Overview

Dataset statistics

Number of variables10
Number of observations24
Missing cells4
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory95.5 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 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 7 other fieldsHigh correlation
동물약품 has 1 (4.2%) missing valuesMissing
일반성분 has 1 (4.2%) missing valuesMissing
보조제 has 1 (4.2%) missing valuesMissing
기타성분 has 1 (4.2%) 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 07:30:10.605422
Analysis finished2024-03-23 07:30:35.842731
Duration25.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.5
Minimum2000
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:36.019427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001.15
Q12005.75
median2011.5
Q32017.25
95-th percentile2021.85
Maximum2023
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation7.0710678
Coefficient of variation (CV)0.0035153208
Kurtosis-1.2
Mean2011.5
Median Absolute Deviation (MAD)6
Skewness0
Sum48276
Variance50
MonotonicityStrictly decreasing
2024-03-23T07:30:36.413672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2023 1
 
4.2%
2010 1
 
4.2%
2000 1
 
4.2%
2001 1
 
4.2%
2002 1
 
4.2%
2003 1
 
4.2%
2004 1
 
4.2%
2005 1
 
4.2%
2006 1
 
4.2%
2007 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
2000 1
4.2%
2001 1
4.2%
2002 1
4.2%
2003 1
4.2%
2004 1
4.2%
2005 1
4.2%
2006 1
4.2%
2007 1
4.2%
2008 1
4.2%
2009 1
4.2%
ValueCountFrequency (%)
2023 1
4.2%
2022 1
4.2%
2021 1
4.2%
2020 1
4.2%
2019 1
4.2%
2018 1
4.2%
2017 1
4.2%
2016 1
4.2%
2015 1
4.2%
2014 1
4.2%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24344.25
Minimum8068
Maximum51870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:36.758183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8068
5-th percentile16565.4
Q118234.75
median19773
Q328323.5
95-th percentile41938.6
Maximum51870
Range43802
Interquartile range (IQR)10088.75

Descriptive statistics

Standard deviation9771.2116
Coefficient of variation (CV)0.40137657
Kurtosis1.70393
Mean24344.25
Median Absolute Deviation (MAD)3374.5
Skewness1.2394108
Sum584262
Variance95476577
MonotonicityNot monotonic
2024-03-23T07:30:36.970088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
27323 1
 
4.2%
18789 1
 
4.2%
8068 1
 
4.2%
16443 1
 
4.2%
18859 1
 
4.2%
18942 1
 
4.2%
17459 1
 
4.2%
18120 1
 
4.2%
18018 1
 
4.2%
17259 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
8068 1
4.2%
16443 1
4.2%
17259 1
4.2%
17459 1
4.2%
18018 1
4.2%
18120 1
4.2%
18273 1
4.2%
18594 1
4.2%
18789 1
4.2%
18859 1
4.2%
ValueCountFrequency (%)
51870 1
4.2%
42547 1
4.2%
38491 1
4.2%
35636 1
4.2%
29608 1
4.2%
28652 1
4.2%
28214 1
4.2%
27323 1
4.2%
25528 1
4.2%
24831 1
4.2%

유해물질
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8103.2917
Minimum5981
Maximum12546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:37.170835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5981
5-th percentile6082.45
Q16653.25
median7054.5
Q39310.5
95-th percentile12321.2
Maximum12546
Range6565
Interquartile range (IQR)2657.25

Descriptive statistics

Standard deviation2120.114
Coefficient of variation (CV)0.26163614
Kurtosis-0.15487323
Mean8103.2917
Median Absolute Deviation (MAD)880
Skewness1.0613721
Sum194479
Variance4494883.2
MonotonicityNot monotonic
2024-03-23T07:30:37.394579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
12383 1
 
4.2%
6704 1
 
4.2%
5981 1
 
4.2%
6170 1
 
4.2%
6936 1
 
4.2%
6833 1
 
4.2%
6179 1
 
4.2%
6067 1
 
4.2%
7580 1
 
4.2%
6467 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
5981 1
4.2%
6067 1
4.2%
6170 1
4.2%
6179 1
4.2%
6467 1
4.2%
6543 1
4.2%
6690 1
4.2%
6704 1
4.2%
6773 1
4.2%
6833 1
4.2%
ValueCountFrequency (%)
12546 1
4.2%
12383 1
4.2%
11971 1
4.2%
11385 1
4.2%
9699 1
4.2%
9450 1
4.2%
9264 1
4.2%
9051 1
4.2%
8342 1
4.2%
7580 1
4.2%

동물약품
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean1513.7391
Minimum47
Maximum7615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:37.683743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile75.6
Q1171.5
median307
Q32285.5
95-th percentile5655.5
Maximum7615
Range7568
Interquartile range (IQR)2114

Descriptive statistics

Standard deviation2051.7601
Coefficient of variation (CV)1.3554251
Kurtosis2.7252916
Mean1513.7391
Median Absolute Deviation (MAD)233
Skewness1.7378744
Sum34816
Variance4209719.4
MonotonicityNot monotonic
2024-03-23T07:30:37.975859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
216 1
 
4.2%
2139 1
 
4.2%
47 1
 
4.2%
90 1
 
4.2%
160 1
 
4.2%
133 1
 
4.2%
281 1
 
4.2%
315 1
 
4.2%
256 1
 
4.2%
93 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
47 1
4.2%
74 1
4.2%
90 1
4.2%
93 1
4.2%
133 1
4.2%
160 1
4.2%
183 1
4.2%
202 1
4.2%
216 1
4.2%
256 1
4.2%
ValueCountFrequency (%)
7615 1
4.2%
5856 1
4.2%
3851 1
4.2%
3527 1
4.2%
2835 1
4.2%
2432 1
4.2%
2139 1
4.2%
1784 1
4.2%
1758 1
4.2%
662 1
4.2%

농약
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8000.7083
Minimum223
Maximum28245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:38.194005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum223
5-th percentile245.8
Q12564
median6053
Q310535.75
95-th percentile23426.65
Maximum28245
Range28022
Interquartile range (IQR)7971.75

Descriptive statistics

Standard deviation7693.9131
Coefficient of variation (CV)0.96165399
Kurtosis1.1038946
Mean8000.7083
Median Absolute Deviation (MAD)4295
Skewness1.24325
Sum192017
Variance59196299
MonotonicityNot monotonic
2024-03-23T07:30:38.717687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10913 1
 
4.2%
5587 1
 
4.2%
383 1
 
4.2%
235 1
 
4.2%
307 1
 
4.2%
223 1
 
4.2%
324 1
 
4.2%
2315 1
 
4.2%
2647 1
 
4.2%
3247 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
223 1
4.2%
235 1
4.2%
307 1
4.2%
324 1
4.2%
383 1
4.2%
2315 1
4.2%
2647 1
4.2%
2835 1
4.2%
3247 1
4.2%
4462 1
4.2%
ValueCountFrequency (%)
28245 1
4.2%
24124 1
4.2%
19475 1
4.2%
17279 1
4.2%
11867 1
4.2%
10913 1
4.2%
10410 1
4.2%
10286 1
4.2%
8651 1
4.2%
8643 1
4.2%

일반성분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4436.8696
Minimum2121
Maximum7344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:39.096333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2121
5-th percentile2364.6
Q13443
median4200
Q35091.5
95-th percentile6894.5
Maximum7344
Range5223
Interquartile range (IQR)1648.5

Descriptive statistics

Standard deviation1493.6639
Coefficient of variation (CV)0.33664814
Kurtosis-0.56769474
Mean4436.8696
Median Absolute Deviation (MAD)1056
Skewness0.28629424
Sum102048
Variance2231031.8
MonotonicityNot monotonic
2024-03-23T07:30:39.477131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2415 1
 
4.2%
2121 1
 
4.2%
6429 1
 
4.2%
7344 1
 
4.2%
6920 1
 
4.2%
6665 1
 
4.2%
5256 1
 
4.2%
3987 1
 
4.2%
4007 1
 
4.2%
3772 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
2121 1
4.2%
2359 1
4.2%
2415 1
4.2%
2424 1
4.2%
3111 1
4.2%
3114 1
4.2%
3772 1
4.2%
3987 1
4.2%
4007 1
4.2%
4139 1
4.2%
ValueCountFrequency (%)
7344 1
4.2%
6920 1
4.2%
6665 1
4.2%
6429 1
4.2%
5791 1
4.2%
5256 1
4.2%
4927 1
4.2%
4885 1
4.2%
4793 1
4.2%
4684 1
4.2%

광물질
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1252.9167
Minimum344
Maximum2921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:39.842304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum344
5-th percentile358.75
Q1617.75
median1116.5
Q31721.5
95-th percentile2338.45
Maximum2921
Range2577
Interquartile range (IQR)1103.75

Descriptive statistics

Standard deviation700.85109
Coefficient of variation (CV)0.55937566
Kurtosis-0.1999341
Mean1252.9167
Median Absolute Deviation (MAD)518.5
Skewness0.66780628
Sum30070
Variance491192.25
MonotonicityNot monotonic
2024-03-23T07:30:40.263608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
582 1
 
4.2%
1072 1
 
4.2%
1696 1
 
4.2%
1999 1
 
4.2%
2324 1
 
4.2%
2921 1
 
4.2%
2341 1
 
4.2%
1969 1
 
4.2%
1798 1
 
4.2%
1549 1
 
4.2%
Other values (14) 14
58.3%
ValueCountFrequency (%)
344 1
4.2%
349 1
4.2%
414 1
4.2%
565 1
4.2%
582 1
4.2%
614 1
4.2%
619 1
4.2%
811 1
4.2%
920 1
4.2%
1013 1
4.2%
ValueCountFrequency (%)
2921 1
4.2%
2341 1
4.2%
2324 1
4.2%
1999 1
4.2%
1969 1
4.2%
1798 1
4.2%
1696 1
4.2%
1549 1
4.2%
1385 1
4.2%
1283 1
4.2%

보조제
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean826.43478
Minimum292
Maximum1790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:40.588403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum292
5-th percentile301
Q1423
median879
Q31140.5
95-th percentile1362.3
Maximum1790
Range1498
Interquartile range (IQR)717.5

Descriptive statistics

Standard deviation429.68538
Coefficient of variation (CV)0.51992655
Kurtosis-0.79497975
Mean826.43478
Median Absolute Deviation (MAD)393
Skewness0.39020841
Sum19008
Variance184629.53
MonotonicityNot monotonic
2024-03-23T07:30:40.951131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
573 1
 
4.2%
372 1
 
4.2%
1182 1
 
4.2%
1365 1
 
4.2%
1273 1
 
4.2%
1338 1
 
4.2%
1790 1
 
4.2%
1092 1
 
4.2%
1099 1
 
4.2%
902 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
292 1
4.2%
298 1
4.2%
328 1
4.2%
356 1
4.2%
368 1
4.2%
372 1
4.2%
474 1
4.2%
500 1
4.2%
533 1
4.2%
573 1
4.2%
ValueCountFrequency (%)
1790 1
4.2%
1365 1
4.2%
1338 1
4.2%
1273 1
4.2%
1272 1
4.2%
1182 1
4.2%
1099 1
4.2%
1092 1
4.2%
1064 1
4.2%
916 1
4.2%

미생물
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.66667
Minimum8
Maximum418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:41.437734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14.15
Q129.75
median75.5
Q3245
95-th percentile351.6
Maximum418
Range410
Interquartile range (IQR)215.25

Descriptive statistics

Standard deviation130.56089
Coefficient of variation (CV)0.9151464
Kurtosis-0.91285559
Mean142.66667
Median Absolute Deviation (MAD)64.5
Skewness0.6796142
Sum3424
Variance17046.145
MonotonicityNot monotonic
2024-03-23T07:30:41.756752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
30 2
 
8.3%
170 1
 
4.2%
49 1
 
4.2%
8 1
 
4.2%
24 1
 
4.2%
15 1
 
4.2%
17 1
 
4.2%
14 1
 
4.2%
29 1
 
4.2%
76 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
8 1
4.2%
14 1
4.2%
15 1
4.2%
17 1
4.2%
24 1
4.2%
29 1
4.2%
30 2
8.3%
44 1
4.2%
49 1
4.2%
74 1
4.2%
ValueCountFrequency (%)
418 1
4.2%
354 1
4.2%
338 1
4.2%
324 1
4.2%
273 1
4.2%
272 1
4.2%
236 1
4.2%
199 1
4.2%
191 1
4.2%
170 1
4.2%

기타성분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean356.86957
Minimum52
Maximum648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-23T07:30:41.992817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile73.5
Q1229.5
median387
Q3471.5
95-th percentile591.5
Maximum648
Range596
Interquartile range (IQR)242

Descriptive statistics

Standard deviation174.40504
Coefficient of variation (CV)0.48870808
Kurtosis-0.87185006
Mean356.86957
Median Absolute Deviation (MAD)136
Skewness-0.30428122
Sum8208
Variance30417.119
MonotonicityNot monotonic
2024-03-23T07:30:42.194121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
71 1
 
4.2%
172 1
 
4.2%
357 1
 
4.2%
478 1
 
4.2%
595 1
 
4.2%
465 1
 
4.2%
413 1
 
4.2%
523 1
 
4.2%
560 1
 
4.2%
548 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
52 1
4.2%
71 1
4.2%
96 1
4.2%
111 1
4.2%
172 1
4.2%
215 1
4.2%
244 1
4.2%
298 1
4.2%
318 1
4.2%
357 1
4.2%
ValueCountFrequency (%)
648 1
4.2%
595 1
4.2%
560 1
4.2%
548 1
4.2%
523 1
4.2%
478 1
4.2%
465 1
4.2%
458 1
4.2%
420 1
4.2%
413 1
4.2%

Interactions

2024-03-23T07:30:32.419493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:11.234874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:13.339904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:15.728166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:17.836958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:20.225733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:22.122421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:24.380592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:27.552232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:30.352322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:32.564971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:11.511597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:13.585914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:15.943949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:18.076112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:20.364113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:22.330363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:24.515700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:27.892160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:30.579284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:32.820858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:11.647384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:13.822701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:16.084738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:18.340223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:20.512368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:22.599555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:24.660559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:28.274247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:30.769657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:33.062409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:11.804178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:14.068134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:16.266134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:18.638172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:20.773700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:22.845119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:24.971766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:28.532302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:31.098015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:33.311861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:11.954379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:14.319303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:16.433697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:18.898350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:21.062789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:23.140873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:25.266856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:28.818140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:31.359507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:33.550431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:12.133830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:14.469126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:16.599551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:19.137898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:21.203336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:23.291412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:25.661633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:29.052416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:31.606089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:33.784574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:12.373403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:14.709867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:16.840935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:19.378721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:21.375673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:23.431649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:25.898717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:29.304413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:31.800052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:34.057693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:12.608472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:14.951912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:17.082122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:19.670357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:21.524363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:23.599777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:26.304227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:29.550639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:31.945323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:34.240475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:12.856690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:15.208895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:17.345195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:19.902072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:21.686245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:23.771785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:26.889117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:29.836354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:32.124793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:34.486200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:13.079474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:15.484947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:17.587669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:20.074468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:21.900899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:24.159573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:27.258450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:30.106847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:30:32.253928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:30:42.367305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
연도1.0000.6640.6110.5720.7940.6790.7160.6700.5920.481
합계0.6641.0000.7230.9530.8920.4370.3090.0000.7840.265
유해물질0.6110.7231.0000.7610.9010.6730.5330.0000.9220.711
동물약품0.5720.9530.7611.0000.8760.0000.0000.0000.8480.307
농약0.7940.8920.9010.8761.0000.2530.5110.3260.8250.687
일반성분0.6790.4370.6730.0000.2531.0000.7390.6520.5540.675
광물질0.7160.3090.5330.0000.5110.7391.0000.5980.0000.849
보조제0.6700.0000.0000.0000.3260.6520.5981.0000.0000.279
미생물0.5920.7840.9220.8480.8250.5540.0000.0001.0000.748
기타성분0.4810.2650.7110.3070.6870.6750.8490.2790.7481.000
2024-03-23T07:30:42.680513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
연도1.0000.8740.8430.7580.924-0.719-0.929-0.8400.759-0.752
합계0.8741.0000.8600.7710.887-0.489-0.804-0.8570.706-0.679
유해물질0.8430.8601.0000.6330.777-0.685-0.779-0.7580.644-0.681
동물약품0.7580.7710.6331.0000.776-0.455-0.712-0.7720.711-0.523
농약0.9240.8870.7770.7761.000-0.709-0.910-0.9060.821-0.722
일반성분-0.719-0.489-0.685-0.455-0.7091.0000.7560.616-0.5910.585
광물질-0.929-0.804-0.779-0.712-0.9100.7561.0000.844-0.7480.847
보조제-0.840-0.857-0.758-0.772-0.9060.6160.8441.000-0.8150.645
미생물0.7590.7060.6440.7110.821-0.591-0.748-0.8151.000-0.428
기타성분-0.752-0.679-0.681-0.523-0.7220.5850.8470.645-0.4281.000

Missing values

2024-03-23T07:30:34.805901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:30:35.305520image/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.
2024-03-23T07:30:35.622477image/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
02023273231238321610913241558257317071
1202228214125462139102862121414372164172
220213563611971283517279235934947427396
32020425479264585624124242434429219152
4201951870113857615282453111619356324215
520183849196993527194754200565368354111
620172960890512432118674684614298418244
72016286529450385186434885920328199376
820152552883421784104103114811533236298
920142483166901758865149271385500272648
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
14200919192735620244624555126987949420
15200818273677393471337721134902338548
162007172596467256324740071549109974560
172006180187580315264739871798109276523
182005181206067281231552561969179029413
19200417459617913332466652341133814465
20200318942683316022369202921127317595
2120021885969369030773442324136515478
2220011644361704723564291999118224357
23200080685981<NA>383<NA>1696<NA>8<NA>