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 7 other fieldsHigh correlation
유해물질 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
동물약품 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
농약 is highly overall correlated with 연도 and 8 other fieldsHigh correlation
일반성분 is highly overall correlated with 연도 and 7 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 (4.3%) 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 07:28:58.453981
Analysis finished2024-03-23 07:29:24.726262
Duration26.27 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
2024-03-23T07:29:24.919387image/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
2024-03-23T07:29:25.491929image/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%
Mean24214.739
Minimum8068
Maximum51870
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:25.851902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8068
5-th percentile16524.6
Q118196.5
median19192
Q328433
95-th percentile42141.4
Maximum51870
Range43802
Interquartile range (IQR)10236.5

Descriptive statistics

Standard deviation9969.7327
Coefficient of variation (CV)0.41172166
Kurtosis1.6367567
Mean24214.739
Median Absolute Deviation (MAD)2749
Skewness1.2660727
Sum556939
Variance99395570
MonotonicityNot monotonic
2024-03-23T07:29:26.210598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
28214 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%
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%
18859 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%
28214 1
4.3%
25528 1
4.3%
24831 1
4.3%
23192 1
4.3%

유해물질
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7917.2174
Minimum5981
Maximum12546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:26.591162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5981
5-th percentile6077.3
Q16616.5
median7031
Q39157.5
95-th percentile11912.4
Maximum12546
Range6565
Interquartile range (IQR)2541

Descriptive statistics

Standard deviation1957.1555
Coefficient of variation (CV)0.24720244
Kurtosis0.43607339
Mean7917.2174
Median Absolute Deviation (MAD)852
Skewness1.2089331
Sum182096
Variance3830457.5
MonotonicityNot monotonic
2024-03-23T07:29:26.963758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
12546 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 (%)
12546 1
4.3%
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%

동물약품
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1572.7273
Minimum47
Maximum7615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:27.347883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile74.8
Q1165.75
median311
Q32358.75
95-th percentile5755.75
Maximum7615
Range7568
Interquartile range (IQR)2193

Descriptive statistics

Standard deviation2079.9857
Coefficient of variation (CV)1.3225342
Kurtosis2.4855311
Mean1572.7273
Median Absolute Deviation (MAD)250.5
Skewness1.6742191
Sum34600
Variance4326340.4
MonotonicityNot monotonic
2024-03-23T07:29:27.653920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2139 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%
2139 1
4.3%
1784 1
4.3%
1758 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%
Mean7874.087
Minimum223
Maximum28245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:27.936557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum223
5-th percentile242.2
Q12481
median5587
Q310348
95-th percentile23659.1
Maximum28245
Range28022
Interquartile range (IQR)7867

Descriptive statistics

Standard deviation7841.2217
Coefficient of variation (CV)0.99582615
Kurtosis1.0905262
Mean7874.087
Median Absolute Deviation (MAD)4699
Skewness1.2832603
Sum181104
Variance61484758
MonotonicityNot monotonic
2024-03-23T07:29:28.286533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10286 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%
10286 1
4.3%
8651 1
4.3%
8643 1
4.3%
8327 1
4.3%

일반성분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean4528.7727
Minimum2121
Maximum7344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:28.632621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2121
5-th percentile2362.25
Q13825.75
median4377.5
Q35173.75
95-th percentile6907.25
Maximum7344
Range5223
Interquartile range (IQR)1348

Descriptive statistics

Standard deviation1460.739
Coefficient of variation (CV)0.32254633
Kurtosis-0.47019018
Mean4528.7727
Median Absolute Deviation (MAD)742
Skewness0.25305888
Sum99633
Variance2133758.5
MonotonicityNot monotonic
2024-03-23T07:29:28.927823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2121 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 (%)
2121 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%
Mean1282.087
Minimum344
Maximum2921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:29.161594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum344
5-th percentile355.5
Q1715
median1134
Q31747
95-th percentile2339.3
Maximum2921
Range2577
Interquartile range (IQR)1032

Descriptive statistics

Standard deviation701.5477
Coefficient of variation (CV)0.54719198
Kurtosis-0.23244198
Mean1282.087
Median Absolute Deviation (MAD)520
Skewness0.60568683
Sum29488
Variance492169.17
MonotonicityNot monotonic
2024-03-23T07:29:29.548275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
414 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 (%)
344 1
4.3%
349 1
4.3%
414 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%
Mean837.95455
Minimum292
Maximum1790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:29.892674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum292
5-th percentile299.5
Q1397.5
median890.5
Q31161.25
95-th percentile1363.65
Maximum1790
Range1498
Interquartile range (IQR)763.75

Descriptive statistics

Standard deviation436.1466
Coefficient of variation (CV)0.52048957
Kurtosis-0.87288161
Mean837.95455
Median Absolute Deviation (MAD)386.5
Skewness0.31568368
Sum18435
Variance190223.85
MonotonicityNot monotonic
2024-03-23T07:29:30.300028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
372 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%
372 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%
Mean141.47826
Minimum8
Maximum418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:30.675593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14.1
Q129.5
median75
Q3254
95-th percentile352.4
Maximum418
Range410
Interquartile range (IQR)224.5

Descriptive statistics

Standard deviation133.36241
Coefficient of variation (CV)0.94263538
Kurtosis-0.97677458
Mean141.47826
Median Absolute Deviation (MAD)61
Skewness0.69816027
Sum3254
Variance17785.534
MonotonicityNot monotonic
2024-03-23T07:29:31.059030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
30 2
 
8.7%
164 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%
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%
164 1
4.3%

기타성분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing1
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean369.86364
Minimum52
Maximum648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-03-23T07:29:31.459087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile96.75
Q1257.5
median395
Q3474.75
95-th percentile593.25
Maximum648
Range596
Interquartile range (IQR)217.25

Descriptive statistics

Standard deviation166.72484
Coefficient of variation (CV)0.4507738
Kurtosis-0.65660896
Mean369.86364
Median Absolute Deviation (MAD)112.5
Skewness-0.3601648
Sum8137
Variance27797.171
MonotonicityNot monotonic
2024-03-23T07:29:31.810114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
172 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%
96 1
4.3%
111 1
4.3%
172 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

2024-03-23T07:29:21.280634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:58.859709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:01.220534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:03.390756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:05.724836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:07.849670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:10.266641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:12.400752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:14.976410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:17.561483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:21.564458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:59.083214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:01.435298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:03.625849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:05.941549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:08.076912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:10.417226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:12.627653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:15.213185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:18.051340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:21.829885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:59.303634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:01.625901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:03.879015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:06.198966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:08.261804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:10.669955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:12.856679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:15.478880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:18.425517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:22.089548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:59.554591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:01.832103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:04.142473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:06.439198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:08.418655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:10.857126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:13.139118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:15.734455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:18.817268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:22.343186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:28:59.867673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:02.028845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:04.350557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:06.683528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:08.574220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:11.030292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:13.392462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:15.988658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:19.251051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:22.584299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:00.095702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:02.247520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:04.587267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:06.864512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:08.786882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:11.271840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:13.592278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:16.230709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:19.673796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:22.854459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:00.334542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:02.427659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:04.826112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:07.070410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:09.024460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:11.449756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:13.816497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:16.475912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:20.119370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:23.093963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:00.548122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:02.663097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:05.043686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:07.225256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:09.262529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:11.688420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:14.220781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:16.730043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:20.479436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:23.342758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:00.793782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:02.918996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:05.280658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:07.396976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:09.536929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:11.946217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:14.513302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:17.028298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:20.793349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:23.598197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:01.009435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:03.144192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:05.498389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:07.607784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:10.029000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:12.203951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:14.742987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:17.303554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:29:21.020722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:29:32.002482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
연도1.0000.7260.4380.6500.6650.6810.5770.7970.3640.635
합계0.7261.0000.7150.9610.8910.5520.2940.3620.7650.495
유해물질0.4380.7151.0000.7820.8950.6000.4330.0000.9140.697
동물약품0.6500.9610.7821.0000.8950.0000.0000.0000.8640.461
농약0.6650.8910.8950.8951.0000.4610.5230.0000.8090.738
일반성분0.6810.5520.6000.0000.4611.0000.5670.5680.4800.517
광물질0.5770.2940.4330.0000.5230.5671.0000.5490.0000.817
보조제0.7970.3620.0000.0000.0000.5680.5491.0000.0000.000
미생물0.3640.7650.9140.8640.8090.4800.0000.0001.0000.792
기타성분0.6350.4950.6970.4610.7380.5170.8170.0000.7921.000
2024-03-23T07:29:32.274157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도합계유해물질동물약품농약일반성분광물질보조제미생물기타성분
연도1.0000.8840.8220.8550.934-0.683-0.930-0.8680.784-0.720
합계0.8841.0000.8680.7970.877-0.466-0.791-0.8400.690-0.661
유해물질0.8220.8681.0000.7210.770-0.641-0.760-0.7790.663-0.645
동물약품0.8550.7970.7211.0000.816-0.508-0.765-0.7830.721-0.596
농약0.9340.8770.7700.8161.000-0.705-0.909-0.9090.819-0.697
일반성분-0.683-0.466-0.641-0.508-0.7051.0000.7350.632-0.6130.534
광물질-0.930-0.791-0.760-0.765-0.9090.7351.0000.855-0.7610.832
보조제-0.868-0.840-0.779-0.783-0.9090.6320.8551.000-0.8040.652
미생물0.7840.6900.6630.7210.819-0.613-0.761-0.8041.000-0.430
기타성분-0.720-0.661-0.645-0.596-0.6970.5340.8320.652-0.4301.000

Missing values

2024-03-23T07:29:23.938307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:29:24.218416image/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.
2024-03-23T07:29:24.560362image/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
0202228214125462139102862121414372164172
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>