Overview

Dataset statistics

Number of variables10
Number of observations23
Missing cells9
Missing cells (%)3.9%
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 7 other fieldsHigh correlation
유해물질 is highly overall correlated with 연도 and 7 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 3 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 6 other fieldsHigh correlation
기타성분 is highly overall correlated with 연도 and 6 other fieldsHigh correlation
동물약품 has 2 (8.7%) missing valuesMissing
농약 has 1 (4.3%) missing valuesMissing
일반성분 has 1 (4.3%) missing valuesMissing
광물질 has 1 (4.3%) missing valuesMissing
보조제 has 2 (8.7%) missing valuesMissing
기타성분 has 2 (8.7%) missing valuesMissing
연도 has unique valuesUnique
합계 has unique valuesUnique
유해물질 has unique valuesUnique

Reproduction

Analysis started2024-03-23 07:29:34.304520
Analysis finished2024-03-23 07:30:00.610656
Duration26.31 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
2024-03-23T07:30:00.877013image/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
2024-03-23T07:30:01.316986image/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%
Mean22994.739
Minimum154
Maximum51870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:01.704272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum154
5-th percentile8905.5
Q118069
median18942
Q327090
95-th percentile42141.4
Maximum51870
Range51716
Interquartile range (IQR)9021

Descriptive statistics

Standard deviation11109.771
Coefficient of variation (CV)0.48314405
Kurtosis1.4697181
Mean22994.739
Median Absolute Deviation (MAD)2499
Skewness0.76924633
Sum528879
Variance1.2342702 × 108
MonotonicityNot monotonic
2024-03-23T07:30:02.085580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
154 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 (%)
154 1
4.3%
8068 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%
Mean7376.6087
Minimum112
Maximum11971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:02.429770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum112
5-th percentile5989.6
Q16505
median6936
Q38696.5
95-th percentile11216.4
Maximum11971
Range11859
Interquartile range (IQR)2191.5

Descriptive statistics

Standard deviation2306.5512
Coefficient of variation (CV)0.3126845
Kurtosis4.0792229
Mean7376.6087
Median Absolute Deviation (MAD)757
Skewness-0.80801672
Sum169662
Variance5320178.5
MonotonicityNot monotonic
2024-03-23T07:30:02.751173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
112 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 (%)
112 1
4.3%
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%
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%
7580 1
4.3%
7356 1
4.3%
7078 1
4.3%

동물약품
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean1545.7619
Minimum47
Maximum7615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:03.013687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile74
Q1160
median307
Q32432
95-th percentile5856
Maximum7615
Range7568
Interquartile range (IQR)2272

Descriptive statistics

Standard deviation2127.407
Coefficient of variation (CV)1.3762838
Kurtosis2.4026219
Mean1545.7619
Median Absolute Deviation (MAD)233
Skewness1.6918243
Sum32461
Variance4525860.7
MonotonicityNot monotonic
2024-03-23T07:30:03.391177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5856 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%
202 1
 
4.3%
Other values (11) 11
47.8%
(Missing) 2
 
8.7%
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%
662 1
4.3%
315 1
4.3%

농약
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean7764.4545
Minimum223
Maximum28245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:03.745283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum223
5-th percentile238.6
Q12398
median5150
Q39970.25
95-th percentile23891.55
Maximum28245
Range28022
Interquartile range (IQR)7572.25

Descriptive statistics

Standard deviation8007.6836
Coefficient of variation (CV)1.031326
Kurtosis1.0412361
Mean7764.4545
Median Absolute Deviation (MAD)4134
Skewness1.3114991
Sum170818
Variance64122996
MonotonicityNot monotonic
2024-03-23T07:30:04.102264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
17279 1
 
4.3%
4462 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 (12) 12
52.2%
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%
Mean4433.6364
Minimum28
Maximum7344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:04.443830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile2362.25
Q13825.75
median4377.5
Q35173.75
95-th percentile6907.25
Maximum7344
Range7316
Interquartile range (IQR)1348

Descriptive statistics

Standard deviation1677.1488
Coefficient of variation (CV)0.37827839
Kurtosis1.1067372
Mean4433.6364
Median Absolute Deviation (MAD)742
Skewness-0.50273562
Sum97540
Variance2812828.2
MonotonicityNot monotonic
2024-03-23T07:30:04.768240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
28 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 (%)
28 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  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1321.5455
Minimum344
Maximum2921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:05.270185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum344
5-th percentile359.8
Q1838.25
median1201.5
Q31772.5
95-th percentile2340.15
Maximum2921
Range2577
Interquartile range (IQR)934.25

Descriptive statistics

Standard deviation691.44064
Coefficient of variation (CV)0.5232061
Kurtosis-0.19836529
Mean1321.5455
Median Absolute Deviation (MAD)538.5
Skewness0.58076623
Sum29074
Variance478090.16
MonotonicityNot monotonic
2024-03-23T07:30:05.700733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
349 1
 
4.3%
1269 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 (12) 12
52.2%
ValueCountFrequency (%)
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%
1099 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 

Distinct21
Distinct (%)100.0%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean860.14286
Minimum292
Maximum1790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:06.044717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum292
5-th percentile298
Q1474
median902
Q31182
95-th percentile1365
Maximum1790
Range1498
Interquartile range (IQR)708

Descriptive statistics

Standard deviation434.00741
Coefficient of variation (CV)0.50457596
Kurtosis-0.84484593
Mean860.14286
Median Absolute Deviation (MAD)371
Skewness0.23671778
Sum18063
Variance188362.43
MonotonicityNot monotonic
2024-03-23T07:30:06.411593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
292 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%
879 1
 
4.3%
Other values (11) 11
47.8%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
292 1
4.3%
298 1
4.3%
328 1
4.3%
356 1
4.3%
368 1
4.3%
474 1
4.3%
500 1
4.3%
533 1
4.3%
742 1
4.3%
879 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 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.95652
Minimum8
Maximum418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:06.773033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q126.5
median74
Q3254
95-th percentile352.4
Maximum418
Range410
Interquartile range (IQR)227.5

Descriptive statistics

Standard deviation135.85536
Coefficient of variation (CV)1.0066602
Kurtosis-0.9467726
Mean134.95652
Median Absolute Deviation (MAD)60
Skewness0.77692758
Sum3104
Variance18456.68
MonotonicityNot monotonic
2024-03-23T07:30:07.130332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
14 2
 
8.7%
30 2
 
8.7%
49 1
 
4.3%
8 1
 
4.3%
24 1
 
4.3%
15 1
 
4.3%
17 1
 
4.3%
29 1
 
4.3%
76 1
 
4.3%
74 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
8 1
4.3%
14 2
8.7%
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%
74 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 

Distinct21
Distinct (%)100.0%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean379.28571
Minimum52
Maximum648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:30:07.468105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile96
Q1298
median403
Q3478
95-th percentile595
Maximum648
Range596
Interquartile range (IQR)180

Descriptive statistics

Standard deviation164.73104
Coefficient of variation (CV)0.43431912
Kurtosis-0.38185834
Mean379.28571
Median Absolute Deviation (MAD)105
Skewness-0.48805173
Sum7965
Variance27136.314
MonotonicityNot monotonic
2024-03-23T07:30:07.830754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
52 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%
420 1
 
4.3%
Other values (11) 11
47.8%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
52 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%
387 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

2024-03-23T07:29:57.432932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:34.940362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:37.355181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:39.773695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:42.142789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:44.265002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:46.381568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:48.837607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:51.102278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:54.569824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:57.630454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:35.175741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:37.590645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:40.035517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:42.318303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:44.582029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:46.619178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:49.068061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:51.335706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:54.894801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:57.850250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:35.409324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:37.821827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:40.414420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:42.501948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:44.865212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:46.864819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:49.286498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:51.555516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:55.153138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:57.999569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:35.647501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:38.068250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:40.565294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:42.753542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:45.022479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:47.110548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:49.574043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:51.849396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:55.453297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:58.193999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:35.904239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:38.292780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:40.754383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:42.914085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:45.233232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:47.357559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:49.783048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:52.210381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:55.963293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:58.421116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:36.137846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:38.553632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:40.989362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:43.072068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:45.468845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:47.592667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:49.936955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:52.604705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:56.198809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:58.647617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:36.384511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:38.800664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:41.163568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:43.251461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:45.650764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:47.848673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:50.074736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:53.153671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:56.464171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:58.882808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:36.614357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:39.026140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:41.615961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:43.491074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:45.837406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:48.130106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:50.232430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:53.517164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:56.726736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:59.138274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:36.873553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:39.286072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:41.775732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:43.749998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:45.994987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:48.373706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:50.562135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:53.846993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:56.988442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:59.399003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:37.097619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:39.508817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:41.923078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:44.017788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:46.140924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:48.644571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:50.847501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:54.258386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:57.247690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:30:08.092581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
연도1.0000.8150.5290.7600.7370.7240.4120.8220.3970.689
합계0.8151.0000.9350.9920.9510.7930.1610.3720.9170.614
유해물질0.5290.9351.0000.8330.5940.6300.3020.0000.7150.468
동물약품0.7600.9920.8331.0000.8920.5410.0000.0000.8690.656
농약0.7370.9510.5940.8921.0000.4970.6310.0660.9260.812
일반성분0.7240.7930.6300.5410.4971.0000.6680.4530.3090.408
광물질0.4120.1610.3020.0000.6310.6681.0000.4710.0000.858
보조제0.8220.3720.0000.0000.0660.4530.4711.0000.0000.000
미생물0.3970.9170.7150.8690.9260.3090.0000.0001.0000.722
기타성분0.6890.6140.4680.6560.8120.4080.8580.0000.7221.000
2024-03-23T07:30:08.414950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
연도1.0000.6550.5720.8610.949-0.683-0.923-0.8740.614-0.687
합계0.6551.0000.8880.7660.864-0.252-0.776-0.8190.708-0.626
유해물질0.5720.8881.0000.7060.764-0.380-0.732-0.7710.717-0.604
동물약품0.8610.7660.7061.0000.797-0.488-0.749-0.7530.709-0.553
농약0.9490.8640.7640.7971.000-0.703-0.911-0.9090.811-0.664
일반성분-0.683-0.252-0.380-0.488-0.7031.0000.6990.630-0.4370.481
광물질-0.923-0.776-0.732-0.749-0.9110.6991.0000.855-0.7790.808
보조제-0.874-0.819-0.771-0.753-0.9090.6300.8551.000-0.8020.622
미생물0.6140.7080.7170.7090.811-0.437-0.779-0.8021.000-0.416
기타성분-0.687-0.626-0.604-0.553-0.6640.4810.8080.622-0.4161.000

Missing values

2024-03-23T07:29:59.713943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:29:59.981766image/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.
2024-03-23T07:30:00.321756image/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
02022154112<NA><NA>28<NA><NA>14<NA>
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>