Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 KiB
Average record size in memory106.3 B

Variable types

Numeric7
Text1
Categorical4

Alerts

시도코드 has constant value ""Constant
시도명 has constant value ""Constant
돼지 is highly overall correlated with 산란계 and 4 other fieldsHigh correlation
산란계 is highly overall correlated with 돼지 and 4 other fieldsHigh correlation
육계 is highly overall correlated with 돼지 and 4 other fieldsHigh correlation
젖소 is highly overall correlated with 돼지 and 4 other fieldsHigh correlation
한우 is highly overall correlated with 돼지 and 4 other fieldsHigh correlation
축산 is highly overall correlated with 돼지 and 4 other fieldsHigh correlation
시군구코드 is highly imbalanced (91.9%)Imbalance
시군구명 is highly imbalanced (91.9%)Imbalance
아이디 has unique valuesUnique
격자번호 has unique valuesUnique
산란계 has 3 (3.0%) zerosZeros
육계 has 4 (4.0%) zerosZeros
젖소 has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:37:21.031746
Analysis finished2023-12-10 11:37:28.201266
Duration7.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:28.315930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:37:28.508341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:37:28.909074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row라사9797
2nd row라사5134
3rd row라사4218
4th row라사3314
5th row라사5235
ValueCountFrequency (%)
라사9797 1
 
1.0%
라사3416 1
 
1.0%
라사4832 1
 
1.0%
라사5022 1
 
1.0%
라사3007 1
 
1.0%
라사5135 1
 
1.0%
라사5321 1
 
1.0%
라사3110 1
 
1.0%
라사4532 1
 
1.0%
라사6020 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:37:29.487776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
라 100
16.7%
사 100
16.7%
3 78
13.0%
1 62
10.3%
2 61
10.2%
4 42
7.0%
5 37
 
6.2%
8 30
 
5.0%
0 29
 
4.8%
9 23
 
3.8%
Other values (2) 38
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 78
19.5%
1 62
15.5%
2 61
15.2%
4 42
10.5%
5 37
9.2%
8 30
 
7.5%
0 29
 
7.2%
9 23
 
5.8%
6 22
 
5.5%
7 16
 
4.0%
Other Letter
ValueCountFrequency (%)
라 100
50.0%
사 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
3 78
19.5%
1 62
15.5%
2 61
15.2%
4 42
10.5%
5 37
9.2%
8 30
 
7.5%
0 29
 
7.2%
9 23
 
5.8%
6 22
 
5.5%
7 16
 
4.0%
Hangul
ValueCountFrequency (%)
라 100
50.0%
사 100
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
라 100
50.0%
사 100
50.0%
ASCII
ValueCountFrequency (%)
3 78
19.5%
1 62
15.5%
2 61
15.2%
4 42
10.5%
5 37
9.2%
8 30
 
7.5%
0 29
 
7.2%
9 23
 
5.8%
6 22
 
5.5%
7 16
 
4.0%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
42
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42
2nd row42
3rd row42
4th row42
5th row42

Common Values

ValueCountFrequency (%)
42 100
100.0%

Length

2023-12-10T20:37:29.670272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:29.787831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42 100
100.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
강원
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강원 100
100.0%

Length

2023-12-10T20:37:29.894516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:30.030664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강원 100
100.0%

시군구코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
42130
99 
42830
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row42830
2nd row42130
3rd row42130
4th row42130
5th row42130

Common Values

ValueCountFrequency (%)
42130 99
99.0%
42830 1
 
1.0%

Length

2023-12-10T20:37:30.179258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:30.392191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
42130 99
99.0%
42830 1
 
1.0%

시군구명
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
원주시
99 
양양군
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row양양군
2nd row원주시
3rd row원주시
4th row원주시
5th row원주시

Common Values

ValueCountFrequency (%)
원주시 99
99.0%
양양군 1
 
1.0%

Length

2023-12-10T20:37:30.520895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:30.655864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
원주시 99
99.0%
양양군 1
 
1.0%

돼지
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-62.6
Minimum-99
Maximum847
Zeros0
Zeros (%)0.0%
Negative89
Negative (%)89.0%
Memory size1.0 KiB
2023-12-10T20:37:30.790920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q3-99
95-th percentile218.35
Maximum847
Range946
Interquartile range (IQR)0

Descriptive statistics

Standard deviation126.75507
Coefficient of variation (CV)-2.0248414
Kurtosis27.963057
Mean-62.6
Median Absolute Deviation (MAD)0
Skewness4.7757756
Sum-6260
Variance16066.848
MonotonicityNot monotonic
2023-12-10T20:37:30.922761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-99 89
89.0%
143 1
 
1.0%
1 1
 
1.0%
130 1
 
1.0%
260 1
 
1.0%
111 1
 
1.0%
847 1
 
1.0%
324 1
 
1.0%
24 1
 
1.0%
268 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
-99 89
89.0%
1 1
 
1.0%
24 1
 
1.0%
111 1
 
1.0%
130 1
 
1.0%
143 1
 
1.0%
218 1
 
1.0%
225 1
 
1.0%
260 1
 
1.0%
268 1
 
1.0%
ValueCountFrequency (%)
847 1
1.0%
324 1
1.0%
268 1
1.0%
260 1
1.0%
225 1
1.0%
218 1
1.0%
143 1
1.0%
130 1
1.0%
111 1
1.0%
24 1
1.0%

산란계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1308.71
Minimum-99
Maximum69917
Zeros3
Zeros (%)3.0%
Negative89
Negative (%)89.0%
Memory size1.0 KiB
2023-12-10T20:37:31.107090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q3-99
95-th percentile689.25
Maximum69917
Range70016
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9205.6947
Coefficient of variation (CV)7.0341746
Kurtosis48.196692
Mean1308.71
Median Absolute Deviation (MAD)0
Skewness6.9851098
Sum130871
Variance84744814
MonotonicityNot monotonic
2023-12-10T20:37:31.297983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-99 89
89.0%
0 3
 
3.0%
1530 1
 
1.0%
69917 1
 
1.0%
645 1
 
1.0%
4278 1
 
1.0%
291 1
 
1.0%
97 1
 
1.0%
2558 1
 
1.0%
60366 1
 
1.0%
ValueCountFrequency (%)
-99 89
89.0%
0 3
 
3.0%
97 1
 
1.0%
291 1
 
1.0%
645 1
 
1.0%
1530 1
 
1.0%
2558 1
 
1.0%
4278 1
 
1.0%
60366 1
 
1.0%
69917 1
 
1.0%
ValueCountFrequency (%)
69917 1
 
1.0%
60366 1
 
1.0%
4278 1
 
1.0%
2558 1
 
1.0%
1530 1
 
1.0%
645 1
 
1.0%
291 1
 
1.0%
97 1
 
1.0%
0 3
 
3.0%
-99 89
89.0%

육계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365.08
Minimum-99
Maximum19530
Zeros4
Zeros (%)4.0%
Negative89
Negative (%)89.0%
Memory size1.0 KiB
2023-12-10T20:37:31.450542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q3-99
95-th percentile142.1
Maximum19530
Range19629
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2670.3378
Coefficient of variation (CV)7.3143908
Kurtosis41.463137
Mean365.08
Median Absolute Deviation (MAD)0
Skewness6.4051442
Sum36508
Variance7130703.9
MonotonicityNot monotonic
2023-12-10T20:37:31.613832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
-99 89
89.0%
0 4
 
4.0%
2 1
 
1.0%
619 1
 
1.0%
19530 1
 
1.0%
7154 1
 
1.0%
117 1
 
1.0%
1035 1
 
1.0%
16862 1
 
1.0%
ValueCountFrequency (%)
-99 89
89.0%
0 4
 
4.0%
2 1
 
1.0%
117 1
 
1.0%
619 1
 
1.0%
1035 1
 
1.0%
7154 1
 
1.0%
16862 1
 
1.0%
19530 1
 
1.0%
ValueCountFrequency (%)
19530 1
 
1.0%
16862 1
 
1.0%
7154 1
 
1.0%
1035 1
 
1.0%
619 1
 
1.0%
117 1
 
1.0%
2 1
 
1.0%
0 4
 
4.0%
-99 89
89.0%

젖소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.86
Minimum-99
Maximum16
Zeros5
Zeros (%)5.0%
Negative89
Negative (%)89.0%
Memory size1.0 KiB
2023-12-10T20:37:31.770747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q3-99
95-th percentile1
Maximum16
Range115
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31.881415
Coefficient of variation (CV)-0.3628661
Kurtosis4.5829931
Mean-87.86
Median Absolute Deviation (MAD)0
Skewness2.5423559
Sum-8786
Variance1016.4246
MonotonicityNot monotonic
2023-12-10T20:37:31.941346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-99 89
89.0%
0 5
 
5.0%
2 2
 
2.0%
1 2
 
2.0%
16 1
 
1.0%
3 1
 
1.0%
ValueCountFrequency (%)
-99 89
89.0%
0 5
 
5.0%
1 2
 
2.0%
2 2
 
2.0%
3 1
 
1.0%
16 1
 
1.0%
ValueCountFrequency (%)
16 1
 
1.0%
3 1
 
1.0%
2 2
 
2.0%
1 2
 
2.0%
0 5
 
5.0%
-99 89
89.0%

한우
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-81.99
Minimum-99
Maximum131
Zeros0
Zeros (%)0.0%
Negative89
Negative (%)89.0%
Memory size1.0 KiB
2023-12-10T20:37:32.082857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q3-99
95-th percentile40.7
Maximum131
Range230
Interquartile range (IQR)0

Descriptive statistics

Standard deviation51.159188
Coefficient of variation (CV)-0.62396863
Kurtosis8.1434065
Mean-81.99
Median Absolute Deviation (MAD)0
Skewness3.0081852
Sum-8199
Variance2617.2625
MonotonicityNot monotonic
2023-12-10T20:37:32.218240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-99 89
89.0%
14 1
 
1.0%
1 1
 
1.0%
32 1
 
1.0%
131 1
 
1.0%
9 1
 
1.0%
86 1
 
1.0%
126 1
 
1.0%
6 1
 
1.0%
40 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
-99 89
89.0%
1 1
 
1.0%
6 1
 
1.0%
9 1
 
1.0%
14 1
 
1.0%
32 1
 
1.0%
40 1
 
1.0%
54 1
 
1.0%
86 1
 
1.0%
113 1
 
1.0%
ValueCountFrequency (%)
131 1
1.0%
126 1
1.0%
113 1
1.0%
86 1
1.0%
54 1
1.0%
40 1
1.0%
32 1
1.0%
14 1
1.0%
9 1
1.0%
6 1
1.0%

축산
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1793.95
Minimum-99
Maximum89840
Zeros0
Zeros (%)0.0%
Negative89
Negative (%)89.0%
Memory size1.0 KiB
2023-12-10T20:37:32.377708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q3-99
95-th percentile1018.25
Maximum89840
Range89939
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11862.158
Coefficient of variation (CV)6.6123126
Kurtosis47.355995
Mean1793.95
Median Absolute Deviation (MAD)0
Skewness6.9039028
Sum179395
Variance1.407108 × 108
MonotonicityNot monotonic
2023-12-10T20:37:32.509688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
-99 89
89.0%
160 1
 
1.0%
6 1
 
1.0%
2315 1
 
1.0%
89840 1
 
1.0%
767 1
 
1.0%
950 1
 
1.0%
11885 1
 
1.0%
440 1
 
1.0%
407 1
 
1.0%
Other values (2) 2
 
2.0%
ValueCountFrequency (%)
-99 89
89.0%
6 1
 
1.0%
160 1
 
1.0%
407 1
 
1.0%
440 1
 
1.0%
767 1
 
1.0%
950 1
 
1.0%
2315 1
 
1.0%
3869 1
 
1.0%
11885 1
 
1.0%
ValueCountFrequency (%)
89840 1
1.0%
77567 1
1.0%
11885 1
1.0%
3869 1
1.0%
2315 1
1.0%
950 1
1.0%
767 1
1.0%
440 1
1.0%
407 1
1.0%
160 1
1.0%

Interactions

2023-12-10T20:37:27.033181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:21.482940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:22.432777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:23.297080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:24.166785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:25.062862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:25.874666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:27.150296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:21.600344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:22.534497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:23.441360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:24.297200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:25.166488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:26.263599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:27.264834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:21.719194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:22.644083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:23.560493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:24.444583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:25.274651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:26.373238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:27.389881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:21.845805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:22.772292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:23.694454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:24.578712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:25.411857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:26.495386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:27.517105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:21.966630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:22.930178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:23.817442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:24.726389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:25.543844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:26.606931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:27.634158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:22.137632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:23.059118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:23.922740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:24.832451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:25.664990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:26.832159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:27.749878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:22.308361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:23.171999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:24.055227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:24.967639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:25.763974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:26.929697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:37:32.638883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디격자번호시군구코드시군구명돼지산란계육계젖소한우축산
아이디1.0001.0000.0410.0410.2640.0510.0470.4680.3050.047
격자번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구코드0.0411.0001.0000.6930.0000.0000.0000.0000.0000.000
시군구명0.0411.0000.6931.0000.0000.0000.0000.0000.0000.000
돼지0.2641.0000.0000.0001.0000.7740.8291.0000.9830.829
산란계0.0511.0000.0000.0000.7741.0001.0000.5990.8491.000
육계0.0471.0000.0000.0000.8291.0001.0000.3460.7141.000
젖소0.4681.0000.0000.0001.0000.5990.3461.0001.0000.346
한우0.3051.0000.0000.0000.9830.8490.7141.0001.0000.714
축산0.0471.0000.0000.0000.8291.0001.0000.3460.7141.000
2023-12-10T20:37:32.793484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드시군구명
시군구코드1.0000.487
시군구명0.4871.000
2023-12-10T20:37:32.941934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디돼지산란계육계젖소한우축산시군구코드시군구명
아이디1.0000.0060.0070.0070.0030.0060.0070.0000.000
돼지0.0061.0000.9970.9960.9980.9990.9980.0000.000
산란계0.0070.9971.0000.9990.9950.9991.0000.0000.000
육계0.0070.9960.9991.0000.9950.9980.9990.0000.000
젖소0.0030.9980.9950.9951.0000.9970.9960.0000.000
한우0.0060.9990.9990.9980.9971.0000.9990.0000.000
축산0.0070.9981.0000.9990.9960.9991.0000.0000.000
시군구코드0.0000.0000.0000.0000.0000.0000.0001.0000.487
시군구명0.0000.0000.0000.0000.0000.0000.0000.4871.000

Missing values

2023-12-10T20:37:27.900490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:37:28.113495image/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

아이디격자번호시도코드시도명시군구코드시군구명돼지산란계육계젖소한우축산
01라사979742강원42830양양군-99-99-99-99-99-99
12라사513442강원42130원주시-99-99-99-99-99-99
23라사421842강원42130원주시-99-99-99-99-99-99
34라사331442강원42130원주시-99-99-99-99-99-99
45라사523542강원42130원주시-99-99-99-99-99-99
56라사293642강원42130원주시-99-99-99-99-99-99
67라사252942강원42130원주시-99-99-99-99-99-99
78라사461742강원42130원주시-99-99-99-99-99-99
89라사260642강원42130원주시-99-99-99-99-99-99
910라사301242강원42130원주시-99-99-99-99-99-99
아이디격자번호시도코드시도명시군구코드시군구명돼지산란계육계젖소한우축산
9091라사311942강원42130원주시-99-99-99-99-99-99
9192라사281542강원42130원주시-99-99-99-99-99-99
9293라사482342강원42130원주시-99-99-99-99-99-99
9394라사483142강원42130원주시-99-99-99-99-99-99
9495라사463042강원42130원주시-99-99-99-99-99-99
9596라사310842강원42130원주시-99-99-99-99-99-99
9697라사551942강원42130원주시-99-99-99-99-99-99
9798라사513042강원42130원주시-99-99-99-99-99-99
9899라사411842강원42130원주시-99-99-99-99-99-99
99100라사283542강원42130원주시-99-99-99-99-99-99