.25 1
2.1%
1.27 1
2.1%
1.29 1
2.1%
1.3 1
2.1%
1.31 2
4.3%
1.32 2
4.3%
ValueCountFrequency (%)
2.17 1
2.1%
2.0 1
2.1%
1.95 1
2.1%
1.88 1
2.1%
1.79 1
2.1%
1.76 1
2.1%
1.73 1
2.1%
1.72 1
2.1%
1.71 1
2.1%
1.69 2
4.3%

총 등록두수
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.31915
Minimum8
Maximum2134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T16:23:34.698069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile13.6
Q139.5
median82
Q3434
95-th percentile1454.2
Maximum2134
Range2126
Interquartile range (IQR)394.5

Descriptive statistics

Standard deviation486.05193
Coefficient of variation (CV)1.4894987
Kurtosis4.8972231
Mean326.31915
Median Absolute Deviation (MAD)63
Skewness2.2373156
Sum15337
Variance236246.48
MonotonicityNot monotonic
2023-12-12T16:23:34.852500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
41 2
 
4.3%
30 2
 
4.3%
45 2
 
4.3%
73 2
 
4.3%
13 2
 
4.3%
234 1
 
2.1%
143 1
 
2.1%
32 1
 
2.1%
8 1
 
2.1%
78 1
 
2.1%
Other values (32) 32
68.1%
ValueCountFrequency (%)
8 1
2.1%
13 2
4.3%
15 1
2.1%
19 1
2.1%
20 1
2.1%
21 1
2.1%
25 1
2.1%
30 2
4.3%
32 1
2.1%
38 1
2.1%
ValueCountFrequency (%)
2134 1
2.1%
1744 1
2.1%
1594 1
2.1%
1128 1
2.1%
1044 1
2.1%
843 1
2.1%
722 1
2.1%
601 1
2.1%
571 1
2.1%
499 1
2.1%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
강원특별자치도 강릉시청
47 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강원특별자치도 강릉시청
2nd row강원특별자치도 강릉시청
3rd row강원특별자치도 강릉시청
4th row강원특별자치도 강릉시청
5th row강원특별자치도 강릉시청

Common Values

ValueCountFrequency (%)
강원특별자치도 강릉시청 47
100.0%

Length

2023-12-12T16:23:34.995095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:35.081704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원특별자치도 47
50.0%
강릉시청 47
50.0%

전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
033-640-5843
47 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row033-640-5843
2nd row033-640-5843
3rd row033-640-5843
4th row033-640-5843
5th row033-640-5843

Common Values

ValueCountFrequency (%)
033-640-5843 47
100.0%

Length

2023-12-12T16:23:35.180980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:35.286763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
033-640-5843 47
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-09-05
47 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-05
2nd row2023-09-05
3rd row2023-09-05
4th row2023-09-05
5th row2023-09-05

Common Values

ValueCountFrequency (%)
2023-09-05 47
100.0%

Length

2023-12-12T16:23:35.391937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:23:35.486762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-05 47
100.0%

Interactions

2023-12-12T16:23:29.545588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.245217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.970665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.709960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.399979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.323538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.236796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.305651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.609461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.657352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.324793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.045914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.794022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.481707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.425909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.413807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.426028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.697242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.758194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.402241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.112507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.858638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.616722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.531484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.517825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.863527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.805661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.862473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.476721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.185300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.921472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.709513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.611478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.625115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.974421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.926005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.958261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.571062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.282019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.995524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.830501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.728828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.740132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.110388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.024689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.058385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.653893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.365837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.074383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.929433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.848800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.846198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.227289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.119565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.142535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.743891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.465269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.150890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.034323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.946638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.948375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.331260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.249282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.240554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.823802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.564904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.238303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.131648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.043583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.078650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.442708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.351125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:30.342113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:22.900486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:23.646214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:24.326599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:25.230847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:26.133272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:27.195588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:28.535421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:23:29.450966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:23:35.561856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동 구분시군구 등록건수위탁대행업체 등록건수기타 등록건수내장형외장형인식표등록품종수동물소유자수1인당 등록두수총 등록두수
읍면동 구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구 등록건수1.0001.0000.6080.4570.8770.6570.8150.5800.8570.2920.804
위탁대행업체 등록건수1.0000.6081.0000.9350.9400.9990.9080.8560.9830.0000.997
기타 등록건수1.0000.4570.9351.0000.8320.9640.6700.7600.8620.0000.941
내장형1.0000.8770.9400.8321.0000.9400.9470.8290.9850.0000.932
외장형1.0000.6570.9990.9640.9401.0000.8990.8040.9720.0000.994
인식표1.0000.8150.9080.6700.9470.8991.0000.7700.9740.0000.908
등록품종수1.0000.5800.8560.7600.8290.8040.7701.0000.8510.0000.840
동물소유자수1.0000.8570.9830.8620.9850.9720.9740.8511.0000.0000.968
1인당 등록두수1.0000.2920.0000.0000.0000.0000.0000.0000.0001.0000.000
총 등록두수1.0000.8040.9970.9410.9320.9940.9080.8400.9680.0001.000
2023-12-12T16:23:35.710852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구 등록건수위탁대행업체 등록건수내장형외장형인식표등록품종수동물소유자수1인당 등록두수총 등록두수기타 등록건수
시군구 등록건수1.0000.8050.7860.8260.8140.7840.8180.0290.8290.446
위탁대행업체 등록건수0.8051.0000.9710.9840.9580.9840.992-0.1460.9960.651
내장형0.7860.9711.0000.9410.9010.9800.974-0.1670.9740.727
외장형0.8260.9840.9411.0000.9420.9620.984-0.1560.9850.719
인식표0.8140.9580.9010.9421.0000.9370.945-0.0620.9590.514
등록품종수0.7840.9840.9800.9620.9371.0000.984-0.1530.9830.580
동물소유자수0.8180.9920.9740.9840.9450.9841.000-0.2080.9950.772
1인당 등록두수0.029-0.146-0.167-0.156-0.062-0.153-0.2081.000-0.1320.000
총 등록두수0.8290.9960.9740.9850.9590.9830.995-0.1321.0000.663
기타 등록건수0.4460.6510.7270.7190.5140.5800.7720.0000.6631.000

Missing values

2023-12-12T16:23:30.477448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:23:30.730994image/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 구분시군구 등록건수위탁대행업체 등록건수기타 등록건수내장형외장형인식표등록품종수동물소유자수1인당 등록두수총 등록두수관리기관명전화번호데이터기준일자
0교동452088110427343587115971.342134강원특별자치도 강릉시청033-640-58432023-09-05
1저동082041291219671.2282강원특별자치도 강릉시청033-640-58432023-09-05
2학동04502512816321.4145강원특별자치도 강릉시청033-640-58432023-09-05
3강동면9529102279861372661.45386강원특별자치도 강릉시청033-640-58432023-09-05
4강문동03802512114231.6538강원특별자치도 강릉시청033-640-58432023-09-05
5견소동520911117331351691.27215강원특별자치도 강릉시청033-640-58432023-09-05
6구정면364356253112863494271.69722강원특별자치도 강릉시청033-640-58432023-09-05
7금학동02501212113181.3925강원특별자치도 강릉시청033-640-58432023-09-05
8난곡동446020181214291.7250강원특별자치도 강릉시청033-640-58432023-09-05
9남문동07303826920621.1873강원특별자치도 강릉시청033-640-58432023-09-05
읍면동 구분시군구 등록건수위탁대행업체 등록건수기타 등록건수내장형외장형인식표등록품종수동물소유자수1인당 등록두수총 등록두수관리기관명전화번호데이터기준일자
37죽헌동0410249815211.9541강원특별자치도 강릉시청033-640-58432023-09-05
38지변동2120060431925911.34122강원특별자치도 강릉시청033-640-58432023-09-05
39청량동2390724108241.7141강원특별자치도 강릉시청033-640-58432023-09-05
40초당동3276012510153352131.31279강원특별자치도 강릉시청033-640-58432023-09-05
41포남동25156817805622526711891.341594강원특별자치도 강릉시청033-640-58432023-09-05
42홍제동188250406284153526471.3843강원특별자치도 강릉시청033-640-58432023-09-05
43회산동3344015011087382601.33347강원특별자치도 강릉시청033-640-58432023-09-05
44남항진동04701821815361.3147강원특별자치도 강릉시청033-640-58432023-09-05
45월호평동03001514114181.6730강원특별자치도 강릉시청033-640-58432023-09-05
46주문진읍1910250541354149637141.461044강원특별자치도 강릉시청033-640-58432023-09-05