Overview

Dataset statistics

Number of variables10
Number of observations23
Missing cells4
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory95.7 B

Variable types

Numeric10

Dataset

Description사료의 품질 및 안전관리를 위한 일반성분, 유해성분 등 검정 정보(유해물질, 동물약품, 농약, 일반성분, 광물질, 보조제, 미생물, 기타성분)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220609000000002100

Alerts

연도 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
합계 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
유해물질 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
동물약품 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
농약 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
일반성분 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
광물질 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
보조제 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
미생물 is highly overall correlated with 연도 and 7 other fieldsHigh correlation
기타성분 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
동물약품 has 1 (4.3%) missing valuesMissing
일반성분 has 1 (4.3%) missing valuesMissing
보조제 has 1 (4.3%) missing valuesMissing
기타성분 has 1 (4.3%) missing valuesMissing
연도 has unique valuesUnique
합계 has unique valuesUnique
유해물질 has unique valuesUnique
농약 has unique valuesUnique
광물질 has unique valuesUnique

Reproduction

Analysis started2024-03-23 07:28:22.069608
Analysis finished2024-03-23 07:28:49.415218
Duration27.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011
Minimum2000
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:49.684830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001.1
Q12005.5
median2011
Q32016.5
95-th percentile2020.9
Maximum2022
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.0033726156
Kurtosis-1.2
Mean2011
Median Absolute Deviation (MAD)6
Skewness0
Sum46253
Variance46
MonotonicityStrictly decreasing
2024-03-23T07:28:50.064469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2022 1
 
4.3%
2021 1
 
4.3%
2000 1
 
4.3%
2001 1
 
4.3%
2002 1
 
4.3%
2003 1
 
4.3%
2004 1
 
4.3%
2005 1
 
4.3%
2006 1
 
4.3%
2007 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2000 1
4.3%
2001 1
4.3%
2002 1
4.3%
2003 1
4.3%
2004 1
4.3%
2005 1
4.3%
2006 1
4.3%
2007 1
4.3%
2008 1
4.3%
2009 1
4.3%
ValueCountFrequency (%)
2022 1
4.3%
2021 1
4.3%
2020 1
4.3%
2019 1
4.3%
2018 1
4.3%
2017 1
4.3%
2016 1
4.3%
2015 1
4.3%
2014 1
4.3%
2013 1
4.3%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23687.913
Minimum8068
Maximum51870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:50.424219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8068
5-th percentile16131.6
Q118069
median18942
Q327090
95-th percentile42141.4
Maximum51870
Range43802
Interquartile range (IQR)9021

Descriptive statistics

Standard deviation10068.453
Coefficient of variation (CV)0.42504603
Kurtosis1.7866563
Mean23687.913
Median Absolute Deviation (MAD)2499
Skewness1.3720709
Sum544822
Variance1.0137375 × 108
MonotonicityNot monotonic
2024-03-23T07:28:50.723358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
16097 1
 
4.3%
35636 1
 
4.3%
8068 1
 
4.3%
16443 1
 
4.3%
18859 1
 
4.3%
18942 1
 
4.3%
17459 1
 
4.3%
18120 1
 
4.3%
18018 1
 
4.3%
17259 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
8068 1
4.3%
16097 1
4.3%
16443 1
4.3%
17259 1
4.3%
17459 1
4.3%
18018 1
4.3%
18120 1
4.3%
18273 1
4.3%
18594 1
4.3%
18789 1
4.3%
ValueCountFrequency (%)
51870 1
4.3%
42547 1
4.3%
38491 1
4.3%
35636 1
4.3%
29608 1
4.3%
28652 1
4.3%
25528 1
4.3%
24831 1
4.3%
23192 1
4.3%
20354 1
4.3%

유해물질
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7716.3043
Minimum5981
Maximum11971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:50.980155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5981
5-th percentile6077.3
Q16616.5
median7031
Q38696.5
95-th percentile11216.4
Maximum11971
Range5990
Interquartile range (IQR)2080

Descriptive statistics

Standard deviation1677.6076
Coefficient of variation (CV)0.21741076
Kurtosis0.90866805
Mean7716.3043
Median Absolute Deviation (MAD)852
Skewness1.2693342
Sum177475
Variance2814367.2
MonotonicityNot monotonic
2024-03-23T07:28:51.353152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
7925 1
 
4.3%
11971 1
 
4.3%
5981 1
 
4.3%
6170 1
 
4.3%
6936 1
 
4.3%
6833 1
 
4.3%
6179 1
 
4.3%
6067 1
 
4.3%
7580 1
 
4.3%
6467 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
5981 1
4.3%
6067 1
4.3%
6170 1
4.3%
6179 1
4.3%
6467 1
4.3%
6543 1
4.3%
6690 1
4.3%
6704 1
4.3%
6773 1
4.3%
6833 1
4.3%
ValueCountFrequency (%)
11971 1
4.3%
11385 1
4.3%
9699 1
4.3%
9450 1
4.3%
9264 1
4.3%
9051 1
4.3%
8342 1
4.3%
7925 1
4.3%
7580 1
4.3%
7356 1
4.3%

동물약품
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1531
Minimum47
Maximum7615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:51.706182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile74.8
Q1165.75
median311
Q32270
95-th percentile5755.75
Maximum7615
Range7568
Interquartile range (IQR)2104.25

Descriptive statistics

Standard deviation2077.2909
Coefficient of variation (CV)1.3568197
Kurtosis2.6828305
Mean1531
Median Absolute Deviation (MAD)250.5
Skewness1.7457254
Sum33682
Variance4315137.6
MonotonicityNot monotonic
2024-03-23T07:28:51.950399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1221 1
 
4.3%
183 1
 
4.3%
47 1
 
4.3%
90 1
 
4.3%
160 1
 
4.3%
133 1
 
4.3%
281 1
 
4.3%
315 1
 
4.3%
256 1
 
4.3%
93 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
47 1
4.3%
74 1
4.3%
90 1
4.3%
93 1
4.3%
133 1
4.3%
160 1
4.3%
183 1
4.3%
202 1
4.3%
256 1
4.3%
281 1
4.3%
ValueCountFrequency (%)
7615 1
4.3%
5856 1
4.3%
3851 1
4.3%
3527 1
4.3%
2835 1
4.3%
2432 1
4.3%
1784 1
4.3%
1758 1
4.3%
1221 1
4.3%
662 1
4.3%

농약
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7639.2174
Minimum223
Maximum28245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:52.224243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum223
5-th percentile242.2
Q12481
median4884
Q39530.5
95-th percentile23659.1
Maximum28245
Range28022
Interquartile range (IQR)7049.5

Descriptive statistics

Standard deviation7846.5951
Coefficient of variation (CV)1.0271465
Kurtosis1.2692697
Mean7639.2174
Median Absolute Deviation (MAD)3767
Skewness1.374806
Sum175702
Variance61569054
MonotonicityNot monotonic
2024-03-23T07:28:52.636335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4884 1
 
4.3%
17279 1
 
4.3%
383 1
 
4.3%
235 1
 
4.3%
307 1
 
4.3%
223 1
 
4.3%
324 1
 
4.3%
2315 1
 
4.3%
2647 1
 
4.3%
3247 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
223 1
4.3%
235 1
4.3%
307 1
4.3%
324 1
4.3%
383 1
4.3%
2315 1
4.3%
2647 1
4.3%
2835 1
4.3%
3247 1
4.3%
4462 1
4.3%
ValueCountFrequency (%)
28245 1
4.3%
24124 1
4.3%
19475 1
4.3%
17279 1
4.3%
11867 1
4.3%
10410 1
4.3%
8651 1
4.3%
8643 1
4.3%
8327 1
4.3%
6519 1
4.3%

일반성분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean4488.3636
Minimum1232
Maximum7344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:53.003249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1232
5-th percentile2362.25
Q13825.75
median4377.5
Q35173.75
95-th percentile6907.25
Maximum7344
Range6112
Interquartile range (IQR)1348

Descriptive statistics

Standard deviation1540.6298
Coefficient of variation (CV)0.34324978
Kurtosis-0.10350984
Mean4488.3636
Median Absolute Deviation (MAD)742
Skewness-0.011044395
Sum98744
Variance2373540.2
MonotonicityNot monotonic
2024-03-23T07:28:53.334352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1232 1
 
4.3%
4139 1
 
4.3%
6429 1
 
4.3%
7344 1
 
4.3%
6920 1
 
4.3%
6665 1
 
4.3%
5256 1
 
4.3%
3987 1
 
4.3%
4007 1
 
4.3%
3772 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
1232 1
4.3%
2359 1
4.3%
2424 1
4.3%
3111 1
4.3%
3114 1
4.3%
3772 1
4.3%
3987 1
4.3%
4007 1
4.3%
4139 1
4.3%
4150 1
4.3%
ValueCountFrequency (%)
7344 1
4.3%
6920 1
4.3%
6665 1
4.3%
6429 1
4.3%
5791 1
4.3%
5256 1
4.3%
4927 1
4.3%
4885 1
4.3%
4793 1
4.3%
4684 1
4.3%

광물질
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1274.8261
Minimum247
Maximum2921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:53.713312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum247
5-th percentile344.5
Q1715
median1134
Q31747
95-th percentile2339.3
Maximum2921
Range2674
Interquartile range (IQR)1032

Descriptive statistics

Standard deviation711.7309
Coefficient of variation (CV)0.55829647
Kurtosis-0.25188249
Mean1274.8261
Median Absolute Deviation (MAD)520
Skewness0.55077639
Sum29321
Variance506560.88
MonotonicityNot monotonic
2024-03-23T07:28:54.088296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
247 1
 
4.3%
349 1
 
4.3%
1696 1
 
4.3%
1999 1
 
4.3%
2324 1
 
4.3%
2921 1
 
4.3%
2341 1
 
4.3%
1969 1
 
4.3%
1798 1
 
4.3%
1549 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
247 1
4.3%
344 1
4.3%
349 1
4.3%
565 1
4.3%
614 1
4.3%
619 1
4.3%
811 1
4.3%
920 1
4.3%
1013 1
4.3%
1072 1
4.3%
ValueCountFrequency (%)
2921 1
4.3%
2341 1
4.3%
2324 1
4.3%
1999 1
4.3%
1969 1
4.3%
1798 1
4.3%
1696 1
4.3%
1549 1
4.3%
1385 1
4.3%
1283 1
4.3%

보조제
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean841.86364
Minimum292
Maximum1790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:54.305343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum292
5-th percentile299.5
Q1462
median890.5
Q31161.25
95-th percentile1363.65
Maximum1790
Range1498
Interquartile range (IQR)699.25

Descriptive statistics

Standard deviation432.13846
Coefficient of variation (CV)0.5133117
Kurtosis-0.83743602
Mean841.86364
Median Absolute Deviation (MAD)386.5
Skewness0.32475817
Sum18521
Variance186743.65
MonotonicityNot monotonic
2024-03-23T07:28:54.526592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
458 1
 
4.3%
742 1
 
4.3%
1182 1
 
4.3%
1365 1
 
4.3%
1273 1
 
4.3%
1338 1
 
4.3%
1790 1
 
4.3%
1092 1
 
4.3%
1099 1
 
4.3%
902 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
292 1
4.3%
298 1
4.3%
328 1
4.3%
356 1
4.3%
368 1
4.3%
458 1
4.3%
474 1
4.3%
500 1
4.3%
533 1
4.3%
742 1
4.3%
ValueCountFrequency (%)
1790 1
4.3%
1365 1
4.3%
1338 1
4.3%
1273 1
4.3%
1272 1
4.3%
1182 1
4.3%
1099 1
4.3%
1092 1
4.3%
1064 1
4.3%
916 1
4.3%

미생물
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.65217
Minimum8
Maximum418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:54.969589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14.1
Q129.5
median74
Q3254
95-th percentile352.4
Maximum418
Range410
Interquartile range (IQR)224.5

Descriptive statistics

Standard deviation134.51381
Coefficient of variation (CV)0.98435176
Kurtosis-0.92462954
Mean136.65217
Median Absolute Deviation (MAD)59
Skewness0.78392177
Sum3143
Variance18093.964
MonotonicityNot monotonic
2024-03-23T07:28:55.363056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
30 2
 
8.7%
53 1
 
4.3%
49 1
 
4.3%
8 1
 
4.3%
24 1
 
4.3%
15 1
 
4.3%
17 1
 
4.3%
14 1
 
4.3%
29 1
 
4.3%
76 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
8 1
4.3%
14 1
4.3%
15 1
4.3%
17 1
4.3%
24 1
4.3%
29 1
4.3%
30 2
8.7%
44 1
4.3%
49 1
4.3%
53 1
4.3%
ValueCountFrequency (%)
418 1
4.3%
354 1
4.3%
338 1
4.3%
324 1
4.3%
273 1
4.3%
272 1
4.3%
236 1
4.3%
199 1
4.3%
191 1
4.3%
76 1
4.3%

기타성분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean365.54545
Minimum52
Maximum648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:28:55.565481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile77.95
Q1257.5
median395
Q3474.75
95-th percentile593.25
Maximum648
Range596
Interquartile range (IQR)217.25

Descriptive statistics

Standard deviation173.19813
Coefficient of variation (CV)0.47380738
Kurtosis-0.70044932
Mean365.54545
Median Absolute Deviation (MAD)112.5
Skewness-0.41412442
Sum8042
Variance29997.593
MonotonicityNot monotonic
2024-03-23T07:28:55.918701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
77 1
 
4.3%
318 1
 
4.3%
357 1
 
4.3%
478 1
 
4.3%
595 1
 
4.3%
465 1
 
4.3%
413 1
 
4.3%
523 1
 
4.3%
560 1
 
4.3%
548 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
52 1
4.3%
77 1
4.3%
96 1
4.3%
111 1
4.3%
215 1
4.3%
244 1
4.3%
298 1
4.3%
318 1
4.3%
357 1
4.3%
376 1
4.3%
ValueCountFrequency (%)
648 1
4.3%
595 1
4.3%
560 1
4.3%
548 1
4.3%
523 1
4.3%
478 1
4.3%
465 1
4.3%
458 1
4.3%
420 1
4.3%
413 1
4.3%

Interactions

2024-03-23T07:28:45.316171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:22.587582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:24.731692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:27.115083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:29.492575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:31.775105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:34.276649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:36.755196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:39.519691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:41.925828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:45.670889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:22.818179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:24.899853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:27.350064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:29.639332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:32.005071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:34.470573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:36.988240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:39.732260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:42.148826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:46.004283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:22.953894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:25.087740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:27.577784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:29.888522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:32.233749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:34.747494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:37.215749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:39.991117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:42.370321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:46.364247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:23.257059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:25.330496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:27.826820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:30.160304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:32.479007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:34.997622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:37.459545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:40.228309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:42.610173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:46.728971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:23.410958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:25.592704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:28.082817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:30.437801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:32.727161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:35.266804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:37.706042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:40.487743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:42.905730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:46.952210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:23.548326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:25.832665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:28.326356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:30.679154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:32.956782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:35.515402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:37.940549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:40.718524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:43.206544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:47.230682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:23.998843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:26.088702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:28.574735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:30.928564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:33.228404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:35.765667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:38.381539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:40.888341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:43.718665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:47.486388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:24.137701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:26.358978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:28.815412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:31.173720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:33.505994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:36.006252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:38.614824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:41.162586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:44.038980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:47.754600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:24.333184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:26.634897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:29.084579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:31.391217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:33.757006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:36.257949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:38.878115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:41.415603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:44.583410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:48.013914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:24.524096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:26.866313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:29.342094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:31.595886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:33.982483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:36.517893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:39.115526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:41.679516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:44.996390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:28:56.182922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
연도1.0000.7580.3490.6870.6730.7480.7890.8360.4220.731
합계0.7581.0000.8820.9230.8950.5800.0000.0000.7770.632
유해물질0.3490.8821.0000.7990.6140.4070.4090.3580.7810.118
동물약품0.6870.9230.7991.0000.9550.3030.0000.0000.9430.560
농약0.6730.8950.6140.9551.0000.0000.8120.0000.9350.773
일반성분0.7480.5800.4070.3030.0001.0000.6690.5640.6140.475
광물질0.7890.0000.4090.0000.8120.6691.0000.6380.0000.724
보조제0.8360.0000.3580.0000.0000.5640.6381.0000.0000.000
미생물0.4220.7770.7810.9430.9350.6140.0000.0001.0000.710
기타성분0.7310.6320.1180.5600.7730.4750.7240.0000.7101.000
2024-03-23T07:28:56.472698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
연도1.0000.6770.7940.8390.887-0.683-0.933-0.8680.745-0.726
합계0.6771.0000.7850.6940.820-0.252-0.570-0.6870.640-0.436
유해물질0.7940.7851.0000.7350.769-0.589-0.738-0.7910.675-0.625
동물약품0.8390.6940.7351.0000.799-0.486-0.735-0.7780.720-0.562
농약0.8870.8200.7690.7991.000-0.639-0.849-0.8880.795-0.626
일반성분-0.683-0.252-0.589-0.486-0.6391.0000.7380.632-0.5790.546
광물질-0.933-0.570-0.738-0.735-0.8490.7381.0000.851-0.7150.832
보조제-0.868-0.687-0.791-0.778-0.8880.6320.8511.000-0.7690.652
미생물0.7450.6400.6750.7200.795-0.579-0.715-0.7691.000-0.365
기타성분-0.726-0.436-0.625-0.562-0.6260.5460.8320.652-0.3651.000

Missing values

2024-03-23T07:28:48.324360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:28:48.781988image/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.
2024-03-23T07:28:49.158044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
020221609779251221488412322474585377
120213563611971283517279235934947427396
22020425479264585624124242434429219152
3201951870113857615282453111619356324215
420183849196993527194754200565368354111
520172960890512432118674684614298418244
62016286529450385186434885920328199376
720152552883421784104103114811533236298
820142483166901758865149271385500272648
92013185946543662283557911013127275403
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
13200919192735620244624555126987949420
14200818273677393471337721134902338548
152007172596467256324740071549109974560
162006180187580315264739871798109276523
172005181206067281231552561969179029413
18200417459617913332466652341133814465
19200318942683316022369202921127317595
2020021885969369030773442324136515478
2120011644361704723564291999118224357
22200080685981<NA>383<NA>1696<NA>8<NA>