Overview

Dataset statistics

Number of variables13
Number of observations35
Missing cells97
Missing cells (%)21.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory114.8 B

Variable types

Numeric7
Text5
Categorical1

Dataset

Description농림축산검역본부_격리재배검역 처분 현황 이중 어떤 식물들이 있는지에 대한 식물명, 격리재배 현황 및 합격가능한 건지 판단하여 합격 및 폐기 여부 기입 ## LINK 미리보기 [![미리보기](http://curate.gimi9.com/linkview/www-data-go-kr-data-filedata-15103005?url=https%3A//data.mafra.go.kr/opendata/data/indexOpenDataDetail.do%3Fdata_id%3D20181019000000000967&version=d7)](https://www.data.go.kr/data/15103005/fileData.do)
URLhttps://www.data.go.kr/data/15103005/fileData.do

Alerts

검출 병해충3 has constant value ""Constant
검출 병해충4 has constant value ""Constant
격리재배 건 is highly overall correlated with 격리재배 수량 and 3 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 3 other fieldsHigh correlation
폐기 건 is highly overall correlated with 격리재배 수량 and 1 other fieldsHigh correlation
폐기 수량 is highly overall correlated with 격리재배 건 and 3 other fieldsHigh correlation
검출 병해충2 has 29 (82.9%) missing valuesMissing
검출 병해충3 has 34 (97.1%) missing valuesMissing
검출 병해충4 has 34 (97.1%) missing valuesMissing
합격 건 has 16 (45.7%) zerosZeros
합격 수량 has 16 (45.7%) zerosZeros

Reproduction

Analysis started2023-12-12 21:46:52.144808
Analysis finished2023-12-12 21:46:57.832789
Duration5.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct8
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5429
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T06:46:57.878294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015.7
Q12017
median2018
Q32020
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.091198
Coefficient of variation (CV)0.0010359939
Kurtosis-0.93807973
Mean2018.5429
Median Absolute Deviation (MAD)2
Skewness0.16119151
Sum70649
Variance4.3731092
MonotonicityIncreasing
2023-12-13T06:46:57.986530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2018 8
22.9%
2016 5
14.3%
2019 5
14.3%
2017 4
11.4%
2021 4
11.4%
2022 4
11.4%
2020 3
 
8.6%
2015 2
 
5.7%
ValueCountFrequency (%)
2015 2
 
5.7%
2016 5
14.3%
2017 4
11.4%
2018 8
22.9%
2019 5
14.3%
2020 3
 
8.6%
2021 4
11.4%
2022 4
11.4%
ValueCountFrequency (%)
2022 4
11.4%
2021 4
11.4%
2020 3
 
8.6%
2019 5
14.3%
2018 8
22.9%
2017 4
11.4%
2016 5
14.3%
2015 2
 
5.7%

식물명
Text

Distinct21
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-13T06:46:58.169942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.2285714
Min length3

Characters and Unicode

Total characters183
Distinct characters52
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

Unique11 ?
Unique (%)31.4%

Sample

1st row호두나무 묘목
2nd row가시여지 묘목
3rd row올리브 묘목
4th row장미 묘목
5th row장미 묘목
ValueCountFrequency (%)
묘목 8
18.6%
올리브묘목 4
 
9.3%
호두나무 3
 
7.0%
포도묘목 3
 
7.0%
개암나무묘목 2
 
4.7%
감귤접수 2
 
4.7%
장미묘목 2
 
4.7%
파인애플묘목 2
 
4.7%
장미 2
 
4.7%
파인애플삽수 2
 
4.7%
Other values (12) 13
30.2%
2023-12-13T06:46:58.475528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
묘 29
 
15.8%
목 28
 
15.3%
8
 
4.4%
수 6
 
3.3%
올 5
 
2.7%
나 5
 
2.7%
리 5
 
2.7%
무 5
 
2.7%
브 5
 
2.7%
두 4
 
2.2%
Other values (42) 83
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 175
95.6%
Space Separator 8
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
묘 29
 
16.6%
목 28
 
16.0%
수 6
 
3.4%
올 5
 
2.9%
나 5
 
2.9%
리 5
 
2.9%
무 5
 
2.9%
브 5
 
2.9%
두 4
 
2.3%
호 4
 
2.3%
Other values (41) 79
45.1%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 175
95.6%
Common 8
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
묘 29
 
16.6%
목 28
 
16.0%
수 6
 
3.4%
올 5
 
2.9%
나 5
 
2.9%
리 5
 
2.9%
무 5
 
2.9%
브 5
 
2.9%
두 4
 
2.3%
호 4
 
2.3%
Other values (41) 79
45.1%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 175
95.6%
ASCII 8
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
묘 29
 
16.6%
목 28
 
16.0%
수 6
 
3.4%
올 5
 
2.9%
나 5
 
2.9%
리 5
 
2.9%
무 5
 
2.9%
브 5
 
2.9%
두 4
 
2.3%
호 4
 
2.3%
Other values (41) 79
45.1%
ASCII
ValueCountFrequency (%)
8
100.0%

수입국
Categorical

Distinct10
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
일본
11 
중국
9 
네덜란드
5 
베트남
3 
대만
2 
Other values (5)
5 

Length

Max length4
Median length2
Mean length2.4285714
Min length2

Unique

Unique5 ?
Unique (%)14.3%

Sample

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

Common Values

ValueCountFrequency (%)
일본 11
31.4%
중국 9
25.7%
네덜란드 5
14.3%
베트남 3
 
8.6%
대만 2
 
5.7%
미국 1
 
2.9%
영국 1
 
2.9%
독일 1
 
2.9%
미얀마 1
 
2.9%
그리스 1
 
2.9%

Length

2023-12-13T06:46:58.630196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:46:58.784509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일본 11
31.4%
중국 9
25.7%
네덜란드 5
14.3%
베트남 3
 
8.6%
대만 2
 
5.7%
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 

Distinct17
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.257143
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T06:46:58.916447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median4
Q310
95-th percentile54.5
Maximum75
Range74
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation19.350244
Coefficient of variation (CV)1.5786912
Kurtosis3.5317253
Mean12.257143
Median Absolute Deviation (MAD)3
Skewness2.0803616
Sum429
Variance374.43193
MonotonicityNot monotonic
2023-12-13T06:46:59.013994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 9
25.7%
2 6
17.1%
4 4
11.4%
7 2
 
5.7%
3 2
 
5.7%
50 1
 
2.9%
30 1
 
2.9%
9 1
 
2.9%
65 1
 
2.9%
11 1
 
2.9%
Other values (7) 7
20.0%
ValueCountFrequency (%)
1 9
25.7%
2 6
17.1%
3 2
 
5.7%
4 4
11.4%
5 1
 
2.9%
6 1
 
2.9%
7 2
 
5.7%
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%
13 1
2.9%
11 1
2.9%
9 1
2.9%

격리재배 수량
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69423.314
Minimum2
Maximum626416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T06:46:59.120790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.1
Q1101
median3683
Q341870
95-th percentile407941.5
Maximum626416
Range626414
Interquartile range (IQR)41769

Descriptive statistics

Standard deviation145335.16
Coefficient of variation (CV)2.0934633
Kurtosis6.7553841
Mean69423.314
Median Absolute Deviation (MAD)3681
Skewness2.6333359
Sum2429816
Variance2.1122309 × 1010
MonotonicityNot monotonic
2023-12-13T06:46:59.238194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2 2
 
5.7%
436120 1
 
2.9%
15181 1
 
2.9%
10701 1
 
2.9%
150 1
 
2.9%
16000 1
 
2.9%
8 1
 
2.9%
5 1
 
2.9%
230 1
 
2.9%
34730 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
2 2
5.7%
5 1
2.9%
8 1
2.9%
13 1
2.9%
16 1
2.9%
30 1
2.9%
45 1
2.9%
92 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%
57500 1
2.9%
43040 1
2.9%
40700 1
2.9%

합격 건
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6571429
Minimum0
Maximum53
Zeros16
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T06:46:59.356319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile36.7
Maximum53
Range53
Interquartile range (IQR)9

Descriptive statistics

Standard deviation13.781793
Coefficient of variation (CV)1.799861
Kurtosis3.5887254
Mean7.6571429
Median Absolute Deviation (MAD)1
Skewness2.1044252
Sum268
Variance189.93782
MonotonicityNot monotonic
2023-12-13T06:46:59.460388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 16
45.7%
3 3
 
8.6%
1 3
 
8.6%
34 2
 
5.7%
10 2
 
5.7%
2 2
 
5.7%
43 1
 
2.9%
30 1
 
2.9%
13 1
 
2.9%
5 1
 
2.9%
Other values (3) 3
 
8.6%
ValueCountFrequency (%)
0 16
45.7%
1 3
 
8.6%
2 2
 
5.7%
3 3
 
8.6%
5 1
 
2.9%
8 1
 
2.9%
10 2
 
5.7%
12 1
 
2.9%
13 1
 
2.9%
30 1
 
2.9%
ValueCountFrequency (%)
53 1
 
2.9%
43 1
 
2.9%
34 2
5.7%
30 1
 
2.9%
13 1
 
2.9%
12 1
 
2.9%
10 2
5.7%
8 1
 
2.9%
5 1
 
2.9%
3 3
8.6%

합격 수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40525.914
Minimum0
Maximum360836
Zeros16
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T06:46:59.559446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60
Q318273
95-th percentile282083
Maximum360836
Range360836
Interquartile range (IQR)18273

Descriptive statistics

Standard deviation95649.16
Coefficient of variation (CV)2.3601975
Kurtosis6.1272677
Mean40525.914
Median Absolute Deviation (MAD)60
Skewness2.662321
Sum1418407
Variance9.1487618 × 109
MonotonicityNot monotonic
2023-12-13T06:46:59.670551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 16
45.7%
349892 1
 
2.9%
10546 1
 
2.9%
8 1
 
2.9%
10260 1
 
2.9%
28232 1
 
2.9%
82 1
 
2.9%
360836 1
 
2.9%
6028 1
 
2.9%
7776 1
 
2.9%
Other values (10) 10
28.6%
ValueCountFrequency (%)
0 16
45.7%
8 1
 
2.9%
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%
10260 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%
28232 1
2.9%
26000 1
2.9%
10546 1
2.9%

폐기 건
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8857143
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T06:46:59.773721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile18.3
Maximum53
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.824254
Coefficient of variation (CV)2.0108122
Kurtosis17.306634
Mean4.8857143
Median Absolute Deviation (MAD)0
Skewness3.8733788
Sum171
Variance96.515966
MonotonicityNot monotonic
2023-12-13T06:46:59.885116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 20
57.1%
2 7
 
20.0%
18 2
 
5.7%
7 2
 
5.7%
53 1
 
2.9%
19 1
 
2.9%
3 1
 
2.9%
12 1
 
2.9%
ValueCountFrequency (%)
1 20
57.1%
2 7
 
20.0%
3 1
 
2.9%
7 2
 
5.7%
12 1
 
2.9%
18 2
 
5.7%
19 1
 
2.9%
53 1
 
2.9%
ValueCountFrequency (%)
53 1
 
2.9%
19 1
 
2.9%
18 2
 
5.7%
12 1
 
2.9%
7 2
 
5.7%
3 1
 
2.9%
2 7
 
20.0%
1 20
57.1%

폐기 수량
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27903.4
Minimum2
Maximum373022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T06:47:00.001408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.1
Q147.5
median2145
Q313280
95-th percentile140330.4
Maximum373022
Range373020
Interquartile range (IQR)13232.5

Descriptive statistics

Standard deviation71239.454
Coefficient of variation (CV)2.5530743
Kurtosis16.89229
Mean27903.4
Median Absolute Deviation (MAD)2137
Skewness3.8941776
Sum976619
Variance5.0750598 × 109
MonotonicityNot monotonic
2023-12-13T06:47:00.133010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
50 3
 
8.6%
8 2
 
5.7%
2 2
 
5.7%
86228 1
 
2.9%
1725 1
 
2.9%
10560 1
 
2.9%
3683 1
 
2.9%
124047 1
 
2.9%
10 1
 
2.9%
35029 1
 
2.9%
Other values (21) 21
60.0%
ValueCountFrequency (%)
2 2
5.7%
5 1
 
2.9%
8 2
5.7%
10 1
 
2.9%
13 1
 
2.9%
30 1
 
2.9%
45 1
 
2.9%
50 3
8.6%
60 1
 
2.9%
150 1
 
2.9%
ValueCountFrequency (%)
373022 1
2.9%
178325 1
2.9%
124047 1
2.9%
86228 1
2.9%
43040 1
2.9%
40700 1
2.9%
35029 1
2.9%
25050 1
2.9%
16000 1
2.9%
10560 1
2.9%

검출 병해충1
Text

Distinct20
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-13T06:47:00.321472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length35
Mean length29.628571
Min length17

Characters and Unicode

Total characters1037
Distinct characters40
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

Unique12 ?
Unique (%)34.3%

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
 
15.4%
leaf 8
 
6.2%
pv 5
 
3.8%
xanthomonas 5
 
3.8%
pineapple 4
 
3.1%
arabis 4
 
3.1%
mealybug 4
 
3.1%
juglandis 4
 
3.1%
mosaic 4
 
3.1%
yellowing-associated 4
 
3.1%
Other values (38) 68
52.3%
2023-12-13T06:47:00.611374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
95
 
9.2%
i 93
 
9.0%
s 91
 
8.8%
a 85
 
8.2%
r 80
 
7.7%
e 71
 
6.8%
l 56
 
5.4%
o 56
 
5.4%
u 49
 
4.7%
n 40
 
3.9%
Other values (30) 321
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 891
85.9%
Space Separator 95
 
9.2%
Uppercase Letter 38
 
3.7%
Dash Punctuation 8
 
0.8%
Other Punctuation 5
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 93
10.4%
s 91
 
10.2%
a 85
 
9.5%
r 80
 
9.0%
e 71
 
8.0%
l 56
 
6.3%
o 56
 
6.3%
u 49
 
5.5%
n 40
 
4.5%
t 39
 
4.4%
Other values (15) 231
25.9%
Uppercase Letter
ValueCountFrequency (%)
C 6
15.8%
X 6
15.8%
O 5
13.2%
P 4
10.5%
A 4
10.5%
J 3
7.9%
E 3
7.9%
T 2
 
5.3%
G 2
 
5.3%
N 1
 
2.6%
Other values (2) 2
 
5.3%
Space Separator
ValueCountFrequency (%)
95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 929
89.6%
Common 108
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 93
 
10.0%
s 91
 
9.8%
a 85
 
9.1%
r 80
 
8.6%
e 71
 
7.6%
l 56
 
6.0%
o 56
 
6.0%
u 49
 
5.3%
n 40
 
4.3%
t 39
 
4.2%
Other values (27) 269
29.0%
Common
ValueCountFrequency (%)
95
88.0%
- 8
 
7.4%
. 5
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1037
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
95
 
9.2%
i 93
 
9.0%
s 91
 
8.8%
a 85
 
8.2%
r 80
 
7.7%
e 71
 
6.8%
l 56
 
5.4%
o 56
 
5.4%
u 49
 
4.7%
n 40
 
3.9%
Other values (30) 321
31.0%

검출 병해충2
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing29
Missing (%)82.9%
Memory size412.0 B
2023-12-13T06:47:00.764228image/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%
2023-12-13T06:47:01.008302image/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%
Missing34
Missing (%)97.1%
Memory size412.0 B
2023-12-13T06:47:01.144209image/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%
2023-12-13T06:47:01.404053image/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%
Missing34
Missing (%)97.1%
Memory size412.0 B
2023-12-13T06:47:01.528636image/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%
2023-12-13T06:47:01.781140image/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

2023-12-13T06:46:56.514626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:52.660392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.353795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.988371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.559273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.198031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.904578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.617044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:52.770013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.460585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.069270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.647467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.312016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.990651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.728758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:52.869147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.559124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.157690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.727912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.411754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.074640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.810693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:52.964375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.657973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.236642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.803618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.511945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.154095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.903090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.066820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.734108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.314848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.887817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.604813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.244284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:57.008309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.160594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.823995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.395506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.975988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.697116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.353814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:57.087466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.254336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:53.903167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:54.470669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.093473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:55.800878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:56.426094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:47:01.896831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도식물명수입국격리재배 건격리재배 수량합격 건합격 수량폐기 건폐기 수량검출 병해충1검출 병해충2
년도1.0000.8180.2400.0000.2790.0000.2400.2110.1680.8611.000
식물명0.8181.0000.6310.0000.0000.0000.0000.0000.0000.9781.000
수입국0.2400.6311.0000.0000.0000.0000.0000.7290.0000.0001.000
격리재배 건0.0000.0000.0001.0000.9611.0000.9770.9020.8640.0001.000
격리재배 수량0.2790.0000.0000.9611.0000.9200.9930.7930.9660.0001.000
합격 건0.0000.0000.0001.0000.9201.0000.9190.8770.7960.0001.000
합격 수량0.2400.0000.0000.9770.9930.9191.0000.7650.9410.0001.000
폐기 건0.2110.0000.7290.9020.7930.8770.7651.0000.8030.0001.000
폐기 수량0.1680.0000.0000.8640.9660.7960.9410.8031.0000.0001.000
검출 병해충10.8610.9780.0000.0000.0000.0000.0000.0000.0001.0001.000
검출 병해충21.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T06:47:02.072142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도격리재배 건격리재배 수량합격 건합격 수량폐기 건폐기 수량수입국
년도1.000-0.405-0.333-0.266-0.274-0.150-0.2510.000
격리재배 건-0.4051.0000.7860.8710.8310.4870.6550.000
격리재배 수량-0.3330.7861.0000.6630.7370.5930.9310.000
합격 건-0.2660.8710.6631.0000.9610.1760.4520.000
합격 수량-0.2740.8310.7370.9611.0000.2580.5280.000
폐기 건-0.1500.4870.5930.1760.2581.0000.7280.344
폐기 수량-0.2510.6550.9310.4520.5280.7281.0000.000
수입국0.0000.0000.0000.0000.0000.3440.0001.000

Missing values

2023-12-13T06:46:57.484795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:46:57.670737image/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.
2023-12-13T06:46:57.781571image/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
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>
312022포도묘목중국30152279122823218124047Grapevine berry inner necrosis virus<NA><NA><NA>
322022올리브묘목네덜란드236830023683Olive leaf yellowing-associated virus<NA><NA><NA>
332022개암나무묘목중국420820210260210560Xanthomonas arboricola pv. corylina<NA><NA><NA>
342022감귤접수일본2161818Citrus bark cracking viroid<NA><NA><NA>