Overview

Dataset statistics

Number of variables10
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory90.9 B

Variable types

Numeric7
Text2
Categorical1

Dataset

Description식물방역법 제13조에 따른 수입 식물에 대한 식물별 격리재배검역 병해충 검출(처분) 현황에 대한 자료를 제공합니다.
URLhttps://www.data.go.kr/data/15117832/fileData.do

Alerts

격리재배건 is highly overall correlated with 격리재배수량 and 4 other fieldsHigh correlation
격리재배수량 is highly overall correlated with 격리재배건 and 4 other fieldsHigh correlation
합격건 is highly overall correlated with 격리재배건 and 2 other fieldsHigh correlation
합격수량 is highly overall correlated with 격리재배건 and 2 other fieldsHigh correlation
폐기건 is highly overall correlated with 격리재배건 and 2 other fieldsHigh correlation
폐기수량 is highly overall correlated with 격리재배건 and 2 other fieldsHigh correlation
합격건 has 18 (52.9%) zerosZeros
합격수량 has 18 (52.9%) zerosZeros

Reproduction

Analysis started2023-12-12 09:38:07.929378
Analysis finished2023-12-12 09:38:14.365201
Duration6.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct9
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2015
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:38:14.419985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015.65
Q12017
median2018
Q32020
95-th percentile2022.35
Maximum2023
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1497005
Coefficient of variation (CV)0.001064999
Kurtosis-0.47089319
Mean2018.5
Median Absolute Deviation (MAD)1.5
Skewness0.38892189
Sum68629
Variance4.6212121
MonotonicityIncreasing
2023-12-12T18:38:14.564773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2018 8
23.5%
2016 5
14.7%
2019 5
14.7%
2017 4
11.8%
2021 4
11.8%
2020 3
 
8.8%
2015 2
 
5.9%
2023 2
 
5.9%
2022 1
 
2.9%
ValueCountFrequency (%)
2015 2
 
5.9%
2016 5
14.7%
2017 4
11.8%
2018 8
23.5%
2019 5
14.7%
2020 3
 
8.8%
2021 4
11.8%
2022 1
 
2.9%
2023 2
 
5.9%
ValueCountFrequency (%)
2023 2
 
5.9%
2022 1
 
2.9%
2021 4
11.8%
2020 3
 
8.8%
2019 5
14.7%
2018 8
23.5%
2017 4
11.8%
2016 5
14.7%
2015 2
 
5.9%

식물명
Text

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T18:38:14.793649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.2352941
Min length3

Characters and Unicode

Total characters178
Distinct characters55
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)41.2%

Sample

1st row호두나무 묘목
2nd row가시여지 묘목
3rd row올리브 묘목
4th row장미 묘목
5th row장미 묘목
ValueCountFrequency (%)
묘목 8
19.0%
포도묘목 4
 
9.5%
호두나무 3
 
7.1%
올리브묘목 3
 
7.1%
장미묘목 2
 
4.8%
파인애플묘목 2
 
4.8%
파인애플삽수 2
 
4.8%
패션후르트묘목 2
 
4.8%
장미 2
 
4.8%
꽃시계덩굴묘 1
 
2.4%
Other values (13) 13
31.0%
2023-12-12T18:38:15.231550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
묘 29
 
16.3%
목 28
 
15.7%
8
 
4.5%
수 5
 
2.8%
리 5
 
2.8%
포 4
 
2.2%
플 4
 
2.2%
애 4
 
2.2%
인 4
 
2.2%
파 4
 
2.2%
Other values (45) 83
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170
95.5%
Space Separator 8
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
묘 29
 
17.1%
목 28
 
16.5%
수 5
 
2.9%
리 5
 
2.9%
포 4
 
2.4%
플 4
 
2.4%
애 4
 
2.4%
인 4
 
2.4%
파 4
 
2.4%
미 4
 
2.4%
Other values (44) 79
46.5%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170
95.5%
Common 8
 
4.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
묘 29
 
17.1%
목 28
 
16.5%
수 5
 
2.9%
리 5
 
2.9%
포 4
 
2.4%
플 4
 
2.4%
애 4
 
2.4%
인 4
 
2.4%
파 4
 
2.4%
미 4
 
2.4%
Other values (44) 79
46.5%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170
95.5%
ASCII 8
 
4.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
묘 29
 
17.1%
목 28
 
16.5%
수 5
 
2.9%
리 5
 
2.9%
포 4
 
2.4%
플 4
 
2.4%
애 4
 
2.4%
인 4
 
2.4%
파 4
 
2.4%
미 4
 
2.4%
Other values (44) 79
46.5%
ASCII
ValueCountFrequency (%)
8
100.0%

수입국
Categorical

Distinct10
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Memory size404.0 B
일본
10 
중국
9 
네덜란드
5 
베트남
3 
대만
2 
Other values (5)
5 

Length

Max length4
Median length2
Mean length2.4411765
Min length2

Unique

Unique5 ?
Unique (%)14.7%

Sample

1st row중국
2nd row베트남
3rd row미국
4th row네덜란드
5th row베트남

Common Values

ValueCountFrequency (%)
일본 10
29.4%
중국 9
26.5%
네덜란드 5
14.7%
베트남 3
 
8.8%
대만 2
 
5.9%
미국 1
 
2.9%
영국 1
 
2.9%
독일 1
 
2.9%
미얀마 1
 
2.9%
그리스 1
 
2.9%

Length

2023-12-12T18:38:15.458410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:38:15.691611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일본 10
29.4%
중국 9
26.5%
네덜란드 5
14.7%
베트남 3
 
8.8%
대만 2
 
5.9%
미국 1
 
2.9%
영국 1
 
2.9%
독일 1
 
2.9%
미얀마 1
 
2.9%
그리스 1
 
2.9%

격리재배건
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.911765
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:38:15.867456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.25
median4
Q312.5
95-th percentile55.25
Maximum75
Range74
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation19.506592
Coefficient of variation (CV)1.5107611
Kurtosis3.2285542
Mean12.911765
Median Absolute Deviation (MAD)3
Skewness1.9963519
Sum439
Variance380.50713
MonotonicityNot monotonic
2023-12-12T18:38:16.005790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 9
26.5%
2 5
14.7%
4 3
 
8.8%
7 2
 
5.9%
3 2
 
5.9%
50 1
 
2.9%
16 1
 
2.9%
30 1
 
2.9%
9 1
 
2.9%
65 1
 
2.9%
Other values (8) 8
23.5%
ValueCountFrequency (%)
1 9
26.5%
2 5
14.7%
3 2
 
5.9%
4 3
 
8.8%
5 1
 
2.9%
6 1
 
2.9%
7 2
 
5.9%
9 1
 
2.9%
11 1
 
2.9%
13 1
 
2.9%
ValueCountFrequency (%)
75 1
2.9%
65 1
2.9%
50 1
2.9%
45 1
2.9%
38 1
2.9%
30 1
2.9%
25 1
2.9%
16 1
2.9%
13 1
2.9%
11 1
2.9%

격리재배수량
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73248.265
Minimum2
Maximum626416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:38:16.139708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.95
Q1102.5
median5250
Q353885
95-th percentile409954.25
Maximum626416
Range626414
Interquartile range (IQR)53782.5

Descriptive statistics

Standard deviation146805.31
Coefficient of variation (CV)2.0042155
Kurtosis6.387816
Mean73248.265
Median Absolute Deviation (MAD)5248
Skewness2.5581805
Sum2490441
Variance2.1551799 × 1010
MonotonicityNot monotonic
2023-12-12T18:38:16.311616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 2
 
5.9%
436120 1
 
2.9%
5 1
 
2.9%
1725 1
 
2.9%
10701 1
 
2.9%
150 1
 
2.9%
16000 1
 
2.9%
8 1
 
2.9%
15181 1
 
2.9%
244651 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
2 2
5.9%
5 1
2.9%
8 1
2.9%
13 1
2.9%
30 1
2.9%
45 1
2.9%
92 1
2.9%
100 1
2.9%
110 1
2.9%
150 1
2.9%
ValueCountFrequency (%)
626416 1
2.9%
436120 1
2.9%
395865 1
2.9%
244651 1
2.9%
218303 1
2.9%
152279 1
2.9%
87079 1
2.9%
85044 1
2.9%
57500 1
2.9%
43040 1
2.9%

합격건
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4411765
Minimum0
Maximum53
Zeros18
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:38:16.446684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.25
95-th percentile37.15
Maximum53
Range53
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation14.011232
Coefficient of variation (CV)1.8829323
Kurtosis3.5193449
Mean7.4411765
Median Absolute Deviation (MAD)0
Skewness2.1103392
Sum253
Variance196.31462
MonotonicityNot monotonic
2023-12-12T18:38:16.602291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 18
52.9%
3 3
 
8.8%
34 2
 
5.9%
10 2
 
5.9%
1 2
 
5.9%
43 1
 
2.9%
30 1
 
2.9%
2 1
 
2.9%
13 1
 
2.9%
5 1
 
2.9%
Other values (2) 2
 
5.9%
ValueCountFrequency (%)
0 18
52.9%
1 2
 
5.9%
2 1
 
2.9%
3 3
 
8.8%
5 1
 
2.9%
8 1
 
2.9%
10 2
 
5.9%
13 1
 
2.9%
30 1
 
2.9%
34 2
 
5.9%
ValueCountFrequency (%)
53 1
 
2.9%
43 1
 
2.9%
34 2
5.9%
30 1
 
2.9%
13 1
 
2.9%
10 2
5.9%
8 1
 
2.9%
5 1
 
2.9%
3 3
8.8%
2 1
 
2.9%

합격수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40585.5
Minimum0
Maximum360836
Zeros18
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:38:16.755278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39853.5
95-th percentile286926.5
Maximum360836
Range360836
Interquartile range (IQR)9853.5

Descriptive statistics

Standard deviation97177.378
Coefficient of variation (CV)2.3943866
Kurtosis5.8499166
Mean40585.5
Median Absolute Deviation (MAD)0
Skewness2.6170297
Sum1379907
Variance9.4434428 × 109
MonotonicityNot monotonic
2023-12-12T18:38:16.898013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 18
52.9%
349892 1
 
2.9%
7450 1
 
2.9%
82 1
 
2.9%
360836 1
 
2.9%
6028 1
 
2.9%
7776 1
 
2.9%
26000 1
 
2.9%
10546 1
 
2.9%
120 1
 
2.9%
Other values (7) 7
 
20.6%
ValueCountFrequency (%)
0 18
52.9%
60 1
 
2.9%
82 1
 
2.9%
120 1
 
2.9%
500 1
 
2.9%
6028 1
 
2.9%
7450 1
 
2.9%
7776 1
 
2.9%
10546 1
 
2.9%
26000 1
 
2.9%
ValueCountFrequency (%)
360836 1
2.9%
349892 1
2.9%
253022 1
2.9%
200531 1
2.9%
84862 1
2.9%
39978 1
2.9%
32224 1
2.9%
26000 1
2.9%
10546 1
2.9%
7776 1
2.9%

폐기건
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8529412
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:38:17.030683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32.75
95-th percentile18.35
Maximum53
Range52
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation9.6988983
Coefficient of variation (CV)1.9985609
Kurtosis19.100441
Mean4.8529412
Median Absolute Deviation (MAD)0
Skewness4.0915086
Sum165
Variance94.068627
MonotonicityNot monotonic
2023-12-12T18:38:17.179990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 19
55.9%
2 6
 
17.6%
7 2
 
5.9%
18 1
 
2.9%
53 1
 
2.9%
19 1
 
2.9%
3 1
 
2.9%
12 1
 
2.9%
9 1
 
2.9%
6 1
 
2.9%
ValueCountFrequency (%)
1 19
55.9%
2 6
 
17.6%
3 1
 
2.9%
6 1
 
2.9%
7 2
 
5.9%
9 1
 
2.9%
12 1
 
2.9%
18 1
 
2.9%
19 1
 
2.9%
53 1
 
2.9%
ValueCountFrequency (%)
53 1
 
2.9%
19 1
 
2.9%
18 1
 
2.9%
12 1
 
2.9%
9 1
 
2.9%
7 2
 
5.9%
6 1
 
2.9%
3 1
 
2.9%
2 6
 
17.6%
1 19
55.9%

폐기수량
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27320.324
Minimum2
Maximum373022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T18:38:17.312459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.95
Q150
median1935
Q318475
95-th percentile118461.95
Maximum373022
Range373020
Interquartile range (IQR)18425

Descriptive statistics

Standard deviation70571.338
Coefficient of variation (CV)2.5831077
Kurtosis18.440298
Mean27320.324
Median Absolute Deviation (MAD)1926
Skewness4.0886726
Sum928891
Variance4.9803138 × 109
MonotonicityNot monotonic
2023-12-12T18:38:17.489348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
50 3
 
8.8%
2 2
 
5.9%
86228 1
 
2.9%
1725 1
 
2.9%
100 1
 
2.9%
19300 1
 
2.9%
71170 1
 
2.9%
10 1
 
2.9%
35029 1
 
2.9%
230 1
 
2.9%
Other values (21) 21
61.8%
ValueCountFrequency (%)
2 2
5.9%
5 1
 
2.9%
8 1
 
2.9%
10 1
 
2.9%
13 1
 
2.9%
30 1
 
2.9%
45 1
 
2.9%
50 3
8.8%
60 1
 
2.9%
100 1
 
2.9%
ValueCountFrequency (%)
373022 1
2.9%
178325 1
2.9%
86228 1
2.9%
71170 1
2.9%
43040 1
2.9%
40700 1
2.9%
35029 1
2.9%
25050 1
2.9%
19300 1
2.9%
16000 1
2.9%

검출병해충
Text

Distinct24
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-12T18:38:17.800108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length105
Median length54
Mean length35
Min length17

Characters and Unicode

Total characters1190
Distinct characters43
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)50.0%

Sample

1st rowXanthomonas campestris pv. Juglandis, Cherry leaf roll virus
2nd rowArabis mosaic virus
3rd rowArabis mosaic nepovirus
4th rowTobacco streak virus
5th rowTobacco streak virus
ValueCountFrequency (%)
virus 25
 
16.7%
leaf 8
 
5.3%
pineapple 5
 
3.3%
grapevine 5
 
3.3%
cherry 5
 
3.3%
arabis 5
 
3.3%
mosaic 5
 
3.3%
mealybug 4
 
2.7%
juglandis 4
 
2.7%
xanthomonas 4
 
2.7%
Other values (46) 80
53.3%
2023-12-12T18:38:18.197271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116
 
9.7%
i 109
 
9.2%
s 105
 
8.8%
r 96
 
8.1%
a 95
 
8.0%
e 85
 
7.1%
l 61
 
5.1%
u 58
 
4.9%
o 56
 
4.7%
n 48
 
4.0%
Other values (33) 361
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1007
84.6%
Space Separator 116
 
9.7%
Uppercase Letter 48
 
4.0%
Other Punctuation 12
 
1.0%
Dash Punctuation 7
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 109
10.8%
s 105
10.4%
r 96
 
9.5%
a 95
 
9.4%
e 85
 
8.4%
l 61
 
6.1%
u 58
 
5.8%
o 56
 
5.6%
n 48
 
4.8%
v 45
 
4.5%
Other values (15) 249
24.7%
Uppercase Letter
ValueCountFrequency (%)
C 7
14.6%
A 6
12.5%
X 6
12.5%
P 5
10.4%
G 5
10.4%
O 5
10.4%
E 4
8.3%
J 3
6.2%
T 2
 
4.2%
N 1
 
2.1%
Other values (4) 4
8.3%
Other Punctuation
ValueCountFrequency (%)
, 8
66.7%
. 4
33.3%
Space Separator
ValueCountFrequency (%)
116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1055
88.7%
Common 135
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 109
 
10.3%
s 105
 
10.0%
r 96
 
9.1%
a 95
 
9.0%
e 85
 
8.1%
l 61
 
5.8%
u 58
 
5.5%
o 56
 
5.3%
n 48
 
4.5%
v 45
 
4.3%
Other values (29) 297
28.2%
Common
ValueCountFrequency (%)
116
85.9%
, 8
 
5.9%
- 7
 
5.2%
. 4
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116
 
9.7%
i 109
 
9.2%
s 105
 
8.8%
r 96
 
8.1%
a 95
 
8.0%
e 85
 
7.1%
l 61
 
5.1%
u 58
 
4.9%
o 56
 
4.7%
n 48
 
4.0%
Other values (33) 361
30.3%

Interactions

2023-12-12T18:38:12.865077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:08.303871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:09.109089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:09.959988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.621222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.255640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:12.116352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:13.006299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:08.432801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:09.260412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.067258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.708343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.374655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:12.215600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:13.126873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:08.543885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:09.405875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.176879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.795813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.471373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:12.309239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:13.242807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:08.644295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:09.554302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.265121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.876513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.578303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:12.399512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:13.345808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:08.745271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:09.666858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.346861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.967437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.688522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:12.488294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:13.818829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:08.845058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:09.769994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.445423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.052501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.805372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:12.611561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:13.929839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:08.973085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:09.863004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:10.524738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.145659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:11.959141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:38:12.713383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:38:18.316547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도식물명수입국격리재배건격리재배수량합격건합격수량폐기건폐기수량검출병해충
년도1.0000.7350.0000.5260.6780.0000.0000.3870.5240.807
식물명0.7351.0000.5410.0000.0000.0000.0000.0000.0000.982
수입국0.0000.5411.0000.0000.0000.0000.0000.1170.0000.000
격리재배건0.5260.0000.0001.0000.9850.9710.9930.9040.8770.000
격리재배수량0.6780.0000.0000.9851.0000.8540.9830.8140.8120.000
합격건0.0000.0000.0000.9710.8541.0000.9510.8670.8380.000
합격수량0.0000.0000.0000.9930.9830.9511.0000.8080.8410.000
폐기건0.3870.0000.1170.9040.8140.8670.8081.0000.9670.632
폐기수량0.5240.0000.0000.8770.8120.8380.8410.9671.0000.534
검출병해충0.8070.9820.0000.0000.0000.0000.0000.6320.5341.000
2023-12-12T18:38:18.452682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도격리재배건격리재배수량합격건합격수량폐기건폐기수량수입국
년도1.000-0.353-0.318-0.432-0.446-0.112-0.2550.000
격리재배건-0.3531.0000.8170.7580.7240.5230.6810.000
격리재배수량-0.3180.8171.0000.5830.6400.5850.9290.000
합격건-0.4320.7580.5831.0000.9700.0610.3560.000
합격수량-0.4460.7240.6400.9701.0000.1300.4210.000
폐기건-0.1120.5230.5850.0610.1301.0000.7100.000
폐기수량-0.2550.6810.9290.3560.4210.7101.0000.000
수입국0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T18:38:14.074267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:38:14.282874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

년도식물명수입국격리재배건격리재배수량합격건합격수량폐기건폐기수량검출병해충
02015호두나무 묘목중국50436120343498921886228Xanthomonas campestris pv. Juglandis, Cherry leaf roll virus
12015가시여지 묘목베트남3824465134200531110000Arabis mosaic virus
22016올리브 묘목미국5110360150Arabis mosaic nepovirus
32016장미 묘목네덜란드4587079438486212145Tobacco streak virus
42016장미 묘목베트남13575001032224125050Tobacco streak virus
52016꽃시계덩굴묘대만722440072244Euphorbia leaf curl virus
62016호두나무 묘목중국756264163025302253373022Xanthomonas campestris pv. Juglandis
72017패션후르트묘목대만74070000740700Euphorbia leaf curl virus, East asian passifolra virus
82017패션후르트묘목일본14500145Euphorbia leaf curl virus
92017깔라만시 묘목베트남480025002300Citrus psorosis virus
년도식물명수입국격리재배건격리재배수량합격건합격수량폐기건폐기수량검출병해충
242020양벚삽수일본180018Cherry necrotic rusty mottle virus, Little cherry virus
252020양벚묘목일본120012Cherry green ring mottle virus
262020사옥묘목일본150015Cherry necrotic rusty mottle virus
272021올리브묘목중국4151813602819153Olive leaf yellowing-associated virus
282021올리브묘목그리스1230001230Olive leaf yellowing-associated virus
292021포도묘목중국65395865533608361235029Grapevine berry inner necrosis virus, Grapevine geminivirus A, Grapevine fabavirus, Xylophilus ampelinnus
302021포도묘목일본992882110Xylophilus ampelinnus
312022포도묘목중국3015227900971170Grapevine berry inner necrosis virus
322023포도묘목중국168504400619300Grapevine berry inner necrosis virus
332023블루베리묘목네덜란드2100002100Dasineura oxycoccana