Overview

Dataset statistics

Number of variables13
Number of observations34
Missing cells94
Missing cells (%)21.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory114.9 B

Variable types

Numeric7
Text5
Categorical1

Dataset

Description농림축산검역본부_격리재배검역 처분 현황(식물명,격리재배,합격,폐기)
Author농림축산검역본부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20181019000000000967

Alerts

검출 병해충3 has constant value ""Constant
검출 병해충4 has constant value ""Constant
격리재배 건 is highly overall correlated with 격리재배 수량 and 4 other fieldsHigh correlation
격리재배 수량 is highly overall correlated with 격리재배 건 and 4 other fieldsHigh correlation
합격 건 is highly overall correlated with 격리재배 건 and 2 other fieldsHigh correlation
합격 수량 is highly overall correlated with 격리재배 건 and 2 other fieldsHigh correlation
폐기 건 is highly overall correlated with 격리재배 건 and 2 other fieldsHigh correlation
폐기 수량 is highly overall correlated with 격리재배 건 and 2 other fieldsHigh correlation
검출 병해충2 has 28 (82.4%) missing valuesMissing
검출 병해충3 has 33 (97.1%) missing valuesMissing
검출 병해충4 has 33 (97.1%) missing valuesMissing
합격 건 has 18 (52.9%) zerosZeros
합격 수량 has 18 (52.9%) zerosZeros

Reproduction

Analysis started2023-12-11 03:27:38.414037
Analysis finished2023-12-11 03:27:46.197797
Duration7.78 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
2023-12-11T12:27:46.267042image/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
2023-12-11T12:27:46.418345image/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
2023-12-11T12:27:46.646330image/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%
2023-12-11T12:27:47.168752image/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

2023-12-11T12:27:47.409947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:27:47.589733image/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%

격리재배 건
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
2023-12-11T12:27:47.763302image/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
2023-12-11T12:27:47.925415image/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%

격리재배 수량
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
2023-12-11T12:27:48.061946image/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
2023-12-11T12:27:48.185487image/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%

합격 건
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
2023-12-11T12:27:48.303254image/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
2023-12-11T12:27:48.418151image/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%

합격 수량
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
2023-12-11T12:27:48.573225image/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
2023-12-11T12:27:48.700971image/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%

폐기 건
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
2023-12-11T12:27:48.846856image/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
2023-12-11T12:27:48.961912image/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%

폐기 수량
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
2023-12-11T12:27:49.083738image/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
2023-12-11T12:27:49.217355image/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%

검출 병해충1
Text

Distinct19
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2023-12-11T12:27:49.435625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length34
Mean length29.235294
Min length17

Characters and Unicode

Total characters994
Distinct characters41
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

Unique11 ?
Unique (%)32.4%

Sample

1st rowXanthomonas campestris pv. Juglandis
2nd rowArabis mosaic virus
3rd rowArabis mosaic nepovirus
4th rowTobacco streak virus
5th rowTobacco streak virus
ValueCountFrequency (%)
virus 20
 
16.0%
leaf 7
 
5.6%
juglandis 4
 
3.2%
pv 4
 
3.2%
xanthomonas 4
 
3.2%
pineapple 4
 
3.2%
mosaic 4
 
3.2%
arabis 4
 
3.2%
wilt-associated 4
 
3.2%
viruses 4
 
3.2%
Other values (36) 66
52.8%
2023-12-11T12:27:49.777632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91
 
9.2%
s 90
 
9.1%
i 88
 
8.9%
a 80
 
8.0%
r 79
 
7.9%
e 73
 
7.3%
o 51
 
5.1%
l 50
 
5.0%
u 49
 
4.9%
n 41
 
4.1%
Other values (31) 302
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 855
86.0%
Space Separator 91
 
9.2%
Uppercase Letter 37
 
3.7%
Dash Punctuation 7
 
0.7%
Other Punctuation 4
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 90
10.5%
i 88
10.3%
a 80
 
9.4%
r 79
 
9.2%
e 73
 
8.5%
o 51
 
6.0%
l 50
 
5.8%
u 49
 
5.7%
n 41
 
4.8%
c 37
 
4.3%
Other values (15) 217
25.4%
Uppercase Letter
ValueCountFrequency (%)
C 5
13.5%
X 5
13.5%
P 4
10.8%
O 4
10.8%
A 4
10.8%
E 3
8.1%
G 3
8.1%
J 3
8.1%
T 2
 
5.4%
N 1
 
2.7%
Other values (3) 3
8.1%
Space Separator
ValueCountFrequency (%)
91
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 892
89.7%
Common 102
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 90
 
10.1%
i 88
 
9.9%
a 80
 
9.0%
r 79
 
8.9%
e 73
 
8.2%
o 51
 
5.7%
l 50
 
5.6%
u 49
 
5.5%
n 41
 
4.6%
c 37
 
4.1%
Other values (28) 254
28.5%
Common
ValueCountFrequency (%)
91
89.2%
- 7
 
6.9%
. 4
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
91
 
9.2%
s 90
 
9.1%
i 88
 
8.9%
a 80
 
8.0%
r 79
 
7.9%
e 73
 
7.3%
o 51
 
5.1%
l 50
 
5.0%
u 49
 
4.9%
n 41
 
4.1%
Other values (31) 302
30.4%

검출 병해충2
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing28
Missing (%)82.4%
Memory size404.0 B
2023-12-11T12:27:49.931273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length25
Mean length23.333333
Min length19

Characters and Unicode

Total characters140
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st rowCherry leaf roll virus
2nd rowEast asian passifolra virus
3rd rowArabis mosaic virus
4th rowPineapple bacilliform CO virus
5th rowLittle cherry virus
ValueCountFrequency (%)
virus 5
23.8%
cherry 2
 
9.5%
leaf 1
 
4.8%
roll 1
 
4.8%
east 1
 
4.8%
asian 1
 
4.8%
passifolra 1
 
4.8%
arabis 1
 
4.8%
mosaic 1
 
4.8%
pineapple 1
 
4.8%
Other values (6) 6
28.6%
2023-12-11T12:27:50.212234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 17
12.1%
r 15
10.7%
15
10.7%
s 12
 
8.6%
a 11
 
7.9%
e 9
 
6.4%
l 8
 
5.7%
v 7
 
5.0%
u 6
 
4.3%
n 4
 
2.9%
Other values (17) 36
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 116
82.9%
Space Separator 15
 
10.7%
Uppercase Letter 9
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 17
14.7%
r 15
12.9%
s 12
10.3%
a 11
9.5%
e 9
 
7.8%
l 8
 
6.9%
v 7
 
6.0%
u 6
 
5.2%
n 4
 
3.4%
o 4
 
3.4%
Other values (9) 23
19.8%
Uppercase Letter
ValueCountFrequency (%)
A 2
22.2%
C 2
22.2%
E 1
11.1%
P 1
11.1%
O 1
11.1%
L 1
11.1%
G 1
11.1%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 125
89.3%
Common 15
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 17
13.6%
r 15
12.0%
s 12
 
9.6%
a 11
 
8.8%
e 9
 
7.2%
l 8
 
6.4%
v 7
 
5.6%
u 6
 
4.8%
n 4
 
3.2%
o 4
 
3.2%
Other values (16) 32
25.6%
Common
ValueCountFrequency (%)
15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 17
12.1%
r 15
10.7%
15
10.7%
s 12
 
8.6%
a 11
 
7.9%
e 9
 
6.4%
l 8
 
5.7%
v 7
 
5.0%
u 6
 
4.3%
n 4
 
2.9%
Other values (17) 36
25.7%

검출 병해충3
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing33
Missing (%)97.1%
Memory size404.0 B
2023-12-11T12:27:50.353590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters19
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowGrapevine fabavirus
ValueCountFrequency (%)
grapevine 1
50.0%
fabavirus 1
50.0%
2023-12-11T12:27:50.673826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3
15.8%
r 2
10.5%
e 2
10.5%
v 2
10.5%
i 2
10.5%
G 1
 
5.3%
p 1
 
5.3%
n 1
 
5.3%
1
 
5.3%
f 1
 
5.3%
Other values (3) 3
15.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17
89.5%
Uppercase Letter 1
 
5.3%
Space Separator 1
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3
17.6%
r 2
11.8%
e 2
11.8%
v 2
11.8%
i 2
11.8%
p 1
 
5.9%
n 1
 
5.9%
f 1
 
5.9%
b 1
 
5.9%
u 1
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
G 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18
94.7%
Common 1
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3
16.7%
r 2
11.1%
e 2
11.1%
v 2
11.1%
i 2
11.1%
G 1
 
5.6%
p 1
 
5.6%
n 1
 
5.6%
f 1
 
5.6%
b 1
 
5.6%
Other values (2) 2
11.1%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3
15.8%
r 2
10.5%
e 2
10.5%
v 2
10.5%
i 2
10.5%
G 1
 
5.3%
p 1
 
5.3%
n 1
 
5.3%
1
 
5.3%
f 1
 
5.3%
Other values (3) 3
15.8%

검출 병해충4
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing33
Missing (%)97.1%
Memory size404.0 B
2023-12-11T12:27:50.825656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters21
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowXylophilus ampelinnus
ValueCountFrequency (%)
xylophilus 1
50.0%
ampelinnus 1
50.0%
2023-12-11T12:27:51.161910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 3
14.3%
p 2
9.5%
i 2
9.5%
u 2
9.5%
s 2
9.5%
n 2
9.5%
X 1
 
4.8%
y 1
 
4.8%
o 1
 
4.8%
h 1
 
4.8%
Other values (4) 4
19.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19
90.5%
Uppercase Letter 1
 
4.8%
Space Separator 1
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 3
15.8%
p 2
10.5%
i 2
10.5%
u 2
10.5%
s 2
10.5%
n 2
10.5%
y 1
 
5.3%
o 1
 
5.3%
h 1
 
5.3%
a 1
 
5.3%
Other values (2) 2
10.5%
Uppercase Letter
ValueCountFrequency (%)
X 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20
95.2%
Common 1
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 3
15.0%
p 2
10.0%
i 2
10.0%
u 2
10.0%
s 2
10.0%
n 2
10.0%
X 1
 
5.0%
y 1
 
5.0%
o 1
 
5.0%
h 1
 
5.0%
Other values (3) 3
15.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 3
14.3%
p 2
9.5%
i 2
9.5%
u 2
9.5%
s 2
9.5%
n 2
9.5%
X 1
 
4.8%
y 1
 
4.8%
o 1
 
4.8%
h 1
 
4.8%
Other values (4) 4
19.0%

Interactions

2023-12-11T12:27:44.108511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:38.920950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:39.945368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:40.850740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.562107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.315204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:43.096050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:44.256236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:39.067757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:40.083568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:40.988416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.655410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.433308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:43.221394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:44.423201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:39.246458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:40.225650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.095419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.776778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.563602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:43.348125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:44.593438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:39.413286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:40.365098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.176164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.881214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.678373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:43.489780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:44.744870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:39.547302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:40.492552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.273127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.991172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.781576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:43.642019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:44.900664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:39.675256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:40.623863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.373929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.111796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.890287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:43.799378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:45.044872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:39.820825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:40.737773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:41.467547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.217900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:42.994013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:27:43.947429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:27:51.270751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도식물명수입국격리재배 건격리재배 수량합격 건합격 수량폐기 건폐기 수량검출 병해충1검출 병해충2
년도1.0000.7350.0000.5260.6780.0000.0000.3870.5240.8091.000
식물명0.7351.0000.5410.0000.0000.0000.0000.0000.0000.9931.000
수입국0.0000.5411.0000.0000.0000.0000.0000.1170.0000.5151.000
격리재배 건0.5260.0000.0001.0000.9850.9710.9930.9040.8770.0001.000
격리재배 수량0.6780.0000.0000.9851.0000.8540.9830.8140.8120.0001.000
합격 건0.0000.0000.0000.9710.8541.0000.9510.8670.8380.0001.000
합격 수량0.0000.0000.0000.9930.9830.9511.0000.8080.8410.0001.000
폐기 건0.3870.0000.1170.9040.8140.8670.8081.0000.9670.0001.000
폐기 수량0.5240.0000.0000.8770.8120.8380.8410.9671.0000.0001.000
검출 병해충10.8090.9930.5150.0000.0000.0000.0000.0000.0001.0001.000
검출 병해충21.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-11T12:27:51.418777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도격리재배 건격리재배 수량합격 건합격 수량폐기 건폐기 수량수입국
년도1.000-0.353-0.318-0.432-0.446-0.112-0.2550.000
격리재배 건-0.3531.0000.8170.7580.7240.5230.6810.000
격리재배 수량-0.3180.8171.0000.5830.6400.5850.9290.000
합격 건-0.4320.7580.5831.0000.9700.0610.3560.000
합격 수량-0.4460.7240.6400.9701.0000.1300.4210.000
폐기 건-0.1120.5230.5850.0610.1301.0000.7100.000
폐기 수량-0.2550.6810.9290.3560.4210.7101.0000.000
수입국0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-11T12:27:45.631170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:27:45.946917image/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.
2023-12-11T12:27:46.113371image/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

년도식물명수입국격리재배 건격리재배 수량합격 건합격 수량폐기 건폐기 수량검출 병해충1검출 병해충2검출 병해충3검출 병해충4
02015호두나무 묘목중국50436120343498921886228Xanthomonas campestris pv. JuglandisCherry leaf roll virus<NA><NA>
12015가시여지 묘목베트남3824465134200531110000Arabis mosaic virus<NA><NA><NA>
22016올리브 묘목미국5110360150Arabis mosaic nepovirus<NA><NA><NA>
32016장미 묘목네덜란드4587079438486212145Tobacco streak virus<NA><NA><NA>
42016장미 묘목베트남13575001032224125050Tobacco streak virus<NA><NA><NA>
52016꽃시계덩굴묘대만722440072244Euphorbia leaf curl virus<NA><NA><NA>
62016호두나무 묘목중국756264163025302253373022Xanthomonas campestris pv. Juglandis<NA><NA><NA>
72017패션후르트묘목대만74070000740700Euphorbia leaf curl virusEast asian passifolra virus<NA><NA>
82017패션후르트묘목일본14500145Euphorbia leaf curl virus<NA><NA><NA>
92017깔라만시 묘목베트남480025002300Citrus psorosis virus<NA><NA><NA>
년도식물명수입국격리재배 건격리재배 수량합격 건합격 수량폐기 건폐기 수량검출 병해충1검출 병해충2검출 병해충3검출 병해충4
242020양벚삽수일본180018Cherry necrotic rusty mottle virusLittle cherry virus<NA><NA>
252020양벚묘목일본120012Cherry green ring mottle virus<NA><NA><NA>
262020사옥묘목일본150015Cherry necrotic rusty mottle virus<NA><NA><NA>
272021올리브묘목중국4151813602819153Olive leaf yellowing-associated virus<NA><NA><NA>
282021올리브묘목그리스1230001230Olive leaf yellowing-associated virus<NA><NA><NA>
292021포도묘목중국65395865533608361235029Grapevine berry inner necrosis virusGrapevine geminivirus AGrapevine fabavirusXylophilus ampelinnus
302021포도묘목일본992882110Xylophilus ampelinnus<NA><NA><NA>
312022포도묘목중국3015227900971170Grapevine berry inner necrosis virus<NA><NA><NA>
322023포도묘목중국168504400619300Grapevine berry inner necrosis virus<NA><NA><NA>
332023블루베리묘목네덜란드2100002100Dasineura oxycoccana<NA><NA><NA>