Overview

Dataset statistics

Number of variables13
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory119.9 B

Variable types

Categorical2
Text1
Numeric10

Dataset

Description시도별 기타가축(마필, 산양, 면양, 사슴, 토끼, 개, 오리, 칠면조 등) 사육농가 및 사육마릿수 자료", "제공 목록 : 년도, 지역, 사육가구 수
Author농림축산식품부
URLhttps://www.data.go.kr/data/3034933/fileData.do

Alerts

사육_가구_수_계 is highly overall correlated with 1가구_4가구 and 8 other fieldsHigh correlation
1가구_4가구 is highly overall correlated with 사육_가구_수_계 and 9 other fieldsHigh correlation
5가구_9가구 is highly overall correlated with 사육_가구_수_계 and 8 other fieldsHigh correlation
10가구_19가구 is highly overall correlated with 사육_가구_수_계 and 9 other fieldsHigh correlation
20가구_29가구 is highly overall correlated with 사육_가구_수_계 and 8 other fieldsHigh correlation
30가구_49가구 is highly overall correlated with 사육_가구_수_계 and 8 other fieldsHigh correlation
50가구_99가구 is highly overall correlated with 사육_가구_수_계 and 9 other fieldsHigh correlation
100가구_299가구 is highly overall correlated with 사육_가구_수_계 and 9 other fieldsHigh correlation
300가구_499가구 is highly overall correlated with 사육_가구_수_계 and 9 other fieldsHigh correlation
500가구_999가구 is highly overall correlated with 사육_가구_수_계 and 8 other fieldsHigh correlation
1000가구_이상 is highly overall correlated with 1가구_4가구 and 4 other fieldsHigh correlation
사육_가구_수_계 has 3 (8.8%) zerosZeros
1가구_4가구 has 4 (11.8%) zerosZeros
5가구_9가구 has 4 (11.8%) zerosZeros
10가구_19가구 has 5 (14.7%) zerosZeros
20가구_29가구 has 3 (8.8%) zerosZeros
30가구_49가구 has 4 (11.8%) zerosZeros
50가구_99가구 has 4 (11.8%) zerosZeros
100가구_299가구 has 5 (14.7%) zerosZeros
300가구_499가구 has 17 (50.0%) zerosZeros
500가구_999가구 has 19 (55.9%) zerosZeros

Reproduction

Analysis started2024-04-17 18:09:35.518455
Analysis finished2024-04-17 18:09:43.137326
Duration7.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2013
17 
2014
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 17
50.0%
2014 17
50.0%

Length

2024-04-18T03:09:43.185147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:09:43.258156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2013 17
50.0%
2014 17
50.0%

지역
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2024-04-18T03:09:43.382053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters68
Distinct characters21
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주
ValueCountFrequency (%)
서울 2
 
5.9%
충북 2
 
5.9%
제주 2
 
5.9%
경남 2
 
5.9%
경북 2
 
5.9%
전남 2
 
5.9%
전북 2
 
5.9%
충남 2
 
5.9%
강원 2
 
5.9%
부산 2
 
5.9%
Other values (7) 14
41.2%
2024-04-18T03:09:43.630136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
전 6
 
8.8%
남 6
 
8.8%
북 6
 
8.8%
경 6
 
8.8%
산 4
 
5.9%
대 4
 
5.9%
충 4
 
5.9%
주 4
 
5.9%
울 4
 
5.9%
강 2
 
2.9%
Other values (11) 22
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 6
 
8.8%
남 6
 
8.8%
북 6
 
8.8%
경 6
 
8.8%
산 4
 
5.9%
대 4
 
5.9%
충 4
 
5.9%
주 4
 
5.9%
울 4
 
5.9%
강 2
 
2.9%
Other values (11) 22
32.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 6
 
8.8%
남 6
 
8.8%
북 6
 
8.8%
경 6
 
8.8%
산 4
 
5.9%
대 4
 
5.9%
충 4
 
5.9%
주 4
 
5.9%
울 4
 
5.9%
강 2
 
2.9%
Other values (11) 22
32.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
전 6
 
8.8%
남 6
 
8.8%
북 6
 
8.8%
경 6
 
8.8%
산 4
 
5.9%
대 4
 
5.9%
충 4
 
5.9%
주 4
 
5.9%
울 4
 
5.9%
강 2
 
2.9%
Other values (11) 22
32.4%

사육_가구_수_계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean603.02941
Minimum0
Maximum2647
Zeros3
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:43.754548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132.5
median104.5
Q31261.25
95-th percentile1923.2
Maximum2647
Range2647
Interquartile range (IQR)1228.75

Descriptive statistics

Standard deviation742.3176
Coefficient of variation (CV)1.2309808
Kurtosis0.3123169
Mean603.02941
Median Absolute Deviation (MAD)104.5
Skewness1.0909048
Sum20503
Variance551035.42
MonotonicityNot monotonic
2024-04-18T03:09:43.855335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 3
 
8.8%
18 2
 
5.9%
1278 2
 
5.9%
37 1
 
2.9%
58 1
 
2.9%
2647 1
 
2.9%
1420 1
 
2.9%
1084 1
 
2.9%
1217 1
 
2.9%
632 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
0 3
8.8%
4 1
 
2.9%
7 1
 
2.9%
17 1
 
2.9%
18 2
5.9%
31 1
 
2.9%
37 1
 
2.9%
46 1
 
2.9%
51 1
 
2.9%
58 1
 
2.9%
ValueCountFrequency (%)
2647 1
2.9%
2230 1
2.9%
1758 1
2.9%
1420 1
2.9%
1395 1
2.9%
1325 1
2.9%
1278 2
5.9%
1276 1
2.9%
1217 1
2.9%
1112 1
2.9%

1가구_4가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.26471
Minimum0
Maximum1427
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:43.940850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.25
median20.5
Q3454
95-th percentile940.9
Maximum1427
Range1427
Interquartile range (IQR)448.75

Descriptive statistics

Standard deviation364.37329
Coefficient of variation (CV)1.421863
Kurtosis3.0716837
Mean256.26471
Median Absolute Deviation (MAD)20.5
Skewness1.7460696
Sum8713
Variance132767.9
MonotonicityNot monotonic
2024-04-18T03:09:44.030488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 4
 
11.8%
16 2
 
5.9%
4 2
 
5.9%
3 1
 
2.9%
1 1
 
2.9%
21 1
 
2.9%
1427 1
 
2.9%
381 1
 
2.9%
570 1
 
2.9%
495 1
 
2.9%
Other values (19) 19
55.9%
ValueCountFrequency (%)
0 4
11.8%
1 1
 
2.9%
2 1
 
2.9%
3 1
 
2.9%
4 2
5.9%
9 1
 
2.9%
13 1
 
2.9%
15 1
 
2.9%
16 2
5.9%
18 1
 
2.9%
ValueCountFrequency (%)
1427 1
2.9%
1262 1
2.9%
768 1
2.9%
593 1
2.9%
590 1
2.9%
570 1
2.9%
498 1
2.9%
495 1
2.9%
457 1
2.9%
445 1
2.9%

5가구_9가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.88235
Minimum0
Maximum591
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:44.133502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.25
median21.5
Q3244.75
95-th percentile386.9
Maximum591
Range591
Interquartile range (IQR)238.5

Descriptive statistics

Standard deviation156.85484
Coefficient of variation (CV)1.2560208
Kurtosis0.84099422
Mean124.88235
Median Absolute Deviation (MAD)21.5
Skewness1.1897096
Sum4246
Variance24603.44
MonotonicityNot monotonic
2024-04-18T03:09:44.221468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 4
 
11.8%
7 3
 
8.8%
1 2
 
5.9%
12 2
 
5.9%
16 1
 
2.9%
37 1
 
2.9%
3 1
 
2.9%
591 1
 
2.9%
229 1
 
2.9%
253 1
 
2.9%
Other values (17) 17
50.0%
ValueCountFrequency (%)
0 4
11.8%
1 2
5.9%
2 1
 
2.9%
3 1
 
2.9%
6 1
 
2.9%
7 3
8.8%
12 2
5.9%
13 1
 
2.9%
16 1
 
2.9%
21 1
 
2.9%
ValueCountFrequency (%)
591 1
2.9%
448 1
2.9%
354 1
2.9%
341 1
2.9%
278 1
2.9%
271 1
2.9%
267 1
2.9%
253 1
2.9%
250 1
2.9%
229 1
2.9%

10가구_19가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.852941
Minimum0
Maximum287
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:44.313614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.5
median26
Q3169.75
95-th percentile250.45
Maximum287
Range287
Interquartile range (IQR)165.25

Descriptive statistics

Standard deviation98.90526
Coefficient of variation (CV)1.1795085
Kurtosis-0.88454458
Mean83.852941
Median Absolute Deviation (MAD)26
Skewness0.8545099
Sum2851
Variance9782.2504
MonotonicityNot monotonic
2024-04-18T03:09:44.426506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 5
 
14.7%
4 2
 
5.9%
229 2
 
5.9%
6 1
 
2.9%
11 1
 
2.9%
2 1
 
2.9%
287 1
 
2.9%
224 1
 
2.9%
212 1
 
2.9%
111 1
 
2.9%
Other values (18) 18
52.9%
ValueCountFrequency (%)
0 5
14.7%
2 1
 
2.9%
3 1
 
2.9%
4 2
 
5.9%
6 1
 
2.9%
8 1
 
2.9%
9 1
 
2.9%
11 1
 
2.9%
12 1
 
2.9%
13 1
 
2.9%
ValueCountFrequency (%)
287 1
2.9%
281 1
2.9%
234 1
2.9%
229 2
5.9%
224 1
2.9%
219 1
2.9%
212 1
2.9%
177 1
2.9%
148 1
2.9%
119 1
2.9%

20가구_29가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.823529
Minimum0
Maximum127
Zeros3
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:44.517370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median14.5
Q374.5
95-th percentile118.7
Maximum127
Range127
Interquartile range (IQR)70.5

Descriptive statistics

Standard deviation44.099385
Coefficient of variation (CV)1.1073701
Kurtosis-0.9851219
Mean39.823529
Median Absolute Deviation (MAD)14
Skewness0.80091657
Sum1354
Variance1944.7558
MonotonicityNot monotonic
2024-04-18T03:09:44.607312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4 4
 
11.8%
0 3
 
8.8%
12 2
 
5.9%
3 2
 
5.9%
13 2
 
5.9%
1 2
 
5.9%
102 2
 
5.9%
28 1
 
2.9%
2 1
 
2.9%
120 1
 
2.9%
Other values (14) 14
41.2%
ValueCountFrequency (%)
0 3
8.8%
1 2
5.9%
2 1
 
2.9%
3 2
5.9%
4 4
11.8%
9 1
 
2.9%
12 2
5.9%
13 2
5.9%
16 1
 
2.9%
20 1
 
2.9%
ValueCountFrequency (%)
127 1
2.9%
120 1
2.9%
118 1
2.9%
111 1
2.9%
102 2
5.9%
97 1
2.9%
89 1
2.9%
75 1
2.9%
73 1
2.9%
70 1
2.9%

30가구_49가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.764706
Minimum0
Maximum120
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:44.694966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median12
Q368.5
95-th percentile92.8
Maximum120
Range120
Interquartile range (IQR)65

Descriptive statistics

Standard deviation36.87083
Coefficient of variation (CV)1.0309278
Kurtosis-1.0230546
Mean35.764706
Median Absolute Deviation (MAD)12
Skewness0.62320642
Sum1216
Variance1359.4581
MonotonicityNot monotonic
2024-04-18T03:09:44.783271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 4
 
11.8%
74 2
 
5.9%
2 2
 
5.9%
12 2
 
5.9%
6 2
 
5.9%
90 2
 
5.9%
5 2
 
5.9%
3 2
 
5.9%
52 1
 
2.9%
46 1
 
2.9%
Other values (14) 14
41.2%
ValueCountFrequency (%)
0 4
11.8%
1 1
 
2.9%
2 2
5.9%
3 2
5.9%
5 2
5.9%
6 2
5.9%
8 1
 
2.9%
9 1
 
2.9%
11 1
 
2.9%
12 2
5.9%
ValueCountFrequency (%)
120 1
2.9%
98 1
2.9%
90 2
5.9%
80 1
2.9%
78 1
2.9%
74 2
5.9%
69 1
2.9%
67 1
2.9%
64 1
2.9%
57 1
2.9%

50가구_99가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.558824
Minimum0
Maximum118
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:44.883394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median8.5
Q359.25
95-th percentile94.1
Maximum118
Range118
Interquartile range (IQR)58

Descriptive statistics

Standard deviation35.70688
Coefficient of variation (CV)1.1684638
Kurtosis-0.20386249
Mean30.558824
Median Absolute Deviation (MAD)8.5
Skewness0.97199867
Sum1039
Variance1274.9813
MonotonicityNot monotonic
2024-04-18T03:09:44.971727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 5
14.7%
0 4
 
11.8%
3 2
 
5.9%
2 2
 
5.9%
78 2
 
5.9%
47 2
 
5.9%
7 2
 
5.9%
118 1
 
2.9%
73 1
 
2.9%
64 1
 
2.9%
Other values (12) 12
35.3%
ValueCountFrequency (%)
0 4
11.8%
1 5
14.7%
2 2
 
5.9%
3 2
 
5.9%
4 1
 
2.9%
6 1
 
2.9%
7 2
 
5.9%
10 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
ValueCountFrequency (%)
118 1
2.9%
111 1
2.9%
85 1
2.9%
78 2
5.9%
73 1
2.9%
66 1
2.9%
64 1
2.9%
60 1
2.9%
57 1
2.9%
47 2
5.9%

100가구_299가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.558824
Minimum0
Maximum93
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:45.062182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7.5
Q344.75
95-th percentile76.4
Maximum93
Range93
Interquartile range (IQR)42.75

Descriptive statistics

Standard deviation27.692238
Coefficient of variation (CV)1.1754508
Kurtosis-0.18330639
Mean23.558824
Median Absolute Deviation (MAD)7.5
Skewness0.99983057
Sum801
Variance766.86007
MonotonicityNot monotonic
2024-04-18T03:09:45.156008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 5
 
14.7%
2 5
 
14.7%
4 2
 
5.9%
3 2
 
5.9%
1 2
 
5.9%
51 1
 
2.9%
14 1
 
2.9%
8 1
 
2.9%
38 1
 
2.9%
79 1
 
2.9%
Other values (13) 13
38.2%
ValueCountFrequency (%)
0 5
14.7%
1 2
 
5.9%
2 5
14.7%
3 2
 
5.9%
4 2
 
5.9%
7 1
 
2.9%
8 1
 
2.9%
14 1
 
2.9%
16 1
 
2.9%
21 1
 
2.9%
ValueCountFrequency (%)
93 1
2.9%
79 1
2.9%
75 1
2.9%
66 1
2.9%
58 1
2.9%
52 1
2.9%
51 1
2.9%
48 1
2.9%
45 1
2.9%
44 1
2.9%

300가구_499가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2058824
Minimum0
Maximum29
Zeros17
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:45.236238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q37.25
95-th percentile22.7
Maximum29
Range29
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation8.193787
Coefficient of variation (CV)1.5739478
Kurtosis1.7019201
Mean5.2058824
Median Absolute Deviation (MAD)0.5
Skewness1.6515486
Sum177
Variance67.138146
MonotonicityNot monotonic
2024-04-18T03:09:45.318750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 17
50.0%
1 2
 
5.9%
2 2
 
5.9%
5 2
 
5.9%
10 1
 
2.9%
8 1
 
2.9%
16 1
 
2.9%
24 1
 
2.9%
13 1
 
2.9%
4 1
 
2.9%
Other values (5) 5
 
14.7%
ValueCountFrequency (%)
0 17
50.0%
1 2
 
5.9%
2 2
 
5.9%
3 1
 
2.9%
4 1
 
2.9%
5 2
 
5.9%
8 1
 
2.9%
10 1
 
2.9%
11 1
 
2.9%
13 1
 
2.9%
ValueCountFrequency (%)
29 1
2.9%
24 1
2.9%
22 1
2.9%
21 1
2.9%
16 1
2.9%
13 1
2.9%
11 1
2.9%
10 1
2.9%
8 1
2.9%
5 2
5.9%

500가구_999가구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4117647
Minimum0
Maximum14
Zeros19
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-04-18T03:09:45.414241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile11.35
Maximum14
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.0981714
Coefficient of variation (CV)1.6992418
Kurtosis1.6544156
Mean2.4117647
Median Absolute Deviation (MAD)0
Skewness1.7116107
Sum82
Variance16.795009
MonotonicityNot monotonic
2024-04-18T03:09:45.490252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 19
55.9%
1 4
 
11.8%
2 3
 
8.8%
12 1
 
2.9%
14 1
 
2.9%
7 1
 
2.9%
6 1
 
2.9%
10 1
 
2.9%
11 1
 
2.9%
9 1
 
2.9%
ValueCountFrequency (%)
0 19
55.9%
1 4
 
11.8%
2 3
 
8.8%
3 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
11 1
 
2.9%
12 1
 
2.9%
ValueCountFrequency (%)
14 1
 
2.9%
12 1
 
2.9%
11 1
 
2.9%
10 1
 
2.9%
9 1
 
2.9%
7 1
 
2.9%
6 1
 
2.9%
3 1
 
2.9%
2 3
8.8%
1 4
11.8%

1000가구_이상
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
0
24 
1
6 
3
 
2
6
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 24
70.6%
1 6
 
17.6%
3 2
 
5.9%
6 2
 
5.9%

Length

2024-04-18T03:09:45.581605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:09:45.656563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24
70.6%
1 6
 
17.6%
3 2
 
5.9%
6 2
 
5.9%

Interactions

2024-04-18T03:09:42.237215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:35.865742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.563494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.277799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.125794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.780848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.418008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.128717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.773098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.373144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.315882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:35.935733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.631376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.339108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.186701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.850231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.485021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.188359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.832167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.434566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.388663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.017369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.697705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.403158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.256471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.916153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.567855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.253340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.898129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.499036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.459817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.096070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.778014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.466227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.326807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.979053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.650666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.314605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.957330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.560041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.524589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.175831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.842915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.537276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.388256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.042676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.722422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.377869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.015756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.622603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.591070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.243724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.909287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.602546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.450776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.104743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.785300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.452408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.074770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.700748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.655805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.311597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.975122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.879197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.517236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.165513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.848036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.522463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.133243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.761626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.721327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.375854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.057766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.939287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.579232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.227310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.912658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.583179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.191253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.050357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.781758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.435425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.131607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.996054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.640804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.285167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.979462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.638833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.245589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.106656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.849505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:36.497647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:37.212846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.058748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:38.705639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:39.354225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.058341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:40.706247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:41.307625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:09:42.168662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:09:45.719089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도지역사육_가구_수_계1가구_4가구5가구_9가구10가구_19가구20가구_29가구30가구_49가구50가구_99가구100가구_299가구300가구_499가구500가구_999가구1000가구_이상
년도1.0000.0000.0000.0000.0000.0000.0000.3280.0000.0000.0430.1210.000
지역0.0001.0000.8190.8750.8590.7840.8770.7450.8810.7770.7980.4560.975
사육_가구_수_계0.0000.8191.0000.9880.9440.8370.9380.8380.8080.8560.7890.8830.799
1가구_4가구0.0000.8750.9881.0000.9540.8610.9360.8580.8390.8570.8240.9090.935
5가구_9가구0.0000.8590.9440.9541.0000.8110.8020.8360.7980.8150.8090.8900.423
10가구_19가구0.0000.7840.8370.8610.8111.0000.8390.9410.9070.9670.9060.8510.840
20가구_29가구0.0000.8770.9380.9360.8020.8391.0000.8540.8440.8350.8260.7920.839
30가구_49가구0.3280.7450.8380.8580.8360.9410.8541.0000.8350.9320.7800.8810.523
50가구_99가구0.0000.8810.8080.8390.7980.9070.8440.8351.0000.9020.9390.9400.761
100가구_299가구0.0000.7770.8560.8570.8150.9670.8350.9320.9021.0000.9360.8800.799
300가구_499가구0.0430.7980.7890.8240.8090.9060.8260.7800.9390.9361.0000.9750.823
500가구_999가구0.1210.4560.8830.9090.8900.8510.7920.8810.9400.8800.9751.0000.590
1000가구_이상0.0000.9750.7990.9350.4230.8400.8390.5230.7610.7990.8230.5901.000
2024-04-18T03:09:45.841487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1000가구_이상년도
1000가구_이상1.0000.000
년도0.0001.000
2024-04-18T03:09:45.915220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사육_가구_수_계1가구_4가구5가구_9가구10가구_19가구20가구_29가구30가구_49가구50가구_99가구100가구_299가구300가구_499가구500가구_999가구년도1000가구_이상
사육_가구_수_계1.0000.9630.9890.9880.9800.9700.9620.8820.8710.8540.0000.427
1가구_4가구0.9631.0000.9600.9420.9330.9270.8950.8420.8390.8560.0000.620
5가구_9가구0.9890.9601.0000.9800.9620.9590.9480.8740.8590.8380.0000.278
10가구_19가구0.9880.9420.9801.0000.9720.9750.9690.8890.8690.8510.0000.606
20가구_29가구0.9800.9330.9620.9721.0000.9570.9650.8800.8680.8510.0000.470
30가구_49가구0.9700.9270.9590.9750.9571.0000.9730.8660.8700.8550.0530.340
50가구_99가구0.9620.8950.9480.9690.9650.9731.0000.9140.8790.8460.0000.550
100가구_299가구0.8820.8420.8740.8890.8800.8660.9141.0000.8830.8730.0000.552
300가구_499가구0.8710.8390.8590.8690.8680.8700.8790.8831.0000.9060.0000.632
500가구_999가구0.8540.8560.8380.8510.8510.8550.8460.8730.9061.0000.1880.370
년도0.0000.0000.0000.0000.0000.0530.0000.0000.0000.1881.0000.000
1000가구_이상0.4270.6200.2780.6060.4700.3400.5500.5520.6320.3700.0001.000

Missing values

2024-04-18T03:09:42.957670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:09:43.086315image/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

년도지역사육_가구_수_계1가구_4가구5가구_9가구10가구_19가구20가구_29가구30가구_49가구50가구_99가구100가구_299가구300가구_499가구500가구_999가구1000가구_이상
02013서울00000000000
12013부산40001102000
22013대구7913221412872100
32013인천651671316940000
42013광주183143322000
52013대전00000000000
62013울산1011816251311108000
72013경기23654374420352716201
82013강원6242331638737522325220
92013충북13254243412291118078511010
년도지역사육_가구_수_계1가구_4가구5가구_9가구10가구_19가구20가구_29가구30가구_49가구50가구_99가구100가구_299가구300가구_499가구500가구_999가구1000가구_이상
242014경기28270444828373221011
252014강원6322851626432462414410
262014충북12784452782191027885581120
272014충남121759025014873574745313
282014전북10844952081115269645222101
292014전남142057025321212764737529116
302014경북127838122922497120118792190
312014경남26471427591287120987838530
322014제주184322213001
332014세종582112114523000